Genetic Diversity and Genome-Wide Association Study for some agronomic traits in durum wheat (Triticum turgidum L.) Using whole genome DArTseq Markers

Genetic Diversity and Genome-Wide Association Study for some agronomic traits in durum wheat (Triticum turgidum L.) Using whole genome DArTseq Markers | 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 Diversity and Genome-Wide Association Study for some agronomic traits in durum wheat ( Triticum turgidum L.) Using whole genome DArTseq Markers Peyman Ebrahimi, Ezzat Karami, Alireza Etminan, Reza Talebi, Reza Mohammadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4237277/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Plant Molecular Biology Reporter → Version 1 posted 7 You are reading this latest preprint version Abstract This research was conducted to study genetic diversity, population structure, linkage disequilibrium, and marker-trait relationship in durum wheat using DArTseq technology. Therefore, 94 durum wheat genotypes were evaluated in the form of augmented design with six repeated controls during the two cropping years, 2016–2017 and 2017–2018. Some Agronomic traits including the number of days to spike emergence, number of days to physiological maturity, plant height, thousand kernel weight, and grain yield were measured and recorded. The DNA samples were processed for DArTseq using the Genotyping by Sequencing (GBS) platform at Diversity Array Technology Pty, Ltd, Australia. The values of polymorphism information content (PIC) of SilicoDArT markers varied from 0.023 to 0.499 with an average of 0.38. The studied durum wheat genotypes were grouped into four separate groups based on the Bayesian model. LD analysis between A and B genomes showed that there is a high number of significant marker pairs (82052) in the B genome compared to the A genome (68885). In general, during two cropping years, 29 markers related to the studied traits were found, and out of these 29 related markers, 19 markers were identified in the first year and 10 markers were identified in the second year. The results revealed that the DArTseq markers are a very powerful tool for evaluating the genetic diversity and population structure of durum wheat genotypes and can be used in genotype screening as breeding parents and marker-assisted selection in breeding programs. Association mapping DArTseq technology Durum wheat Polymorphism information content Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Among cereal crops, wheat is one of the most grown crops that is essential for the human diet due to providing carbohydrates, proteins, zinc, calcium, fiber, and energy (Mehrabi et al. 2020 ). Durum wheat ( Triticum turgidum L. Var. Durum ) is a tetraploid species with an AABB genome that has been created from hybridization between two wild species Aegilops speltoides Tausch (B genome) and Triticum urartu Ghandilyan (A genome) (Giunta et al. 2019 ). This crop is well-adapted to various climatic conditions, and it’s mainly cultivated in the Mediterranean region, North Africa, Northern plains, Southwestern USA, and Southern Europe. However, it has an important role in supplying food to local people in the Mediterranean region, which produces more than 50% of the world's durum wheat production (Mérida-García et al. 2020 ). Hence, detailed genetic information on important agronomic traits and grain yield of durum wheat is required as a basis in breeding programs (Mantovani et al. 2008 ). Investigation of genetic diversity among plant populations is an important component of genetics and plant breeding programs. Indeed, the study of phenotypic and genotypic variation in plant genetic materials provides various insights for better utilizing undiscovered features that can be used to improve crop productivity and its adaptation to a wide range of environmental conditions (Mehrabi et al. 2020 ). Population genetic structure analysis and evaluation of the level of genetic diversity in durum wheat have a long history and so far numerous researches have been done using different markers from agro-morphological to molecular markers (Etminan et al. 2018 ). Characterization of genetic diversity using phenotypic markers is often not completely successful due to environmental influences on them, whereas molecular markers disclose genetic similarities in a better context without interference from environmental factors. Hence, molecular markers can provide complete information regarding genetic diversity and population structure in the targeted plant populations (Pour-Aboughadareh et al. 2017 ). Several types of genetic markers, such as microsatellites, start codon targeted polymorphism (Scot), sequence-tagged sites (STS), CAAT-box derived polymorphism (CBDP), diverse array technology (DArT), and single-nucleotide polymorphisms (SNP) have been used for evaluating the genetic diversity and population structure analyses in durum wheat e.g. (Mantovani et al. 2008 ; Pozniak et al. 2012 ; Hu et al. 2015 ; Etminan et al. 2016 , 2018 ; Baloch et al. 2017 ; Mehrabi et al. 2020 ; Mérida-García et al. 2020 ; Sansaloni et al. 2020 ). Of these, the SNP and DArT are identified as higher throughput molecular markers. These techniques are the most common polymorphism among plant materials (Deschamps and Campbell 2010 ). SNPs show the most frequent type of DNA polymorphism markers that therefore can provide a high density of genetic markers near a target locus (Batley and Edwards 2007 ). Hence, they are useful DNA-based markers to use in genetic studies. Besides, DArT is another high-throughput genotyping platform based on hybridizing DNA to microarrays (Li et al. 2002 ). The main advantage of this technique is that it does not require prior sequence information of test individuals. The provision of high-quality dominant markers with a cost- and time-competitive trade-off is another important advantage usage of the DArT technique in genetic studies (Li et al. 2002 ). In general, the accessibility of DArT and SNP genotyping techniques would facilitate the genetic analysis and the application of marker-assisted selection in breeding pragmas (Trebbi et al. 2011 ; Mehrabi et al. 2020 ). One of the most efficient approaches currently used for the analysis of quantitative agronomic traits and quality features is association mapping or AM analysis (Flint-Garcia et al. 2003 ). This method is based on the conception that each quantitative trait that has entered a population will still be linked to the genetic background of the evolutionary ancestor (Gibson and Muse 2009 ). Hence, AM analysis can discover specific functional genetic alleles or loci associated with phenotypic variation in a trait to simplify the correlation between phenotypic and genome sequence polymorphisms (Ranieri 2015 ). In other words, this analysis is a powerful approach to discovering the relationship between phenotypic variations and genome polymorphisms in natural germplasm collections (Mehrabi et al. 2020 ). As a promising approach, several advantages such as much finer mapping resolution, providing broader genomic region coverage, and minimum confidence intervals of the detected loci have distinguished this approach compared to classical linkage analysis (Zhu et al. 2008 ). Albeit, many studies have represented the genetic diversity and AM analysis in durum wheat germplasm e.g. (Mantovani et al. 2008 ; Hu et al. 2015 ; Mwadzingeni et al. 2017 ; Fayaz et al. 2019 ; Mehrabi et al. 2020 ; Sansaloni et al. 2020 ), there are few studies on genetic diversity and AM analysis using SNP and DArT markers together in durum wheat e.g.(Baloch et al. 2017 ; Mérida-García et al. 2020 ). Hence, the main objectives of this study were (i) to the analysis of genetic diversity, genetic population structure, and linkage disequilibrium (LD) in a set of durum wheat genotypes, and (ii) to dissection associations between the several agronomic characters with the SNP and DArT markers. Materials and Methods Genetic materials Ninety-six durum wheat genotypes including 92 breeding lines along with 4 local cultivars (as the check genotypes) were investigated. All plant genetic materials were provided by the Dryland Agricultural Research Institute (DARI), Kermanshah, Iran. Additional information about the pedigree of investigated genotypes is presented in Table S1 . Phenotypic assay The field experiment was performed at the Sara-rood Dryland Agricultural Research Institute, Kermanshah, Iran (Latitude 34°20ʹN, Longitude 47°17ʹE, and Altitude 1351 m above sea level) during the 2017–2018 and 2018–2019 cropping seasons. The weather condition of the genotype evaluation site is shown in Fig. 1 . In each cropping season, all genotypes were planted in an augmented design with 5 blocks and 4 repeated check genotypes. Sowing and crop management were done based on experts’ advice. The plot size was six rows, 3 m in length, spaced 20 cm apart. Grain sowing was done manually. During plant growth and development, five agro-morphological traits were recorded based on the standard system of the National Gene Bank of Tunisia (NGBT). The measured traits were: (1) the number of days-to-heading, (2) the number of days-to-physiological maturities, (3) plant height, (4) thousand kernel weight, and (5) grain yield. After adjusting means for measured traits, Variance analysis on morphological data was done based on the Best Linear Unbiased Estimation model(BLUE) using the MIXED procedure of the SAS-STAT statistical package. DNA Extraction Ten grains from each genotype were sown in plastic pots under controlled conditions at the research greenhouse. The DNA of 94 durum wheat genotypes was extracted by the CTAB method with a few changes from fresh leaves(Doyle and Doyle 1990 ). To check the absence of breakage in the extracted DNA molecules, a standard 0.8% agarose gel was used. In this method, 5 microliters of the extracted DNA solution were stained and loaded with a dye, and gel electrophoresis was performed with a current of 75 volts for 30 minutes. To determine the quality of DNA, it is possible to compare the pattern and color intensity of the used lambda phage DNA with the position and intensity of the band obtained from the electrophoresed DNA. After determining the concentration of the extracted DNA by the spectrophotometric method in comparison with the control DNA, each of the extracted DNA tubes was diluted to a concentration of 50 ng/µl. Genotyping assay For genetic evaluation in 94 wheat genotypes, the extracted DNA was prepared at a concentration of 50 ng/µl. From each genotype, at least 20 microliters of each sample were loaded into 96 DNA plates. The plates containing the samples were sent to the Diversity Arrays Technology Pty Ltd Center located at the Building 3, Level D, University of Canberra Bruce, ACT 2617, Australia ( https://www.diversityarrays.com ) for genotypic evaluation by Sililco DArT and SNP markers. DArT markers were first filtered in terms of unsuitable parameters such as minor allele frequency > 2%, repeatability > 90%, and unread rate > 90%. Finally, 7882 Silico DArT and 5455 SNP markers were yielded for further analysis. The obtained data are of two categories: Sililco DArT markers based on the presence and absence of a gene array sequence in each sample. Another category of SNP markers is based on the presence or absence of a mutation in a gene region. Sililco DArT markers are dominant markers and SNP markers are common markers(Alam et al. 2018 ). Genetic analysis The amount of polymorphism information content (PIC) for SilicoDArT markers was calculated by Power Marker V.3.25 software(Powell et al. 1996 ). Analysis of population genetic structure (Population Structure) was read using STRUCTURE HARVESTER software. The number of sub-populations resulting from the population analysis by SilicoDArT markers data was determined by the number of K, and based on this, the genetic information between the sub-populations and their graphic plot were extracted from the population software(Evanno et al. 2005 ). The amount of linkage disequilibrium (LD) of molecular data (SilicoDArT and SNP) was calculated using TASSEL software and its graphical analysis was done in the R environment using the GAPIT package (Lipka et al., 2012). Finally, Association mapping was calculated separately by TASSEL and GAPIT software and compared to ensure their correctness. In both software, the FarmCPU method was calculated using PCA, KINSHIP MATRIX, and Q matrix data. Markers that had a significant level (P < 0.001) were considered correlated markers with each of the phenotypic data. Due to the high number of markers used, therefore, to determine markers with higher efficiency, Q-Q-Plot analysis was also performed simultaneously with FarmCPU and markers that had Log10 (P) greater than 3 were selected as markers with very high correlation. To identify the candidate genes, the physical location of each marker was searched in the IWGSC Refseq v1.0 genome, and if a gene is found 5–10 kb above the desired location, it will be considered correlated with the desired marker. Results Genetic diversity with pheno-morphological markers The results of variance analysis using the BLUE & BLUP method and descriptive statistics are presented in Table 1, separately for the two years of the experiment (E1 and E2, respectively). According to the results of this analysis, it was found that the genotypic effect was significant for all traits in both years (except for seed yield in the first year). The estimated genotypic variance for each of the measured traits showed that in both crop years, the highest amount of variation was related to grain yield. In addition, the coefficient of variation for grain yield was estimated more than other traits. The highest and lowest heritability in the first year belonged to plant height and grain yield traits, which were 0.93 and 0.33, respectively. In the second year, grain yield was the lowest with 0.45, and thousand grain weight was the highest heritability with 0.91(Table 1). The GT charts shown separately in Fig. 2 for both experiment years. Based on the first year's data, 52.92% of the total genetic diversity among genotypes was justified by the first two components. By justifying the polygon diagram drawn in this bi-plot, genotypes No. 3, 4, 38, 77, 24, 43, and 86 are located at the polygon's vertices. Therefore, it can be said that genotype No. 43 had the highest value in terms of thousand kernel weight, and genotypes No. 3 and 4 had the highest value in terms of plant height. Also, the highest amount of grain yield belonged to genotype No. 86 (Fig. 2A). The bi-plot drawn based on the obtained data from the second year of the experiment showed that the first two components accounted for 33.22 and 26.53 % of the total variation related to studied traits among genotypes, respectively (Fig. 2B). Based on the second year's data, genotypes No. 18, 14, 91, 44, 72, 41, 45, 47, and 65 were placed in the vertices of polygons. As shown in Fig.2B can be seen, genotype 72 was superior to other genotypes in terms of thousand kernel weight. On the other hand, the best genotypes in terms of plant height and the number of days to physiological maturity were genotypes No. 47 and 65. Genotype No. 41 was also identified as the latest genotype. In terms of grain yield, genotypes No. 9, 11, and 86 had better potential than other genotypes. Molecular diversity by SilicoDArT markers The SilicoDArT SNP markers with very high genome coverage were used to study genome diversity in a panel consisting of 92 durum wheat genotypes, which is a valuable and powerful tool for GWAS analysis in the future. Also, these germplasms will be valuable resources for extracting alleles and selecting genotypes that give resistance genes to various biotic and abiotic stresses through linkage mapping in the whole genome. Candidate SilicoDArT and SNP markers can be used to establish marker-trait association (MTA) concerning specific genes or large QTLs controlling valuable traits in durum wheat. The information (data) of these markers was estimated by polymorphism information content (PIC). Polymorphism information content (PIC) is used to provide information about the variation of a gene or segment of DNA in a population to indicate evolutionary pressure on an allele and mutations at a gene locus that may have occurred over some time. When the scoring of a marker is (0) and (1) with ratios of 50% to 50%, the maximum PIC value is calculated as 0.5. In this research, a large number (7882 and 8948) of high-quality SilicoDArT and SNP polymorphic markers were filtered out of 62269 and 54543 markers, respectively, and used for the analysis of genetic diversity and population structure in a set of 92 durum wheat genotypes. The average PIC values for all SilicoDArT markers were equal to 0.38, which indicates the high efficiency of these markers for measuring the genomic diversity of durum wheat (Table 2). In general, the distribution of PIC values among the markers was asymmetric and tended towards values higher than the average, so about 60% of the markers showed PIC above the average (Fig.S1). The average content of polymorphic information in SNP markers was equal to 0.25. The lowest value of PIC was observed in the chromosomal group (Chr2A) with a value of 0.235 and the highest value in the chromosomal group (Chr6A) and (Chr5B) was 0.261. The distribution of PIC values for SilicoDArT and SNP markers is shown in Fig. S1 and Fig.S2, respectively. The distribution of PIC values among markers was almost non-uniform, and more than 3000 SilicoDArT markers had PIC values close to 0.5. About 75% of SilicoDArT markers had PIC values greater than 0.3. In addition to the PIC values some other quality parameters, such as the call rate and the reproducibility of each marker within the panel of examined durum wheat genotypes, were also estimated. The average reproducibility in both SilicoDArT and SNP markers was more than 0.98. The call rate average in SilicoDArT and SNP markers were equal to 0.92 and 0.98, respectively (Table 2). Among 14 linkage groups, the distribution frequency of SNP markers was higher than SilicoDArT markers. The average distribution of SilicoDArT markers in each chromosome was equal to 563, and the same index in SNP markers was equal to 639 in each chromosome. The lowest number of identified SNP markers was observed in the linkage group (Chr4A) and (Chr6A) and the highest number of SNP markers was observed in the chromosomal group (Chr2B). Also, the Chr7B linkage group showed the highest markers with an average of 1.14 SilicoDArT markers per Mbp. Kinship coefficients between pairs of genotypes Based on the kinship coefficients matrix, the investigated genotypes were divided into 3 groups for gene data. Also, the results showed that about 60% of the kinship coefficients between pairs of genotypes were between zero (except zero) and 0.1 and about 39% of the values were between 0.1 (except 0.1) and 0.5. Only about one percent of the coefficients had values greater than 0.5. Therefore, the results of this analysis indicated that there is very little kinship in the studied genotypes (Fig.3). Bayesian clustering According to the results, K and Delta K statistics were extracted, and two-dimensional diagrams were drawn that clearly showed the curve at the maximum of K=4 (Fig.S3). Therefore, according to the obtained results, durum wheat genotypes were grouped into four separate subpopulations based on the Bayesian model (Fig. 4). The four subpopulations showed relatively high genetic diversity, which ranged from 0.02 in a subpopulation (POP4) to 0.34 in a subpopulation (POP3). Net Nucleotide Distance is a parameter to measure genetic diversity among populations that revealed the highest (0.270) and lowest (0.07) genetic distance between POP2 and POP4 subpopulations and POP1 and POP2 subpopulations (Table 3). Analysis of linkage disequilibrium (LD) The linkage disequilibrium index was evaluated using 605212 pairs of DArTseq markers with specific map locations along 14 durum wheat