Construction of high resolution genetic linkage maps to improve the soybean genome sequence assembly Glyma1.01.

BACKGROUND: A landmark in soybean research, Glyma1.01, the first whole genome sequence of variety Williams 82 (Glycine max L. Merr.) was completed in 2010 and is widely used. However, because the assembly was primarily built based on the linkage maps constructed with a limited number of markers and recombinant inbred lines (RILs), the assembled sequence, especially in some genomic regions with sparse numbers of anchoring markers, needs to be improved. Molecular markers are being used by researchers in the soybean community, however, with the updating of the Glyma1.01 build based on the high-resolution linkage maps resulting from this research, the genome positions of these markers need to be mapped. RESULTS: Two high density genetic linkage maps were constructed based on 21,478 single nucleotide polymorphism loci mapped in the Williams 82 x G. soja (Sieb. & Zucc.) PI479752 population with 1083 RILs and 11,922 loci mapped in the Essex x Williams 82 population with 922 RILs. There were 37 regions or single markers where marker order in the two populations was in agreement but was not consistent with the physical position in the Glyma1.01 build. In addition, 28 previously unanchored scaffolds were positioned. Map data were used to identify false joins in the Glyma1.01 assembly and the corresponding scaffolds were broken and reassembled to the new assembly, Wm82.a2.v1. Based upon the plots of the genetic on physical distance of the loci, the euchromatic and heterochromatic regions along each chromosome in the new assembly were delimited. Genomic positions of the commonly used markers contained in BARCSOYSSR_1.0 database and the SoySNP50K BeadChip were updated based upon the Wm82.a2.v1 assembly. CONCLUSIONS: The information will facilitate the study of recombination hot spots in the soybean genome, identification of genes or quantitative trait loci controlling yield, seed quality and resistance to biotic or abiotic stresses as well as other genetic or genomic research.

Genome sequence of the palaeopolyploid soybean.

Soybean (Glycine max) is one of the most important crop plants for seed protein and oil content, and for its capacity to fix atmospheric nitrogen through symbioses with soil-borne microorganisms. We sequenced the 1.1-gigabase genome by a whole-genome shotgun approach and integrated it with physical and high-density genetic maps to create a chromosome-scale draft sequence assembly. We predict 46,430 protein-coding genes, 70% more than Arabidopsis and similar to the poplar genome which, like soybean, is an ancient polyploid (palaeopolyploid). About 78% of the predicted genes occur in chromosome ends, which comprise less than one-half of the genome but account for nearly all of the genetic recombination. Genome duplications occurred at approximately 59 and 13 million years ago, resulting in a highly duplicated genome with nearly 75% of the genes present in multiple copies. The two duplication events were followed by gene diversification and loss, and numerous chromosome rearrangements. An accurate soybean genome sequence will facilitate the identification of the genetic basis of many soybean traits, and accelerate the creation of improved soybean varieties.

A genome-wide association study of seed protein and oil content in soybean.

BACKGROUND: Association analysis is an alternative to conventional family-based methods to detect the location of gene(s) or quantitative trait loci (QTL) and provides relatively high resolution in terms of defining the genome position of a gene or QTL. Seed protein and oil concentration are quantitative traits which are determined by the interaction among many genes with small to moderate genetic effects and their interaction with the environment. In this study, a genome-wide association study (GWAS) was performed to identify quantitative trait loci (QTL) controlling seed protein and oil concentration in 298 soybean germplasm accessions exhibiting a wide range of seed protein and oil content. RESULTS: A total of 55,159 single nucleotide polymorphisms (SNPs) were genotyped using various methods including Illumina Infinium and GoldenGate assays and 31,954 markers with minor allele frequency >0.10 were used to estimate linkage disequilibrium (LD) in heterochromatic and euchromatic regions. In euchromatic regions, the mean LD (r2) rapidly declined to 0.2 within 360 Kbp, whereas the mean LD declined to 0.2 at 9,600 Kbp in heterochromatic regions. The GWAS results identified 40 SNPs in 17 different genomic regions significantly associated with seed protein. Of these, the five SNPs with the highest associations and seven adjacent SNPs were located in the 27.6-30.0 Mbp region of Gm20. A major seed protein QTL has been previously mapped to the same location and potential candidate genes have recently been identified in this region. The GWAS results also detected 25 SNPs in 13 different genomic regions associated with seed oil. Of these markers, seven SNPs had a significant association with both protein and oil. CONCLUSIONS: This research indicated that GWAS not only identified most of the previously reported QTL controlling seed protein and oil, but also resulted in narrower genomic regions than the regions reported as containing these QTL. The narrower GWAS-defined genome regions will allow more precise marker-assisted allele selection and will expedite positional cloning of the causal gene(s).

