In silico single nucleotide polymorphism discovery and application to marker-assisted selection in soybean

Abstract

The general approach to discovering single nucleotide polymorphisms (SNPs) requires locus-specific PCR amplification. To enhance the efficiency of SNP discovery in soybean, we used in silico analysis prior to re-sequencing as it is both rapid and inexpensive. In silico analysis was performed to detect putative SNPs in expressed sequence tag (EST) contigs assembled using publicly available ESTs from 18 different soybean genotypes. SNP validation by direct sequencing of six soybean cultivars and a wild soybean genotype was performed with PCR primers designed from EST contigs aligned with at least 5 out of 18 soybean genotypes. The efficiency of SNP discovery among the confirmation genotypes was 81.2%. Furthermore, the efficiency of SNP discovery between Pureunkong and Jinpumkong 2 genotypes was 47.4%, a great improvement on our previous finding based on direct sequencing (22.3%). Using SNPs between Pureunkong and Jinpumkong 2 in EST contigs, which were linked to target traits, we were able to genotype 90 recombinant inbred lines by high-resolution melting (HRM) analysis. These SNPs were mapped onto the expected locations near quantitative trait loci for water-logging tolerance and seed pectin concentration. Thus, our protocol for HRM analysis can be applied successfully not only to genetic diversity studies, but also to marker-assisted selection (MAS). Our study suggests that a combination of in silico analysis and HRM can reduce the cost and labor involved in developing SNP markers and genotyping SNPs. The markers developed in this study can also easily be applied to MAS if the markers are associated with the target traits.