chromosomes. Pairwise linkage disequilibrium values were estimated using the square of allelic frequency correlations (r 2 ) between markers. To identify differences in intra-chromosomal linkage disequilibrium, the average values of r 2 between pairs of markers are divided into five groups based on the genetic distance between them: markers with very high linkage (Distance less than 5 cM), Contiguous markers (with a distance of 5-10 cM), markers with medium continuity (with a distance of 10-12 cM), weakly connected markers (with a distance of 20-50 cM) and independent markers (with a distance of more than 50 cM). The evaluation of intra-chromosomal linkage disequilibrium showed that the linkage disequilibrium decreases with increasing genetic distance. Analysis of linkage disequilibrium was also investigated at the genome level. To show the extent of linkage disequilibrium at the level of the genome, the average reduction in disequilibrium was obtained by plotting intra-chromosomal r 2 values against the genetic distance between markers (Fig.5). The amount of LD within the chromosomes based on both SilicoDArT and SNP markers for both durum wheat genomes (A and B) is shown in Table 4. The amount of LD in both types of markers was very wide, in such a way that 605,212 pairs of significant markers were observed in the entire population. LD analysis between the A and B genomes revealed that there are a high number of significant marker pairs (82052) in the B genome compared to the A genome (68885). The average r2 values for the whole population were equal to 0.14, whereas about A and B genomes were equal to 0.12 and 0.11, respectively (Table 4). A graphic analysis of the distribution of LD markers is shown in Fig.5. In general, 15 kb of the genome was covered in LD. The highest amount of linkage disequilibrium was observed in chromosome 4B. Some of the markers are shown on this chromosome (Fig.6). Genome-Wide Association Study The correlation analysis was performed using markers with a frequency of more than ten percent, and the P-value statistic was considered with 1000 permutations. Also, the lowest P-value was used as the basis for selecting the correlated markers. The distribution of markers was studied based on the coefficient of determination of the marker in the regression model. The coefficient of determination (r 2 ) is the proportion of the phenotypic variance accounted for by the QTL for each locus. The association mapping of days-to-heading, day-to-physiological maturity, plant height, thousand kernel weight, and grain yield of durum wheat was performed using genotypic and phenotypic data. Association mapping analysis for each trait was done based on FarmCPU. To determine the significant markers associated with the studied traits (MATs), QQPlot analysis was also performed, and finally, the markers with -log 10(p) were selected. The Manhattan plots are shown based on all measured traits for two cropping years in Fig.7 and Fig.8, respectively. The full report of significant associations of markers related to all traits in both two years of the experiment is presented in Tables 5 and 6. The results of a genome-wide association study by the FarmCPU method in 2017 showed that 19 markers had a significant association with the studied traits (Table 5). The results of association mapping for studied traits in the first year of the experiment are summarized as follows: Two significant relationships were identified for the numbers of days to heading by 1703829 and 1087984 markers located on chromosomes 6B and 7A, respectively. The correlation of 3940462 markers on chromosome 4A with days to maturity was significant. Ten significant relationships were identified between plant height and DArT markers. These markers had chromosomal locations 3A, 3B, 4B, 5A, 5B, 6A, 6B, and 7B. Four significant associated markers with thousand-grain weight were identified (3570140, 992437, 3534094, and 3533907) on chromosomes 7A, 3A, 5B, and 3B, respectively. The effective trait of Grain yield had three significant relationships with 1108172, 4989009, and 1233550 markers located on chromosomal positions 3A, 2B, and 1B, respectively. The results of genome-wide association analysis by the FarmCPU method in 2018 indicated that ten markers had a significant association with the studied traits (Table 6). The summary of the results was as follows: Association mapping for the numbers of days-to-heading revealed two significant markers (3935863 and 1211191) with the chromosomal locus 1A and 5B, respectively. Three markers (3064874, 1034732, and 1025860) with gene loci (794315843, 673038094, and 666991274) on chromosomes chr3B, chr4A, and chr4A respectively, had a significant relationship with the numbers of day to physiological maturity. The relationship of plant height was significant with the marker of 1057654 with gene locus 691163974 on chromosome chr7A. This is even though ten significant associations were identified for plant height in the previous year. The trait of a thousand-grain weight had a significant relationship with 3025786 and 981221 markers. These markers with gene loci 609888794 and 684637596 are located on chr6A and chr3B chromosomes, respectively. Two significant associations were identified between grain yield and DArT markers. The locus of these markers is located on chromosomes 6A and 7A, respectively. In general, according to the results, the same marker (1057654) was identified for grain yield and plant height in the second experiment year. This marker with genetic locus 691163974 is located on chromosome 7A. By comparing the significant marker-trait relationships as well as the durum wheat consensus map, the position of the marker can be identified and checked by comparing them with physical maps. If the marker distance is more than +10 or -10 cM that position can be identified as a new QTL. Based on this, 29 quantitative positions were identified and confirmed in this study. Among the identified loci, 9 loci were not previously recorded in Durum's Consensus map (Table 7). Discussion Durum wheat ( Triticum turgidum ssp. durum) in Iran and other major producer countries in the world, such as Canada, Mexico, the United States of America, Australia, Italy, Turkey, et cetera. is mainly grown under rain-fed conditions, so due to environmental stresses, especially drought stress, mostly has a low grain yield (Beres et al. 2020). The initial loss of durum wheat’s genetic diversity during the domestication process is estimated at 84%. Therefore, as a consequence of this event, kinds of wheat (bread and durum) were domesticated, which has narrowed their genetic base and diversity, currently (Zeven 2000 ).One of the primary goals of any breeding program is to identify and select the desired genotypes in terms of various traits(Mohammadi et al. 2014 ). To achieve this goal, the traits used should be clearly defined in terms of quantity and quality, so that in combination with important economic traits, they can be used to maximize the Grain yield of the selected genotypes. In this regard, several statistical methods, including correlation analysis, multiple regression analysis, and path coefficient analysis, have been proposed and widely used by experts and breeders. In the meantime, one of the most important statistical methods introduced to achieve this goal is genotype-trait graphical analysis (GT), which is obtained through analysis into principal components(Yan 2001 ). In this method, the bi-plot used is obtained through the first two components extracted through the analysis of eigenvalues on the desired attribute data in several environments (year, place, or a combination of year and place). Although this method was introduced to analyze multi-environment experiments, it can be used for any type of data that has a two-way structure(Yan 2001 ). Generally, according to Yan and Rajcan ( 2002 ), the desired genotypes identified through this analysis can be used as parents in cross-breeding programs or directly to produce commercial cultivars. The best linear unbiased estimation (BLUE & BLUP) analysis revealed that the investigated durum wheat genotypes in this research have a high genetic diversity in grain yield and other agronomy traits (Table 1 ). Also, the different responses of each genotype in terms of measured traits to different environmental conditions (year) revealed the existence of the genotype × environment interaction effect. However, a more accurate evaluation of the selected genotypes to grain yield and other traits is necessary for their use in crossbreeding programs or to check their stability and adaptability to different regions. Graphical analysis of genotype-trait (GT) indicated genotypes 24, 80, and 83 were superior to other genotypes in terms of studied traits (Fig. 2 ). Therefore, it's made possible to use them in breeding programs for the improvement of several characters simultaneously. Considering that the bi-plots showed all the available changes did not justify, these predictions may not accurately reflect the observed numbers. However, the predictions are highly close to reality, so it is possible to select genotypes with superior values compared to previously. Yan and Kang ( 2002 ) in soybean, Yan and Frégeau-Reid ( 2008 ) in oats, Dehghani et al. ( 2008 ) in winter rapeseed, and Rahmati et al. ( 2020 ) in cantaloupe have applied this method to evaluate, compare and select superior cultivars and indicated that this method in addition to showing the relationship between different traits graphically, can make the visual comparison between genotypes very possible. The average SNP and Silico-DArT PIC values (were 0.252 and 0.383, respectively) in the recent study were higher than the PIC value reported by Ren et al. ( 2013 ) using SNP markers in the global collection of 150 durum wheat genotypes (it was equal to 0.18888). Moragues et al.( 2007) investigated the genetic diversity of 63 indigenous populations of durum wheat from Mediterranean countries using AFLP and SSR markers, the average PIC values for AFLP and SSR markers were 0.24 and 0.70, respectively. Sequence reading rate (Call Rate) is the percentage of valid scores among all possible scores for a marker, while reproducibility is measured as the percentage of scoring repeatability for repeated samples. Therefore, both of these parameters were estimated to determine the quality of each marker within the panel of durum wheat genotypes in this research (Table 2). Although the combination of restriction enzymes as well as the amount of diversity in the representative panel is different from other plant species; the proportion of technical replicate assay pairs for markers score and the success of reading the marker sequence across the samples were high and comparable with previous applications of SilicoDArTs in other plant species (Grzebelus et al. 2014 ; Ndjiondjop et al. 2017 ; O’Connor et al. 2019 ). The genomic coverage of SilicoDArT and SNP markers was generally homogenous for most of chromosomes. The highest number of SilicoDArT and SNP markers (853, 818) were observed in Chr7B and Chr2B linkage groups, respectively. Also, chromosome size covered by these markers were highly similar and its highest and lowest value were observed in the Chr3B and the Chr1A linkage groups, respectively (Table 2) which is similar to previous studies in durum wheat using high-performance GBS (Baloch et al. 2017 ). Based on the Kinship matrix information, the studied genotypes were divided into 3 groups. Relative kinship among individuals in the population can lead to the identification of false associations between markers and traits and therefore it should be taken into consideration in relational mapping studies. The value of the matrix of kinship coefficients can vary from 3 to -3, as much as its average tends to be zero or less, it indicates a very high diversity in the population. Genotypes with high kinship coefficients are highly likely to have a common ancestry (Salgotra et al., 2015). The results of population structure analysis based on the Bayesian model put the studied genotypes into four groups. Since some clusters showed a mixture of durum wheat genotypes, it can be concluded that most probably the same parental lines were used in the breeding programs of these genotypes. The existence of groups and subgroups in large populations can be reasons such as different geographical origins of genotypes, natural or human selection, genetic drift, etc. (Flint-Garcia et al., 2003 ). Genome-wide Association study is the method that determines the QTL's location based on linkage disequilibrium (Gupta and Varshney 2005). Therefore, for association mapping, in addition to the composition of the population structure, the extent of linkage disequilibrium in the genome has fundamental importance (Al-Maskri et al. 2012). The results revealed that linkage disequilibrium varies from chromosome to chromosome. In our research, the highest amount of linkage disequilibrium was observed for markers with a distance of less than 5 cM on chromosome 4B, which indicates that linkage is one of the most effective forces that cause disequilibrium in the population. Previous studies have demonstrated that linkage disequilibrium varies along the chromosome and from chromosome to chromosome (Chao et al. 2010; Hao et al. 2011). The results also showed that increasing the distance between the markers does not completely remove the imbalance. This is due to the presence of other effective factors such as population structure, genetic drift, migration, selection, and mutation on linkage disequilibrium (Reich and Lander 2001). In the present study, the marker-trait association was investigated using the FarmCPU model. In general, 29 significant "marker-trait" associations were found during two cropping years, and out of these 29 related markers, 19 markers were identified in the first cropping year and 10 markers were identified in the second cropping year. Among the identified markers, there were 11 markers related to plant height. One of the most promising approaches to improving traits is the introduction of new alleles. As a first step, allele extraction approaches can be performed in different ways and with different sets of plant germplasm. Therefore, performing multiple trials in different environments and the phenotypic analysis of the studied germplasm can identify associated markers for a genetic description of complex traits. In addition, genetic relationships can be used to choose the desirable parents for breeding programs, if the genetic pedigree data is not available. The information produced here can be used to breeding programs tailored to regional and local needs. The presence of many SilicoDArT and SNP markers, the cost-effectiveness, and the high relatively polymorphism information content (PIC) have provided excellent aspects for widespread screening of the whole genome for genetic diversity, linkage disequilibrium studies, and genome-wide association analysis purposes. Conclusion Genetic diversity is the basis of each breeding program. Therefore, it is expected that the comprehensive knowledge about the genetic diversity of durum wheat from various eco-geographic regions will have a significant impact on the protection and use of desirable germplasms. This can help breeders in designing new approaches to achieve profitable diversity in breeding programs. The study of genetic diversity subordination from geographical and climatic diversity indicates the possible adaptation of plant germplasms with different habitats and environments. MTAs are the key elements to detecting genomic regions related to durum wheat morpho-phenologic traits. The current experiment found 29 highly significant MTAs under two consecutive cropping year. The markers detected would be useful genomic sources for cloning and fine mapping of underlying genes, and for conducting gene introgression and marker-based selection in durum wheat under rain fed condition. Further research attempts are needed for validating the markers detected in the current project using a larger durum wheat population. Declarations Acknowledgments: We would like to express our gratitude to the Islamic Azad University of Sanandaj branch and the Dryland Agricultural Research Institute, Sararud, Kermanshah, for their cooperation in carrying out the present research as best as possible. Author’s contribution: A. Etminan conceived and the study, P. Ebrahimi and R. Mohammadi conducted the experiments, R. Talebi analyzed the data, E. Karami wrote the manuscript, all authors read and approved the final version. Conflict of Interest: The authors declare that they have no conflict of interest. Declaration of Funding: This project did not have a financial supporter . Data Availability Statement: Data sharing is not applicable as no new data were generated or analyzed during this study References Alam M, Neal J, O’Connor K, et al (2018) Ultra-high-throughput DArTseq-based silicoDArT and SNP markers for genomic studies in macadamia. PLoS One 13:e0203465. https://doi.org/10.1371/JOURNAL.PONE.0203465 Baloch FS, Alsaleh A, Shahid MQ, et al (2017) A whole genome DArTseq and SNP analysis for genetic diversity assessment in durum wheat from central fertile crescent. PLoS One 12:. https://doi.org/10.1371/journal.pone.0167821 Batley J, Edwards D (2007) SNP Applications in Plants. In: Association Mapping in Plants. Springer, New York, NY, pp 95–102 Dehghani H, Omidi H, Sabaghnia N (2008) Graphic Analysis of Trait Relations of Rapeseed Using the Biplot Method. Agron J 100:1443–1449. https://doi.org/10.2134/AGRONJ2007.0275 Deschamps S, Campbell MA (2010) Utilization of next-generation sequencing platforms in plant genomics and genetic variant discovery. Mol Breed 25:553–570. https://doi.org/10.1007/S11032-009-9357-9 Doyle J., Doyle J. (1990) “Isolation of Plant DNA from Fresh Tissue,.” Focus (Madison) 12:13–15 Etminan A, Pour-Aboughadareh A, Mohammadi R, et al (2018) Applicability of CAAT box-derived polymorphism (CBDP) markers for analysis of genetic diversity in durum wheat. Cereal Res Commun 46:1–9. https://doi.org/10.1556/0806.45.2017.054 Etminan A, Pour-Aboughadareh A, Mohammadi R, et al (2016) Applicability of start codon targeted (SCoT) and inter-simple sequence repeat (ISSR) markers for genetic diversity analysis in durum wheat genotypes Applicability of start codon targeted (SCoT) and inter-simple sequence repeat (ISSR) markers for genetic d. Biotechnol Biotechnol Equip 30:1075–1081. https://doi.org/10.1080/13102818.2016.1228478 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620. https://doi.org/10.1111/J.1365-294X.2005.02553.X Fayaz F, Aghaee Sarbarzeh M, Talebi R, Azadi A (2019) Genetic Diversity and Molecular Characterization of Iranian Durum Wheat Landraces (Triticum turgidum durum (Desf.) Husn.) Using DArT Markers. Biochem Genet 57:98–116. https://doi.org/10.1007/S10528-018-9877-2 Flint-Garcia SA, Thornsberry JM, Edward IV SB (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374. https://doi.org/10.1146/ANNUREV.ARPLANT.54.031902.134907 Gibson G, Muse S. (2009) A Primer of Genome Science - Greg Gibson; Spencer V. Muse - Oxford University Press. Oxford University Press Giunta F, Pruneddu G, Motzo R (2019) Grain yield and grain protein of old and modern durum wheat cultivars grown under different cropping systems. F Crop Res 230:107–120. https://doi.org/10.1016/J.FCR.2018.10.012 Grzebelus D, Iorizzo M, Senalik D, et al (2014) Diversity, genetic mapping, and signatures of domestication in the carrot (Daucus carota L.) genome, as revealed by Diversity Arrays Technology (DArT) markers. Mol Breed 33:625–637. https://doi.org/10.1007/S11032-013-9979-9/FIGURES/3 Hu X, Ren J, Ren X, et al (2015) Association of Agronomic Traits with SNP Markers in Durum Wheat (Triticum turgidum L. durum (Desf.)). PLoS One 10:e0130854. https://doi.org/10.1371/JOURNAL.PONE.0130854 Li YC, Korol AB, Fahima T, et al (2002) Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Mol Ecol 11:2453–2465. https://doi.org/10.1046/J.1365-294X.2002.01643.X