Genome Sequence and Association Analysis Reveal Allelic Variants for Agronomically Important Traits in Foxtail Millet (Setaria italica L.) Germplasm

Genome Sequence and Association Analysis Reveal Allelic Variants for Agronomically Important Traits in Foxtail Millet (Setaria italica L.) 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Germplasm Sameena Shaik, Anand Kumar, Bhushan B. Dholakia, Konda Sravansimha Reddy, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7465120/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 Foxtail millet ( Setaria italica L.), a small-grained, diploid C4 panicoid millet with a genome size of 515 Mb, is the second most widely cultivated crop globally. It serves as a model species for agronomic and nutritional research. This study employed double-digest restriction-site-associated DNA sequencing (ddRAD-seq) to characterize genomic variation within 30 foxtail millet genetic resources. The analysis identified 206,483 high-quality polymorphic SNPs through NGS-ddRAD path. Number of SNPs on individual genotypes ranged from 1184(Si-7) to 131873 (Si-3) at Read Depth 10. Results revealed three distinct clusters, effectively separating most landraces and released cultivars, thereby indicating population differentiation based on their classification and geographic origin. Among the landraces, genotypes Si-17, Si-15, Si-5, and Si-16 exhibited high yields, early flowering, and early maturation compared to release cultivars Further examination of SNPs in landraces uncovered variations in minor allele frequency (MAF), highlighting high-frequency alleles within the Setaria genotype. A total 83 significant MTAs (Marker-trait associations) were identified by GWAS for Eight traits across the genome. High confidence MTAs for three important traits including total tiller number per panicle (TNPP), Plant base color (PBC), and Grain weight per panicle (GWPa) were identified. These MTAs facilitated the identification of 57 candidate genes with potential applications in molecular breeding. Additionally, a mini-core selection of 30 genotypes, representing the majority of genetic diversity indicated notable genetic traces of landraces, particularly for those three traits. These landraces show promise for use in breeding programs aimed at developing climate-resilient foxtail millet varieties. ddRAD-Seq Foxtail millet Genome-wide analysis Landrace Molecular markers SNPs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Foxtail millet ( Setaria italica L.) is a nutritionally rich crop known for its resilience to harsh environmental conditions, including drought and elevated temperatures. Originating in China (Charulata, 2013), it is a staple in Asia and Africa; Because of its high nutritional content and adaptability to climate change, foxtail millet is a valuable feed and fodder source that is suited for livestock and supports sustainable farming methods (Vetriventhan et al., 2012 & Anuradha et al., 2020). This crop possesses several advantageous traits, including drought-avoidance mechanisms, low harvesting costs, nutritional value, and high tolerance to biotic stresses. Foxtail millet's short life cycle, compact genome size (515 Mb, second only to rice), inbreeding nature, and efficient seed setting make it a valuable model species for agronomic and nutritional research (Ramesh et al., 2023). In India, it is predominantly cultivated in semi-arid regions such as Andhra Pradesh, Tamil Nadu, Karnataka, Rajasthan, Uttar Pradesh, and the North-Eastern states (Kumari et al., 2013). Landraces are domesticated plant species that have undergone extensive local adaptation, serving as crucial genetic resources for sustainable agriculture and frequently utilized by local farmers. Compared to release cultivars, landraces generally exhibit lower yields (Pradhan et al., 2020). However, they are characterized by greater genetic heterogeneity and exhibit remarkable resilience to both abiotic and biotic stresses (Ramesh et al., 2023). Enhancing landraces for improved yields and adaptability to diverse environmental stresses holds significant potential for increasing crop productivity and improving farmers' livelihoods. Yield-focused breeding programs have been a key driver of genetic diversity loss in foxtail millet, as seen in many other crop species. DNA-based studies on millet species have demonstrated high levels of genetic diversity in foxtail millet and green millet (Le Thierry d'Ennequin et al., 2000). To mitigate genetic erosion, a strategic approach involving the collection, evaluation, and preservation of these invaluable landraces is essential for their future utilization (Ramesh et al., 2023; Palakurthi et al., 2023). The complete genome sequence and associated bioinformatics resources of foxtail millet have been transformative for millet research, facilitating genome mapping and genetic characterization to develop elite cultivars. Advances in genomics and genetics have been significantly bolstered by high-throughput genotyping and sequencing technologies (Zhang et al., 2018). The rapid evolution of next-generation sequencing (NGS) technologies has enabled the generation of extensive single nucleotide polymorphism (SNP) datasets in both model and non-model plant species (Cloutier et al., 2019). Early efforts to estimate genetic diversity in foxtail millet landraces utilized molecular markers (Liu et al., 2019). Genotyping-by-sequencing (GBS), an NGS-based approach for SNP discovery in reduced representations of genomes, has proven effective (Deschamps et al., 2012). Among GBS methods, double-digest restriction-site-associated DNA sequencing (ddRAD-seq) is a refined technique employing two restriction enzymes—one rare-cutting and one frequent-cutting—to generate stable, reproducible-sized genomic fragments (Peterson et al., 2012). This approach enables the generation of high-density marker datasets, crucial for advanced genetic analyses. Concurrent advancements in statistical methods have enhanced the accuracy of genomic studies by reducing false positives arising from population structure and multiple testing corrections (Gupta et al., 2014). Genome-wide association studies (GWAS) have become a pivotal tool for dissecting the genetic basis of quantitative traits. Applications of ddRAD-seq technology in GWAS have been successfully demonstrated across various crops, including tomato (Bodanapu et al., 2019), pea (Gali et al., 2019), sesame (Ruperao et al., 2023), wheat (Arora et al., 2017), sorghum (Morris et al., 2013), maize (Xiao et al., 2017), and rice (Zhao et al., 2018). In this study, we analyzed foxtail millet germplasms, encompassing both landraces and released cultivars of Setaria , using an integrated approach combining ddRAD-seq with morpho-physiological, yield, and yield-related trait characterization. Germplasm separation was achieved through the single-seed descent method, enabling precise analysis of allelic variants (Ramesh et al., 2023). The resulting large-scale SNP markers and the identification of genotypes carrying novel, superior alleles provide valuable resources for foxtail millet improvement programs, particularly for enhancing climate resilience in response to future agricultural challenges. Materials and methods Plant material and Phenotyping Thirty diverse genotypes of Setaria were collected from various locations within the Rayalaseema region of Andhra Pradesh, India. The collection comprised 20 landraces, 7 released cultivars and 3 germplasm lines. The methodology for the pure line development was previously described (Ramesh et al., 2023). These genotypes were evaluated for eight agronomic traits: plant base color, leaf base color, bristle (awn) color, grain weight per panicle, tiller number per plant, chlorophyll content, plant height, and stomatal density. The phenotyping was conducted at Yogi Vemana University, Kadapa, Andhra Pradesh, India using a randomized complete block design with three replications during the Kharif season of 2020. The average data from three replicates for all traits were used in the analysis. Descriptive statistical measures including minimum, maximum, mean, and standard error were calculated using Microsoft Excel. DNA extraction, ddRAD sequencing and SNP calling Genomic DNA was extracted from the leaves of five-week-old plants using a modified CTAB method (Murray and Thompson, 1980). DNA quantity was assessed based on absorbance values at 260 nm and 280 nm using a Nanodrop spectrophotometer (Eppendorf BioSpectrometer Fluorescence, Germany). DNA quality and intact double-stranded DNA integrity were evaluated using a Qubit fluorometer (Thermo Fisher Scientific, USA) and 2% agarose gel electrophoresis. Library quality control (QC) was performed using a Bioanalyzer (Agilent Technologies, USA). The quantified DNA was subsequently used for ddRAD-sequencing on an Illumina HiSeq2000 platform (Agri Genome Labs Pvt. Ltd., Hyderabad, India). Following sequencing, reads were processed for quality assurance. Base and adapter trimming were performed and sequences containing restriction-site-associated DNA (RAD) tags were filtered. A quality distribution plot was generated using custom scripts developed at Agri Genome Labs Pvt. Ltd. Sample reads were demultiplexed with allowance for one mismatch and low-quality bases, as well as regions with base bias at the start or end were trimmed. Illumina 5' and 3' adapter sequences were also removed. The Setaria reference genome was obtained from Phytozome v7.0 ( ftp://ftp.jgipsf.org/pub/compgen/phytozome/v7.0/Sitalica/annotation/Sitalica_164_gene.gff3.gz ). Paired-end reads were aligned to this reference genome using the Bowtie2 program (v2.1.0) (Langmead et al., 2012) with default parameters. Variant calling was performed based on these alignments to identify genomic variations. Zygosity, diversity, phylogenetic, kinship and Principal component analyses The diversity, zygosity, kinship and phylogenetic analyses of the 30 genotypes were conducted using genotype data filtered for a read depth (RD) > 10 and minor allele frequency (MAF) < 0.05. A radial phylogenetic tree was constructed to illustrate genetic relationships based on a similarity matrix generated using the neighbor-joining (NJ) algorithm in MEGA X (Kumari et al., 2023). Kinship analysis among individuals was calculated using VanRaden's method based on their genotypic data (VanRaden, 2008). This analysis employed a centered identical-by-state (IBS) matrix and was further explored using the Trait Analysis by Association and Evolution (TASSEL v5.2.28) software (Bradbury et al., 2007). Principal component analysis (PCA) was performed on the filtered genotype data (MAF < 0.05) using PLINK v1.90 (Purcell et al., 2007) and analyzed with TASSEL to investigate genetic diversity. The PCA results highlighted the genetic differentiation among landraces, released cultivars, and germplasm lines providing insights into their evolutionary and genetic relationships. Marker-trait association For GWAS, SNPs with less than 30% missing data and a MAF greater than 5% were included. The Fixed and Random Model Circulating Probability Unification (FarmCPU) method (Liu et al., 2016) was employed for the association test. This recently developed technique is computationally efficient and effectively addresses challenges related to multiple testing corrections and kinship effects. Farm CPU integrates both fixed effect models (FEM) and random effect models (REM) in its framework. The FEM analyzes markers using pseudo-quantitative trait nucleotides (QTNs), which are calculated by the REM and subsequently used as covariates. The association test model included the first three principal components (PCA) as covariates to account for population structure. SNPs with a p-value < 0.005 were considered significant marker-trait associations (MTAs), and a Bonferroni-corrected p-value threshold of 0.05 was applied (Liu et al., 2016). Quantile-quantile (Q-Q) plots were analyzed to evaluate model fitting and assess population structure correction. The Q-Q plots illustrated the distribution of observed versus expected p-values from the association tests. Proper model fitting was indicated by a close alignment of the observed p-values with the diagonal line, suggesting minimal bias. Sharp deviations at the curve ends signified a small number of true associations among the numerous SNPs tested. The degree of deviation from the diagonal at the curve’s tail served as a measure of the power of the test statistics (Turner, 2014). Candidate gene identification To identify potential candidate genes located near high-confidence SNPs, the associated SNPs were mapped to the Setaria italica reference genome (v2.2) (available at Phytozome). Transcripts within a 25 kb region flanking each associated SNP were extracted along with their functional annotations (Vandana et al., 2019). Results The phenotypic distribution of traits revealed a wide range of variability Descriptive statistics, including the minimum, maximum, mean, and standard deviation, revealed substantial phenotypic variation across the eight traits evaluated in 30 genotypes (Suppl. Table S1). For instance, grain weight per panicle ranged from 4.7 g to 15.1 g, with a mean of 9.28 g and a standard deviation of 2.8 g. Similar variability was observed in other traits, such as plant height (112.55 ± 15.2 cm), tiller number per plant ( 6.9 ± 4.3), chlorophyll content ( 45.34 ± 4.08), number of stomata (72.6 ± 10.6), plant base color (1.3 ± 0.47), leaf base color ( 1.2 ± 0.44) and awn color (1.3 ± 0.4). Detection of polymorphic variants indicated the presence of SNPs on all chromosomes Detailed information about the 30 Setaria genotypes including variety names and collection origins is provided in Supplementary Table S2. Genomic DNA was isolated from each genotype and digested using the restriction enzymes MluCI and SphI , which have distinct recognition sites and cutting frequencies. Quality assessment of the fragmented DNA confirmed that all samples met the required screening criteria. Further processing, including quality checks, screening, and filtering of raw sequencing data, produced various read statistics (Fig. 1 , Table 1 ). The average GC content across samples was 46%. Among the genotypes, the Si-27 landrace exhibited the highest proportion of reads containing RAD tags (47%), while Si-21 showed the lowest (43%). The percentage of uniquely aligned reads to the Setaria italica reference genome for all germplasms is summarized in Table 1 . Table 1 Sequence read measurements of 30 genotypes with annotated SNPs and INDELs at read depth (RD) > 10 Sample Total reads Reads after processing Total reads aligned Reads aligned (%) Total uniquely aligned reads Uniquely aligned reads (%) Si-1 16,89,274 16,40,278 1,467,092 89.44% 1,409,655 96.09 Si-2 44,16,956 44,16,956 2,318,216 52.48% 1,804,356 77.83 Si-3 65,55,018 65,55,018 3,664,652 55.91% 2,897,245 79.06 Si-4 41,59,494 41,59,494 1,845,754 44.37% 1,434,943 77.74 Si-5 46,78,988 46,78,988 3,839,849 82.07% 2,906,577 75.7 Si-6 12,21,540 11,77,974 1,077,474 91.47% 1,044,431 96.93 Si-7 39,08,672 39,08,672 9,28,634 23.76% 3,91,733 42.18 Si-8 36,90,080 36,90,080 2,640,236 71.55% 2,061,422 78.08 Si-9 35,06,900 35,06,900 2,977,316 84.90% 2,377,308 79.85 Si-10 10,47,420 10,20,720 9,23,580 90.48% 8,87,660 96.11 Si-11 16,42,510 16,42,510 1,403,285 85.44% 1,091,035 77.75 Si-12 22,17,334 22,17,334 1,832,703 82.65% 1,441,068 78.63 Si-13 20,06,868 20,06,868 1,767,216 88.06% 1,416,355 80.15 Si-14 57,09,834 57,09,834 4,891,724 85.67% 3,740,042 76.46 Si-15 19,72,192 19,15,244 1,723,369 89.98% 1,663,770 96.54 Si-16 15,30,772 14,89,590 1,333,941 89.55% 1,286,660 96.46 Si-17 19,04,782 18,62,970 1,651, 673 88.66% 1,580,563 95.69 Si-18 10,39,734 10,07,330 8,98,181 89.16% 8,61,901 95.96 Si-19 20,61,392 20,35,660 1,884,005 92.55% 1,826,673 96.96 Si-20 17,68,238 17,46,624 1,659, 997 95.04% 1,620,263 97.61 Si-21 7,88,114 7,88,114 6,62,773 84.10% 4,97,729 75.1 Si-22 22,84,416 21,95,924 1,993,092 90.76% 1,921,886 96.43 Si-23 17,74,064 17,53,854 1,506,919 85.92% 1,458,523 96.79 Si-24 37,76,914 37,76,914 3,084,336 81.66% 2,326,285 75.42 Si-25 15,63,192 15,43,232 1,433,299 92.88% 1,389,854 96.97 Si-26 24,94,850 24,53,852 2,290,061 93.33% 2,231,018 97.42 Si-27 54,03,826 54,03,826 4,623,005 85.55% 3,570,870 77.24 Si-28 14,60,844 14,39,730 1,352,689 93.95% 1,320,030 97.59 Si-29 21,37,930 21,16,944 1,977,249 93.40% 1,929,895 97.61 Si-30 30,27,958 30,27,958 2,566,819 84.77% 2,014,461 78.48 In this study, landraces displayed an average number of reads aligning to the S. italica reference genome, with coverage spanning all nine chromosomes