Assessment of Power and False Discovery Rate in Genome-Wide Association Studies using the BarleyCAP Germplasm

Success in genome-wide association studies (GWAS) is dependent on the power to detect quantitative trait loci (QTL) with a minimal rate of false discovery. The objective of this study was to determine the potential for GWAS within barley (Hordeum vulgare L.) by evaluating several linear models that varied in the way they accounted for population structure (model-based STRUCTURE or principle component analysis [PCA]) and familial relatedness. Using genotype data from the Barley Coordinated Agricultural Project (BarleyCAP), phenotypic effects were simulated for different numbers of QTL with three heritability levels. Under each scenario, power and false discovery rate were calculated for sample sizes of 100 or 300 individuals. A mixed model that accounted for familial relatedness but not population structure performed as well as or better than all other models across all heritability levels, QTL numbers, and sample sizes tested. Simulations with 100 lines performed poorly for QTL detection but simulations with 300 lines performed adequately, suggesting that the BarleyCAP data can be used successfully for GWAS if sample sizes are adequate.