Mantovani P, Maccaferri M, Sanguineti MC, et al (2008) An integrated DArT-SSR linkage map of durum wheat. Mol Breed 22:629–648. https://doi.org/10.1007/S11032-008-9205-3 Mehrabi A., Pour-Aboughadareh A., Mansouri S, Hosseini A. (2020) Genome-wide association analysis of root system architecture features and agronomic traits in durum wheat | Enhanced Reader. Mol Breed 40–55 Mérida-García R, Bentley AR, Gálvez S, et al (2020) Mapping Agronomic and Quality Traits in Elite Durum Wheat Lines under Differing Water Regimes. Agron 2020, Vol 10, Page 144 10:144. https://doi.org/10.3390/AGRONOMY10010144 Mohammadi R, Dehghani H, Karimzadeh G (2014) Graphic analysis of trait relations of cantaloupe using the Biplot method . J Plant Prod Res 21: Moragues M, Moralejo M, Sorrells ME, Royo C (2007) Dispersal of durum wheat [Triticum turgidum L. ssp. turgidum convar. durum (Desf.) MacKey] landraces across the Mediterranean basin assessed by AFLPs and microsatellites. Genet Resour Crop Evol 54:1133–1144. https://doi.org/10.1007/S10722-006-9005-8 Mwadzingeni L, Shimelis H, Rees DJG, Tsilo TJ (2017) Genome-wide association analysis of agronomic traits in wheat under drought-stressed and non-stressed conditions. PLoS One 12:e0171692. https://doi.org/10.1371/JOURNAL.PONE.0171692 Ndjiondjop MN, Semagn K, Gouda AC, et al (2017) Genetic variation and population structure of Oryza glaberrima and development of a mini-core collection using DArTseq. Front Plant Sci 8:1748. https://doi.org/10.3389/FPLS.2017.01748/BIBTEX NGBT NGBT descriptors for durum wheat 1.0. https://www.genesys-pgr.org/descriptorlists/b6544d11-be40-4638-be35-1b3f5349c5c9. Accessed 26 Mar 2023 O’Connor K, Kilian A, Hayes B, et al (2019) Population structure, genetic diversity and linkage disequilibrium in a macadamia breeding population using SNP and silicoDArT markers. Tree Genet Genomes 15:. https://doi.org/10.1007/S11295-019-1331-Z Pour-Aboughadareh A, Mahmoudi M, Moghaddam M, et al (2017) Agro-morphological and molecular variability in Triticum boeoticum accessions from Zagros Mountains, Iran. Genet Resour Crop Evol 64:545–556. https://doi.org/10.1007/S10722-016-0381-4 Powell W, Morgante M, Andre C, et al (1996) The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed 2:225–238. https://doi.org/10.1007/BF00564200/METRICS Pozniak CJ, Clarke JM, Clarke FR (2012) Potential for detection of marker-trait associations in durum wheat using unbalanced, historical phenotypic datasets. Mol Breed 30:1537–1550. https://doi.org/10.1007/S11032-012-9737-4 Rahmati M, Hoseinpour T, Ahmadi A (2020) Assessment of interrelationships between agronomic traits of wheat genotypes under rain-fed conditions using double and triple biplots of genotype, trait and yield. Iran J Dryl Agric 9:1–20 Ranieri R (2015) Geography of the durum wheat crop -. In: Pastaria Open Fields. Collecchio,Italy Ren J, Sun D, Chen L, et al (2013) Genetic Diversity Revealed by Single Nucleotide Polymorphism Markers in a Worldwide Germplasm Collection of Durum Wheat. Int J Mol Sci 14:7061. https://doi.org/10.3390/IJMS14047061 Sansaloni C, Franco J, Santos B, et al (2020) Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints. Nat Commun 2020 111 11:1–12. https://doi.org/10.1038/s41467-020-18404-w Trebbi D, Maccaferri M, de Heer P, et al (2011) High-throughput SNP discovery and genotyping in durum wheat (Triticum durum Desf.). Theor Appl Genet 123:555–569. https://doi.org/10.1007/S00122-011-1607-7 Yan W (2001) GGEbiplot—A Windows Application for Graphical Analysis of Multienvironment Trial Data and Other Types of Two-Way Data. Agron J 93:1111–1118. https://doi.org/10.2134/AGRONJ2001.9351111X Yan W, Frégeau-Reid J (2008) Breeding Line Selection Based on Multiple Traits. Crop Sci 48:417–423. https://doi.org/10.2135/CROPSCI2007.05.0254 Yan W, Kang MS (2002) GGE Biplot Analysis : A Graphical Tool for Breeders, Geneticists, and Agronomists. GGE Biplot Anal. https://doi.org/10.1201/9781420040371 Yan W, Rajcan I (2002) Biplot Analysis of Test Sites and Trait Relations of Soybean in Ontario. Crop Sci 42:11–20. https://doi.org/10.2135/CROPSCI2002.1100 Zeven AC (2000) Traditional maintenance breeding of landraces: 1. Data by crop. Euphytica 116:65–85. https://doi.org/10.1023/A:1004089816030/METRICS Zhu C, Gore M, Buckler ES, Yu J (2008) Status and Prospects of Association Mapping in Plants. Plant Genome 1:5–20. https://doi.org/10.3835/PLANTGENOME2008.02.0089 Tables Table 1. Analysis of variance for measured traits in investigated durum wheat genotypes based on BLUE models Traits GY TKW PH DM DF Statistics Environments 0.33 0.91 0.93 0.90 0.90 Heritability First Year(Env1) 26734.65 12.06 54.16 2.02 6.27 Genotypic Variance 323321.56 6.83 24.10 1.27 4.23 Residual 2275.8 31.3 62.8 171.3 109.2 MIN 5375.8 54.7 112.7 183.7 132.2 MAX 4071.91 44.55 80.20 175.65 124.32 Mean 306.80 3.99 7.80 1.69 3.04 LSD 13.72 5.92 6.12 0.64 1.66 Coefficients of Variation 680.91 4.44 8.74 1.92 3.47 Standard Deviation 0.64 0.0004 0.00001 0.00001 0.006 P-Value 0.45 0.91 0.81 0.73 0.76 Heritability Second Year(Env2) 30107 14.18 35 3.83 1.89 Genotypic Variance 402432.41 8.34 50.05 8.59 3.54 Residual 1441.5 29.2 67.5 161.8 118.7 MIN 4802.0 61.7 122.5 181.5 131.9 MAX 3472.40 39.04 89.65 167.89 125.03 Mean 1090.54 4.38 8.73 3.14 2.14 LSD 18.34 7.43 8.00 1.75 1.50 Coefficients of Variation 759.34 5.31 9.87 4.24 2.68 Standard Deviation 0.03 0.04 0.00002 0.0001 0.0002 P-Value Table 2. SilicoDArT and SNP Markers Distribution on Durum Wheat Genome, Polymorphism Information Content (PIC), Call Rate, and Average of Reproducibility Linkage Groups No. DArT Markers No. SNP Markers Chr Size(kbp) Silico DArT Chr Size (kbp) SNP DArT Call Rate (mean) SNP Call Rate (mean) DArT One Ratio (mean) SNP One Ratio(mean) SNP PIC (mean) DArT PIC (mean) AV. of SNP Reproducibility Av. of DArT Reproducibility SNP position Chr1A 300 582 589293.8 591612.294 0.926811 0.983 0.53253 0.423 0.257 0.392783 0.984 0.987783 29.42 Chr2A 574 695 779665.3 780499.087 0.928376 0.982 0.531615 0.396 0.235 0.378022 0.981 0.987819 31.5 Chr3A 454 685 750610.4 750170.104 0.926852 0.983 0.528354 0.411 0.251 0.371944 0.981 0.987748 31.08 Chr4A 489 445 742808.8 743151.257 0.929105 0.984 0.507459 0.434 0.253 0.382376 0.982 0.986216 28.09 Chr5A 436 726 708977.0 708511.745 0.928146 0.982 0.520776 0.421 0.259 0.375594 0.981 0.98842 29.66 Chr6A 456 445 616320.1 617227.837 0.928312 0.982 0.510363 0.468 0.261 0.392759 0.981 0.987314 30.57 Chr7A 789 750 735262.1 736431.403 0.929175 0.981 0.510735 0.383 0.241 0.384203 0.981 0.986959 31.84 Ch1B 681 591 688589.5 688256.279 0.92758 0.981 0.533268 0.427 0.244 0.385003 0.98 0.987277 27.97 Chr2B 759 818 800769.3 800987.529 0.927394 0.98 0.531767 0.41 0.243 0.393895 0.982 0.987843 30.64 Chr3B 582 772 829200.1 829329.004 0.922085 0.983 0.564573 0.391 0.241 0.369268 0.984 0.9861 30.27 Chr4B 333 456 671754.7 671277.981 0.924849 0.984 0.538444 0.42 0.259 0.37495 0.98 0.987985 30.11 Chr5B 586 757 708929.4 712833.876 0.923233 0.983 0.566275 0.43 0.261 0.382289 0.982 0.986366 28.4 Chr6B 590 638 720840.0 720898.912 0.926843 0.982 0.534025 0.41 0.241 0.382743 0.981 0.986644 29.69 Chr7B 853 588 749946.8 750031.822 0.92991 0.982 0.515027 0.449 0.282 0.392338 0.978 0.987082 29.8 Sum 7882 8948 - - - - - - - - - - - Mean 563 639 720926.2 721515.7 0.927048 0.98229 0.530372 0.4195 0.252 0.382726 0.9813 0.987254 29.9314286 Group A 499.72 618 703276.78 703943.4 0.928111 0.982 0.520261 0.419 0.251 0.382525 0.981 0.987465 30.30 Group B 626.3 660 738575.7 739087.9 0.925985 0.982 0.540483 0.419 0.253 0.382927 0.981 0.987042 29.55 Table 3. Genetic divergence among (Net Nucleotide Distance) and within (expected heterozygosity) population, the proportion of membership, and the mean value of Fst observed from the study of population structure of 92 durum wheat genotypes using SilicoDArT markers. Population Net Nucleotide Distance Expected Heterozygosity % 0f membership Mean fixation index (Fst) POP2 POP3 POP4 POP1 0.070 0.084 0.247 0.20 46 0.41 POP2 0.085 0.270 0.20 26 0.46 POP3 0.249 0.34 19.5 0.21 POP4 0.02 8.5 0.95 Table 4. Characteristics of linkage disequilibrium in durum wheat genome based on DArTseq markers Genome Total pairs Significant pairs (P≤0.001) % of Significant pairs Mean r 2 Pairs in complete LD Critical r 2 value A-genome 269891 68885 24.7 0.13 1337 0.12 B-genome 335322 82052 24.4 0.12 1146 0.11 Whole-genome 605212 148937 24.6 0.13 2438 0.14 Table 5 Associated markers with studied traits in 94 durum wheat genotypes using linkage analysis in the 2017 cropping year. Traits Marker Linkage Group Locus P- value Maf Consensus map(cM) Days to Flowering 1703829 Chr6B 4863112 0.000075 0.309783 3.34 1087984 Chr7A 42448091 0.000083 0.141304 3.34 Days to Maturity 3940462 Chr4A 606757033 0.000087 0.943478 54.26 Plant Height 1228105 Chr5A 4994308 0.000000 0.288043 5.94 3946051 Chr3A 697061451 0.000000 0.13587 - 4004958 Chr5B 648289673 0.000001 0.130435 - 4405812 Chr5A 109937709 0.000002 0.369565 - 1068144 Chr6A 613084911 0.000013 0.70652 98.22 1093423 Chr6B 33427529 0.000026 0.375 13.14 2275869 Chr3B 19232431 0.000034 0.483696 17.44 5410721 Chr7B 625701808 0.000041 0.065217 81.31 1671913 Chr3A 497297304 0.000043 0.233696 - 1090315 Chr4B 629706516 0.000067 0.413043 - Thousand-grain weight 3570140 Chr7A 71591095 0.000031 0.211957 149.67 992437 Chr3A 541246518 0.000041 0.391304 68.59 3534094 Chr5B 27137735 0.000044 0.255435 161.82 3533907 Chr3B 739839965 0.000056 0.461957 - Grain Yield 1108172 Chr3A 652905402 0.000071 0.059783 65.11 4989009 Chr2B 106775933 0.000021 0.179348 - 1233550 Chr1B 558575664 0.000033 0.190217 - Table 6 Correlated markers with studied traits in 94 durum wheat genotypes using linkage analysis in the 2018 cropping year. Traits Marker Linkage Group Locus P- value Maf Consensus map(cM) Days to Heading 3935863 Chr1A 30133162 0.000032 0.086957 35.82 1211191 Chr5B 700372980 0.000037 0.065217 137.12 Days to Maturity 3064874 Chr3B 794315843 0.000006 0.097826 133.21 1034732 Chr4A 673038094 0.0000028 0.25 96.08 1025860 Chr4A 666991274 0.000078 0.304348 96.16 Plant Height 1057654 Chr7A 691163974 0.000051 0.097826 129.67 Thousand Grain Weight 3025786 Chr6A 609888794 0.000044 0.070652 98.83 981221 Chr3B 684637594 0.000090 0.0157609 81.17 Grain Yield 1057654 Chr7A 691163974 0.000016 0.097826 129.67 7918245 Chr6A 611855760 0.000040 0.141304 - Table 7 Comparison of gene locations identified by Consensus map of durum wheat Traits Marker Linkage Group cM Days to Heading 1703829 Chr6B 3.34 1087984 Chr7A 3.34 3935863 Chr1A 35.82 1211191 Chr5B 137.12 Days to Maturity 3940462 Chr4A 54.26 3064874 Chr3B 133.21 1034732 Chr4A 96.08 1025860 Chr4A 96.16 Plant Height 1228105 Chr5A 5.94 3946051 Chr3A - 4004958 Chr5B - 4405812 Chr5A - 1068144 Chr6A 98.22 1093423 Chr6B 13.14 2275869 Chr3B 17.44 5410721 Chr7B 81.31 1671913 Chr3A - 1090315 Chr4B - 1057654 Chr7A 129.67 Thousand-grain weight 3570140 Chr7A 149.67 992437 Chr3A 68.59 3534094 Chr5B 161.82 3533907 Chr3B - 3025786 Chr6A 98.83 981221 Chr3B 81.17 Grain Yield 1108172 Chr3A 65.11 4989009 Chr2B - 1233550 Chr1B - 1057654 Chr7A 129.67 7918245 Chr6A - Additional Declarations No competing interests reported. Supplementary Files FigureSupplementary.docx TableS1.docx Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Plant Molecular Biology Reporter → Version 1 posted Editorial decision: Revision requested 30 Nov, 2024 Reviews received at journal 12 Jun, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers invited by journal 29 May, 2024 Submission checks completed at journal 09 Apr, 2024 Editor assigned by journal 09 Apr, 2024 First submitted to journal 08 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4237277","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290942211,"identity":"c1d62d1e-4ef9-4194-bb21-13c0371a8074","order_by":0,"name":"Peyman Ebrahimi","email":"","orcid":"","institution":"Islamic Azad University of Kermanshah","correspondingAuthor":false,"prefix":"","firstName":"Peyman","middleName":"","lastName":"Ebrahimi","suffix":""},{"id":290942212,"identity":"5a06c634-ca46-40b9-960d-722d788b7867","order_by":1,"name":"Ezzat Karami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBADOTb2BgZm4tUfYDAw5uM5QKKWxHkSCURq4ec//vDzx7Y/xmySbww/F1TYMPC3dyfg1SLZcCBZ4mCbgRybdI6x9IwzaQwSZ85uwKvF4GDDAZAWY6AWA2netsMMBhK5+LXYH2Zs/gHUktgmecb4N1FaDNiY2STAWiR4zIizReIMG5vFmXPGxmw8aWXWPGfSeAj6hb//+OMbFWVycvLthzff5qmwkeNv78WvBQlwGIBIHmKVgwD7A1JUj4JRMApGwQgCAD+mQekYAgCFAAAAAElFTkSuQmCC","orcid":"","institution":"Islamic Azad University Sanandaj Branch","correspondingAuthor":true,"prefix":"","firstName":"Ezzat","middleName":"","lastName":"Karami","suffix":""},{"id":290942213,"identity":"a5d5f3ff-8638-4b62-9695-96a1c252dab5","order_by":2,"name":"Alireza Etminan","email":"","orcid":"","institution":"Islamic Azad University of Kermanshah","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Etminan","suffix":""},{"id":290942214,"identity":"a125bc09-9bcc-4d93-a8bb-070fca0675f8","order_by":3,"name":"Reza Talebi","email":"","orcid":"","institution":"Islamic Azad University Sanandaj Branch","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Talebi","suffix":""},{"id":290942215,"identity":"5989e0d7-717c-4857-bbab-8de8b3d76f62","order_by":4,"name":"Reza Mohammadi","email":"","orcid":"","institution":"Islamic Azad University of Kermanshah","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Mohammadi","suffix":""}],"badges":[],"createdAt":"2024-04-08 14:48:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4237277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4237277/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11105-025-01559-5","type":"published","date":"2025-03-18T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54806088,"identity":"1b831911-890c-4c5e-90c6-979d5504a17c","added_by":"auto","created_at":"2024-04-17 04:35:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":534773,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of temperature and precipitation indices in two consecutive years\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/c507e3506a6588a5131ffe8e.png"},{"id":54806099,"identity":"3172fe22-e68a-4a29-8bec-8b0156a59292","added_by":"auto","created_at":"2024-04-17 04:35:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":554681,"visible":true,"origin":"","legend":"\u003cp\u003ePolygon bi-plot drawn based on the first two components for the measured traits in the first (A) and second (B) years of the experiment. DHE, DMA, PLH, TKW, and YLD represent the number of days until the emergence of the spike, the number of days to the physiological maturity, the plant height, the thousand kernel weight, and the grain yield, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/7cb444e9f56c31e46cdcb41a.png"},{"id":54806095,"identity":"95166055-29f1-44a4-b076-5c393d02d5a0","added_by":"auto","created_at":"2024-04-17 04:35:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44015,"visible":true,"origin":"","legend":"\u003cp\u003eGrouping of genotypes based on relative kinship coefficients between pairs of genotypes\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/9841e8b56389eeeab484caf1.png"},{"id":54806092,"identity":"78df7ebd-c788-4993-be31-3e7598f2aeca","added_by":"auto","created_at":"2024-04-17 04:35:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":415518,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the 92 durum wheat genotypes using SilicoDArT and SNP markers in subpopulations according to the structure analysis (\u003cem\u003ek \u003c/em\u003e= 4). The individuals were represented in vertical bars, each color associated with a different group.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/4a7b2419f765c422096c3e0a.png"},{"id":54806100,"identity":"86269ae6-dca7-40d9-af9a-3a6af294fc6e","added_by":"auto","created_at":"2024-04-17 04:35:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":446597,"visible":true,"origin":"","legend":"\u003cp\u003eReduction of linkage disequilibrium at the genome level\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/c876d417c74b89e239f58c46.png"},{"id":54806091,"identity":"6d57dfb7-8c20-48ad-ab75-54182425d213","added_by":"auto","created_at":"2024-04-17 04:35:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1109976,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of some DArTseq markers on chromosome 4B\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/2a84b5a23fc89a23477725b8.png"},{"id":54806102,"identity":"4d47a253-0ec5-4f33-a10c-c470078d8029","added_by":"auto","created_at":"2024-04-17 04:35:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":634463,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot resulting from linkage location along the length of durum wheat chromosomes in the crop year 2017 for the traits of day to flowering (DHE), day to maturity (DMA), plant height (PLH), thousand-grain weight (TKW) and grain yield\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/3fb32674cd660151fb9ca435.png"},{"id":54806085,"identity":"fb5a9485-478a-4d7c-82c3-1bf7a5ab28d8","added_by":"auto","created_at":"2024-04-17 04:35:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":573828,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot resulting from linkage location along the length of durum wheat chromosomes in the 2018 crop year for the traits of day to flowering (DHE), day to maturity (DMA), plant height (PLH), thousand-grain weight (TKW) and grain yield\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/356fe387bd71f7c2869c8cfd.png"},{"id":79120697,"identity":"2a740cf3-eaa3-426c-990f-b07ca36e1688","added_by":"auto","created_at":"2025-03-24 16:11:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6532878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/3d7331f1-6c66-4e55-878a-18e3c1123e3f.pdf"},{"id":54806094,"identity":"c8eac1e3-2d78-482a-982a-6f30fc3aa93a","added_by":"auto","created_at":"2024-04-17 04:35:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":142665,"visible":true,"origin":"","legend":"","description":"","filename":"FigureSupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/5ea56d42acf5095bfc5fb2a9.docx"},{"id":54806079,"identity":"55c2fed2-75d0-4237-89c1-fa69ef99b7aa","added_by":"auto","created_at":"2024-04-17 04:35:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17536,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4237277/v1/101cd98277dc6c7591e3fe5c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGenetic Diversity and Genome-Wide Association Study for some agronomic traits in durum wheat (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTriticum turgidum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e L.) Using whole genome DArTseq Markers\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmong cereal crops, wheat is one of the most grown crops that is essential for the human diet due to providing carbohydrates, proteins, zinc, calcium, fiber, and energy (Mehrabi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Durum wheat (\u003cem\u003eTriticum turgidum\u003c/em\u003e L. Var. \u003cem\u003eDurum\u003c/em\u003e) is a tetraploid species with an AABB genome that has been created from hybridization between two wild species \u003cem\u003eAegilops speltoides\u003c/em\u003e Tausch (B genome) and \u003cem\u003eTriticum urartu\u003c/em\u003e Ghandilyan (A genome) (Giunta et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This crop is well-adapted to various climatic conditions, and it\u0026rsquo;s mainly cultivated in the Mediterranean region, North Africa, Northern plains, Southwestern USA, and Southern Europe. However, it has an important role in supplying food to local people in the Mediterranean region, which produces more than 50% of the world's durum wheat production (M\u0026eacute;rida-Garc\u0026iacute;a et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, detailed genetic information on important agronomic traits and grain yield of durum wheat is required as a basis in breeding programs (Mantovani et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInvestigation of genetic diversity among plant populations is an important