Identification and validation of quantitative trait loci for seed yield, oil and protein contents in two recombinant inbred line populations of soybean.

Soybean seeds contain high levels of oil and protein, and are the important sources of vegetable oil and plant protein for human consumption and livestock feed. Increased seed yield, oil and protein contents are the main objectives of soybean breeding. The objectives of this study were to identify and validate quantitative trait loci (QTLs) associated with seed yield, oil and protein contents in two recombinant inbred line populations, and to evaluate the consistency of QTLs across different environments, studies and genetic backgrounds. Both the mapping population (SD02-4-59 × A02-381100) and validation population (SD02-911 × SD00-1501) were phenotyped for the three traits in multiple environments. Genetic analysis indicated that oil and protein contents showed high heritabilities while yield exhibited a lower heritability in both populations. Based on a linkage map constructed previously with the mapping population and using composite interval mapping and/or interval mapping analysis, 12 QTLs for seed yield, 16 QTLs for oil content and 11 QTLs for protein content were consistently detected in multiple environments and/or the average data over all environments. Of the QTLs detected in the mapping population, five QTLs for seed yield, eight QTLs for oil content and five QTLs for protein content were confirmed in the validation population by single marker analysis in at least one environment and the average data and by ANOVA over all environments. Eight of these validated QTLs were newly identified. Compared with the other studies, seven QTLs for seed yield, eight QTLs for oil content and nine QTLs for protein content further verified the previously reported QTLs. These QTLs will be useful for breeding higher yield and better quality cultivars, and help effectively and efficiently improve yield potential and nutritional quality in soybean.

Structural variants in the soybean genome localize to clusters of biotic stress-response genes.

Genome-wide structural and gene content variations are hypothesized to drive important phenotypic variation within a species. Structural and gene content variations were assessed among four soybean (Glycine max) genotypes using array hybridization and targeted resequencing. Many chromosomes exhibited relatively low rates of structural variation (SV) among genotypes. However, several regions exhibited both copy number and presence-absence variation, the most prominent found on chromosomes 3, 6, 7, 16, and 18. Interestingly, the regions most enriched for SV were specifically localized to gene-rich regions that harbor clustered multigene families. The most abundant classes of gene families associated with these regions were the nucleotide-binding and receptor-like protein classes, both of which are important for plant biotic defense. The colocalization of SV with plant defense response signal transduction pathways provides insight into the mechanisms of soybean resistance gene evolution and may inform the development of new approaches to resistance gene cloning.

Structural and functional divergence of a 1-Mb duplicated region in the soybean (Glycine max) genome and comparison to an orthologous region from Phaseolus vulgaris.

Soybean (Glycine max) has undergone at least two rounds of polyploidization, resulting in a paleopolyploid genome that is a mosaic of homoeologous regions. To determine the structural and functional impact of these duplications, we sequenced two ~1-Mb homoeologous regions of soybean, Gm8 and Gm15, derived from the most recent ~13 million year duplication event and the orthologous region from common bean (Phaseolus vulgaris), Pv5. We observed inversions leading to major structural variation and a bias between the two chromosome segments as Gm15 experienced more gene movement (gene retention rate of 81% in Gm15 versus 91% in Gm8) and a nearly twofold increase in the deletion of long terminal repeat (LTR) retrotransposons via solo LTR formation. Functional analyses of Gm15 and Gm8 revealed decreases in gene expression and synonymous substitution rates for Gm15, for instance, a 38% increase in transcript levels from Gm8 relative to Gm15. Transcriptional divergence of homoeologs was found based on expression patterns among seven tissues and developmental stages. Our results indicate asymmetric evolution between homoeologous regions of soybean as evidenced by structural changes and expression variances of homoeologous genes.

The composition and origins of genomic variation among individuals of the soybean reference cultivar Williams 82.

