Comparative QTL analysis and candidate genes identification of seed size, shape and weight in soybean (Glycine max L.)

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Mahmoud A Elattar, Benjamin Karikari, Shuguang Li, Shiyu Song, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-206236/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dissecting the genetic mechanism underlying seed size, shape and weight is essential to these traits for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line populations, LM6 and ZM6, evaluated in multiple environments to identify candidate genes behind seed-related traits major and stable QTLs. A total of 239 and 43 M-QTL were mapped by composite interval mapping and mixed-model based composite interval mapping approaches, respectively, from which 22 common QTLs including four major and novel QTLs. CIM and MCIM approaches identified 180 and 18 novel M-QTLs, respectively. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Seed flatness index QTLs (34 QTLs) were identified and reported for the first time. Seven QTL clusters underlying the inheritance of seed size, shape and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17 and 19 were identified. Gene annotations, gene ontology (GO) enrichment and RNA-seq analyses identified 47 candidate genes for seed-related traits within the genomic regions of those 7 QTL clusters. These genes are highly expressed in seed-related tissues and nodules, that might be deemed as potential candidate genes regulating the above traits in soybean. This study provides detailed information for the genetic bases of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning as well as for MAS targeted at improving these traits individually or concurrently. Molecular Genetics Plant Molecular Biology and Genetics Glycine max Soybean seed QTL mapping QTL clusters epistatic interactions marker assisted breeding Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Soybean ( Glycine max L. Merr.) is one of the most important food crops, being a rich source of dietary protein (69 %) and provides more than 50 % edible oil globally, as well as has a significant role in health and biofuel ( Hoeck et al. 2003 ). It is used in human food and animal feed due to its high nutritional value and improves soil fertility by integrating atmospheric nitrogen in the soil through a synergetic interaction with microorganisms ( Wang et al. 2019 ). Throughout the last five decades, soybean production in China has suffered continuous annual decline and reduced yields. To meet domestic demands, China imports nearly 80 % to meet its domestic demand, therefore improving soybean production has been the main objective of soybean breeders to make the country self-sufficient ( Liu et al. 2018 ). Accordingly, most plant breeders are targeting yield-related traits to improve soybean production. In this regard, seed size traits, i.e., seed length (SL), thickness (ST) and width (SW), and seed shape traits, i.e., length to-thickness (SLT), length-to-width (SLW), width-to-thickness (SWT) ratios and flatness index (FI) determine seed vigor, quality and yield in soybean ( Cha-um and Kirdmanee 2011 ; Salas et al. 2006 ). Flatness index contributes a lot towards seed vigor which impacts the seedling emergence time by determining the development of Soybean plant throughout their growth cycle as contributing factor to higher leaf area index from early vegetative stages (V 1 ) to reproductive stages (R 2 ) ( Ebone et al. 2020 ). Vigor reduced meaning flatness index reduced, showing the increase in variability among the plants in the field. It generates differences among distinct groups as a result of higher trifoliate leaf area, allowing the plants to use more resources (light) for their development. Thus, shooting the yield of the plant because of higher leaf area index, higher accumulation of photoassimilates and finally contributed to seed yield. These plants therefore not need to increase their average internode length ( Wu et al. 2017 ) and have a higher number of total nodes and a higher pod number which explains the differences in grain mass ( Ainsworth et al. 2012 ). Moreover, seed size is an essential trait in flowering plants and plays a critical role in adaptation to the environment ( Tao et al. 2017 ). However, these traits are complex quantitative traits regulated by polygenes and strongly influenced by environment and genotype × environment (G×E) interaction, and hence it is more difficult to select for based on phenotype compare with monogenic traits ( Yao et al. 2014 ). A positive correlation between seed size/weight and seed yield has been reported in several studies ( Burris et al. 1973 ; Smith and Camper Jr 1975 ). Besides, seed weight/size has been demonstrated to be associated with seed germination capability and vigor, thereby significantly affecting the competitive capability of the seedling for nutrient and water resources and light, hence enhance stress tolerance ( Edwards Jr and Hartwig 1971 ; Haig 2013 ). All soybean varieties evolved in tropical and subtropical countries, such as Indonesia and India have small seed size compared to the temperate region varieties, such as China, the USA and Japan. Besides, seed size, shape and weight are important seed quality traits with great influence on seed use, for example, round seeds are often desirable for food-type soybean, and large-seeded cultivars are typically used for green soybeans (edamame), soymilk, miso, boiled soybean ( nimame ) and soybean curd ( tofu ), whereas, small-grained cultivars are desirable for sprout production and fermented soybean ( nātto ) ( Basra 1995 ; Teng et al. 2017 ; Wu et al. 2018 ). Therefore, depending on the end-use or biographical area of growth, many soybean varieties with different seed sizes and shapes with a 100-seed weight ranged from 3.0 - 77.5 g have been developed ( Panthee et al. 2005 ). Quantitative trait locus (QTL) analysis provides an efficient tool for crop breeders to study the genetic factors underlying quantitatively inherited traits and search for new sources of variation. The key influence of quantitative trait loci (QTLs), phenotypic variance (PV) of quantitative traits is often regulated by epistasis and QTL ×environment (QEs) interactions, which greatly lead to variations in complex traits ( Yang et al. 2005 ). Furthermore, QTL analysis of complex traits may increase the accuracy of QTL mapping if QTL by QTL interactions are considered. However, epistatic interaction has a stronger effect on inbreeding depression, heterosis, adaptation, speciation and reproductive isolation ( Ma et al. 2015 ). Most of the previous studies focused on the detection of main-effect QTLs for seed sizes, shapes and 100-seed weight in soybean. To date, at least 441, 52 and 297 QTLs for seed size, shape and 100-seed weight (HSW) have been reported (www.soybase.org) based on various genetic contexts, advances in marker technology, statistical methods and specific environments. However, most of these QTLs are minor (R 2 <10 %), not stable and with larger genomic region/confidence interval. Salas et al. ( Salas et al. 2006 ) found that 26 QTLs for seed size and shape on 13 soybean linkage groups. Han et al. ( Han et al. 2012 ) mapped 46 QTLs associated with HSW in 3 RIL soybean populations with one common male parent (Hefeng25). Furthermore, Hu et al. ( Hu et al. 2013 ) detected 10 QTLs for seed shape on 6 chromosomes in soybean. Moreover, Kato et al. ( Kato et al. 2014 ) identified 15 major QTLs for single seed weight among 11 chromosomes by using two RIL soybean populations developed from crosses between the US and Japanese cultivars of soybean. Recently, there have been limited studies on detecting QTLs with epistatic effects and their interactions with the environment (QEs) ( Liang et al. 2016 ; Panthee et al. 2005 ; Xu et al. 2011 ; Zhang et al. 2018 ). Most of the previous QTL analysis researches have relied on low-density genetic maps used biochemical and morphological, simple sequence repeats (SSRs), restriction fragment length polymorphisms (RFLPs), or other low-quantity markers, which resulted in large QTLs confidence intervals with a low resolution compared with high-density SNP markers that are valuable for high-throughput QTL mapping ( Hu et al. 2013 ; Moongkanna et al. 2011 ; Salas et al. 2006 ). With the recent advancement in next-generation sequencing (NGS) techniques, a number of techniques of high-throughput sequencing have been developed to generate large scale markers. Among them include genotyping-by-sequencing (GBS) ( Poland et al. 2012 ), restriction-site associated DNA sequencing (RAD-seq) ( Miller et al. 2007 ; Peterson et al. 2012 ) and specific length amplified fragment sequencing (SLAF-seq) ( Sun et al. 2013 ). These sequencing technologies have facilitated the production of hundreds to millions of single-nucleotide polymorphisms (SNPs) throughout the whole genome that promote the development of high-density linkage maps. The RAD-seq produces markers that have been proved to be a promising tool for SNP detection and genetic map construction ( Chutimanitsakun et al. 2011 ; Xie et al. 2018 ). A number of genetic maps produced by RAD-seq have been generated and used for QTL mapping in several crops such as barley ( Chutimanitsakun et al. 2011 ), soybean ( Hina et al. 2020 ), cowpea ( Pan et al. 2017 ), jute ( Kundu et al. 2015 ), sorghum ( Kajiya-Kanegae et al. 2020 ), alfalfa ( Zhang et al. 2019a ) among others. In addition to the above, knowledge of molecular mechanisms underlying soybean seed size, shape and weight is still limited. So far, only two seed sizes/weight-related genes have been isolated from the soybean. The gene Glyma20g25000 (ln) has a significant impact on seed size and the number of seeds per pod ( Jeong et al. 2012 ), and the PP2C-1 allele underlying Glyma17g33690 from the wild soybean accession ‘ZYD7’ was reported and demonstrated to increase seed size/weight ( Lu et al. 2017 ). Therefore, it is essential to identify major and stable QTLs and candidate genes related to seed size, shape and weight to improve our understanding of genetic mechanisms controlling these important traits in soybean ( Kato et al. 2014 ; Zhang et al. 2018 ). Due to the shortage in the available molecular markers and the lack of high-density linkage maps which resulted in low resolution and large confidence intervals of identified QTLs, the present study used RAD-seq to generate over 2200 bin-markers for either of the two-related recombinant inbred line (RIL) populations for QTL mapping and candidate gene(s) identification for seed size, shape and weight. The two-related RIL populations (ZM6 and LM6) were derived from a common male parent Meng 8206 ( M8206 ) crossed with either Zhengyang (Z) and Linhefenqingdou (L) and RILs and their parents were evaluated across multiple environments. The study was aimed to: (i). map main-effect QTLs (M-QTLs), additive by additive QTLs and QE for seed size, shape and weight traits, (ii). analyze epistatic QTL pairs and their interactions with the environment for further utilization of these QTLs in soybean genetic improvement, and (iii). mine potential candidate genes for the major and stable QTLs. These findings would be useful for the application of marker-assisted breeding (MAB) in soybean and provide comprehensive knowledge on the genetic bases for these traits as well as mined candidate genes would serve as a foundation for functional validation and verification of some genes for seed size, shape and weight in soybean. Materials And Methods Plant materials and experiments Two recombinant inbred lines (RIL) populations, i.e., ZM6 and LM6 ( Karikari et al. 2019 ; Zhang et al. 2019b ), consisting of 126 and 104 lines, respectively, were used in the present study. The two populations were developed by single seed descent (SSD) with the genotypes Zhengyang (Z) and Linhefenqingdou (L) were used as female parents and the M8206 (M6) genotype was used as the male parent. The two female parents, Z and L, have an average 100-seed weight of 17.1 and 35 g, respectively, whereas the male parent has an average 100-seed weight of 13.7 g. The two RIL populations along with their parents were evaluated for seed size and shape across multiple environments. Experiments were conducted in the Jiangpu Experimental Station (33 ◦ 030 N and 63 ◦ 1180 E), Nanjing, Jiangsu Province, in 2012, 2013, 2014 and 2017 growing seasons (designated as 12JP, 13JP, 14JP, and 17JP, respectively), the Fengyang Experimental Station, Chuzhou, Anhui Province (32 ◦ 870 N and 117 ◦ 560 E), in 2012 growing season (designated as 12FY) and the Yancheng Experimental Station, Yangcheng, Jiangsu Province, (33◦410 N and 120◦200 E) in 2014 (designated as 14YC). Plants were sown in June and harvest were done in October of the same year. Experiments were designed in a randomized complete blocks design (RCBD) with three replications. The experimental plot was one row of 2 m long at 5 cm plant to plant distance and 50 cm row to row distance. Planting and post-planting operations were carried out following the recommended agronomical practices. Phenotypic Measurement and Statistical Analyses Eight seed-related traits including seed length (SL), seed width (SW), seed thickness (ST), seed length/seed width (SLW), seed length/seed thickness (SLT), seed width/seed thickness (SWT), flatness index (FI), and 100-seed weight (HSW) were evaluated in LM6 and ZM6 populations under all environments. Phenotypic data were measured and recorded according to standard procedures ( Cheng et al. 2006 ; Tomooka et al. 2002 ). In brief, seeds harvested from 10 guarded plants in the middle of each row were used for estimating SL, SW, ST and HSW. The SL was measured as the longest dimension over the seed equivalent to the hilum. SW was measured as the longest dimension across the seed vertical to the hilum. ST was measured as the longest dimension from top to bottom of the seed. The SL, SW, and ST were estimated in millimeters (mm) utilizing the Vernier caliper instrument, according to ( Kaushik et al. 2007 ) (Fig. 1). The seed shape was identified by calculating three different ratios, i.e., SL/SW (SLW), SL/ST (SLT), and SW/ST (SWT), as well as flatness index (FI). The ratios between the SL, SW and ST were estimated from the individual values of the length, width, and thickness of the seeds according to ( Omokhafe and Alika 2004 ). Flatness index is an indicator for high or lower seed vigor which impacts the seedling uniformity index as it reduces if the seed vigor reduces. Thus, showing that the plants with FI near to one has good seed vigor concluding higher leaf area index at early vegetative stages and hence higher yield per plant as they have a good number of pods due to the higher accumulation of photoassimilates. These plants, therefore, do not need to increase their average internode length and have a higher number of total nodes and a higher pod number. While flatness index (FI) was calculated following the formula elaborated by ( Cailleux 1945 ) and ( Cerdà and Garcıa-Fayos 2002 ) to describe seed shape: where 𝐿 is the seed length, 𝑊 is seed width and T is seed thickness. It extended from a value of 1 for the round seeds to more than 2 for skinny seeds. The HSW was expressed as an average of five measurements of 100 randomly selected seeds. The descriptive statistics of the seed size, seed shape, and HSW traits were calculated using the SPSS software, version 24 ( http://www.spss.com ). The analysis of variance (ANOVA) for each environment and the combined overall environments (CE) were performed using the PROC GLM procedure in SAS software based on the random model (SAS Institute Inc. v. 9.02, 2010, Cary, NC, USA). The broad-sense heritability ( h 2 ) in individual environments was estimated as: where σ 2 g , σ 2 e and σ 2 ge are the variance components estimated from the analysis of variance for the genotypic, error and genotype × experiment variances, respectively, with r as the number of replicates and n as the number of environments. All the parameters were assessed from the expected mean squares in ANOVA. Pearson correlation coefficient ( r ) between seed size, seed shape, and HSW traits was calculated from the mean data utilizing the SAS PROC CORR with data obtained for CE (average across environments) for each population. Construction of Genetic Maps and QTL Analysis High-density genetic maps of the ZM6 and LM6 populations consist of 2601 and 2267 bin markers by using RAD-seq technique, respectively ( Karikari et al. 2019 ; Zhang et al. 2019b ) (Suppl. Table 1). The total length of the ZM6 and LM6 maps were 2630.22 and 2453.79 cM, with an average distance between the markers 1.01 and 1.08 cM, respectively (Suppl. Table 1). The Average marker per chromosome was 130 and 113 for ZM6 and LM6 linkage maps, respectively, with an average genetic distance per chromosome 131.51 and 122.69 cM (Suppl. Table 1). Main- and Epistatic-Effect QTLs Mapping The WinQTLCart 2.5 software ( Wang et al. 2006 ) was employed to identify the M-QTLs using the average values of seed size, seed shape, and 100-seed weight from the individual environments and overall environments with the composite interval mapping model (CIM) ( Zeng 1994 ). The software running features were 10 cM window size, 1 cM running speed, the logarithm of odds (LOD) ( Morton 1955 ) threshold was computed using 1000 permutations due to an experiment-wide error proportion of P < 0.05 ( Churchill and Doerge 1994 ), and the confidence interval was determined utilizing a 1-LOD support interval, which was controlled by finding the local on the two sides of a QTL top that compatible with a reduction of 1 LOD score. The QTL detected within the overlapping intervals in different environments were considered the same ( Palomeque et al. 2009 ; Palomeque et al. 2010 ; Zhaoming et al. 2017 ). Moreover, to identify the genetic effects of the QTLs, i.e., additive QTLs, additive × additive (AA), additive × environment (AE) and AA × environment (AAE), the mixed-model based composite interval mapping (MCIM) procedure was employed in the QTLNetwork V2.1 software ( Yang et al. 2008 ). Critical F-value was calculated by a permutation test with 1000 permutations for MCIM. The effects of QTLs were assessed using the Markov Chain Monte Carlo (MCMC) approach. Epistatic effects, candidate interval selection, and putative QTL detection were estimated with an experiment-wide error proportion of P 10% were considered as major and stable QTLs ( Zhaoming et al. 2017 ). Regions on chromosomes with several M-QTLs related to different traits studied in this research were termed as a QTL Cluster. The Phytozome (http:/phytozome.jgi.doe.gov) and SoyBase (http:/www.soybase.org) online platform repositories, were employed to retrieve all model genes within the physical interval position of the main "QTL Clusters". Possible candidate genes were predicted based on gene annotations (http:/www.soybase.org and https:/phytozome.jgi.doe.gov) as well as the reported putative function of genes implicated in these traits. Gene ontology (GO) information was obtained from SoyBase via the online resources, i.e., The Center for Biotechnology Information (NCBI: https:/www.ncbi.nlm.nih.gov), GeneMania (http:/genemania.org/) and the Kyoto Encyclopedia of Genes and Genomes (KEGG, www.kegg.jp). These online tools were employed to further screen the predicted candidate genes. Gene ontology (GO) enrichment analysis was conducted for all the genes within each QTL cluster region using AgriGO V2.0 (http:/systemsbiology.cau.edu.cn) ( Tian et al. 2017 ). Gene classification was then carried out using Web Gene Ontology (WeGO) Annotation Plotting tool, Version 2.0 ( Ye et al. 2006 ). The publicly available RNA-Seq database on the SoyBase website was used to analyze the expression of the predicted candidate genes in various soybean tissues and the development stages. A heatmap to visualize the fold-change patterns of these candidate genes was developed using the TBtools_JRE 1.068 software ( Chen et al. 2020a ). Results Phenotypic evaluation of seed size, seed shape, and 100-seed weight traits All measured (SL, ST, SW, and HSW), and calculated (SLW, SLT, SWT and FI) phenotypic traits exhibited significant differences among the three parental lines across all environments as indicated by the analysis of variance (Tables S2 and S3). Analysis of variance (ANOVA) revealed that all studied traits were significantly (P < 0. 001 or P < 0.05) influenced by the environment, genotypes and the genotype × environment interaction (Tables S4 and S5), indicating the differential response of the genotypes to the changes in environmental cues. Furthermore, the two populations showed continuous phenotypic variations in all studied traits, implying a polygenic inheritance of these traits (Fig. 2 ). Besides, the estimation of skewness, kurtosis and coefficient of variation (CV) for all studied traits across all environments showed that most of the recorded skewness and kurtosis values were 3 %, emphasizing that these traits in both populations are controlled by polygenes and are fit for QTL mapping (Tables S2 and S3). The differences in mean phenotypic values among the three parental lines for seed size, seed shape, and HSW traits were constantly high across all studied environments, and their multi-environment means for both populations (Fig. 3 ). The female parent of the LM6 population, Linhefenqingdou, exhibited an average increase of 27.80, 28.19, 31.10 and 41.37 % in SL, ST, SW and HSW, respectively, compared to the male parent, M8206. Meanwhile, in the ZM6 population the female parent Zhengyang surpassed the male parent M8206 by an average of 11.00, 9.66, 7.65 and 17.53 % in SL, ST, SW and HSW across all environments, respectively (Fig. 3 , Tables S2 and S3). In both populations, several lines overstep their parents in both directions in all studied traits across all environments, suggesting the occurrence of transgressive segregations within the two populations (Fig. 2 , 3 ). The broad-sense heritability (h 2 ) under individual environments ranged from 69.58–99.04%, 69.08–97.9% and 78.49–98.68% for seed size, HSW and seed shape (Tables S2 and S3). Meanwhile, h 2 under combined environments (CE) ranged from 90.82–94.48%, 87.51–94.46% and 97.58–98.39% for seed size, shape and HSW, respectively. The correlation coefficient (r 2 ) among SL, ST and SW exhibited significant positive correlations with each other and with two of the seed shape traits (SLT and SLW) in both populations with r 2 values ranged from 0.79–0.91. Meanwhile, SL, ST and SW exhibited significant negative correlations with the other two seed shape traits (SWT and FI) (Suppl. Table 6). Except for the correlation between SLW and SWT, all the seed shape traits showed significant positive correlations with each other in both populations with r 2 values ranged from 0.33–0.95. Furthermore, all seed size traits, i.e., SL, SW, and ST showed significant positive correlations with HSW with r 2 values ranged from 0.29 to 0.70 in both populations. Mapping of seed size main-effect QTLs in the two-related RIL populations A total of 92 M-QTLs were mapped for seed size related traits, i.e., SL, SW and ST, on all chromosomes in soybean, except chromosomes 1 and 12, with logarithm of odd (LOD) scores and phenotypic variations (R 2 ) ranged from 2.5–10.3 and 5.0-19.7 %, respectively, in the two populations (Suppl. Table 7, Suppl. Figure 1a, b, c). Out of these, 30 M-QTLs for SL, 35 for SW and 27 for ST with alleles underlying QTLs emanated from either of parents. Seventy-two M-QTLs were mapped in a specific environment while the remaining 20 were mapped within overlapping regions in at least one specific environment together with or without CE. Forty-seven QTLs that exhibited R 2 > 10 %, hence were considered as major QTLs. The most prominent QTL was the qSW-17-2 LM6 (LOD = 6.70-10.29, and R 2 = 16.60–18.30 %) was detected within the physical position 6844412–9645325 bp in 14JP and CE (Fig. 4 b). Likewise, the qSL-10-2 ZM6, LM6 (LOD = 6.08–6.89, and R 2 = 15.4–17.1 % in ZM6 (17JP) and LM6 (14JP) populations) was located to the physical position between 41454163–43944243 bp. Aside, qSL-10-2 ZM6, LM6 , in terms of stability across at least two specific environments with or without the CE, qSL-10-1 LM6 , qSL-11-1 ZM6 , qSL-18-3 LM6 , qSL-20-1 ZM6 , qSW-10-2 ZM6 , qSW-16-1 LM6 , qST-4-1 LM6, ZM6 , qST-16-1 LM6 and qST-20-1 ZM6 accounted for averages of 11.17, 14.00, 10.70, 7.65, 11.30, 6.75, 8.85, 13.30 and 11.13 %, respectively (Suppl. Table 7, Suppl. Figure 1a, b, c). Mapping of seed shape main-effect QTLs in the two populations. In total, ninety-nine M-QTLs related to seed shape traits (SLT, SLW, SWT, and FI) were mapped to 19 soybean chromosomes excluding chromosome (Chr02) in both populations (ZM6 and LM6) among four environments plus CE with LOD scores of (2.50-10.44) and R 2 (5.12–31.56 %) by the composite interval mapping (CIM) approach (Suppl. Table 8, Suppl. Figure 1d, e, f, g). From the 99 M-QTLs, 22, 33, 11, and 22 were detected for SLT, SLW, SWT, and FI, respectively (Suppl. Table 8). Among them, seventy-one M-QTLs were detected in specific environments, while 28 were mapped in at least one specific environment together either with or without the CE. Eight M-QTLs for SLW ( qSLW-3-2 LM6 , qSLW-5-3 ZM6, LM6 , qSLW-9-3 LM6, ZM6 , qSLW-13-4 ZM6 , LM6 , qSLW-15-1 LM6 , qSLW-15-3 LM6 , qSLW-16-2 ZM6 and qSLW-16-3 ZM6 ) were mapped in at least one specific environment with or without the CE. Similarly, seven M-QTLs for SLT ( qSLT-1-3 LM6 , qSLT-5-3 ZM6 , qSLT-11-1 LM6 , qSLT-13-1 LM6 , qSLT-14-1 LM6 , qSLT-16-1 LM6 and qSLT-20-1 ZM6 ) were mapped in at least one specific environment with or without CE (Suppl. Table 8, Suppl. Figure 1e). Likewise, four M-QTLs ( qSWT-8-1 LM6 , qSWT-11-2 ZM6, LM6 , qSWT-13-1 ZM6 and qSWT-17-1 ZM6 ) were mapped for SWT (Suppl. Table 8, Suppl. Figure 1f). Also, a total of 10 M-QTLs ( qFI-1-1 ZM6 , qFI-1-2 ZM6, LM6 , qFI-1-3 LM6 , qFI-1-4 ZM6, LM6 , qFI-3-1 LM6 , qFI-3-3 ZM6 , qFI-5-2 ZM6 , qFI-11-1 LM6 , qFI-14-1 LM6 , qFI-17-1 ZM6 and qFI-20-1 ZM6 ) were considered as stable. Several physical regions identified harbored at least two seed shape related traits, i.e., 1730667–3014518 bp harbored qSLT-1-2 ZM6 , qSWT-1-1 ZM6 and qFI-1-2 ZM6, LM6 , and 4946300–35955471 bp had qSLT-1-3 LM6 , qSWT-1-2 LM6 and qFI-1-3 LM6 on Chr01 (Suppl. Figure 2, Suppl. Table 8). Two M-QTLs ( qSLW-3-2 LM6 and qFI-3-1 LM6 ) colocalized within the physical region of 1509548–3515594 bp. Moreover, qSLT-5-3 ZM6 , qFI-5-2 ZM6 and qSLW-5-3 ZM6, LM6 overlapped within the physical region of 38035798–41186985 bp (Suppl. Figure 2, Suppl. Table 8). Furthermore, qSLT-11-1 LM6 and qFI-11-1 LM6 (Chr11); qSLT-13-1 LM6 , qSLW-13-4 ZM6, LM6 and qSWT-13-1 ZM6 ( Chr13 ); qFI-14-1 LM6 , qSLT-14-1 LM6 and qFI-14-1 LM6 (Chr14) located within regions of 17145381–23469672, 33303067–39562563 and 3468251–8668367 bp, respectively (Suppl. Figure 2, Suppl. Table 8). qSLW-16-2 ZM6 , qSLT-16-1 LM6 and qSLW-16-3 ZM6 QTLs qFI-17-1 ZM6 and qSWT-17-1 ZM6 and qSLT-20-1 ZM6 and qFI-20- 1 ZM6 were located to the chromosomal regions of 26903205–31959397 on Chr16, 40207655–41672092 bp on Chr17 and 1 -1115156 bp on Chr20, respectively (Suppl. Figure 2, Tables S8). Mapping of hundred-seed weight main-effect QTLs in the two populations A total of 48 M-QTLs for HSW were detected, from which 27 were detected in a specific environment and 21 were mapped in at least one specific environment and/without CE (Suppl. Table 9, Suppl. Figure 1h). The LOD scores and R 2 values of these M-QTLs ranged from 2.51–10.61 and 4.8–24.5 %, respectively. The highest number of M-QTLs of 6 were mapped on Chr04 followed by Chr10 with 5 M-QTLs and the lowest number of one M-QTL was mapped to Chr02, Chr09, Chr17 and Chr18. The most prominent M-QTLs were qHSW-14-2 ZM6 , qHSW-10-3 LM6 and qHSW-10-4 LM6 with LOD scores and R 2 values of 10.61 and 24.50 % (Fig. 4 ), 7.57 and 17.60 %, and 7.20 and 16.90 %, respectively. Among those 21 M-QTLs, qHSW-4-3 LM6 , ZM6 , qHSW-6-2 LM6 , qHSW-10-1 LM6 , qHSW-13-1 ZM6 , qHSW-15-2 LM6 and qHSW-15-4 LM6 were mapped in at least three environments with an average R 2 of 13.01 %. Comparative analysis of main-effect QTLs for seed size, shape and weight in the two-related populations Regions on chromosomes with several identified M-QTLs for different studied seed phenotypic traits were designated as a QTL cluster. Accordingly, twenty-four QTL clusters located on 17 chromosomes with exception of Chr02, Chr12 and Chr18, were identified (Suppl. Table 10, Suppl. Figure 2). Among the identified 24 clusters, seven clusters harbored QTLs related to seed size, seed shape, and HSW, five clusters harbored QTLs related only to seed size and seed shape traits, nine clusters comprised