Genome-Wide SNP Identification and Characterization in Two Soybean Cultivars with Contrasting Mungbean Yellow Mosaic India Virus Disease Resistance Traits

Mungbean yellow mosaic India virus (MYMIV) is a bipartite Geminivirus, which causes severe yield loss in soybean (Glycine max). Considering this, the present study was conducted to develop large-scale genome-wide single nucleotide polymorphism (SNP) markers and identify potential markers linked with known disease resistance loci for their effective use in genomics-assisted breeding to impart durable MYMIV tolerance. The whole-genome re-sequencing of MYMIV resistant cultivar ‘UPSM-534’ and susceptible Indian cultivar ‘JS-335’ was performed to identify high-quality SNPs and InDels (insertion and deletions). Approximately 234 and 255 million of 100-bp paired-end reads were generated from UPSM-534 and JS-335, respectively, which provided ~98% coverage of reference soybean genome. A total of 3083987 SNPs (1559556 in UPSM-534 and 1524431 in JS-335) and 562858 InDels (281958 in UPSM-534 and 280900 in JS-335) were identified. Of these, 1514 SNPs were found to be present in 564 candidate disease resistance genes. Among these, 829 non-synonymous and 671 synonymous SNPs were detected in 266 and 286 defence-related genes, respectively. Noteworthy, a non-synonymous SNP (in chromosome 18, named 18-1861613) at the 149^th base-pair of LEUCINE-RICH REPEAT RECEPTOR-LIKE PROTEIN KINASE gene responsible for a G/C transversion [proline (CCC) to alanine(GCC)] was identified and validated in a set of 12 soybean cultivars. Taken together, the present study generated a large-scale genomic resource such as, SNPs and InDels at a genome-wide scale that will facilitate the dissection of various complex traits through construction of high-density linkage maps and fine mapping. In the present scenario, these markers can be effectively used to design high-density SNP arrays for their large-scale validation and high-throughput genotyping in diverse natural and mapping populations, which could accelerate genomics-assisted MYMIV disease resistance breeding in soybean.     

Genome-Wide Analysis of Seed Acid Detergent Lignin (ADL) and Hull Content in Rapeseed (Brassica napus L.)

A stable yellow-seeded variety is the breeding goal for obtaining the ideal rapeseed (Brassica napus L.) plant, and the amount of acid detergent lignin (ADL) in the seeds and the hull content (HC) are often used as yellow-seeded rapeseed screening indices. In this study, a genome-wide association analysis of 520 accessions was performed using the Q + K model with a total of 31,839 single-nucleotide polymorphism (SNP) sites. As a result, three significant associations on the B. napus chromosomes A05, A09, and C05 were detected for seed ADL content. The peak SNPs were within 9.27, 14.22, and 20.86 kb of the key genes BnaA.PAL4, BnaA.CAD2/BnaA.CAD3, and BnaC.CCR1, respectively. Further analyses were performed on the major locus of A05, which was also detected in the seed HC examination. A comparison of our genome-wide association study (GWAS) results and previous linkage mappings revealed a common chromosomal region on A09, which indicates that GWAS can be used as a powerful complementary strategy for dissecting complex traits in B. napus. Genomic selection (GS) utilizing the significant SNP markers based on the GWAS results exhibited increased predictive ability, indicating that the predictive ability of a given model can be substantially improved by using GWAS and GS.     

Genome-wide association study reveals the genetic architecture of flowering time in rapeseed (Brassica napus L.)

Flowering time adaptation is a major breeding goal in the allopolyploid species Brassica napus. To investigate the genetic architecture of flowering time, a genome-wide association study (GWAS) of flowering time was conducted with a diversity panel comprising 523 B. napus cultivars and inbred lines grown in eight different environments. Genotyping was performed with a Brassica 60K Illumina Infinium SNP array. A total of 41 single-nucleotide polymorphisms (SNPs) distributed on 14 chromosomes were found to be associated with flowering time, and 12 SNPs located in the confidence intervals of quantitative trait loci (QTL) identified in previous researches based on linkage analyses. Twenty-five candidate genes were orthologous to Arabidopsis thaliana flowering genes. To further our understanding of the genetic factors influencing flowering time in different environments, GWAS was performed on two derived traits, environment sensitivity and temperature sensitivity. The most significant SNPs were found near Bn-scaff_16362_1-p380982, just 13 kb away from BnaC09g41990D, which is orthologous to A. thaliana CONSTANS (CO), an important gene in the photoperiod flowering pathway. These results provide new insights into the genetic control of flowering time in B. napus and indicate that GWAS is an effective method by which to reveal natural variations of complex traits in B. napus.     

