Using mendelian inheritance to improve high-throughput SNP discovery

Abstract

Restriction site-associated DNA sequencing or genotyping-by-sequencing (GBS) approaches allow for rapid and costeffective discovery and genotyping of thousands of single-nucleotide polymorphisms (SNPs) in multiple individuals. However, rigorous quality control practices are needed to avoid high levels of error and bias with these reduced representation methods. We developed a formal statistical framework for filtering spurious loci, using Mendelian inheritance patterns in nuclear families, that accommodates variable-quality genotype calls and missing data—both rampant issues with GBS data—and for identifying sex-linked SNPs. Simulations predict excellent performance of both the Mendelian filter and the sex-linkage assignment under a variety of conditions. We further evaluate our method by applying it to real GBS data and validating a subset of high-quality SNPs. These results demonstrate that our metric of Mendelian inheritance is a powerful quality filter for GBS loci that is complementary to standard coverage and HardyWeinberg filters. The described method, implemented in the software MendelChecker, will improve quality control during SNP discovery in nonmodel as well as model organisms.