Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics

Abstract

Summary Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole‐genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single‐nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1‐Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene‐centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss‐of‐function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.

The pan‐genome of the cultivated soybean (PanSoy) reveals an extraordinarily conserved gene content

“…The advent of high‐throughput sequencing technologies and availability of a high‐quality reference genome has provided an exceptional opportunity to systematically detect the DNA sequence variation among soybeans (Chung et al., 2014 ; Fang et al., 2017 ; Lam et al., 2010 ; Maldonado dos Santos et al., 2016 ; Song et al., 2017 ; Torkamaneh et al., 2017 ; Torkamaneh et al., 2020 ; Valliyodan et al., 2016 ; Zhou et al., 2015 ). All these studies used a single reference genome (Williams 82 (Schmutz et al., 2010 )) to align reads and call variants.…”

“…Here, in contrast, we present a pan‐genome focused uniquely and specifically on cultivated soybean (PanSoy). It is constructed using the de novo genome assembly of 204 phylogenetically and geographically representative accessions of improved G. max selected from the larger GmHapMap collection (Torkamaneh et al., 2020 ). The objective of this study was to describe the entire gene repertoire in the cultivated soybean gene pool and thereby dispense with the added complexity of a non‐cultivated wild species.…”

A bumper crop of SNPs in soybean through high‐density genotyping‐by‐sequencing (HD‐GBS)

“…SoySNP50K and Std‐GBS data for 96 samples were obtained from SoyBase and Torkamaneh et al . (2020b). The dramatically reduced depth of coverage of Skim‐Seq results in reduced cost per sample but this occurs at the expense of a higher proportion of missing data (>50%) (Figure 1f,g).…”

“…To assess the performance of HD-GBS in terms of the number of variants, proportion of missing data and cost per sample, six genotypic data sets were created for 96 accessions including Skim-Seq@1x (1.5M SNPs), Skim-Seq@0.5x (645K), Skim-Seq@0.2x (301K), Std-GBS (98K), SoySNP50K (41K) and HD-GBS (823K) (Figure 1e). The Skim-Seq data sets were generated for the 96 samples by randomly sampling raw reads from the original WGRS data from the GmHapMap dataset (Torkamaneh et al, 2020b; 1007 accessions, 15M SNPs). SoySNP50K and Std-GBS data for 96 samples were obtained from SoyBase and Torkamaneh et al (2020b).…”

“…The Skim-Seq data sets were generated for the 96 samples by randomly sampling raw reads from the original WGRS data from the GmHapMap dataset (Torkamaneh et al, 2020b; 1007 accessions, 15M SNPs). SoySNP50K and Std-GBS data for 96 samples were obtained from SoyBase and Torkamaneh et al (2020b). The dramatically reduced depth of coverage of Skim-Seq results in reduced cost per sample but this occurs at the expense of a higher proportion of missing data (>50%) (Figure 1f,g).…”