QTLs related to seed size and HSW traits, and 3 clusters harbor QTLs for only seed shape traits (Suppl. Table 10). The majority of these clusters contained major QTLs. Furthermore, QTLs within 15 clusters revealed positive additive effects with the beneficial alleles inherited from the big seed size and heavy seed weight parents (either Zhengyang or Linhefenqingdou ). Eight clusters out of 24 contain QTLs that have been detected in both populations (Suppl. Table 10). The most prominent M-QTL ( qFI-1-3 LM6 ) with a LOD score of 3.71–10.44 and R 2 (10.45–31.50 %) was located in Cluster-01. Each cluster comprised a different number of QTLs, with the highest number of QTLs, i.e., seven associated with seed size, shape, and HSW traits were in Cluster-03 at the physical position of 1,509,548-6,780,840 bp allocated as two QTLs related to seed size ( qSL-3-1 LM6 and qSL-3-2 LM6 ), four QTLs for seed shape ( qSLW-3-2 LM6 , qFI-3-1 LM6 , qSLT-3-1 LM6 , and qFI-3-2 LM6 ) and one QTL HSW ( qHSW-3-1 LM6 ). In addition, except for qHSW-3-1 LM6 , all QTLs in this cluster were major QTLs with R 2 > 10 %. Furthermore, each of clusters-13, 16.2, and 17.1 contained five or six QTLs only related to seed size and HSW traits and displayed R 2 of 8.85–13.43 %, 5.96–11.26 %, and 6.8–18.30 %, respectively, and these clusters comprised M-QTLs from only one of the two RIL populations (Suppl. Figure 2, Suppl. Table 10). Another rich region of QTLs was cluster-20 on Chr20 with a physical length of 1.2Mb. This region harbored 5 M-QTLs related to seed size and shape, i.e., qFI-20-1 ZM6 , qSLT-20-1 ZM6 , qSLW-20-1 ZM6 , qST-20-1 ZM6 , and qSW-20-1 ZM6 , out of these five QTLs, three are major QTLs with R² of 11.2–19.2% (Suppl. Table 10). Cluster-09 contained 5 QTLs related to seed size, shape, and HSW and displayed R² of 16.3 % and 12.5 % for qHSW-9-1 ZM6 and qSLW-9-3 LM6 , respectively, across the two populations. Cluster-14.1 consisted of four major M-QTLs within the physical region between 5834015-9844637bp in both populations with R 2 values ranged from10.4-18.4%, one from which ( qSW-14-2 ZM6 ) are related to seed size traits, and three ( qSLW-14-1 LM6 , qFI-14-1 LM6 , and qSLT-14-1 LM6 ) for seed shape traits all were (Suppl. Table 10). Six clusters harbored 4 M-QTLs each, were identified, from which four clusters harbored QTLs associated with HSW as well as some seed size and seed shape traits, i.e., cluster-07 and cluster-19.1, cluster-08 and cluster-14.2 cluster-10.2 mapped on Chr10 and contained M-QTLs for seed size and seed shape traits, and cluster-16.1 that contained only M-QTLs related to seed shape traits (Suppl. Table 10). The remaining 9 clusters have three QTLs each, out of them cluster-01 and cluster-17.2 that contain major QTLs related only to seed shape traits. Conversely, cluster-04.1 and cluster-19.2 contain minor M-QTLs associated with SW, SL and HSW. Another two clusters contained M-QTLs for both seed size and seed shape traits (Suppl. Table 10). Moreover, the other three clusters of M-QTLs, i.e., cluster-10.1, cluster-11 and cluster-15 contained both major and minor QTLs for seed size traits and HSW. The Cluster-04.2 had two QTLs for two traits, qHSW-4-3 LM6, ZM6 , and qSL-4-1 ZM6 with R 2 of 13.1–17.7 %. Among the identified 24 clusters, seven clusters harbored QTLs related to seed size, seed shape, and HSW, five clusters harbored QTLs related to only seed size and seed shape traits, nine clusters had QTLs related to seed size and HSW traits, and three clusters harbored QTLs for only seed shape traits (Suppl. Table 10). The majority of these clusters had major QTLs. Furthermore, QTLs within 15 clusters revealed positive additive effects with the beneficial alleles inherited from the big seed size and heavy seed weight parents (either Zhengyang or Linhefenqingdou ). Eight clusters out of twenty-four contained QTLs that have been detected in both populations (Suppl. Table 10). Analyses of additive effect QTL and additive QTL ×environment interactions for seed size, shape and weight. The mixed-model based composite interval mapping (MCIM) method was implemented in QTL Network V2.1 software to map for additive effect ( A ) QTLs and their interactions with the environment ( AE ) was performed for both RIL populations across multi- environments. In total, thirty-five AA on 17 chromosomes related to seven seed size and seed shape traits were identified. These comprised 9, 3, 7, 3, 4, 1, and 8 A QTLs associated with SL, SW, ST, SLW, SLT, SWT, and FI, respectively, in the LM6 and ZM6 populations across all environments (Table 1 ). Furthermore, the contributed allele of 11 QTLs of them was inherited from the M8206 parent that decreased seed size and seed shape values through significant additive effects. Meanwhile, the contributed allele of the remaining 24 QTLs descended from either Zhengyang or Linhefenqingdou parent of the ZM6 or LM6 population, respectively, that increased seed size and shape values through significant additive effects (Table 1 ). On the other hand, thirteen out of 35 QTLs revealed significant AE effects in at least one environment. However, five QTLs, i.e., qSW-13-5 LM6 , qSW-19-2 LM6 , qFI-8-6 LM6 , qSL-10-2 ZM6 and qST-13-5 ZM6 , showed significant or highly significant AE among all studied environments (Table 1 ). Furthermore, the influence of AE effects on seed size and seed shape values was environmentally dependent (Table 1 ). Eight AA QTLs associated with HSW were identified on 6 chromosomes, i.e., Chr03, Chr08, Chr09, Chr13, Chr14 and Chr16 in LM6 and ZM6 populations across six environments (Table 2 ). Six of those 8 QTLs displayed a positive additive effect with the beneficial allele inherited from the female parent ( Linhefenqingdou or Zhengyang in the LM6 or ZM6 population) which could increase HSW. Meanwhile, the remaining two QTLs, i.e., qHSW-13-3 ZM6 and qHSW-14-2 ZM6 , revealed negative additive effects with the alleles are inherited from the common male parent ( Meng8206 ) which could reduce HSW (Table 2 ). Additionally, two QTLs, i.e., qHSW-14-3 LM6 and qHSW-8-3 ZM6 , displayed significant AE effects in two individual environments. Whereas, the qHSW-13-3 ZM6 showed a significant AE only in the 13JP environment. In addition, the qHSW-14-4 ZM6 revealed significant or highly significant AE effects across three different environments, i.e., 12FY, 12JP, and 17JP (Table 2 ). Table 1 Additive and additive × environment interaction effect of QTLs associated with seed size traits (SL, SW and ST) and seed shape (SLW, SLT, SWT and FI) traits in soybean seeds. QTL 1 Marker interval Position (cM) 2 Physical position (bp) Additive -Effect (A) 3 Additive × Environment Effect (AE) 4 Reference A H²% AE1 AE2 AE3 AE4 H²% qSL-7-1 LM6 bin744-bin745 15.25 3324836–3459470 0.16** 17.45 NS NS NS NS 0.07 Hu et al. 2013 qSL-13-6 LM6 bin1535-bin1536 140.15 43244220–44026619 0.13** 6.08 NS NS NS NS 0.28 Salas et al. 2006 qSW-13-5 LM6 bin1536-bin1537 143.04 43953331–44408971 0.51** 10.52 0.18* -0.18* 0.16* 0.21** 0.15 Salas et al. 2006 qSW-19-2 LM6 bin2100-bin2101 42.57 34493194–34882495 0.18** 19.47 -0.13** -0.11* 0.06* 0.09* 0.18 New qST-9-5 LM6 bin1022-bin1023 51.88 7308659–7459924 0.12** 8.6 NS NS NS NS 0.03 Salas et al. 2006 qST-18-4 LM6 bin1979-bin1980 43.69 9222099–10402370 0.06** 11.34 NS NS NS NS 0.07 Fang et al. 2017 qSLT-3-1 LM6 bin247-bin248 19.49 3119582–3515594 0.12** 2.67 NS − .22** NS NS 4.93 New qSLT-14-1 LM6 bin1586-bin1587 42.38 7850227–8143522 -0.09** 1.96 NS 0.27** NS NS 6.73 Li et al. 2010 qSLT-17-5 LM6 bin1883-bin1884 70.26 13441932–13696232 -0.13** 2.41 NS NS NS NS 1.05 New qSWT-7-5 LM6 bin816-bin817 79.69 29822346–35034728 -0.67** 12.58 NS NS NS NS 1.27 Fang et al. 2017 qFI-3-3 LM6 bin244-bin245 17.30 2790829–2980527 0.16** 11.48 NS NS NS NS 0.13 New qFI-5-1 LM6 bin476-bin477 0.21 1- 529217 0.17** 21.22 NS NS NS NS 0.15 New qFI-8-6 LM6 bin954-bin955 95.88 35158414–37964850 0.17** 15.74 0.21* -0.19* 0.14** 0.21* 0.11 New qFI-9-5 LM6 bin1030-bin1031 56.45 20192294–27035074 -0.13** 4.27 NS NS NS NS 0.21 New qFI-11-3 LM6 bin1290-bin1291 69.93 18546688–18767705 0.1** 2.27 NS NS NS NS 1.47 New qFI-16-1 LM6 bin1745-bin1746 1.66 697999–908917 -0.08** 14.74 NS NS NS NS 0.11 New qSL-1-4 ZM6 bin4-bin5 3.23 754691–1375000 0.05** 3.33 NS NS NS NS 0.08 New qSL-9-2 ZM6 bin1174-bin1175 90.55 38507474–38736001 0.04* 2.06 NS NS 0.07* NS 2.29 New qSL-10-1 ZM6 bin1236-bin1237 24.62 3150454–3297961 0.05** 13.67 NS NS NS NS 1.4 New qSL-10-2 ZM6 bin1334-bin1335 106.35 44226599–44378813 0.05** 5.57 -0.1** 0.06* 0.09** -0.07* 3.58 Li et al. 2010 qSL-12-4 ZM6 bin1553-bin1554 97.99 38615116–38812896 -0.05** 3.17 NS NS NS NS 0.83 New qSL-13-2 ZM6 bin1612-bin1613 71.65 25830321–26065585 -0.08** 6.17 NS NS NS NS 1.33 Fang et al. 2017 qSL-15-5 ZM6 bin1918-bin1919 85.59 17503517–17963129 0.14** 3.56 NS NS NS NS 0.08 Salas et al. 2006 qSW-8-5 ZM6 bin959-bin960 73.74 11970511–12228336 0.04** 3 NS NS NS NS 0.65 New qST-10-5 ZM6 bin1334-bin1335 106.35 44226599–44378813 0.42** 3.99 NS NS NS NS 3.07 Hu et al. 2013 qST-10-6 ZM6 bin1336-bin1337 107.17 44378814–44741960 -0.41** 2.25 NS NS 0.15** NS 3.17 New qST-13-5 ZM6 bin1609-bin1610 67.36 24985496–25641179 -0.41** 4.1 0.8** -0.6* -0.7** 0.61* 3.41 New qST-14-3 ZM6 bin1809-bin1810 104.68 47489495–47717306 0.04** 2.87 NS NS 0.05** NS 2.42 New qST-20-1 ZM6 bin2463-bin2464 4.37 662753–1045131 -0.06** 3.78 NS NS NS NS 0.71 New qSLW-9-4 ZM6 bin1172-bin1173 89.06 38139739–38507473 0.99** 2.51 NS NS NS NS 1.25 Li et al. 2010 qSLW-10-2 ZM6 bin1275-bin1279 60.40 14218565–17808941 0.82** 12.74 NS -0.92* 0.95* NS 2.3 New qSLW-13-5 ZM6 bin1653-bin1654 102.63 32704220–33303066 -0.1** 1.15 NS 0.3** NS NS 3.95 Salas et al. 2006 qSLT-5-3 ZM6 bin600-bin599 93.89 40328493–40882874 0.013** 11.18 NS NS NS NS 0.32 Salas et al. 2006 qFI-17-6 ZM6 bin2177-bin2178 130.75 41009636–41399912 0.06** 14.81 NS NS NS NS 0.6 New qFI-20-1 ZM6 bin2461-bin2462 2.80 1- 662752 0.08** 5.45 NS NS 0.12* 0.09* 0.28 New Chr., chromosome. * p < 0.05; ** p < 0.01; NS, non-significant. A indicates additive effects, those with positive values show beneficial alleles from parents Zhengyang and Linhefenqingdou while those with negative values show beneficial alleles from parent Meng 8206.H2 indicates phenotypic variation explained by additive effects. AE1, FY2012; AE2, JP2012; AE3, JP2014; AE4, JP2017. Table 2 Additive and additive × environment interaction effect of QTLs associated with 100-seed weight trait in soybean seeds. QTL 1 Marker interval Position (cM) 2 Physical position (bp) Additive -Effect (A) 3 Additive × Environment Effect (AE) 4 Reference A H²% AE1 AE2 AE3 AE4 AE5 AE6 H²% qHSW-3-2 LM6 bin255-bin256 30.64 5833775–6780840 0.61** 10.75 NS NS NS NS NS NS 0.16 Li et al. 2008 qHSW-14-3 LM6 bin1640-bin1641 101.33 48267526–48523627 0.48** 0.28 0.52* NS NS NS -0.16* NS 1.99 New qHSW-8-3 ZM6 bin963-bin964 80.64 12871276–13803222 0.29** 3.21 NS NS NS 0.34* -0.41* NS 0.42 Han et al. 2012 qHSW-9-1 ZM6 bin1162-bin1163 77.51 35758796–36561550 0.40** 2.47 NS NS NS NS NS NS 0.88 New qHSW-13-3 ZM6 bin1611-bin1612 69.99 25641180–26012595 -0.33** 5.7 NS NS -0.38* NS NS NS 1.13 Funatsuki et al. 2005 qHSW-14-2 ZM6 bin1746-bin1747 28.67 4176245–4861311 -0.15** 1.66 NS NS NS NS NS NS 0.17 New qHSW-14-4 ZM6 bin1809-bin1810 104.68 47489495–47717306 0.46** 2.78 0.54** NS NS 0.43* NS -0.69** 4.75 New qHSW-16-3 ZM6 bin2043-bin2044 103.81 35441262–35607069 0.21** 1.67 NS NS NS NS NS NS 0.29 New Chr., chromosome. * p < 0.05; ** p < 0.01; NS, non-significant. A indicates additive effects, those with positive values show beneficial alleles from parents Zhengyang and Linhefenqingdou while those with negative values show beneficial alleles from parent Meng 8206.H2 indicates phenotypic variation explained by additive effects. AE1, FY2012; AE2, JP2012; AE3, JP2013; AE4, JP2014; AE5, YC2014; AE6, JP2017. Comparison Of Two Mapping Approaches A total of 92, 99, and 48 M-QTLs associated with seed size, seed shape, and HSW, respectively, were mapped by the CIM approach (Tables S7-S9). Meanwhile, forty-three QTLs were identified for seed size, shape and HSW by using MCIM approach (Tables 1 and 2 ). Among these, twenty-two QTLs were identified by both approaches within the same physical chromosomal position, indicating the dependability and stability of these QTLs. Moreover, a comparison of the physical chromosomal regions of the QTLs detected by both approaches revealed that four QTLs, i.e., qSL-7-1 LM6 , qSW-19-2 LM6 , qFI-3-1 LM6 , and qHSW-3-2 LM6 , were identified for the first time in the two populations (LM6 and ZM6) with an R 2 > 10 %. Therefore, we considered these QTLs as novel and most stable QTLs that could be validated and utilized for map-based cloning, candidate genes identification and QTL stacking into elite cultivars targeted at improving seed size, shape and HSW in soybean. Analyses of epistatic-effect QTLs and their interaction with the environment Analysis of the seed size and shape traits data under all four environments identified 38 pairwise epistatic effects (AA) QTLs, from which 2, 13, 6, 2, 3, 5, and 7 pairs were related to SL, SW, ST, SLW, SLT, SWT, and FI traits, respectively, with R 2 values ranged 0.51–11.35 % (Table 3 ). All QTL pairs displayed a high significant additive × additive ( AA ) effect. Further analyses revealed that 20 AA QTLs showed significant or highly significant pairwise additive-additive-environment ( AAE ) interaction effects in at least one environment with R 2 values ranged 0.13–5.31 % (Table 3 ). Furthermore, ten pairs showed significant AAE in two environments, i.e., 12FY (AAE1), and 12JP (AAE2), while three pairs displayed significant AAE in 12JP (AAE2) and 14JP (AAE3) environments (Table 3 ). This indicates the effect of the environment on gene expression on phenotype development through epistatic effects. Out of 38 QTLs, sixteen pairwise interactions exhibited negative epistatic effects ( AA ) that decreased the values of seed size and shape traits, whereas 22 pairwise interactions exhibited positive epistatic effects ( AA ) that increased the values of seed size and shape traits (Table 3 ). The pairwise interaction between qFI-1-1 ZM6 and qFI-7-3 ZM6 revealed the strongest positive epistatic effect (0.65), whereas the pairwise qSLT-6-1 LM6 and qSLW-9-1 LM6 , revealed the weakest positive epistatic effect (0.02). Conversely, qSWT-3-1 LM6 and qSWT-13-1 LM6 resulted in the strongest negative epistatic effect (-0.71), whereas the pairwise qSLW-2-6 ZM6 and qSLW-18-3 ZM6 pairwise resulted in the weakest negative epistatic effect (-0.02) (Table 3 ). Table 3 Estimated epistatic effects (AA) and environmental (AAE) interaction of QTLs for soybean seed size traits (SL, SW, and ST) and seed shape (SLW, SLT, SWT, and FI) traits across all environments. RIL Trait QTL_i Chr_i Interval_i Pos_i QTL_j Chr_j Interval_j Pos_j Epistasis -Effect (AA) Epistasis × Environment Effect (AAE) AA H²% AAE1 AAE2 AAE3 AAE4 H²% LM6 SW qSW-2-2 LM6 2 bin124-bin125 23.92 qSW-16-2 LM6 16 bin1757-bin1758 18.19 0.08** 7.24 NS NS NS NS 1.11 qSW-16-2 LM6 3 bin229-bin230 1.96 qSW-13-1 LM6 13 bin1509-bin1510 107.72 -0.1** 5.40 NS NS NS NS 0.24 qSW-4-2 LM6 4 bin353-bin354 13.86 qSW-15-3 LM6 15 bin1738-bin1739 106.86 -0.53** 2.85 NS NS NS NS 2.34 qSW-4-2 LM6 4 bin353-bin354 13.86 qSW-15-4 LM6 15 bin1740-bin1741 110.00 0.33** 7.30 0.11** -0.11** NS NS 3.95 qSW-5-1 LM6 5 bin525-bin526 74.76 qSW-12-1 LM6 12 bin1352-bin1353 21.59 -0.11** 3.96 NS NS NS NS 0.96 qSW-7-1 LM6 7 bin784-bin785 53.84 qSW-15-4 LM6 15 bin1740-bin1741 110.00 -0.10** 5.83 NS NS NS NS 0.05 qSW-8-1 LM6 8 bin984-bin985 130.62 qSW-10-3 LM6 10 bin1219-bin1220 97.61 -0.21** 3.20 -0.13* 0.12* NS NS 1.39 qSW-10-1 LM6 10 bin1184-bin1185 62.20 qSW-20-1 LM6 20 bin2231-bin2232 69.64 -0.09** 2.20 NS NS NS NS 1.19 qSW-10-3 LM6 10 bin1219-bin1220 97.61 qSW-16-4 LM6 16 bin1810-bin1811 87.22 0.14** 1.47 NS NS NS NS 0.18 qSW-11-1 LM6 11 bin1291-bin1292 70.56 qSW-15-1 LM6 15 bin1715-bin1716 85.15 -0.17** 4.79 NS NS NS NS 1.08 qSW-11-2 LM6 11 bin1292-bin1296 71.58 qSW-15-2 LM6 15 bin1717-bin1718 85.58 0.23** 9.22 NS NS NS 0.10** 1.48 ST qST-3-1 LM6 3 bin237-bin238 8.12 qST-3-3 LM6 3 bin344-bin345 114.06 0.05** 0.51 NS NS NS NS 0.56 qST-6-3 LM6 6 bin647-bin648 109.3 qST-11-1 LM6 11 bin1274-bin1275 54.45 0.1** 5.55 NS NS NS 0.12** 1.40 qST-7-2 LM6 7 bin749-bin750 19.81 qST-16-3 LM6 16 bin1755-bin1756 17.12 0.09** 6.31 NS NS NS NS 0.57 qST-7-3 LM6 7 bin783-bin784 53.32 qST-15-2 LM6 15 bin1741-bin1742 110.5 -0.12** 5.74 NS NS NS NS 0.69 qST-16-1 LM6 16 bin1744-bin1745 1.66 qST-17-2 LM6 17 bin1886-bin1887 72.87 -0.14** 9.36 0.06* -0.06* NS NS 2.04 SLW qSLW- 8-2 LM6 8 bin941-bin942 86.24 qST- 14-3 LM6 14 bin1625-bin1626 84.91 0.03** 11.35 NS NS NS NS 1.41 SLT qSLT-5-3 LM6 5 bin560-bin543 92.74 qSLW-6-2 LM6 6 bin625-bin626 82.38 -0.04** 5.40 0.03* NS NS NS 2.85 qSLT-6-1 LM6 6 bin584-bin585 28.72 qSLW-9-1 LM6 9 bin1095-bin1096 128.66 0.02** 1.61 -0.03* 0.03* NS NS 3.00 SWT qSWT-3-1 LM6 3 bin236-bin237 7.79 qSWT-13-1 LM6 13 bin1435-bin1434 15.23 -0.71** 5.19 0.81** -0.8** NS NS 4.57 qSWT-6-1 LM6 6 bin588-bin589 33.52 qSWT-18-1 LM6 18 bin2036-bin2037 95.49 -0.62** 3.88 -0.64* 0.63* NS NS 0.67 qSWT-11-1 LM6 11 bin1262-bin1263 40.91 qSWT-20-1 LM6 20 bin2177-bin2178 19.84 0.14** 8.03 -0.11** 0.15** NS NS 4.19 qSWT-16-1 LM6 16 bin1744-bin1745 1.66 qSWT-17-3 LM6 17 bin1886-bin1887 72.87 0.11** 11.08 -0.15** 0.17** NS NS 5.31 FI qFI-1-5 LM6 1 bin59-bin60 55.61 qFI-14-4 LM6 14 bin1627-bin1628 85.96 0.17** 2.89 NS NS 0.09* NS 2.24 qFI-5-2LM6 5 bin516-bin517 65.79 qFI-10-2 LM6 10 bin1223-bin1224 108.26 0.04** 10.02 NS NS NS NS 0.02 qFI-16-2 LM6 16 bin1745-bin1746 1.66 qSWT-17-2 LM6 17 bin1886-bin1887 38.76 0.02** 6.76 -0.18* 0.02* NS 0.03* 2.75 SL qSL-12-4 ZM6 12 bin1553-bin1554 97.99 qSL-15-5 ZM6 15 bin1919-bin1920 85.99 -0.06** 1.88 NS NS NS NS 0.13 qSL-2-3 ZM6 2 bin214-bin211 96.57 qSL-8-6 ZM6 8 bin1084-bin1085 186.90 0.2** 8.61 NS NS NS NS 0.10 ZM6 SW qSW-4-4 ZM6 4 bin434-bin435 62.42 qSW-20-5 ZM6 20 bin2590-bin2591 97.79 -0.12** 6.68 NS NS NS 0.07* 0.33 qSW-6-3 ZM6 6 bin684-bin685 86.36 qSW-6-5 ZM6 6 bin703-bin704 109.84 0.05** 0.66 -0.08** 0.08** NS NS 3.35 ST qST-10-5 ZM6 10 bin1334-bin1335 106.35 qST-10-6 ZM6 10 bin1336-bin1337 107.17 -0.7** 2.36 NS NS NS NS 0.48 SLW qSLW-2-6 ZM6 2 bin260-bin261 158.24 qSLW-18-3 ZM6 18 bin2336-bin2337 123.90 -0.02** 1.14 NS 0.04** -0.03** NS 3.74 SLT qSLT-1-4 ZM6 1 bin53-bin54 43.95 qSLT-7-2 ZM6 7 bin884-bin885 103.56 0.03** 4.22 NS -0.03* 0.03** NS 2.58 SWT qSWT-1-3 ZM6 1 bin62-bin63 47.73 qSWT-8-1 ZM6 8 bin914-bin915 15.98 0.13** 5.30 NS NS NS NS 0.29 FI qFI-17-6 ZM6 17 bin2177-bin2178 130.75 qFI-20-1 ZM6 20 bin2461-bin2462 2.79 0.51** 1.75 NS NS NS NS 1.20 qFI-1-1 ZM6 1 bin58-bin59 46.05 qFI-7-3 ZM6 7 bin884-bin885 103.56 0.65** 5.14 NS NS 1.01** NS 1.26 qFI-1-3 ZM6 1 bin72-bin73 66.36 qFI-7-1 ZM6 7 bin872-bin873 96.62 0.3** 6.01 NS NS NS NS 0.65 qFI-3-1 ZM6 3 bin289-bin290 27.39 qFI-18-2 ZM6 18 bin2313-bin2314 112.03 0.12** 7.29 NS 0.9* -0.86* NS 1.88 Chr_i and Chr_j indicate the two sites involved in epistatic interactions; Pos indicates genetic position for each of the sites. * p < 0.05; ** p < 0.01; NS, non-significant. AA indicates epistatic effects between two QTLs, those with positive values show two loci genotypes being the same as those in parent Linhefenqingdou, Zhengyang (or Meng 8206) have the beneficial effects, while the two-loci recombinants take the negative effects. The case of negative values is the opposite. H² indicates phenotypic variation explained by epistatic effects. AE1, FY2012; AE2, JP2012; AE3, JP2014; AE4, JP2017. Two digenic pairwise epistatic QTLs for HSW with highly significant additive × additive ( AA ) effects were identified on 4 chromosomes (Table 4 ). The first pairwise is composed of 2 QTLs, qHSW-11-1 LM6 , and qHSW-20-1 LM6 with an R² of 3.46 %, whereas the second pairwise comprises the two QTLs qHSW-9-1 ZM6 and qHSW-16-3 ZM6 with an R² of 1.38 %. In addition, the two pairwise interactions exhibited positive epistatic effects that could increase the HSW in both populations. Meanwhile, the two pairs did not show any significant AAE interaction effects across all six environments (Table 4 ). Table 4 Estimated epistatic effects (AA) and environmental (AAE) interaction of QTLs for soybean 100-seed weight across all environments. QTL_i Chr_i Interval_i Pos_i Physical position (bp)_i QTL_j Chr_j Interval_j Pos_j Physical position (bp)_j Epistasis -Effect (AA) Epistasis × Environment Effect (AAE) AA H²% AAE1 AAE2 AAE3 AAE4 AAE5 AAE6 H²% qHSW-11-1 LM6 11 bin1245-bin1246 27.25 6135584–6494224 qHSW-20-1 LM6 20 bin2175-bin2176 17.48 1272590–1470471 0.51** 3.46 NS NS NS NS NS NS 0.32 qHSW-9-1 ZM6 9 bin1162-bin1163 77.51 35758796–36561550 qHSW-16-3 ZM6 16 bin2043-bin2044 103.81 35441262–35607069 0.34** 1.38 NS NS NS NS NS NS 0.06 Chr_i and Chr_j indicate the two sites involved in epistatic interactions; Pos indicates genetic position for each of the sites. * p < 0.05; ** p < 0.01; NS, non-significant. AA indicates epistatic effects between two QTLs, those with positive values show two loci genotypes being the same as those in parent Linhefenqingdou, Zhengyang (or Meng 8206) have the beneficial effects, while the two-loci recombinants take the negative effects. The case of negative values is the opposite. H² indicates phenotypic variation explained by epistatic effects. AE1, FY2012; AE2, JP2012; AE3, JP2013; AE4, JP2014; AE5, YC2014; AE6, JP2017. Mining Major M-QTLS Clusters For Candidate Genes Identification The 24 M-QTL clusters were filtered based on the richness in QTLs related to all or some of the seed size, shape and HSW or those with at least one QTL for seed size, shape and HSW traits. As a result, seven QTL clusters, i.e., cluster-03, 04.1, 05.1, 07, 09, 17.1, and 19.1, were used to predict candidate genes based on publicly available databases such as SoyBase and Phytozome, and published papers. According to the physical intervals of the 7 QTL clusters, a total of 242, 190, 444, 367, 437, 523, and 116 genes were identified within cluster-03, 04.1, 05.1, 07, 09, 17.1, and 19, respectively, were retrieved from the SoyBase database ( www.soybase.org ; Suppl. Table 11). Gene ontology (GO) enrichment analyses via AgriGO V2.0 ( http:/systemsbiology.cau.edu.cn ) (Tian et al. 2017 ) were used to classify the model genes in each cluster. The classification was based on molecular function, biological process, and cellular components visualized on the Web-based GO (WeGO) V2.0 https://wego.genomics.cn (Ye et al. 2006 ) In all seven QTL clusters, high percentages of genes were related to catalytic activity, cell part, cell, cellular process, binding, and metabolic process terms, in addition to the response to stimulus in cluster-03 (Fig. 5 ). These indicate essential roles of these terms in the seed size, shape and seed weight development in soybean. Probable candidate genes underlying these QTL clusters responsible for seed size, shape, and HSW in soybean were further predicted based on gene annotations, GO enrichment analysis and the previously known putative biological function of the gene. Based on these, a total of 19, 12, 26, 18, 22, 30, and 16 candidate genes were identified within QTL clusters-03, 04.1, 05.1, 07, 09, 17.1, and 19.1, respectively (Suppl. Table 12). These genes may work directly or indirectly in regulating seed development in soybean, which in turn regulating seed size, shape, and HSW. These genes are involved in response to brassinosteroid stimulus, regulation of cell proliferation and differentiation, regulation of transcription, secondary metabolism and signaling, storage of proteins and lipids, hormone-mediated signaling pathway, regulation of cell cycle process, transport, ubiquitin-dependent protein catabolic process, embryonic pattern specification, and response to auxin stimulus (Table 5 ). However, the RNS-seq data of expression of genes in soybean genome developed by (Severin et al. 2010 ) and publicly available on SoyBase was used to heatmapped the expression of 19, 12, 26, 18, 22, 30, and 16 candidate genes were identified within QTL clusters-03, 04.1, 05.1, 07, 09, 17.1, and 19.1, respectively, in the young leaf, flower, pod, seed, root and nodule (Fig. 6 , Suppl. Table 13). From the heatmaps, forty-seven genes out of the identified 143 candidate genes are highly expressed during seed developmental stages as well as in seed-related tissues (Fig. 6 , Suppl. Table 13), hence these could be considered as potential candidate genes, however, they need screening and validation for utilization for seed size, shape, and weight improvement in soybean. Table 5 Candidate genes identified within the seven QTL clusters that are highly expressed in soybean seed. QTL Clusters Gene Start Stop Gene Functional Annotation Cluster-03 Glyma03g01880 1668601 1674475 Seed dormancy process; protein ubiquitination; lipid storage Glyma03g03210 3001933 3005606 Pollen development; embryo sac egg cell differentiation; DNA-dependent Glyma03g03760 3581308 3584468 Maintenance of shoot apical meristem identity; cell differentiation Glyma03g04330 3581308 3584468 Embryo development; regulation of seed maturation Glyma03g04620 4798039 4801122 Regulation of meristem growth; protein deubiquitination Cluster-04.1 Glyma04g02970 2146489 2152500 Embryo sac egg cell differentiation Glyma04g03210 2347024 2349849 Fatty acid beta-oxidation; response to auxin stimulus; ovule development Glyma04g03610 2630227 2632308 Brassinosteroid mediated signaling pathway; seed development; ovule development Glyma04g04460 3305860 3308715 Response to cytokinin stimulus; response to brassinosteroid stimulus; seed development Glyma04g04540 3395831 3397238 Response to ethylene stimulus; seed dormancy process; floral organ morphogenesis Glyma04g04870 3628743 3634478 Embryo development ending in seed dormancy Cluster-05.1 Glyma05g28950 34669156 34678593 Nucleotide biosynthetic process; embryo development ending in seed dormancy Glyma05g29700 35236284 35242029 Brassinosteroid biosynthetic process; starch biosynthetic process Glyma05g30380 35754306 35755603 Embryo development; protein ubiquitination; lipid storage; anther development Glyma05g31450 36578952 36583516 Post-embryonic development Glyma05g31490 36611301 36615160 Embryo development ending in seed dormancy Glyma05g31830 36870586 36873840 Ubiquitin-dependent protein catabolic process Glyma05g32030 37026301 37031440 Ubiquitin-dependent protein catabolic process; multicellular organismal development Glyma05g33790 38337126 38341410 Phosphatidylcholine biosynthetic process; metabolic process Glyma05g34070 38511154 38513219 Cellular response to abscisic acid stimulus Cluster-07 Glyma07g13230 11764552 11784123 Embryo sac egg cell differentiation; protein ubiquitination; lipid storage Glyma07g13730 12749034 12753558 Embryo development; positive regulation of gene expression Glyma07g14460 13903037 13906228 Embryo development ending in seed dormancy Glyma07g15050 14900705 14909235 Seed dormancy process; regulation of cell cycle process Glyma07g15640 15378798 15384642 Response to hormone stimulus and auxin stimulus; response to brassinosteroid stimulus Glyma07g15840 15528948 15544150 Ubiquitin-dependent protein catabolic process; regulation of lipid catabolic process Cluster-09 Glyma09g28640 35573357 35579018 Embryo development ending in seed dormancy; cellular response to abscisic acid stimulus Glyma09g29030 35989729 35993075 Ubiquitin-dependent protein catabolic process; fatty acid beta-oxidation Glyma09g29720 36540972 36548174 Response to auxin stimulus; auxin metabolic process Glyma09g30130 37014420 37023261 Protein import into nucleus; embryo sac egg cell differentiation Glyma09g30650 37426876 37433118 Phosphatidylcholine biosynthetic process; metabolic process; pollen development Glyma09g31620 38298193 38307446 Response to abscisic acid stimulus; embryo development Glyma09g32600 39100482 39107332 Translational elongation; embryo development ending in seed dormancy Glyma09g32680 39173955 39183935 Regulation of protein phosphorylation Glyma09g33630 40063507 40067999 Response to auxin stimulus; seed dormancy process Cluster-17.1 Glyma17g09320 6889969 6894069 Seed maturation; histone deacetylation; response to abscisic acid stimulus Glyma17g09690 7171761 7186015 Seed maturation; protein ubiquitination; lipid storage Glyma17g10290 7707775 7711360 Pollen tube growth; seed dormancy process; ovule development Glyma17g10380 7768561 7778131 Ubiquitin-dependent protein catabolic process Glyma17g10990 8262700 8267178 Carbohydrate metabolic process Glyma17g11410 8557013 8563158 Regulation of embryo sac egg cell differentiation Glyma17g12950 9873806 9891306 Protein folding; embryo development response to starvation Glyma17g15490 12218497 12226562 Ubiquitin-dependent protein catabolic process Glyma17g15550 12302621 12306143 N-terminal protein myristoylation; pollen development; pollen tube growth