Comparative Analysis of Disease-Linked Single Nucleotide Polymorphic Markers from Brassica rapa for Their Applicability to Brassica oleracea

Numerous studies using single nucleotide polymorphisms (SNPs) have been conducted in humans, and other animals, and in major crops, including rice, soybean, and Chinese cabbage. However, the number of SNP studies in cabbage is limited. In this present study, we evaluated whether 7,645 SNPs previously identified as molecular markers linked to disease resistance in the Brassica rapa genome could be applied to B. oleracea. In a BLAST analysis using the SNP sequences of B. rapa and B. oleracea genomic sequence data registered in the NCBI database, 256 genes for which SNPs had been identified in B. rapa were found in B. oleracea. These genes were classified into three functional groups: molecular function (64 genes), biological process (96 genes), and cellular component (96 genes). A total of 693 SNP markers, including 145 SNP markers [BRH—developed from the B. rapa genome for high-resolution melt (HRM) analysis], 425 SNP markers (BRP—based on the B. rapa genome that could be applied to B. oleracea), and 123 new SNP markers (BRS—derived from BRP and designed for HRM analysis), were investigated for their ability to amplify sequences from cabbage genomic DNA. In total, 425 of the SNP markers (BRP-based on B. rapa genome), selected from 7,645 SNPs, were successfully applied to B. oleracea. Using PCR, 108 of 145 BRH (74.5%), 415 of 425 BRP (97.6%), and 118 of 123 BRS (95.9%) showed amplification, suggesting that it is possible to apply SNP markers developed based on the B. rapa genome to B. oleracea. These results provide valuable information that can be utilized in cabbage genetics and breeding programs using molecular markers derived from other Brassica species.     

In silico integration of quantitative trait loci for seed yield and yield-related traits in Brassica napus

Mapping quantitative trait loci (QTLs) is a foundation for molecular marker-assisted selection and map-based gene cloning. During the past decade, numerous QTLs for seed yield (SY) and yield-related traits in Brassica napus L. have been identified. However, integration of these results in order to compare QTLs from different mapping populations has not been undertaken, due to the lack of common molecular markers between studies. Using previously reported Brassica rapa and Brassica oleracea genome sequences, we carried out in silico integration of 1,960 QTLs associated with 13 SY and yield-related traits from 15 B. napus mapping experiments over the last decade. A total of 736 SY and yield-related QTLs were mapped onto 283 loci in the A and C genomes of B. napus. These QTLs were unevenly distributed across the 19 B. napus chromosomes, with the most on chromosome A3 and the least on chromosome C6. Our integrated QTL map identified 142 loci where the conserved QTLs were detected and 25 multifunctional loci, mostly for the traits of flowering time (FT), plant height, 1,000-seed weight, maturity time and SY. These conserved QTLs and multifunctional loci may result from pleiotropism or clustered genes. At the same time, a total of 146 genes underlying the QTLs for FT and other yield-related traits were identified by comparative mapping with the Arabidopsis genome. These results facilitate the retrieval of B. napus SY and yield-related QTLs for research communities, increase the density of targeted QTL-linked markers, validate the existence of QTLs across different populations, and advance the fine mapping of genes.     

Identification of genome-wide single nucleotide polymorphisms in allopolyploid crop Brassica napus

Background

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation. Identification of large numbers of SNPs is helpful for genetic diversity analysis, map-based cloning, genome-wide association analyses and marker-assisted breeding. Recently, identifying genome-wide SNPs in allopolyploid Brassica napus (rapeseed, canola) by resequencing many accessions has become feasible, due to the availability of reference genomes of Brassica rapa (2n = AA) and Brassica oleracea (2n = CC), which are the progenitor species of B. napus (2n = AACC). Although many SNPs in B. napus have been released, the objective in the present study was to produce a larger, more informative set of SNPs for large-scale and efficient genotypic screening. Hence, short-read genome sequencing was conducted on ten elite B. napus accessions for SNP discovery. A subset of these SNPs was randomly selected for sequence validation and for genotyping efficiency testing using the Illumina GoldenGate assay.

