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

Area of Science:

• Plant Genomics
• Agricultural Biotechnology

Background:

• High-density single nucleotide polymorphism (SNP) genotyping in soybean often requires costly whole-genome resequencing.
• Publicly available high-depth resequencing data can serve as a valuable resource for imputation.

Purpose of the Study:

• To develop and validate a cost-effective method for obtaining high-density SNP information in soybean using skim sequencing and imputation.
• To assess the accuracy and feasibility of low-coverage sequencing for soybean genotyping.

Main Methods:

• A reference panel was created from 99 soybean lines resequenced at ~17.1X coverage, identifying over 10 million SNPs.
• 114 ungenotyped soybean lines underwent skim sequencing at ~1X coverage, with data subsets analyzed down to 0.1X.
• SNPs were genotyped, and missing data imputed using Beagle 4.1, with accuracy evaluated against sequencing depth.

Main Results:

• High-density SNP genotyping accuracy of 97.8% was achieved with sequencing depths as low as 0.3X.
• Accuracy was inversely correlated with minor allele frequency and positively correlated with marker linkage disequilibrium.
• The imputation accuracy remained high even with significantly reduced sequencing coverage.

Conclusions:

• Skim sequencing coupled with imputation provides a low-cost, high-accuracy alternative to deep resequencing for soybean genotyping.
• This method facilitates widespread application of dense genotypic data in soybean genomics research and breeding.