of Setaria . A total of 206,483 SNPs were identified across all germplasms when compared to the reference genome. Of these, the Si-3 landrace contributed the largest number of SNPs (131,873), while Si-7 showed the lowest contribution (1,184 SNPs) at a read depth (RD) of 10 (Fig. 2 & Table 1 ). Following the identification of polymorphic variants, the genetic relationships among the 30 genotypes were analyzed. Structure and genetic diversity among genotypes In this study, 203,422 genomic sites were predicted across 30 Setaria genotypes by comparison with the reference genome. Based on genotyping data with a RD > 10 and a MAF < 0.05, a total of 12,612,000 gametes were aligned with no data points excluded. The percentage of heterozygosity was lowest in Si-17 and Si-25 (0.23%) followed by Si-16 (0.26%), Si-15 (0.28%), and both Si-10 and Si-20 (0.29%) when compared to the reference genome (Fig. 3 a & Suppl. Table S3). This low heterozygosity suggested a higher degree of homology to the reference genome. The expected heterozygosity ranged from 0–0.70%, with an average of 0.25%, whereas the observed heterozygosity varied from 0–19.85%, with an average of 6.66%. (Fig. 3 b). These findings emphasized the importance of selecting appropriate parental lines with homozygous polymorphic markers, which ranged from 569 to 61,739 SNPs distributed across the entire Setaria genome (Table 2 ). This approach can significantly contribute to the development of breeding programs targeting genetic improvement in Setaria . Table 2 Overall SNP Summary among the Genotypes at RD10 Sample Total markers No. of heterozygous loci Heterozygous loci (%) No. of homozygous loci Homozygous loci (%) S. italica 164 203422 0 0 203422 100 Si-1 203422 607 0.3 202815 99.7 Si-2 203422 26092 12.83 177330 87.17 Si-3 203422 38693 19.02 164729 80.98 Si-4 203422 26792 13.17 176630 86.83 Si-5 203422 25653 12.61 177769 87.39 Si-6 203422 1692 0.83 201730 99.17 Si-7 203422 693 0.34 202729 99.66 Si-8 203422 32329 15.89 171093 84.11 Si-9 203422 36274 17.83 167148 82.17 Si-10 203422 591 0.29 202831 99.71 Si-11 203422 22447 11.03 180975 88.97 Si-12 203422 19227 9.45 184195 90.55 Si-13 203422 27959 13.74 175463 86.26 Si-14 203422 31296 15.38 172126 84.62 Si-15 203422 574 0.28 202848 99.72 Si-16 203422 529 0.26 202893 99.74 Si-17 203422 476 0.23 202946 99.77 Si-18 203422 649 0.32 202773 99.68 Si-19 203422 744 0.37 202678 99.63 Si-20 203422 584 0.29 202838 99.71 Si-21 203422 14762 7.26 188660 92.74 Si-22 203422 664 0.33 202758 99.67 Si-23 203422 1620 0.8 201802 99.2 Si-24 203422 30142 14.82 173280 85.18 Si-25 203422 477 0.23 202945 99.77 Si-26 203422 705 0.35 202717 99.65 Si-27 203422 40375 19.85 163047 80.15 Si-28 203422 5165 2.54 198257 97.46 Si-29 203422 1410 0.69 202012 99.31 Si-30 203422 31309 15.39 172113 84.61 Principal component analysis and phylogenetic relationship showed significant variations among Setaria genotypes We analyzed the genetic variation among Setaria landraces, released cultivars, and germplasm lines using PCA based on 206,483 SNPs. The top five principal components (PCs) were extracted using default parameters (Suppl. Table S4), and the first three PCs (PC1, PC2 and PC3) were visualized in R (v. 3.3.1; R Core Team, 2016) to assess genetic diversity among the 30 genotypes (Fig. 4 ). The PCA results demonstrated a well-dispersed genetic structure reflecting clear differentiation among the genotypes which corresponded to their regional origins. The genotypes were primarily grouped into landraces, released cultivars and germplasm lines. Phylogenetic analysis based on SNP clustering further supported these findings by dividing the 30 genotypes and the reference Setaria genome into three distinct clades (Fig. 5 ). Clade 1 included the reference genome along with 14 genotypes: Si-8, Si-21, Si-13, Si-2, Si-5, Si-3, Si-13, Si-4, Si-24, Si-11, Si-9, Si-27, Si-28 and Si-12. Clade 2 comprised 8 genotypes: Si-18, Si-22, Si-1, Si-6, Si-10, Si-15, Si-16 and Si-17. Clade 3 consisted of another 8 genotypes: Si-25, Si-23, Si-26, Si-29, Si-28, Si-20, Si-19 and Si-7. These groupings highlighted significant divergence among the selected genotypes. The largest clade displayed two prominent subclades, potentially indicating the presence of closely related orthologs. Overall, the PCA results were consistent with the clustering patterns observed in the radial dendrogram, further corroborating the genetic variation and diversity among the analyzed Setaria genotypes. GWAS identified high-confidence marker-trait association for selected agronomic traits A total of 82 significant MTAs (-log10 p > 4.0) were identified for eight traits using the Farm CPU model with a p-value threshold of < 0.05 (Table 3 ). These MTAs were distributed across all nine chromosomes of the foxtail millet genome (Fig. 6 ). Among these, the trait TNPP had the highest number of MTAs, with 36 associations spanning all chromosomes. The most significant MTAs for TNPP were predominantly located on chromosomes 3 and 9. For GWpa, 12 MTAs were detected across six chromosomes ( 3 , and 5 – 9 ), with the most significant association on chromosome 3. Table 3 Details of significant MTAs along with their chromosome, position, SNP effect and p -value cut-off Trait SNP Chromosome -log(p) SNP effect MAF TNPP U1.P.7810928 1 4.51065441 -4.14611 0.137931 U1.P.1686015 1 4.871089734 11.34653 0.327586 U2.P.29140605 2 4.190382344 4.655844 0.293103 U2.P.20227992 2 4.195025703 9.376994 0.344828 U2.P.32723821 2 4.77850309 4.915417 0.155172 U2.P.32775439 2 4.909348074 4.549813 0.155172 U2.P.32765288 2 5.213821995 -4.21476 0.189655 U2.P.26263756 2 5.653138206 6.287075 0.068966 U2.P.32673002 2 5.773905487 5.186474 0.137931 U3.P.36902456 3 4.063555849 -8.59198 0.344828 U3.P.37623561 3 4.338051378 -5.58157 0.086207 U3.P.88759 3 4.571251608 -4.31637 0.172414 U3.P.17048505 3 4.871089734 11.34653 0.327586 U4.P.39627594 4 4.044480769 3.062524 0.293103 U4.P.8734529 4 4.403062891 -4.99981 0.448276 U4.P.1287751 4 5.09674574 7.627616 0.051724 U5.P.9351113 5 4.131248453 4.710379 0.103448 U5.P.8755075 5 4.243048711 -4.00264 0.206897 U5.P.8755078 5 4.243048711 -4.00264 0.206897 U5.P.8755090 5 4.243048711 4.002636 0.206897 U5.P.36939765 5 4.376501903 -7.11579 0.482759 U6.P.21553011 6 4.08053225 3.418203 0.258621 U7.P.21435470 7 3.952038362 2.975882 0.362069 U7.P.22190549 7 4.528113937 8.965163 0.344828 U7.P.30066461 7 4.958372051 -3.8794 0.206897 U8.P.32134455 8 4.51065441 -4.14611 0.137931 U8.P.28192415 8 5.757902993 -6.90113 0.344828 U8.P.28192393 8 5.757902993 6.901126 0.344828 U9.P.45558178 9 4.638588133 -5.03268 0.310345 U9.P.45558191 9 4.638588133 -5.03268 0.310345 U9.P.32110399 9 4.668270053 -5.10951 0.12069 U9.P.21138360 9 4.871089734 11.34653 0.327586 U9.P.48367936 9 4.871089734 11.34653 0.327586 U9.P.424584 9 4.877187635 3.782103 0.293103 U9.P.32110401 9 4.892237021 8.643724 0.362069 U9.P.55319287 9 5.299583744 -9.70576 0.344828 GWPa U3.P.26134706 3 4.342843907 -4.58263 0.396552 U3.P.2130440 3 4.438322103 -4.34619 0.068966 U3.P.7003009 3 4.508147784 6.732641 0.344828 U3.P.48594755 3 4.508147784 -6.73264 0.344828 U5.P.6504655 5 4.096600693 3.439549 0.344828 U6.P.24872538 6 4.512850913 3.577091 0.086207 U6.P.20678085 6 5.204750316 3.765414 0.344828 U7.P.22568466 7 4.508147784 -6.73264 0.344828 U8.P.32393157 8 4.048654437 3.255125 0.103448 U9.P.43245595 9 4.438322103 4.346191 0.068966 U9.P.48339773 9 4.508147784 6.732641 0.344828 U9.P.8894136 9 4.508147784 6.732641 0.344828 AC U1.P.34713158 1 3.964368665 -0.63546 0.310345 U4.P.5383889 4 4.042255266 0.932299 0.362069 U5.P.34217851 5 4.051430958 0.741664 0.413793 U5.P.23902170 5 4.466401385 0.937002 0.37931 U7.P.11108796 7 3.909406031 -0.55644 0.137931 U9.P.22952108 9 4.466401385 0.468501 0.241379 LBC U1.P.2465258 1 3.930104256 -0.97443 0.362069 U1.P.2465260 1 3.930104256 -0.97443 0.362069 U5.P.19096852 5 4.082105944 0.546267 0.37931 U5.P.5619936 5 4.103632084 0.527403 0.310345 U6.P.2387149 6 3.970549286 -0.42045 0.206897 NS U1.P.10586306 1 4.083269876 17.32689 0.396552 U5.P.8171593 5 4.013930874 -14.3424 0.086207 U7.P.13558003 7 4.051098726 11.90777 0.155172 U7.P.13558005 7 4.051098726 11.90777 0.155172 U7.P.13558007 7 4.051098726 11.90777 0.155172 U7.P.13558008 7 4.051098726 11.90777 0.155172 U7.P.13558009 7 4.051098726 11.90777 0.155172 PBC U1.P.10254412 1 4.740718101 0.815994 0.413793 U2.P.48541889 2 3.955817718 0.532029 0.206897 U5.P.19096852 5 3.984821803 0.54043 0.37931 U5.P.5619936 5 4.06441339 0.525075 0.310345 U6.P.2387149 6 5.00700628 -0.46155 0.206897 U7.P.10981753 7 4.040905414 0.931684 0.362069 PH U1.P.10886040 1 4.625174362 14.42422 0.275862 U5.P.18384997 5 4.199329944 -20.9148 0.448276 U6.P.28254812 6 4.424033326 10.92063 0.362069 U7.P.31998098 7 3.930925109 14.94568 0.155172 U7.P.31998099 7 3.930925109 14.94568 0.155172 U7.P.31998096 7 3.930925109 -14.9457 0.155172 U7.P.31998100 7 3.930925109 -14.9457 0.155172 U7.P.31998101 7 3.930925109 -14.9457 0.155172 SPAD U1.P.20351799 1 3.63583685 -3.42676 0.241379 U5.P.28095651 5 4.368148363 7.807428 0.482759 Other traits showed the following distribution of MTAs: eight for PH, seven for NS, six each for PBC and AC, five for LBC, and two for SPAD. All 82 SNPs exhibited single-trait associations. Detailed information on these MTAs, including their chromosomal positions, p-values, minor allele frequencies and SNP effects, is provided in Suppl. Table S4. To minimize false positives caused by multiple testing, MTAs were filtered using the Bonferroni correction. Of the 82 MTAs, 16 met the stringent Bonferroni threshold and were designated as high-confidence MTAs. These high-confidence MTAs were associated with three traits: TNPP (14 MTAs), GWPa (1 MTA), and PBC (1 MTA) (Table 4 ). Table 4 List of 16 high-confidence MTAs that fulfilled the Bonferroni correction Trait SNP Chromosome Position P -value TNPP U1.P.1686015 1 1686015 4.87109 U2.P.32775439 2 32775439 4.909348 U2.P.32765288 2 32765288 5.213822 U2.P.26263756 2 26263756 5.653138 U2.P.32673002 2 32673002 5.773905 U3.P.17048505 3 17048505 4.87109 U4.P.1287751 4 1287751 5.096746 U8.P.28192415 8 28192415 5.757903 U8.P.28192393 8 28192393 5.757903 U9.P.21138360 9 21138360 4.87109 U9.P.48367936 9 48367936 4.87109 U9.P.424584 9 424584 4.877188 U9.P.32110401 9 32110401 4.892237 U9.P.55319287 9 55319287 5.299584 GWPa U6.P.20678085 6 20678085 5.20475 PBC U6.P.2387149 6 2387149 5.007006 The 14 MTAs linked to TNPP were located on chromosomes 1, 2, 3, 4, 8, and 9, with the most significant associations being: C1.P.1686015 (p-value: 1.35 × 10⁻⁵) C2.P.32775439 (p-value: 1.23 × 10⁻⁵) C2.P.32765288 (p-value: 6.11 × 10⁻⁶) C2.P.26263756 (p-value: 2.22 × 10⁻⁶) C2.P.32673002 (p-value: 1.68 × 10⁻⁶) C3.P.17048505 (p-value: 1.35 × 10⁻⁵) C4.P.1287751 (p-value: 8.00 × 10⁻⁶) C8.P.28192415 (p-value: 1.75 × 10⁻⁶) C8.P.28192393 (p-value: 1.75 × 10⁻⁶) C9.P.21138360 (p-value: 1.35 × 10⁻⁵) C9.P.48367936 (p-value: 1.35 × 10⁻⁵) C9.P.424584 (p-value: 1.33 × 10⁻⁵) C9.P.32110401 (p-value: 1.28 × 10⁻⁵) C9.P.55319287 (p-value: 5.02 × 10⁻⁶). The single high-confidence MTA for GWPa was located on chromosome 6 (C6.P.20678085, p-value: 6.24 × 10⁻⁶), while the MTA for PBC was also on chromosome 6 (C6.P.2387149, p-value: 9.84 × 10⁻⁶). Identification of candidate genes suggested the potential involvement of different pathways A total of 57 candidate genes were identified within the 25 Kb flanking regions (upstream and downstream) of the 16 high-confidence MTAs associated with three traits: TNPP, GWPa, and PBC (Table 4 ). Notably, none of the associated SNPs were located within gene-coding regions. For the 14 MTAs linked to TNPP, 52 candidate genes were identified across various chromosomes. These genes encoded diverse proteins, including EF-hand domain-containing proteins, 1,3-beta-glucan synthase, 1,3-beta-glucan synthase components, FKS1-like domain-containing proteins, Myb-like domain-containing proteins, and cysteine dioxygenase, among others. In the genomic regions associated with the single MTA for PBC, four candidate genes were identified. These included Obg-like ATPase-1 , Pentatricopeptide-repeat region of PRORP domain-containing protein , Photosystem II 10kDa chloroplastic polypeptide , and K Homology domain-containing protein . For GWPa, a single candidate gene with a known function, MATH domain-containing protein , was identified within the 25 Kb upstream region of the associated SNP (MTA). The detailed list of candidate genes, along with their chromosomal positions and putative functions, is provided in Table 5 . Table 5 Details of the identified candidate genes residing in close vicinity (25kb either side) of the associated SNPs Trait Associated SNP Transcript Start End Strand Description TNPP U1.P.1686015 SETIT_016772mg 1686015 1662015 - EF-hand domain-containing protein SETIT_016150mg 1686015 1710015 + 1,3-beta-glucan synthase SETIT_016487mg 1686015 1710015 + 1,3-beta-glucan synthase component FKS1-like domain-containing protein U1.P.7810928 SETIT_019086mg 7810928 7786928 - Calmodulin-binding domain-containing protein SETIT_019554mg 7810928 7786928 - Homeobox domain-containing protein U2.P.26263756 SETIT_029444mg 26239756 26263756 - Myb-like domain-containing protein SETIT_030953mg 26239756 26263756 - cysteine dioxygenase U2.P.32673002 SETIT_029077mg 32649002 32673002 - Potassium transporter SETIT_032857mg 32649002 32673002 - F-box domain-contain SETIT_030244mg 32649002 32673002 - Protein XRI1 U2.P.32723821 SETIT_032446mg 32699821 32723821 - Zinc finger PHD-type U2.P.32765288 SETIT_029726mg 32741288 32765288 - C2H2-type domain-co U2.P.32775439 SETIT_033245mg 32751439 32775439 - Preprotein translocase subunit- SecE U3.P.88759 SETIT_022424mg 64759 88759 - RING-CH-type domain-containing protein SETIT_025676mg 64759 88759 - CAP-Gly domain-containing protein SETIT_022584mg 64759 88759 - Sugar phosphate transporter domain-containing protein SETIT_021320mg 64759 88759 - Methyltransferase SETIT_020982mg 64759 88759 - 1-phosphatidylinositol-3-phosphate 5-kinase U3.P.17048505 SETIT_021362mg III:17048505–17072505 + Protein kinase domain-containing protein SETIT_023473mg III:17048505–17072505 + Yippee domain-containing protein SETIT_022793mg III:17048505–17072505 + FCP1 homology domain-containing protein SETIT_023745mg III:17048505–17072505 + Histone H4 U4.P.1287751 SETIT_005759mg IV:39627594–39651594 + Protein kinase domain-containing protein U7.P.22190549 SETIT_010421mg 22166549 22190549 - SAM domain-containing protein SETIT_012723mg 22166549 22190549 - DUF6598 domain-containing protein SETIT_009246mg 22166549 22190549 - CRM domain-containing protein U7.P.30066461 SETIT_009982mg 30042461 30066461 - F-box domain-containing protein SETIT_010903mg 30042461 30066461 - Multiple organellar RNA editing factor 3, mitochondrial SETIT_011506mg 30042461 30066461 - Wall-associated receptor kinase galacturonan-binding domain-containing protein U9.P.21138360 SETIT_038219mg 21114360 21138360 - BHLH domain-containing protein U9.P.32110399 SETIT_037461mg 32086399 32110399 - Proline-rich protein U9.P.424584 SETIT_034495mg 400584 424584 - Metallo-beta-lactamase domain-containing protein SETIT_037834mg 400584 424584 - H15 domain-containing protein SETIT_037173mg 400584 424584 - Derlin SETIT_038671mg 400584 424584 - Pentacotripeptide-repeat region of PRORP domain-containing protein SETIT_039302mg 400584 424584 - Transcription factor CBF/NF-Y/archaeal histone domain-containing protein SETIT_037626mg 400584 424584 - Ycf20-like protein SETIT_035086mg 55295287 55319287 - Pentacotripeptide-repeat region of PRORP domain-containing protein U9.P.45558178 SETIT_039321mg 45558178 45582178 Peroxidase SETIT_033939mg 45534178 45558178 - NB-ARC domain-containing protein U9.P.48367936 SETIT_036189mg 48343936 48367936 - VAN3-binding protein-like auxin canalisation domain-containing protein SETIT_034343mg 48343936 48367936 - DEAD-box ATP-dependent RNA helicase 24 SETIT_036926mg 48343936 48367936 + ATP-dependent Clp protease proteolytic subunit SETIT_034160mg 48367936 48391936 + [histone H3]-lysine( 27 ) N-trimethyltransferase SETIT_036751mg 48367936 48391936 + UBC core domain-containing protein SETIT_034564mg 48367936 48391936 + Trichome birefringence-like N-terminal domain-containing protein U9.P.55319287 SETIT_035086mg 55295287 55319287 - Pentacotripeptide-repeat region of PRORP domain-containing protein SETIT_038756mg 55295287 55319287 - Thioredoxin domain-containing protein SETIT_036985mg 55295287 55319287 - DUF1997 domain-containing protein SETIT_036078mg 55319287 55343287 + Serine/threonine-protein phosphatase SETIT_035602mg 55319287 55343287 + Major facilitator superfamily (MFS) profile domain-containing protein SETIT_039016mg 55319287 55343287 + F-box domain-containing protein PBC U6.P.2387149 SETIT_013409mg 2363149 2387149 - Obg-like ATPase 1 SETIT_013265mg 2387149 2411149 + Pentacotripeptide-repeat region of PRORP domain-containing protein SETIT_014601mg 2387149 2411149 + Photosystem II 10 kDa polypeptide, chloroplastic SETIT_013409mg 2387149 2411149 + K Homology do main-containing protein GWPa U6.P.20678085 SETIT_015259mg 20653315 20653962 - MATH Domain containing protein Discussion Enhancing grain yield remains one of the most challenging objectives in crop improvement. In this study, the selection of 30 genetically diverse Setaria genotypes demonstrated their suitability for GWAS. To facilitate effective genetic improvement, conservation, and resource management, it is essential to harness crop diversity. The Setaria genotypes used in this study were sourced from various districts of the Rayalaseema region in Andhra Pradesh, including Nandyala, Kadapa, Chittoor, Kurnool and Anantapur (Ramesh et al., 2023). Descriptive statistics revealed significant phenotypic diversity across the panel for all eight agronomic traits (Suppl. Table S1). Understanding the genetic structure of a population is crucial for genotype conservation and plant breeding efforts. A comprehensive understanding of the genetic structure of a species is critical for implementing breeding programs that prioritize the efficient preservation and utilization of plant genetic resources (Izzatullayeva et al., 2014; Tripathi et al., 2012). ddRAD-seq has emerged as a highly efficient and cost-effective method for SNP genotyping, offering a simplified protocol with high accuracy. Despite its potential, relatively few studies have utilized this advanced technology to investigate the genetic relationships and structure within the Setaria genus (Ramesh et al., 2023; Jaiswal, 2019). The higher-than-expected heterozygosity highlights the substantial genetic variation present in landraces compared to released cultivars In this study, ddRAD-seq was conducted using the restriction enzyme pair MlucI and SphI to identify genome-wide SNPs. This approach yielded high read depth suitable for reliable SNP calling across all genotypes. Approximately 86% of the genomic segments generated were uniquely aligned to the reference genome, a result comparable to findings in genome-wide SNP discovery studies in Solanum lycopersicum (Bodanapu et al., 2019). The experimental design and methodology employed in this research were adapted from a tomato study (Bodanapu et al., 2019), further underscoring the utility of this approach for exploring genetic diversity in crop species. Genetic diversity plays a critical role in determining a population's ability to adapt to changing environmental conditions. Insufficient genetic diversity in breeding populations poses a significant risk of severe declines under major environmental stress events (Novaes et al., 2008). The reduced productivity, yield, and stress resilience observed in this study underscore the urgent need to manage and conserve Setaria resources and biodiversity effectively. In this study, approximately 519,867 to 1,142,208 paired-end reads (100 bp each) were generated for the 30 Setaria genotypes, resulting in an average genome coverage of 51.99 to 114.22 MB per line, with a targeted depth of 10–20×. This extensive dataset revealed minimal contamination levels and unexpectedly low sequence redundancy. Despite certain complications, 206,483 SNPs were identified following the initial quality check. The identification of these SNPs significantly enhances the likelihood of discovering efficient molecular markers for Setaria . The moderate frequency of retained SNPs, particularly those located in transcribed and regulatory regions, highlights the priority of generating genome-wide SNPs for functional genomic studies in Setaria species (Jo et al., 2017). Using robust tools and methodologies, this study effectively estimated heterozygosity levels across genotypes, ranging from 0 to 19.85%. While previous studies predicted heterozygosity levels between 0 and 0.70% with an average of 0.25% (Ramesh et al., 2023), the significantly higher heterozygosity observed in this study suggests that the Setaria population may have recently experienced a genetic bottleneck (Cornuet et al., 1996; Zhang et al., 2018). Similar findings were reported in onion inbred lines, where observed heterozygosity (Ho) exceeded expected heterozygosity (He) (Lee et al., 2018). The low percentage of polymorphic loci identified across species indicates substantial genetic variation within the population, influenced by both genetic and environmental factors (Zhao et al., 2011). These findings reinforce the importance of conserving genetic diversity within Setaria species to support future breeding programs and environmental adaptation strategies. Maximum likelihood phylogenetic analysis based on 206,483 SNPs identified three major clusters among the assembled Setaria genotypes. Cluster 1 contained the majority of the genotypes ( 14 ), while Clusters 2 and 3 each comprised eight genotypes. The results indicated that species within the same cluster exhibited the highest SNP locus similarity. These findings align with previous studies (Vetriventhan et al., 2014; Ali et al., 2016), which reported a strong correlation between geographic origin and phylogenetic relationships. RAD-seq has proven to be an effective tool for phylogenetic reconstruction in species with sufficient orthologous restriction sites conserved across populations (Rubin et al., 2012). Similar phylogenetic analyses using RAD-seq have been conducted in onion (Lee, 2018), tomato (Bodanapu et al., 2019), grapevine (Laucou et al., 2018), and Rhododendron meddianum (Zhang et al., 2021), with conclusions that support the findings of the Neighbor-Joining (NJ) tree analysis in the present study. In addition to phylogenetic analysis, cluster, and PCA analyses have proven to be highly effective for exploring genetic diversity, tracing species evolution, selecting parental lines, and identifying centers of origin (Eivazi et al., 2007). The PCA results in this study revealed three major groupings of Setaria genotypes, consistent with the clustering patterns observed in the phylogenetic analysis. This approach has been successfully applied in numerous population genetics studies (Gupta et al., 2012), demonstrating its utility in understanding genetic relationships and diversity in Setaria . GWAS have long been a powerful tool for dissecting the genetic basis of complex traits and have been successfully applied to various crops, including cotton, rice, wheat, maize, and pearl millet (Jaiswal et al., 2016). However, there has been limited research applying GWAS to Setaria (foxtail millet), particularly for identifying the genetic regions controlling desirable traits (Gupta et al., 2014). The genetic diversity of the study panel was a critical prerequisite for the success of GWAS. Additionally, kinship results posed challenges in interpreting the GWAS outcomes, and the power of GWAS is influenced by both population size and trait diversity (Flint-Garcia et al., 2005). While the population size in this study was relatively small, it still yielded findings comparable to those from GWAS in other cereal crops (Anuradha et al., 2017). Despite the stringent Bonferroni correction, 16 significant MTAs were identified across three traits out of a total of 82 MTAs associated with eight traits. The use of thousands of markers increases the risk of false negatives, making multiple testing corrections essential. Therefore, rigorous parameters were applied to minimize false positives, and further validation of all 82 MTAs is necessary. The 16 MTAs that passed the multiple testing correction were deemed high-confidence associations. These high-confidence MTAs, identified for three key agronomic traits—TNPP, GWPa and PBC could significantly contribute to foxtail millet breeding through marker-assisted selection, pending further validation. Among the candidate genes identified near the associated SNP for GWPa, the MATH domain-containing protein was of particular interest, as it plays a role in various functions, including flower development, fatty acid biosynthesis, and stress tolerance (Weber and Hellmann, 2009; Lechner et al., 2011; Chen et al., 2013; Ma et al., 2013; Chen et al., 2015). In a wheat RNAi study, MATH domain-containing proteins were found to regulate plant growth and development, especially during early embryogenesis (Bauer et al., 2019). These candidate genes, located near the associated SNPs, could be validated in future studies and deployed in breeding programs to enhance foxtail millet. Overall, the insights gained from ddRAD sequencing in this study advance our understanding of Setaria species. The identified high-confidence SNPs also facilitated the discovery of 57 candidate genes associated with important traits. Identifying SNPs linked to yield-related characteristics, such as TNPP, GWPa, and PBC, is crucial for genomics-assisted breeding in foxtail millet. Plant breeders, when supported by genotype-based relatedness analysis, can make informed decisions regarding the selection of parental lines, ultimately improving breeding programs and the long-term sustainability of millet cultivation (Lee et al., 2018). These findings offer significant insights into the genetic architecture underlying key agronomic traits in foxtail millet, elucidating the molecular basis of trait variation. They also highlight potential genetic targets for further functional validation, thereby laying the groundwork for enhancing foxtail millet improvement through precision breeding strategies. Declarations Credit authorship contribution statement ACS conceived the idea; ACS, PCOR, SS, and RP collected landraces from the farmer’s field; SS and AK conducted all the experiments; KSSR, VBR, and LSP performed genomic and data analysis; ACS, PCOR, BBD, SS, AK, ARK, RP, ARR and PT prepared as well as edited the entire MS; ACS and PCOR contributed consumables. All authors read and approved the final manuscript. Ethical Statements: This manuscript does not include any of the Human / Animal Models / Cell Lines or any other of this kind that are subjected to ethical issues. Hence this declaration is “NOT APPLICABLE” Funding: This work was supported by the Department of Science and Technology (DST)-INSPIRE fellowships, New Delhi, India as part of a Ph. D degree scholarship to SS (IF170752) and KSSR (IF220369). ACS and PCOR from YVU acknowledged the partial utilization of the financial support for consumables (No. CRG/2018/003280 dated May 30th, 2019) from DST-SERB, New Delhi, India. AK thanked for the JRF (No. CRG/2018/003280 dated May 30th, 2019) from DST- Science and Engineering Research Board (SERB), New Delhi. Author Contribution ACS conceived the idea; ACS, PCOR, SS, and RP collected landraces from the farmer’s field; SS and AK conducted all the experiments; KSSR, VBR, and LSP performed genomic and data analysis; ACS, PCOR, BBD, SS, AK, ARK, RP, ARR and PT prepared as well as edited the entire MS; ACS and PCOR contributed consumables. All authors read and approved the final manuscript. Acknowledgement This work was supported by the Department of Science and Technology (DST)-INSPIRE fellowships, New Delhi, India as part of a Ph. D degree scholarship to SS (IF170752) and KSSR (IF220369). 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Plant diversity , 43 (6),.472–479. https://doi.org/10.1016/j.pld.2021.05.005 Zhao, K., et al.(2011). Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature communications , 2 (1), 467. https://doi.org/10.1038/ncomms1467 Zhao, Y., et al.(2018). Genetic architecture and candidate genes for deep-sowing tolerance in rice revealed by non-syn GWAS. Frontiers in Plant Science , 9 , 332 .https://doi.org/10.3389/fpls.2018.00332 Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Significant MTA threshold [−log 10 (p) \u0026lt; 10−03] and Bonferroni threshold were represented by dash (red) and continuous (green) lines, respectively. x-axis represented chromosomes\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7465120/v1/dbb44ee75925aaf72883081a.jpg"},{"id":94636717,"identity":"93097462-258c-43bd-bbdd-fbb07a223635","added_by":"auto","created_at":"2025-10-29 07:09:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2496050,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7465120/v1/e8f947b7-16d5-4cfc-9e7b-3d4148c4740e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome Sequence and Association Analysis Reveal Allelic Variants for Agronomically Important Traits in Foxtail Millet (Setaria italica L.) Germplasm","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFoxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e L.) is a nutritionally rich crop known for its resilience to harsh environmental conditions, including drought and elevated temperatures. Originating in China (Charulata, 2013), it is a staple in Asia and Africa; Because of its high nutritional content and adaptability to climate change, foxtail millet is a valuable feed and fodder source that is suited for livestock and supports sustainable farming methods (Vetriventhan et al., 2012 \u0026amp; Anuradha et al., 2020). This crop possesses several advantageous traits, including drought-avoidance mechanisms, low harvesting costs, nutritional value, and high tolerance to biotic stresses.\u003c/p\u003e\u003cp\u003eFoxtail millet's short life cycle, compact genome size (515 Mb, second only to rice), inbreeding nature, and efficient seed setting make it a valuable model species for agronomic and nutritional research (Ramesh et al., 2023). In India, it is predominantly cultivated in semi-arid regions such as Andhra Pradesh, Tamil Nadu, Karnataka, Rajasthan, Uttar Pradesh, and the North-Eastern states (Kumari et al., 2013).\u003c/p\u003e\u003cp\u003eLandraces are domesticated plant species that have undergone extensive local adaptation, serving as crucial genetic resources for sustainable agriculture and frequently utilized by local farmers. Compared to release cultivars, landraces generally exhibit lower yields (Pradhan et al., 2020). However, they are characterized by greater genetic heterogeneity and exhibit remarkable resilience to both abiotic and biotic stresses (Ramesh et al., 2023). Enhancing landraces for improved yields and adaptability to diverse environmental stresses holds significant potential for increasing crop productivity and improving farmers' livelihoods. Yield-focused breeding programs have been a key driver of genetic diversity loss in foxtail millet, as seen in many other crop species. DNA-based studies on millet species have demonstrated high levels of genetic diversity in foxtail millet and green millet (Le Thierry d'Ennequin et al., 2000). To mitigate genetic erosion, a strategic approach involving the collection, evaluation, and preservation of these invaluable landraces is essential for their future utilization (Ramesh et al., 2023; Palakurthi et al., 2023).\u003c/p\u003e\u003cp\u003eThe complete genome sequence and associated bioinformatics resources of foxtail millet have been transformative for millet research, facilitating genome mapping and genetic characterization to develop elite cultivars. Advances in genomics and genetics have been significantly bolstered by high-throughput genotyping and sequencing technologies (Zhang et al., 2018). The rapid evolution of next-generation sequencing (NGS) technologies has enabled the generation of extensive single nucleotide polymorphism (SNP) datasets in both model and non-model plant species (Cloutier et al., 2019).\u003c/p\u003e\u003cp\u003eEarly efforts to estimate genetic diversity in foxtail millet landraces utilized molecular markers (Liu et al., 2019). Genotyping-by-sequencing (GBS), an NGS-based approach for SNP discovery in reduced representations of genomes, has proven effective (Deschamps et al., 2012). Among GBS methods, double-digest restriction-site-associated DNA sequencing (ddRAD-seq) is a refined technique employing two restriction enzymes\u0026mdash;one rare-cutting and one frequent-cutting\u0026mdash;to generate stable, reproducible-sized genomic fragments (Peterson et al., 2012). This approach enables the generation of high-density marker datasets, crucial for advanced genetic analyses.