Soybean (Glycine max) is a self-pollinating species that has relatively low nucleotide polymorphism rates compared with other crop species. Despite the low rate of nucleotide polymorphisms, a wide range of heritable phenotypic variation exists. There is even evidence for heritable phenotypic variation among individuals within some cultivars. Williams 82, the soybean cultivar used to produce the reference genome sequence, was derived from backcrossing a Phytophthora root rot resistance locus from the donor parent Kingwa into the recurrent parent Williams. To explore the genetic basis of intracultivar variation, we investigated the nucleotide, structural, and gene content variation of different Williams 82 individuals. Williams 82 individuals exhibited variation in the number and size of introgressed Kingwa loci. In these regions of genomic heterogeneity, the reference Williams 82 genome sequence consists of a mosaic of Williams and Kingwa haplotypes. Genomic structural variation between Williams and Kingwa was maintained between the Williams 82 individuals within the regions of heterogeneity. Additionally, the regions of heterogeneity exhibited gene content differences between Williams 82 individuals. These findings show that genetic heterogeneity in Williams 82 primarily originated from the differential segregation of polymorphic chromosomal regions following the backcross and single-seed descent generations of the breeding process. We conclude that soybean haplotypes can possess a high rate of structural and gene content variation, and the impact of intracultivar genetic heterogeneity may be significant. This detailed characterization will be useful for interpreting soybean genomic data sets and highlights important considerations for research communities that are developing or utilizing a reference genome sequence.

Molecular mapping of soybean rust resistance in soybean accession PI 561356 and SNP haplotype analysis of the Rpp1 region in diverse germplasm.

Soybean rust (SBR), caused by Phakopsora pachyrhizi Sydow, is one of the most economically important and destructive diseases of soybean [Glycine max (L.) Merr.] and the discovery of novel SBR resistance genes is needed because of virulence diversity in the pathogen. The objectives of this research were to map SBR resistance in plant introduction (PI) 561356 and to identify single nucleotide polymorphism (SNP) haplotypes within the region on soybean chromosome 18 where the SBR resistance gene Rpp1 maps. One-hundred F(2:3) lines derived from a cross between PI 561356 and the susceptible experimental line LD02-4485 were genotyped with genetic markers and phenotyped for resistance to P. pachyrhizi isolate ZM01-1. The segregation ratio of reddish brown versus tan lesion type in the population supported that resistance was controlled by a single dominant gene. The gene was mapped to a 1-cM region on soybean chromosome 18 corresponding to the same interval as Rpp1. A haplotype analysis of diverse germplasm across a 213-kb interval that included Rpp1 revealed 21 distinct haplotypes of which 4 were present among 5 SBR resistance sources that have a resistance gene in the Rpp1 region. Four major North American soybean ancestors belong to the same SNP haplotype as PI 561356 and seven belong to the same haplotype as PI 594538A, the Rpp1-b source. There were no North American soybean ancestors belonging to the SNP haplotypes found in PI 200492, the source of Rpp1, or PI 587886 and PI 587880A, additional sources with SBR resistance mapping to the Rpp1 region.

Genotyping Platforms for Genome-Wide Association Studies: Options and Practical Considerations.

Genome-wide association studies (GWAS) in crops requires genotyping platforms that are capable of producing accurate high density genotyping data on hundreds of plants in a cost-effective manner. Currently there are multiple commercial platforms available that are being effectively used across crops. These platforms include genotyping arrays such as the Illumina Infinium arrays and the Applied Biosystems Axiom Arrays along with a variety of resequencing methods. These methods are being used to genotype tens of thousands of markers up to millions of markers on GWAS panels. They are being used on crops with simple genomes to crops with very complex, large, polyploid genomes. Depending on the crop and the goal of the GWAS, there are several options and practical considerations to take into account when selecting a genotyping technology to ensure that the right coverage, accuracy, and cost for the study is achieved.

An integrative approach to genomic introgression mapping.

Near-isogenic lines (NILs) are valuable genetic resources for many crop species, including soybean (Glycine max). The development of new molecular platforms promises to accelerate the mapping of genetic introgressions in these materials. Here, we compare some existing and emerging methodologies for genetic introgression mapping: single-feature polymorphism analysis, Illumina GoldenGate single nucleotide polymorphism (SNP) genotyping, and de novo SNP discovery via RNA-Seq analysis of next-generation sequence data. We used these methods to map the introgressed regions in an iron-inefficient soybean NIL and found that the three mapping approaches are complementary when utilized in combination. The comparative RNA-Seq approach offers several additional advantages, including the greatest mapping resolution, marker depth, and de novo marker utility for downstream fine-mapping analysis. We applied the comparative RNA-Seq method to map genetic introgressions in an additional pair of NILs exhibiting differential seed protein content. Furthermore, we attempted to optimize the comparative RNA-Seq approach by assessing the impact of sequence depth, SNP identification methodology, and post hoc analyses on SNP discovery rates. We conclude that the comparative RNA-Seq approach can be optimized with sufficient sampling and by utilizing a post hoc correction accounting for gene density variation that controls for false discoveries.

