A resource-efficient tool for mixed model association analysis of large-scale data
A resource-efficient tool for mixed model association analysis of large-scale data
ABSTRACT 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.
Zheng Zhili、Qi Ting、Kemper Kathryn E.、Wray Naomi R.、Jiang Longda、Yang Jian、Visscher Peter M.
Institute for Molecular Bioscience, The University of Queensland||Institute for Advanced Research, Wenzhou Medical UniversityInstitute for Molecular Bioscience, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandInstitute for Molecular Bioscience, The University of Queensland||Queensland Brain Institute, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandInstitute for Molecular Bioscience, The University of Queensland||Institute for Advanced Research, Wenzhou Medical University||Queensland Brain Institute, The University of QueenslandInstitute for Molecular Bioscience, The University of Queensland||Queensland Brain Institute, The University of Queensland
生物科学研究方法、生物科学研究技术基础医学生物科学理论、生物科学方法
Zheng Zhili,Qi Ting,Kemper Kathryn E.,Wray Naomi R.,Jiang Longda,Yang Jian,Visscher Peter M..A resource-efficient tool for mixed model association analysis of large-scale data[EB/OL].(2025-03-28)[2025-04-29].https://www.biorxiv.org/content/10.1101/598110.点此复制
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