\u003c/p\u003e\u003cp\u003eConcurrent advancements in statistical methods have enhanced the accuracy of genomic studies by reducing false positives arising from population structure and multiple testing corrections (Gupta et al., 2014). Genome-wide association studies (GWAS) have become a pivotal tool for dissecting the genetic basis of quantitative traits. Applications of ddRAD-seq technology in GWAS have been successfully demonstrated across various crops, including tomato (Bodanapu et al., 2019), pea (Gali et al., 2019), sesame (Ruperao et al., 2023), wheat (Arora et al., 2017), sorghum (Morris et al., 2013), maize (Xiao et al., 2017), and rice (Zhao et al., 2018). In this study, we analyzed foxtail millet germplasms, encompassing both landraces and released cultivars of \u003cem\u003eSetaria\u003c/em\u003e, using an integrated approach combining ddRAD-seq with morpho-physiological, yield, and yield-related trait characterization. Germplasm separation was achieved through the single-seed descent method, enabling precise analysis of allelic variants (Ramesh et al., 2023). The resulting large-scale SNP markers and the identification of genotypes carrying novel, superior alleles provide valuable resources for foxtail millet improvement programs, particularly for enhancing climate resilience in response to future agricultural challenges.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant material and Phenotyping\u003c/h2\u003e\u003cp\u003eThirty diverse genotypes of \u003cem\u003eSetaria\u003c/em\u003e were collected from various locations within the Rayalaseema region of Andhra Pradesh, India. The collection comprised 20 landraces, 7 released cultivars and 3 germplasm lines. The methodology for the pure line development was previously described (Ramesh et al., 2023). These genotypes were evaluated for eight agronomic traits: plant base color, leaf base color, bristle (awn) color, grain weight per panicle, tiller number per plant, chlorophyll content, plant height, and stomatal density. The phenotyping was conducted at Yogi Vemana University, Kadapa, Andhra Pradesh, India using a randomized complete block design with three replications during the Kharif season of 2020. The average data from three replicates for all traits were used in the analysis. Descriptive statistical measures including minimum, maximum, mean, and standard error were calculated using Microsoft Excel.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA extraction, ddRAD sequencing and SNP calling\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from the leaves of five-week-old plants using a modified CTAB method (Murray and Thompson, 1980). DNA quantity was assessed based on absorbance values at 260 nm and 280 nm using a Nanodrop spectrophotometer (Eppendorf BioSpectrometer Fluorescence, Germany). DNA quality and intact double-stranded DNA integrity were evaluated using a Qubit fluorometer (Thermo Fisher Scientific, USA) and 2% agarose gel electrophoresis. Library quality control (QC) was performed using a Bioanalyzer (Agilent Technologies, USA).\u003c/p\u003e\u003cp\u003eThe quantified DNA was subsequently used for ddRAD-sequencing on an Illumina HiSeq2000 platform (Agri Genome Labs Pvt. Ltd., Hyderabad, India). Following sequencing, reads were processed for quality assurance. Base and adapter trimming were performed and sequences containing restriction-site-associated DNA (RAD) tags were filtered. A quality distribution plot was generated using custom scripts developed at Agri Genome Labs Pvt. Ltd. Sample reads were demultiplexed with allowance for one mismatch and low-quality bases, as well as regions with base bias at the start or end were trimmed. Illumina 5' and 3' adapter sequences were also removed. The \u003cem\u003eSetaria\u003c/em\u003e reference genome was obtained from Phytozome v7.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.jgipsf.org/pub/compgen/phytozome/v7.0/Sitalica/annotation/Sitalica_164_gene.gff3.gz\u003c/span\u003e\u003cspan address=\"http://ftp://ftp.jgipsf.org/pub/compgen/phytozome/v7.0/Sitalica/annotation/Sitalica_164_gene.gff3.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Paired-end reads were aligned to this reference genome using the Bowtie2 program (v2.1.0) (Langmead et al., 2012) with default parameters. Variant calling was performed based on these alignments to identify genomic variations.\u003c/p\u003e\n\u003ch3\u003eZygosity, diversity, phylogenetic, kinship and Principal component analyses\u003c/h3\u003e\n\u003cp\u003eThe diversity, zygosity, kinship and phylogenetic analyses of the 30 genotypes were conducted using genotype data filtered for a read depth (RD)\u0026thinsp;\u0026gt;\u0026thinsp;10 and minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A radial phylogenetic tree was constructed to illustrate genetic relationships based on a similarity matrix generated using the neighbor-joining (NJ) algorithm in MEGA X (Kumari et al., 2023).\u003c/p\u003e\u003cp\u003eKinship analysis among individuals was calculated using VanRaden's method based on their genotypic data (VanRaden, 2008). This analysis employed a centered identical-by-state (IBS) matrix and was further explored using the Trait Analysis by Association and Evolution (TASSEL v5.2.28) software (Bradbury et al., 2007). Principal component analysis (PCA) was performed on the filtered genotype data (MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.05) using PLINK v1.90 (Purcell et al., 2007) and analyzed with TASSEL to investigate genetic diversity. The PCA results highlighted the genetic differentiation among landraces, released cultivars, and germplasm lines providing insights into their evolutionary and genetic relationships.\u003c/p\u003e\n\u003ch3\u003eMarker-trait association\u003c/h3\u003e\n\u003cp\u003eFor GWAS, SNPs with less than 30% missing data and a MAF greater than 5% were included. The Fixed and Random Model Circulating Probability Unification (FarmCPU) method (Liu et al., 2016) was employed for the association test. This recently developed technique is computationally efficient and effectively addresses challenges related to multiple testing corrections and kinship effects. Farm CPU integrates both fixed effect models (FEM) and random effect models (REM) in its framework. The FEM analyzes markers using pseudo-quantitative trait nucleotides (QTNs), which are calculated by the REM and subsequently used as covariates. The association test model included the first three principal components (PCA) as covariates to account for population structure. SNPs with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005 were considered significant marker-trait associations (MTAs), and a Bonferroni-corrected p-value threshold of 0.05 was applied (Liu et al., 2016).\u003c/p\u003e\u003cp\u003eQuantile-quantile (Q-Q) plots were analyzed to evaluate model fitting and assess population structure correction. The Q-Q plots illustrated the distribution of observed versus expected p-values from the association tests. Proper model fitting was indicated by a close alignment of the observed p-values with the diagonal line, suggesting minimal bias. Sharp deviations at the curve ends signified a small number of true associations among the numerous SNPs tested. The degree of deviation from the diagonal at the curve\u0026rsquo;s tail served as a measure of the power of the test statistics (Turner, 2014).\u003c/p\u003e\n\u003ch3\u003eCandidate gene identification\u003c/h3\u003e\n\u003cp\u003eTo identify potential candidate genes located near high-confidence SNPs, the associated SNPs were mapped to the \u003cem\u003eSetaria italica\u003c/em\u003e reference genome (v2.2) (available at Phytozome). Transcripts within a 25 kb region flanking each associated SNP were extracted along with their functional annotations (Vandana et al., 2019).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eThe phenotypic distribution of traits revealed a wide range of variability\u003c/h2\u003e\u003cp\u003eDescriptive statistics, including the minimum, maximum, mean, and standard deviation, revealed substantial phenotypic variation across the eight traits evaluated in 30 genotypes (Suppl. Table S1). For instance, grain weight per panicle ranged from 4.7 g to 15.1 g, with a mean of 9.28 g and a standard deviation of 2.8 g. Similar variability was observed in other traits, such as plant height (112.55\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2 cm), tiller number per plant ( 6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3), chlorophyll content ( 45.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08), number of stomata (72.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6), plant base color (1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47), leaf base color ( 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44) and awn color (1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDetection of polymorphic variants indicated the presence of SNPs on all chromosomes\u003c/h3\u003e\n\u003cp\u003eDetailed information about the 30 \u003cem\u003eSetaria\u003c/em\u003e genotypes including variety names and collection origins is provided in Supplementary Table S2. Genomic DNA was isolated from each genotype and digested using the restriction enzymes \u003cem\u003eMluCI\u003c/em\u003e and \u003cem\u003eSphI\u003c/em\u003e, which have distinct recognition sites and cutting frequencies. Quality assessment of the fragmented DNA confirmed that all samples met the required screening criteria. Further processing, including quality checks, screening, and filtering of raw sequencing data, produced various read statistics (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average GC content across samples was 46%. Among the genotypes, the Si-27 landrace exhibited the highest proportion of reads containing RAD tags (47%), while Si-21 showed the lowest (43%). The percentage of uniquely aligned reads to the \u003cem\u003eSetaria italica\u003c/em\u003e reference genome for all germplasms is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSequence read measurements of 30 genotypes with annotated SNPs and INDELs at read depth (RD)\u0026thinsp;\u0026gt;\u0026thinsp;10\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal reads\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReads after processing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal reads aligned\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReads aligned (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal uniquely aligned reads\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUniquely aligned reads (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16,89,274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16,40,278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,467,092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.44%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,409,655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44,16,956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44,16,956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,318,216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.48%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,804,356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e77.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65,55,018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65,55,018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,664,652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.91%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,897,245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e79.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41,59,494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41,59,494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,845,754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,434,943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e77.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46,78,988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46,78,988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,839,849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.07%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,906,577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e75.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,21,540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11,77,974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,077,474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e91.47%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,044,431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39,08,672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,08,672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,28,634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.76%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3,91,733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36,90,080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36,90,080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,640,236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e71.55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,061,422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e78.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35,06,900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35,06,900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,977,316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,377,308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e79.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,47,420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10,20,720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,23,580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e90.48%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8,87,660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16,42,510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16,42,510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,403,285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85.44%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,091,035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e77.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22,17,334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22,17,334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,832,703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,441,068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e78.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20,06,868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20,06,868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,767,216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88.06%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,416,355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57,09,834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57,09,834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,891,724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85.67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3,740,042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e76.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19,72,192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19,15,244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,723,369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,663,770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15,30,772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14,89,590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,333,941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,286,660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19,04,782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18,62,970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,651, 673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88.66%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,580,563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e95.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,39,734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10,07,330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,98,181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.16%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8,61,901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e95.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20,61,392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20,35,660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,884,005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92.55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,826,673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17,68,238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17,46,624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,659, 997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e95.04%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,620,263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,88,114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7,88,114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,62,773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4,97,729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e75.