Cluster-19.1 Glyma19g32990 40666918 40669847 Glucose catabolic process; response to auxin stimulus Glyma19g33620 41194146 41196743 Maltose metabolic process; starch biosynthetic process; glucosinolate biosynthetic process Glyma19g33650 41237306 41242657 Abscisic acid biosynthetic process; plant-type cell wall modification; pollen tube growth Discussion Dissecting the genetic factors underlying seed size, shape and weight and their relationship to the ambient environment is essential for improving soybean yield and quality-related traits. In addition, understanding the additive and additive × environment effects of QTLs and their contribution to the phenotypic variations would facilitate the application marker-assisted selection (MAS) because it will prominently lead the breeders in the QTL selection and expectation of the outcomes of MAS (Jannink et al. 2009 ). A major objective of utilizing linkage mapping in plant breeding is to deepen our understanding of the inheritance and genetic architecture of quantitative traits and detect markers that can be employed as indirect selection tools in plant breeding (Abou-Elwafa 2016 ; Bernardo 2008 ). In this regard, QTL mapping has been regularly used for detecting the QTL/gene underlying the quantitative traits such as seed size, shape, and weight in crop plants. As known, parental diversity and marker density greatly influence the accuracy and precision of QTL mapping. Besides, the population size used in most of the previously published reports for genetic mapping studies usually varied from 50–250 individuals, but larger populations are needed for high-resolution mapping. Moreover, a high-density genetic map facilitates the detection of narrow linked markers associated with QTLs and provide a good base for investigating quantitative traits (Galal et al. 2014 ; Mohan et al. 1997 ; Tewodros and Zelalem 2016 ). Besides, the statistical difference between phenotypic data obtained from various environments could enhance the accuracy to detect QTL position (Zhao and Xu 2012 ). Previous studies identified important seed size and shape QTLs, which were also classified to be associated with HSW, however, most of the studies utilized low-density genetic maps based on RFLP, SSR markers, biochemical and morphological markers which have large confidence interval with low resolution of QTLs not suitable for detecting candidate gene (Abou-Elwafa 2016 ; Bernardo 2008 ; Han et al. 2012 ). Therefore, it is crucial to employ high-density genetic maps to detect more new recombination in a population, which in turn will increase the accuracy of QTL mapping and MAS (Cao et al. 2019 ; Hina et al. 2020 ). In the present study, high-density genetic maps constructed from the two-related RIL populations LM6 and ZM6 consist of 2267 and 2601 bin markers, respectively, were used. The markers in the LM6 and ZM6 linkage maps were distributed to all 20 linkage groups and covered the length of 2453.79 and 2630.22 cM, with 1.08 cM and 1.01 cM average distance between adjacent markers, respectively (Li et al. 2017 ). To minimize the environmental errors, the two RIL populations were evaluated in four environments (including different geographical areas and years). The high-density linkage maps of LM6 and ZM6 RIL populations across multi-environment as well as the combined environment were employed to map major main-effect, additive-effect and epistatic-effect QTLs together with interactions with environments and the candidate genes underly seed size, shape and seed weight traits. The parents of the two mapping populations showed high phenotypic variations across all environments in all studied traits, i.e., SL, SW, ST, SLW, SLT, SWT, FI and HSW. Consequently, the transgressive segregation and continuous variations observed in the two populations in all studied phenotypic traits facilitate the identification of a high number of both major and minor effect QTLs including some novel QTLs associated with all studied traits (Teng et al. 2009 ; Xu et al. 2011 ; Zhang et al. 2018 ). All measured and calculated traits in both populations were significantly ( P < 0.01) influenced by genotype (G), environment (E) and their interactions (G×E), suggesting that the seed size, shape, and weight traits are not only governed by both genetic and environment but also there is an effect of the G×E interaction (Hu et al. 2013 ; Liang et al. 2016 ; Sun et al. 2012 ). This explains the observed high h 2 (99.04 %), and accordingly deduced that these traits are amenable to manipulation by selection without the assistance of molecular markers, indicating that these traits may produce the same phenotypic values when evaluated in the same geographical area. Except for SL, SW and ST that exhibited a highly significant correlation between each other and with HSW, our data showed that seed size, shape, and weight traits are not correlated which is favorable when breeding for a round-type with smaller or bigger seed size (Cober et al. 1997 ; Salas et al. 2006 ). Comparative QTL results using the CIM QTL mapping approach with SoyBase database identified 69, 82 and 29 novel QTLs for seed size, shape, and HSW, respectively, indicating the distinct genetic architecture of the LM6 and ZM6 populations. These novel QTLs together explain more than 88.00 % of phenotypic variance for seed size, shape, and weight, signifying their potential value for improving soybean cultivars. Besides, the identification of novel QTLs in the present study suggests that more germplasms are needed to be used for unraveling the complex genetic basis for seed size and shape traits in soybean. Among these novel QTLs, the qFI-1-3 LM6 showed the highest R 2 and LOD values and therefore may be the major QTL underlies flatness index (FI). Noteworthily, all FI QTLs were identified for the first time in this study, thus we considered them as novel QTLs. Chr01 and Chr03 harbored 4 and 3 FI QTLs, suggesting crucial roles of Chr01 and Chr03 in controlling the inheritance of seed FI in soybean. Round soybean seeds are required for soybean varieties used for food. Furthermore, the data revealed that the average flatness indices across all environments in both the LM6 and ZM6 populations have sphere seeds (FI ≈ 1.0), indicating that it is essential to start with at least one round seeded parent to get a segregant with round seed shape. Remarkably, eight novel major QTLs for HSW, including qHSW-4-1 LM6 , qHSW-6-2 LM6 , qHSW-7-3 LM6 , qHSW-9-1 ZM6 , qHSW-10-2 LM6 , qHSW-10-3 LM6 , qHSW-14-1 LM6, ZM6 and qHSW-14-2 ZM6 where their physical intervals did not overlap with any of the previously reported HSW QTLs, suggesting them as potential loci for HSW and major QTLs for future fine mapping to delimit the physical interval. The qHSW-4-3 LM6, ZM6 was detected in the physical interval of 42.7–45.8 Mb on Chr04 that overlapped with the previously identified seed-weight QTLs, i.e., seed weight 47 − 1 and seed wtQTL4.1 (Hacisalihoglu et al. 2018 ; Li et al. 2010 ). Nine QTLs for SL identified in this study were colocalized as previously reported (Hu et al. 2013 ; Jun et al. 2014 ; Salas et al. 2006 ). Two major QTLs related to SW, i.e., qSW-4-4 ZM6 and qSW-6-1 LM6 , both residing a genomic sequence of approximate 128 kb colocalized with the previously identified SW QTL A063-1 (Salas et al. 2006 ) and SW QTL detected previously in another independent soybean populations (Hina et al. 2020 ; Moongkanna et al. 2011 ), respectively. Thus, qSW-4-4 ZM6 and qSW-6-1 LM6 could be considered as stable QTLs for further fine mapping and map-based cloning to clarify the genetic mechanisms underlying SW. The qSLW-5-1 LM6 overlapped with qSLW-5-2 (Satt449) which was previously reported by (Jun et al. 2014 ). Furthermore, the three QTLs associated with SLT ( qSLT-5-3 ZM6 , qSLT-16-1 LM6 , and qSLT-20-1 ZM6 ) are colocalized with previously reported QTLs (Fang et al. 2017 ; Jun et al. 2014 ; Salas et al. 2006 ). The SWT QTL between Satt508 and Satt421 on Chr08 that have been previously mapped (Salas et al. 2006 ) are colocalized to the qSWT-8-1 LM6 QTL that was mapped to the physical interval 93–96.3 cM of Chr08. Additionally, our study identified for the first time 13 major QTLs (R² >10 %) related to FI, thus we considered them as novel QTLs. Among these novel QTLs, the qFI-1-3 LM6 showed the highest R 2 and LOD values and therefore might be the major QTL underlies FI. Besides, Chr01 and Chr03 harbored 4 and 3 FI QTLs, suggesting crucial roles of Chr01 and Chr03 in controlling the inheritance of seed FI in soybean. Moreover, the positive alleles for seed size, shape and HSW traits were inherited from both parents of the two RIL populations. Therefore, it is likely that not only the higher seed size and heavy weight parent ( Linhefenqingdou or Zhengyang ) contributed favorable alleles but also the lighter seed weight parent ( M8206 ) might play a role (Cao et al. 2019 ; Hina et al. 2020 ). Mapping of QTLs associated with seed size, shape and weight related traits using the MCIM approach was performed to; i) dissect the additive effect QTLs and Q × E interactions which is essential for selecting the most compatible varieties adapted to particular environments, and ii) further validate the QTLs identified by the CIM approach. The MCIM method approach identified 18 QTLs for seed sizes, shapes, and weight traits that are colocalized in the same physical interval of the CIM-mapped QTLs as previous studies. The major SL QTL qSL-7-1 LM6 is colocalized with Satt150 QTL (Salas et al. 2006 ). Furthermore, the SW QTLs qSW-13-5 LM6 , qST-18-4 LM6 , qSWT-7-5 LM6 , and qSLT-5-3 ZM6 were mapped in the same position as reported in previous studies (Fang et al. 2017 ; Salas et al. 2006 ). Additionally, the qHSW-3-2 LM6 , qHSW-8-3 ZM6 and qHSW-13-3 ZM6 QTLs are colocalized to the previously identified SW QTL Seed weight 32 − 3 ( Satt675 ), Seed weight 35 − 1 and Seed weight 19 − 2 ( Satt114 ) QTLs, respectively (Funatsuki et al. 2005 ; Han et al. 2012 ; Li et al. 2008 ). Therefore, these QTLs could also be considered as stable QTLs for further fine mapping and map-based cloning to uncover the genetic control and mechanisms of seed size, shape, and weight traits in soybean, and molecular markers tightly linked to these QTLs could be used for MAS. Dissecting the epistatic and QTL × environment effects are crucial for understanding the genetic mechanisms that greatly contributed to the phenotypic variations of complex traits (Kaushik et al. 2007 ). The genetic construction of seed size, shape, and weight also contains epistatic interactions between QTLs (Kato et al. 2014 ; Liang et al. 2016 ). Therefore, disregarding intergenic interactions will lead to the over-estimation of individual QTL effects, and the under-estimation of genetic variance (Nyquist and Baker 1991 ). Consequently, this might result in a large drop in the genetic response to MAS especially in late generations (Zhang et al. 2004 ). The identified 40 pairwise digenic epistatic QTLs for seed size, shape and weight related traits in the present study could be considered as modifying genes that do not exhibit only additive effects but could affect the expression of seed size, shape and weight related genes through epistatic interactions. Similar results for the epistatic interaction of seed size, shape and weight QTLs have been also previously reported by (Xin et al. 2016 ; Zhang et al. 2018 ). The appearance of epistatic interactions for a specific trait makes selection difficult. Noteworthily, all main-effect QTLs detected in our study had no epistatic effect, which raises the heritability of the trait guiding to easier selection. Genomic regions were identified as QTL clusters based on the presence of a large number of QTLs related to all or some of seed size, shape and HSW. The identification of 24 QTL clusters located on 17 different chromosomes. Accordingly, twenty-four QTL clusters were identified on 17 chromosomes each contained three or more QTLs related to seed size, shape, and HSW traits. These QTL clusters have not been reported previously and added to the developing knowledge of the genetic control of these traits. Moreover, the colocalization of QTLs for seed size, shape, and HSW and the way that they have exceptionally corresponded support the highly significant correlation with each other (Cai and Morishima 2002 ) (Suppl. Table 10). Besides, the occurrence of the QTL clustering could signify a linkage of QTLs/genes or outcome from the multiple effects of one QTL in the same genomic region (Cao et al. 2017 ; Liu et al. 2017 ; Wang et al. 2006 ). Furthermore, the QTL clusters displayed that the QTLs linkage/ gathering could make the enhancement of seed size and shape more easily than single QTLs (Hina et al. 2020 ). Significant positive correlations between soybean seed protein and oil contents and seed size and seed shape have been demonstrated, therefore, both traits are directly associated with seed size and shape in soybean (Hacisalihoglu and Settles 2017 ; Qi et al. 2011 ; Wu et al. 2018 ). This notion would explain the colocalization of QTLs associated with seed protein and oil contents in the genomic regions of several QTL clusters including clusters 1, 04.1, 04.2, 06, 07, 09, 10.1, 14.1, 14.2, 17.1, and 17.2. (Moongkanna et al. 2011 ; Panthee et al. 2005 ; Salas et al. 2006 ; Vieira et al. 2006 ; Yang et al. 2011 ). Additionally, the position of the first flower and the number of days to flowering have large effects on seed number per plant in soybean (Khan et al. 2008 ; Tasma et al. 2001 ; Yamanaka et al. 2001 ), which in turn affects seed size and HSW indicating the existence of common genetic factors for these traits. QTLs associated with the position of the first flower identified previously (Han et al. 2012 ; Tasma et al. 2001 ), are located to the genomic region of clusters 16.1, 19.2, and 20 (Hyten et al. 2004 ). The extensive analysis of QTLs clusters in our study suggests that breeding programs aiming to improve seed size, shape, and weight with enhanced quality should focus on QTL clustering and select QTLs within these regions. Besides, the existence of QTL clusters provides evidence that some traits related-genes are more densely concentrated in specific genomic regions of crop genomes than others (Fang et al. 2017 ). Identification of candidate genes underlying QTL regions is of great interest for breeding programs (Abou-Elwafa 2018 ; Abou-Elwafa and Shehzad 2018 ). So far, only two seed sizes/weight-related genes have been cloned from the soybean, i.e., the Glyma20g25000 (ln) gene that has a significant impact on seed size and the number of seeds per pod (Jeong et al. 2012 ), and the PP2C-1 allele underlying Glyma17g33690 has been reported to increase seed size/weight (Lu et al. 2017 ). In our study, a bioinformatics pipeline implementing genomic sequences of identified QTL clusters was employed to identify candidate genes. The pipeline consists of three complementary steps, i.e., 1) retrieving candidate genes from the SoyBase database ( www.soybase.org ) visualize the molecular function of candidate genes by GO enrichment analyses and gene classification, and 3) the implication of candidate genes in seed size, shape and weight based on their expression profiles. Accordingly, one-hundred forty-three genes were considered as potential candidates. The GO enrichment and gene classification analyses showed that most of the identified candidate genes behind QTL clusters are related to the terms of catalytic activity, cell part, cell, cellular process, binding and metabolic process terms in addition to the response to stimulus in cluster-03, and these terms are reported as being vital elements in seed development (Li and Li 2014 ; Mao et al. 2010 ). For example, Glyma07g14460 gene underlying QTL cluster-7 belongs to the oxygenase (CYP51G1) protein class, which has been confirmed to regulate seed size in soybean (Zhao et al. 2016 ). These predicted 143 genes have functions that are related/involved in seed development, which in turn influences the size, shape, and weight of seeds, such as brassinosteroid mediated signaling pathway, regulation of cell differentiation and proliferation, fatty acid beta-oxidation, peroxisome organization, double fertilization forming a zygote and endosperm, lipid transport and storage, regulation of hormone levels transport and metabolic processes, ubiquitin-dependent protein catabolic process, and sugar mediated signaling pathway (Li and Li 2014 ; Mao et al. 2010 ). Furthermore, ten candidate genes were identified as a regulator of ubiquitin-dependent protein catabolic process, RING-type E3 ubiquitin ligases and lipid catabolic process (Table 5 ). Several components of the ubiquitin pathway such as the ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2) and ubiquitin protein ligase (E3) have been reported to play important roles in regulation seed and organ size (Li and Li 2014 ). Similarly, 16 candidate genes have functions in pollen tube development, embryo sac egg cell differentiation, post-embryonic development, regulation of seed maturation, positive regulation of gene expression, regulation of cell cycle process, ovule development, anther development, seed dormancy process and seed maturation (Table 5 ) and hence they are likely to participate in regulating seed size, shape and weight in plants, including soybean (Meng et al. 2016 ). Additionally, ten candidate genes are involved in response to auxin stimulus, response to ethylene stimulus, abscisic acid biosynthetic process that are known to be implicated in promoting seed size and weight in Arabidopsis (Table 5 ) (Xie et al. 2014 ). Furthermore, six genes are known to play functions in glucose catabolic process, phosphatidylcholine biosynthetic process, carbohydrate metabolic process, maltose metabolic process and starch biosynthetic process which some of them are known to be involved in partitioning and translocation of photo-assimilates and grain filling in rice (Table 5 ) (Chen et al. 2020b ; Zhang et al. 2020 ). Conclusions The present study employed high-density maps of two-related RIL populations, LM6 and ZM6 evaluated in multiple environments to identify M-QTLs as well candidate genes controlling seed size, shape, and weight in soybean. Besides, this is the first detailed and comprehensive investigation of QTLs for flatness index as a seed shape trait in soybean. A total of 180 and 18 M-QTLs were reported for the first time in this study using the CIM and MCIM QTL mapping approaches, respectively. Besides, sixty-nine QTLs were considered as stable as detected in more than one specific environment or one individual environment together with CE. All the positive alleles of 282 identified QTLs were inherited from the female parents. Our data revealed 7 major and stable QTL clusters underlying the inheritance of seed size, shape and weight located to genomic regions on chromosomes 3, 4, 5, 7, 9, 17 and 19 in soybean. The implemented bioinformatics pipeline delimits the number of the identified candidate genes to 47 genes within the physical interval of the previously mentioned 7 genomic regions involved directly or indirectly in seed size, shape and weight. These genes are highly expressed in seed-related tissues and nodules, indicating that they may be involved in regulating the above traits in soybean. Furthermore, some of the potential 47 candidate genes have been included in our on-going projects for functional validation to confirm their effect on seed size, shape, and weight. Our study provides detailed information for genetic bases of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning as well as for MAS targeted at improving seed size, shape and weight. Declarations Author Contributions T.Z. designed the project. M.A.E. performed the experiments. M.A.E., B.K., S.S., S.L., Y.C., M.A. and A.H. analyzed the data. M.A.E. drafted the manuscript. T.Z. and S.F.A. revised the paper. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by the National Key R & D Program of China (2018YFD0100800), the National Natural Science Foundation of China (31871646), the MOE Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT_17R55), the Fundamental Research Funds for the Central Universities (KYT201801), the Jiangsu Collaborative Innovation Center for Modern Crop Production (JCICMCP) Program. Conflicts of Interest The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Availability of data and material All data are included within the manuscript and its supplementary material. Code availability Not applicable. Ethics approval and consent to participate Not applicable. Consent for Publication Not applicable. References Abou-Elwafa SF (2016) Association mapping for drought tolerance in barley at the reproductive stage. Cr Biol 339:51-59 Abou-Elwafa SF (2018) Identification of genes associated with drought tolerance in barley. Biologia Plantarum 62:299-306 Abou-Elwafa SF, Shehzad T (2018) Genetic identification and expression profiling of drought responsive genes in sorghum. 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(a) seed length associated QTLs, (b) seed width associated QTLs, (c) seed thickness associated QTLs, (d) seed length/width associated QTLs, (e) seed length/ thickness associated QTLs, (f) seed width/thickness associated QTLs, (g) Flatness index associated QTLs and (h) QTLs associated with HSW. The LOD threshold (2.5) is indicated by a green line. The X and Y-axis represent chromosomes and LOD score, respectively. Suppl.Fig.2.tif Supplementary Fig. 2: Diagram showing Chromosomal locations of the identified 24 QTL clusters on 17 different chromosomes, i.e., Chr01, Chr03, Chr04, Chr05, Chr06, Chr07, Chr08, Chr09, Chr10, Chr11, Chr13, Chr14, Chr15, Chr16, Chr17, Chr19 and Chr20 in LM6 and ZM6 RIL populations for SL, SW, ST, SLW, SLT, SWT, FI, and HSW traits under multiple environments. Suppl.Table1.xlsx Supplementary Table 1: Distribution of SNPs, recombination bins and markers mapped on soybean chromosomes/linkage groups. Bin-map (RAD-sequencing). Suppl.Table2.xlsx Supplementary Table 2: Descriptive statistics, broad-sense heritability (h2) of Seed Shape & size traits evaluated in two recombinant inbred lines (RILs) LM6 & ZM6. The RILs and their parents were grown in different field environments of years and locations. * &** represent significance at 5% and 1%, respectively. Suppl.Table3.xlsx Supplementary Table 3: Descriptive statistics, broad-sense heritability (h2) of 100-Seed weight trait evaluated in two recombinant inbred lines (RILs) LM6 & ZM6. The RILs and their parents were grown in different field environments of years and locations. * &** represent significance at 5% and 1%, respectively. Suppl.Table4.xlsx Supplementary Table 4: Combined analysis of variance (ANOVA) for Seed shape & size trait (SL, SW, ST, SLW, SLT, SWT, FI) in ZM6 & LM6 RIL Populations across three different environments (2012FY, 2012JP, 2014JP and 2017JP) Suppl.Table5.xlsx Supplementary Table 5: Combined analysis of variance (ANOVA) for 100-Seed weight trait in ZM6 & LM6 RIL Populations across six different environments (2012FY, 2012JP, 2013JP, 2014JP, 2014YC, and 2017JP) Suppl.Table6.xlsx Supplementary Table 6: Correlation analysis among seven Seed size/Shape traits (SL, SW, ST, FI, SLT, SLW, SWT), and 100-seed weight (HSW). * &** represent significance at 5% and 1%, respectively. Suppl.Table7.xlsx Supplementary Table 7: Main-effect quantitative trait loci (M-QTLs) identified for three seed-size traits (seed length (SL), seed width (SW), and seed thickness (ST) in ZM6 and LM6 recombinant inbred line (RIL) populations across multiple environments and combined environment. Suppl.Table8.xlsx Supplementary Table 8: Main-effect quantitative trait loci (M-QTLs) identified for four seed-size traits (Seed length/width ratio (SLW), Seed length thickness ratio (SLT), Seed width thickness ratio (SWT), and Flatness index (FI) in ZM6 and LM6 recombinant inbred line (RIL) populations across multiple environments and combined environment. Suppl.Table9.xlsx Supplementary Table 9: Main-effect quantitative trait loci (M-QTLs) identified for 100-Seed weight in ZM6 and LM6 recombinant inbred line (RIL) populations across multiple environments and combined environment. Suppl.Table10.xlsx Supplementary Table 10: Twenty-Four QTL clusters detected in LM6 and ZM6 RIL populations across multiple environments. Suppl.Table11.xlsx Supplementary Table 11: Model genes within Cluster-03, Cluster-04.1, Cluster-05.1, Cluster-07, Cluster-09, Cluster-17.1, and Cluster-19.1 regions Suppl.Table12.xlsx Supplementary Table 12: Predicted candidate genes within Cluster-03, Cluster-04.1, Cluster-05.1, Cluster-07, Cluster-09, Cluster-17.1, and Cluster-19.1 regions in both RIL Populations (LM6 & ZM6) based on known functional annotation. Suppl.Table13.xlsx Supplementary Table 13: Candidate genes within seven QTL clusters and their relative expression data retrieved from RNA-seq data available in SoyBase. (DAF= Days after flowering) Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-206236","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":10572328,"identity":"a4dd82eb-5afe-4251-874e-68fff20d1a48","order_by":0,"name":"Mahmoud A Elattar","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"A","lastName":"Elattar","suffix":""},{"id":10572329,"identity":"251a459f-d357-4669-a18a-50292190b6b9","order_by":1,"name":"Benjamin Karikari","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Karikari","suffix":""},{"id":10572330,"identity":"cf166212-7d5a-4ffd-bfd4-4399c7d586b9","order_by":2,"name":"Shuguang Li","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuguang","middleName":"","lastName":"Li","suffix":""},{"id":10572331,"identity":"ba62b8cb-3069-403c-95af-f0dddef14308","order_by":3,"name":"Shiyu Song","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shiyu","middleName":"","lastName":"Song","suffix":""},{"id":10572332,"identity":"2218aee8-5471-4305-a74a-c2c27dd5da48","order_by":4,"name":"Yongce Cao","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongce","middleName":"","lastName":"Cao","suffix":""},{"id":10572333,"identity":"cc66189c-9c30-4f09-8a9f-f42b039ff1f2","order_by":5,"name":"Muhammed Aslam","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"","lastName":"Aslam","suffix":""},{"id":10572334,"identity":"5b20b67f-54bf-4a7f-8816-28d34bf5103a","order_by":6,"name":"Aiman Hina","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aiman","middleName":"","lastName":"Hina","suffix":""},{"id":10572335,"identity":"ddc4f327-70d5-4ea7-be6f-3dd468e3be37","order_by":7,"name":"Salah Fatouh Abou-Elwafa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie3PsQrCMBSF4ZRCs6RkvSLqK0QKVXDwVVocXIoIgjgIBoS62VcRhM5XCrpkcHR0cnLoJE5qi5NLsJtgfsgQyMchhJhMvxgtzow8F/x9tb8g5RtF7KAmKxOB3xJus3aOc2fkHZR/JrNeKOnqqCW1JfMA92ziq6gjiBqGkqmxlojM3dRzB6wUIx+sOAslRIGW9DN3e8eHsLbJtSCPgrSueiJsN4VdHIQbKFdkuUJRSyDjt+5ujR6cLlMI9kMvZpFWEL6KBye8YZMngxTyea+R0MNZbz4qP+EQJiqQd7TKislkMv1BL4jwR5GkFlwzAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9018-598X","institution":"Assiut University Faculty of Agriculture","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Salah","middleName":"Fatouh","lastName":"Abou-Elwafa","suffix":""},{"id":10572336,"identity":"9d5cd326-0847-491b-ba16-2bd966dd24c5","order_by":8,"name":"Tuanjie Zhao","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tuanjie","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2021-02-04 05:45:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-206236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-206236/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":5698568,"identity":"64deb6f0-2879-41e8-b5e8-12d5908a7885","added_by":"auto","created_at":"2021-02-06 19:01:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":264429,"visible":true,"origin":"","legend":"Measuring seed width (SW), length (SL) and thickness (ST).","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/45ba62f9c3b5242e3c2bf2c6.jpg"},{"id":5698195,"identity":"18aab60c-c22a-448d-b3ef-f821484b9b3b","added_by":"auto","created_at":"2021-02-06 18:55:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7338814,"visible":true,"origin":"","legend":"Boxplot for seed size, seed shape and 100-seed weight traits. The black line in the middle of the box shows the median, the white box indicates the range from the lower quartile to the upper quartile, the dashed black line and yellow dots represent the dispersion and frequency distribution of the phenotypic data in each of the six environments, i.e., 12FY, 12JP, 13JP, 14JP, 14YC, and 17JP. While a and b represent LM6 and ZM6 populations","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/ba27d905673a0b912314ee17.jpg"},{"id":5698604,"identity":"b5f9e4c6-c79c-4c2a-b855-b8584f72cbbe","added_by":"auto","created_at":"2021-02-06 19:07:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":333274,"visible":true,"origin":"","legend":"Performance of the three parents of the RILs mapping populations, i.e., Meng8206, Linhefenqingdou, and Zhengyanghuangdou along with the two derived RIL populations, LM6 and ZM6, for seed sizes and shapes traits as well as 100-seed weight among multiple environments. 