Genetic loci underlying important shoot morphological traits of wild emmer wheat revealed by GWAS

Genetic loci underlying important shoot morphological traits of wild emmer wheat revealed by GWAS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic loci underlying important shoot morphological traits of wild emmer wheat revealed by GWAS Shanjida Rahman, Shahidul Islam, Penghao Wang, Darshan Sharma, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3036278/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 Wild emmer wheat (WEW) played a central role in wheat evolution. During the long process of evolution, rapid alteration and sporadic genomic changes occurred in wheat resulting gene modifications and loss to some extent. WEW contains numerous genes that are absent in modern wheat gene pool, which might be useful for improving wheat. But, there is a lack of investigation in exploring genotype to phenotype associations in WEW. This study conducted genome wide association study (GWAS) on 19 shoot morphological traits and identified the genetic loci associated with several phenotypes from a collection of 263 WEW accessions using 90K SNP (single nucleotide polymorphism). A total of 11,393 SNP markers which passed the data quality screening, were used to conduct the GWAS analysis using a mixed linear model in TASSEL (Trait Analysis by Association, Evolution, and Linkage) software. A total of 857 significant MTAs (marker-trait association) were identified harbouring on all fourteen chromosomes, among which 81 were highly significant. On average, each significant MTA explained approximately 7% of phenotypic variance. The most significant MTAs were for tiller number, biomass, and some of yield related traits such as yield/plant and seed size. Putative candidate genes were also predicted for highly significant MTAs using the bioinformatics platform. The majority of the selected MTAs showed significant differences between alternative alleles for the corresponding phenotypes indicating their potential to be used in the breeding program. The genetic loci, contributing significantly to phenotypic variation, identified from this study will be useful in improving wheat morphological traits. Wild emmer wheat GWAS morphological traits 90K SNP Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Key Message A number of genetic loci associated with several shoot morphological traits have been identified in wild emmer wheat with the potential to be utilized for improving bread wheat. Introduction Wild emmer wheat ( Triticum turgidum ssp. dicoccoides ) is the tetraploid parent (2n = 4x = 28, AABB) of modern wheat, which originated through hybridization between diploid T. urartu (AA) and Aegilops speltoides (BB) and the domestication of which resulted in cultivated emmer ( Triticum turgidum ssp. dicoccon ) (Zaharieva et al. 2010 ). Native landraces of wild emmer can be found throughout the Near Eastern Fertile Crescent region (Harlan and Zohary 1966 ). They have a huge diverse gene pool in traits of agronomy (biomass, growth stages, yield), quality (grain protein content; gliadins and glutenins content; amino acid composition; micronutrient content), biotic stresses (resistance to powdery mildew; fusarium head mildew; tan spot; leaf, stem and stripe rust) and abiotic stresses (resistance to salt; drought; heat; herbicide) (Chatzav et al. 2010 ; Nevo et al. 2013 ). Several genes and QTL (quantitative trait loci) have already been mapped in wild emmer, such as Pm16 and Pm36 on 5B for powdery mildew tolerance (Chen et al. 2005 ), Yr36 on 6B for stripe rust tolerance (Uauy et al. 2005 ), Qfhs.fcu-7AL on 7A for fusarium head blight (Kumar et al. 2007 ), Gpc-B1 on 6B and QGpc-ndsu·5B3 on 5B for grain protein content (Blanco et al. 2006 ). However, considering the large gene pool for a wide range of traits, the gene mapping work has been far less than it should be. There are two common approaches for gene mapping (i) linkage analysis and (ii) association mapping (Yu and Buckler 2006 ); both depend on the co-inheritance of a functional polymorphism and neighboring DNA variants. The difference is, in linkage analysis, biparental populations with contrasting parents are used, where recombination opportunity is low. On the other hand, an unstructured population with historical recombination and natural diversity is explored in association mapping, which ultimately results in a higher resolution mapping with a greater allele number and a wider reference population. The most common association mapping method is genome wide association study (GWAS), which is a powerful approach to revealing the genetic architecture of complex traits by identifying novel gene/QTL responsible for a particular phenotype (Li et al. 2018 ; Marcotuli et al. 2015 ; Sun et al. 2017 ). It generally surveys genetic variation in the whole genome from a set of germplasm with huge recombination to find a signal for marker-trait associations (MTAs) from linkage disequilibrium (LD) between the causal gene/QTL and the assayed markers (Arora et al. 2017 ). Although thousands of molecular markers are needed in GWAS, rapid advances in next-generation sequencing based genotyping has made it easy. The SNP (single nucleotide polymorphism) markers cover the whole genome in a cost-effective way (Zhu et al. 2008 ). A high-density wheat 90K SNP iSelect array comprising approximately 90,000 gene associated SNP covering the whole genome is available, which is suitable for GWAS analysis in both tetraploid and hexaploid wheat (Sun et al. 2017 ; Wang et al. 2014 ). The advantages of GWAS include (i) exploring a large number of accessions simultaneously, (ii) utilizing already existing genetic stocks, breeding lines, or natural population of wider genetic diversity with historical recombinations, and (iii) obtaining the position of causal gene/s with higher resolution (Jamann et al. 2015 ; Liu et al. 2017 ). However, it also has limitations that include failure in detecting alleles with low frequency and the chance of false positive results due to spurious relations between traits and markers (Rafalski 2010 ). GWAS has been used widely in association studies on cereals such as rice (Huang et al. 2010 ), maize (Yu and Buckler 2006 ) and wheat (Chen et al. 2014 ) for determining the genetic architecture of grain quality, abiotic and biotic stress, and yield or yield related traits (Turuspekov et al. 2017 ). GWAS in wheat was relatively challenging because of the larger genome and polyploidization until recently (Ain et al. 2015 ). After the development of the wheat 90K SNP array, GWAS studies have become more popular in both hexaploid and tetraploid wheat (Wang et al. 2014 ). GWAS studies have been reported by using 90K SNP for diploid wheat Aegilops tauschii for grain architecture (Arora et al. 2017 ), cultivated emmer wheat for stripe rust resistance (Liu et al. 2017 ), durum wheat for arabinoxylan content (Marcotuli et al. 2015 ), and bread wheat for vitamin B1 and B2 content (Li et al. 2018 ), yield components (Turuspekov et al. 2017 ), grain yield related traits (Ain et al. 2015 ), Ca accumulation (Alomari et al. 2017 ) and panicle traits (Liu et al. 2018a ). Recently, the GWAS approach was used for studying wild emmer on grain protein content (Liu et al. 2019 ), grain micronutrient content (Liu et al. 2021 ), and powdery mildew resistance (Li et al. 2019a ). Domestication of wheat led to several phenotypic changes, which resulted in a narrowed gene pool in modern durum and bread wheat. For example, wild emmer is characterized by brittle rachis, i.e., the spikelets of a spike disarticulate at the maturity stage, which is deleterious for agricultural purposes. This brittle rachis is controlled by Br1 , Br2 and Br3 genes, and transformation from brittle to non-brittle type is the first symbol of domestication (Nalam et al. 2007 ). Soft glume or free threshing is another domestication event, controlled by sog and Q genes , respectively, absent in wild emmer. As a result, wild emmer has tough glumes (due to the presence of the Tg1 gene) with difficulty to thresh (Jantasuriyarat et al. 2004 ). For flowering time, wild emmer has alleles for late flowering on 5A and for early flowering on 2A, 4B and 6B (Peng et al. 2011a ). It was postulated that alleles on 5A were positioned at the same location with the Vrn1 (Peng et al. 2011b ). Domestication resulted in the emergence of spring wheat that does not require specific vernalization (Gegas et al. 2010 ; Peng et al. 2003 ). Similarly, domestication also resulted in changes in kernel size and shape, controlled by GS3 (grain size), GW2 (grain width) and SW5 (seed width), resulting in a long and thin primitive grains to wider and shorter modern grain (Gegas et al. 2010 ). These indicate that wild emmer has many features not present in today’s bread wheat or durum wheat. Though not all are desirable, some features are potentially useful in breeding. For example, tall plant with large biomass is one of the wild emmer features that have highly significant correlations with yield (Ormoli et al. 2015 ). GWAS has provided a solution for identifying novel genes underlying the useful and desirable traits of wild emmer. Despite several QTLs/genes for a particular trait having been identified and mapped from WEW, most of those were reported for stress related traits. The genetic variation of most of the agronomic traits across WEW has not been explored yet. In particular, GWAS, an advanced method for revealing genetic architecture, has not been used in studying agronomic traits of the WEW population. For the first time, this study conducted GWAS on 19 shoot morphological traits using 90K SNP from a germplasm collection of 263 WEW accessions collected from different parts of Israel, Turkey, Syria, and Lebanon. Materials and methods Plant materials For this study, 263 wild emmer accessions ( Triticum dicoccoides ) collected from Israel, Turkey, Syria, Lebanon, and Iran were used, which were obtained from the Gene Bank of the Institute of Evolution, University of Haifa, Israel. The accessions can be grouped into ten populations based on their geographical location (Kato et al. 1997 ), among which six belong to Israel as majority of the accession were collected from different part of Israel: (1) Central, Israel: 129 accession collected from Qazrin, Yehudiyya, Gamla, Rosh-Pinha and Tabigha were categorized under this population; (2) South, Israel: 17 accessions were from Mt. Gilboa, Mt. Gerizim, Gitit, Kokhav-Hashahar, Taiyaba, Sanhedriyya, Bet-Meir and Jaba were grouped under this population; (3) West, Israel: 11 accessions collected from Amirim, Nesher, Beit-Oreon, Daliyya and Bat-Shelomo were categorized under this population; (4) North, Israel: only one accession collected from Mt. Hermon was obtained for population; (5) Evolution canyon 1, Israel: 13 accessions were collected from an optimal natural microscale model named “Evolution canyon 1 (EC1)” near the Mount Carmel, Israel for studying evolutionary process; (6) Admixed, Israel: 41 accessions, collected from Israel but information on exact location was not unavailable, were grouped into this population; (7) Turkey: with 14 accessions; (8) Lebanon: with 16 accessions; (9) Syria: with 19 accessions and (10) Iran: with only one accession. Detailed information on the collection location, and the corresponding population is mentioned in Supplementary Table 1. Trials and phenotyping For phenotyping, three trials were conducted, two in a glasshouse and one in the field. Both glasshouse (GH) trials were conducted at Murdoch University, Western Australia (WA) in 2019 (E1), maintaining 20-25 o C day temperature and 10–20 o C night temperature, and 70% Relative Humidity (RH); and in 2020 (E2) maintaining the same temperature and relative humidity. A completely randomized design (CRD) with six replications was used for both GH trials. The field trial was conducted in South Perth, the Department of Primary Industries and Regional Development (DPIRD) research farm, WA, in 2020, following randomized complete block design (RCBD) with three replications. Due to having brittle rachis, spikes of wild emmer were harvested at physiological maturity (Chatzav et al. 2010 ). A total of 19 shoot morphological traits were measured and recorded from those trials, which included: heading time (HT) in days, flowering time (FT) in days, maturity time (MT) in days, plant height (PH) in cm, total tiller number (TTN), effective tiller number (ETN), spike length (SpL) in cm, peduncle length (PL) in cm, flag leaf area (FLA) in cm 2 , second leaf area (SLA) in cm 2 , yield/plant (YPP) in gm, biomass/plant (BPP) in gm, shoot angle (SAng) in degree, thousand kernel weight (TKW) in gm, seed length (SL) in mm, seed width (SW) in mm, seed thickness (ST) in mm, seed area (SA) in mm 2 , and threshability (T). The detailed procedure for recording each data is mentioned in Supplementary Table 2. The descriptive analysis, ANOVA (Analysis of variation), heritability and correlation analysis were conducted in the statistical package RStudio (version 2022.02.2.0). The heritability estimates were conducted using the formula H 2 = Vg/(Vg + Ve) given by Johnson et al. (Johnson et al. 1955 ), where Vg and Ve indicate genetic and environmental variance, respectively. Genotyping and marker selection Genomic DNA was extracted from fresh and healthy leave of two-week old seedlings of the wild emmer accessions using SDS extraction protocol (Ren et al. 2013 ). Genotyping with a high-density Illumina 90K infinium SNP array (Wang et al. 2014 ) was carried out by DJPR Victoria (Victoria Department of Jobs, Precincts and Regions, Australia). Raw intensity data were loaded into GenomeStudio and the NormTheta (x-axis coordinate) and NormR (y-axis coordinate) values from the SNP cluster plots were exported. Then, a customized Perl script was used to perform sample clustering and to assign SNP genotypes for known polymorphism. SNP marker with an unknown location on a chromosome or > 30% missing value or < 5% minor allele frequency was removed. The SNPs with duplicated values were also removed. Genetic diversity and population structure PowerMarker V3.25 was used to calculate the genetic diversity and polymorphism information content (PIC) (Liu et al. 2017 ). The population structure was estimated for the 263 wild emmer accessions using STRUCTURE software 2.3.4, which implements a model-based Bayesian cluster analysis (Pritchard et al. 2000 ). The accessions were considered as an admixture population with correlated allele frequencies. A total of 50,000 burn-in iterations followed by 100,000 times of Markov Chain Monte Carlo (MCMC) iterations for k = 2–9 clusters were used to identify the optimal cluster (k). To estimate the sampling variance (robustness) of the inferred population structure, five independent runs were produced for each k. The output generated by the STRUCTURE software was accumulated by another software STRUCTURE HARVESTER (Liu et al. 2017 ). An ad-hoc statistic Δk, based on the rate of change in log probability of data [ln P(D)] between successive k values, was used to detect the number of clusters (k) that best represented this population (Evanno et al. 2005 ). Phylogenetic analysis was performed using the Nearest Neighbour approach on the genetic distance calculated from the filtered SNPs by Plink version 1.9 with 1000 times bootstrap implemented by R package Phangorn, visualised by SNPhylo. The PCA and MDS plots were generated on the genotypes using R package SNPRelate and GGplot2 with in-house codes where lines in plots were coloured according to their collection origins. Linkage disequilibrium (LD) and Genome wide association study (GWAS) Linkage disequilibrium (LD) analysis was performed using the LD function in TASSEL (Trait Analysis by Association, Evolution and linkage) software version 5.2.64 (Elias et al. 1996 ). The analysis produced two outputs: squared allele frequency correlation (R 2 ) and normalized coefficient of linkage disequilibrium (DPrime). The loci at P < 0.05 were considered as significant for LD. The LD triangle plot was generated for each chromosome using LD Block Show from the VCF files obtained using in-house R scripts where the R 2 value was used. Genome-wide association study (GWAS) was performed through marker trait associations (MTAs) for the 19 above-ground traits using TASSEL software version 5.2.64. A mixed linear model (MLM) was used for the analysis, where both the PC matrix and kinship matrix (PCA + K) were considered (Liu et al. 2017 ). Quantile-quantile (Q-Q) plots and Manhattan plots were generated for individual traits from TASSEEL. Q-Q plots are effective in understanding the presence of false positive or false negative MTAs. Manhattan plots were generated to visualise the GWAS output with chromosome position on X-axis and -log(P-value) on Y-axis. SNP markers with P < 0.05 were considered significant and corrected for multiple tests by calculating the q-value (FDR-adjusted P-value). SNP markers with q-values < 0.05 were selected to avoid false positive MTAs. False discovery rate (FDR) correction was performed using Benjamini-Hochberg multiple test correction to determine the actual significant MTAs (Benjamini and Hochberg 1995 ). Finally, significant MTAs were determined using the threshold P value (1.0e-3). To obtain highly significant MTAs, the threshold P value was increased using the formula: P = 1/n, where n is the total SNP markers used for GWAS analysis (Sun et al. 2017 ). The MTAs identified in multiple environments were considered robust. Candidate gene identification To identify putative candidate genes or related proteins, the genome browser GrainGenes/jbrowse-Zavitan ( https://wheat.pw.usda.gov/GG3/jbrowse_Zavitan ) was used, which was developed by the wild emmer wheat sequencing consortium (WEWseq) where the wild emmer reference genome Zavitan was used (Avni et al. 2017 ). The chromosomal position of the associated SNP of the highly significant MTAs was extended by 10 kb in both directions to identify candidate genes along with the annotated proteins (Arora et al. 2017 ). The identified genes were again blasted in Ensembl Plants T. dicoccoides WEWSeq_v.1.0 database for confirmation. Results Phenotypic evaluation The descriptive statistics of 19 shoot morphological traits revealed a wide range of variation in studied wild emmer germplasm, and ANOVA showed significant differences (P < 0.001) among accessions and environments for all traits (Table 1 ). Considerable diversity was observed in all traits in three environments, even though that varied between the environments. Heritability ranged from 40.86 for seed thickness to 98.40 for flowering time, where high heritability (> 60%) was found for the majority of the traits. Correlation analysis showed several significant associations between traits (Supplementary Fig. 1). In general, highly significant positive correlations were found between the traits related to life cycle (HT, FT and MT), leaf morphology (flag and second leaf area), and kernel properties (thousand kernel weight, seed length, seed width, seed weight, seed thickness and seed area). On the contrary, life cycle related traits (HT, FT and MT) negatively correlated with most of the other traits, except total and effective tiller number. On the other hand, BPP and YPP had significant positive associations with most of the other traits except life cycle related traits and threshability. Table 1 Basic statistics for 19 shoot morphological traits in wild emmer collections