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22,84,416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21,95,924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,993,092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e90.76%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,921,886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17,74,064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17,53,854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,506,919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85.92%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,458,523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37,76,914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37,76,914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,084,336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81.66%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,326,285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e75.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15,63,192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15,43,232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,433,299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92.88%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,389,854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24,94,850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24,53,852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,290,061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93.33%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,231,018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54,03,826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54,03,826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,623,005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85.55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3,570,870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e77.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14,60,844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14,39,730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,352,689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93.95%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,320,030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21,37,930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21,16,944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,977,249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,929,895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30,27,958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30,27,958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,566,819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.77%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2,014,461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e78.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this study, landraces displayed an average number of reads aligning to the \u003cem\u003eS. italica\u003c/em\u003e reference genome, with coverage spanning all nine chromosomes of \u003cem\u003eSetaria\u003c/em\u003e. A total of 206,483 SNPs were identified across all germplasms when compared to the reference genome. Of these, the Si-3 landrace contributed the largest number of SNPs (131,873), while Si-7 showed the lowest contribution (1,184 SNPs) at a read depth (RD) of 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Following the identification of polymorphic variants, the genetic relationships among the 30 genotypes were analyzed.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStructure and genetic diversity among genotypes\u003c/h2\u003e\u003cp\u003eIn this study, 203,422 genomic sites were predicted across 30 \u003cem\u003eSetaria\u003c/em\u003e genotypes by comparison with the reference genome. Based on genotyping data with a RD\u0026thinsp;\u0026gt;\u0026thinsp;10 and a MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.05, a total of 12,612,000 gametes were aligned with no data points excluded.\u003c/p\u003e\u003cp\u003eThe percentage of heterozygosity was lowest in Si-17 and Si-25 (0.23%) followed by Si-16 (0.26%), Si-15 (0.28%), and both Si-10 and Si-20 (0.29%) when compared to the reference genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u0026amp; Suppl. Table S3). This low heterozygosity suggested a higher degree of homology to the reference genome. The expected heterozygosity ranged from 0\u0026ndash;0.70%, with an average of 0.25%, whereas the observed heterozygosity varied from 0\u0026ndash;19.85%, with an average of 6.66%. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThese findings emphasized the importance of selecting appropriate parental lines with homozygous polymorphic markers, which ranged from 569 to 61,739 SNPs distributed across the entire \u003cem\u003eSetaria\u003c/em\u003e genome (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This approach can significantly contribute to the development of breeding programs targeting genetic improvement in \u003cem\u003eSetaria\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverall SNP Summary among the Genotypes at RD10\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal markers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo. of heterozygous\u003c/p\u003e\u003cp\u003eloci\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeterozygous loci (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo. of homozygous loci\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHomozygous\u003c/p\u003e\u003cp\u003eloci (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eS. italica\u003c/em\u003e 164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e177330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e164729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e176630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e177769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e201730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e171093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e167148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e180975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e184195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e175463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e172126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e188660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e201802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e173280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e163047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e198257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e97.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e172113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrincipal component analysis and phylogenetic relationship showed significant variations among\u003c/b\u003e \u003cb\u003eSetaria\u003c/b\u003e \u003cb\u003egenotypes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed the genetic variation among \u003cem\u003eSetaria\u003c/em\u003e landraces, released cultivars, and germplasm lines using PCA based on 206,483 SNPs. The top five principal components (PCs) were extracted using default parameters (Suppl. Table S4), and the first three PCs (PC1, PC2 and PC3) were visualized in R (v. 3.3.1; R Core Team, 2016) to assess genetic diversity among the 30 genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The PCA results demonstrated a well-dispersed genetic structure reflecting clear differentiation among the genotypes which corresponded to their regional origins. The genotypes were primarily grouped into landraces, released cultivars and germplasm lines.\u003c/p\u003e\u003cp\u003ePhylogenetic analysis based on SNP clustering further supported these findings by dividing the 30 genotypes and the reference \u003cem\u003eSetaria\u003c/em\u003e genome into three distinct clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Clade 1 included the reference genome along with 14 genotypes: Si-8, Si-21, Si-13, Si-2, Si-5, Si-3, Si-13, Si-4, Si-24, Si-11, Si-9, Si-27, Si-28 and Si-12. Clade 2 comprised 8 genotypes: Si-18, Si-22, Si-1, Si-6, Si-10, Si-15, Si-16 and Si-17. Clade 3 consisted of another 8 genotypes: Si-25, Si-23, Si-26, Si-29, Si-28, Si-20, Si-19 and Si-7. These groupings highlighted significant divergence among the selected genotypes.\u003c/p\u003e\u003cp\u003eThe largest clade displayed two prominent subclades, potentially indicating the presence of closely related orthologs. Overall, the PCA results were consistent with the clustering patterns observed in the radial dendrogram, further corroborating the genetic variation and diversity among the analyzed \u003cem\u003eSetaria\u003c/em\u003e genotypes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGWAS identified high-confidence marker-trait association for selected agronomic traits\u003c/h2\u003e\u003cp\u003eA total of 82 significant MTAs (-log10 p\u0026thinsp;\u0026gt;\u0026thinsp;4.0) were identified for eight traits using the Farm CPU model with a p-value threshold of \u0026lt;\u0026thinsp;0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These MTAs were distributed across all nine chromosomes of the foxtail millet genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among these, the trait TNPP had the highest number of MTAs, with 36 associations spanning all chromosomes. The most significant MTAs for TNPP were predominantly located on chromosomes 3 and 9. For GWpa, 12 MTAs were detected across six chromosomes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, and \u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), with the most significant association on chromosome 3.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetails of significant MTAs along with their chromosome, position, SNP effect and \u003cem\u003ep\u003c/em\u003e-value cut-off\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChromosome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-log(p)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSNP effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMAF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"34\" rowspan=\"35\"\u003e\u003cp\u003eTNPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.7810928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.51065441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.14611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.137931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.1686015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.871089734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.34653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.327586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.29140605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.190382344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.655844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.293103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.20227992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.195025703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.376994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32723821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.77850309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.915417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32775439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.909348074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.549813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32765288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.213821995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.21476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.189655\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.26263756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.653138206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.287075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32673002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.773905487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.186474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.137931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU3.P.36902456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.063555849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.59198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU3.P.37623561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.338051378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.58157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.086207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU3.P.88759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.571251608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.31637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.172414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU3.P.17048505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.871089734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.34653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.327586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU4.P.39627594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.044480769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.062524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.293103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU4.P.8734529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.403062891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.99981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.448276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU4.P.1287751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.09674574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.627616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.051724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.9351113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.131248453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.710379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.103448\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.8755075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.243048711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.00264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.206897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.8755078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.243048711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.00264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.206897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.8755090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.243048711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.002636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.206897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.36939765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.376501903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.11579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.482759\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6.P.21553011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.08053225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.418203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.258621\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.21435470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.952038362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.975882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.362069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.22190549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.528113937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.965163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.30066461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.958372051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.8794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.206897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU8.P.32134455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.51065441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.14611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.137931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU8.P.28192415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