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

Abstract

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.

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

“…DNA was isolated from lyophilized leaf tissue collected from twenty plants per genotype using a CTAB based extraction method scaled down for a 96 well plate by dividing all reagent volumes by 40 ( Keim, 1988 ). To generate a high density marker panel that enabled a fine mapping resolution while remaining cost effective, whole genome skim sequencing with genotype imputation was used ( Happ et al, 2019 ). The reference panel for imputation was generated from 99 soybean genotypes with publicly available whole genome sequence data, and consisted of 10,803,148 biallelic homozygous single nucleotide polymorphisms (SNPs).…”

“…All sequence data was deposited in the NCBI Short Read Archive database accession no: PRJNA699266 . Pre imputation processing and quality control was performed according to the previously published protocol ( Happ et al, 2019 ). Plink1.9 ( Purcell et al, 2007 ) was used to eliminate individual low quality imputations with a genotype probability (GP) score of less than 0.9.…”

“…Recently, Pour-Aboughadareh et al (2019) reported the development of an R script to calculate a range of phenotypic stability estimates, providing a manageable way to calculate sixteen popular stability estimates using a single R function. This included Plaisted and Peterson’s mean variance component, Plaisted’s GE variance component, Wricke’s ecovalence stability index, regression coefficient, deviation from regression, Shukla’s stability variance, environmental coefficient of variance, Nassar and Huhn’s statistics (S1 and S2), Huhn’s equation (S3 and S6), Thennarasu’s non-parametric statistics (NP1-4), and Kang’s rank-sum ( Happ et al, 2019 ). While this covered many of the prevalent univariate stability analysis methods, it did not include the multivariate additive main effect and multiplicative interaction (AMMI) analyses methods and subsequent stability values ( Sabaghnia et al, 2008 ).…”

Recommendations for Choosing the Genotyping Method and Best Practices for Quality Control in Crop Genome-Wide Association Studies

“…WGR allows the highest number of SNP calls, up to several millions as reported in peach (Cao et al, 2016) and cotton (Du et al, 2018). This is a clear advantage when, rather than MAS, gene isolation is the main aim of the GWAS project (Wang et al, 2016;Happ et al, 2019). Indeed, in high-resolution GWAS, SNP loci showing the highest evidence of association are usually in tight linkage, or may even coincide, with loci underlying phenotypic variation (e.g., Shang et al, 2014;Yano et al, 2016).…”

“…In practice, WGR in crops has been usually performed with average sequencing depth ranging from ∼5×, as for cotton (Du et al, 2018), tomato (Lin et al, 2014), and peach (Cao et al, 2019), to ∼15×, as for watermelon (Guo et al, 2019) and grapevine (Liang et al, 2019). A notable exception is represented by strict self-pollinating species, such as rice and soybean, for which very low average sequencing depth (1× or lower) has been successfully applied (Wang et al, 2016;Happ et al, 2019). Indeed, homozygous populations of pure lines are effectively haploid, thus allowing easy reconstruction of haplotypes and, consequently, accurate imputation of missing data (Wang et al, 2016).…”