evaluated in three environments. Traits Environment Mean Range Heritability F-value from ANOVA Genotype Environment Heading time (days) HT E1 184.69 188.53 97.88 232.02 *** 26228.2 *** E2 166.72 224.25 E3 108.71 46.00 Flowering time (days) FT E1 190.85 187.60 98.40 308.9 *** 36704.2 *** E2 171.56 225.25 E3 114.62 43.33 Maturity time (days) MT E1 246.06 135.00 92.00 58.46 *** 32740.6 *** E2 269.20 211.50 E3 143.11 25.00 Plant height (cm) PH E1 128.51 139.00 83.22 25.8 *** 349.4 *** E2 119.81 115.00 E3 119.95 66.00 Total tiller number TTN E1 5.92 11.67 52.91 6.62 *** 393.6 *** E2 8.22 17.00 E3 6.75 7.00 Effective tiller number ETN E1 3.40 6.75 58.31 7.9 *** 447.4 *** E2 4.69 13.00 E3 4.62 6.00 Spike length (cm) SpL E1 6.59 5.50 60.31 8.6 *** 1962.6 *** E2 7.50 7.25 E3 9.64 6.50 Peduncle length (cm) PL E1 43.41 65.50 66.83 11.1 *** 763.97 *** E2 52.28 58.50 E3 60.49 46.00 First leaf area (cm 2 ) FLA E1 24.17 55.80 70.08 12.71 *** 324.7 *** E2 29.62 72.38 E3 20.41 30.73 Second leaf area (cm 2 ) SLA E1 32.60 58.84 73.76 15.1 *** 497.9 *** E2 34.80 78.30 E3 25.09 28.28 Shoot angle ( O ) SAng E1 51.19 81.37 81.59 23.16 *** 246.3 *** E2 57.60 77.65 E3 62.18 70.00 Biomass/plant (g) BPP E1 9.03 23.00 90.63 49.4 *** 5768.14 *** E2 14.11 53.60 E3 6.41 9.74 Yield/plant (g) YPP E1 1.05 2.36 85.98 31.7 *** 2735.9 *** E2 1.58 4.28 E3 0.66 4.19 Thousand kernel weight (g), TKW E1 26.59 36.55 75.93 16.8 *** 645.11 *** E2 30.81 38.75 E3 24.44 37.80 Seed length (mm) SL E1 8.84 4.26 53.65 6.8 *** 91.45 *** E2 9.04 5.27 E3 8.57 3.56 Seed width (mm) SW E1 2.54 1.65 47.61 5.5 *** 94.72 *** E2 2.64 1.78 E3 2.44 1.81 Seed thickness (mm), ST E1 2.39 1.83 40.86 4.5 *** 167.22 *** E2 2.37 1.77 E3 2.16 1.66 Seed area (mm 2 ) SA E1 14.85 12.03 68.75 11.9 *** 279.62 *** E2 15.81 11.20 E3 14.13 10.10 Threshability T E1 2.88 3.00 67.01 11.15 *** 218.53 *** E2 3.35 3.00 E3 3.01 3.00 *** р < 0.001 SNP marker quality A total of 263 wild emmer accessions were genotyped using 90K (80,222 actual markers) Illumina Infinium SNP array. After SNP analysis, tetraploid cluster calls were assigned for 84,222 markers. Of these, 48,771 were successfully converted to genotype calls based on allelic state, of which 31,771 SNPs had > = 80% call rate. A total of 14,793 SNPs were retained after removing the markers with > 30% missing value and < 5% minor allele frequency. After removing SNPs with unknown positions and duplicate values, the rest 11,393 SNPs were utilised in the downstream analysis. Genetic diversity The highest number of markers (1248 SNPs) were mapped on chromosome 2B, while the lowest (578 SNPs) were mapped on chromosome 4B (Table 2 ). All chromosomes showed high genetic diversity and the polymorphism information content (PIC). The overall genetic diversity and PIC of the studied wild emmer germplasm were 0.4294 and 0.3808, respectively (Table 2 ). Chromosome 6B had the highest genetic diversity (0.4607) and PIC (0.4057) whereas chromosome 5B had the lowest genetic diversity (0.4014) and PIC (0.3578). At the genome level, more SNPs were mapped in the B genome (6637 SNPs) compared to the A genome (4756 SNPs), but the two genomes’ genetic diversities (A genome = 0.4368; B genome = 0.4221) and polymorphism information contents (PIC) (A genome = 0.3868; B genome = 0.3747) were not significantly different based on the paired t -test (Table 2 ). In the A genome, the SNP marker distribution ranged from 619 to 759 markers per chromosome, with an average density of 679 markers per chromosome. In contrast, in the B genome, the marker distribution ranged from 578 to 1248 with an average of 948 markers per chromosome. Table 2 Number of markers, genetic diversity, and polymorphism information content (PIC) values in the studied wild emmer wheat germplasm. No. of SNP markers Mean value of genetic diversity Mean PIC value Chromosome A Genome B Genome A Genome B Genome A Genome B Genome 1 643 1083 0.4434 0.4092 0.3924 0.3644 2 742 1248 0.4402 0.4298 0.3893 0.3812 3 699 841 0.4497 0.4264 0.3977 0.3782 4 619 578 0.4384 0.4028 0.3876 0.3588 5 638 1077 0.4067 0.4014 0.3622 0.3578 6 656 911 0.4390 0.4607 0.3884 0.4057 7 759 899 0.4402 0.4243 0.3900 0.3770 Subtotal/mean 4756 6637 0.4368 0.4221 0.3868 0.3747 Total/Grand mean 11393 0.4294 0.3808 The genetic diversity and PIC were also calculated for the eight wild emmer populations (Supplementary table 3). Here, two populations named ‘North area, Israel’ and ‘Iran’ were discarded due to only containing 2 and 1 accessions, respectively. Higher genetic diversities were observed for south area (Israel), Turkey, and Lebanon than the others, with mean genetic diversities of 0.4896, 0.4817 and 0.4802, and PIC values of 0.4257, 0.4165 and 0.4197, respectively. In contrast, the lowest diversity was observed for the Evolutionary Canyon 1 (EC1), Israel, with genetic diversity of 0.1954 and PIC of 0.1707. The remaining populations had intermediate levels of genetic diversity and PIC values. Population structure The population structure of the 263 wild emmer accessions was estimated based on 11,393 SNP markers. The delta k was plotted against the number of putative k (= 2–9) (Fig. 1 A). The peak of the line graph was observed at k = 2, which indicates that the studied wild emmer population can be divided into two subpopulations as shown in two different colours in Fig. 1 B. On the other hand, the PCA analysis also grouped the 263 wild emmer accessions into two clusters, supporting the result of structure analysis (Fig. 1 C). The phylogenetic tree based on Nearest Neighbour method also divided the germplasm into two clusters (Fig. 1 D). MDS_IBS and PCA analysis based on the origin of the studied wild emmer accessions was also conducted (Supplementary Fig. 2). In both the cases no specific stratification pattern was observed, which indicates the grouping pattern is not related to geographical distribution. Linkage Disequilibrium (LD ) LD triangle plot generated for each chromosome showed low LD blocks; clear strong LD triangle blocks (r 2 = 1) were visible only for chromosomes 2B, 3B,5A, 5B, 6A, 7A, and 7B (Supplementary Fig. 3). Marker-trait associations for shoot trait Genome-wide association study identified 857 significant (P value at 1.0e-3; -log[P] = 3.0) marker-trait associations (MTAs) for the traits in three environments (Fig. 2 ). The number of associated MTAs varied for individual traits, which on average explained approximately 7% of the phenotypic variance. To determine the MTAs significance, the threshold was raised to 4.0 (-log[P] = -log [1/11393] = 4.0). The MTAs with this threshold were considered highly significant MTAs. MTAs identified in multiple environments were also considered highly robust. A total of 81 highly significant MTAs were identified, mostly for HT, FT, MT, TTN, ETN, BPP, YPP, SW, ST, SA, and T (Table 3 ). Table 3 Details of highly significant MTAs for the 19 traits. Traits SNP ID Chr. Position E1 E2 E3 -Log(P) R 2 (%) -Log(P) R 2 (%) -Log(P) R 2 (%) Heading time (HT) IWB19587 2A 309954062 3.28 5.5 - - 3.39 7.7 IWB60825 2B 139693372 - - - - 4.19 9.5 IWB44218 4B 755027845 - - 4.67 9.3 - - IWB2714 5A 424219181 4.96 9.4 - - 1.60 3.1 IWB56520 5A 476078236 - - - - 4.08 7.0 IWB35325 5A 628946362 2.87 4.5 5.33 11.1 - - IWB74620 6A 602637715 3.48 5.2 3.13 5.6 4.21 8.0 IWB58299 7A 668673203 - - 4.76 9.2 - - IWB9063 7A 636875338 - - - - 4.16 8.1 Flowering time (FT) IWB67869 1B 648758891 3.06 4.7 1.41 2.1 - - IWB44218 4B 755027845 - - 4.53 8.9 - - IWB2714 5A 424219181 4.86 9.0 - - 1.44 2.4 IWB35325 5A 628946362 2.61 4.1 5.19 10.8 - - IWB74620 6A 602637715 3.22 4.8 3.46 6.3 - - IWB58299 7A 668673203 - - 4.89 9.5 1.64 3.3 Maturity time (MT) IWB50416 2A 183851107 2.04 2.6 - - 5.45 11.2 IWB19587 2A 309954062 2.28 3.4 - - 5.86 13.9 IWB12290 2B 780878357 4.25 7.2 - - - - IWB5392 4A 107779117 - - - - 4.03 9.3 IWB2714 5A 424219181 5.66 10.3 2.20 4.9 - - IWB73413 6A 573292400 2.23 3.1 4.24 8.5 - - IWB28559 6A 16755288 1.79 2.2 - - 4.59 9.1 IWB74620 6A 602637715 2.65 3.6 3.21 5.8 1.98 3.4 IWB71856 7A 454798397 - - - - 5.43 10.5 IWB8215 7B 690033603 2.01 2.7 6.59 15.4 - - Plant height (PH) IWB63062 1B 307265116 4.25 7.1 - - - - Total tiller number (TTN) IWB3715 1B 648945313 - - 4.75 9.2 - - IWB62774 1B 666554291 1.40 2.3 - - 3.34 7.4 IWB24354 5B 467644027 - - 4.08 8.5 - - IWA4622 5B 467796529 - - 4.30 11.9 - - IWB47417 6B 119674222 - - - - 4.16 8.0 IWB4154 7B 718931217 - - - - 5.23 11.1 Effective tiller number (ETN) IWB74353 1B 318231805 3.31 6.5 1.53 2.8 - - IWB64507 4A 707443679 - - - - 4.03 8.4 IWB73323 4A 707441940 - - - - 4.31 8.5 IWB43886 5A 513607997 4.08 8.0 - - - - IWB37996 5B 533724565 3.76 6.1 2.80 5.0 - - IWB23613 6A 356525458 3.12 4.8 1.68 2.7 - - Spike length (SpL) IWB44509 1B 592872144 - - 1.64 2.7 3.07 5.9 Flag leaf area (FLA) IWB65386 4B 453692425 2.20 3.0 - - 3.34 6.3 IWB11785 6A 1819300 3.08 5.8 - - 1.81 5.0 Second leaf area (SLA) IWB10401 5B 675765355 1.79 2.3 3.62 7.0 - - Biomass/plant (BPP) IWB51019 2A 442612818 - - 5.44 11.2 - - IWB30769 2B 435835642 - - 5.01 10.4 - - IWB71337 3B 632009019 - - 4.78 10.1 - - IWB73910 4B 651373491 - - 4.76 9.5 - - IWB3256 4B 672925079 2.31 4.5 6.37 17.5 - - IWB50762 7A 603945748 - - 4.55 9.2 - - IWB57171 7A 570566311 1.37 1.7 4.82 10.8 - - IWB72437 7B 466652832 - - 4.08 8.4 - - IWB62673 7B 494892488 - - 5.43 12.3 1.46 2.3 Yield/plant (YPP) IWB24823 3B 827635687 4.73 8.0 - - - - IWB57707 5A 431829169 1.42 1.8 - - 4.88 6.4 IWB41324 5A 431823018 3.91 7.8 - - 1.99 2.5 IWA2892 5A 428391480 1.61 2.1 - - 3.93 5.6 IWB19933 6B 4049546 2.67 5.2 - - 3.73 8.9 Thousand kernel weight (TKW) IWB71993 1B 622806648 - - - - 4.67 8.9 IWB73662 1B 374558838 3.04 6.1 2.07 5.1 - - Seed length (SL) IWB72916 2B 104154443 1.46 1.7 3.56 6.1 - - IWB57206 4A 633814526 1.90 2.7 4.01 9.1 - - IWB3123 4A 576360975 1.43 1.7 3.61 6.1 - - Seed width (SW) IWA1608 1A 477295885 2.24 2.9 2.96 5.4 5.19 9.4 IWB20856 1A 477292942 1.57 2.1 2.04 3.8 5.28 9.9 IWB43477 1B 524856652 - - - - 4.08 8.7 IWB71993 1B 622806648 - - 1.82 3.0 5.94 11.2 IWB73064 4B 569477550 - - - - 4.08 8.5 IWB67456 4B 59146559 4.24 6.6 - - - - IWB57449 7B 139944719 - - - - 4.08 7.3 IWB38649 7B 139942108 - - 1.61 2.8 4.98 9.5 Seed thickness (ST) IWB20856 1A 477292942 - - 2.03 3.0 4.03 7.0 IWB32627 2B 91643549 1.58 2.0 3.21 6.0 - - IWB36023 4B 58800780 4.10 7.6 - - 1.68 2.8 IWB51790 5A 428392074 5.09 7.8 - - - - Seed area (SA) IWA1609 1A 477295885 1.37 1.6 - - 4.06 8.1 IWB71993 1B 622806648 - - - - 4.00 7.8 IWB57449 7B 139944719 - - 3.19 6.1 3.74 7.5 Thresh-ability (T) IWB10078 2B 230076863 - - 3.69 6.8 3.13 5.3 IWB32896 4A 136402000 - - 1.36 2.3 4.15 9.2 IWB2614 4A 111095762 - - 2.20 4.0 4.19 7.8 IWB51790 5A 428392074 2.07 2.5 4.16 7.4 2.41 3.6 IWA4996 7A 571700366 - - - - 4.41 9.2 Note: E1, E2 and E3 represent glasshouse trial-2019, glasshouse trial-2020 and field trial-2020, respectively. R 2 indicates phenotypic variance explained by corresponding SNP. Growth stage related traits Out of the 52 significant MTAs identified for heading time (HT), 9 highly significant MTAs were found on chromosomes 2A, 2B, 4B, 5A, 6A, and 7A (Table 3 ; Supplementary Fig. 4). The phenotypic variance explained (R 2 ) by each SNP marker ranged from 4.5 to 11.1%. Among the 9 highly significant MTAs, IWB74620 on 6A was detected in all three environments, which explained 5.2 to 8.0% of phenotypic variance. Besides, IWB19587 on 2A, and IWB2714 and IWB35325 on 5A were found to be associated with HT in multiple environments. For flowering time (FT), only 6 MTAs were highly significant out of 232 detected MTAs. Those MTAs were distributed on 1B, 4B, 5A, 6A and 7A, and explained 6.0% of the phenotypic variance on average. A total of 57 significant MTAs were found for MT, among which 10 highly significant MTAs were distributed on chromosomes 2A, 2B, 4A, 5A, 6A, 7A, and 7B. IWB74620 on 6A was detected in all three environments and explained 3.4 to 5.8% of the phenotypic variances. Two MTAs, IWB2714 on 5A and IWB74620 on 6A, were significantly associated with all growth stage related traits, i.e., HT, FT, and MT (Table 3 , Table 4 ). Similarly, IWB35323 on 5A and IWB58299 on 7A were identified for HT and FT, whereas IWB19587 on 2A for HT and MT. Biomass related traits There were 20 significant MTAs for the total tiller number (TTN), of which 6 were highly significant (Table 3 ; Supplementary Fig. 4) on chromosomes 1B, 5B, 6B, and 7B that explained on average 8.4% of the phenotypic variances. IWB62774 on 1B was detected in two environments, explaining 2.3 to 7.4% of the phenotypic variances. For the effective tiller number (ETN), a total of 63 significant MTAs was identified, of which 6 on chromosome 1B, 4A, 5A, 5B, and 6A were highly significant and explained 2.7 to 8.5% of the phenotypic variance. IWB74353 on 1B, IWB37996 on 5B and IWB23613 on 6A were detected in multiple environments for ETN. A total of 32 significant MTAs were identified for biomass/plant (BPP) and 9 MTAs on chromosomes 2A, 2B, 3B, 4B, 7A, and 7B were highly significant. IWB3256 on 4B was detected in multiple environments (E1 and E2), explaining an average of 11% of the phenotypic variances. Similarly, IWB57171 on 7A and IWB62673 on 7B were also detected in multiple environments and explained an average of 6.25 and 7.3% of the phenotypic variances, respectively. Very few highly significant MTAs were detected for other biomass related traits, such as one for plant height (PH) on 1B, one for spike length (SpL) on 1B, two for flag leaf area (FLA) on 4B and 6A and one for the second leaf area (SLA) on 5B. No highly significant MTA was found for peduncle length (PL) and shoot angle (SAng). Yield related traits A total of 28 significant MTAs was identified for yield/plant (YPP), among which 5 on chromosome 3B, 5A and 6B were highly significant and explained on average 5.4% of the phenotypic variances (Table 3 ; Supplementary Fig. 4). For seed width (SW), out of 68 significant MTAs, 8 on chromosome 1A, 1B, 4B and 7B were highly significant. Among them, IWA1608 and IWB20856, both on chromosome 1A, were found to be associated with SW in all three environments, particularly in E3, which explained 9.4 and 9.9% phenotypic variances, respectively. A total of 113 significant MTAs were identified for seed thickness (ST) and only 4 were highly significant. Those MTAs were detected on chromosomes 1A, 2B, 4B, and 5A, which explained on average 5.2% of the phenotypic variances. Out of 78 significant MTAs identified for threshability (T), 5 on chromosome 2B, 4A, 5A, and 7A were highly significant. Among them, IWB51790 on 5A was found in all three environments and explained 2.5 to 7.4% of the phenotypic variances. In addition to this, very few highly significant MTAs were identified for other kernel related traits, such as two for thousand kernel weight (TKW) on chromosome 1B, three for seed length (SL) on chromosomes 2B and 4A, and three for seed area (SA) on chromosome 1A, 1B and 7B. Two MTAs had pleiotropic effects, including IWB51790 on 5A for ST and T and IWB57449 on 7B for SW and SA (Table 3 , Table 4 ). Putative candidate gene annotation for above ground traits Genes and annotations were searched on the 10 Kb upstream and downstream regions of tagged SNPs for all of the 81 highly significant MTAs in Table 3 . Among them, annotations matched with the corresponding traits according to previous studies were considered putative candidate genes (Table 4 ). A total of ten candidate genes were selected for the growth stage related traits from nine MTAs. For example, IWB2714 on chromosome 5A, was identified for heading time (HT), flowering time (FT) and maturity time (MT) in multiple environments and the predicted protein is the RING/U-box superfamily protein. Similarly, another MTA, IWB74620 on 6A, detected for all the growth stage related traits in multiple environments, was predicted with two genes, including the C2 domain containing protein and ABC transporter family protein. Other proteins predicted for growth stage related traits include aconitate hydratase for IWB35325 on 5A, receptor-like protein kinase for IWB58299 on 7A, ankyrin repeat family protein for IWB19587 on 2A, 60 KDa jasmonate induced protein for IWB9063 on 7A, and NAD-specific glutamate dehydrogenase for IWB5392 on 4A. In addition to these, two MTAs (IWB50416 on 2A and IWB8215 on 7B) were detected for MT in multiple environments, which did not have corresponding trait related annotations. For the total tiller number (TTN), five genes were predicted for the five associated MTAs. Three were annotated as lysine-specific demethylase for IWB62774 on 1B, F-box domain containing protein for IWA4622 on 5B, and transport inhibitor response protein for IWB24354 on 5B. For the effective tiller number (ETN), four MTAs were selected, of which two MTAs on 4A (IWB64507 and IWB73323) had trait related annotation such as phosphoglucan, and the other two, including IWB37996 on 5B and IWB23613 on 6A did not have proper annotations. For biomass/plant (BPP), eight candidate genes were predicted from seven MTAs, among which only four had biomass related annotations, such as RNA-binding protein for IWB51019 on 2A, Myb-like transcription factor family protein for IWB50762 on 7A, F-box family protein IWB62673 on 7B, and histone deacetylase for IWB72437 on 7B. For yield/plant (YPP), four MTAs were selected, among which only one, IWA2892 on 5A, had a proper yield related annotation, methyl-CPG-binding domain. For the other yield related traits (SL, SW, SA, ST, and T), five putative genes were predicted for five MTAs, with two having pleiotropic effect. IWB51790 on 5A for ST and T was annotated as methyl-CPG-binding domain and IWB57449 on 7B for SW and SA was annotated as fasciclin-like arabinogalactan. Favourable alleles and their effects The phenotypic values of each trait were calculated for the two alternative alleles of each of the selected MTA and t-test was conducted to test the significance between the mean values of the two alternative alleles. All selected MTAs for growth stage related traits showed significant differences between alleles, which ranged from 6 to 55 days for heading time (HT), 27 to 62 days for flowering time (FT), and 3 to 41 days for maturity time (MT) (Fig. 3 ). For HT, the highest allelic difference was observed for IWB35325 on 5A where heading occurred 55 days earlier in accessions with the ‘A’ allele compared to accessions with ‘G’ allele. This indicates that the ‘A’ allele of IWB35325 was favourable for HT. For FT, IWB74620 on 6A showed the highest allelic difference, where accession with the favourable ‘A’ allele flowered almost 62 days earlier than the ‘G’ allele accessions. For MT, the highest allelic difference was found for the IWB8215 on 7B, where accessions with ‘C’ allele completed the life cycle 41 days earlier than accessions with the other allele. For the total tiller number (TTN), four MTAs out of five showed significant allelic variations where the highest difference (3.73 tiller/plant) was observed between A and G alleles of IWA4622 on 5B (Fig. 4 ). Another MTA on the same chromosome (5B), IWB24354, also showed a significant difference between the alternative alleles and located close to the previous one, which indicates the presence of an outstanding cluster controlling the trait (Fig. 4 : LD triangle plot). For the effective tiller number (ETN), only two closely positioned MTAs on chromosome 4A showed significant allelic variances where the ‘C’ allele of IWB64507 and the ‘A’ allele of IWB73323 were the favourable alleles (Fig. 4 ). On the other hand, for BPP, four MTAs out of seven showed significant differences