Mutational analysis of the major soybean UreF paralogue involved in urease activation.

The soybean genome duplicated ∼14 and 45 million years ago and has many paralogous genes, including those in urease activation (emplacement of Ni and CO(2) in the active site). Activation requires the UreD and UreF proteins, each encoded by two paralogues. UreG, a third essential activation protein, is encoded by the single-copy Eu3, and eu3 mutants lack activity of both urease isozymes. eu2 has the same urease-negative phenotype, consistent with Eu2 being a single-copy gene, possibly encoding a Ni carrier. Unexpectedly, two eu2 alleles co-segregated with missense mutations in the chromosome 2 UreF paralogue (Ch02UreF), suggesting lack of expression/function of Ch14UreF. However, Ch02UreF and Ch14UreF transcripts accumulate at the same level. Further, it had been shown that expression of the Ch14UreF ORF complemented a fungal ureF mutant. A third, nonsense (Q2*) allelic mutant, eu2-c, exhibited 5- to 10-fold more residual urease activity than missense eu2-a or eu2-b, though eu2-c should lack all Ch02UreF protein. It is hypothesized that low-level activation by Ch14UreF is 'spoiled' by the altered missense Ch02UreF proteins ('epistatic dominant-negative'). In agreement with active 'spoiling' by eu2-b-encoded Ch02UreF (G31D), eu2-b/eu2-c heterozygotes had less than half the urease activity of eu2-c/eu2-c siblings. Ch02UreF (G31D) could spoil activation by Chr14UreF because of higher affinity for the activation complex, or because Ch02UreF (G31D) is more abundant than Ch14UreF. Here, the latter is favoured, consistent with a reported in-frame AUG in the 5' leader of Chr14UreF transcript. Translational inhibition could represent a form of 'functional divergence' of duplicated genes.

Identification of a second Asian soybean rust resistance gene in Hyuuga soybean.

ABSTRACT Asian soybean rust (ASR) is an economically significant disease caused by the fungus Phakopsora pachyrhizi. The soybean genes Rpp3 and Rpp?(Hyuuga) confer resistance to specific isolates of the pathogen. Both genes map to chromosome 6 (Gm06) (linkage group [LG] C2). We recently identified 12 additional soybean accessions that harbor ASR resistance mapping to Gm06, within 5 centimorgans of Rpp3 and Rpp?(Hyuuga). To further characterize genotypes with resistance on Gm06, we used a set of eight P. pachyrhizi isolates collected from geographically diverse areas to inoculate plants and evaluate them for differential phenotypic responses. Three isolates elicited different responses from soybean accessions PI 462312 (Ankur) (Rpp3) and PI 506764 (Hyuuga) (Rpp?[Hyuuga]). In all, 11 of the new accessions yielded responses identical to either PI 462312 or Hyuuga and 1 of the new accessions, PI 417089B (Kuro daizu), differed from all others. Additional screening of Hyuuga-derived recombinant inbred lines indicated that Hyuuga carries two resistance genes, one at the Rpp3 locus on Gm06 and a second, unlinked ASR resistance gene mapping to Gm03 (LG-N) near Rpp5. These findings reveal a natural case of gene pyramiding for ASR resistance in Hyuuga and underscore the importance of utilizing multiple isolates of P. pachyrhizi when screening for ASR resistance.

Comparing a Mixed Model Approach to Traditional Stability Estimators for Mapping Genotype by Environment Interactions and Yield Stability in Soybean [Glycine max (L.) Merr.].

