RT Journal Article
SR Electronic
T1 A resource-efficient tool for mixed model association analysis of large-scale data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 598110
DO 10.1101/598110
A1 Jiang, Longda
A1 Zheng, Zhili
A1 Qi, Ting
A1 Kemper, Kathryn E.
A1 Wray, Naomi R.
A1 Visscher, Peter M.
A1 Yang, Jian
YR 2019
UL http://biorxiv.org/content/early/2019/04/11/598110.abstract
AB The genome-wide association study (GWAS) has been widely used as an experimental design to detect associations between genetic variants and a phenotype. Two major confounding factors, population stratification and relatedness, could potentially lead to inflated GWAS test-statistics and thereby spurious associations. Mixed linear model (MLM)-based approaches can be used to account for sample structure. However, genome-wide association (GWA) analyses in biobank samples such as the UK Biobank (UKB) often exceed the capability of most existing MLM-based tools especially if the number of traits is large. Here, we developed an MLM-based tool (called fastGWA) that controls for population stratification by principal components and relatedness by a sparse genetic relationship matrix for GWA analyses of biobank-scale data. We demonstrated by extensive simulations that fastGWA is reliable, robust and highly resource-efficient. We then applied fastGWA to 3,613 traits on 456,422 array-genotyped and imputed individuals and 2,090 traits on 46,191 whole-exome-sequenced (WES) individuals in the UKB.