between the alternative alleles where accession containing the favourable allele showed an average 4.25 g higher BPP compared to the accessions with unfavourable alleles (Fig. 5 ). The highest difference between the alternative alleles (5.54 g/plant) was observed for IWB50762 on 7A, where ‘T’ allele showed a higher BPP (18.99g) compared to the ‘C’ allele (13.44g). For the yield per plant (YPP), three out of four MTAs showed significant differences between the alternative alleles. Notably, all of them were situated very close to each other on chromosome 5A, indicating the presence of an outstanding cluster controlling the trait (Fig. 5 ). The favourable alleles of these three MTAs (IWB57707, IWB41324 and IWA2892) showed an average of 1.15 g higher YPP. For seed width (SW), only one MTA (IWB57449 on 7B) out of two showed a significant difference between the alternative alleles. This MTA is also associated with seed area (SA), which showed a significant allelic difference. In both traits, the ‘A’ allele was favourable, which had 6% higher SW and 14% higher SA compared to the unfavourable ‘G’ allele (Fig. 6 ). For seed thickness (ST) and threshability (T), all selected MTAs showed significant allelic differences, among which the IWB51790 on 5A was pleiotropic for both traits (Fig. 7 ). In both cases the ‘T’ was favourable, which led to 14% higher ST and 46% lower threshability score as lower T score indicates easy to thresh. Table 4 List of highly significant MTAs for the above ground traits, along with the predicted genes and annotations obtained from the “GrainGenes” browser Traits SNP ID Environments Chr. Alleles Putative Gene ID Annotations Functions Growth stage related traits HT, FT, MT IWB2714 E1 (HT, FT, MT), E2 (MT), E3 (HT, FT) 5A C/T TRIDC5AG038830 RING/U-box superfamily protein Promotes leaf senescence and aging in Arabidopsis (Zhang et al. 2020 ). HT, FT, MT IWB74620 E1 and E2 (HT, FT, MT), E3 (HT, MT) 6A A/G TRIDC6AG058260 C2 domain-containing protein Initiates floral transition in Arabidopsis (Liu et al. 2018b ). TRIDC6AG058330 ABC transporter family protein Develops anther and pollen in rice (Qin et al. 2013 ). HT, FT IWB35325 E1 and E2 (HT, FT) 5A A/G TRIDC5AG071920 Aconitate hydratase 1 Regulates flag leaf senescence in wheat (Gregersen and Holm 2007 ). HT, FT IWB58299 E2 (HT, FT), E3 (FT) 7A A/C TRIDC7AG067460 Receptor-like protein kinase 4 Involves in biological process including senescence (Li et al. 2019b ). HT, MT IWB19587 E1 and E3 (HT, MT) 2A G/T TRIDC2AG032420 Ankyrin repeat family protein Causes flower senescence in 4 o’clock plant (Xu et al. 2007 ). HT IWB9063 E3 7A A/G TRIDC7AG062140 60 kDa jasmonate-induced protein Involves in leaf senescence in barley (Rustgi et al. 2014 ). MT IWB50416 E1, E3 2A A/C TRIDC2AG026800 Undescribed protein - MT IWB5392 E3 4A A/C TRIDC4AG013330 NAD-specific glutamate dehydrogenase Involves in leaf senescence in wheat (Kar and Feierabend 1984 ). MT IWB8215 E1, E2 7B C/T TRIDC7BG070240 Chaperone protein DnaJ 1 Unrelated function Biomass related traits TTN IWB3715 E2 1B C/T TRIDC1BG068390 Ribosomal protein S10 Unrelated function TTN IWB62774 E1, E3 1B G/T TRIDC1BG072460 Lysine-specific demethylase 5B Stem elongation (EnsemblePlants) TTN IWA4622 E2 5B A/G TRIDC5BG046110 F-box domain containing protein Associated with tiller bud activity in rice (Ishikawa et al. 2005 ). TTN IWB24354 E2 5B C/T TRIDC5BG046090 Transport inhibitor response 1-like protein Involves in early flowering and tillering capacity in Arabidopsis (Qiao et al. 2020 ). TTN IWB4154 E3 7B A/G TRIDC7BG064220 Sulfate transporter 91 Unrelated function ETN IWB64507 E3 4A C/T TRIDC4AG065850 Phosphoglucan Causes higher yield and biomass including increased tiller number (Ral et al. 2012 ) ETN IWB73323 E3 4A A/G TRIDC4AG065851 Phosphoglucan Same above ETN IWB37996 E1 5B A/G TRIDC5BG056150 - - ETN IWB23613 E1 6A G/T TRIDC6AG031240 DNA mismatch repair protein Unrelated function BPP IWB51019 E2 2A C/T TRIDC2AG039400 RNA-binding protein 8A Regulates photosynthesis in Arabidopsis (Bach-Pages et al. 2020 ). BPP IWB30769 E2 2B A/G TRIDC2BG045480 - - BPP IWB3256 E2 4B C/T TRIDC4BG064100 Undescribed protein - BPP IWB50762 E2 7A C/T TRIDC7AG057990 Myb-like transcription factor family protein Upregulates photosynthesis gene expression (Zhou and Li 2016 ). BPP IWB57171 E2 7A C/T TRIDC7AG054970 Dihydroflavonol 4-reductase-like1 Unrelated function TRIDC7AG054980 Histone-lysine N-methyltransferase ATXR4 Unrelated function BPP IWB62673 E2, E3 7B C/T TRIDC7BG045140 F-box family protein Regulates stomatal closure in wheat (An et al. 2019 ). BPP IWB72437 E2 7B C/T TRIDC7BG042690 Histone deacetylase 1 Involves in plant growth and development (Ma et al. 2013 ) Yield related traits YPP IWB57707 E3 5A A/G TRIDC5AG039490 Cellulose synthase like E1 Unrelated function YPP IWB41324 E1 5A C/T TRIDC5AG039480 Ribosomal RNA small subunit methyltransferase B Unrelated function YPP IWA2892 E1, E3 5A G/T TRIDC5AG039080 Methyl-CPG-binding domain 8 Grain development in wheat controlling yield (Shi et al. 2016 ) YPP IWB19933 E3 6B A/C TRIDC6BG001120 Pleckstrin homology (PH) domain-containing protein /lipid-binding START domain-containing protein Unrelated function SL IWB3123 E1, E2 4A A/G TRIDC4AG042370 Plastid-lipid associated protein PAP/fibrillin family protein Responsible for seed development (Gangurde et al. 2020 ). SW IWB73064 E3 4B A/G TRIDC4BG048580 Tetratricopeptide repeat (TPR)-like superfamily protein Overexpression of TPR is associated with seed development in rice (Jain et al. 2007 ). SW, SA IWB57449 E2 (SA), E3 (SW, SA) 7B A/G TRIDC7BG017530 FASCICLIN-like arabinogalactan 1 Involves in microspore development of Arabidopsis (Johnson et al. 2011 ). ST, T IWB51790 E1 (ST, T), E2 and E3 (T) 5A C/T TRIDC5AG039080 Methyl-CPG-binding domain 8 Grain development in wheat controlling yield (Shi et al. 2016 ). ST IWB36023 E1, E3 4B C/T TRIDC4BG010410 Inosine-5'-monophosphate dehydrogenase Expressed differentially in response to stress (Pradhan et al. 2019 ). Note: HT: heading time; FT: flowering time; MT: maturity time; TTN: total tiller number; ETN: effective tiller number; BPP: biomass/plant; YPP: yield/plant; SL: seed length; SW: seed width; ST: seed thickness; SA: seed area; T: threshability. E1, E2 and E3 represent glasshouse trial-2019, glasshouse trial-2020 and field trial-2020, respectively. Discussion Wild emmer is an important genetic resource for wheat improvement as it possesses novel gene pool with large diversity. However, only a few traits of wild emmer have been explored until now. Particularly, the agronomic shoot traits in wild emmer germplasm were almost untapped due to sophisticated morphological characteristics. Although the majority of wild emmer accessions are characterized with undesirable phenotypes, some accessions, for example the out layers do contain desirable phenotypes for several traits, such as early flowering, high tillering capacity, huge biomass, well developed root system, a higher number of lateral roots and many more (Unpublished data). In the current study, GWAS was conducted on 19 shoot morphological traits and grain protein content (GPC) in wild emmer accessions for identifying novel alleles or genes for those traits, which can be potentially used for improving modern wheat. Linkage Disequilibrium (LD) Linkage disequilibrium (LD) between the marker and causal gene determines the precision of GWAS (Sela et al. 2014 ). A small LD extent is ideal for precise mapping with high resolution of marker trait association (Liu et al. 2017 ; Sela et al. 2014 ). There are two ways to visualize LD: decay plot and LD triangle plot (Abdurakhmonov and Abdukarimov 2008 ). The decay plot gives a rough estimate of the extent of LD throughout the genome or individual chromosomes. For example, LD for hexaploid wheat can be calculated considering the whole genome or separately for each A, B and D genome or for the 21 individual chromosomes. Nevertheless, the LD range is not uniform throughout the genome, even not within the same genome. Rather, it varies from chromosome to chromosome. Even within the same chromosome, the LD could vary in different regions. In this study, we presented our result in LD triangle form so that the LD block for the individual region of interest can be observed clearly (Supplementary Fig. 3). Moreover, the extent of LD from the decay plot and the size of the red block from the LD triangle plot complements each other as both are proportional to recombination (Abdurakhmonov and Abdukarimov 2008 ). This study observed a very small red block for all chromosomes. This indicates a high recombination rate in the studied wild germplasm leading to higher diversity. The small LD for wild emmer was also observed previously (Sela et al. 2014 ), even smaller than the cultivated emmer wheat (Liu et al. 2017 ). In contrast, comparatively longer LD was observed for some elite durum and bread wheat (Ain et al. 2015 ; Arora et al. 2017 ; Turuspekov et al. 2017 ). It is worth mentioning that the rapid LD decay, i.e., smaller LD, was observed for wild populations in many plant species, including Arabidopsis , barley and Aegilops tauschii (Liu et al. 2021 ; Liu et al. 2019 ; Spielmeyer and Richards 2004 ). This difference in the extent of LD between wild emmer and modern wheat (durum and bread) can be explained by the breeding and selection process towards modern variety development, which results in the population bottleneck (Sela et al. 2014 ). MTAs detected for shoot morphological traits along with putative function While the number of detected MTAs were quite large (857 for 19 traits) on the first occasion, it was narrowed down to 81 as highly significant MTAs. It was further narrowed to 34 based on the available annotations matched with the corresponding traits. MTAs for growth stage related traits After narrowing down, a total of 9 MTAs was identified for the life cycle related traits, i.e., heading time (HT), flowering time (FT) and maturity time (MT) on chromosomes 2A, 4A, 5A, 6A, 7A, and 7B (Table 4 ). Some previous findings suggested that genes/alleles associated with HT, FT and MT were also distributed on chromosome 2A (Hu et al. 2020 ), 4A (Hu et al. 2020 ; Sheoran et al. 2019 ), 5A (Fowler et al. 2016 ; Hu et al. 2020 ; Sheoran et al. 2019 ), 6A (Fowler et al. 2016 ) and 7A (Hu et al. 2020 ). In this study, IWB2714 was mapped around 424 Mb on 5A for HT, FT and MT, indicating strong genetic associations between these traits. An MTA on 5A chromosome (around 412 Mb) was also reported previously for heading and maturity time, which supports the findings of this study (Sheoran et al. 2019 ). It is well known that the heading and flowering of wheat are largely controlled by vernalization ( Vrn ), photoperiod ( Ppd ), and earliness ( Eps ) genes, and VRN1 , VRN2 and VRN3 are the three major alleles for vernalization on chromosome groups 5 and 7 (Quraishi et al. 2011 ). However, Vrn1-5A (TRIDC5A057030; 5A: 582 Mb) positioned 160 Mb far away from this MTA and might not be the candidate gene for the growth stage phenotype. Rather, another MTA IWB35325 detected for HT and FT was much closer (within 50 Mb) to the region reported for Vrn1-5A . However, IWB2714 was annotated as RING/U-box superfamily protein. The gene encoding this protein, AtUSR1 , also promoted leaf senescence and aging in the model plant Arabidopsis through the jasmonic acid signalling pathway (Zhang et al. 2020 ). On the other hand, IWB35325 was annotated as aconitate hydratase_1, of which the upregulation in cytoplasm resulted in flag leaf senescence in wheat (Gregersen and Holm 2007 ). Another MTA, IWB74620, was identified at 602 Mb on 6A, within the previously reported QTL (599–603 Mb) range for flowering time (Corsi et al. 2021 ). The IWB74620 was predicted for two genes annotation, of the C2 domain containing protein and ABC transporter family protein, respectively. C2 domains cooperated in initiating floral transition in Arabidopsis , which is strong evidence of its involvement in regulating flowering time (Liu et al. 2018b ). Similarly, the ABC transporter protein, ABCG15, was necessary for anther and pollen development in rice, indicating its relation to flowering time determination (Qin et al. 2013 ). Another MTA IWB58299 on 7A was detected for HT and FT, which was annotated as receptor like protein kinase - an important cell surface receptor, and involved in multiple biological processes, including leaf senescence in Arabidopsis (Li et al. 2019b ). Two more MTAs on 7A (IWB9063) and 4A (IWB5392), detected for HT and MT, respectively, were annotated as 60 Kda jasmonate induced protein and NAD specific glutamate dehydrogenase, respectively. Both were reported to be involved in barley leaf senescence (Rustgi et al. 2014 ) and wheat (Kar and Feierabend 1984 ). Overall, all these MTAs can be considered candidates for functional studies. Particularly, IWB2714 on 5A and IWB74620 on 6A, detected for all growth stage traits in all environments with significant allelic effects, can be considered as strong candidates. Moreover, the newly identified MTAs for MT, IWB50416 on 2A and IWB8215 on 7B, also deserve further investigation to determine their roles in wheat aging or senescence. MTAs for biomass related traits At a high significant level, five MTAs were identified for total tiller number (TTN) on chromosomes 1B, 5B and 7B and four for effective tiller number (ETN) on chromosomes 4A, 5B, and 6A. Previous studies on wheat also reported genes/alleles for tiller number on chromosome 1B (Chen et al. 2017 ), 5B (Chen et al. 2017 ; Guo et al. 2018 ; Muhammad et al. 2021 ), and 4A (Guo et al. 2018 ). Two MTAs detected on 5B, IWA4622 and IWB24354, were annotated as F-box domain containing protein and transport inhibitor response like protein, respectively. The functions of three F-box proteins have been described in rice, among which D3 (dwarf 3) F-box proteins are associated with tiller bud activity that ultimately influence tillering capacity (Ishikawa et al. 2005 ). In Arabidopsis , a transport inhibitor response like protein - ‘siR109944’ showed an increased tillering capacity and exhibited early flowering when overexpressed in transgenic lines (Qiao et al. 2020 ). The MTAs detected for ETN on 4A (IWB64507 and IWB73323) were annotated as phosphoglucan. Downregulation of one type of phosphoglucan, named GWD (glucan water dikinase), results in higher yield and biomass with an increased tiller number (Ral et al. 2012 ). These findings indicate that the MTAs in this study are truly associated with tillering capacity. Besides, two MTAs for TTN, IWB3715 on 1B and IWB4154 on 7B did not show tillering related annotation. Still, they had higher -log(P) values and clearly showed significant allelic differences in phenotype (Fig. 4 ), which indicates that the genes predicted for these two MTAs could be strong candidates. Notably, two neighbouring MTAs for TTN (IWA4622 and IWB24354) were within a strong LD block on 5B (Fig. 4 ), suggesting a gene nearby controlling the trait. Similarly, two MTAs for ETN showing significant allelic differences, IWB64507 and IWB73323, were positioned closely on 4A, which also suggests the presence of a gene nearby controlling the traits. Seven MTAs were detected for biomass/plant (BPP) on 2A, 2B, 4B, 7A and 7B. According to previous studies, significant genes/alleles for biomass were also mapped on chromosome 2A (Guo et al. 2018 ), 7A (Khadka et al. 2020 ; Rehman Arif et al. 2020 ; Tricker et al. 2018 ) and 7B (Guo et al. 2018 ; Rehman Arif et al. 2020 ; Schmidt et al. 2020 ; Tricker et al. 2018 ). The MTA detected on 2A, IWB51019, was annotated as RNA binding protein, which is important in regulating photosynthesis in Arabidopsis , as related to biomass (Bach-Pages et al. 2020 ). Another MTA for BPP, IWB50762 on 7A, was annotated as a Myb-like transcription factor family protein, which is also responsible for increasing photosynthesis through up-regulating photosynthetic gene expression (Zhou and Li 2016 ). Similarly, two more MTAs for BPP on 7B, IWB62673 and IWB72437, were annotated as F-box family protein and histone deacetylase, respectively. The F-box family protein, TaFBA1 from wheat, negatively controls stomatal closure, ultimately affecting photosynthesis. Hence biomass is also affected (An et al. 2019 ); whereas histone deacetylase plays a key role in plant growth and development in Arabidopsis , rice, and maize (Ma et al. 2013 ). Three other MTAs have also been selected with high-log (P) values. Still, the predicted genes close to them did not have a trait related annotation, indicating that they are novel for biomass, specially IWB57171 on 7A is promising as it showed a significant allelic effect (Fig. 5 ). MTAs for yield related traits There were nine significant MTAs for yield related traits, including yield/plant (YPP), seed length (SL), seed width (SW), seed area (SA), seed thickness (ST) and threshability (T). Out of four MTAs identified for YPP three were located on 5A. Previous studies also reported genes/alleles for wheat yield on 5A (Hu et al. 2020 ; Liu et al. 2018a ; Sun et al. 2017 ; Tricker et al. 2018 ). Within those four MTAs, only one (IWA2892) had trait related annotation such as methyl CPG binding domain. A gene encoding this protein in wheat, TaMBD6 , was found to be involved in the process of grain development (Shi et al. 2016 ). Two other MTAs (IWB41324 and IWB57707) did not have yield related annotation. Still, they demonstrated significant allelic differences (Fig. 5 ), which indicates genes close to these two MTAs have not been reported before for this trait. It is worth mentioning that all three MTAs on 5A for YPP were positioned in a strong LD block (Fig. 5 ), which suggests that genes within this LD block cumulatively control the yield in wild emmer. One MTA identified for SL, IWB3123 on 4A, was annotated as a plastid lipid associated protein (PAP), previously reported as a responsible protein for seed development (Gangurde et al. 2020 ). Two MTAs were identified for SW, among which IWB57449 on 7B had pleiotropic effect on SA, which indicates a genetic association between these two traits. IWB57449 related gene was annotated as fasciclin like arabinogalactan (FLA), which was previously reported to be involved in the shoot development of Arabidopsis . Particularly, FLA3 is responsible for microspore development, assumed to influence grain characteristics (Johnson et al. 2011 ). Another MTA for SW, IWB73064, was detected on 4B, which agrees with a previous study (Liu et al. 2018a ; Sun et al. 2017 ). This MTA was annotated as tetratricopeptide repeat (TPR) like superfamily protein with one F-box domain. The overexpression of F-box protein encoding gene in rice is associated with seed development (Jain et al. 2007 ). Similarly, another MTA (IWB51790 on 5A) showed pleiotropic effects for ST and T, which was annotated as the methyl-CPG-binding domain. One MTA (IWB36023) on 4B was detected for ST and predicted as inosine-5-monophosphate dehydrogenase, which was detected for grain number, spike fertility, and thousand grain weight of wheat (Pradhan et al. 2019 ). Overall, all MTAs for the yield related traits demonstrated highly significant allelic effects (Fig. 6 and Fig. 7 ). Particularly, two MTAs, IWB51790 on 5A and IWB57449 on 7B, were detected in multiple environments with pleiotropic effects and highly significant allelic effects. Therefore, these two MTAs can be considered as strong candidates for future functional validation studies. Conclusions Wild emmer wheat has been explored earlier for grain quality and biotic, and abiotic stress related traits. This study is the first attempt to explore shoot morphological traits using the GWAS approach. The study identified 857 significant MTAs, among which 81 were highly significant. The most robust MTAs belong to growth stage related traits, total and effective tiller number, biomass per plant, yield per plant, seed width, thickness, leaf area, and threshability. These traits can be of great importance for breeding purposes. For example, the favourable alleles of significant MTAs of life cycle related traits can reduce the total life span, which is one of the main targets in the breeding industry. Wild emmer can potentially be used as parental lines in breeding or to identify novel genes/alleles for modern wheat improvement. Declarations Acknowledgments: The authors would like to acknowledge State Agricultural Biotechnology Centre (SABC) for providing laboratory and glasshouse facilities and Department of Primary Industries and Regional Development (DPIRD) for providing field trial facilities. Funding: This research was funded by Murdoch University, Australia. Competing Interest: The Authors declare no conflict of interest. Author contribution statement: Shanjida Rahman conducted experimental trials, collected data, performed data analysis, and prepared the first draft. Shahidul Islam sourced plant material, conceptualised experimental design, provided supervision, and edited the manuscript. Penghao Wang helped in genomic data curation and association analysis. Darshan Sharma helped in genomic data production and edited the manuscript. Mirza Dowla assisted in genomic data production. Eviatar Nevo provided plant material from Israel. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3036278","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":212791045,"identity":"bedbfe2a-509b-4a59-a229-ba3354fb3149","order_by":0,"name":"Shanjida Rahman","email":"","orcid":"","institution":"Murdoch University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shanjida","middleName":"","lastName":"Rahman","suffix":""},{"id":212791046,"identity":"e331acc8-6ed9-47b1-849d-e9ba5879d17f","order_by":1,"name":"Shahidul Islam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie3PMUvDQBjG8acUkuWVrBds7Fc4OIiDQb9KSqFdbj4cCwU3nSP6IYRC5iuBunUO2CFdnBQEQXSQemfbzTOODvdfkuPyu3sD+Hz/MYYAeYAeuuhos6YI4HajM2khZAgs6cWTvxBYgi3JuG4h/ZvpY9OoihCGjf6QIPEwnb0SsuRO/0z4anHM86UhXeLzyxKUrhbqkDASTsLylA0uvgn0gSW1TM17NXCRfjF+25GwmX8aIgopzGAbJ4E5c0fAK3sLZ5KbwbST8Foqli/HFJh/qZKSEatHKr7lQ3HtHqyM39XJURTdr9fPZXYWFcPZy9P5aXLlGmxfsH2w/e0tn/t8Pp/v174AcfVWmyzuL1wAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5994-0521","institution":"North Dakota State University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shahidul","middleName":"","lastName":"Islam","suffix":""},{"id":212791047,"identity":"cccc325c-efa4-49da-9d2f-9133bbbe5532","order_by":2,"name":"Penghao Wang","email":"","orcid":"","institution":"Murdoch University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Penghao","middleName":"","lastName":"Wang","suffix":""},{"id":212791048,"identity":"ac2d726f-5575-4ca4-bfff-ae31591b3838","order_by":3,"name":"Darshan Sharma","email":"","orcid":"","institution":"Western Australia Department of Primary Industries and Regional Development","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Darshan","middleName":"","lastName":"Sharma","suffix":""},{"id":212791049,"identity":"b81585ca-a720-4af3-9c60-63cccb5706fb","order_by":4,"name":"Mirza Dowla","email":"","orcid":"","institution":"Edstar Genetics","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mirza","middleName":"","lastName":"Dowla","suffix":""},{"id":212791050,"identity":"15cffd3b-69fc-4796-b052-9c906719164f","order_by":5,"name":"Eviatar Nevo","email":"","orcid":"","institution":"University of Haifa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eviatar","middleName":"","lastName":"Nevo","suffix":""},{"id":212791051,"identity":"4febb3e6-4a1e-4ee7-a120-3ba1f3a51e7f","order_by":6,"name":"Jingjuan Zhang","email":"","orcid":"","institution":"Murdoch University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingjuan","middleName":"","lastName":"Zhang","suffix":""},{"id":212791052,"identity":"7b3b444a-f2b5-43d3-b4c1-6b5f1ab6fb30","order_by":7,"name":"Wujun Ma","email":"","orcid":"","institution":"Murdoch University - Perth Campus: Murdoch University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wujun","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2023-06-08 00:48:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3036278/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3036278/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39306066,"identity":"1a353aea-60cb-466b-a3c8-936083ae1935","added_by":"auto","created_at":"2023-06-29 15:03:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125005,"visible":true,"origin":"","legend":"\u003cp\u003eThe population structure of the 263 wild emmer germplasms based on genotypic data. (a) Plot of delta k plotted against putative k = 2-9; (b) Stacked bar plot of wild emmer accession shown in single line sorted by Q; (c) Plot of PC1 against PC2 showing population structure of 263 wild emmer accession using SNP marker; and (d) Phylogenetic tree using the Nearest neighbour approach.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3036278/v1/6358181f81771ac31417ebb7.png"},{"id":39306065,"identity":"8370a45a-aa9c-40b5-ac56-2bd16e075058","added_by":"auto","created_at":"2023-06-29 15:03:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54951,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of significant MTAs for the 19 traits.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3036278/v1/77ac44219ef277ab0bc04e86.png"},{"id":39306111,"identity":"240ab7d2-3f0c-4422-81c2-78d3a4be5ec9","added_by":"auto","created_at":"2023-06-29 15:03:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334086,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and Q-Q plots for heading time (HT), Flowering time (FT) and Maturity time (MT) along with allelic effects of the highly significant MTAs shown as bar plot and LD triangle plots showing the positions of some highly significant MTAs on chromosome 5A, 6A and 7A which were pleiotropic for life cycle related traits\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3036278/v1/a7b9ec46da584dba1cafbeb8.png"},{"id":39306095,"identity":"21abeeeb-bdd4-4017-b25e-951b6d8458f5","added_by":"auto","created_at":"2023-06-29 15:03:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156455,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and Q-Q plots for total tiller number, TTN) (Upper part) and effective tiller number, ETN (Lower part) along with allelic effects of the highly significant MTAs shown as bar plot and LD triangle plots showing the positions of some highly significant MTAs on chromosome 1B and 5B for TTN (Upper part); and on chromosome 4A for ETN (Lower part).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3036278/v1/cfee6dd71035386d6221d34c.png"},{"id":39306134,"identity":"8349ec20-3e6b-44dc-a05b-06047112d148","added_by":"auto","created_at":"2023-06-29 15:03:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171886,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and Q-Q plots for biomass/plant, BPP (Upper part) and YPP (Lower part) along with allelic effects of the highly significant MTAs shown as bar plot and LD triangle plots showing the positions of some highly significant MTAs on chromosome 7A and 7B for BPP (Upper part); and on chromosome 5A for YPP (Lower part).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3036278/v1/f9d261dd78bfe77b544055ed.png"},{"id":39307345,"identity":"a2581fd1-4d12-4fe2-9ebe-fa76a345acb6","added_by":"auto","created_at":"2023-06-29 15:11:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135365,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and Q-Q plots for seed width (SW) and seed area (SA) along with allelic effects of the highly significant MTAs shown as bar plot and LD triangle plot showing the position of IWB57449 on chromosome 7B, which was pleiotropic for both traits.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3036278/v1/6f67059dcc4621f1b2ef50c0.png"},{"id":39307350,"identity":"04c91729-8eea-41a7-b433-c012dc34222a","added_by":"auto","created_at":"2023-06-29 15:11:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":146202,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and Q-Q plots for seed thickness (ST) and threshability (T) along with allelic effects of the highly significant MTAs shown as bar plot and LD triangle plot showing the position of IWB51790 on chromosome 5A, which was pleiotropic for both traits.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3036278/v1/99c932edd2b50d5cc78b1319.png"},{"id":40374173,"identity":"b2445ecb-a236-4111-ac01-938a50b3b6d2","added_by":"auto","created_at":"2023-07-21 13:11:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1645939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3036278/v1/69ea5fbb-edc2-4ae0-80bc-9a39534559a8.pdf"},{"id":39306135,"identity":"47a34b1c-e0ec-4129-950a-603b6b7d46cd","added_by":"auto","created_at":"2023-06-29 15:03:38","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4770412,"visible":true,"origin":"","legend":"","description":"","filename":"Paper2ShootGWASV8forTAGSupplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3036278/v1/753e328c574f2beac462b389.pdf"}],"financialInterests":"","formattedTitle":"Genetic loci underlying important shoot morphological traits of wild emmer wheat revealed by GWAS","fulltext":[{"header":"Key Message","content":"\u003cp\u003eA number of genetic loci associated with several shoot morphological traits have been identified in wild emmer wheat with the potential to be utilized for improving bread wheat.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWild emmer wheat (\u003cem\u003eTriticum turgidum\u003c/em\u003e ssp. \u003cem\u003edicoccoides\u003c/em\u003e) is the tetraploid parent (2n\u0026thinsp;=\u0026thinsp;4x\u0026thinsp;=\u0026thinsp;28, AABB) of modern wheat, which originated through hybridization between diploid \u003cem\u003eT. urartu\u003c/em\u003e (AA) and \u003cem\u003eAegilops speltoides\u003c/em\u003e (BB) and the domestication of which resulted in cultivated emmer (\u003cem\u003eTriticum turgidum\u003c/em\u003e ssp. \u003cem\u003edicoccon\u003c/em\u003e) (Zaharieva et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Native landraces of wild emmer can be found throughout the Near Eastern Fertile Crescent region (Harlan and Zohary \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1966\u003c/span\u003e). They have a huge diverse gene pool in traits of agronomy (biomass, growth stages, yield), quality (grain protein content; gliadins and glutenins content; amino acid composition; micronutrient content), biotic stresses (resistance to powdery mildew; fusarium head mildew; tan spot; leaf, stem and stripe rust) and abiotic stresses (resistance to salt; drought; heat; herbicide) (Chatzav et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nevo et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Several genes and QTL (quantitative trait loci) have already been mapped in wild emmer, such as \u003cem\u003ePm16\u003c/em\u003e and \u003cem\u003ePm36\u003c/em\u003e on 5B for powdery mildew tolerance (Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), \u003cem\u003eYr36\u003c/em\u003e on 6B for stripe rust tolerance (Uauy et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), \u003cem\u003eQfhs.fcu-7AL\u003c/em\u003e on 7A for fusarium head blight (Kumar et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), \u003cem\u003eGpc-B1\u003c/em\u003e on 6B and \u003cem\u003eQGpc-ndsu\u0026middot;5B3\u003c/em\u003e on 5B for grain protein content (Blanco et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, considering the large gene pool for a wide range of traits, the gene mapping work has been far less than it should be.\u003c/p\u003e \u003cp\u003eThere are two common approaches for gene mapping (i) linkage analysis and (ii) association mapping (Yu and Buckler \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); both depend on the co-inheritance of a functional polymorphism and neighboring DNA variants. The difference is, in linkage analysis, biparental populations with contrasting parents are used, where recombination opportunity is low. On the other hand, an unstructured population with historical recombination and natural diversity is explored in association mapping, which ultimately results in a higher resolution mapping with a greater allele number and a wider reference population. The most common association mapping method is genome wide association study (GWAS), which is a powerful approach to revealing the genetic architecture of complex traits by identifying novel gene/QTL responsible for a particular phenotype (Li et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Marcotuli et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It generally surveys genetic variation in the whole genome from a set of germplasm with huge recombination to find a signal for marker-trait associations (MTAs) from linkage disequilibrium (LD) between the causal gene/QTL and the assayed markers (Arora et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although thousands of molecular markers are needed in GWAS, rapid advances in next-generation sequencing based genotyping has made it easy. The SNP (single nucleotide polymorphism) markers cover the whole genome in a cost-effective way (Zhu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). A high-density wheat 90K SNP iSelect array comprising approximately 90,000 gene associated SNP covering the whole genome is available, which is suitable for GWAS analysis in both tetraploid and hexaploid wheat (Sun et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe advantages of GWAS include (i) exploring a large number of accessions simultaneously, (ii) utilizing already existing genetic stocks, breeding lines, or natural population of wider genetic diversity with historical recombinations, and (iii) obtaining the position of causal gene/s with higher resolution (Jamann et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, it also has limitations that include failure in detecting alleles with low frequency and the chance of false positive results due to spurious relations between traits and markers (Rafalski \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). GWAS has been used widely in association studies on cereals such as rice (Huang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), maize (Yu and Buckler \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and wheat (Chen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for determining the genetic architecture of grain quality, abiotic and biotic stress, and yield or yield related traits (Turuspekov et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). GWAS in wheat was relatively challenging because of the larger genome and polyploidization until recently (Ain et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). After the development of the wheat 90K SNP array, GWAS studies have become more popular in both hexaploid and tetraploid wheat (Wang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). GWAS studies have been reported by using 90K SNP for diploid wheat \u003cem\u003eAegilops tauschii\u003c/em\u003e for grain architecture (Arora et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), cultivated emmer wheat for stripe rust resistance (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), durum wheat for arabinoxylan content (Marcotuli et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and bread wheat for vitamin B1 and B2 content (Li et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), yield components (Turuspekov et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), grain yield related traits (Ain et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Ca accumulation (Alomari et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and panicle traits (Liu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). Recently, the GWAS approach was used for studying wild emmer on grain protein content (Liu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), grain micronutrient content (Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and powdery mildew resistance (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDomestication of wheat led to several phenotypic changes, which resulted in a narrowed gene pool in modern durum and bread wheat. For example, wild emmer is characterized by brittle rachis, i.e., the spikelets of a spike disarticulate at the maturity stage, which is deleterious for agricultural purposes. This brittle rachis is controlled by \u003cem\u003eBr1\u003c/em\u003e, \u003cem\u003eBr2\u003c/em\u003e and \u003cem\u003eBr3\u003c/em\u003e genes, and transformation from brittle to non-brittle type is the first symbol of domestication (Nalam et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Soft glume or free threshing is another domestication event, controlled by \u003cem\u003esog\u003c/em\u003e and \u003cem\u003eQ genes\u003c/em\u003e, respectively, absent in wild emmer. As a result, wild emmer has tough glumes (due to the presence of the \u003cem\u003eTg1\u003c/em\u003e gene) with difficulty to thresh (Jantasuriyarat et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For flowering time, wild emmer has alleles for late flowering on 5A and for early flowering on 2A, 4B and 6B (Peng et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011a\u003c/span\u003e). It was postulated that alleles on 5A were positioned at the same location with the \u003cem\u003eVrn1\u003c/em\u003e (Peng et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). Domestication resulted in the emergence of spring wheat that does not require specific vernalization (Gegas et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Similarly, domestication also resulted in changes in kernel size and shape, controlled by \u003cem\u003eGS3\u003c/em\u003e (grain size), \u003cem\u003eGW2\u003c/em\u003e (grain width) and \u003cem\u003eSW5\u003c/em\u003e (seed width), resulting in a long and thin primitive grains to wider and shorter modern grain (Gegas et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These indicate that wild emmer has many features not present in today\u0026rsquo;s bread wheat or durum wheat. Though not all are desirable, some features are potentially useful in breeding. For example, tall plant with large biomass is one of the wild emmer features that have highly significant correlations with yield (Ormoli et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). GWAS has provided a solution for identifying novel genes underlying the useful and desirable traits of wild emmer.\u003c/p\u003e \u003cp\u003eDespite several QTLs/genes for a particular trait having been identified and mapped from WEW, most of those were reported for stress related traits. The genetic variation of most of the agronomic traits across WEW has not been explored yet. In particular, GWAS, an advanced method for revealing genetic architecture, has not been used in studying agronomic traits of the WEW population. For the first time, this study conducted GWAS on 19 shoot morphological traits using 90K SNP from a germplasm collection of 263 WEW accessions collected from different parts of Israel, Turkey, Syria, and Lebanon.