Identifying genetic loci associated with yield stability has helped plant breeders and geneticists begin to understand the role and influence of genotype by environment (GxE) interactions in soybean [Glycine max (L.) Merr.] productivity, as well as other crops. Quantifying a genotype's range of performance across testing locations has been developed over decades with dozens of methodologies available. This includes directly modeling GxE interactions as part of an overall model for yield, as well as methods which generate overall yield "stability" values from multi-environment trial data. Correspondence between these methods as it pertains to the outcomes of genome wide association studies (GWAS) has not been well defined. In this study, the GWAS results for yield and yield stability were compared in 213 soybean lines across 11 environments to determine their utility and potential intersection. Both univariate and multivariate conventional stability estimates were considered alongside a mixed model for yield that fit marker by environment interactions as a random effect. One-hundred and six total QTL were discovered across all mapping results, however, genetic loci that were significant in the mixed model for grain yield that fit marker by environment interactions were completely distinct from those that were significant when mapping using traditional stability measures as a phenotype. Furthermore, 73.21% of QTL discovered in the mixed model were determined to cause a crossover interaction effect which cause genotype rank changes between environments. Overall, the QTL discovered via explicitly mapping GxE interactions also explained more yield variance that those QTL associated with differences in traditional stability estimates making their theoretical impact on selection greater. A lack of intersecting results between mapping approaches highlights the importance of examining stability in multiple contexts when attempting to manipulate GxE interactions in soybean.

High-throughput SNP discovery and assay development in common bean.

BACKGROUND: Next generation sequencing has significantly increased the speed at which single nucleotide polymorphisms (SNPs) can be discovered and subsequently used as molecular markers for research. Unfortunately, for species such as common bean (Phaseolus vulgaris L.) which do not have a whole genome sequence available, the use of next generation sequencing for SNP discovery is much more difficult and costly. To this end we developed a method which couples sequences obtained from the Roche 454-FLX system (454) with the Illumina Genome Analyzer (GA) for high-throughput SNP discovery. RESULTS: Using a multi-tier reduced representation library we discovered a total of 3,487 SNPs of which 2,795 contained sufficient flanking genomic sequence for SNP assay development. Using Sanger sequencing to determine the validation rate of these SNPs, we found that 86% are likely to be true SNPs. Furthermore, we designed a GoldenGate assay which contained 1,050 of the 3,487 predicted SNPs. A total of 827 of the 1,050 SNPs produced a working GoldenGate assay (79%). CONCLUSIONS: Through combining two next generation sequencing techniques we have developed a method that allows high-throughput SNP discovery in any diploid organism without the need of a whole genome sequence or the creation of normalized cDNA libraries. The need to only perform one 454 run and one GA sequencer run allows high-throughput SNP discovery with sufficient sequence for assay development to be performed in organisms, such as common bean, which have limited genomic resources.

High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence.

BACKGROUND: The Soybean Consensus Map 4.0 facilitated the anchoring of 95.6% of the soybean whole genome sequence developed by the Joint Genome Institute, Department of Energy, but its marker density was only sufficient to properly orient 66% of the sequence scaffolds. The discovery and genetic mapping of more single nucleotide polymorphism (SNP) markers were needed to anchor and orient the remaining genome sequence. To that end, next generation sequencing and high-throughput genotyping were combined to obtain a much higher resolution genetic map that could be used to anchor and orient most of the remaining sequence and to help validate the integrity of the existing scaffold builds. RESULTS: A total of 7,108 to 25,047 predicted SNPs were discovered using a reduced representation library that was subsequently sequenced by the Illumina sequence-by-synthesis method on the clonal single molecule array platform. Using multiple SNP prediction methods, the validation rate of these SNPs ranged from 79% to 92.5%. A high resolution genetic map using 444 recombinant inbred lines was created with 1,790 SNP markers. Of the 1,790 mapped SNP markers, 1,240 markers had been selectively chosen to target existing unanchored or un-oriented sequence scaffolds, thereby increasing the amount of anchored sequence to 97%. CONCLUSION: We have demonstrated how next generation sequencing was combined with high-throughput SNP detection assays to quickly discover large numbers of SNPs. Those SNPs were then used to create a high resolution genetic map that assisted in the assembly of scaffolds from the 8x whole genome shotgun sequences into pseudomolecules corresponding to chromosomes of the organism.

Fine mapping of the soybean aphid-resistance gene Rag2 in soybean PI 200538.