component of genetics and plant breeding programs. Indeed, the study of phenotypic and genotypic variation in plant genetic materials provides various insights for better utilizing undiscovered features that can be used to improve crop productivity and its adaptation to a wide range of environmental conditions (Mehrabi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Population genetic structure analysis and evaluation of the level of genetic diversity in durum wheat have a long history and so far numerous researches have been done using different markers from agro-morphological to molecular markers (Etminan et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Characterization of genetic diversity using phenotypic markers is often not completely successful due to environmental influences on them, whereas molecular markers disclose genetic similarities in a better context without interference from environmental factors. Hence, molecular markers can provide complete information regarding genetic diversity and population structure in the targeted plant populations (Pour-Aboughadareh et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral types of genetic markers, such as microsatellites, start codon targeted polymorphism (Scot), sequence-tagged sites (STS), CAAT-box derived polymorphism (CBDP), diverse array technology (DArT), and single-nucleotide polymorphisms (SNP) have been used for evaluating the genetic diversity and population structure analyses in durum wheat e.g. (Mantovani et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pozniak et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Etminan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Baloch et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mehrabi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; M\u0026eacute;rida-Garc\u0026iacute;a et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sansaloni et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Of these, the SNP and DArT are identified as higher throughput molecular markers. These techniques are the most common polymorphism among plant materials (Deschamps and Campbell \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). SNPs show the most frequent type of DNA polymorphism markers that therefore can provide a high density of genetic markers near a target locus (Batley and Edwards \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Hence, they are useful DNA-based markers to use in genetic studies. Besides, DArT is another high-throughput genotyping platform based on hybridizing DNA to microarrays (Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The main advantage of this technique is that it does not require prior sequence information of test individuals. The provision of high-quality dominant markers with a cost- and time-competitive trade-off is another important advantage usage of the DArT technique in genetic studies (Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In general, the accessibility of DArT and SNP genotyping techniques would facilitate the genetic analysis and the application of marker-assisted selection in breeding pragmas (Trebbi et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mehrabi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the most efficient approaches currently used for the analysis of quantitative agronomic traits and quality features is association mapping or AM analysis (Flint-Garcia et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This method is based on the conception that each quantitative trait that has entered a population will still be linked to the genetic background of the evolutionary ancestor (Gibson and Muse \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Hence, AM analysis can discover specific functional genetic alleles or loci associated with phenotypic variation in a trait to simplify the correlation between phenotypic and genome sequence polymorphisms (Ranieri \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In other words, this analysis is a powerful approach to discovering the relationship between phenotypic variations and genome polymorphisms in natural germplasm collections (Mehrabi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a promising approach, several advantages such as much finer mapping resolution, providing broader genomic region coverage, and minimum confidence intervals of the detected loci have distinguished this approach compared to classical linkage analysis (Zhu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Albeit, many studies have represented the genetic diversity and AM analysis in durum wheat germplasm e.g. (Mantovani et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mwadzingeni et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fayaz et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mehrabi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sansaloni et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), there are few studies on genetic diversity and AM analysis using SNP and DArT markers together in durum wheat e.g.(Baloch et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; M\u0026eacute;rida-Garc\u0026iacute;a et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, the main objectives of this study were (i) to the analysis of genetic diversity, genetic population structure, and linkage disequilibrium (LD) in a set of durum wheat genotypes, and (ii) to dissection associations between the several agronomic characters with the SNP and DArT markers.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenetic materials\u003c/h2\u003e \u003cp\u003eNinety-six durum wheat genotypes including 92 breeding lines along with 4 local cultivars (as the check genotypes) were investigated. All plant genetic materials were provided by the Dryland Agricultural Research Institute (DARI), Kermanshah, Iran. Additional information about the pedigree of investigated genotypes is presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic assay\u003c/h2\u003e \u003cp\u003eThe field experiment was performed at the Sara-rood Dryland Agricultural Research Institute, Kermanshah, Iran (Latitude 34\u0026deg;20ʹN, Longitude 47\u0026deg;17ʹE, and Altitude 1351 m above sea level) during the 2017\u0026ndash;2018 and 2018\u0026ndash;2019 cropping seasons. The weather condition of the genotype evaluation site is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In each cropping season, all genotypes were planted in an augmented design with 5 blocks and 4 repeated check genotypes. Sowing and crop management were done based on experts\u0026rsquo; advice. The plot size was six rows, 3 m in length, spaced 20 cm apart. Grain sowing was done manually. During plant growth and development, five agro-morphological traits were recorded based on the standard system of the National Gene Bank of Tunisia (NGBT). The measured traits were: (1) the number of days-to-heading, (2) the number of days-to-physiological maturities, (3) plant height, (4) thousand kernel weight, and (5) grain yield. After adjusting means for measured traits, Variance analysis on morphological data was done based on the Best Linear Unbiased Estimation model(BLUE) using the MIXED procedure of the SAS-STAT statistical package.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA Extraction\u003c/h2\u003e \u003cp\u003eTen grains from each genotype were sown in plastic pots under controlled conditions at the research greenhouse. The DNA of 94 durum wheat genotypes was extracted by the CTAB method with a few changes from fresh leaves(Doyle and Doyle \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). To check the absence of breakage in the extracted DNA molecules, a standard 0.8% agarose gel was used. In this method, 5 microliters of the extracted DNA solution were stained and loaded with a dye, and gel electrophoresis was performed with a current of 75 volts for 30 minutes. To determine the quality of DNA, it is possible to compare the pattern and color intensity of the used lambda phage DNA with the position and intensity of the band obtained from the electrophoresed DNA. After determining the concentration of the extracted DNA by the spectrophotometric method in comparison with the control DNA, each of the extracted DNA tubes was diluted to a concentration of 50 ng/\u0026micro;l.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eGenotyping assay\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eFor genetic evaluation in 94 wheat genotypes, the extracted DNA was prepared at a concentration of 50 ng/\u0026micro;l. From each genotype, at least 20 microliters of each sample were loaded into 96 DNA plates. The plates containing the samples were sent to the Diversity Arrays Technology Pty Ltd Center located at the Building 3, Level D, University of Canberra Bruce, ACT 2617, Australia (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.diversityarrays.com\u003c/span\u003e\u003cspan address=\"https://www.diversityarrays.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for genotypic evaluation by Sililco DArT and SNP markers. DArT markers were first filtered in terms of unsuitable parameters such as minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;2%, repeatability\u0026thinsp;\u0026gt;\u0026thinsp;90%, and unread rate\u0026thinsp;\u0026gt;\u0026thinsp;90%. Finally, 7882 Silico DArT and 5455 SNP markers were yielded for further analysis. The obtained data are of two categories: Sililco DArT markers based on the presence and absence of a gene array sequence in each sample. Another category of SNP markers is based on the presence or absence of a mutation in a gene region. Sililco DArT markers are dominant markers and SNP markers are common markers(Alam et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGenetic analysis\u003c/h2\u003e \u003cp\u003eThe amount of polymorphism information content (PIC) for SilicoDArT markers was calculated by Power Marker \u003csub\u003e\u003cem\u003eV.3.25\u003c/em\u003e\u003c/sub\u003e software(Powell et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Analysis of population genetic structure (Population Structure) was read using STRUCTURE HARVESTER software. The number of sub-populations resulting from the population analysis by SilicoDArT markers data was determined by the number of K, and based on this, the genetic information between the sub-populations and their graphic plot were extracted from the population software(Evanno et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The amount of linkage disequilibrium (LD) of molecular data (SilicoDArT and SNP) was calculated using TASSEL software and its graphical analysis was done in the R environment using the GAPIT package (Lipka et al., 2012). Finally, Association mapping was calculated separately by TASSEL and GAPIT software and compared to ensure their correctness. In both software, the FarmCPU method was calculated using PCA, KINSHIP MATRIX, and Q matrix data. Markers that had a significant level (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were considered correlated markers with each of the phenotypic data. Due to the high number of markers used, therefore, to determine markers with higher efficiency, Q-Q-Plot analysis was also performed simultaneously with FarmCPU and markers that had Log10 (P) greater than 3 were selected as markers with very high correlation. To identify the candidate genes, the physical location of each marker was searched in the IWGSC Refseq v1.0 genome, and if a gene is found 5\u0026ndash;10 kb above the desired location, it will be considered correlated with the desired marker.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGenetic diversity with pheno-morphological markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of variance analysis using the BLUE \u0026amp; BLUP method\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eand descriptive statistics are presented in Table 1, separately for the two years of the experiment (E1 and E2, respectively). According to the results of this analysis, it was found that the genotypic effect was significant for all traits in both years (except for seed yield in the first year). The estimated genotypic variance for each of the measured traits showed that in both crop years, the highest amount of variation was related to grain yield. In addition, the coefficient of variation for grain yield was estimated more than other traits. The highest and lowest heritability in the first year belonged to plant height and grain yield traits, which were 0.93 and 0.33, respectively. In the second year, grain yield was the lowest with 0.45, and thousand grain weight was the highest heritability with 0.91(Table 1). The GT charts shown separately in Fig. 2 for both experiment years. Based on the first year\u0026apos;s data, 52.92% of the total genetic diversity among genotypes was justified by the first two components. By justifying the polygon diagram drawn in this bi-plot, genotypes No. 3, 4, 38, 77, 24, 43, and 86 are located at the polygon\u0026apos;s vertices. Therefore, it can be said that genotype No. 43 had the highest value in terms of thousand kernel weight, and genotypes No. 3 and 4 had the highest value in terms of plant height. Also, the highest amount of grain yield belonged to genotype No. 86 (Fig. 2A). The bi-plot drawn based on the obtained data from the second year of the experiment showed that the first two components accounted for 33.22 and 26.53 % of the total variation related to studied traits among genotypes, respectively (Fig. 2B). Based on the second year\u0026apos;s data, genotypes No. 18, 14, 91, 44, 72, 41, 45, 47, and 65 were placed in the vertices of polygons. As shown in Fig.2B can be seen, genotype 72 was superior to other genotypes in terms of thousand kernel weight. On the other hand, the best genotypes in terms of plant height and the number of days to physiological maturity were genotypes No. 47 and 65. Genotype No. 41 was also identified as the latest genotype. In terms of grain yield, genotypes No. 9, 11, and 86 had better potential than other genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular diversity by SilicoDArT markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SilicoDArT SNP markers with very high genome coverage were used to study genome diversity in a panel consisting of 92 durum wheat genotypes, which is a valuable and powerful tool for GWAS analysis in the future. Also, these germplasms will be valuable resources for extracting alleles and selecting genotypes that give resistance genes to various biotic and abiotic stresses through linkage mapping in the whole genome. Candidate SilicoDArT and SNP markers can be used to establish marker-trait association (MTA) concerning specific genes or large QTLs controlling valuable traits in durum wheat. The information (data) of these markers was estimated by polymorphism information content (PIC). Polymorphism information content (PIC) is used to provide information about the variation of a gene or segment of DNA in a population to indicate evolutionary pressure on an allele and mutations at a gene locus that may have occurred over some time. When the scoring of a marker is (0) and (1) with ratios of 50% to 50%, the maximum PIC value is calculated as 0.5. In this research, a large number (7882 and 8948) of high-quality SilicoDArT and SNP polymorphic markers were filtered out of 62269 and 54543 markers, respectively, and used for the analysis of genetic diversity and population structure in a set of 92 durum wheat genotypes. The average PIC values for all SilicoDArT markers were equal to 0.38, which indicates the high efficiency of these markers for measuring the genomic diversity of durum wheat (Table 2). In general, the distribution of PIC values among the markers was asymmetric and tended towards values higher than the average, so about 60% of the markers showed PIC above the average (Fig.S1). The average content of polymorphic information in SNP markers was equal to 0.25. The lowest value of PIC was observed in the chromosomal group (Chr2A) with a value of 0.235 and the highest value in the chromosomal group (Chr6A) and (Chr5B) was 0.261. The distribution of PIC values for SilicoDArT and SNP markers is shown in Fig. S1 and Fig.S2, respectively. The distribution of PIC values among markers was almost non-uniform, and more than 3000 SilicoDArT markers had PIC values close to 0.5. About 75% of SilicoDArT markers had PIC values greater than 0.3. In addition to the PIC values some other quality parameters, such as the call rate and the reproducibility of each marker within the panel of examined durum wheat genotypes, were also estimated. The average reproducibility in both SilicoDArT and SNP markers was more than 0.98. The call rate average in SilicoDArT and SNP markers were equal to 0.92 and 0.98, respectively (Table 2). Among 14 linkage groups, the distribution frequency of SNP markers was higher than SilicoDArT markers. The average distribution of SilicoDArT markers in each chromosome was equal to 563, and the same index in SNP markers was equal to 639 in each chromosome. The lowest number of identified SNP markers was observed in the linkage group (Chr4A) and (Chr6A) and the highest number of SNP markers was observed in the chromosomal group (Chr2B). Also, the Chr7B linkage group showed the highest markers with an average of 1.14 SilicoDArT markers per Mbp.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKinship coefficients between pairs of genotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the kinship coefficients matrix, the investigated genotypes were divided into 3 groups for gene data. Also, the results showed that about 60% of the kinship coefficients between pairs of genotypes were between zero (except zero) and 0.1 and about 39% of the values were between 0.1 (except 0.1) and 0.5. Only about one percent of the coefficients had values greater than 0.5. Therefore, the results of this analysis indicated that there is very little kinship in the studied genotypes (Fig.