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eFor this study, 263 wild emmer accessions (\u003cem\u003eTriticum dicoccoides\u003c/em\u003e) collected from Israel, Turkey, Syria, Lebanon, and Iran were used, which were obtained from the Gene Bank of the Institute of Evolution, University of Haifa, Israel. The accessions can be grouped into ten populations based on their geographical location (Kato et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), among which six belong to Israel as majority of the accession were collected from different part of Israel: (1) Central, Israel: 129 accession collected from Qazrin, Yehudiyya, Gamla, Rosh-Pinha and Tabigha were categorized under this population; (2) South, Israel: 17 accessions were from Mt. Gilboa, Mt. Gerizim, Gitit, Kokhav-Hashahar, Taiyaba, Sanhedriyya, Bet-Meir and Jaba were grouped under this population; (3) West, Israel: 11 accessions collected from Amirim, Nesher, Beit-Oreon, Daliyya and Bat-Shelomo were categorized under this population; (4) North, Israel: only one accession collected from Mt. Hermon was obtained for population; (5) Evolution canyon 1, Israel: 13 accessions were collected from an optimal natural microscale model named \u0026ldquo;Evolution canyon 1 (EC1)\u0026rdquo; near the Mount Carmel, Israel for studying evolutionary process; (6) Admixed, Israel: 41 accessions, collected from Israel but information on exact location was not unavailable, were grouped into this population; (7) Turkey: with 14 accessions; (8) Lebanon: with 16 accessions; (9) Syria: with 19 accessions and (10) Iran: with only one accession. Detailed information on the collection location, and the corresponding population is mentioned in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTrials and phenotyping\u003c/h2\u003e \u003cp\u003eFor phenotyping, three trials were conducted, two in a glasshouse and one in the field. Both glasshouse (GH) trials were conducted at Murdoch University, Western Australia (WA) in 2019 (E1), maintaining 20-25\u003csup\u003eo\u003c/sup\u003eC day temperature and 10\u0026ndash;20 \u003csup\u003eo\u003c/sup\u003eC night temperature, and 70% Relative Humidity (RH); and in 2020 (E2) maintaining the same temperature and relative humidity. A completely randomized design (CRD) with six replications was used for both GH trials. The field trial was conducted in South Perth, the Department of Primary Industries and Regional Development (DPIRD) research farm, WA, in 2020, following randomized complete block design (RCBD) with three replications. Due to having brittle rachis, spikes of wild emmer were harvested at physiological maturity (Chatzav et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A total of 19 shoot morphological traits were measured and recorded from those trials, which included: heading time (HT) in days, flowering time (FT) in days, maturity time (MT) in days, plant height (PH) in cm, total tiller number (TTN), effective tiller number (ETN), spike length (SpL) in cm, peduncle length (PL) in cm, flag leaf area (FLA) in cm\u003csup\u003e2\u003c/sup\u003e, second leaf area (SLA) in cm\u003csup\u003e2\u003c/sup\u003e, yield/plant (YPP) in gm, biomass/plant (BPP) in gm, shoot angle (SAng) in degree, thousand kernel weight (TKW) in gm, seed length (SL) in mm, seed width (SW) in mm, seed thickness (ST) in mm, seed area (SA) in mm\u003csup\u003e2\u003c/sup\u003e, and threshability (T). The detailed procedure for recording each data is mentioned in Supplementary Table\u0026nbsp;2. The descriptive analysis, ANOVA (Analysis of variation), heritability and correlation analysis were conducted in the statistical package RStudio (version 2022.02.2.0). The heritability estimates were conducted using the formula H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Vg/(Vg\u0026thinsp;+\u0026thinsp;Ve) given by Johnson et al. (Johnson et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1955\u003c/span\u003e), where Vg and Ve indicate genetic and environmental variance, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping and marker selection\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from fresh and healthy leave of two-week old seedlings of the wild emmer accessions using SDS extraction protocol (Ren et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Genotyping with a high-density Illumina 90K infinium SNP array (Wang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was carried out by DJPR Victoria (Victoria Department of Jobs, Precincts and Regions, Australia). Raw intensity data were loaded into GenomeStudio and the NormTheta (x-axis coordinate) and NormR (y-axis coordinate) values from the SNP cluster plots were exported. Then, a customized Perl script was used to perform sample clustering and to assign SNP genotypes for known polymorphism. SNP marker with an unknown location on a chromosome or \u0026gt;\u0026thinsp;30% missing value or \u0026lt;\u0026thinsp;5% minor allele frequency was removed. The SNPs with duplicated values were also removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity and population structure\u003c/h2\u003e \u003cp\u003ePowerMarker V3.25 was used to calculate the genetic diversity and polymorphism information content (PIC) (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The population structure was estimated for the 263 wild emmer accessions using STRUCTURE software 2.3.4, which implements a model-based Bayesian cluster analysis (Pritchard et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The accessions were considered as an admixture population with correlated allele frequencies. A total of 50,000 burn-in iterations followed by 100,000 times of Markov Chain Monte Carlo (MCMC) iterations for k\u0026thinsp;=\u0026thinsp;2\u0026ndash;9 clusters were used to identify the optimal cluster (k). To estimate the sampling variance (robustness) of the inferred population structure, five independent runs were produced for each k. The output generated by the STRUCTURE software was accumulated by another software STRUCTURE HARVESTER (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). An ad-hoc statistic Δk, based on the rate of change in log probability of data [ln P(D)] between successive k values, was used to detect the number of clusters (k) that best represented this population (Evanno et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Phylogenetic analysis was performed using the Nearest Neighbour approach on the genetic distance calculated from the filtered SNPs by Plink version 1.9 with 1000 times bootstrap implemented by R package Phangorn, visualised by SNPhylo. The PCA and MDS plots were generated on the genotypes using R package SNPRelate and GGplot2 with in-house codes where lines in plots were coloured according to their collection origins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLinkage disequilibrium (LD) and Genome wide association study (GWAS)\u003c/h2\u003e \u003cp\u003eLinkage disequilibrium (LD) analysis was performed using the LD function in TASSEL (Trait Analysis by Association, Evolution and linkage) software version 5.2.64 (Elias et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The analysis produced two outputs: squared allele frequency correlation (R\u003csup\u003e2\u003c/sup\u003e) and normalized coefficient of linkage disequilibrium (DPrime). The loci at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as significant for LD. The LD triangle plot was generated for each chromosome using LD Block Show from the VCF files obtained using in-house R scripts where the R\u003csup\u003e2\u003c/sup\u003e value was used.\u003c/p\u003e \u003cp\u003eGenome-wide association study (GWAS) was performed through marker trait associations (MTAs) for the 19 above-ground traits using TASSEL software version 5.2.64. A mixed linear model (MLM) was used for the analysis, where both the PC matrix and kinship matrix (PCA\u0026thinsp;+\u0026thinsp;K) were considered (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Quantile-quantile (Q-Q) plots and Manhattan plots were generated for individual traits from TASSEEL. Q-Q plots are effective in understanding the presence of false positive or false negative MTAs. Manhattan plots were generated to visualise the GWAS output with chromosome position on X-axis and -log(P-value) on Y-axis. SNP markers with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant and corrected for multiple tests by calculating the q-value (FDR-adjusted P-value). SNP markers with q-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected to avoid false positive MTAs. False discovery rate (FDR) correction was performed using Benjamini-Hochberg multiple test correction to determine the actual significant MTAs (Benjamini and Hochberg \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Finally, significant MTAs were determined using the threshold P value (1.0e-3). To obtain highly significant MTAs, the threshold P value was increased using the formula: P\u0026thinsp;=\u0026thinsp;1/n, where n is the total SNP markers used for GWAS analysis (Sun et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The MTAs identified in multiple environments were considered robust.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCandidate gene identification\u003c/h2\u003e \u003cp\u003eTo identify putative candidate genes or related proteins, the genome browser GrainGenes/jbrowse-Zavitan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wheat.pw.usda.gov/GG3/jbrowse_Zavitan\u003c/span\u003e\u003cspan address=\"https://wheat.pw.usda.gov/GG3/jbrowse_Zavitan\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used, which was developed by the wild emmer wheat sequencing consortium (WEWseq) where the wild emmer reference genome Zavitan was used (Avni et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The chromosomal position of the associated SNP of the highly significant MTAs was extended by 10 kb in both directions to identify candidate genes along with the annotated proteins (Arora et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The identified genes were again blasted in Ensembl Plants \u003cem\u003eT. dicoccoides\u003c/em\u003e WEWSeq_v.1.0 database for confirmation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic evaluation\u003c/h2\u003e \u003cp\u003eThe descriptive statistics of 19 shoot morphological traits revealed a wide range of variation in studied wild emmer germplasm, and ANOVA showed significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among accessions and environments for all traits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Considerable diversity was observed in all traits in three environments, even though that varied between the environments. Heritability ranged from 40.86 for seed thickness to 98.40 for flowering time, where high heritability (\u0026gt;\u0026thinsp;60%) was found for the majority of the traits. Correlation analysis showed several significant associations between traits (Supplementary Fig.\u0026nbsp;1). In general, highly significant positive correlations were found between the traits related to life cycle (HT, FT and MT), leaf morphology (flag and second leaf area), and kernel properties (thousand kernel weight, seed length, seed width, seed weight, seed thickness and seed area). On the contrary, life cycle related traits (HT, FT and MT) negatively correlated with most of the other traits, except total and effective tiller number. On the other hand, BPP and YPP had significant positive associations with most of the other traits except life cycle related traits and threshability.\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\u003eBasic statistics for 19 shoot morphological traits in wild emmer collections evaluated in three environments.\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=\"left\" 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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHeritability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eF-value from ANOVA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHeading time (days)\u003c/p\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e97.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e232.02\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e26228.2\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e224.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFlowering time (days)\u003c/p\u003e \u003cp\u003eFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e98.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e308.9\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e36704.2\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMaturity time (days)\u003c/p\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e92.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e58.46\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e32740.6\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e83.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e349.4\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTotal tiller number\u003c/p\u003e \u003cp\u003eTTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e52.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.62\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e393.6\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEffective tiller number\u003c/p\u003e \u003cp\u003eETN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e58.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e447.4\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSpike length (cm)\u003c/p\u003e \u003cp\u003eSpL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e60.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1962.6\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePeduncle length (cm)\u003c/p\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e66.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e763.97\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFirst leaf area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eFLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e70.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12.71\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e324.7\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSecond leaf area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eSLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e73.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e497.9\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eShoot angle\u003c/p\u003e \u003cp\u003e(\u003csup\u003eO\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eSAng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e81.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e23.16\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e246.3\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBiomass/plant (g)\u003c/p\u003e \u003cp\u003eBPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e90.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5768.14\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eYield/plant\u003c/p\u003e \u003cp\u003e(g)\u003c/p\u003e \u003cp\u003eYPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e85.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2735.9\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThousand kernel weight (g), TKW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e75.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e645.11\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSeed length (mm)\u003c/p\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e53.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e91.45\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSeed width (mm)\u003c/p\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e47.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e94.72\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSeed thickness (mm),\u003c/p\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e40.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e167.22\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSeed area (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e68.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e279.62\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThreshability\u003c/p\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e67.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e11.15\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e218.53\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** р \u0026lt; 0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSNP marker quality\u003c/h2\u003e \u003cp\u003eA total of 263 wild emmer accessions were genotyped using 90K (80,222 actual markers) Illumina Infinium SNP array. After SNP analysis, tetraploid cluster calls were assigned for 84,222 markers. Of these, 48,771 were successfully converted to genotype calls based on allelic state, of which 31,771 SNPs had\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;80% call rate. A total of 14,793 SNPs were retained after removing the markers with \u0026gt;\u0026thinsp;30% missing value and \u0026lt;\u0026thinsp;5% minor allele frequency. After removing SNPs with unknown positions and duplicate values, the rest 11,393 SNPs were utilised in the downstream analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity\u003c/h2\u003e \u003cp\u003eThe highest number of markers (1248 SNPs) were mapped on chromosome 2B, while the lowest (578 SNPs) were mapped on chromosome 4B (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All chromosomes showed high genetic diversity and the polymorphism information content (PIC). The overall genetic diversity and PIC of the studied wild emmer germplasm were 0.4294 and 0.3808, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Chromosome 6B had the highest genetic diversity (0.4607) and PIC (0.4057) whereas chromosome 5B had the lowest genetic diversity (0.4014) and PIC (0.3578). At the genome level, more SNPs were mapped in the B genome (6637 SNPs) compared to the A genome (4756 SNPs), but the two genomes\u0026rsquo; genetic diversities (A genome\u0026thinsp;=\u0026thinsp;0.4368; B genome\u0026thinsp;=\u0026thinsp;0.4221) and polymorphism information contents (PIC) (A genome\u0026thinsp;=\u0026thinsp;0.3868; B genome\u0026thinsp;=\u0026thinsp;0.3747) were not significantly different based on the paired \u003cem\u003et\u003c/em\u003e-test (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the A genome, the SNP marker distribution ranged from 619 to 759 markers per chromosome, with an average density of 679 markers per chromosome. In contrast, in the B genome, the marker distribution ranged from 578 to 1248 with an average of 948 markers per chromosome.\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\u003eNumber of markers, genetic diversity, and polymorphism information content (PIC) values in the studied wild emmer wheat germplasm.