The discovery of biotype diversity of soybean aphid (SA: Aphis glycines Matsumura) in North America emphasizes the necessity to identify new aphid-resistance genes. The soybean [Glycine max (L.) Merr.] plant introduction (PI) 200538 is a promising source of SA resistance because it shows a high level of resistance to a SA biotype that can overcome the SA-resistance gene Rag1 from 'Dowling'. The SA-resistance gene Rag2 was previously mapped from PI 200538 to a 10-cM marker interval on soybean chromosome 13 [formerly linkage group (LG) F]. The objective of this study was to fine map Rag2. This fine mapping was carried out using lines derived from 5,783 F(2) plants at different levels of backcrossing that were screened with flanking genetic markers for the presence of recombination in the Rag2 interval. Fifteen single nucleotide polymorphism (SNP) markers and two dominant polymerase chain reaction-based markers near Rag2 were developed by re-sequencing target intervals and sequence-tagged sites. These efforts resulted in the mapping of Rag2 to a 54-kb interval on the Williams 82 8x assembly (Glyma1). This Williams 82 interval contains seven predicted genes, which includes one nucleotide-binding site-leucine-rich repeat gene. SNP marker and candidate gene information identified in this study will be an important resource in marker-assisted selection for aphid resistance and for cloning the gene.

Fine mapping the soybean aphid resistance gene Rag1 in soybean.

The soybean aphid (Aphis glycines Matsumura) is an important soybean [Glycine max (L.) Merr.] pest in North America. The dominant aphid resistance gene Rag1 was previously mapped from the cultivar 'Dowling' to a 12 cM marker interval on soybean chromosome 7 (formerly linkage group M). The development of additional genetic markers mapping closer to Rag1 was needed to accurately position the gene to improve the effectiveness of marker-assisted selection (MAS) and to eventually clone it. The objectives of this study were to identify single nucleotide polymorphisms (SNPs) near Rag1 and to position these SNPs relative to Rag1. To generate a fine map of the Rag1 interval, 824 BC(4)F(2) and 1,000 BC(4)F(3) plants segregating for the gene were screened with markers flanking Rag1. Plants with recombination events close to the gene were tested with SNPs identified in previous studies along with new SNPs identified from the preliminary Williams 82 draft soybean genome shotgun sequence using direct re-sequencing and gene-scanning melt-curve analysis. Progeny of these recombinant plants were evaluated for aphid resistance. These efforts resulted in the mapping of Rag1 between the two SNP markers 46169.7 and 21A, which corresponds to a physical distance on the Williams 82 8x draft assembly (Glyma1.01) of 115 kilobase pair (kb). Several candidate genes for Rag1 are present within the 115-kb interval. The markers identified in this study that are closely linked to Rag1 will be a useful resource in MAS for this important aphid resistance gene.

Generating High Density, Low Cost Genotype Data in Soybean [Glycine max (L.) Merr.].

Obtaining genome-wide genotype information for millions of SNPs in soybean [Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage. Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI Short Read Archive. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using GATK's Haplotype Caller tool. Whole genome resequencing at approximately 1X depth was performed on 114 previously ungenotyped experimental soybean lines. Coverages down to 0.1X were analyzed by randomly subsetting raw reads from the original 1X sequence data. SNPs discovered in the reference panel were genotyped in the experimental lines after aligning to the soybean reference genome, and missing markers imputed using Beagle 4.1. Sequencing depth of the experimental lines could be reduced to 0.3X while still retaining an accuracy of 97.8%. Accuracy was inversely related to minor allele frequency, and highly correlated with marker linkage disequilibrium. The high accuracy of skim sequencing combined with imputation provides a low cost method for obtaining dense genotypic information that can be used for various genomics applications in soybean.

Highly variable patterns of linkage disequilibrium in multiple soybean populations.

Prospects for utilizing whole-genome association analysis in autogamous plant populations appear promising due to the reported high levels of linkage disequilibrium (LD). To determine the optimal strategies for implementing association analysis in soybean (Glycine max L. Merr.), we analyzed the structure of LD in three regions of the genome varying in length from 336 to 574 kb. This analysis was conducted in four distinct groups of soybean germplasm: 26 accessions of the wild ancestor of soybean (Glycine soja Seib. et Zucc.); 52 Asian G. max Landraces, the immediate results of domestication from G. soja; 17 Asian Landrace introductions that became the ancestors of North American (N. Am.) cultivars, and 25 Elite Cultivars from N. Am. In G. soja, LD did not extend past 100 kb; however, in the three cultivated G. max groups, LD extended from 90 to 574 kb, likely due to the impacts of domestication and increased self-fertilization. The three genomic regions were highly variable relative to the extent of LD within the three cultivated soybean populations. G. soja appears to be ideal for fine mapping of genes, but due to the highly variable levels of LD in the Landraces and the Elite Cultivars, whole-genome association analysis in soybean may be more difficult than first anticipated.