12JP, 13JP, 14JP and 17JP indicate phenotyping at the Jiangpu Experimental Station in 2012, 2013, 2014 and 2017 growing seasons, respectively. 12FY indicates the Fengyang Experimental Station, Chuzhou in 2012 growing season. 14YC indicates ate the Yancheng Experimental Station in 2014. ","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/5fb2ec31c0441f6075a30c21.jpg"},{"id":5698391,"identity":"0397d598-21b8-4552-8163-15b562dc4e69","added_by":"auto","created_at":"2021-02-06 18:58:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3389581,"visible":true,"origin":"","legend":"Position of most prominent QTL detected by CIM approach associated with seed size and seed shape traits in the LM6 and ZM6 RIL populations grown in multiple environments indicated as with E1, FY2012; E2, JP2012; E3, JP2013; E4, JP2014; E5, YC2014; E6, JP2017 respectively, in addition to the combined environment (CE). (a) LOD curve for qSL-10-1LM6, (b) LOD curve for qSW-17-2LM6, (c) LOD curve for qST-13-2ZM6, (d) LOD curve for qSLW-13-4ZM6, (e) LOD curve for qSLT-14-1LM6, (f) LOD curve for qSWT-8-1LM6, (g) LOD curve for qFI-1-3LM6, and (h) LOD curve for qHSW-14-2ZM6. The LOD threshold (2.5) is indicated by a pink line. The double-headed arrow denotes the location of prominent QTL. The X and Y-axis represent chromosome and LOD score, respectively.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/8f349c568383e3239e11d0e9.jpg"},{"id":5698574,"identity":"211e08bf-4fb9-4e62-bc39-5f393a9709dd","added_by":"auto","created_at":"2021-02-06 19:01:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":233771,"visible":true,"origin":"","legend":"WeGO analysis of the genes located within the seven major QTL clusters: (a) Cluster-03; (b) Cluster- 4.1; (c) Cluster-5.1; (d) Cluster-07; (e) Cluster-09; (f) Cluster-17.1; (g) Cluster-19.1","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/0cce65be013ed9f9c02a9105.png"},{"id":5698208,"identity":"94b225b3-9b27-4fe8-91a9-e5f4ab44f231","added_by":"auto","created_at":"2021-02-06 18:55:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":150221,"visible":true,"origin":"","legend":"WeGO analysis of the genes located within the seven major QTL clusters: (a) Cluster-03; (b) Cluster- 4.1; (c) Cluster-5.1; (d) Cluster-07; (e) Cluster-09; (f) Cluster-17.1; (g) Cluster-19.1","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/de18746810af8d612f213fa1.png"},{"id":13655223,"identity":"5d303cc3-6aee-4bd9-8ca0-bafc0c9939df","added_by":"auto","created_at":"2021-09-17 10:01:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2062780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/1e172aec-ae9c-4d65-bef6-ef4f7b0add82.pdf"},{"id":5698379,"identity":"3d1a2b87-b0b9-425f-89f3-98a68240c74f","added_by":"auto","created_at":"2021-02-06 18:58:26","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2108968,"visible":true,"origin":"","legend":"Supplementary Fig. 1: Seed size, shape, and seed weight traits related QTLs mapped using CIM approach in the two soybean RIL populations LM6 and ZM6 across multiple environments indicated with E1, FY2012; E2, JP2012; E3, JP2013; E4, JP2014; E5, YC2014; E6, JP2017 respectively, in addition to the combined environment (CE). (a) seed length associated QTLs, (b) seed width associated QTLs, (c) seed thickness associated QTLs, (d) seed length/width associated QTLs, (e) seed length/ thickness associated QTLs, (f) seed width/thickness associated QTLs, (g) Flatness index associated QTLs and (h) QTLs associated with HSW. The LOD threshold (2.5) is indicated by a green line. The X and Y-axis represent chromosomes and LOD score, respectively.","description":"","filename":"Suppl.Fig.1.tif","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/27ee2ad680914e1afcdcf537.tif"},{"id":5698190,"identity":"6221e4b1-a989-4c17-90af-78ec2ec8f874","added_by":"auto","created_at":"2021-02-06 18:55:26","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":578324,"visible":true,"origin":"","legend":"Supplementary Fig. 2: Diagram showing Chromosomal locations of the identified 24 QTL clusters on 17 different chromosomes, i.e., Chr01, Chr03, Chr04, Chr05, Chr06, Chr07, Chr08, Chr09, Chr10, Chr11, Chr13, Chr14, Chr15, Chr16, Chr17, Chr19 and Chr20 in LM6 and ZM6 RIL populations for SL, SW, ST, SLW, SLT, SWT, FI, and HSW traits under multiple environments. ","description":"","filename":"Suppl.Fig.2.tif","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/e46fa8e57c1e0378d30ebef6.tif"},{"id":5698381,"identity":"c6331fa6-d8c0-4f12-8246-cefcdfcc8b94","added_by":"auto","created_at":"2021-02-06 18:58:26","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12423,"visible":true,"origin":"","legend":"Supplementary Table 1: Distribution of SNPs, recombination bins and markers mapped on soybean chromosomes/linkage groups. Bin-map (RAD-sequencing).","description":"","filename":"Suppl.Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/313479a428184804f765443b.xlsx"},{"id":5698593,"identity":"bd075783-c309-4814-8a92-08479dc5a305","added_by":"auto","created_at":"2021-02-06 19:04:26","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21199,"visible":true,"origin":"","legend":"Supplementary Table 2: Descriptive statistics, broad-sense heritability (h2) of Seed Shape \u0026 size traits evaluated in two recombinant inbred lines (RILs) LM6 \u0026 ZM6. The RILs and their parents were grown in different field environments of years and locations. * \u0026** represent significance at 5% and 1%, respectively.","description":"","filename":"Suppl.Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/089dab070ad05ae5f1d42062.xlsx"},{"id":5698570,"identity":"0dc9ab97-6ca1-4e1d-9d30-cea82ec6cf48","added_by":"auto","created_at":"2021-02-06 19:01:26","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":13070,"visible":true,"origin":"","legend":"Supplementary Table 3: Descriptive statistics, broad-sense heritability (h2) of 100-Seed weight trait evaluated in two recombinant inbred lines (RILs) LM6 \u0026 ZM6. The RILs and their parents were grown in different field environments of years and locations. * \u0026** represent significance at 5% and 1%, respectively.","description":"","filename":"Suppl.Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/e84d954d8c0f6910af58322d.xlsx"},{"id":5698603,"identity":"3550f00e-96b9-4804-9e47-05baa6923279","added_by":"auto","created_at":"2021-02-06 19:07:26","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14803,"visible":true,"origin":"","legend":"Supplementary Table 4: Combined analysis of variance (ANOVA) for Seed shape \u0026 size trait (SL, SW, ST, SLW, SLT, SWT, FI) in ZM6 \u0026 LM6 RIL Populations across three different environments (2012FY, 2012JP, 2014JP and 2017JP)","description":"","filename":"Suppl.Table4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/d207b151936da4bbd7fb1192.xlsx"},{"id":5698384,"identity":"993e222d-e540-4c7a-bbd7-3e33d0438355","added_by":"auto","created_at":"2021-02-06 18:58:26","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":10474,"visible":true,"origin":"","legend":"Supplementary Table 5: Combined analysis of variance (ANOVA) for 100-Seed weight trait in ZM6 \u0026 LM6 RIL Populations across six different environments (2012FY, 2012JP, 2013JP, 2014JP, 2014YC, and 2017JP)","description":"","filename":"Suppl.Table5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/9eed1ff01bb5d6ddd8572e14.xlsx"},{"id":5698198,"identity":"7f7207c1-001e-4903-8e2b-5f452ed611bc","added_by":"auto","created_at":"2021-02-06 18:55:26","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":11409,"visible":true,"origin":"","legend":"Supplementary Table 6: Correlation analysis among seven Seed size/Shape traits (SL, SW, ST, FI, SLT, SLW, SWT), and 100-seed weight (HSW). * \u0026** represent significance at 5% and 1%, respectively.","description":"","filename":"Suppl.Table6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/1c4404b09d1d66ef025b91c5.xlsx"},{"id":5698202,"identity":"199018dc-d33d-49b0-abc5-d9246e7a35fb","added_by":"auto","created_at":"2021-02-06 18:55:26","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":24042,"visible":true,"origin":"","legend":"Supplementary Table 7: Main-effect quantitative trait loci (M-QTLs) identified for three seed-size traits (seed length (SL), seed width (SW), and seed thickness (ST) in ZM6 and LM6 recombinant inbred line (RIL) populations across multiple environments and combined environment.","description":"","filename":"Suppl.Table7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/948d02c0fce19d50dd0a8520.xlsx"},{"id":5698572,"identity":"dfb29174-1bbd-45bf-8088-370e52fc7166","added_by":"auto","created_at":"2021-02-06 19:01:26","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":26003,"visible":true,"origin":"","legend":"Supplementary Table 8: Main-effect quantitative trait loci (M-QTLs) identified for four seed-size traits (Seed length/width ratio (SLW), Seed length thickness ratio (SLT), Seed width thickness ratio (SWT), and Flatness index (FI) in ZM6 and LM6 recombinant inbred line (RIL) populations across multiple environments and combined environment.","description":"","filename":"Suppl.Table8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/58c37f17fbe3f6eaf7aa2f36.xlsx"},{"id":5698605,"identity":"48aab265-e18e-4f41-b711-a9e7e59273ee","added_by":"auto","created_at":"2021-02-06 19:07:26","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":18854,"visible":true,"origin":"","legend":"Supplementary Table 9: Main-effect quantitative trait loci (M-QTLs) identified for 100-Seed weight in ZM6 and LM6 recombinant inbred line (RIL) populations across multiple environments and combined environment.","description":"","filename":"Suppl.Table9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/f84a76c338ba88b929551fd6.xlsx"},{"id":5698596,"identity":"b2c95c1d-cf4b-4c77-aafc-a73beb0e94f0","added_by":"auto","created_at":"2021-02-06 19:04:26","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":19431,"visible":true,"origin":"","legend":"Supplementary Table 10: Twenty-Four QTL clusters detected in LM6 and ZM6 RIL populations across multiple environments.","description":"","filename":"Suppl.Table10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/bd37a429a963cad0aa0fac61.xlsx"},{"id":5698577,"identity":"9c14d05d-d039-4583-b715-5ded0b634892","added_by":"auto","created_at":"2021-02-06 19:01:26","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":698623,"visible":true,"origin":"","legend":"Supplementary Table 11: Model genes within Cluster-03, Cluster-04.1, Cluster-05.1, Cluster-07, Cluster-09, Cluster-17.1, and Cluster-19.1 regions","description":"","filename":"Suppl.Table11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/7944bffb498431a597fbbd47.xlsx"},{"id":5698387,"identity":"2e53137c-efe8-42d5-854d-34431882da61","added_by":"auto","created_at":"2021-02-06 18:58:26","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":32718,"visible":true,"origin":"","legend":"Supplementary Table 12: Predicted candidate genes within Cluster-03, Cluster-04.1, Cluster-05.1, Cluster-07, Cluster-09, Cluster-17.1, and Cluster-19.1 regions in both RIL Populations (LM6 \u0026 ZM6) based on known functional annotation.","description":"","filename":"Suppl.Table12.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/4e9c0fb3653a87f08d517255.xlsx"},{"id":5698393,"identity":"a151faa3-c080-44ba-9823-f5ab412b9871","added_by":"auto","created_at":"2021-02-06 18:58:27","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":22334,"visible":true,"origin":"","legend":"Supplementary Table 13: Candidate genes within seven QTL clusters and their relative expression data retrieved from RNA-seq data available in SoyBase. (DAF= Days after flowering)","description":"","filename":"Suppl.Table13.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-206236/v1/69419f1d7d93eacaed1d3597.xlsx"}],"financialInterests":"","formattedTitle":"Comparative QTL analysis and candidate genes identification of seed size, shape and weight in soybean (Glycine max L.)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoybean (\u003cem\u003eGlycine max \u003c/em\u003eL. Merr.) is one of the most important food crops, being a rich source of dietary protein (69 %) and provides more than 50 % edible oil globally, as well as has a significant role in health and biofuel (\u003ca href=\"#_ENREF_30\"\u003eHoeck et al. 2003\u003c/a\u003e). It is used in human food and animal feed due to its high nutritional value and improves soil fertility by integrating atmospheric nitrogen in the soil through a synergetic interaction with microorganisms (\u003ca href=\"#_ENREF_80\"\u003eWang et al. 2019\u003c/a\u003e). Throughout the last five decades, soybean production in China has suffered continuous annual decline and reduced yields. To meet domestic demands, China imports nearly \u0026nbsp;80 % to meet its domestic demand, therefore improving soybean production has been the main objective of soybean breeders to make the country self-sufficient (\u003ca href=\"#_ENREF_47\"\u003eLiu et al. 2018\u003c/a\u003e). Accordingly, most plant breeders are targeting yield-related traits to improve soybean production. In this regard, seed size traits, i.e., seed length (SL), thickness (ST) and width (SW), and seed shape traits, i.e., length to-thickness (SLT), length-to-width (SLW), width-to-thickness (SWT) ratios and flatness index (FI) determine seed vigor, quality and yield in soybean (\u003ca href=\"#_ENREF_13\"\u003eCha-um and Kirdmanee 2011\u003c/a\u003e; \u003ca href=\"#_ENREF_66\"\u003eSalas et al. 2006\u003c/a\u003e). Flatness index contributes a lot towards seed vigor which impacts the seedling emergence time by determining the development of Soybean plant throughout their growth cycle as contributing factor to higher leaf area index from early vegetative stages (V\u003csub\u003e1\u003c/sub\u003e) to reproductive stages (R\u003csub\u003e2\u003c/sub\u003e) (\u003ca href=\"#_ENREF_20\"\u003eEbone et al. 2020\u003c/a\u003e). Vigor reduced meaning flatness index reduced, showing the increase in variability among the plants in the field. It generates differences among distinct groups as a result of higher trifoliate leaf area, allowing the plants to use more resources (light) for their development. Thus, shooting the yield of the plant because of higher leaf area index, higher accumulation of photoassimilates and finally contributed to seed yield. These plants therefore not need to increase their average internode length (\u003ca href=\"#_ENREF_83\"\u003eWu et al. 2017\u003c/a\u003e) and have a higher number of total nodes and a higher pod number which explains the differences in grain mass (\u003ca href=\"#_ENREF_4\"\u003eAinsworth et al. 2012\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eMoreover, seed size is an essential trait in flowering plants and plays a critical role in adaptation to the environment (\u003ca href=\"#_ENREF_71\"\u003eTao et al. 2017\u003c/a\u003e).\u0026nbsp; However, these traits are complex quantitative traits regulated by polygenes and strongly influenced by environment and genotype \u0026times; environment (G\u0026times;E) interaction, and hence it is more difficult to select for based on phenotype compare with monogenic traits (\u003ca href=\"#_ENREF_94\"\u003eYao et al. 2014\u003c/a\u003e). A positive correlation between seed size/weight and seed yield has been reported in several studies (\u003ca href=\"#_ENREF_7\"\u003eBurris et al. 1973\u003c/a\u003e; \u003ca href=\"#_ENREF_68\"\u003eSmith and Camper Jr 1975\u003c/a\u003e). Besides, seed weight/size has been demonstrated to be associated with seed germination capability and vigor, thereby significantly affecting the competitive capability of the seedling for nutrient and water resources and light, hence enhance stress tolerance (\u003ca href=\"#_ENREF_21\"\u003eEdwards Jr and Hartwig 1971\u003c/a\u003e; \u003ca href=\"#_ENREF_27\"\u003eHaig 2013\u003c/a\u003e). All soybean varieties evolved in tropical and subtropical countries, such as Indonesia and India have small seed size compared to the temperate region varieties, such as China, the USA and Japan. Besides, seed size, shape and weight are important seed quality traits with great influence on seed use, for example, round seeds are often desirable for food-type soybean, and large-seeded cultivars are typically used for green soybeans (edamame), soymilk, miso, boiled soybean (\u003cem\u003enimame\u003c/em\u003e) and soybean curd (\u003cem\u003etofu\u003c/em\u003e), whereas, small-grained cultivars are desirable for sprout production and fermented soybean (\u003cem\u003enātto\u003c/em\u003e) (\u003ca href=\"#_ENREF_5\"\u003eBasra 1995\u003c/a\u003e; \u003ca href=\"#_ENREF_73\"\u003eTeng et al. 2017\u003c/a\u003e; \u003ca href=\"#_ENREF_82\"\u003eWu et al. 2018\u003c/a\u003e). Therefore, depending on the end-use or biographical area of growth, many soybean varieties with different seed sizes and shapes with a \u0026nbsp;100-seed weight ranged from 3.0 - 77.5 g have been developed (\u003ca href=\"#_ENREF_62\"\u003ePanthee et al. 2005\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eQuantitative trait locus (QTL) analysis provides an efficient tool for crop breeders to study the genetic factors underlying quantitatively inherited traits and search for new sources of variation. The key influence of quantitative trait loci (QTLs), phenotypic variance (PV) of quantitative traits is often regulated by epistasis and QTL \u0026times;environment (QEs) interactions, which greatly lead to variations in complex traits (\u003ca href=\"#_ENREF_90\"\u003eYang et al. 2005\u003c/a\u003e). Furthermore, QTL analysis of complex traits may increase the accuracy of QTL mapping if QTL by QTL interactions are considered. However, epistatic interaction has a stronger effect on inbreeding depression, heterosis, adaptation, speciation and reproductive isolation (\u003ca href=\"#_ENREF_50\"\u003eMa et al. 2015\u003c/a\u003e). Most of the previous studies focused on the detection of main-effect QTLs for seed sizes, shapes and 100-seed weight in soybean. To date, at least 441, 52 and 297 QTLs for seed size, shape and 100-seed weight (HSW) have been reported (www.soybase.org) based on various genetic contexts, advances in marker technology, statistical methods and specific environments. However, most of these QTLs are minor (R\u003csup\u003e2\u003c/sup\u003e\u0026lt;10 %), not stable and with larger genomic region/confidence interval. Salas et al. (\u003ca href=\"#_ENREF_66\"\u003eSalas et al. 2006\u003c/a\u003e) found that 26 QTLs for seed size and shape on 13 soybean linkage groups. Han et al. (\u003ca href=\"#_ENREF_28\"\u003eHan et al. 2012\u003c/a\u003e) mapped 46 QTLs associated with HSW in 3 RIL soybean populations with one common male parent (Hefeng25). Furthermore, Hu et al. (\u003ca href=\"#_ENREF_31\"\u003eHu et al. 2013\u003c/a\u003e) detected 10 QTLs for seed shape on 6 chromosomes in soybean. Moreover, Kato et al. (\u003ca href=\"#_ENREF_38\"\u003eKato et al. 2014\u003c/a\u003e) identified 15 major QTLs for single seed weight among 11 chromosomes by using two RIL soybean populations developed from crosses between the US and Japanese cultivars of soybean. Recently, there have been limited studies on detecting QTLs with epistatic effects and their interactions with the environment (QEs) (\u003ca href=\"#_ENREF_46\"\u003eLiang et al. 2016\u003c/a\u003e; \u003ca href=\"#_ENREF_62\"\u003ePanthee et al. 2005\u003c/a\u003e; \u003ca href=\"#_ENREF_88\"\u003eXu et al. 2011\u003c/a\u003e; \u003ca href=\"#_ENREF_101\"\u003eZhang et al. 2018\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eMost of the previous QTL analysis researches have relied on low-density genetic maps used biochemical and morphological, simple sequence repeats (SSRs), restriction fragment length polymorphisms (RFLPs), or other low-quantity markers, which resulted in large QTLs confidence intervals with a low resolution compared with high-density SNP markers that are valuable for high-throughput QTL mapping (\u003ca href=\"#_ENREF_31\"\u003eHu et al. 2013\u003c/a\u003e; \u003ca href=\"#_ENREF_55\"\u003eMoongkanna et al. 2011\u003c/a\u003e; \u003ca href=\"#_ENREF_66\"\u003eSalas et al. 2006\u003c/a\u003e). With the recent advancement in next-generation sequencing (NGS) techniques, a number of techniques of high-throughput sequencing have been developed to generate large scale markers. Among them include genotyping-by-sequencing (GBS) (\u003ca href=\"#_ENREF_64\"\u003ePoland et al. 2012\u003c/a\u003e), restriction-site associated DNA sequencing (RAD-seq) (\u003ca href=\"#_ENREF_53\"\u003eMiller et al. 2007\u003c/a\u003e; \u003ca href=\"#_ENREF_63\"\u003ePeterson et al. 2012\u003c/a\u003e) and specific length amplified fragment sequencing (SLAF-seq) (\u003ca href=\"#_ENREF_69\"\u003eSun et al. 2013\u003c/a\u003e). These sequencing technologies have facilitated the production of hundreds to millions of single-nucleotide polymorphisms (SNPs) throughout the whole genome that promote the development of high-density linkage maps. The RAD-seq produces markers that have been proved to be a promising tool for SNP detection and genetic map construction (\u003ca href=\"#_ENREF_18\"\u003eChutimanitsakun et al. 2011\u003c/a\u003e; \u003ca href=\"#_ENREF_85\"\u003eXie et al. 2018\u003c/a\u003e). A number of genetic maps produced by RAD-seq have been generated and used for QTL mapping in several crops such as barley (\u003ca href=\"#_ENREF_18\"\u003eChutimanitsakun et al. 2011\u003c/a\u003e), soybean (\u003ca href=\"#_ENREF_29\"\u003eHina et al. 2020\u003c/a\u003e), cowpea (\u003ca href=\"#_ENREF_61\"\u003ePan et al. 2017\u003c/a\u003e), jute (\u003ca href=\"#_ENREF_41\"\u003eKundu et al. 2015\u003c/a\u003e), sorghum (\u003ca href=\"#_ENREF_36\"\u003eKajiya-Kanegae et al. 2020\u003c/a\u003e), alfalfa (\u003ca href=\"#_ENREF_97\"\u003eZhang et al. 2019a\u003c/a\u003e) among others. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to the above, knowledge of molecular mechanisms underlying soybean seed size, shape and weight is still limited. So far, only two seed sizes/weight-related genes have been isolated from the soybean. The gene \u003cem\u003eGlyma20g25000 (ln)\u003c/em\u003e has a significant impact on seed size and the number of seeds per pod (\u003ca href=\"#_ENREF_34\"\u003eJeong et al. 2012\u003c/a\u003e), and the PP2C-1 allele underlying \u003cem\u003eGlyma17g33690 \u003c/em\u003efrom the wild soybean accession \u0026lsquo;ZYD7\u0026rsquo; was reported and demonstrated to increase seed size/weight (\u003ca href=\"#_ENREF_49\"\u003eLu et al. 2017\u003c/a\u003e). Therefore, it is essential to identify major and stable QTLs and candidate genes related to seed size, shape and weight to improve our understanding of genetic mechanisms controlling these important traits in soybean (\u003ca href=\"#_ENREF_38\"\u003eKato et al. 2014\u003c/a\u003e; \u003ca href=\"#_ENREF_101\"\u003eZhang et al. 2018\u003c/a\u003e). Due to the shortage in the available molecular markers and the lack of high-density linkage maps which resulted in low resolution and large confidence intervals of identified QTLs, the present study used RAD-seq to generate over 2200 bin-markers for either of the two-related recombinant inbred line (RIL) populations for QTL mapping and candidate gene(s) identification for seed size, shape and weight. The two-related RIL populations (ZM6 and LM6) were derived from a common male parent \u003cem\u003eMeng 8206\u003c/em\u003e (\u003cem\u003eM8206\u003c/em\u003e) crossed with either \u003cem\u003eZhengyang\u003c/em\u003e (Z) and \u003cem\u003eLinhefenqingdou\u003c/em\u003e (L) and RILs and their parents were evaluated across multiple environments. The study was aimed to: (i). map main-effect QTLs (M-QTLs), additive by additive QTLs and QE for seed size, shape and weight traits, (ii). analyze epistatic QTL pairs and their interactions with the environment for further utilization of these QTLs in soybean genetic improvement, and (iii). mine potential candidate genes for the major and stable QTLs. These findings would be useful for the application of marker-assisted breeding (MAB) in soybean and provide comprehensive knowledge on the genetic bases for these traits as well as mined candidate genes would serve as a foundation for functional validation and verification of some genes for seed size, shape and weight in soybean.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant materials and experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo recombinant inbred lines (RIL) populations, i.e.,\u0026nbsp; ZM6 and LM6 (\u003ca href=\"#_ENREF_37\"\u003eKarikari et al. 2019\u003c/a\u003e; \u003ca href=\"#_ENREF_100\"\u003eZhang et al. 2019b\u003c/a\u003e), consisting of 126 and 104 lines, respectively, were used in the present study. The two populations were developed by single seed descent (SSD) with the genotypes \u003cem\u003eZhengyang\u003c/em\u003e (Z) and \u003cem\u003eLinhefenqingdou\u003c/em\u003e (L) were used as female parents and the \u003cem\u003eM8206\u003c/em\u003e (M6) genotype was used as the male parent. The two female parents, Z and L, have an average 100-seed weight of 17.1 and 35 g, respectively, whereas the male parent has an average 100-seed weight of 13.7 g.\u003c/p\u003e\n\u003cp\u003eThe two RIL populations along with their parents were evaluated for seed size and shape across multiple environments.\u0026nbsp; Experiments were conducted in the Jiangpu Experimental Station (33\u003csup\u003e◦\u003c/sup\u003e030 N and 63\u003csup\u003e◦\u003c/sup\u003e1180 E), Nanjing, Jiangsu Province, in 2012, 2013, 2014 and 2017 growing seasons (designated as 12JP, 13JP, 14JP, and 17JP, respectively), the Fengyang Experimental Station, Chuzhou, Anhui Province (32\u003csup\u003e◦\u003c/sup\u003e870 N and 117\u003csup\u003e◦\u003c/sup\u003e560 E), in 2012 growing season (designated as 12FY) and the Yancheng Experimental Station, Yangcheng, Jiangsu Province, (33◦410 N and 120◦200 E) in 2014 (designated as 14YC). Plants were sown in June and harvest were done in October of the same year. Experiments were designed in a randomized complete blocks design (RCBD) with three replications. The experimental plot was one row of 2 m long at 5 cm plant to plant distance and 50 cm row to row distance. Planting and post-planting operations were carried out following the recommended agronomical practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic Measurement and Statistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEight seed-related traits including seed length (SL), seed width (SW), seed thickness (ST), seed length/seed width (SLW), seed length/seed thickness (SLT), seed width/seed thickness (SWT), flatness index (FI), and 100-seed weight (HSW) were evaluated in LM6 and ZM6 populations under all environments. Phenotypic data were measured and recorded according to standard procedures (\u003ca href=\"#_ENREF_16\"\u003eCheng et al. 2006\u003c/a\u003e; \u003ca href=\"#_ENREF_77\"\u003eTomooka et al. 2002\u003c/a\u003e). In brief, seeds harvested from 10 guarded plants in the middle of each row were used for estimating SL, SW, ST and HSW. The SL was measured as the longest dimension over the seed equivalent to the hilum. SW was measured as the longest dimension across the seed vertical to the hilum. ST was measured as the longest dimension from top to bottom of the seed. The SL, SW, and ST were estimated in millimeters (mm) utilizing the Vernier caliper instrument, according to (\u003ca href=\"#_ENREF_39\"\u003eKaushik et al. 2007\u003c/a\u003e) (Fig. 1). The seed shape was identified by calculating three different ratios, i.e., SL/SW (SLW), SL/ST (SLT), and SW/ST (SWT), as well as flatness index (FI). The ratios between the SL, SW and ST were estimated from the individual values of the length, width, and thickness of the seeds according to (\u003ca href=\"#_ENREF_58\"\u003eOmokhafe and Alika 2004\u003c/a\u003e). Flatness index is an indicator for high or lower seed vigor which impacts the seedling uniformity index as it reduces if the seed vigor reduces. Thus, showing that the plants with FI near to one has good seed vigor concluding higher leaf area index at early vegetative stages and hence higher yield per plant as they have a good number of pods due to the higher accumulation of photoassimilates. These plants, therefore, do not need to increase their average internode length and have a higher number of total nodes and a higher pod number. While flatness index (FI) was calculated following the formula elaborated by (\u003ca href=\"#_ENREF_9\"\u003eCailleux 1945\u003c/a\u003e) and (\u003ca href=\"#_ENREF_12\"\u003eCerd\u0026agrave; and Garcıa-Fayos 2002\u003c/a\u003e) to describe seed shape:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58653_1b1c6aeb34a62c68/58653_custom_files/img1612556825.jpg\"\u003e\u003c/p\u003e\u003cp\u003ewhere 𝐿 is the seed length, 𝑊 is seed width and T is seed thickness.