Data from: SNP-skimming: a fast approach to map loci generating quantitative variation in natural populations

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 non-model species has been generating dense
genome-wide markers that satisfy the key requirement that marker data is
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 dataset 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.