.757902993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.90113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU8.P.28192393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.757902993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.901126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.45558178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.638588133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.03268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.310345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.45558191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.638588133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.03268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.310345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.32110399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.668270053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.10951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.12069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.21138360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.871089734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.34653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.327586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.871089734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.34653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.327586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.877187635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.782103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.293103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.32110401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.892237021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.643724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.362069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.299583744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-9.70576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eGWPa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU3.P.26134706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.342843907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.58263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.396552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU3.P.2130440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.438322103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.34619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU3.P.7003009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.508147784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.732641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU3.P.48594755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.508147784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.73264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.6504655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.096600693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.439549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6.P.24872538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.512850913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.577091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.086207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6.P.20678085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.204750316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.765414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.22568466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.508147784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.73264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU8.P.32393157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.048654437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.255125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.103448\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.43245595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.438322103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.346191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.48339773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.508147784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.732641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.8894136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.508147784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.732641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.34713158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.964368665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.63546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.310345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU4.P.5383889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.042255266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.932299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.362069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.34217851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.051430958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.741664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.413793\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.23902170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.466401385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.937002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.37931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.11108796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.909406031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.55644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.137931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.22952108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.466401385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.468501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.241379\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eLBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.2465258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.930104256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.97443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.362069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.2465260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.930104256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.97443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.362069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.19096852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.082105944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.546267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.37931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.5619936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.103632084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.527403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.310345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6.P.2387149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.970549286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.42045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.206897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.10586306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.083269876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.32689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.396552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.8171593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.013930874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-14.3424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.086207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.13558003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.051098726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.90777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.13558005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.051098726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.90777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.13558007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.051098726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.90777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.13558008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.051098726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.90777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.13558009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.051098726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.90777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003ePBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.10254412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.740718101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.815994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.413793\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.48541889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.955817718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.532029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.206897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.19096852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.984821803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.54043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.37931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.5619936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.06441339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.525075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.310345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6.P.2387149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.00700628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.46155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.206897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.10981753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.040905414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.931684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.362069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.10886040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.625174362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.42422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.275862\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.18384997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.199329944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-20.9148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.448276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6.P.28254812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.424033326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.92063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.362069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.31998098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.930925109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.94568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.31998099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.930925109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.94568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.31998096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.930925109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-14.9457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.31998100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.930925109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-14.9457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU7.P.31998101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.930925109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-14.9457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSPAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.20351799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.63583685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.42676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.241379\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU5.P.28095651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.368148363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.807428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.482759\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOther traits showed the following distribution of MTAs: eight for PH, seven for NS, six each for PBC and AC, five for LBC, and two for SPAD. All 82 SNPs exhibited single-trait associations. Detailed information on these MTAs, including their chromosomal positions, p-values, minor allele frequencies and SNP effects, is provided in Suppl. Table S4.\u003c/p\u003e\u003cp\u003eTo minimize false positives caused by multiple testing, MTAs were filtered using the Bonferroni correction. Of the 82 MTAs, 16 met the stringent Bonferroni threshold and were designated as high-confidence MTAs. These high-confidence MTAs were associated with three traits: TNPP (14 MTAs), GWPa (1 MTA), and PBC (1 MTA) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of 16 high-confidence MTAs that fulfilled the Bonferroni correction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChromosome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePosition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e\u003cp\u003eTNPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU1.P.1686015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1686015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.87109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32775439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32775439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.909348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32765288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32765288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.213822\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.26263756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26263756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.653138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32673002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32673002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.773905\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU3.P.17048505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17048505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.87109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU4.P.1287751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1287751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.096746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU8.P.28192415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28192415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.757903\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU8.P.28192393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28192393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.757903\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.21138360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21138360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.87109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.87109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.877188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.32110401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32110401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.892237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.299584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGWPa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6.P.20678085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20678085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.20475\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6.P.2387149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2387149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.007006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe 14 MTAs linked to TNPP were located on chromosomes 1, 2, 3, 4, 8, and 9, with the most significant associations being:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eC1.P.1686015 (p-value: 1.35 \u0026times; 10⁻⁵)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC2.P.32775439 (p-value: 1.23 \u0026times; 10⁻⁵)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC2.P.32765288 (p-value: 6.11 \u0026times; 10⁻⁶)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC2.P.26263756 (p-value: 2.22 \u0026times; 10⁻⁶)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC2.P.32673002 (p-value: 1.68 \u0026times; 10⁻⁶)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC3.P.17048505 (p-value: 1.35 \u0026times; 10⁻⁵)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC4.P.1287751 (p-value: 8.00 \u0026times; 10⁻⁶)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC8.P.28192415 (p-value: 1.75 \u0026times; 10⁻⁶)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC8.P.28192393 (p-value: 1.75 \u0026times; 10⁻⁶)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC9.P.21138360 (p-value: 1.35 \u0026times; 10⁻⁵)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC9.P.48367936 (p-value: 1.35 \u0026times; 10⁻⁵)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC9.P.424584 (p-value: 1.33 \u0026times; 10⁻⁵)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC9.P.32110401 (p-value: 1.28 \u0026times; 10⁻⁵)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC9.P.55319287 (p-value: 5.02 \u0026times; 10⁻⁶).