\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=\"left\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo. of SNP markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMean value of genetic diversity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMean PIC value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA Genome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB Genome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA Genome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB Genome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA Genome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB Genome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtotal/mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal/Grand mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e11393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.4294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.3808\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 genetic diversity and PIC were also calculated for the eight wild emmer populations (Supplementary table 3). Here, two populations named \u0026lsquo;North area, Israel\u0026rsquo; and \u0026lsquo;Iran\u0026rsquo; were discarded due to only containing 2 and 1 accessions, respectively. Higher genetic diversities were observed for south area (Israel), Turkey, and Lebanon than the others, with mean genetic diversities of 0.4896, 0.4817 and 0.4802, and PIC values of 0.4257, 0.4165 and 0.4197, respectively. In contrast, the lowest diversity was observed for the Evolutionary Canyon 1 (EC1), Israel, with genetic diversity of 0.1954 and PIC of 0.1707. The remaining populations had intermediate levels of genetic diversity and PIC values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure\u003c/h2\u003e \u003cp\u003eThe population structure of the 263 wild emmer accessions was estimated based on 11,393 SNP markers. The delta k was plotted against the number of putative k (=\u0026thinsp;2\u0026ndash;9) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The peak of the line graph was observed at k\u0026thinsp;=\u0026thinsp;2, which indicates that the studied wild emmer population can be divided into two subpopulations as shown in two different colours in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. On the other hand, the PCA analysis also grouped the 263 wild emmer accessions into two clusters, supporting the result of structure analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The phylogenetic tree based on Nearest Neighbour method also divided the germplasm into two clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). MDS_IBS and PCA analysis based on the origin of the studied wild emmer accessions was also conducted (Supplementary Fig.\u0026nbsp;2). In both the cases no specific stratification pattern was observed, which indicates the grouping pattern is not related to geographical distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLinkage Disequilibrium (LD\u003c/b\u003e)\u003c/p\u003e \u003cp\u003eLD triangle plot generated for each chromosome showed low LD blocks; clear strong LD triangle blocks (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1) were visible only for chromosomes 2B, 3B,5A, 5B, 6A, 7A, and 7B (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMarker-trait associations for shoot trait\u003c/h2\u003e \u003cp\u003eGenome-wide association study identified 857 significant (P value at 1.0e-3; -log[P]\u0026thinsp;=\u0026thinsp;3.0) marker-trait associations (MTAs) for the traits in three environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The number of associated MTAs varied for individual traits, which on average explained approximately 7% of the phenotypic variance. To determine the MTAs significance, the threshold was raised to 4.0 (-log[P] = -log [1/11393]\u0026thinsp;=\u0026thinsp;4.0). The MTAs with this threshold were considered highly significant MTAs. MTAs identified in multiple environments were also considered highly robust. A total of 81 highly significant MTAs were identified, mostly for HT, FT, MT, TTN, ETN, BPP, YPP, SW, ST, SA, and T (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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 highly significant MTAs for the 19 traits.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSNP ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-Log(P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-Log(P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-Log(P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eHeading time (HT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB19587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e309954062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB60825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139693372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB2714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e424219181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB56520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e476078236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB35325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e628946362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB74620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602637715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB58299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e668673203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB9063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e636875338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eFlowering time (FT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB67869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e648758891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB44218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e755027845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB2714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e424219181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB35325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e628946362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB74620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602637715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB58299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e668673203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eMaturity time (MT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB50416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183851107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB19587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e309954062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB12290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e780878357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB5392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107779117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB2714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e424219181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB73413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e573292400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB28559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16755288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB74620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602637715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB71856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e454798397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB8215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e690033603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height (PH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB63062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e307265116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eTotal tiller number (TTN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB3715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e648945313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB62774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e666554291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB24354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467644027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWA4622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467796529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB47417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119674222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB4154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e718931217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eEffective tiller number (ETN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB74353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e318231805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB64507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e707443679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB73323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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colname=\"c6\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB23613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356525458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpike length (SpL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB44509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e592872144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFlag leaf area (FLA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB65386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e453692425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB11785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1819300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond leaf area (SLA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB10401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e675765355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eBiomass/plant (BPP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB51019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e442612818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB30769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e435835642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e 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colname=\"c3\"\u003e \u003cp\u003e4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e672925079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB50762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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colname=\"c7\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB62673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e494892488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB57707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e431829169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4049546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThousand kernel weight (TKW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB71993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e622806648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB73662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374558838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSeed length (SL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB72916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eSeed width (SW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWA1608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e477295885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB20856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e477292942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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colname=\"c10\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB73064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e569477550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB67456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59146559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB57449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB51790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e428392074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSeed area (SA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWA1609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e477295885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB71993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e622806648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB57449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139944719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eThresh-ability (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB10078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230076863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB32896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136402000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB2614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111095762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWB51790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e428392074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWA4996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e571700366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: E1, E2 and E3 represent glasshouse trial-2019, glasshouse trial-2020 and field trial-2020, respectively. R\u003csup\u003e2\u003c/sup\u003e indicates phenotypic variance explained by corresponding SNP.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGrowth stage related traits\u003c/h2\u003e \u003cp\u003eOut of the 52 significant MTAs identified for heading time (HT), 9 highly significant MTAs were found on chromosomes 2A, 2B, 4B, 5A, 6A, and 7A (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Fig.\u0026nbsp;4). The phenotypic variance explained (R\u003csup\u003e2\u003c/sup\u003e) by each SNP marker ranged from 4.5 to 11.1%. Among the 9 highly significant MTAs, IWB74620 on 6A was detected in all three environments, which explained 5.2 to 8.0% of phenotypic variance. Besides, IWB19587 on 2A, and IWB2714 and IWB35325 on 5A were found to be associated with HT in multiple environments. For flowering time (FT), only 6 MTAs were highly significant out of 232 detected MTAs. Those MTAs were distributed on 1B, 4B, 5A, 6A and 7A, and explained 6.0% of the phenotypic variance on average. A total of 57 significant MTAs were found for MT, among which 10 highly significant MTAs were distributed on chromosomes 2A, 2B, 4A, 5A, 6A, 7A, and 7B. IWB74620 on 6A was detected in all three environments and explained 3.4 to 5.8% of the phenotypic variances. Two MTAs, IWB2714 on 5A and IWB74620 on 6A, were significantly associated with all growth stage related traits, i.e., HT, FT, and MT (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, IWB35323 on 5A and IWB58299 on 7A were identified for HT and FT, whereas IWB19587 on 2A for HT and MT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBiomass related traits\u003c/h2\u003e \u003cp\u003eThere were 20 significant MTAs for the total tiller number (TTN), of which 6 were highly significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Fig.\u0026nbsp;4) on chromosomes 1B, 5B, 6B, and 7B that explained on average 8.4% of the phenotypic variances. IWB62774 on 1B was detected in two environments, explaining 2.3 to 7.4% of the phenotypic variances. For the effective tiller number (ETN), a total of 63 significant MTAs was identified, of which 6 on chromosome 1B, 4A, 5A, 5B, and 6A were highly significant and explained 2.7 to 8.5% of the phenotypic variance. IWB74353 on 1B, IWB37996 on 5B and IWB23613 on 6A were detected in multiple environments for ETN.\u003c/p\u003e \u003cp\u003eA total of 32 significant MTAs were identified for biomass/plant (BPP) and 9 MTAs on chromosomes 2A, 2B, 3B, 4B, 7A, and 7B were highly significant. IWB3256 on 4B was detected in multiple environments (E1 and E2), explaining an average of 11% of the phenotypic variances. Similarly, IWB57171 on 7A and IWB62673 on 7B were also detected in multiple environments and explained an average of 6.25 and 7.3% of the phenotypic variances, respectively. Very few highly significant MTAs were detected for other biomass related traits, such as one for plant height (PH) on 1B, one for spike length (SpL) on 1B, two for flag leaf area (FLA) on 4B and 6A and one for the second leaf area (SLA) on 5B. No highly significant MTA was found for peduncle length (PL) and shoot angle (SAng).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eYield related traits\u003c/h2\u003e \u003cp\u003eA total of 28 significant MTAs was identified for yield/plant (YPP), among which 5 on chromosome 3B, 5A and 6B were highly significant and explained on average 5.4% of the phenotypic variances (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Fig.\u0026nbsp;4). For seed width (SW), out of 68 significant MTAs, 8 on chromosome 1A, 1B, 4B and 7B were highly significant. Among them, IWA1608 and IWB20856, both on chromosome 1A, were found to be associated with SW in all three environments, particularly in E3, which explained 9.4 and 9.9% phenotypic variances, respectively. A total of 113 significant MTAs were identified for seed thickness (ST) and only 4 were highly significant. Those MTAs were detected on chromosomes 1A, 2B, 4B, and 5A, which explained on average 5.2% of the phenotypic variances. Out of 78 significant MTAs identified for threshability (T), 5 on chromosome 2B, 4A, 5A, and 7A were highly significant. Among them, IWB51790 on 5A was found in all three environments and explained 2.5 to 7.4% of the phenotypic variances. In addition to this, very few highly significant MTAs were identified for other kernel related traits, such as two for thousand kernel weight (TKW) on chromosome 1B, three for seed length (SL) on chromosomes 2B and 4A, and three for seed area (SA) on chromosome 1A, 1B and 7B. Two MTAs had pleiotropic effects, including IWB51790 on 5A for ST and T and IWB57449 on 7B for SW and SA (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePutative candidate gene annotation for above ground traits\u003c/h2\u003e \u003cp\u003eGenes and annotations were searched on the 10 Kb upstream and downstream regions of tagged SNPs for all of the 81 highly significant MTAs in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among them, annotations matched with the corresponding traits according to previous studies were considered putative candidate genes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A total of ten candidate genes were selected for the growth stage related traits from nine MTAs. For example, IWB2714 on chromosome 5A, was identified for heading time (HT), flowering time (FT) and maturity time (MT) in multiple environments and the predicted protein is the RING/U-box superfamily protein. Similarly, another MTA, IWB74620 on 6A, detected for all the growth stage related traits in multiple environments, was predicted with two genes, including the C2 domain containing protein and ABC transporter family protein. Other proteins predicted for growth stage related traits include aconitate hydratase for IWB35325 on 5A, receptor-like protein kinase for IWB58299 on 7A, ankyrin repeat family protein for IWB19587 on 2A, 60 KDa jasmonate induced protein for IWB9063 on 7A, and NAD-specific glutamate dehydrogenase for IWB5392 on 4A. In addition to these, two MTAs (IWB50416 on 2A and IWB8215 on 7B) were detected for MT in multiple environments, which did not have corresponding trait related annotations.\u003c/p\u003e \u003cp\u003eFor the total tiller number (TTN), five genes were predicted for the five associated MTAs. Three were annotated as lysine-specific demethylase for IWB62774 on 1B, F-box domain containing protein for IWA4622 on 5B, and transport inhibitor response protein for IWB24354 on 5B. For the effective tiller number (ETN), four MTAs were selected, of which two MTAs on 4A (IWB64507 and IWB73323) had trait related annotation such as phosphoglucan, and the other two, including IWB37996 on 5B and IWB23613 on 6A did not have proper annotations.