We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the single nucleotide polymorphisms (SNPs). This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed models. Unlike GWAS applied to clinical traits, where only a handful of phenotypes are analyzed per study, in eQTL studies, tens of thousands of gene expression levels are measured, and the GWAS approach is applied to each gene expression level. More recently, the GWAS approach has been utilized to identify regions of the genome that harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). (Author/JKS)Įfficiently Identifying Significant Associations in Genome-wide Association StudiesĪbstract Over the past several years, genome-wide association studies (GWAS) have implicated hundreds of genes in common disease. The technique requires true difference scores, the reliability of obtained differences, and their standard error of measurement. A Practical Method for Identifying Significant Change ScoresĮRIC Educational Resources Information CenterĪ test of significance for identifying individuals who are most influenced by an experimental treatment as measured by pre-post test change score is presented.