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBayesian clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the results, K and Delta K statistics were extracted, and two-dimensional diagrams were drawn that clearly showed the curve at the maximum of K=4 (Fig.S3). Therefore, according to the obtained results, durum wheat genotypes were grouped into four separate subpopulations based on the Bayesian model (Fig. 4). The four subpopulations showed relatively high genetic diversity, which ranged from 0.02 in a subpopulation (POP4) to 0.34 in a subpopulation (POP3). Net Nucleotide Distance is a parameter to measure genetic diversity among populations that revealed the highest (0.270) and lowest (0.07) genetic distance between POP2 and POP4 subpopulations and POP1 and POP2 subpopulations (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of linkage disequilibrium (LD)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe linkage disequilibrium index was evaluated using 605212 pairs of DArTseq markers with specific map locations along 14 durum wheat chromosomes. Pairwise linkage disequilibrium values were estimated using the square of allelic frequency correlations (r\u003csup\u003e2\u003c/sup\u003e) between markers. To identify differences in intra-chromosomal linkage disequilibrium, the average values of r\u003csup\u003e2\u003c/sup\u003e between pairs of markers are divided into five groups based on the genetic distance between them: markers with very high linkage (Distance less than 5 cM), Contiguous markers (with a distance of 5-10 cM), markers with medium continuity (with a distance of 10-12 cM), weakly connected markers (with a distance of 20-50 cM) and independent markers (with a distance of more than 50 cM). The evaluation of intra-chromosomal linkage disequilibrium showed that the linkage disequilibrium decreases with increasing genetic distance. Analysis of linkage disequilibrium was also investigated at the genome level. To show the extent of linkage disequilibrium at the level of the genome, the average reduction in disequilibrium was obtained by plotting intra-chromosomal r\u003csup\u003e2\u003c/sup\u003e values against the genetic distance between markers (Fig.5). The amount of LD within the chromosomes based on both SilicoDArT and SNP markers for both durum wheat genomes (A and B) is shown in Table 4. The amount of LD in both types of markers was very wide, in such a way that 605,212 pairs of significant markers were observed in the entire population. LD analysis between the A and B genomes revealed that there are a high number of significant marker pairs (82052) in the B genome compared to the A genome (68885). The average r2 values for the whole population were equal to 0.14, whereas about A and B genomes were equal to 0.12 and 0.11, respectively (Table 4). A graphic analysis of the distribution of LD markers is shown in Fig.5. In general, 15 kb of the genome was covered in LD. The highest amount of linkage disequilibrium was observed in chromosome 4B. Some of the markers are shown on this chromosome (Fig.6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-Wide Association Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation analysis was performed using markers with a frequency of more than ten percent, and the P-value statistic was considered with 1000 permutations. Also, the lowest P-value was used as the basis for selecting the correlated markers. The distribution of markers was studied based on the coefficient of determination\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eof the marker in the regression model. The coefficient of determination (r\u003csup\u003e2\u003c/sup\u003e) is the proportion of the phenotypic variance accounted for by the QTL for each locus. The association mapping of days-to-heading, day-to-physiological maturity, plant height, thousand kernel weight, and grain yield of durum wheat was performed using genotypic and phenotypic data. Association mapping analysis for each trait was done based on FarmCPU. To determine the significant markers associated with the studied traits (MATs), QQPlot analysis was also performed, and finally, the markers with -log 10(p) were selected. The Manhattan plots are shown based on all measured traits for two cropping years in Fig.7 and Fig.8, respectively. The full report of significant associations of markers related to all traits in both two years of the experiment is presented in Tables 5 and 6. The results of a genome-wide association study by the FarmCPU method in 2017 showed that 19 markers had a significant association with the studied traits (Table 5). The results of association mapping for studied traits in the first year of the experiment are summarized as follows:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eTwo significant relationships were identified for the numbers of days to heading by 1703829 and 1087984 markers located on chromosomes 6B and 7A, respectively.\u003c/li\u003e\n \u003cli\u003eThe correlation of 3940462 markers on chromosome 4A with days to maturity was significant.\u003c/li\u003e\n \u003cli\u003eTen significant relationships were identified between plant height and DArT markers. These markers had chromosomal locations 3A, 3B, 4B, 5A, 5B, 6A, 6B, and 7B.\u003c/li\u003e\n \u003cli\u003eFour significant associated markers with thousand-grain weight were identified (3570140, 992437, 3534094, and 3533907) on chromosomes 7A, 3A, 5B, and 3B, respectively.\u003c/li\u003e\n \u003cli\u003eThe effective trait of Grain yield had three significant relationships with 1108172, 4989009, and 1233550 markers located on chromosomal positions 3A, 2B, and 1B, respectively.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe results of genome-wide association analysis by the FarmCPU method in 2018 indicated that ten markers had a significant association with the studied traits (Table 6). The summary of the results was as follows:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAssociation mapping for the numbers of days-to-heading revealed two significant markers (3935863 and 1211191) with the chromosomal locus 1A and 5B, respectively.\u003c/li\u003e\n \u003cli\u003eThree markers (3064874, 1034732, and 1025860) with gene loci (794315843, 673038094, and 666991274) on chromosomes chr3B, chr4A, and chr4A respectively, had a significant relationship with the numbers of day to physiological maturity.\u003c/li\u003e\n \u003cli\u003eThe relationship of plant height was significant with the marker of 1057654 with gene locus 691163974 on chromosome chr7A. This is even though ten significant associations were identified for plant height in the previous year.\u003c/li\u003e\n \u003cli\u003eThe trait of a thousand-grain weight had a significant relationship with 3025786 and 981221 markers. These markers with gene loci 609888794 and 684637596 are located on chr6A and chr3B chromosomes, respectively.\u003c/li\u003e\n \u003cli\u003eTwo significant associations were identified between grain yield and DArT markers. The locus of these markers is located on chromosomes 6A and 7A, respectively.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn general, according to the results, the same marker (1057654) was identified for grain yield and plant height in the second experiment year. This marker with genetic locus 691163974 is located on chromosome 7A. By comparing the significant marker-trait relationships as well as the durum wheat consensus map, the position of the marker can be identified and checked by comparing them with physical maps. If the marker distance is more than +10 or -10 cM that position can be identified as a new QTL. Based on this, 29 quantitative positions were identified and confirmed in this study. Among the identified loci, 9 loci were not previously recorded in Durum\u0026apos;s Consensus map (Table 7).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDurum wheat (\u003cem\u003eTriticum turgidum\u003c/em\u003e ssp. durum) in Iran and other major producer countries in the world, such as Canada, Mexico, the United States of America, Australia, Italy, Turkey, et cetera. is mainly grown under rain-fed conditions, so due to environmental stresses, especially drought stress, mostly has a low grain yield (Beres et al. 2020). The initial loss of durum wheat\u0026rsquo;s genetic diversity during the domestication process is estimated at 84%. Therefore, as a consequence of this event, kinds of wheat (bread and durum) were domesticated, which has narrowed their genetic base and diversity, currently (Zeven \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).One of the primary goals of any breeding program is to identify and select the desired genotypes in terms of various traits(Mohammadi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). To achieve this goal, the traits used should be clearly defined in terms of quantity and quality, so that in combination with important economic traits, they can be used to maximize the Grain yield of the selected genotypes. In this regard, several statistical methods, including correlation analysis, multiple regression analysis, and path coefficient analysis, have been proposed and widely used by experts and breeders. In the meantime, one of the most important statistical methods introduced to achieve this goal is genotype-trait graphical analysis (GT), which is obtained through analysis into principal components(Yan \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In this method, the bi-plot used is obtained through the first two components extracted through the analysis of eigenvalues on the desired attribute data in several environments (year, place, or a combination of year and place). Although this method was introduced to analyze multi-environment experiments, it can be used for any type of data that has a two-way structure(Yan \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Generally, according to Yan and Rajcan (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), the desired genotypes identified through this analysis can be used as parents in cross-breeding programs or directly to produce commercial cultivars. The best linear unbiased estimation (BLUE \u0026amp; BLUP) analysis revealed that the investigated durum wheat genotypes in this research have a high genetic diversity in grain yield and other agronomy traits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Also, the different responses of each genotype in terms of measured traits to different environmental conditions (year) revealed the existence of the genotype \u0026times; environment interaction effect. However, a more accurate evaluation of the selected genotypes to grain yield and other traits is necessary for their use in crossbreeding programs or to check their stability and adaptability to different regions. Graphical analysis of genotype-trait (GT) indicated genotypes 24, 80, and 83 were superior to other genotypes in terms of studied traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, it's made possible to use them in breeding programs for the improvement of several characters simultaneously. Considering that the bi-plots showed all the available changes did not justify, these predictions may not accurately reflect the observed numbers. However, the predictions are highly close to reality, so it is possible to select genotypes with superior values compared to previously. Yan and Kang (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) in soybean, Yan and Fr\u0026eacute;geau-Reid (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) in oats, Dehghani et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) in winter rapeseed, and Rahmati et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in cantaloupe have applied this method to evaluate, compare and select superior cultivars and indicated that this method in addition to showing the relationship between different traits graphically, can make the visual comparison between genotypes very possible.\u003c/p\u003e \u003cp\u003eThe average SNP and Silico-DArT PIC values (were 0.252 and 0.383, respectively) in the recent study were higher than the PIC value reported by Ren et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) using SNP markers in the global collection of 150 durum wheat genotypes (it was equal to 0.18888). Moragues et al.( 2007) investigated the genetic diversity of 63 indigenous populations of durum wheat from Mediterranean countries using AFLP and SSR markers, the average PIC values for AFLP and SSR markers were 0.24 and 0.70, respectively. Sequence reading rate (Call Rate) is the percentage of valid scores among all possible scores for a marker, while reproducibility is measured as the percentage of scoring repeatability for repeated samples. Therefore, both of these parameters were estimated to determine the quality of each marker within the panel of durum wheat genotypes in this research (Table\u0026nbsp;2). Although the combination of restriction enzymes as well as the amount of diversity in the representative panel is different from other plant species; the proportion of technical replicate assay pairs for markers score and the success of reading the marker sequence across the samples were high and comparable with previous applications of SilicoDArTs in other plant species (Grzebelus et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ndjiondjop et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; O\u0026rsquo;Connor et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The genomic coverage of SilicoDArT and SNP markers was generally homogenous for most of chromosomes. The highest number of SilicoDArT and SNP markers (853, 818) were observed in Chr7B and Chr2B linkage groups, respectively. Also, chromosome size covered by these markers were highly similar and its highest and lowest value were observed in the Chr3B \u003cb\u003eand\u003c/b\u003e the Chr1A linkage groups, respectively (Table\u0026nbsp;2) which is similar to previous studies in durum wheat using high-performance GBS (Baloch et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the Kinship matrix information, the studied genotypes were divided into 3 groups. Relative kinship among individuals in the population can lead to the identification of false associations between markers and traits and therefore it should be taken into consideration in relational mapping studies. The value of the matrix of kinship coefficients can vary from 3 to -3, as much as its average tends to be zero or less, it indicates a very high diversity in the population. Genotypes with high kinship coefficients are highly likely to have a common ancestry (Salgotra et al., 2015).\u003c/p\u003e \u003cp\u003eThe results of population structure analysis based on the \u003cem\u003eBayesian\u003c/em\u003e model put the studied genotypes into four groups. Since some clusters showed a mixture of durum wheat genotypes, it can be concluded that most probably the same parental lines were used in the breeding programs of these genotypes. The existence of groups and subgroups in large populations can be reasons such as different geographical origins of genotypes, natural or human selection, genetic drift, etc. (Flint-Garcia et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenome-wide Association study is the method that determines the QTL's location based on linkage disequilibrium (Gupta and Varshney 2005). Therefore, for association mapping, in addition to the composition of the population structure, the extent of linkage disequilibrium in the genome has fundamental importance (Al-Maskri et al. 2012). The results revealed that linkage disequilibrium varies from chromosome to chromosome. In our research, the highest amount of linkage disequilibrium was observed for markers with a distance of less than 5 cM on chromosome 4B, which indicates that linkage is one of the most effective forces that cause disequilibrium in the population. Previous studies have demonstrated that linkage disequilibrium varies along the chromosome and from chromosome to chromosome (Chao et al. 2010; Hao et al. 2011). The results also showed that increasing the distance between the markers does not completely remove the imbalance. This is due to the presence of other effective factors such as population structure, genetic drift, migration, selection, and mutation on linkage disequilibrium (Reich and Lander 2001). In the present study, the marker-trait association was investigated using the FarmCPU model. In general, 29 significant \"marker-trait\" associations were found during two cropping years, and out of these 29 related markers, 19 markers were identified in the first cropping year and 10 markers were identified in the second cropping year. Among the identified markers, there were 11 markers related to plant height. One of the most promising approaches to improving traits is the introduction of new alleles. As a first step, allele extraction approaches can be performed in different ways and with different sets of plant germplasm. Therefore, performing multiple trials in different environments and the phenotypic analysis of the studied germplasm can identify associated markers for a genetic description of complex traits. In addition, genetic relationships can be used to choose the desirable parents for breeding programs, if the genetic pedigree data is not available. The information produced here can be used to breeding programs tailored to regional and local needs. The presence of many SilicoDArT and SNP markers, the cost-effectiveness, and the high relatively polymorphism information content (PIC) have provided excellent aspects for widespread screening of the whole genome for genetic diversity, linkage disequilibrium studies, and genome-wide association analysis purposes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGenetic diversity is the basis of each breeding program. Therefore, it is expected that the comprehensive knowledge about the genetic diversity of durum wheat from various eco-geographic regions will have a significant impact on the protection and use of desirable germplasms. This can help breeders in designing new approaches to achieve profitable diversity in breeding programs. The study of genetic diversity subordination from geographical and climatic diversity indicates the possible adaptation of plant germplasms with different habitats and environments. MTAs are the key elements to detecting genomic regions related to durum wheat morpho-phenologic traits. The current experiment found 29 highly significant MTAs under two consecutive cropping year. The markers detected would be useful genomic sources for cloning and fine mapping of underlying genes, and for conducting gene introgression and marker-based selection in durum wheat under rain fed condition. Further research attempts are needed for validating the markers detected in the current project using a larger durum wheat population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the Islamic Azad University of Sanandaj branch and the Dryland Agricultural Research Institute, Sararud, Kermanshah, for their cooperation in carrying out the present research as best as possible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Etminan conceived and the study, P. Ebrahimi and R. Mohammadi conducted the experiments, R. Talebi analyzed the data, E. Karami wrote the manuscript, all authors read and approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Funding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project did not have a financial supporter\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data sharing is not applicable as no new data were generated or analyzed during this study\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlam M, Neal J, O\u0026rsquo;Connor K, et al (2018) Ultra-high-throughput DArTseq-based silicoDArT and SNP markers for genomic studies in macadamia. PLoS One 13:e0203465. https://doi.org/10.1371/JOURNAL.PONE.0203465\u003c/li\u003e\n \u003cli\u003eBaloch FS, Alsaleh A, Shahid MQ, et al (2017) A whole genome DArTseq and SNP analysis for genetic diversity assessment in durum wheat from central fertile crescent. PLoS One 12:. https://doi.org/10.1371/journal.pone.0167821\u003c/li\u003e\n \u003cli\u003eBatley J, Edwards D (2007) SNP Applications in Plants. In: Association Mapping in Plants. Springer, New York, NY, pp 95\u0026ndash;102\u003c/li\u003e\n \u003cli\u003eDehghani H, Omidi H, Sabaghnia N (2008) Graphic Analysis of Trait Relations of Rapeseed Using the Biplot Method. Agron J 100:1443\u0026ndash;1449. https://doi.org/10.2134/AGRONJ2007.0275\u003c/li\u003e\n \u003cli\u003eDeschamps S, Campbell MA (2010) Utilization of next-generation sequencing platforms in plant genomics and genetic variant discovery. Mol Breed 25:553\u0026ndash;570. https://doi.org/10.1007/S11032-009-9357-9\u003c/li\u003e\n \u003cli\u003eDoyle J., Doyle J. (1990) \u0026ldquo;Isolation of Plant DNA from Fresh Tissue,.