\u003c/p\u003e \u003cp\u003eFor biomass/plant (BPP), eight candidate genes were predicted from seven MTAs, among which only four had biomass related annotations, such as RNA-binding protein for IWB51019 on 2A, Myb-like transcription factor family protein for IWB50762 on 7A, F-box family protein IWB62673 on 7B, and histone deacetylase for IWB72437 on 7B. For yield/plant (YPP), four MTAs were selected, among which only one, IWA2892 on 5A, had a proper yield related annotation, methyl-CPG-binding domain. For the other yield related traits (SL, SW, SA, ST, and T), five putative genes were predicted for five MTAs, with two having pleiotropic effect. IWB51790 on 5A for ST and T was annotated as methyl-CPG-binding domain and IWB57449 on 7B for SW and SA was annotated as fasciclin-like arabinogalactan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFavourable alleles and their effects\u003c/h2\u003e \u003cp\u003eThe phenotypic values of each trait were calculated for the two alternative alleles of each of the selected MTA and \u003cem\u003et-test\u003c/em\u003e was conducted to test the significance between the mean values of the two alternative alleles. All selected MTAs for growth stage related traits showed significant differences between alleles, which ranged from 6 to 55 days for heading time (HT), 27 to 62 days for flowering time (FT), and 3 to 41 days for maturity time (MT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For HT, the highest allelic difference was observed for IWB35325 on 5A where heading occurred 55 days earlier in accessions with the \u0026lsquo;A\u0026rsquo; allele compared to accessions with \u0026lsquo;G\u0026rsquo; allele. This indicates that the \u0026lsquo;A\u0026rsquo; allele of IWB35325 was favourable for HT. For FT, IWB74620 on 6A showed the highest allelic difference, where accession with the favourable \u0026lsquo;A\u0026rsquo; allele flowered almost 62 days earlier than the \u0026lsquo;G\u0026rsquo; allele accessions. For MT, the highest allelic difference was found for the IWB8215 on 7B, where accessions with \u0026lsquo;C\u0026rsquo; allele completed the life cycle 41 days earlier than accessions with the other allele.\u003c/p\u003e \u003cp\u003eFor the total tiller number (TTN), four MTAs out of five showed significant allelic variations where the highest difference (3.73 tiller/plant) was observed between A and G alleles of IWA4622 on 5B (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Another MTA on the same chromosome (5B), IWB24354, also showed a significant difference between the alternative alleles and located close to the previous one, which indicates the presence of an outstanding cluster controlling the trait (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: LD triangle plot). For the effective tiller number (ETN), only two closely positioned MTAs on chromosome 4A showed significant allelic variances where the \u0026lsquo;C\u0026rsquo; allele of IWB64507 and the \u0026lsquo;A\u0026rsquo; allele of IWB73323 were the favourable alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On the other hand, for BPP, four MTAs out of seven showed significant differences between the alternative alleles where accession containing the favourable allele showed an average 4.25 g higher BPP compared to the accessions with unfavourable alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The highest difference between the alternative alleles (5.54 g/plant) was observed for IWB50762 on 7A, where \u0026lsquo;T\u0026rsquo; allele showed a higher BPP (18.99g) compared to the \u0026lsquo;C\u0026rsquo; allele (13.44g).\u003c/p\u003e \u003cp\u003eFor the yield per plant (YPP), three out of four MTAs showed significant differences between the alternative alleles. Notably, all of them were situated very close to each other on chromosome 5A, indicating the presence of an outstanding cluster controlling the trait (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The favourable alleles of these three MTAs (IWB57707, IWB41324 and IWA2892) showed an average of 1.15 g higher YPP. For seed width (SW), only one MTA (IWB57449 on 7B) out of two showed a significant difference between the alternative alleles. This MTA is also associated with seed area (SA), which showed a significant allelic difference. In both traits, the \u0026lsquo;A\u0026rsquo; allele was favourable, which had 6% higher SW and 14% higher SA compared to the unfavourable \u0026lsquo;G\u0026rsquo; allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For seed thickness (ST) and threshability (T), all selected MTAs showed significant allelic differences, among which the IWB51790 on 5A was pleiotropic for both traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In both cases the \u0026lsquo;T\u0026rsquo; was favourable, which led to 14% higher ST and 46% lower threshability score as lower T score indicates easy to thresh.\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 highly significant MTAs for the above ground traits, along with the predicted genes and annotations obtained from the \u0026ldquo;GrainGenes\u0026rdquo; browser\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnvironments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlleles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePutative Gene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAnnotations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFunctions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eGrowth stage related traits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHT, FT, MT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB2714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1 (HT, FT, MT), E2 (MT), E3 (HT, FT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC5AG038830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRING/U-box superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePromotes leaf senescence and aging in \u003cem\u003eArabidopsis\u003c/em\u003e (Zhang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHT, FT, MT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIWB74620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eE1 and E2 (HT, FT, MT), E3 (HT, MT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC6AG058260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC2 domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInitiates floral transition in \u003cem\u003eArabidopsis\u003c/em\u003e (Liu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC6AG058330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eABC transporter family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDevelops anther and pollen in rice (Qin et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHT, FT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB35325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1 and E2 (HT, FT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC5AG071920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAconitate hydratase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRegulates flag leaf senescence in wheat (Gregersen and Holm \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHT, FT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB58299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2 (HT, FT), E3 (FT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7AG067460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReceptor-like protein kinase 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInvolves in biological process including senescence (Li et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHT, MT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB19587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1 and E3 (HT, MT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC2AG032420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAnkyrin repeat family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCauses flower senescence in 4 o\u0026rsquo;clock plant (Xu et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB9063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7AG062140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60 kDa jasmonate-induced protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInvolves in leaf senescence in barley (Rustgi et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB50416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC2AG026800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUndescribed protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB5392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC4AG013330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNAD-specific glutamate dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInvolves in leaf senescence in wheat (Kar and Feierabend \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB8215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1, E2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7BG070240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChaperone protein DnaJ 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnrelated function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"16\" rowspan=\"17\"\u003e \u003cp\u003e\u003cb\u003eBiomass related traits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB3715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC1BG068390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRibosomal protein S10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnrelated function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB62774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC1BG072460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLysine-specific demethylase 5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStem elongation (EnsemblePlants)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWA4622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC5BG046110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF-box domain containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAssociated with tiller bud activity in rice (Ishikawa et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB24354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC5BG046090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTransport inhibitor response 1-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInvolves in early flowering and tillering capacity in \u003cem\u003eArabidopsis\u003c/em\u003e (Qiao et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB4154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7BG064220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSulfate transporter 91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnrelated function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB64507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC4AG065850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhosphoglucan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCauses higher yield and biomass including increased tiller number (Ral et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB73323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC4AG065851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhosphoglucan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSame above\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB37996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC5BG056150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB23613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC6AG031240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDNA mismatch repair protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnrelated function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB51019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC2AG039400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRNA-binding protein 8A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRegulates photosynthesis in \u003cem\u003eArabidopsis\u003c/em\u003e (Bach-Pages et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB30769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC2BG045480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB3256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC4BG064100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUndescribed protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB50762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7AG057990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMyb-like transcription factor family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpregulates photosynthesis gene expression (Zhou and Li \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIWB57171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7AG054970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDihydroflavonol 4-reductase-like1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnrelated function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7AG054980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHistone-lysine N-methyltransferase ATXR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnrelated function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB62673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7BG045140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF-box family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRegulates stomatal closure in wheat (An et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB72437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7BG042690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHistone deacetylase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInvolves in plant growth and development (Ma et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u003cb\u003eYield related traits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB57707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC5AG039490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCellulose synthase like E1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnrelated function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB41324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC5AG039480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRibosomal RNA small subunit methyltransferase B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnrelated function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWA2892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC5AG039080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMethyl-CPG-binding domain 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGrain development in wheat controlling yield (Shi et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB19933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC6BG001120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePleckstrin homology (PH) domain-containing protein /lipid-binding START domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnrelated function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB3123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1, E2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC4AG042370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePlastid-lipid associated protein PAP/fibrillin family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eResponsible for seed development (Gangurde et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB73064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC4BG048580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTetratricopeptide repeat (TPR)-like superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOverexpression of TPR is associated with seed development in rice (Jain et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSW, SA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB57449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2 (SA), E3 (SW, SA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC7BG017530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFASCICLIN-like arabinogalactan 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInvolves in microspore development of \u003cem\u003eArabidopsis\u003c/em\u003e (Johnson et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST, T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB51790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1 (ST, T), E2 and E3 (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC5AG039080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMethyl-CPG-binding domain 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGrain development in wheat controlling yield (Shi et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIWB36023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE1, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIDC4BG010410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInosine-5'-monophosphate dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eExpressed differentially in response to stress (Pradhan et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: HT: heading time; FT: flowering time; MT: maturity time; TTN: total tiller number; ETN: effective tiller number; BPP: biomass/plant; YPP: yield/plant; SL: seed length; SW: seed width; ST: seed thickness; SA: seed area; T: threshability. E1, E2 and E3 represent glasshouse trial-2019, glasshouse trial-2020 and field trial-2020, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWild emmer is an important genetic resource for wheat improvement as it possesses novel gene pool with large diversity. However, only a few traits of wild emmer have been explored until now. Particularly, the agronomic shoot traits in wild emmer germplasm were almost untapped due to sophisticated morphological characteristics. Although the majority of wild emmer accessions are characterized with undesirable phenotypes, some accessions, for example the out layers do contain desirable phenotypes for several traits, such as early flowering, high tillering capacity, huge biomass, well developed root system, a higher number of lateral roots and many more (Unpublished data). In the current study, GWAS was conducted on 19 shoot morphological traits and grain protein content (GPC) in wild emmer accessions for identifying novel alleles or genes for those traits, which can be potentially used for improving modern wheat.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLinkage Disequilibrium (LD)\u003c/h2\u003e \u003cp\u003eLinkage disequilibrium (LD) between the marker and causal gene determines the precision of GWAS (Sela et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A small LD extent is ideal for precise mapping with high resolution of marker trait association (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sela et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). There are two ways to visualize LD: decay plot and LD triangle plot (Abdurakhmonov and Abdukarimov \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The decay plot gives a rough estimate of the extent of LD throughout the genome or individual chromosomes. For example, LD for hexaploid wheat can be calculated considering the whole genome or separately for each A, B and D genome or for the 21 individual chromosomes. Nevertheless, the LD range is not uniform throughout the genome, even not within the same genome. Rather, it varies from chromosome to chromosome. Even within the same chromosome, the LD could vary in different regions. In this study, we presented our result in LD triangle form so that the LD block for the individual region of interest can be observed clearly (Supplementary Fig.