\u003c/p\u003e\n\u003cp\u003eIt extended from a value of 1 for the round seeds to more than 2 for skinny seeds. The HSW was expressed as an average of five measurements of 100 randomly selected seeds.\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics of the seed size, seed shape, and HSW traits were calculated using the SPSS software, version 24 (\u003ca href=\"http://www.spss.com\"\u003ehttp://www.spss.com\u003c/a\u003e). The analysis of variance (ANOVA) for each environment and the combined overall environments (CE) were performed using the PROC GLM procedure in SAS software based on the random model (SAS Institute Inc. v. 9.02, 2010, Cary, NC, USA). The broad-sense heritability (\u003cem\u003eh\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e) in individual environments was estimated as:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58653_1b1c6aeb34a62c68/58653_custom_files/img1612556846.jpg\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003e\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003e\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/em\u003e and \u003cem\u003e\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ege\u003c/sub\u003e\u003c/em\u003e are the variance components estimated from the analysis of variance for the genotypic, error and genotype \u0026times; experiment variances, respectively, with r as the number of replicates and n as the number of environments. All the parameters were assessed from the expected mean squares in ANOVA. Pearson correlation coefficient (\u003cem\u003er\u003c/em\u003e) between seed size, seed shape, and HSW traits was calculated from the mean data utilizing the SAS PROC CORR with data obtained for CE (average across environments) for each population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Genetic Maps and QTL Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-density genetic maps of the ZM6 and LM6 populations consist of 2601 and 2267 bin markers by using RAD-seq technique, respectively (\u003ca href=\"#_ENREF_37\"\u003eKarikari et al. 2019\u003c/a\u003e; \u003ca href=\"#_ENREF_100\"\u003eZhang et al. 2019b\u003c/a\u003e) (Suppl. Table 1). The total length of the ZM6 and LM6 maps were 2630.22 and 2453.79 cM, with an average distance between the markers 1.01 and 1.08 cM, respectively (Suppl. Table 1). The Average marker per chromosome was 130 and 113 for ZM6 and LM6 linkage maps, respectively, with an average genetic distance per chromosome 131.51 and 122.69 cM (Suppl. Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain- and Epistatic-Effect QTLs Mapping \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe WinQTLCart 2.5 software (\u003ca href=\"#_ENREF_81\"\u003eWang et al. 2006\u003c/a\u003e) was employed to identify the M-QTLs using the average values of seed size, seed shape, and 100-seed weight from the individual environments and overall environments with the composite interval mapping model (CIM) (\u003ca href=\"#_ENREF_96\"\u003eZeng 1994\u003c/a\u003e). The software running features were 10 cM window size, 1 cM running speed, the logarithm of odds (LOD) (\u003ca href=\"#_ENREF_56\"\u003eMorton 1955\u003c/a\u003e) threshold was computed using 1000 permutations due to an experiment-wide error proportion of\u0026nbsp; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (\u003ca href=\"#_ENREF_17\"\u003eChurchill and Doerge 1994\u003c/a\u003e), and the confidence interval was determined utilizing a 1-LOD support interval, which was controlled by finding the local on the two sides of a QTL top that compatible with a reduction of 1 LOD score. The QTL detected within the overlapping intervals in different environments were considered the same (\u003ca href=\"#_ENREF_59\"\u003ePalomeque et al. 2009\u003c/a\u003e; \u003ca href=\"#_ENREF_60\"\u003ePalomeque et al. 2010\u003c/a\u003e; \u003ca href=\"#_ENREF_104\"\u003eZhaoming et al. 2017\u003c/a\u003e). Moreover, to identify the genetic effects of the QTLs, i.e., additive QTLs, additive \u0026times; additive (AA), additive \u0026times; environment (AE) and AA \u0026times; environment (AAE), the mixed-model based composite interval mapping (MCIM) procedure was employed in the QTLNetwork V2.1 software (\u003ca href=\"#_ENREF_91\"\u003eYang et al. 2008\u003c/a\u003e). Critical F-value was calculated by a permutation test with 1000 permutations for MCIM. The effects of QTLs were assessed using the Markov Chain Monte Carlo (MCMC) approach. Epistatic effects, candidate interval selection, and putative QTL detection were estimated with an experiment-wide error proportion of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (\u003ca href=\"#_ENREF_79\"\u003eWang et al. 1994\u003c/a\u003e; \u003ca href=\"#_ENREF_87\"\u003eXing et al. 2012\u003c/a\u003e; \u003ca href=\"#_ENREF_92\"\u003eYang et al. 2007\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMining of Candidate Genes for QTL C\u003c/strong\u003e\u003cstrong\u003elusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQTLs identified in two or more environments with an R\u003csup\u003e2\u003c/sup\u003e \u0026gt; 10% were considered as major and stable QTLs (\u003ca href=\"#_ENREF_104\"\u003eZhaoming et al. 2017\u003c/a\u003e). Regions on chromosomes with several M-QTLs related to different traits studied in this research were termed as a QTL Cluster. The Phytozome (http:/phytozome.jgi.doe.gov) and SoyBase (http:/www.soybase.org) online platform repositories, were employed to retrieve all model genes within the physical interval position of the main \"QTL Clusters\". Possible candidate genes were predicted based on gene annotations (http:/www.soybase.org and https:/phytozome.jgi.doe.gov) as well as the reported putative function of genes implicated in these traits. Gene ontology (GO) information was obtained from SoyBase via the online resources, i.e., The Center for Biotechnology Information (NCBI: https:/www.ncbi.nlm.nih.gov), GeneMania (http:/genemania.org/) and the Kyoto Encyclopedia of Genes and Genomes (KEGG, www.kegg.jp). These online tools were employed to further screen the predicted candidate genes. Gene ontology (GO) enrichment analysis was conducted for all the genes within each QTL cluster region using AgriGO V2.0 (http:/systemsbiology.cau.edu.cn) (\u003ca href=\"#_ENREF_76\"\u003eTian et al. 2017\u003c/a\u003e). Gene classification was then carried out using Web Gene Ontology (WeGO) Annotation Plotting tool, Version 2.0 (\u003ca href=\"#_ENREF_95\"\u003eYe et al. 2006\u003c/a\u003e). The publicly available RNA-Seq database on the SoyBase website was used to analyze the expression of the predicted candidate genes in various soybean tissues and the development stages. A heatmap to visualize the fold-change patterns of these candidate genes was developed using the TBtools_JRE 1.068 software (\u003ca href=\"#_ENREF_14\"\u003eChen et al. 2020a\u003c/a\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic evaluation of seed size, seed shape, and 100-seed weight traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll measured (SL, ST, SW, and HSW), and calculated (SLW, SLT, SWT and FI) phenotypic traits exhibited significant differences among the three parental lines across all environments as indicated by the analysis of variance (Tables S2 and S3). Analysis of variance (ANOVA) revealed that all studied traits were significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0. 001 or P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) influenced by the environment, genotypes and the genotype \u0026times; environment interaction (Tables S4 and S5), indicating the differential response of the genotypes to the changes in environmental cues. Furthermore, the two populations showed continuous phenotypic variations in all studied traits, implying a polygenic inheritance of these traits (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Besides, the estimation of skewness, kurtosis and coefficient of variation (CV) for all studied traits across all environments showed that most of the recorded skewness and kurtosis values were \u0026lt;\u0026thinsp;1 with CV values of \u0026gt;\u0026thinsp;3 %, emphasizing that these traits in both populations are controlled by polygenes and are fit for QTL mapping (Tables S2 and S3). The differences in mean phenotypic values among the three parental lines for seed size, seed shape, and HSW traits were constantly high across all studied environments, and their multi-environment means for both populations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The female parent of the LM6 population, Linhefenqingdou, exhibited an average increase of 27.80, 28.19, 31.10 and 41.37 % in SL, ST, SW and HSW, respectively, compared to the male parent, M8206. Meanwhile, in the ZM6 population the female parent Zhengyang surpassed the male parent M8206 by an average of 11.00, 9.66, 7.65 and 17.53 % in SL, ST, SW and HSW across all environments, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Tables S2 and S3). In both populations, several lines overstep their parents in both directions in all studied traits across all environments, suggesting the occurrence of transgressive segregations within the two populations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The broad-sense heritability (h\u003csup\u003e2\u003c/sup\u003e) under individual environments ranged from 69.58\u0026ndash;99.04%, 69.08\u0026ndash;97.9% and 78.49\u0026ndash;98.68% for seed size, HSW and seed shape (Tables S2 and S3). Meanwhile, h\u003csup\u003e2\u003c/sup\u003e under combined environments (CE) ranged from 90.82\u0026ndash;94.48%, 87.51\u0026ndash;94.46% and 97.58\u0026ndash;98.39% for seed size, shape and HSW, respectively. The correlation coefficient (r\u003csup\u003e2\u003c/sup\u003e) among SL, ST and SW exhibited significant positive correlations with each other and with two of the seed shape traits (SLT and SLW) in both populations with r\u003csup\u003e2\u003c/sup\u003e values ranged from 0.79\u0026ndash;0.91. Meanwhile, SL, ST and SW exhibited significant negative correlations with the other two seed shape traits (SWT and FI) (Suppl. Table\u0026nbsp;6). Except for the correlation between SLW and SWT, all the seed shape traits showed significant positive correlations with each other in both populations with r\u003csup\u003e2\u003c/sup\u003e values ranged from 0.33\u0026ndash;0.95. Furthermore, all seed size traits, i.e., SL, SW, and ST showed significant positive correlations with HSW with r\u003csup\u003e2\u003c/sup\u003e values ranged from 0.29 to 0.70 in both populations.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eMapping of seed size main-effect QTLs in the two-related RIL populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 92 M-QTLs were mapped for seed size related traits, i.e., SL, SW and ST, on all chromosomes in soybean, except chromosomes 1 and 12, with logarithm of odd (LOD) scores and phenotypic variations (R\u003csup\u003e2\u003c/sup\u003e) ranged from 2.5\u0026ndash;10.3 and 5.0-19.7 %, respectively, in the two populations (Suppl. Table\u0026nbsp;7, Suppl. Figure\u0026nbsp;1a, b, c). Out of these, 30 M-QTLs for SL, 35 for SW and 27 for ST with alleles underlying QTLs emanated from either of parents. Seventy-two M-QTLs were mapped in a specific environment while the remaining 20 were mapped within overlapping regions in at least one specific environment together with or without CE. Forty-seven QTLs that exhibited R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;10 %, hence were considered as major QTLs. The most prominent QTL was the \u003cem\u003eqSW-17-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e (LOD\u0026thinsp;=\u0026thinsp;6.70-10.29, and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;16.60\u0026ndash;18.30 %) was detected within the physical position 6844412\u0026ndash;9645325 bp in 14JP and CE (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). Likewise, the \u003cem\u003eqSL-10-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6, LM6\u003c/em\u003e\u003c/sub\u003e (LOD\u0026thinsp;=\u0026thinsp;6.08\u0026ndash;6.89, and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;15.4\u0026ndash;17.1 % in ZM6 (17JP) and LM6 (14JP) populations) was located to the physical position between 41454163\u0026ndash;43944243 bp. Aside, \u003cem\u003eqSL-10-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6, LM6\u003c/em\u003e,\u003c/sub\u003e in terms of stability across at least two specific environments with or without the CE, \u003cem\u003eqSL-10-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSL-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSL-18-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSL-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSW-10-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSW-16-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqST-4-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6, ZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqST-16-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqST-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e accounted for averages of 11.17, 14.00, 10.70, 7.65, 11.30, 6.75, 8.85, 13.30 and 11.13 %, respectively (Suppl. Table\u0026nbsp;7, Suppl. Figure\u0026nbsp;1a, b, c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMapping of seed shape main-effect QTLs in the two populations.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, ninety-nine M-QTLs related to seed shape traits (SLT, SLW, SWT, and FI) were mapped to 19 soybean chromosomes excluding chromosome (Chr02) in both populations (ZM6 and LM6) among four environments plus CE with LOD scores of (2.50-10.44) and R\u003csup\u003e2\u003c/sup\u003e (5.12\u0026ndash;31.56 %) by the composite interval mapping (CIM) approach (Suppl. Table\u0026nbsp;8, Suppl. Figure\u0026nbsp;1d, e, f, g). From the 99 M-QTLs, 22, 33, 11, and 22 were detected for SLT, SLW, SWT, and FI, respectively (Suppl. Table\u0026nbsp;8). Among them, seventy-one M-QTLs were detected in specific environments, while 28 were mapped in at least one specific environment together either with or without the CE. Eight M-QTLs for SLW (\u003cem\u003eqSLW-3-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLW-5-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6, LM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLW-9-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6, ZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLW-13-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLW-15-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLW-15-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLW-16-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSLW-16-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e) were mapped in at least one specific environment with or without the CE. Similarly, seven M-QTLs for SLT (\u003cem\u003eqSLT-1-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-5-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-13-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-16-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSLT-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e) were mapped in at least one specific environment with or without CE (Suppl. Table\u0026nbsp;8, Suppl. Figure\u0026nbsp;1e). Likewise, four M-QTLs (\u003cem\u003eqSWT-8-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSWT-11-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6, LM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSWT-13-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eand qSWT-17-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e) were mapped for SWT (Suppl. Table\u0026nbsp;8, Suppl. Figure\u0026nbsp;1f). Also, a total of 10 M-QTLs (\u003cem\u003eqFI-1-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-1-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6, LM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-1-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-1-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6, LM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-3-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-5-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-17-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqFI-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e) were considered as stable.\u003c/p\u003e\n\u003cp\u003eSeveral physical regions identified harbored at least two seed shape related traits, i.e., 1730667\u0026ndash;3014518 bp harbored \u003cem\u003eqSLT-1-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSWT-1-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqFI-1-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6, LM6\u003c/em\u003e\u003c/sub\u003e, and 4946300\u0026ndash;35955471 bp had \u003cem\u003eqSLT-1-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSWT-1-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqFI-1-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e on Chr01 (Suppl. Figure\u0026nbsp;2, Suppl. Table\u0026nbsp;8). Two M-QTLs (\u003cem\u003eqSLW-3-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqFI-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e) colocalized within the physical region of 1509548\u0026ndash;3515594 bp. Moreover, \u003cem\u003eqSLT-5-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-5-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSLW-5-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6, LM6\u003c/em\u003e\u003c/sub\u003e overlapped within the physical region of 38035798\u0026ndash;41186985 bp (Suppl. Figure\u0026nbsp;2, Suppl. Table\u0026nbsp;8). Furthermore, \u003cem\u003eqSLT-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqFI-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e (Chr11); \u003cem\u003eqSLT-13-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLW-13-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6, LM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSWT-13-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(\u003c/em\u003eChr13\u003cem\u003e); qFI-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqFI-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e (Chr14) located within regions of 17145381\u0026ndash;23469672, 33303067\u0026ndash;39562563 and 3468251\u0026ndash;8668367 bp, respectively (Suppl. Figure\u0026nbsp;2, Suppl. Table\u0026nbsp;8). \u003cem\u003eqSLW-16-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-16-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSLW-16-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e QTLs \u003cem\u003eqFI-17-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSWT-17-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSLT-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eand qFI-20-\u003c/em\u003e1\u003csub\u003eZM6\u003c/sub\u003e were located to the chromosomal regions of 26903205\u0026ndash;31959397 on Chr16, 40207655\u0026ndash;41672092 bp on Chr17 and 1 -1115156 bp on Chr20, respectively (Suppl. Figure\u0026nbsp;2, Tables S8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMapping of hundred-seed weight main-effect QTLs in the two populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 48 M-QTLs for HSW were detected, from which 27 were detected in a specific environment and 21 were mapped in at least one specific environment and/without CE (Suppl. Table\u0026nbsp;9, Suppl. Figure\u0026nbsp;1h). The LOD scores and R\u003csup\u003e2\u003c/sup\u003e values of these M-QTLs ranged from 2.51\u0026ndash;10.61 and 4.8\u0026ndash;24.5 %, respectively. The highest number of M-QTLs of 6 were mapped on Chr04 followed by Chr10 with 5 M-QTLs and the lowest number of one M-QTL was mapped to Chr02, Chr09, Chr17 and Chr18. The most prominent M-QTLs were \u003cem\u003eqHSW-14-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-10-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqHSW-10-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e with LOD scores and R\u003csup\u003e2\u003c/sup\u003e values of 10.61 and 24.50 % (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), 7.57 and 17.60 %, and 7.20 and 16.90 %, respectively. Among those 21 M-QTLs, \u003cem\u003eqHSW-4-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-6-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-10-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-13-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-15-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqHSW-15-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e were mapped in at least three environments with an average R\u003csup\u003e2\u003c/sup\u003e of 13.01 %.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative analysis of main-effect QTLs for seed size, shape and weight in the two-related populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegions on chromosomes with several identified M-QTLs for different studied seed phenotypic traits were designated as a QTL cluster. Accordingly, twenty-four QTL clusters located on 17 chromosomes with exception of Chr02, Chr12 and Chr18, were identified (Suppl. Table\u0026nbsp;10, Suppl. Figure\u0026nbsp;2). Among the identified 24 clusters, seven clusters harbored QTLs related to seed size, seed shape, and HSW, five clusters harbored QTLs related only to seed size and seed shape traits, nine clusters comprised QTLs related to seed size and HSW traits, and 3 clusters harbor QTLs for only seed shape traits (Suppl. Table\u0026nbsp;10). The majority of these clusters contained major QTLs. Furthermore, QTLs within 15 clusters revealed positive additive effects with the beneficial alleles inherited from the big seed size and heavy seed weight parents (either \u003cem\u003eZhengyang\u003c/em\u003e or \u003cem\u003eLinhefenqingdou\u003c/em\u003e). Eight clusters out of 24 contain QTLs that have been detected in both populations (Suppl. Table\u0026nbsp;10). The most prominent M-QTL (\u003cem\u003eqFI-1-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e) with a LOD score of 3.71\u0026ndash;10.44 and R\u003csup\u003e2\u003c/sup\u003e (10.45\u0026ndash;31.50 %) was located in Cluster-01. Each cluster comprised a different number of QTLs, with the highest number of QTLs, i.e., seven associated with seed size, shape, and HSW traits were in Cluster-03 at the physical position of 1,509,548-6,780,840 bp allocated as two QTLs related to seed size (\u003cem\u003eqSL-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSL-3-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e), four QTLs for seed shape (\u003cem\u003eqSLW-3-2\u003c/em\u003e \u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eqFI-3-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e) and one QTL HSW (\u003cem\u003eqHSW-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e). In addition, except for \u003cem\u003eqHSW-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, all QTLs in this cluster were major QTLs with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;10 %. Furthermore, each of clusters-13, 16.2, and 17.1 contained five or six QTLs only related to seed size and HSW traits and displayed R\u003csup\u003e2\u003c/sup\u003e of 8.85\u0026ndash;13.43 %, 5.96\u0026ndash;11.26 %, and 6.8\u0026ndash;18.30 %, respectively, and these clusters comprised M-QTLs from only one of the two RIL populations (Suppl. Figure\u0026nbsp;2, Suppl. Table\u0026nbsp;10). Another rich region of QTLs was cluster-20 on Chr20 with a physical length of 1.2Mb. This region harbored 5 M-QTLs related to seed size and shape, i.e., \u003cem\u003eqFI-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLW-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqST-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eqSW-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, out of these five QTLs, three are major QTLs with R\u0026sup2; of 11.2\u0026ndash;19.2% (Suppl. Table\u0026nbsp;10). Cluster-09 contained 5 QTLs related to seed size, shape, and HSW and displayed R\u0026sup2; of 16.3 % and 12.5 % for \u003cem\u003eqHSW-9-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSLW-9-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, respectively, across the two populations. Cluster-14.1 consisted of four major M-QTLs within the physical region between 5834015-9844637bp in both populations with R\u003csup\u003e2\u003c/sup\u003e values ranged from10.4-18.4%, one from which (\u003cem\u003eqSW-14-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e) are related to seed size traits, and three (\u003cem\u003eqSLW-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eqSLT-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e) for seed shape traits all were (Suppl. Table\u0026nbsp;10). Six clusters harbored 4 M-QTLs each, were identified, from which four clusters harbored QTLs associated with HSW as well as some seed size and seed shape traits, i.e., cluster-07 and cluster-19.1, cluster-08 and cluster-14.2 cluster-10.2 mapped on Chr10 and contained M-QTLs for seed size and seed shape traits, and cluster-16.1 that contained only M-QTLs related to seed shape traits (Suppl. Table\u0026nbsp;10). The remaining 9 clusters have three QTLs each, out of them cluster-01 and cluster-17.2 that contain major QTLs related only to seed shape traits. Conversely, cluster-04.1 and cluster-19.2 contain minor M-QTLs associated with SW, SL and HSW. Another two clusters contained M-QTLs for both seed size and seed shape traits (Suppl. Table\u0026nbsp;10). Moreover, the other three clusters of M-QTLs, i.e., cluster-10.1, cluster-11 and cluster-15 contained both major and minor QTLs for seed size traits and HSW. The Cluster-04.2 had two QTLs for two traits, \u003cem\u003eqHSW-4-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6, ZM6\u003c/em\u003e,\u003c/sub\u003e and \u003cem\u003eqSL-4-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e with R\u003csup\u003e2\u003c/sup\u003e of 13.1\u0026ndash;17.7 %. Among the identified 24 clusters, seven clusters harbored QTLs related to seed size, seed shape, and HSW, five clusters harbored QTLs related to only seed size and seed shape traits, nine clusters had QTLs related to seed size and HSW traits, and three clusters harbored QTLs for only seed shape traits (Suppl. Table\u0026nbsp;10). The majority of these clusters had major QTLs. Furthermore, QTLs within 15 clusters revealed positive additive effects with the beneficial alleles inherited from the big seed size and heavy seed weight parents (either \u003cem\u003eZhengyang\u003c/em\u003e or \u003cem\u003eLinhefenqingdou\u003c/em\u003e). Eight clusters out of twenty-four contained QTLs that have been detected in both populations (Suppl. Table\u0026nbsp;10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalyses of additive effect QTL and additive QTL \u0026times;environment interactions for seed size, shape and weight.