Efficient Control of Population Structure in Model Organism Association Mapping

摘要: Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic studies are confronted by problem complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied genomic control or structured association. Recent demonstrated that mixed models successfully correct relatedness maize and Arabidopsis panel data sets. currently available mixed-model methods suffer from computational inefficiency. In this article, we propose new method, efficient (EMMA), corrects organism mapping. Our method takes advantage specific nature optimization applying mapping, allows us substantially increase speed reliability results. We EMMA silico whole-genome involving hundreds thousands SNPs, addition also performed extensive simulation estimate statistical power under various SNP effects, varying degrees structure, differing numbers multiple measurements per strain. Despite limited due number strains, able identify significantly associated fall into known QTL genes identified through previous while avoiding an inflation positives. An R package implementation webserver our publicly available.