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe single high-confidence MTA for GWPa was located on chromosome 6 (C6.P.20678085, p-value: 6.24 \u0026times; 10⁻⁶), while the MTA for PBC was also on chromosome 6 (C6.P.2387149, p-value: 9.84 \u0026times; 10⁻⁶).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of candidate genes suggested the potential involvement of different pathways\u003c/h2\u003e\u003cp\u003eA total of 57 candidate genes were identified within the 25 Kb flanking regions (upstream and downstream) of the 16 high-confidence MTAs associated with three traits: TNPP, GWPa, and PBC (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, none of the associated SNPs were located within gene-coding regions.\u003c/p\u003e\u003cp\u003eFor the 14 MTAs linked to TNPP, 52 candidate genes were identified across various chromosomes. These genes encoded diverse proteins, including EF-hand domain-containing proteins, 1,3-beta-glucan synthase, 1,3-beta-glucan synthase components, FKS1-like domain-containing proteins, Myb-like domain-containing proteins, and cysteine dioxygenase, among others.\u003c/p\u003e\u003cp\u003eIn the genomic regions associated with the single MTA for PBC, four candidate genes were identified. These included \u003cem\u003eObg-like ATPase-1\u003c/em\u003e, \u003cem\u003ePentatricopeptide-repeat region of PRORP domain-containing protein\u003c/em\u003e, \u003cem\u003ePhotosystem II 10kDa chloroplastic polypeptide\u003c/em\u003e, and \u003cem\u003eK Homology domain-containing protein\u003c/em\u003e. For GWPa, a single candidate gene with a known function, \u003cem\u003eMATH domain-containing protein\u003c/em\u003e, was identified within the 25 Kb upstream region of the associated SNP (MTA). The detailed list of candidate genes, along with their chromosomal positions and putative functions, is provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetails of the identified candidate genes residing in close vicinity (25kb either side) of the associated SNPs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssociated SNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTranscript\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStart\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"51\" rowspan=\"52\"\u003e\u003cp\u003eTNPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eU1.P.1686015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_016772mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1686015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1662015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEF-hand domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_016150mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1686015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1710015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,3-beta-glucan synthase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_016487mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1686015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1710015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,3-beta-glucan synthase component FKS1-like domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eU1.P.7810928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_019086mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7810928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7786928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCalmodulin-binding domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_019554mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7810928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7786928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHomeobox domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eU2.P.26263756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_029444mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26239756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26263756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMyb-like domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_030953mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26239756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26263756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ecysteine dioxygenase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eU2.P.32673002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_029077mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32649002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32673002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePotassium transporter\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_032857mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32649002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32673002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF-box domain-contain\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_030244mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32649002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32673002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eProtein XRI1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32723821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_032446mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32699821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32723821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eZinc finger PHD-type\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32765288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_029726mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32741288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32765288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eC2H2-type domain-co\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU2.P.32775439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_033245mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32751439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32775439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePreprotein translocase subunit-\u003c/p\u003e\u003cp\u003eSecE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eU3.P.88759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_022424mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRING-CH-type domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_025676mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCAP-Gly domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_022584mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSugar phosphate transporter domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_021320mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMethyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_020982mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1-phosphatidylinositol-3-phosphate 5-kinase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eU3.P.17048505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_021362mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII:17048505\u0026ndash;17072505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eProtein kinase domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_023473mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII:17048505\u0026ndash;17072505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYippee domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_022793mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII:17048505\u0026ndash;17072505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFCP1 homology domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_023745mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII:17048505\u0026ndash;17072505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHistone H4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU4.P.1287751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_005759mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIV:39627594\u0026ndash;39651594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eProtein kinase domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eU7.P.22190549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_010421mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22166549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22190549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSAM domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_012723mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22166549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22190549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDUF6598 domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_009246mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22166549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22190549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCRM domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eU7.P.30066461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_009982mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30042461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30066461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF-box domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_010903mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30042461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30066461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMultiple organellar RNA editing factor 3, mitochondrial\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_011506mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30042461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30066461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWall-associated receptor kinase galacturonan-binding domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.21138360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_038219mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21114360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21138360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBHLH domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU9.P.32110399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_037461mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32086399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32110399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eProline-rich protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eU9.P.424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_034495mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMetallo-beta-lactamase domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_037834mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eH15 domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_037173mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDerlin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_038671mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePentacotripeptide-repeat region of PRORP domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_039302mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTranscription factor CBF/NF-Y/archaeal histone domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_037626mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYcf20-like protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_035086mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55295287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePentacotripeptide-repeat region of PRORP domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eU9.P.45558178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_039321mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45558178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45582178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePeroxidase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_033939mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45534178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45558178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNB-ARC domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eU9.P.48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_036189mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48343936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVAN3-binding protein-like auxin canalisation domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_034343mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48343936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDEAD-box ATP-dependent RNA helicase 24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_036926mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48343936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eATP-dependent Clp protease proteolytic subunit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_034160mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48391936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[histone H3]-lysine(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e) N-trimethyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_036751mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48391936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUBC core domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_034564mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48367936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48391936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTrichome birefringence-like N-terminal domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eU9.P.55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_035086mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55295287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePentacotripeptide-repeat region of PRORP domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_038756mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55295287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eThioredoxin domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_036985mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55295287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDUF1997 domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_036078mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55343287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSerine/threonine-protein phosphatase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_035602mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55343287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMajor facilitator superfamily (MFS) profile domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_039016mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55319287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55343287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF-box domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eU6.P.2387149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_013409mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2363149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2387149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eObg-like ATPase 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_013265mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2387149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2411149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePentacotripeptide-repeat region of PRORP domain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_014601mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2387149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2411149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePhotosystem II 10 kDa polypeptide, chloroplastic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_013409mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2387149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2411149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eK Homology do\u003c/p\u003e\u003cp\u003emain-containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGWPa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6.P.20678085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSETIT_015259mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20653315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20653962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMATH Domain containing protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eEnhancing grain yield remains one of the most challenging objectives in crop improvement. In this study, the selection of 30 genetically diverse \u003cem\u003eSetaria\u003c/em\u003e genotypes demonstrated their suitability for GWAS. To facilitate effective genetic improvement, conservation, and resource management, it is essential to harness crop diversity. The \u003cem\u003eSetaria\u003c/em\u003e genotypes used in this study were sourced from various districts of the Rayalaseema region in Andhra Pradesh, including Nandyala, Kadapa, Chittoor, Kurnool and Anantapur (Ramesh et al., 2023). Descriptive statistics revealed significant phenotypic diversity across the panel for all eight agronomic traits (Suppl. Table S1).