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mixed-model based composite interval mapping (MCIM) method was implemented in QTL Network V2.1 software to map for additive effect (\u003cem\u003eA\u003c/em\u003e) QTLs and their interactions with the environment (\u003cem\u003eAE\u003c/em\u003e) was performed for both RIL populations across multi- environments. In total, thirty-five \u003cem\u003eAA\u003c/em\u003e on 17 chromosomes related to seven seed size and seed shape traits were identified. These comprised 9, 3, 7, 3, 4, 1, and 8 \u003cem\u003eA\u003c/em\u003e QTLs associated with SL, SW, ST, SLW, SLT, SWT, and FI, respectively, in the LM6 and ZM6 populations across all environments (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, the contributed allele of 11 QTLs of them was inherited from the \u003cem\u003eM8206\u003c/em\u003e parent that decreased seed size and seed shape values through significant additive effects. Meanwhile, the contributed allele of the remaining 24 QTLs descended from either \u003cem\u003eZhengyang\u003c/em\u003e or \u003cem\u003eLinhefenqingdou\u003c/em\u003e parent of the ZM6 or LM6 population, respectively, that increased seed size and shape values through significant additive effects (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). On the other hand, thirteen out of 35 QTLs revealed significant \u003cem\u003eAE\u003c/em\u003e effects in at least one environment. However, five QTLs, i.e., \u003cem\u003eqSW-13-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSW-19-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-8-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSL-10-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqST-13-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, showed significant or highly significant \u003cem\u003eAE\u003c/em\u003e among all studied environments (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, the influence of \u003cem\u003eAE\u003c/em\u003e effects on seed size and seed shape values was environmentally dependent (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Eight \u003cem\u003eAA\u003c/em\u003e QTLs associated with HSW were identified on 6 chromosomes, i.e., Chr03, Chr08, Chr09, Chr13, Chr14 and Chr16 in LM6 and ZM6 populations across six environments (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Six of those 8 QTLs displayed a positive additive effect with the beneficial allele inherited from the female parent (\u003cem\u003eLinhefenqingdou\u003c/em\u003e or \u003cem\u003eZhengyang\u003c/em\u003e in the LM6 or ZM6 population) which could increase HSW. Meanwhile, the remaining two QTLs, i.e., \u003cem\u003eqHSW-13-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqHSW-14-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, revealed negative additive effects with the alleles are inherited from the common male parent (\u003cem\u003eMeng8206\u003c/em\u003e) which could reduce HSW (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, two QTLs, i.e., \u003cem\u003eqHSW-14-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqHSW-8-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, displayed significant \u003cem\u003eAE\u003c/em\u003e effects in two individual environments. Whereas, the \u003cem\u003eqHSW-13-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e showed a significant \u003cem\u003eAE\u003c/em\u003e only in the 13JP environment. In addition, the \u003cem\u003eqHSW-14-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e revealed significant or highly significant \u003cem\u003eAE\u003c/em\u003e effects across three different environments, i.e., 12FY, 12JP, and 17JP (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAdditive and additive \u0026times; environment interaction effect of QTLs associated with seed size traits (SL, SW and ST) and seed shape (SLW, SLT, SWT and FI) traits in soybean seeds.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQTL\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMarker interval\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePosition (cM)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePhysical position (bp)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAdditive -Effect (A)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eAdditive \u0026times; Environment Effect (AE)\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eH\u0026sup2;%\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eH\u0026sup2;%\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-7-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin744-bin745\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3324836\u0026ndash;3459470\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHu et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-13-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1535-bin1536\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e140.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43244220\u0026ndash;44026619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.13**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSalas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-13-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1536-bin1537\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e143.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43953331\u0026ndash;44408971\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.51**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.18*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.18*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSalas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-19-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2100-bin2101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34493194\u0026ndash;34882495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.13**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-9-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1022-bin1023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7308659\u0026ndash;7459924\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.12**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSalas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-18-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1979-bin1980\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9222099\u0026ndash;10402370\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFang et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLT-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin247-bin248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3119582\u0026ndash;3515594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.12**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.22**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLT-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1586-bin1587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7850227\u0026ndash;8143522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.09**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLi et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLT-17-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1883-bin1884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13441932\u0026ndash;13696232\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.13**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-7-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin816-bin817\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29822346\u0026ndash;35034728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.67**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFang et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-3-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin244-bin245\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2790829\u0026ndash;2980527\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-5-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin476-bin477\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1- 529217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-8-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin954-bin955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35158414\u0026ndash;37964850\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.19*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-9-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1030-bin1031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20192294\u0026ndash;27035074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.13**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-11-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1290-bin1291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e69.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18546688\u0026ndash;18767705\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-16-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1745-bin1746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697999\u0026ndash;908917\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.08**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-1-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin4-bin5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e754691\u0026ndash;1375000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-9-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1174-bin1175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38507474\u0026ndash;38736001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-10-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1236-bin1237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3150454\u0026ndash;3297961\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-10-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1334-bin1335\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e106.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44226599\u0026ndash;44378813\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.07*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLi et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-12-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1553-bin1554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38615116\u0026ndash;38812896\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.05**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-13-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1612-bin1613\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e71.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25830321\u0026ndash;26065585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.08**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFang et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-15-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1918-bin1919\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e85.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17503517\u0026ndash;17963129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.14**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSalas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-8-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin959-bin960\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11970511\u0026ndash;12228336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-10-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1334-bin1335\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e106.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44226599\u0026ndash;44378813\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.42**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHu et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-10-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1336-bin1337\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44378814\u0026ndash;44741960\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.41**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-13-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1609-bin1610\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24985496\u0026ndash;25641179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.41**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.6*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.7**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.61*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-14-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1809-bin1810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e104.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47489495\u0026ndash;47717306\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2463-bin2464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e662753\u0026ndash;1045131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.06**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLW-9-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1172-bin1173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38139739\u0026ndash;38507473\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.99**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLi et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLW-10-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1275-bin1279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e60.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14218565\u0026ndash;17808941\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.92*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLW-13-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1653-bin1654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e102.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32704220\u0026ndash;33303066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.1**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSalas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLT-5-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin600-bin599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e93.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40328493\u0026ndash;40882874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSalas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-17-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2177-bin2178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e130.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41009636\u0026ndash;41399912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2461-bin2462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1- 662752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.08**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"12\"\u003eChr., chromosome. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; NS, non-significant. A indicates additive effects, those with positive values show beneficial alleles from parents Zhengyang and Linhefenqingdou while those with negative values show beneficial alleles from parent Meng 8206.H2 indicates phenotypic variation explained by additive effects. AE1, FY2012; AE2, JP2012; AE3, JP2014; AE4, JP2017.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAdditive and additive \u0026times; environment interaction effect of QTLs associated with 100-seed weight trait in soybean seeds.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQTL\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMarker interval\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePosition (cM)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePhysical position (bp)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAdditive -Effect (A)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAdditive \u0026times; Environment Effect (AE)\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eH\u0026sup2;%\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE5\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAE6\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eH\u0026sup2;%\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-3-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin255-bin256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5833775\u0026ndash;6780840\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLi et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-14-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1640-bin1641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e101.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48267526\u0026ndash;48523627\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.48**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.52*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.16*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-8-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin963-bin964\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12871276\u0026ndash;13803222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.29**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.34*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.41*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHan et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-9-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1162-bin1163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e77.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35758796\u0026ndash;36561550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.40**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-13-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1611-bin1612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e69.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25641180\u0026ndash;26012595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.33**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.38*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFunatsuki et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-14-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1746-bin1747\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4176245\u0026ndash;4861311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.15**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-14-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1809-bin1810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e104.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47489495\u0026ndash;47717306\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.46**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.54**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.43*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.69**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-16-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2043-bin2044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e103.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35441262\u0026ndash;35607069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.21**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"14\"\u003eChr., chromosome. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; NS, non-significant. A indicates additive effects, those with positive values show beneficial alleles from parents Zhengyang and Linhefenqingdou while those with negative values show beneficial alleles from parent Meng 8206.H2 indicates phenotypic variation explained by additive effects. AE1, FY2012; AE2, JP2012; AE3, JP2013; AE4, JP2014; AE5, YC2014; AE6, JP2017.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eComparison Of Two Mapping Approaches\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA total of 92, 99, and 48 M-QTLs associated with seed size, seed shape, and HSW, respectively, were mapped by the CIM approach (Tables S7-S9). Meanwhile, forty-three QTLs were identified for seed size, shape and HSW by using MCIM approach (Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these, twenty-two QTLs were identified by both approaches within the same physical chromosomal position, indicating the dependability and stability of these QTLs. Moreover, a comparison of the physical chromosomal regions of the QTLs detected by both approaches revealed that four QTLs, i.e., \u003cem\u003eqSL-7-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSW-19-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqFI-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eqHSW-3-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, were identified for the first time in the two populations (LM6 and ZM6) with an R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;10 %. Therefore, we considered these QTLs as novel and most stable QTLs that could be validated and utilized for map-based cloning, candidate genes identification and QTL stacking into elite cultivars targeted at improving seed size, shape and HSW in soybean.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalyses of epistatic-effect QTLs and their interaction with the environment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of the seed size and shape traits data under all four environments identified 38 pairwise epistatic effects (AA) QTLs, from which 2, 13, 6, 2, 3, 5, and 7 pairs were related to SL, SW, ST, SLW, SLT, SWT, and FI traits, respectively, with R\u003csup\u003e2\u003c/sup\u003e values ranged 0.51\u0026ndash;11.35 % (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). All QTL pairs displayed a high significant additive \u0026times; additive (\u003cem\u003eAA\u003c/em\u003e) effect. Further analyses revealed that 20 AA QTLs showed significant or highly significant pairwise additive-additive-environment (\u003cem\u003eAAE\u003c/em\u003e) interaction effects in at least one environment with R\u003csup\u003e2\u003c/sup\u003e values ranged 0.13\u0026ndash;5.31 % (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, ten pairs showed significant \u003cem\u003eAAE\u003c/em\u003e in two environments, i.e., 12FY (AAE1), and 12JP (AAE2), while three pairs displayed significant \u003cem\u003eAAE\u003c/em\u003e in 12JP (AAE2) and 14JP (AAE3) environments (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This indicates the effect of the environment on gene expression on phenotype development through epistatic effects. Out of 38 QTLs, sixteen pairwise interactions exhibited negative epistatic effects (\u003cem\u003eAA\u003c/em\u003e) that decreased the values of seed size and shape traits, whereas 22 pairwise interactions exhibited positive epistatic effects (\u003cem\u003eAA\u003c/em\u003e) that increased the values of seed size and shape traits (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The pairwise interaction between \u003cem\u003eqFI-1-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqFI-7-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e revealed the strongest positive epistatic effect (0.65), whereas the pairwise \u003cem\u003eqSLT-6-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSLW-9-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, revealed the weakest positive epistatic effect (0.02). Conversely, \u003cem\u003eqSWT-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSWT-13-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e resulted in the strongest negative epistatic effect (-0.71), whereas the pairwise \u003cem\u003eqSLW-2-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSLW-18-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e pairwise resulted in the weakest negative epistatic effect (-0.02) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEstimated epistatic effects (AA) and environmental (AAE) interaction of QTLs for soybean seed size traits (SL, SW, and ST) and seed shape (SLW, SLT, SWT, and FI) traits across all environments.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRIL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTrait\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQTL_i\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eChr_i\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInterval_i\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePos_i\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQTL_j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eChr_j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInterval_j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePos_j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEpistasis -Effect (AA)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eEpistasis \u0026times; Environment Effect (AAE)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH\u0026sup2;%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAE1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAE2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAE3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAE4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH\u0026sup2;%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"28\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLM6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"11\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSW\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-2-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin124-bin125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-16-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1757-bin1758\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-16-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin229-bin230\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-13-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1509-bin1510\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-4-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin353-bin354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-15-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1738-bin1739\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.53**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-4-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin353-bin354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-15-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1740-bin1741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-5-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin525-bin526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-12-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1352-bin1353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-7-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin784-bin785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-15-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1740-bin1741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.10**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-8-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin984-bin985\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-10-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1219-bin1220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.21**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.13*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-10-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1184-bin1185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2231-bin2232\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.09**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-10-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1219-bin1220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-16-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1810-bin1811\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1291-bin1292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-15-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1715-bin1716\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.17**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-11-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1292-bin1296\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-15-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1717-bin1718\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eST\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin237-bin238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-3-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin344-bin345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-6-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin647-bin648\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1274-bin1275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-7-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin749-bin750\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-16-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1755-bin1756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-7-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin783-bin784\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-15-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1741-bin1742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.12**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-16-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1744-bin1745\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-17-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1886-bin1887\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.14**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSLW\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLW- 8-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin941-bin942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST- 14-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1625-bin1626\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.41\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSLT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLT-5-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin560-bin543\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLW-6-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin625-bin626\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLT-6-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin584-bin585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLW-9-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1095-bin1096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.03*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSWT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin236-bin237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-13-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1435-bin1434\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.71**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.8**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-6-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin588-bin589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-18-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2036-bin2037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.62**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.64*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1262-bin1263\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2177-bin2178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-16-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1744-bin1745\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-17-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1886-bin1887\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.15**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-1-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin59-bin60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-14-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1627-bin1628\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-5-2LM6\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin516-bin517\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-10-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1223-bin1224\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-16-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1745-bin1746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-17-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1886-bin1887\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.18*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.75\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSL\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-12-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1553-bin1554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-15-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1919-bin1920\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-2-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin214-bin211\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSL-8-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1084-bin1085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e186.