Optimization of genotyping by sequencing (GBS) data in common bean (Phaseolus vulgaris L.)

Abstract

Genotyping by sequencing (GBS) is a technique to discover large numbers of single nucleotide polymorphisms (SNPs) within a sample pool. The standard version of the GBS method uses a pool of relatively large DNA fragments and is typically sequenced at low coverage (1×). This often results in mis-scoring of heterozygotes as homozygotes and a high rate (≥30 %) of missing data points. The purpose of this study was to improve the quality and the coverage of GBS data in common bean (Phaseolus vulgaris L.) in order to increase the number of SNPs available for genome-wide association studies (GWAS). An improved and Phaseolus-specific GBS method was developed, which utilizes an in silico digest of the bean genome to predict the best fragment density and length, a double digest with the restriction enzymes MseI and TaqαI, and size selection after library preparation to achieve a reduced fragment pool that can be sequenced at higher coverage. The study consisted of 25 diverse common bean genotypes belonging to the Mesoamerican gene pool and compared libraries using ApeKI fragments with MseI/TaqαI double-digest fragments. The new improved bean-specific GBS library provided a 3.8- to 12.5-fold increase in SNPs, based on a minimum coverage (3×, 5× and 8×). These results provide insight for future GBS library constructs, and how to achieve a higher SNP density for GWAS studies.