Results

A total of 892,536 bi-allelic SNPs were discovered throughout the B. napus genome. A total of 36,458 putative amino acid variants were located in 13,552 protein-coding genes, which were predicted to have enriched binding and catalytic activity as a result. Using the GoldenGate genotyping platform, 94 of 96 SNPs sampled could effectively distinguish genotypes of 130 lines from two mapping populations, with an average call rate of 92%.

Conclusions

Despite the polyploid nature of B. napus, nearly 900,000 simple SNPs were identified by whole genome resequencing. These SNPs were predicted to be effective in high-throughput genotyping assays (51% polymorphic SNPs, 92% average call rate using the GoldenGate assay, leading to an estimated >450 000 useful SNPs). Hence, the development of a much larger genotyping array of informative SNPs is feasible. SNPs identified in this study to cause non-synonymous amino acid substitutions can also be utilized to directly identify causal genes in association studies.     

Development of gene-targeted SNP markers for genomic mapping in Pacific abalone Haliotis discus hannai Ino

Useful and novel DNA markers are needed for aquaculture genetics and breeding. In this study, we report the discovery and development of gene-targeted single nucleotide polymorphisms (SNPs) for genomic mapping in the Pacific abalone Haliotis discus hannai Ino. Single EST or EST-contigs from 66 genes that had positive BLASTx matches (E-value ≤ 1e-8) were used for polymerase chain reaction (PCR) amplification. PCR products from the two parents of one mapping family were directly sequenced, and 83 SNP loci were found from 17 genes. Allele-specific PCR (AS-PCR) was developed and optimized for genotyping of 11 SNP loci in 120 progeny of the mapping family. Nine of the loci conformed to the expected Mendelian ratio of 1:1 based on the χ² test (P > 0.05) and could potentially be used for linkage map construction. Our data also indicate that the sequencing of two parents may be a practical strategy for the discovery of informative SNPs for linkage mapping in a particular mapping population.     

Single nucleotide polymorphisms generated by genotyping by sequencing to characterize genome-wide diversity,linkage disequilibrium,and selective sweeps in cultivated watermelon

Background

A large single nucleotide polymorphism (SNP) dataset was used to analyze genome-wide diversity in a diverse collection of watermelon cultivars representing globally cultivated, watermelon genetic diversity. The marker density required for conducting successful association mapping depends on the extent of linkage disequilibrium (LD) within a population. Use of genotyping by sequencing reveals large numbers of SNPs that in turn generate opportunities in genome-wide association mapping and marker-assisted selection, even in crops such as watermelon for which few genomic resources are available. In this paper, we used genome-wide genetic diversity to study LD, selective sweeps, and pairwise F_ST distributions among worldwide cultivated watermelons to track signals of domestication.

Results

We examined 183 Citrullus lanatus var. lanatus accessions representing domesticated watermelon and generated a set of 11,485 SNP markers using genotyping by sequencing. With a diverse panel of worldwide cultivated watermelons, we identified a set of 5,254 SNPs with a minor allele frequency of ≥ 0.05, distributed across the genome. All ancestries were traced to Africa and an admixture of various ancestries constituted secondary gene pools across various continents. A sliding window analysis using pairwise F_ST values was used to resolve selective sweeps. We identified strong selection on chromosomes 3 and 9 that might have contributed to the domestication process. Pairwise analysis of adjacent SNPs within a chromosome as well as within a haplotype allowed us to estimate genome-wide LD decay. LD was also detected within individual genes on various chromosomes. Principal component and ancestry analyses were used to account for population structure in a genome-wide association study. We further mapped important genes for soluble solid content using a mixed linear model.

Conclusions

Information concerning the SNP resources, population structure, and LD developed in this study will help in identifying agronomically important candidate genes from the genomic regions underlying selection and for mapping quantitative trait loci using a genome-wide association study in sweet watermelon.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-767) contains supplementary material, which is available to authorized users.     