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"10\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eZM6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSW\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-4-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin434-bin435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-20-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2590-bin2591\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.12**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-6-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin684-bin685\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSW-6-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin703-bin704\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.08**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eST\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-10-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1334-bin1335\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqST-10-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1336-bin1337\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.7**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSLW\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLW-2-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin260-bin261\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLW-18-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2336-bin2337\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.03**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSLT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLT-1-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin53-bin54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSLT-7-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin884-bin885\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.03*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSWT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-1-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin62-bin63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqSWT-8-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin914-bin915\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-17-6\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2177-bin2178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2461-bin2462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-1-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin58-bin59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-7-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin884-bin885\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.65**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-1-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin72-bin73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-7-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin872-bin873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-3-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin289-bin290\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqFI-18-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2313-bin2314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.86*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.88\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"17\"\u003eChr_i and Chr_j indicate the two sites involved in epistatic interactions; Pos indicates genetic position for each of the sites. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; NS, non-significant. AA indicates epistatic effects between two QTLs, those with positive values show two loci genotypes being the same as those in parent Linhefenqingdou, Zhengyang (or Meng 8206) have the beneficial effects, while the two-loci recombinants take the negative effects. The case of negative values is the opposite. H\u0026sup2; indicates phenotypic variation explained by epistatic effects. AE1, FY2012; AE2, JP2012; AE3, JP2014; AE4, JP2017.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo digenic pairwise epistatic QTLs for HSW with highly significant additive \u0026times; additive (\u003cem\u003eAA\u003c/em\u003e) effects were identified on 4 chromosomes (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The first pairwise is composed of 2 QTLs, \u003cem\u003eqHSW-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eqHSW-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e with an R\u0026sup2; of 3.46 %, whereas the second pairwise comprises the two QTLs \u003cem\u003eqHSW-9-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqHSW-16-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e with an R\u0026sup2; of 1.38 %. In addition, the two pairwise interactions exhibited positive epistatic effects that could increase the HSW in both populations. Meanwhile, the two pairs did not show any significant \u003cem\u003eAAE\u003c/em\u003e interaction effects across all six environments (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEstimated epistatic effects (AA) and environmental (AAE) interaction of QTLs for soybean 100-seed weight across all environments.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQTL_i\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eChr_i\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInterval_i\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePos_i\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePhysical position (bp)_i\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQTL_j\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eChr_j\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInterval_j\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePos_j\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePhysical position (bp)_j\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEpistasis -Effect (AA)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eEpistasis \u0026times; Environment Effect (AAE)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eH\u0026sup2;%\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAAE1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAAE2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAAE3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAAE4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAAE5\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAAE6\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eH\u0026sup2;%\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-11-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1245-bin1246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6135584\u0026ndash;6494224\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e20\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2175-bin2176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1272590\u0026ndash;1470471\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.51**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-9-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin1162-bin1163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e77.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35758796\u0026ndash;36561550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eqHSW-16-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin2043-bin2044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e103.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35441262\u0026ndash;35607069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.34**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"19\"\u003eChr_i and Chr_j indicate the two sites involved in epistatic interactions; Pos indicates genetic position for each of the sites. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; NS, non-significant. AA indicates epistatic effects between two QTLs, those with positive values show two loci genotypes being the same as those in parent Linhefenqingdou, Zhengyang (or Meng 8206) have the beneficial effects, while the two-loci recombinants take the negative effects. The case of negative values is the opposite. H\u0026sup2; indicates phenotypic variation explained by epistatic effects. AE1, FY2012; AE2, JP2012; AE3, JP2013; AE4, JP2014; AE5, YC2014; AE6, JP2017.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eMining Major M-QTLS Clusters For Candidate Genes Identification\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe 24 M-QTL clusters were filtered based on the richness in QTLs related to all or some of the seed size, shape and HSW or those with at least one QTL for seed size, shape and HSW traits. As a result, seven QTL clusters, i.e., cluster-03, 04.1, 05.1, 07, 09, 17.1, and 19.1, were used to predict candidate genes based on publicly available databases such as SoyBase and Phytozome, and published papers. According to the physical intervals of the 7 QTL clusters, a total of 242, 190, 444, 367, 437, 523, and 116 genes were identified within cluster-03, 04.1, 05.1, 07, 09, 17.1, and 19, respectively, were retrieved from the SoyBase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.soybase.org\" target=\"_blank\"\u003ewww.soybase.org\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e; Suppl. Table\u0026nbsp;11). Gene ontology (GO) enrichment analyses via AgriGO V2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp:/systemsbiology.cau.edu.cn\u003c/span\u003e\u003c/span\u003e) (Tian et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) were used to classify the model genes in each cluster. The classification was based on molecular function, biological process, and cellular components visualized on the Web-based GO (WeGO) V2.0 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wego.genomics.cn\u003c/span\u003e\u003c/span\u003e (Ye et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) In all seven QTL clusters, high percentages of genes were related to catalytic activity, cell part, cell, cellular process, binding, and metabolic process terms, in addition to the response to stimulus in cluster-03 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). These indicate essential roles of these terms in the seed size, shape and seed weight development in soybean. Probable candidate genes underlying these QTL clusters responsible for seed size, shape, and HSW in soybean were further predicted based on gene annotations, GO enrichment analysis and the previously known putative biological function of the gene. Based on these, a total of 19, 12, 26, 18, 22, 30, and 16 candidate genes were identified within QTL clusters-03, 04.1, 05.1, 07, 09, 17.1, and 19.1, respectively (Suppl. Table\u0026nbsp;12). These genes may work directly or indirectly in regulating seed development in soybean, which in turn regulating seed size, shape, and HSW. These genes are involved in response to brassinosteroid stimulus, regulation of cell proliferation and differentiation, regulation of transcription, secondary metabolism and signaling, storage of proteins and lipids, hormone-mediated signaling pathway, regulation of cell cycle process, transport, ubiquitin-dependent protein catabolic process, embryonic pattern specification, and response to auxin stimulus (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). However, the RNS-seq data of expression of genes in soybean genome developed by (Severin et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) and publicly available on SoyBase was used to heatmapped the expression of 19, 12, 26, 18, 22, 30, and 16 candidate genes were identified within QTL clusters-03, 04.1, 05.1, 07, 09, 17.1, and 19.1, respectively, in the young leaf, flower, pod, seed, root and nodule (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, Suppl. Table\u0026nbsp;13). From the heatmaps, forty-seven genes out of the identified 143 candidate genes are highly expressed during seed developmental stages as well as in seed-related tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, Suppl. Table\u0026nbsp;13), hence these could be considered as potential candidate genes, however, they need screening and validation for utilization for seed size, shape, and weight improvement in soybean.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCandidate genes identified within the seven QTL clusters that are highly expressed in soybean seed.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQTL Clusters\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStart\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStop\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene Functional Annotation\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCluster-03\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma03g01880\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1668601\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1674475\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeed dormancy process; protein ubiquitination; lipid storage\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma03g03210\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3001933\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3005606\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePollen development; embryo sac egg cell differentiation; DNA-dependent\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma03g03760\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3581308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3584468\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaintenance of shoot apical meristem identity; cell differentiation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma03g04330\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3581308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3584468\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmbryo development; regulation of seed maturation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma03g04620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4798039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4801122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegulation of meristem growth; protein deubiquitination\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCluster-04.1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma04g02970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2146489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2152500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmbryo sac egg cell differentiation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma04g03210\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2347024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2349849\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFatty acid beta-oxidation; response to auxin stimulus; ovule development\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma04g03610\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2630227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2632308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBrassinosteroid mediated signaling pathway; seed development; ovule development\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma04g04460\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3305860\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3308715\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to cytokinin stimulus; response to brassinosteroid stimulus; seed development\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma04g04540\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3395831\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3397238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to ethylene stimulus; seed dormancy process; floral organ morphogenesis\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma04g04870\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3628743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3634478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmbryo development ending in seed dormancy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCluster-05.1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma05g28950\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34669156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34678593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNucleotide biosynthetic process; embryo development ending in seed dormancy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma05g29700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35236284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35242029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBrassinosteroid biosynthetic process; starch biosynthetic process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma05g30380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35754306\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35755603\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmbryo development; protein ubiquitination; lipid storage; anther development\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma05g31450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36578952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36583516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePost-embryonic development\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma05g31490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36611301\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36615160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmbryo development ending in seed dormancy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma05g31830\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36870586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36873840\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUbiquitin-dependent protein catabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma05g32030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37026301\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37031440\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUbiquitin-dependent protein catabolic process; multicellular organismal development\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma05g33790\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38337126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38341410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhosphatidylcholine biosynthetic process; metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma05g34070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38511154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38513219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellular response to abscisic acid stimulus\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCluster-07\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma07g13230\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11764552\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11784123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmbryo sac egg cell differentiation; protein ubiquitination; lipid storage\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma07g13730\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12749034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12753558\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmbryo development; positive regulation of gene expression\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma07g14460\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13903037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13906228\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmbryo development ending in seed dormancy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma07g15050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14900705\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14909235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeed dormancy process; regulation of cell cycle process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma07g15640\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15378798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15384642\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to hormone stimulus and auxin stimulus; response to brassinosteroid stimulus\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma07g15840\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15528948\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15544150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUbiquitin-dependent protein catabolic process; regulation of lipid catabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCluster-09\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma09g28640\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35573357\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35579018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmbryo development ending in seed dormancy; cellular response to abscisic acid stimulus\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma09g29030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35989729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35993075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUbiquitin-dependent protein catabolic process; fatty acid beta-oxidation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma09g29720\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36540972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36548174\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to auxin stimulus; auxin metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma09g30130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37014420\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37023261\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProtein import into nucleus; embryo sac egg cell differentiation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma09g30650\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37426876\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37433118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhosphatidylcholine biosynthetic process; metabolic process; pollen development\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma09g31620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38298193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38307446\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to abscisic acid stimulus; embryo development\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma09g32600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39100482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39107332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTranslational elongation; embryo development ending in seed dormancy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma09g32680\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39173955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39183935\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegulation of protein phosphorylation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma09g33630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40063507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40067999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to auxin stimulus; seed dormancy process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCluster-17.1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma17g09320\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6889969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6894069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeed maturation; histone deacetylation; response to abscisic acid stimulus\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma17g09690\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7171761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7186015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeed maturation; protein ubiquitination; lipid storage\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma17g10290\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7707775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7711360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePollen tube growth; seed dormancy process; ovule development\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma17g10380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7768561\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7778131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUbiquitin-dependent protein catabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma17g10990\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8262700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8267178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCarbohydrate metabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma17g11410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8557013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8563158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegulation of embryo sac egg cell differentiation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma17g12950\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9873806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9891306\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProtein folding; embryo development response to starvation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma17g15490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12218497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12226562\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUbiquitin-dependent protein catabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma17g15550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12302621\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12306143\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN-terminal protein myristoylation; pollen development; pollen tube growth\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCluster-19.1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma19g32990\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40666918\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40669847\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlucose catabolic process; response to auxin stimulus\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma19g33620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41194146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41196743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaltose metabolic process; starch biosynthetic process; glucosinolate biosynthetic process\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlyma19g33650\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41237306\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41242657\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbscisic acid biosynthetic process; plant-type cell wall modification; pollen tube