\u003c/p\u003e\u003cp\u003eUnderstanding the genetic structure of a population is crucial for genotype conservation and plant breeding efforts. A comprehensive understanding of the genetic structure of a species is critical for implementing breeding programs that prioritize the efficient preservation and utilization of plant genetic resources (Izzatullayeva et al., 2014; Tripathi et al., 2012). ddRAD-seq has emerged as a highly efficient and cost-effective method for SNP genotyping, offering a simplified protocol with high accuracy. Despite its potential, relatively few studies have utilized this advanced technology to investigate the genetic relationships and structure within the \u003cem\u003eSetaria\u003c/em\u003e genus (Ramesh et al., 2023; Jaiswal, 2019). The higher-than-expected heterozygosity highlights the substantial genetic variation present in landraces compared to released cultivars\u003c/p\u003e\u003cp\u003eIn this study, ddRAD-seq was conducted using the restriction enzyme pair \u003cem\u003eMlucI\u003c/em\u003e and \u003cem\u003eSphI\u003c/em\u003e to identify genome-wide SNPs. This approach yielded high read depth suitable for reliable SNP calling across all genotypes. Approximately 86% of the genomic segments generated were uniquely aligned to the reference genome, a result comparable to findings in genome-wide SNP discovery studies in \u003cem\u003eSolanum lycopersicum\u003c/em\u003e (Bodanapu et al., 2019). The experimental design and methodology employed in this research were adapted from a tomato study (Bodanapu et al., 2019), further underscoring the utility of this approach for exploring genetic diversity in crop species. Genetic diversity plays a critical role in determining a population's ability to adapt to changing environmental conditions. Insufficient genetic diversity in breeding populations poses a significant risk of severe declines under major environmental stress events (Novaes et al., 2008). The reduced productivity, yield, and stress resilience observed in this study underscore the urgent need to manage and conserve \u003cem\u003eSetaria\u003c/em\u003e resources and biodiversity effectively.\u003c/p\u003e\u003cp\u003eIn this study, approximately 519,867 to 1,142,208 paired-end reads (100 bp each) were generated for the 30 \u003cem\u003eSetaria\u003c/em\u003e genotypes, resulting in an average genome coverage of 51.99 to 114.22 MB per line, with a targeted depth of 10\u0026ndash;20\u0026times;. This extensive dataset revealed minimal contamination levels and unexpectedly low sequence redundancy. Despite certain complications, 206,483 SNPs were identified following the initial quality check. The identification of these SNPs significantly enhances the likelihood of discovering efficient molecular markers for \u003cem\u003eSetaria\u003c/em\u003e. The moderate frequency of retained SNPs, particularly those located in transcribed and regulatory regions, highlights the priority of generating genome-wide SNPs for functional genomic studies in \u003cem\u003eSetaria\u003c/em\u003e species (Jo et al., 2017).\u003c/p\u003e\u003cp\u003eUsing robust tools and methodologies, this study effectively estimated heterozygosity levels across genotypes, ranging from 0 to 19.85%. While previous studies predicted heterozygosity levels between 0 and 0.70% with an average of 0.25% (Ramesh et al., 2023), the significantly higher heterozygosity observed in this study suggests that the \u003cem\u003eSetaria\u003c/em\u003e population may have recently experienced a genetic bottleneck (Cornuet et al., 1996; Zhang et al., 2018). Similar findings were reported in onion inbred lines, where observed heterozygosity (Ho) exceeded expected heterozygosity (He) (Lee et al., 2018).\u003c/p\u003e\u003cp\u003eThe low percentage of polymorphic loci identified across species indicates substantial genetic variation within the population, influenced by both genetic and environmental factors (Zhao et al., 2011). These findings reinforce the importance of conserving genetic diversity within \u003cem\u003eSetaria\u003c/em\u003e species to support future breeding programs and environmental adaptation strategies.\u003c/p\u003e\u003cp\u003eMaximum likelihood phylogenetic analysis based on 206,483 SNPs identified three major clusters among the assembled \u003cem\u003eSetaria\u003c/em\u003e genotypes. Cluster 1 contained the majority of the genotypes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), while Clusters 2 and 3 each comprised eight genotypes. The results indicated that species within the same cluster exhibited the highest SNP locus similarity. These findings align with previous studies (Vetriventhan et al., 2014; Ali et al., 2016), which reported a strong correlation between geographic origin and phylogenetic relationships.\u003c/p\u003e\u003cp\u003eRAD-seq has proven to be an effective tool for phylogenetic reconstruction in species with sufficient orthologous restriction sites conserved across populations (Rubin et al., 2012). Similar phylogenetic analyses using RAD-seq have been conducted in onion (Lee, 2018), tomato (Bodanapu et al., 2019), grapevine (Laucou et al., 2018), and \u003cem\u003eRhododendron meddianum\u003c/em\u003e (Zhang et al., 2021), with conclusions that support the findings of the Neighbor-Joining (NJ) tree analysis in the present study.\u003c/p\u003e\u003cp\u003eIn addition to phylogenetic analysis, cluster, and PCA analyses have proven to be highly effective for exploring genetic diversity, tracing species evolution, selecting parental lines, and identifying centers of origin (Eivazi et al., 2007). The PCA results in this study revealed three major groupings of \u003cem\u003eSetaria\u003c/em\u003e genotypes, consistent with the clustering patterns observed in the phylogenetic analysis. This approach has been successfully applied in numerous population genetics studies (Gupta et al., 2012), demonstrating its utility in understanding genetic relationships and diversity in \u003cem\u003eSetaria\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eGWAS have long been a powerful tool for dissecting the genetic basis of complex traits and have been successfully applied to various crops, including cotton, rice, wheat, maize, and pearl millet (Jaiswal et al., 2016). However, there has been limited research applying GWAS to \u003cem\u003eSetaria\u003c/em\u003e (foxtail millet), particularly for identifying the genetic regions controlling desirable traits (Gupta et al., 2014). The genetic diversity of the study panel was a critical prerequisite for the success of GWAS. Additionally, kinship results posed challenges in interpreting the GWAS outcomes, and the power of GWAS is influenced by both population size and trait diversity (Flint-Garcia et al., 2005).\u003c/p\u003e\u003cp\u003eWhile the population size in this study was relatively small, it still yielded findings comparable to those from GWAS in other cereal crops (Anuradha et al., 2017). Despite the stringent Bonferroni correction, 16 significant MTAs were identified across three traits out of a total of 82 MTAs associated with eight traits. The use of thousands of markers increases the risk of false negatives, making multiple testing corrections essential. Therefore, rigorous parameters were applied to minimize false positives, and further validation of all 82 MTAs is necessary. The 16 MTAs that passed the multiple testing correction were deemed high-confidence associations. These high-confidence MTAs, identified for three key agronomic traits\u0026mdash;TNPP, GWPa and PBC could significantly contribute to foxtail millet breeding through marker-assisted selection, pending further validation.\u003c/p\u003e\u003cp\u003eAmong the candidate genes identified near the associated SNP for GWPa, the MATH domain-containing protein was of particular interest, as it plays a role in various functions, including flower development, fatty acid biosynthesis, and stress tolerance (Weber and Hellmann, 2009; Lechner et al., 2011; Chen et al., 2013; Ma et al., 2013; Chen et al., 2015). In a wheat RNAi study, MATH domain-containing proteins were found to regulate plant growth and development, especially during early embryogenesis (Bauer et al., 2019). These candidate genes, located near the associated SNPs, could be validated in future studies and deployed in breeding programs to enhance foxtail millet.\u003c/p\u003e\u003cp\u003eOverall, the insights gained from ddRAD sequencing in this study advance our understanding of \u003cem\u003eSetaria\u003c/em\u003e species. The identified high-confidence SNPs also facilitated the discovery of 57 candidate genes associated with important traits. Identifying SNPs linked to yield-related characteristics, such as TNPP, GWPa, and PBC, is crucial for genomics-assisted breeding in foxtail millet. Plant breeders, when supported by genotype-based relatedness analysis, can make informed decisions regarding the selection of parental lines, ultimately improving breeding programs and the long-term sustainability of millet cultivation (Lee et al., 2018). These findings offer significant insights into the genetic architecture underlying key agronomic traits in foxtail millet, elucidating the molecular basis of trait variation. They also highlight potential genetic targets for further functional validation, thereby laying the groundwork for enhancing foxtail millet improvement through precision breeding strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCredit authorship contribution statement\u003c/h2\u003e\u003cp\u003eACS conceived the idea; ACS, PCOR, SS, and RP collected landraces from the farmer\u0026rsquo;s field; SS and AK conducted all the experiments; KSSR, VBR, and LSP performed genomic and data analysis; ACS, PCOR, BBD, SS, AK, ARK, RP, ARR and PT prepared as well as edited the entire MS; ACS and PCOR contributed consumables. All authors read and approved the final manuscript.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthical Statements:\u003c/h2\u003e\u003cp\u003eThis manuscript does not include any of the Human / Animal Models / Cell Lines or any other of this kind that are subjected to ethical issues. Hence this declaration is \u0026ldquo;NOT APPLICABLE\u0026rdquo;\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis work was supported by the Department of Science and Technology (DST)-INSPIRE fellowships, New Delhi, India as part of a Ph. D degree scholarship to SS (IF170752) and KSSR (IF220369). ACS and PCOR from YVU acknowledged the partial utilization of the financial support for consumables (No. CRG/2018/003280 dated May 30th, 2019) from DST-SERB, New Delhi, India. AK thanked for the JRF (No. CRG/2018/003280 dated May 30th, 2019) from DST- Science and Engineering Research Board (SERB), New Delhi.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eACS conceived the idea; ACS, PCOR, SS, and RP collected landraces from the farmer\u0026rsquo;s field; SS and AK conducted all the experiments; KSSR, VBR, and LSP performed genomic and data analysis; ACS, PCOR, BBD, SS, AK, ARK, RP, ARR and PT prepared as well as edited the entire MS; ACS and PCOR contributed consumables. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the Department of Science and Technology (DST)-INSPIRE fellowships, New Delhi, India as part of a Ph. D degree scholarship to SS (IF170752) and KSSR (IF220369). ACS and PCOR from YVU acknowledged the partial utilization of the financial support for consumables (No. CRG/2018/003280 dated May 30th, 2019) from DST-SERB, New Delhi, India. AK thanked for the JRF (No. CRG/2018/003280 dated May 30th, 2019) from DST- Science and Engineering Research Board (SERB), New Delhi. All the authors acknowledge Dr. Saurabh Gupta and Dr. Nisarg Vays of Department of Generative AI \u0026amp; Bioinformatics, Infocusp Innovations, Gala Hub, Bopal, Ahmedabad, Gujarat for their help in Bioinformatic analysis and association studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli, Asjad., et al. (2016). EST-SSR based genetic diversity and population structure among Korean landraces of foxtail millet (Setaria italica L.). \u003cem\u003eKorean J. 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Genetic architecture and candidate genes for deep-sowing tolerance in rice revealed by non-syn GWAS. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 332\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.3389/fpls.2018.00332\u003c/span\u003e\u003cspan address=\".10.3389/fpls.2018.00332\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"ddRAD-Seq, Foxtail millet, Genome-wide analysis, Landrace, Molecular markers, SNPs","lastPublishedDoi":"10.21203/rs.3.rs-7465120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7465120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFoxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e L.), a small-grained, diploid C4 panicoid millet with a genome size of 515 Mb, is the second most widely cultivated crop globally. It serves as a model species for agronomic and nutritional research. This study employed double-digest restriction-site-associated DNA sequencing (ddRAD-seq) to characterize genomic variation within 30 foxtail millet genetic resources. The analysis identified 206,483 high-quality polymorphic SNPs through NGS-ddRAD path. Number of SNPs on individual genotypes ranged from 1184(Si-7) to 131873 (Si-3) at Read Depth 10. Results revealed three distinct clusters, effectively separating most landraces and released cultivars, thereby indicating population differentiation based on their classification and geographic origin. Among the landraces, genotypes Si-17, Si-15, Si-5, and Si-16 exhibited high yields, early flowering, and early maturation compared to release cultivars Further examination of SNPs in landraces uncovered variations in minor allele frequency (MAF), highlighting high-frequency alleles within the \u003cem\u003eSetaria\u003c/em\u003e genotype. A total 83 significant MTAs (Marker-trait associations) were identified by GWAS for Eight traits across the genome. High confidence MTAs for three important traits including total tiller number per panicle (TNPP), Plant base color (PBC), and Grain weight per panicle (GWPa) were identified. These MTAs facilitated the identification of 57 candidate genes with potential applications in molecular breeding. Additionally, a mini-core selection of 30 genotypes, representing the majority of genetic diversity indicated notable genetic traces of landraces, particularly for those three traits. These landraces show promise for use in breeding programs aimed at developing climate-resilient foxtail millet varieties.\u003c/p\u003e","manuscriptTitle":"Genome Sequence and Association Analysis Reveal Allelic Variants for Agronomically Important Traits in Foxtail Millet (Setaria italica L.) Germplasm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 16:32:56","doi":"10.21203/rs.3.rs-7465120/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":"8018b21f-8e8a-44ca-8516-7fd0fbd4c70f","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T07:08:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 16:32:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7465120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7465120","identity":"rs-7465120","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}