\u0026rdquo; Focus (Madison) 12:13\u0026ndash;15\u003c/li\u003e\n \u003cli\u003eEtminan A, Pour-Aboughadareh A, Mohammadi R, et al (2018) Applicability of CAAT box-derived polymorphism (CBDP) markers for analysis of genetic diversity in durum wheat. Cereal Res Commun 46:1\u0026ndash;9. https://doi.org/10.1556/0806.45.2017.054\u003c/li\u003e\n \u003cli\u003eEtminan A, Pour-Aboughadareh A, Mohammadi R, et al (2016) Applicability of start codon targeted (SCoT) and inter-simple sequence repeat (ISSR) markers for genetic diversity analysis in durum wheat genotypes Applicability of start codon targeted (SCoT) and inter-simple sequence repeat (ISSR) markers for genetic d. Biotechnol Biotechnol Equip 30:1075\u0026ndash;1081. https://doi.org/10.1080/13102818.2016.1228478\u003c/li\u003e\n \u003cli\u003eEvanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611\u0026ndash;2620. https://doi.org/10.1111/J.1365-294X.2005.02553.X\u003c/li\u003e\n \u003cli\u003eFayaz F, Aghaee Sarbarzeh M, Talebi R, Azadi A (2019) Genetic Diversity and Molecular Characterization of Iranian Durum Wheat Landraces (Triticum turgidum durum (Desf.) Husn.) Using DArT Markers. Biochem Genet 57:98\u0026ndash;116. https://doi.org/10.1007/S10528-018-9877-2\u003c/li\u003e\n \u003cli\u003eFlint-Garcia SA, Thornsberry JM, Edward IV SB (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357\u0026ndash;374. https://doi.org/10.1146/ANNUREV.ARPLANT.54.031902.134907\u003c/li\u003e\n \u003cli\u003eGibson G, Muse S. (2009) A Primer of Genome Science - Greg Gibson; Spencer V. Muse - Oxford University Press. Oxford University Press\u003c/li\u003e\n \u003cli\u003eGiunta F, Pruneddu G, Motzo R (2019) Grain yield and grain protein of old and modern durum wheat cultivars grown under different cropping systems. F Crop Res 230:107\u0026ndash;120. https://doi.org/10.1016/J.FCR.2018.10.012\u003c/li\u003e\n \u003cli\u003eGrzebelus D, Iorizzo M, Senalik D, et al (2014) Diversity, genetic mapping, and signatures of domestication in the carrot (Daucus carota L.) genome, as revealed by Diversity Arrays Technology (DArT) markers. Mol Breed 33:625\u0026ndash;637. https://doi.org/10.1007/S11032-013-9979-9/FIGURES/3\u003c/li\u003e\n \u003cli\u003eHu X, Ren J, Ren X, et al (2015) Association of Agronomic Traits with SNP Markers in Durum Wheat (Triticum turgidum L. durum (Desf.)). PLoS One 10:e0130854. https://doi.org/10.1371/JOURNAL.PONE.0130854\u003c/li\u003e\n \u003cli\u003eLi YC, Korol AB, Fahima T, et al (2002) Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Mol Ecol 11:2453\u0026ndash;2465. https://doi.org/10.1046/J.1365-294X.2002.01643.X\u003c/li\u003e\n \u003cli\u003eMantovani P, Maccaferri M, Sanguineti MC, et al (2008) An integrated DArT-SSR linkage map of durum wheat. Mol Breed 22:629\u0026ndash;648. https://doi.org/10.1007/S11032-008-9205-3\u003c/li\u003e\n \u003cli\u003eMehrabi A., Pour-Aboughadareh A., Mansouri S, Hosseini A. (2020) Genome-wide association analysis of root system architecture features and agronomic traits in durum wheat | Enhanced Reader. Mol Breed 40\u0026ndash;55\u003c/li\u003e\n \u003cli\u003eM\u0026eacute;rida-Garc\u0026iacute;a R, Bentley AR, G\u0026aacute;lvez S, et al (2020) Mapping Agronomic and Quality Traits in Elite Durum Wheat Lines under Differing Water Regimes. Agron 2020, Vol 10, Page 144 10:144. https://doi.org/10.3390/AGRONOMY10010144\u003c/li\u003e\n \u003cli\u003eMohammadi R, Dehghani H, Karimzadeh G (2014) Graphic analysis of trait relations of cantaloupe using the \u0026nbsp;Biplot method . J Plant Prod Res 21:\u003c/li\u003e\n \u003cli\u003eMoragues M, Moralejo M, Sorrells ME, Royo C (2007) Dispersal of durum wheat [Triticum turgidum L. ssp. turgidum convar. durum (Desf.) MacKey] landraces across the Mediterranean basin assessed by AFLPs and microsatellites. Genet Resour Crop Evol 54:1133\u0026ndash;1144. https://doi.org/10.1007/S10722-006-9005-8\u003c/li\u003e\n \u003cli\u003eMwadzingeni L, Shimelis H, Rees DJG, Tsilo TJ (2017) Genome-wide association analysis of agronomic traits in wheat under drought-stressed and non-stressed conditions. PLoS One 12:e0171692. https://doi.org/10.1371/JOURNAL.PONE.0171692\u003c/li\u003e\n \u003cli\u003eNdjiondjop MN, Semagn K, Gouda AC, et al (2017) Genetic variation and population structure of Oryza glaberrima and development of a mini-core collection using DArTseq. Front Plant Sci 8:1748. https://doi.org/10.3389/FPLS.2017.01748/BIBTEX\u003c/li\u003e\n \u003cli\u003eNGBT NGBT descriptors for durum wheat 1.0. https://www.genesys-pgr.org/descriptorlists/b6544d11-be40-4638-be35-1b3f5349c5c9. Accessed 26 Mar 2023\u003c/li\u003e\n \u003cli\u003eO\u0026rsquo;Connor K, Kilian A, Hayes B, et al (2019) Population structure, genetic diversity and linkage disequilibrium in a macadamia breeding population using SNP and silicoDArT markers. Tree Genet Genomes 15:. https://doi.org/10.1007/S11295-019-1331-Z\u003c/li\u003e\n \u003cli\u003ePour-Aboughadareh A, Mahmoudi M, Moghaddam M, et al (2017) Agro-morphological and molecular variability in Triticum boeoticum accessions from Zagros Mountains, Iran. Genet Resour Crop Evol 64:545\u0026ndash;556. https://doi.org/10.1007/S10722-016-0381-4\u003c/li\u003e\n \u003cli\u003ePowell W, Morgante M, Andre C, et al (1996) The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed 2:225\u0026ndash;238. https://doi.org/10.1007/BF00564200/METRICS\u003c/li\u003e\n \u003cli\u003ePozniak CJ, Clarke JM, Clarke FR (2012) Potential for detection of marker-trait associations in durum wheat using unbalanced, historical phenotypic datasets. Mol Breed 30:1537\u0026ndash;1550. https://doi.org/10.1007/S11032-012-9737-4\u003c/li\u003e\n \u003cli\u003eRahmati M, Hoseinpour T, Ahmadi A (2020) Assessment of interrelationships between agronomic traits of wheat genotypes under rain-fed conditions using double and triple biplots of genotype, trait and yield. Iran J Dryl Agric 9:1\u0026ndash;20\u003c/li\u003e\n \u003cli\u003eRanieri R (2015) Geography of the durum wheat crop -. In: Pastaria Open Fields. Collecchio,Italy\u003c/li\u003e\n \u003cli\u003eRen J, Sun D, Chen L, et al (2013) Genetic Diversity Revealed by Single Nucleotide Polymorphism Markers in a Worldwide Germplasm Collection of Durum Wheat. Int J Mol Sci 14:7061. https://doi.org/10.3390/IJMS14047061\u003c/li\u003e\n \u003cli\u003eSansaloni C, Franco J, Santos B, et al (2020) Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints. Nat Commun 2020 111 11:1\u0026ndash;12. https://doi.org/10.1038/s41467-020-18404-w\u003c/li\u003e\n \u003cli\u003eTrebbi D, Maccaferri M, de Heer P, et al (2011) High-throughput SNP discovery and genotyping in durum wheat (Triticum durum Desf.). Theor Appl Genet 123:555\u0026ndash;569. https://doi.org/10.1007/S00122-011-1607-7\u003c/li\u003e\n \u003cli\u003eYan W (2001) GGEbiplot\u0026mdash;A Windows Application for Graphical Analysis of Multienvironment Trial Data and Other Types of Two-Way Data. Agron J 93:1111\u0026ndash;1118. https://doi.org/10.2134/AGRONJ2001.9351111X\u003c/li\u003e\n \u003cli\u003eYan W, Fr\u0026eacute;geau-Reid J (2008) Breeding Line Selection Based on Multiple Traits. Crop Sci 48:417\u0026ndash;423. https://doi.org/10.2135/CROPSCI2007.05.0254\u003c/li\u003e\n \u003cli\u003eYan W, Kang MS (2002) GGE Biplot Analysis : A Graphical Tool for Breeders, Geneticists, and Agronomists. GGE Biplot Anal. https://doi.org/10.1201/9781420040371\u003c/li\u003e\n \u003cli\u003eYan W, Rajcan I (2002) Biplot Analysis of Test Sites and Trait Relations of Soybean in Ontario. Crop Sci 42:11\u0026ndash;20. https://doi.org/10.2135/CROPSCI2002.1100\u003c/li\u003e\n \u003cli\u003eZeven AC (2000) Traditional maintenance breeding of landraces: 1. Data by crop. Euphytica 116:65\u0026ndash;85. https://doi.org/10.1023/A:1004089816030/METRICS\u003c/li\u003e\n \u003cli\u003eZhu C, Gore M, Buckler ES, Yu J (2008) Status and Prospects of Association Mapping in Plants. Plant Genome 1:5\u0026ndash;20. https://doi.org/10.3835/PLANTGENOME2008.02.0089\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Table 1. Analysis of variance for measured traits in investigated durum wheat genotypes based on BLUE models\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.37579617834395%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.248407643312103%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.375796178343949%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.490445859872612%\"\u003e\n \u003cp\u003eGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eTKW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.248407643312103%\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.375796178343949%\"\u003e\n \u003cp\u003eEnvironments\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.490445859872612%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\" valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\" valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.248407643312103%\" valign=\"top\"\u003e\n \u003cp\u003eHeritability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.375796178343949%\" rowspan=\"10\"\u003e\n \u003cp\u003eFirst Year(Env1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e26734.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e12.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e54.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eGenotypic Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e323321.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e24.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e2275.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e62.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e171.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e109.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\"\u003e\n \u003cp\u003eMIN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e5375.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e112.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e183.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e132.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\"\u003e\n \u003cp\u003eMAX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e4071.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e44.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e80.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e175.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e124.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e306.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eLSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eCoefficients of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e680.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e8.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.490445859872612%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\" valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.248407643312103%\" valign=\"top\"\u003e\n \u003cp\u003eHeritability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.375796178343949%\" rowspan=\"10\"\u003e\n \u003cp\u003eSecond Year(Env2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e30107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e14.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eGenotypic Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e402432.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e50.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e1441.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e161.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e118.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\"\u003e\n \u003cp\u003eMIN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e4802.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e122.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e181.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e131.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\"\u003e\n \u003cp\u003eMAX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e3472.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e39.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e89.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e167.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e125.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e1090.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e8.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eLSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e18.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eCoefficients of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e759.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e9.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.727941176470587%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e0.00002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.83823529411765%\" valign=\"top\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"972\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.493827160493828%\" colspan=\"14\" valign=\"top\" style=\"width: 92.2311%;\"\u003e\n \u003cp dir=\"\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003cspan dir=\"LTR\"\u003eTable 2. SilicoDArT and SNP Markers Distribution on Durum Wheat Genome, Polymorphism Information Content (PIC), Call Rate, and Average of Reproducibility\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eLinkage Groups\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eNo. DArT Markers\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eNo. SNP Markers\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr Size(kbp) Silico DArT\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr Size (kbp) SNP\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eDArT Call Rate (mean)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eSNP Call Rate\u003c/span\u003e\u003cspan dir=\"LTR\"\u003e(mean)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eDArT One Ratio (mean)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eSNP One Ratio(mean)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;SNP PIC\u0026nbsp;\u003c/span\u003e\u003cspan dir=\"LTR\"\u003e(mean)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eDArT PIC\u003c/span\u003e\u003c/p\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;(mean)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eAV. of \u0026nbsp; \u0026nbsp; SNP Reproducibility\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eAv. of DArT Reproducibility\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eSNP position\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eChr1A\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e300\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e582\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e589293.8\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e591612.294\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.926811\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.983\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.53253\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.423\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.257\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.392783\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.984\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987783\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e29.42\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr2A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e574\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e695\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e779665.3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e780499.087\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.928376\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.531615\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.396\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.235\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.378022\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987819\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e31.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr3A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e454\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e685\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e750610.4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e750170.104\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.926852\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.983\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.528354\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.411\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.251\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.371944\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987748\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e31.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eChr4A\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e489\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e445\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e742808.8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e743151.257\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.929105\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.984\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.507459\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.434\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.253\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.382376\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.986216\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e28.09\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr5A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e436\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e726\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e708977.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e708511.745\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.928146\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.520776\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.421\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.259\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.375594\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.98842\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e29.66\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eChr6A\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e456\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e445\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e616320.1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e617227.837\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.928312\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.510363\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.468\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.261\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.392759\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987314\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e30.57\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr7A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e789\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e750\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e735262.1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e736431.403\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.929175\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.510735\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.383\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.241\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.384203\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.986959\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e31.84\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eCh1B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e681\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e591\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e688589.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e688256.279\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.92758\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.533268\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.427\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.244\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.385003\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.98\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987277\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e27.97\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eChr2B\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e759\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e818\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e800769.3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e800987.529\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.927394\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.98\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.531767\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.243\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.393895\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987843\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e30.64\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eChr3B\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e582\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e772\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e829200.1\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e829329.004\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.922085\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.983\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.564573\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.391\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.241\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.369268\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.984\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.9861\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e30.