growth\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDissecting the genetic factors underlying seed size, shape and weight and their relationship to the ambient environment is essential for improving soybean yield and quality-related traits. In addition, understanding the additive and additive \u0026times; environment effects of QTLs and their contribution to the phenotypic variations would facilitate the application marker-assisted selection (MAS) because it will prominently lead the breeders in the QTL selection and expectation of the outcomes of MAS (Jannink et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). A major objective of utilizing linkage mapping in plant breeding is to deepen our understanding of the inheritance and genetic architecture of quantitative traits and detect markers that can be employed as indirect selection tools in plant breeding (Abou-Elwafa \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bernardo \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). In this regard, QTL mapping has been regularly used for detecting the QTL/gene underlying the quantitative traits such as seed size, shape, and weight in crop plants. As known, parental diversity and marker density greatly influence the accuracy and precision of QTL mapping. Besides, the population size used in most of the previously published reports for genetic mapping studies usually varied from 50\u0026ndash;250 individuals, but larger populations are needed for high-resolution mapping. Moreover, a high-density genetic map facilitates the detection of narrow linked markers associated with QTLs and provide a good base for investigating quantitative traits (Galal et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mohan et al. \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e; Tewodros and Zelalem \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Besides, the statistical difference between phenotypic data obtained from various environments could enhance the accuracy to detect QTL position (Zhao and Xu \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Previous studies identified important seed size and shape QTLs, which were also classified to be associated with HSW, however, most of the studies utilized low-density genetic maps based on RFLP, SSR markers, biochemical and morphological markers which have large confidence interval with low resolution of QTLs not suitable for detecting candidate gene (Abou-Elwafa \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bernardo \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Han et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, it is crucial to employ high-density genetic maps to detect more new recombination in a population, which in turn will increase the accuracy of QTL mapping and MAS (Cao et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hina et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the present study, high-density genetic maps constructed from the two-related RIL populations LM6 and ZM6 consist of 2267 and 2601 bin markers, respectively, were used. The markers in the LM6 and ZM6 linkage maps were distributed to all 20 linkage groups and covered the length of 2453.79 and 2630.22 cM, with 1.08 cM and 1.01 cM average distance between adjacent markers, respectively (Li et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). To minimize the environmental errors, the two RIL populations were evaluated in four environments (including different geographical areas and years). The high-density linkage maps of LM6 and ZM6 RIL populations across multi-environment as well as the combined environment were employed to map major main-effect, additive-effect and epistatic-effect QTLs together with interactions with environments and the candidate genes underly seed size, shape and seed weight traits. The parents of the two mapping populations showed high phenotypic variations across all environments in all studied traits, i.e., SL, SW, ST, SLW, SLT, SWT, FI and HSW. Consequently, the transgressive segregation and continuous variations observed in the two populations in all studied phenotypic traits facilitate the identification of a high number of both major and minor effect QTLs including some novel QTLs associated with all studied traits (Teng et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Xu et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). All measured and calculated traits in both populations were significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) influenced by genotype (G), environment (E) and their interactions (G\u0026times;E), suggesting that the seed size, shape, and weight traits are not only governed by both genetic and environment but also there is an effect of the G\u0026times;E interaction (Hu et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liang et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sun et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). This explains the observed high h\u003csup\u003e2\u003c/sup\u003e (99.04 %), and accordingly deduced that these traits are amenable to manipulation by selection without the assistance of molecular markers, indicating that these traits may produce the same phenotypic values when evaluated in the same geographical area. Except for SL, SW and ST that exhibited a highly significant correlation between each other and with HSW, our data showed that seed size, shape, and weight traits are not correlated which is favorable when breeding for a round-type with smaller or bigger seed size (Cober et al. \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e; Salas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eComparative QTL results using the CIM QTL mapping approach with SoyBase database identified 69, 82 and 29 novel QTLs for seed size, shape, and HSW, respectively, indicating the distinct genetic architecture of the LM6 and ZM6 populations. These novel QTLs together explain more than 88.00 % of phenotypic variance for seed size, shape, and weight, signifying their potential value for improving soybean cultivars. Besides, the identification of novel QTLs in the present study suggests that more germplasms are needed to be used for unraveling the complex genetic basis for seed size and shape traits in soybean. Among these novel QTLs, the \u003cem\u003eqFI-1-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e showed the highest R\u003csup\u003e2\u003c/sup\u003e and LOD values and therefore may be the major QTL underlies flatness index (FI). Noteworthily, all FI QTLs were identified for the first time in this study, thus we considered them as novel QTLs. Chr01 and Chr03 harbored 4 and 3 FI QTLs, suggesting crucial roles of Chr01 and Chr03 in controlling the inheritance of seed FI in soybean. Round soybean seeds are required for soybean varieties used for food. Furthermore, the data revealed that the average flatness indices across all environments in both the LM6 and ZM6 populations have sphere seeds (FI\u0026thinsp;\u0026asymp;\u0026thinsp;1.0), indicating that it is essential to start with at least one round seeded parent to get a segregant with round seed shape. Remarkably, eight novel major QTLs for HSW, including \u003cem\u003eqHSW-4-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-6-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-7-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-9-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-10-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-10-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-14-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6, ZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqHSW-14-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e where their physical intervals did not overlap with any of the previously reported HSW QTLs, suggesting them as potential loci for HSW and major QTLs for future fine mapping to delimit the physical interval. The \u003cem\u003eqHSW-4-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6, ZM6\u003c/em\u003e\u003c/sub\u003e was detected in the physical interval of 42.7\u0026ndash;45.8 Mb on Chr04 that overlapped with the previously identified seed-weight QTLs, i.e., \u003cem\u003eseed weight 47\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e and \u003cem\u003eseed wtQTL4.1\u003c/em\u003e (Hacisalihoglu et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Nine QTLs for SL identified in this study were colocalized as previously reported (Hu et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jun et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Salas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Two major QTLs related to SW, i.e., \u003cem\u003eqSW-4-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSW-6-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, both residing a genomic sequence of approximate 128 kb colocalized with the previously identified SW QTL \u003cem\u003eA063-1\u003c/em\u003e (Salas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) and SW QTL detected previously in another independent soybean populations (Hina et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Moongkanna et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), respectively. Thus, \u003cem\u003eqSW-4-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqSW-6-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e could be considered as stable QTLs for further fine mapping and map-based cloning to clarify the genetic mechanisms underlying SW. The \u003cem\u003eqSLW-5-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e overlapped with \u003cem\u003eqSLW-5-2\u003c/em\u003e (Satt449) which was previously reported by (Jun et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, the three QTLs associated with SLT (\u003cem\u003eqSLT-5-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSLT-16-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eqSLT-20-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e) are colocalized with previously reported QTLs (Fang et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jun et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Salas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The SWT QTL between \u003cem\u003eSatt508\u003c/em\u003e and \u003cem\u003eSatt421\u003c/em\u003e on Chr08 that have been previously mapped (Salas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) are colocalized to the \u003cem\u003eqSWT-8-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e QTL that was mapped to the physical interval 93\u0026ndash;96.3 cM of Chr08. Additionally, our study identified for the first time 13 major QTLs (R\u0026sup2; \u0026gt;10 %) related to FI, thus we considered them as novel QTLs. Among these novel QTLs, the \u003cem\u003eqFI-1-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e showed the highest R\u003csup\u003e2\u003c/sup\u003e and LOD values and therefore might be the major QTL underlies FI. Besides, Chr01 and Chr03 harbored 4 and 3 FI QTLs, suggesting crucial roles of Chr01 and Chr03 in controlling the inheritance of seed FI in soybean. Moreover, the positive alleles for seed size, shape and HSW traits were inherited from both parents of the two RIL populations. Therefore, it is likely that not only the higher seed size and heavy weight parent (\u003cem\u003eLinhefenqingdou\u003c/em\u003e or \u003cem\u003eZhengyang\u003c/em\u003e) contributed favorable alleles but also the lighter seed weight parent (\u003cem\u003eM8206\u003c/em\u003e) might play a role (Cao et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hina et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMapping of QTLs associated with seed size, shape and weight related traits using the MCIM approach was performed to; i) dissect the additive effect QTLs and Q \u0026times; E interactions which is essential for selecting the most compatible varieties adapted to particular environments, and ii) further validate the QTLs identified by the CIM approach. The MCIM method approach identified 18 QTLs for seed sizes, shapes, and weight traits that are colocalized in the same physical interval of the CIM-mapped QTLs as previous studies. The major SL QTL \u003cem\u003eqSL-7-1\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e is colocalized with \u003cem\u003eSatt150\u003c/em\u003e QTL (Salas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Furthermore, the SW QTLs \u003cem\u003eqSW-13-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqST-18-4\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqSWT-7-5\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eqSLT-5-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e were mapped in the same position as reported in previous studies (Fang et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Salas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Additionally, the \u003cem\u003eqHSW-3-2\u003c/em\u003e\u003csub\u003e\u003cem\u003eLM6\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eqHSW-8-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eqHSW-13-3\u003c/em\u003e\u003csub\u003e\u003cem\u003eZM6\u003c/em\u003e\u003c/sub\u003e QTLs are colocalized to the previously identified SW QTL \u003cem\u003eSeed weight 32\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/em\u003e (\u003cem\u003eSatt675\u003c/em\u003e), \u003cem\u003eSeed weight 35\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e and \u003cem\u003eSeed weight 19\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/em\u003e (\u003cem\u003eSatt114\u003c/em\u003e) QTLs, respectively (Funatsuki et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Han et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). Therefore, these QTLs could also be considered as stable QTLs for further fine mapping and map-based cloning to uncover the genetic control and mechanisms of seed size, shape, and weight traits in soybean, and molecular markers tightly linked to these QTLs could be used for MAS.\u003c/p\u003e\n\u003cp\u003eDissecting the epistatic and QTL \u0026times; environment effects are crucial for understanding the genetic mechanisms that greatly contributed to the phenotypic variations of complex traits (Kaushik et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The genetic construction of seed size, shape, and weight also contains epistatic interactions between QTLs (Kato et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liang et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, disregarding intergenic interactions will lead to the over-estimation of individual QTL effects, and the under-estimation of genetic variance (Nyquist and Baker \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e). Consequently, this might result in a large drop in the genetic response to MAS especially in late generations (Zhang et al. \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). The identified 40 pairwise digenic epistatic QTLs for seed size, shape and weight related traits in the present study could be considered as modifying genes that do not exhibit only additive effects but could affect the expression of seed size, shape and weight related genes through epistatic interactions. Similar results for the epistatic interaction of seed size, shape and weight QTLs have been also previously reported by (Xin et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The appearance of epistatic interactions for a specific trait makes selection difficult. Noteworthily, all main-effect QTLs detected in our study had no epistatic effect, which raises the heritability of the trait guiding to easier selection.\u003c/p\u003e\n\u003cp\u003eGenomic regions were identified as QTL clusters based on the presence of a large number of QTLs related to all or some of seed size, shape and HSW. The identification of 24 QTL clusters located on 17 different chromosomes. Accordingly, twenty-four QTL clusters were identified on 17 chromosomes each contained three or more QTLs related to seed size, shape, and HSW traits. These QTL clusters have not been reported previously and added to the developing knowledge of the genetic control of these traits. Moreover, the colocalization of QTLs for seed size, shape, and HSW and the way that they have exceptionally corresponded support the highly significant correlation with each other (Cai and Morishima \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) (Suppl. Table\u0026nbsp;10). Besides, the occurrence of the QTL clustering could signify a linkage of QTLs/genes or outcome from the multiple effects of one QTL in the same genomic region (Cao et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Furthermore, the QTL clusters displayed that the QTLs linkage/ gathering could make the enhancement of seed size and shape more easily than single QTLs (Hina et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Significant positive correlations between soybean seed protein and oil contents and seed size and seed shape have been demonstrated, therefore, both traits are directly associated with seed size and shape in soybean (Hacisalihoglu and Settles \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qi et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wu et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). This notion would explain the colocalization of QTLs associated with seed protein and oil contents in the genomic regions of several QTL clusters including clusters 1, 04.1, 04.2, 06, 07, 09, 10.1, 14.1, 14.2, 17.1, and 17.2. (Moongkanna et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Panthee et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Salas et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vieira et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yang et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, the position of the first flower and the number of days to flowering have large effects on seed number per plant in soybean (Khan et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tasma et al. \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Yamanaka et al. \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e), which in turn affects seed size and HSW indicating the existence of common genetic factors for these traits. QTLs associated with the position of the first flower identified previously (Han et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tasma et al. \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e), are located to the genomic region of clusters 16.1, 19.2, and 20 (Hyten et al. \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). The extensive analysis of QTLs clusters in our study suggests that breeding programs aiming to improve seed size, shape, and weight with enhanced quality should focus on QTL clustering and select QTLs within these regions. Besides, the existence of QTL clusters provides evidence that some traits related-genes are more densely concentrated in specific genomic regions of crop genomes than others (Fang et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIdentification of candidate genes underlying QTL regions is of great interest for breeding programs (Abou-Elwafa \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Abou-Elwafa and Shehzad \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). So far, only two seed sizes/weight-related genes have been cloned from the soybean, i.e., the \u003cem\u003eGlyma20g25000 (ln)\u003c/em\u003e gene that has a significant impact on seed size and the number of seeds per pod (Jeong et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the PP2C-1 allele underlying \u003cem\u003eGlyma17g33690\u003c/em\u003e has been reported to increase seed size/weight (Lu et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). In our study, a bioinformatics pipeline implementing genomic sequences of identified QTL clusters was employed to identify candidate genes. The pipeline consists of three complementary steps, i.e., 1) retrieving candidate genes from the SoyBase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.soybase.org\" target=\"_blank\"\u003ewww.soybase.org\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e) visualize the molecular function of candidate genes by GO enrichment analyses and gene classification, and 3) the implication of candidate genes in seed size, shape and weight based on their expression profiles. Accordingly, one-hundred forty-three genes were considered as potential candidates. The GO enrichment and gene classification analyses showed that most of the identified candidate genes behind QTL clusters are related to the terms of catalytic activity, cell part, cell, cellular process, binding and metabolic process terms in addition to the response to stimulus in cluster-03, and these terms are reported as being vital elements in seed development (Li and Li \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mao et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). For example, \u003cem\u003eGlyma07g14460\u003c/em\u003e gene underlying QTL cluster-7 belongs to the oxygenase (CYP51G1) protein class, which has been confirmed to regulate seed size in soybean (Zhao et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). These predicted 143 genes have functions that are related/involved in seed development, which in turn influences the size, shape, and weight of seeds, such as brassinosteroid mediated signaling pathway, regulation of cell differentiation and proliferation, fatty acid beta-oxidation, peroxisome organization, double fertilization forming a zygote and endosperm, lipid transport and storage, regulation of hormone levels transport and metabolic processes, ubiquitin-dependent protein catabolic process, and sugar mediated signaling pathway (Li and Li \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mao et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Furthermore, ten candidate genes were identified as a regulator of ubiquitin-dependent protein catabolic process, RING-type E3 ubiquitin ligases and lipid catabolic process (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Several components of the ubiquitin pathway such as the ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2) and ubiquitin protein ligase (E3) have been reported to play important roles in regulation seed and organ size (Li and Li \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, 16 candidate genes have functions in pollen tube development, embryo sac egg cell differentiation, post-embryonic development, regulation of seed maturation, positive regulation of gene expression, regulation of cell cycle process, ovule development, anther development, seed dormancy process and seed maturation (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) and hence they are likely to participate in regulating seed size, shape and weight in plants, including soybean (Meng et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, ten candidate genes are involved in response to auxin stimulus, response to ethylene stimulus, abscisic acid biosynthetic process that are known to be implicated in promoting seed size and weight in Arabidopsis (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) (Xie et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, six genes are known to play functions in glucose catabolic process, phosphatidylcholine biosynthetic process, carbohydrate metabolic process, maltose metabolic process and starch biosynthetic process which some of them are known to be involved in partitioning and translocation of photo-assimilates and grain filling in rice (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) (Chen et al. \u003cspan class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":" \u003cp\u003eThe present study employed high-density maps of two-related RIL populations, LM6 and ZM6 evaluated in multiple environments to identify M-QTLs as well candidate genes controlling seed size, shape, and weight in soybean. Besides, this is the first detailed and comprehensive investigation of QTLs for flatness index as a seed shape trait in soybean. A total of 180 and 18 M-QTLs were reported for the first time in this study using the CIM and MCIM QTL mapping approaches, respectively. Besides, sixty-nine QTLs were considered as stable as detected in more than one specific environment or one individual environment together with CE. All the positive alleles of 282 identified QTLs were inherited from the female parents. Our data revealed 7 major and stable QTL clusters underlying the inheritance of seed size, shape and weight located to genomic regions on chromosomes 3, 4, 5, 7, 9, 17 and 19 in soybean. The implemented bioinformatics pipeline delimits the number of the identified candidate genes to 47 genes within the physical interval of the previously mentioned 7 genomic regions involved directly or indirectly in seed size, shape and weight. These genes are highly expressed in seed-related tissues and nodules, indicating that they may be involved in regulating the above traits in soybean. Furthermore, some of the potential 47 candidate genes have been included in our on-going projects for functional validation to confirm their effect on seed size, shape, and weight. Our study provides detailed information for genetic bases of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning as well as for MAS targeted at improving seed size, shape and weight.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.Z. designed the project. M.A.E. performed the experiments. M.A.E., B.K., S.S., S.L., Y.C., M.A. and A.H. analyzed the data. M.A.E. drafted the manuscript. T.Z. and S.F.A. revised the paper. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R \u0026amp; D Program of China (2018YFD0100800), the National Natural Science Foundation of China (31871646), the MOE Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT_17R55), the Fundamental Research Funds for the Central Universities (KYT201801), the Jiangsu Collaborative Innovation Center for Modern Crop Production (JCICMCP) Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll\u0026nbsp;data\u0026nbsp;are included within the manuscript and its supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAbou-Elwafa SF (2016) Association mapping for drought tolerance in barley at the reproductive stage. 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Euphytica 213:162\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glycine max, Soybean seed, QTL mapping, QTL clusters, epistatic interactions, marker assisted breeding","lastPublishedDoi":"10.21203/rs.3.rs-206236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-206236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Dissecting the genetic mechanism underlying seed size, shape and weight is essential to these traits for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line populations, LM6 and ZM6, evaluated in multiple environments to identify candidate genes behind seed-related traits major and stable QTLs. A total of 239 and 43 M-QTL were mapped by composite interval mapping and mixed-model based composite interval mapping approaches, respectively, from which 22 common QTLs including four major and novel QTLs. CIM and MCIM approaches identified 180 and 18 novel M-QTLs, respectively. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Seed flatness index QTLs (34 QTLs) were identified and reported for the first time. Seven QTL clusters underlying the inheritance of seed size, shape and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17 and 19 were identified. Gene annotations, gene ontology (GO) enrichment and RNA-seq analyses identified 47 candidate genes for seed-related traits within the genomic regions of those 7 QTL clusters. These genes are highly expressed in seed-related tissues and nodules, that might be deemed as potential candidate genes regulating the above traits in soybean. This study provides detailed information for the genetic bases of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning as well as for MAS targeted at improving these traits individually or concurrently.","manuscriptTitle":"Comparative QTL analysis and candidate genes identification of seed size, shape and weight in soybean (Glycine max L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-06 18:55:24","doi":"10.21203/rs.3.rs-206236/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dab90393-255d-44ea-9f47-e0cfb8415aa4","owner":[],"postedDate":"February 6th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2275272,"name":"Molecular Genetics"},{"id":2275273,"name":"Plant Molecular Biology and Genetics"}],"tags":[],"updatedAt":"2021-02-07T05:01:29+00:00","versionOfRecord":[],"versionCreatedAt":"2021-02-06 18:55:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-206236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-206236","identity":"rs-206236","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}