<Emphasis Type="Bold">A user guide to the</Emphasis><Emphasis Type="BoldItalic">Brassica</Emphasis><Emphasis Type="Bold">60K Illumina Infinium</Emphasis>™ <Emphasis Type="Bold">SNP genotyping array</Emphasis>

The Brassica napus 60K Illumina Infinium? SNP array has had huge international uptake in the rapeseed community due to the revolutionary speed of acquisition and ease of analysis of this high-throughput genotyping data, particularly when coupled with the newly available reference genome sequence. However, further utilization of this valuable resource can be optimized by better understanding the promises and pitfalls of SNP arrays. We outline how best to analyze Brassica SNP marker array data for diverse applications, including linkage and association mapping, genetic diversity and genomic introgression studies. We present data on which SNPs are locus-specific in winter, semi-winter and spring B. napus germplasm pools, rather than amplifying both an A-genome and a C-genome locus or multiple loci. Common issues that arise when analyzing array data will be discussed, particularly those unique to SNP markers and how to deal with these for practical applications in Brassica breeding applications.     

Selection of haplotype variables from a high-density marker map for genomic prediction

Background

Using haplotype blocks as predictors rather than individual single nucleotide polymorphisms (SNPs) may improve genomic predictions, since haplotypes are in stronger linkage disequilibrium with the quantitative trait loci than are individual SNPs. It has also been hypothesized that an appropriate selection of a subset of haplotype blocks can result in similar or better predictive ability than when using the whole set of haplotype blocks. This study investigated genomic prediction using a set of haplotype blocks that contained the SNPs with large effects estimated from an individual SNP prediction model. We analyzed protein yield, fertility and mastitis of Nordic Holstein cattle, and used high-density markers (about 770k SNPs). To reach an optimum number of haplotype variables for genomic prediction, predictions were performed using subsets of haplotype blocks that contained a range of 1000 to 50 000 main SNPs.

Results

The use of haplotype blocks improved the prediction reliabilities, even when selection focused on only a group of haplotype blocks. In this case, the use of haplotype blocks that contained the 20 000 to 50 000 SNPs with the highest effect was sufficient to outperform the model that used all individual SNPs as predictors (up to 1.3 % improvement in prediction reliability for mastitis, compared to individual SNP approach), and the achieved reliabilities were similar to those using all haplotype blocks available in the genome data (from 0.6 % lower to 0.8 % higher reliability).

Conclusions

Haplotype blocks used as predictors can improve the reliability of genomic prediction compared to the individual SNP model. Furthermore, the use of a subset of haplotype blocks that contains the main SNP effects from genomic data could be a feasible approach to genomic prediction in dairy cattle, given an increase in density of genotype data available. The predictive ability of the models that use a subset of haplotype blocks was similar to that obtained using either all haplotype blocks or all individual SNPs, with the benefit of having a much lower computational demand.     

Genomic DNA Enrichment Using Sequence Capture Microarrays: a Novel Approach to Discover Sequence Nucleotide Polymorphisms (SNP) in Brassica napus L

Targeted genomic selection methodologies, or sequence capture, allow for DNA enrichment and large-scale resequencing and characterization of natural genetic variation in species with complex genomes, such as rapeseed canola (Brassica napus L., AACC, 2n=38). The main goal of this project was to combine sequence capture with next generation sequencing (NGS) to discover single nucleotide polymorphisms (SNPs) in specific areas of the B. napus genome historically associated (via quantitative trait loci –QTL– analysis) to traits of agronomical and nutritional importance. A 2.1 million feature sequence capture platform was designed to interrogate DNA sequence variation across 47 specific genomic regions, representing 51.2 Mb of the Brassica A and C genomes, in ten diverse rapeseed genotypes. All ten genotypes were sequenced using the 454 Life Sciences chemistry and to assess the effect of increased sequence depth, two genotypes were also sequenced using Illumina HiSeq chemistry. As a result, 589,367 potentially useful SNPs were identified. Analysis of sequence coverage indicated a four-fold increased representation of target regions, with 57% of the filtered SNPs falling within these regions. Sixty percent of discovered SNPs corresponded to transitions while 40% were transversions. Interestingly, fifty eight percent of the SNPs were found in genic regions while 42% were found in intergenic regions. Further, a high percentage of genic SNPs was found in exons (65% and 64% for the A and C genomes, respectively). Two different genotyping assays were used to validate the discovered SNPs. Validation rates ranged from 61.5% to 84% of tested SNPs, underpinning the effectiveness of this SNP discovery approach. Most importantly, the discovered SNPs were associated with agronomically important regions of the B. napus genome generating a novel data resource for research and breeding this crop species.     

