Genomewide Markers for Controlling Background Variation in Association Mapping

Abstract

Current procedures for association mapping in plants account for population structure (Q) and kinship (K). Here I propose an association mapping procedure that uses genomewide markers (G) to account for quantitative trait loci (QTL) on background chromosomes. My objective was to determine if the G and QG models are superior to the K and QK models. I simulated mapping population sizes ofN= 384, 768, and 1536 inbreds that belonged to three known subpopulations. The G and QG models showed the best adherence to the significance level (P) specified by the investigator for declaring QTL. Across different genetic models (15 or 30 QTL), population sizes, andPlevels, the Q model suffered from a high number of false positives (NFP). With the K and QK models, a relaxedPlevel led to a reasonable number of true QTL detected (NTQ) withN= 384 or 768 but it led to highNFPwithN= 1536. Compared with the K and QK models, the G and QG models had a better balance between highNTQand lowNFP. The results strongly indicated that the G and QG models are superior to the K and QK models.