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.Floral phenotypic dataMean phenotypic measurements for the 291 sampled individuals. Here, Indiv is our identication number for the plant, "TubeL" is floral tube length, "Width" is floral tube width, and "StamenL" is stamen filament length. Units for the three measurements is millimeters.floral.phenos.txtslimsnps2.4.vcfVariant call file listing genotypes for the filtered set of SNPs in the initial scan used in downstream analyses. Individuals are present in numerical order in the file.slimsnps2.4.reest.AFsMaximum likelihood estimates for allele frequency of the reference allele (q) for the filtered set of SNPs (initial scan).slimsnps2.4.MLE.TubeLMaximum likelihood estimates for genetic effects on floral tube length (initial scan).slimsnps2.4.MLE.StamenLMaximum likelihood estimates for genetic effects on stamen filament length (initial scan).slimsnps2.4.MLE.WidthMaximum likelihood estimates for genetic effects on floral tube width (initial scan).amplicons.slim.RPI400Variant call file listing genotypes for filtered SNPs after amplicon sequencing used in downstream analyses.amplicons.slim.RPI400.MLE.StamenLMaximum likelihood estimates for genetic effects on stamen filament length (amplicon SNPs).amplicons.slim.RPI400.MLE.TubeLMaximum likelihood estimates for genetic effects on floral tube length (amplicon SNPs).amplicons.slim.RPI400.WidthMaximum likelihood estimates for genetic effects on floral tube width (amplicon SNPs).initial.data.near.focalsitesVariant call file listing genotypes for the all SNPs in the initial scan in the vicinity of SNPs targeted for amplicon sequencing (including sites that did not meet our strict filtering requirements in the initial scan).initial.data.near.focalsites.MLE.StamenLMaximum likelihood estimates for genetic effects on stamen filament length (initial scan, SNPs near targeted sites).initial.data.near.focalsites.MLE.TubeLMaximum likelihood estimates for genetic effects on floral tube length (initial scan, SNPs near targeted sites)