Genome-wide association mapping of salinity tolerance in rice (Oryza sativa)

Salinity tolerance in rice is highly desirable to sustain production in areas rendered saline due to various reasons. It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS). Here, we implemented GWAS to identify loci controlling salinity tolerance in rice. A custom-designed array based on 6,000 single nucleotide polymorphisms (SNPs) in as many stress-responsive genes, distributed at an average physical interval of <100 kb on 12 rice chromosomes, was used to genotype 220 rice accessions using Infinium high-throughput assay. Genetic association was analysed with 12 different traits recorded on these accessions under field conditions at reproductive stage. We identified 20 SNPs (loci) significantly associated with Na⁺/K⁺ ratio, and 44 SNPs with other traits observed under stress condition. The loci identified for various salinity indices through GWAS explained 5–18% of the phenotypic variance. The region harbouring Saltol, a major quantitative trait loci (QTLs) on chromosome 1 in rice, which is known to control salinity tolerance at seedling stage, was detected as a major association with Na⁺/K⁺ ratio measured at reproductive stage in our study. In addition to Saltol, we also found GWAS peaks representing new QTLs on chromosomes 4, 6 and 7. The current association mapping panel contained mostly indica accessions that can serve as source of novel salt tolerance genes and alleles. The gene-based SNP array used in this study was found cost-effective and efficient in unveiling genomic regions/candidate genes regulating salinity stress tolerance in rice.     

Genomic Characterization of Burkholderia pseudomallei Isolates Selected for Medical Countermeasures Testing: Comparative Genomics Associated with Differential Virulence

Burkholderia pseudomallei is the causative agent of melioidosis and a potential bioterrorism agent. In the development of medical countermeasures against B. pseudomallei infection, the US Food and Drug Administration (FDA) animal Rule recommends using well-characterized strains in animal challenge studies. In this study, whole genome sequence data were generated for 6 B. pseudomallei isolates previously identified as candidates for animal challenge studies; an additional 5 isolates were sequenced that were associated with human inhalational melioidosis. A core genome single nucleotide polymorphism (SNP) phylogeny inferred from a concatenated SNP alignment from the 11 isolates sequenced in this study and a diverse global collection of isolates demonstrated the diversity of the proposed Animal Rule isolates. To understand the genomic composition of each isolate, a large-scale blast score ratio (LS-BSR) analysis was performed on the entire pan-genome; this demonstrated the variable composition of genes across the panel and also helped to identify genes unique to individual isolates. In addition, a set of ~550 genes associated with pathogenesis in B. pseudomallei were screened against the 11 sequenced genomes with LS-BSR. Differential gene distribution for 54 virulence-associated genes was observed between genomes and three of these genes were correlated with differential virulence observed in animal challenge studies using BALB/c mice. Differentially conserved genes and SNPs associated with disease severity were identified and could be the basis for future studies investigating the pathogenesis of B. pseudomallei. Overall, the genetic characterization of the 11 proposed Animal Rule isolates provides context for future studies involving B. pseudomallei pathogenesis, differential virulence, and efficacy to therapeutics.     

Refining Susceptibility Loci of Chronic Obstructive Pulmonary Disease with Lung eqtls

Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of mortality worldwide. Recent genome-wide association studies (GWAS) have identified robust susceptibility loci associated with COPD. However, the mechanisms mediating the risk conferred by these loci remain to be found. The goal of this study was to identify causal genes/variants within susceptibility loci associated with COPD. In the discovery cohort, genome-wide gene expression profiles of 500 non-tumor lung specimens were obtained from patients undergoing lung surgery. Blood-DNA from the same patients were genotyped for 1,2 million SNPs. Following genotyping and gene expression quality control filters, 409 samples were analyzed. Lung expression quantitative trait loci (eQTLs) were identified and overlaid onto three COPD susceptibility loci derived from GWAS; 4q31 (HHIP), 4q22 (FAM13A), and 19q13 (RAB4B, EGLN2, MIA, CYP2A6). Significant eQTLs were replicated in two independent datasets (n = 363 and 339). SNPs previously associated with COPD and lung function on 4q31 (rs1828591, rs13118928) were associated with the mRNA expression of HHIP. An association between mRNA expression level of FAM13A and SNP rs2045517 was detected at 4q22, but did not reach statistical significance. At 19q13, significant eQTLs were detected with EGLN2. In summary, this study supports HHIP, FAM13A, and EGLN2 as the most likely causal COPD genes on 4q31, 4q22, and 19q13, respectively. Strong lung eQTL SNPs identified in this study will need to be tested for association with COPD in case-control studies. Further functional studies will also be needed to understand the role of genes regulated by disease-related variants in COPD.     