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr4B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e333\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e456\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e671754.7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e671277.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.924849\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.984\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.538444\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.42\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.259\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.37495\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.98\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987985\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e30.11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr5B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e586\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e757\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e708929.4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e712833.876\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.923233\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.983\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.566275\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.43\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.261\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.382289\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.986366\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e28.4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr6B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e590\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e638\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e720840.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e720898.912\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.926843\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.534025\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.241\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.382743\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.986644\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e29.69\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eChr7B\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e853\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e588\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e749946.8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e750031.822\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.92991\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.515027\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.449\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.282\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.392338\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.978\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987082\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e29.8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSum\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e7882\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e8948\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eMean\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e563\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e639\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e720926.2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e721515.7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.927048\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e0.98229\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.530372\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.4195\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e0.252\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.382726\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.9813\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987254\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e29.9314286\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eGroup A\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e499.72\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e618\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e703276.78\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e703943.4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.928111\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.520261\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.419\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e0.251\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.382525\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987465\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e30.30\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eGroup B\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e626.3\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e660\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e738575.7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.024691358024691%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e739087.9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.925985\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e0.982\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.540483\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.407407407407407%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.419\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e0.253\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.382927\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.981\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.987042\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e29.55\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Genetic divergence among (Net Nucleotide Distance) and within (expected heterozygosity) population, the proportion of membership, and the mean value of Fst observed from the study of population structure of 92 durum wheat genotypes using SilicoDArT markers.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.969335604770016%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePopulation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.04940374787053%\" colspan=\"3\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eNet Nucleotide Distance\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.08006814310051%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eExpected Heterozygosity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1839863713799%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e% 0f \u0026nbsp;membership\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.717206132879046%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eMean fixation index (Fst)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.969335604770016%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePOP2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.199318568994888%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePOP3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.458262350936968%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePOP4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.08006814310051%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1839863713799%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.717206132879046%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.969335604770016%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePOP1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.070\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.199318568994888%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.084\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.458262350936968%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.247\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.08006814310051%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1839863713799%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.717206132879046%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.969335604770016%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePOP2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.199318568994888%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.085\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.458262350936968%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.270\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.08006814310051%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1839863713799%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.717206132879046%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.969335604770016%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePOP3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.199318568994888%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.458262350936968%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.249\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.08006814310051%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1839863713799%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e19.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.717206132879046%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.969335604770016%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePOP4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.199318568994888%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.458262350936968%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.08006814310051%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.02\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1839863713799%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e8.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.717206132879046%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.95\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003eTable 4. Characteristics of linkage disequilibrium in durum wheat genome based on DArTseq markers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.264750378214828%\"\u003e\n \u003cp\u003eGenome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\"\u003e\n \u003cp\u003eTotal pairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.128593040847202%\"\u003e\n \u003cp\u003eSignificant pairs (P\u0026le;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.826021180030256%\"\u003e\n \u003cp\u003e% of Significant pairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531013615733738%\"\u003e\n \u003cp\u003eMean r\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.61573373676248%\"\u003e\n \u003cp\u003ePairs in complete LD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.10287443267776%\"\u003e\n \u003cp\u003eCritical r\u003csup\u003e2\u003c/sup\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.75642965204236%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.264750378214828%\"\u003e\n \u003cp\u003eA-genome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\"\u003e\n \u003cp\u003e269891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.128593040847202%\"\u003e\n \u003cp\u003e68885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.826021180030256%\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531013615733738%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.61573373676248%\"\u003e\n \u003cp\u003e1337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.10287443267776%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.75642965204236%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.264750378214828%\"\u003e\n \u003cp\u003eB-genome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\"\u003e\n \u003cp\u003e335322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.128593040847202%\"\u003e\n \u003cp\u003e82052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.826021180030256%\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531013615733738%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.61573373676248%\"\u003e\n \u003cp\u003e1146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.10287443267776%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.75642965204236%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.264750378214828%\"\u003e\n \u003cp\u003eWhole-genome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\"\u003e\n \u003cp\u003e605212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.128593040847202%\"\u003e\n \u003cp\u003e148937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.826021180030256%\"\u003e\n \u003cp\u003e24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.531013615733738%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.61573373676248%\"\u003e\n \u003cp\u003e2438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.10287443267776%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.75642965204236%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"88.17567567567568%\" colspan=\"7\" valign=\"top\" style=\"width: 99.8654%;\"\u003e\n \u003cp\u003eTable 5 Associated markers with studied traits in 94 durum wheat genotypes using linkage analysis in the 2017 cropping year.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.33502538071066%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eTraits\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.720812182741117%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eMarker\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.197969543147208%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eLinkage Group\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.874788494077833%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eLocus\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.182741116751268%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eP-\u003c/span\u003e\u003c/em\u003e\u003cspan dir=\"LTR\"\u003evalue\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eMaf\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConsensus map(cM)\u003c/span\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.33502538071066%\" rowspan=\"2\"\u003e\n \u003cp\u003eDays to Flowering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.720812182741117%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1703829\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.197969543147208%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr6B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.874788494077833%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4863112\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.182741116751268%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000075\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.309783\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1087984\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr7A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e42448091\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000083\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.141304\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.33502538071066%\"\u003e\n \u003cp\u003eDays to Maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.720812182741117%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3940462\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.197969543147208%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr4A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.874788494077833%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e606757033\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.182741116751268%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000087\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.943478\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e54.26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.33502538071066%\" rowspan=\"10\"\u003e\n \u003cp\u003ePlant Height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.720812182741117%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1228105\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.197969543147208%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr5A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.874788494077833%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4994308\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.182741116751268%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.288043\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5.94\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3946051\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr3A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e697061451\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.13587\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4004958\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr5B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e648289673\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.130435\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4405812\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr5A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e109937709\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000002\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.369565\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1068144\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr6A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e613084911\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000013\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.70652\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e98.22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1093423\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr6B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e33427529\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000026\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.375\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e13.14\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2275869\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr3B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e19232431\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000034\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.483696\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e17.44\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5410721\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr7B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e625701808\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000041\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.065217\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e81.31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1671913\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr3A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e497297304\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000043\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.233696\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1090315\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr4B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e629706516\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000067\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.413043\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.33502538071066%\" rowspan=\"4\"\u003e\n \u003cp\u003eThousand-grain weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.720812182741117%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3570140\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.197969543147208%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr7A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.874788494077833%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e71591095\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.182741116751268%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000031\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.211957\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e149.