SNP‐skimming: A fast approach to map loci generating quantitative variation in natural populations

Abstract

Genome-wide association mapping (GWAS) is a method to estimate the contribution of segregating genetic loci to trait variation. A major challenge for applying GWAS to nonmodel species has been generating dense genome-wide markers that satisfy the key requirement that marker data are error-free. Here, we present an approach to map loci within natural populations using inexpensive shallow genome sequencing. This "SNP-skimming" approach involves two steps: an initial genome-wide scan to identify putative targets followed by deep sequencing for confirmation of targeted loci. We apply our method to a test data set of floral dimension variation in the plant Penstemon virgatus, a member of a genus that has experienced dynamic floral adaptation that reflects repeated transitions in primary pollinator. The ability to detect SNPs that generate phenotypic variation depends on population genetic factors such as population allele frequency, effect size and epistasis, as well as sampling effects contingent on missing data and genotype uncertainty. However, both simulations and the Penstemon data suggest that the most significant tests from the initial SNP skim are likely to be true positives-loci with subtle but significant quantitative effects on phenotype. We discuss the promise and limitations of this method and consider optimal experimental design for a given sequencing effort. Simulations demonstrate that sampling a larger number of individual at the expense of average read depth per individual maximizes the power to detect loci.