A high-throughput SNP array in the amphidiploid species Brassica napus shows diversity in resistance genes

Single-nucleotide polymorphisms (SNPs)are molecular markers based on nucleotide variation and can be used for genotyping assays across populations and to track genomic inheritance. SNPs offer a comprehensive genotyping alternative to whole-genome sequencing for both agricultural and research purposes including molecular breeding and diagnostics, genome evolution and genetic diversity analyses, genetic mapping, and trait association studies. Here genomic SNPs were discovered between four cultivars of the important amphidiploid oilseed species Brassica napus and used to develop a B. napus Infinium? array containing 5,306 SNPs randomly dispersed across the genome. Assay success was high, with >94 % of these producing a reproducible, polymorphic genotype in the 1,070 samples screened. Although the assay was designed to B. napus, successful SNP amplification was achieved in the B. napus progenitor species, Brassica rapa and Brassica oleracea, and to a lesser extent in the related species Brassica nigra. Phylogenetic analysis was consistent with the expected relationships between B. napus individuals. This study presents an efficient custom SNP assay development pipeline in the complex polyploid Brassica genome and demonstrates the utility of the array for high-throughput genotyping in a number of related Brassica species. It also demonstrates the utility of this assay in genotyping resistance genes on chromosome A7, which segregate amongst the 1,070 samples.     

Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.)

Genome-wide mapping approaches in diverse populations are powerful tools to unravel the genetic architecture of complex traits. The main goals of our study were to investigate the potential and limits to unravel the genetic architecture and to identify the factors determining the accuracy of prediction of the genotypic variation of Fusarium head blight (FHB) resistance in wheat (Triticum aestivum L.) based on data collected with a diverse panel of 372 European varieties. The wheat lines were phenotyped in multi-location field trials for FHB resistance and genotyped with 782 simple sequence repeat (SSR) markers, and 9k and 90k single-nucleotide polymorphism (SNP) arrays. We applied genome-wide association mapping in combination with fivefold cross-validations and observed surprisingly high accuracies of prediction for marker-assisted selection based on the detected quantitative trait loci (QTLs). Using a random sample of markers not selected for marker–trait associations revealed only a slight decrease in prediction accuracy compared with marker-based selection exploiting the QTL information. The same picture was confirmed in a simulation study, suggesting that relatedness is a main driver of the accuracy of prediction in marker-assisted selection of FHB resistance. When the accuracy of prediction of three genomic selection models was contrasted for the three marker data sets, no significant differences in accuracies among marker platforms and genomic selection models were observed. Marker density impacted the accuracy of prediction only marginally. Consequently, genomic selection of FHB resistance can be implemented most cost-efficiently based on low- to medium-density SNP arrays.     

Evaluation of Linkage Disequilibrium Pattern and Association Study on Seed Oil Content in Brassica napus Using ddRAD Sequencing

High-density genetic markers are the prerequisite for understanding linkage disequilibrium (LD) and genome-wide association studies (GWASs) of complex traits in crops. To evaluate the LD pattern in oilseed rape, we sequenced a previous association panel containing 189 B. napus inbred lines using double-digested restriction-site associated DNA (ddRAD) and genotyped 19,327 RAD tags. A total of 15,921 RAD tags were assigned to a published genetic linkage map and the majority (71.1%) of these tags was uniquely mapped to the draft reference genome “Darmor-bzh.” The distance of LD decay was 1,214 kb across the genome at the background level (r² = 0.26), with the distances of LD decay being 405 kb and 2,111 kb in the A and C subgenomes, respectively. A total of 361 haplotype blocks with length > 100 kb were identified in the entire genome. The association panel could be classified into two groups, P1 and P2, which are essentially consistent with the geographical origins of varieties. A large number of group-specific haplotypes were identified, reflecting that varieties in the P1 and P2 groups experienced distinct selection in breeding programs to adapt their different growth habitats. GWAS repeatedly detected two loci significantly associated with oil content of seeds based on the developed SNPs, suggesting that the high-density SNPs were useful for understanding the genetic determinants of complex traits in GWAS.