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e992437\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr3A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e541246518\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000041\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.391304\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e68.59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3534094\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr5B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e27137735\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000044\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.255435\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e161.82\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3533907\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr3B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e739839965\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000056\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.461957\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.33502538071066%\" rowspan=\"3\"\u003e\n \u003cp\u003eGrain Yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.720812182741117%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1108172\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.197969543147208%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr3A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.874788494077833%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e652905402\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.182741116751268%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000071\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.059783\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.844331641285956%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e65.11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4989009\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr2B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e106775933\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000021\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.179348\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.954248366013072%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1233550\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99346405228758%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr1B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.864923747276688%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e558575664\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000033\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.190217\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.250544662309368%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"89.1025641025641%\" colspan=\"7\" valign=\"top\" style=\"width: 99.8397%;\"\u003e\n \u003cp\u003eTable 6 Correlated markers with studied traits in 94 durum wheat genotypes using linkage analysis in the 2018 cropping year.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.76%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eTraits\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eMarker\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.84%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eLinkage Group\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eLocus\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eP-\u003c/span\u003e\u003c/em\u003e\u003cspan dir=\"LTR\"\u003evalue\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.36%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eMaf\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.88%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConsensus map(cM)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.76%\" rowspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eDays to Heading\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3935863\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.84%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr1A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e30133162\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000032\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.36%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.086957\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.88%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e35.82\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1211191\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr5B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.67241379310345%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e700372980\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.810344827586206%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000037\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.301724137931034%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.065217\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e137.12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.76%\" rowspan=\"3\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eDays to Maturity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3064874\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.84%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr3B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e794315843\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000006\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.36%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.097826\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.88%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e133.21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1034732\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr4A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.67241379310345%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e673038094\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.810344827586206%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0000028\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.301724137931034%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e96.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1025860\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr4A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.67241379310345%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e666991274\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.810344827586206%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000078\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.301724137931034%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.304348\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e96.16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.76%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePlant Height\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1057654\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.84%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr7A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e691163974\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000051\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.36%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.097826\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.88%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e129.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.76%\" rowspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eThousand Grain Weight\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3025786\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.84%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr6A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e609888794\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000044\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.36%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.070652\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.88%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e98.83\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e981221\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr3B\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.67241379310345%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e684637594\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.810344827586206%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000090\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.301724137931034%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0157609\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e81.17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.76%\" rowspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eGrain Yield\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1057654\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.84%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr7A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e691163974\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000016\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.36%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.097826\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.88%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e129.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e7918245\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eChr6A\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.67241379310345%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e611855760\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.810344827586206%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000040\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.301724137931034%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.141304\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTable 7 Comparison of gene locations identified by Consensus map of durum wheat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eTraits\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865079365079364%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eMarker\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.1984126984127%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eLinkage Group\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.19047619047619%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ecM\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" rowspan=\"4\"\u003e\n \u003cp\u003eDays to Heading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865079365079364%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1703829\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.1984126984127%\"\u003e\n \u003cp\u003eChr6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.19047619047619%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1087984\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3935863\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e35.82\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1211191\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e137.12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" rowspan=\"4\"\u003e\n \u003cp\u003eDays to Maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865079365079364%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3940462\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.1984126984127%\"\u003e\n \u003cp\u003eChr4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.19047619047619%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e54.26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3064874\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e133.21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1034732\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e96.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1025860\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e96.16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" rowspan=\"11\"\u003e\n \u003cp\u003ePlant Height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865079365079364%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1228105\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.1984126984127%\"\u003e\n \u003cp\u003eChr5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.19047619047619%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5.94\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3946051\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4004958\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4405812\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1068144\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e98.22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1093423\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e13.14\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2275869\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e17.44\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5410721\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e81.31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1671913\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1090315\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1057654\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e129.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" rowspan=\"6\"\u003e\n \u003cp\u003eThousand-grain weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865079365079364%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3570140\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.1984126984127%\"\u003e\n \u003cp\u003eChr7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.19047619047619%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e149.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e992437\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e68.59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3534094\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e161.82\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3533907\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3025786\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e98.83\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e981221\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e81.17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" rowspan=\"5\"\u003e\n \u003cp\u003eGrain Yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.865079365079364%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1108172\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.1984126984127%\"\u003e\n \u003cp\u003eChr3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.19047619047619%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e65.11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4989009\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1233550\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1057654\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e129.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.709302325581394%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e7918245\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91860465116279%\"\u003e\n \u003cp\u003eChr6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.372093023255815%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-molecular-biology-reporter","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pmbr","sideBox":"Learn more about [Plant Molecular Biology Reporter](http://link.springer.com/journal/11105)","snPcode":"11105","submissionUrl":"https://submission.nature.com/new-submission/11105/3","title":"Plant Molecular Biology Reporter","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Association mapping, DArTseq technology, Durum wheat, Polymorphism information content","lastPublishedDoi":"10.21203/rs.3.rs-4237277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4237277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research was conducted to study genetic diversity, population structure, linkage disequilibrium, and marker-trait relationship in durum wheat using DArTseq technology. Therefore, 94 durum wheat genotypes were evaluated in the form of augmented design with six repeated controls during the two cropping years, 2016\u0026ndash;2017 and 2017\u0026ndash;2018. Some Agronomic traits including the number of days to spike emergence, number of days to physiological maturity, plant height, thousand kernel weight, and grain yield were measured and recorded. The DNA samples were processed for DArTseq using the Genotyping by Sequencing (GBS) platform at Diversity Array Technology Pty, Ltd, Australia. The values of polymorphism information content (PIC) of SilicoDArT markers varied from 0.023 to 0.499 with an average of 0.38. The studied durum wheat genotypes were grouped into four separate groups based on the Bayesian model. LD analysis between A and B genomes showed that there is a high number of significant marker pairs (82052) in the B genome compared to the A genome (68885). In general, during two cropping years, 29 markers related to the studied traits were found, and out of these 29 related markers, 19 markers were identified in the first year and 10 markers were identified in the second year. The results revealed that the DArTseq markers are a very powerful tool for evaluating the genetic diversity and population structure of durum wheat genotypes and can be used in genotype screening as breeding parents and marker-assisted selection in breeding programs.\u003c/p\u003e","manuscriptTitle":"Genetic Diversity and Genome-Wide Association Study for some agronomic traits in durum wheat (Triticum turgidum L.) Using whole genome DArTseq Markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 04:35:08","doi":"10.21203/rs.3.rs-4237277/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-30T20:07:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-12T04:17:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74308977162616117416977574345927329813","date":"2024-05-29T22:08:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-29T15:07:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-09T07:01:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-09T07:01:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Molecular Biology Reporter","date":"2024-04-08T14:46:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-molecular-biology-reporter","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pmbr","sideBox":"Learn more about [Plant Molecular Biology Reporter](http://link.springer.com/journal/11105)","snPcode":"11105","submissionUrl":"https://submission.nature.com/new-submission/11105/3","title":"Plant Molecular Biology Reporter","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"73873279-b02f-4876-b9a0-78e6efca33a5","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:07:12+00:00","versionOfRecord":{"articleIdentity":"rs-4237277","link":"https://doi.org/10.1007/s11105-025-01559-5","journal":{"identity":"plant-molecular-biology-reporter","isVorOnly":false,"title":"Plant Molecular Biology Reporter"},"publishedOn":"2025-03-18 15:57:11","publishedOnDateReadable":"March 18th, 2025"},"versionCreatedAt":"2024-04-17 04:35:08","video":"","vorDoi":"10.1007/s11105-025-01559-5","vorDoiUrl":"https://doi.org/10.1007/s11105-025-01559-5","workflowStages":[]},"version":"v1","identity":"rs-4237277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4237277","identity":"rs-4237277","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}