\u0026nbsp;3). Moreover, the extent of LD from the decay plot and the size of the red block from the LD triangle plot complements each other as both are proportional to recombination (Abdurakhmonov and Abdukarimov \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This study observed a very small red block for all chromosomes. This indicates a high recombination rate in the studied wild germplasm leading to higher diversity. The small LD for wild emmer was also observed previously (Sela et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), even smaller than the cultivated emmer wheat (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, comparatively longer LD was observed for some elite durum and bread wheat (Ain et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Arora et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Turuspekov et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is worth mentioning that the rapid LD decay, i.e., smaller LD, was observed for wild populations in many plant species, including \u003cem\u003eArabidopsis\u003c/em\u003e, barley and \u003cem\u003eAegilops tauschii\u003c/em\u003e (Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Spielmeyer and Richards \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This difference in the extent of LD between wild emmer and modern wheat (durum and bread) can be explained by the breeding and selection process towards modern variety development, which results in the population bottleneck (Sela et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMTAs detected for shoot morphological traits along with putative function\u003c/h2\u003e \u003cp\u003eWhile the number of detected MTAs were quite large (857 for 19 traits) on the first occasion, it was narrowed down to 81 as highly significant MTAs. It was further narrowed to 34 based on the available annotations matched with the corresponding traits.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMTAs for growth stage related traits\u003c/h2\u003e \u003cp\u003eAfter narrowing down, a total of 9 MTAs was identified for the life cycle related traits, i.e., heading time (HT), flowering time (FT) and maturity time (MT) on chromosomes 2A, 4A, 5A, 6A, 7A, and 7B (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Some previous findings suggested that genes/alleles associated with HT, FT and MT were also distributed on chromosome 2A (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), 4A (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sheoran et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), 5A (Fowler et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sheoran et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), 6A (Fowler et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and 7A (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, IWB2714 was mapped around 424 Mb on 5A for HT, FT and MT, indicating strong genetic associations between these traits. An MTA on 5A chromosome (around 412 Mb) was also reported previously for heading and maturity time, which supports the findings of this study (Sheoran et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is well known that the heading and flowering of wheat are largely controlled by vernalization (\u003cem\u003eVrn\u003c/em\u003e), photoperiod (\u003cem\u003ePpd\u003c/em\u003e), and earliness (\u003cem\u003eEps\u003c/em\u003e) genes, and \u003cem\u003eVRN1\u003c/em\u003e, \u003cem\u003eVRN2\u003c/em\u003e and \u003cem\u003eVRN3\u003c/em\u003e are the three major alleles for vernalization on chromosome groups 5 and 7 (Quraishi et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, \u003cem\u003eVrn1-5A\u003c/em\u003e (TRIDC5A057030; 5A: 582 Mb) positioned 160 Mb far away from this MTA and might not be the candidate gene for the growth stage phenotype. Rather, another MTA IWB35325 detected for HT and FT was much closer (within 50 Mb) to the region reported for \u003cem\u003eVrn1-5A\u003c/em\u003e. However, IWB2714 was annotated as RING/U-box superfamily protein. The gene encoding this protein, \u003cem\u003eAtUSR1\u003c/em\u003e, also promoted leaf senescence and aging in the model plant \u003cem\u003eArabidopsis\u003c/em\u003e through the jasmonic acid signalling pathway (Zhang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, IWB35325 was annotated as aconitate hydratase_1, of which the upregulation in cytoplasm resulted in flag leaf senescence in wheat (Gregersen and Holm \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother MTA, IWB74620, was identified at 602 Mb on 6A, within the previously reported QTL (599\u0026ndash;603 Mb) range for flowering time (Corsi et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The IWB74620 was predicted for two genes annotation, of the C2 domain containing protein and ABC transporter family protein, respectively. C2 domains cooperated in initiating floral transition in \u003cem\u003eArabidopsis\u003c/em\u003e, which is strong evidence of its involvement in regulating flowering time (Liu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). Similarly, the ABC transporter protein, ABCG15, was necessary for anther and pollen development in rice, indicating its relation to flowering time determination (Qin et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Another MTA IWB58299 on 7A was detected for HT and FT, which was annotated as receptor like protein kinase - an important cell surface receptor, and involved in multiple biological processes, including leaf senescence in \u003cem\u003eArabidopsis\u003c/em\u003e (Li et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). Two more MTAs on 7A (IWB9063) and 4A (IWB5392), detected for HT and MT, respectively, were annotated as 60 Kda jasmonate induced protein and NAD specific glutamate dehydrogenase, respectively. Both were reported to be involved in barley leaf senescence (Rustgi et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and wheat (Kar and Feierabend \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, all these MTAs can be considered candidates for functional studies. Particularly, IWB2714 on 5A and IWB74620 on 6A, detected for all growth stage traits in all environments with significant allelic effects, can be considered as strong candidates. Moreover, the newly identified MTAs for MT, IWB50416 on 2A and IWB8215 on 7B, also deserve further investigation to determine their roles in wheat aging or senescence.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMTAs for biomass related traits\u003c/h2\u003e \u003cp\u003eAt a high significant level, five MTAs were identified for total tiller number (TTN) on chromosomes 1B, 5B and 7B and four for effective tiller number (ETN) on chromosomes 4A, 5B, and 6A. Previous studies on wheat also reported genes/alleles for tiller number on chromosome 1B (Chen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), 5B (Chen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muhammad et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and 4A (Guo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Two MTAs detected on 5B, IWA4622 and IWB24354, were annotated as F-box domain containing protein and transport inhibitor response like protein, respectively. The functions of three F-box proteins have been described in rice, among which D3 (dwarf 3) F-box proteins are associated with tiller bud activity that ultimately influence tillering capacity (Ishikawa et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In \u003cem\u003eArabidopsis\u003c/em\u003e, a transport inhibitor response like protein - \u0026lsquo;siR109944\u0026rsquo; showed an increased tillering capacity and exhibited early flowering when overexpressed in transgenic lines (Qiao et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The MTAs detected for ETN on 4A (IWB64507 and IWB73323) were annotated as phosphoglucan. Downregulation of one type of phosphoglucan, named GWD (glucan water dikinase), results in higher yield and biomass with an increased tiller number (Ral et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These findings indicate that the MTAs in this study are truly associated with tillering capacity. Besides, two MTAs for TTN, IWB3715 on 1B and IWB4154 on 7B did not show tillering related annotation. Still, they had higher -log(P) values and clearly showed significant allelic differences in phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which indicates that the genes predicted for these two MTAs could be strong candidates. Notably, two neighbouring MTAs for TTN (IWA4622 and IWB24354) were within a strong LD block on 5B (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting a gene nearby controlling the trait. Similarly, two MTAs for ETN showing significant allelic differences, IWB64507 and IWB73323, were positioned closely on 4A, which also suggests the presence of a gene nearby controlling the traits.\u003c/p\u003e \u003cp\u003eSeven MTAs were detected for biomass/plant (BPP) on 2A, 2B, 4B, 7A and 7B. According to previous studies, significant genes/alleles for biomass were also mapped on chromosome 2A (Guo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), 7A (Khadka et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rehman Arif et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tricker et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and 7B (Guo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rehman Arif et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schmidt et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tricker et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The MTA detected on 2A, IWB51019, was annotated as RNA binding protein, which is important in regulating photosynthesis in \u003cem\u003eArabidopsis\u003c/em\u003e, as related to biomass (Bach-Pages et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another MTA for BPP, IWB50762 on 7A, was annotated as a Myb-like transcription factor family protein, which is also responsible for increasing photosynthesis through up-regulating photosynthetic gene expression (Zhou and Li \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, two more MTAs for BPP on 7B, IWB62673 and IWB72437, were annotated as F-box family protein and histone deacetylase, respectively. The F-box family protein, TaFBA1 from wheat, negatively controls stomatal closure, ultimately affecting photosynthesis. Hence biomass is also affected (An et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); whereas histone deacetylase plays a key role in plant growth and development in \u003cem\u003eArabidopsis\u003c/em\u003e, rice, and maize (Ma et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Three other MTAs have also been selected with high-log (P) values. Still, the predicted genes close to them did not have a trait related annotation, indicating that they are novel for biomass, specially IWB57171 on 7A is promising as it showed a significant allelic effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMTAs for yield related traits\u003c/h2\u003e \u003cp\u003eThere were nine significant MTAs for yield related traits, including yield/plant (YPP), seed length (SL), seed width (SW), seed area (SA), seed thickness (ST) and threshability (T). Out of four MTAs identified for YPP three were located on 5A. Previous studies also reported genes/alleles for wheat yield on 5A (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tricker et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Within those four MTAs, only one (IWA2892) had trait related annotation such as methyl CPG binding domain. A gene encoding this protein in wheat, \u003cem\u003eTaMBD6\u003c/em\u003e, was found to be involved in the process of grain development (Shi et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Two other MTAs (IWB41324 and IWB57707) did not have yield related annotation. Still, they demonstrated significant allelic differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which indicates genes close to these two MTAs have not been reported before for this trait. It is worth mentioning that all three MTAs on 5A for YPP were positioned in a strong LD block (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which suggests that genes within this LD block cumulatively control the yield in wild emmer.\u003c/p\u003e \u003cp\u003eOne MTA identified for SL, IWB3123 on 4A, was annotated as a plastid lipid associated protein (PAP), previously reported as a responsible protein for seed development (Gangurde et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Two MTAs were identified for SW, among which IWB57449 on 7B had pleiotropic effect on SA, which indicates a genetic association between these two traits. IWB57449 related gene was annotated as fasciclin like arabinogalactan (FLA), which was previously reported to be involved in the shoot development of \u003cem\u003eArabidopsis\u003c/em\u003e. Particularly, FLA3 is responsible for microspore development, assumed to influence grain characteristics (Johnson et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Another MTA for SW, IWB73064, was detected on 4B, which agrees with a previous study (Liu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This MTA was annotated as tetratricopeptide repeat (TPR) like superfamily protein with one F-box domain. The overexpression of F-box protein encoding gene in rice is associated with seed development (Jain et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Similarly, another MTA (IWB51790 on 5A) showed pleiotropic effects for ST and T, which was annotated as the methyl-CPG-binding domain. One MTA (IWB36023) on 4B was detected for ST and predicted as inosine-5-monophosphate dehydrogenase, which was detected for grain number, spike fertility, and thousand grain weight of wheat (Pradhan et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Overall, all MTAs for the yield related traits demonstrated highly significant allelic effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Particularly, two MTAs, IWB51790 on 5A and IWB57449 on 7B, were detected in multiple environments with pleiotropic effects and highly significant allelic effects. Therefore, these two MTAs can be considered as strong candidates for future functional validation studies.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWild emmer wheat has been explored earlier for grain quality and biotic, and abiotic stress related traits. This study is the first attempt to explore shoot morphological traits using the GWAS approach. The study identified 857 significant MTAs, among which 81 were highly significant. The most robust MTAs belong to growth stage related traits, total and effective tiller number, biomass per plant, yield per plant, seed width, thickness, leaf area, and threshability. These traits can be of great importance for breeding purposes. For example, the favourable alleles of significant MTAs of life cycle related traits can reduce the total life span, which is one of the main targets in the breeding industry. Wild emmer can potentially be used as parental lines in breeding or to identify novel genes/alleles for modern wheat improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge State Agricultural Biotechnology Centre (SABC) for providing laboratory and glasshouse facilities and Department of Primary Industries and Regional Development (DPIRD) for providing field trial facilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by Murdoch University, Australia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest:\u0026nbsp;\u003c/strong\u003eThe Authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement: Shanjida Rahman\u0026nbsp;\u003c/strong\u003econducted experimental trials, collected data, performed data analysis, and prepared the first draft. \u003cstrong\u003eShahidul Islam\u0026nbsp;\u003c/strong\u003esourced plant material, conceptualised experimental design, provided supervision, and edited the manuscript. \u003cstrong\u003ePenghao Wang\u003c/strong\u003e helped in genomic data curation and association analysis. \u003cstrong\u003eDarshan Sharma\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003ehelped in genomic data production and edited the manuscript. \u003cstrong\u003eMirza Dowla\u003c/strong\u003e assisted in genomic data production. \u003cstrong\u003eEviatar Nevo\u0026nbsp;\u003c/strong\u003eprovided plant material from Israel. \u003cstrong\u003eJingjuan Zhang\u003c/strong\u003e assisted in association analysis and edited the manuscript. \u003cstrong\u003eWujun Ma\u0026nbsp;\u003c/strong\u003eprovided supervision, managed funding, and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e \u003cem\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdurakhmonov IY, Abdukarimov A (2008) Application of association mapping to understanding the genetic diversity of plant germplasm resources. 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The plant genome 1:5\u0026ndash;20\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":"Wild emmer wheat, GWAS, morphological traits, 90K SNP","lastPublishedDoi":"10.21203/rs.3.rs-3036278/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3036278/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWild emmer wheat (WEW) played a central role in wheat evolution. During the long process of evolution, rapid alteration and sporadic genomic changes occurred in wheat resulting gene modifications and loss to some extent. WEW contains numerous genes that are absent in modern wheat gene pool, which might be useful for improving wheat. But, there is a lack of investigation in exploring genotype to phenotype associations in WEW. This study conducted genome wide association study (GWAS) on 19 shoot morphological traits and identified the genetic loci associated with several phenotypes from a collection of 263 WEW accessions using 90K SNP (single nucleotide polymorphism). A total of 11,393 SNP markers which passed the data quality screening, were used to conduct the GWAS analysis using a mixed linear model in TASSEL (Trait Analysis by Association, Evolution, and Linkage) software. A total of 857 significant MTAs (marker-trait association) were identified harbouring on all fourteen chromosomes, among which 81 were highly significant. On average, each significant MTA explained approximately 7% of phenotypic variance. The most significant MTAs were for tiller number, biomass, and some of yield related traits such as yield/plant and seed size. Putative candidate genes were also predicted for highly significant MTAs using the bioinformatics platform. The majority of the selected MTAs showed significant differences between alternative alleles for the corresponding phenotypes indicating their potential to be used in the breeding program. The genetic loci, contributing significantly to phenotypic variation, identified from this study will be useful in improving wheat morphological traits.\u003c/p\u003e","manuscriptTitle":"Genetic loci underlying important shoot morphological traits of wild emmer wheat revealed by GWAS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-29 15:03:33","doi":"10.21203/rs.3.rs-3036278/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":"0f2fd041-dcd7-4584-b9f5-afb55763f9ec","owner":[],"postedDate":"June 29th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-21T13:11:03+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-29 15:03:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3036278","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3036278","identity":"rs-3036278","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}