A resource-efficient tool for mixed model association analysis of large-scale data

By Longda Jiang, Zhili Zheng, Ting Qi, Kathryn E. Kemper, Naomi R. Wray, Peter M. Visscher, Jian Yang. Nature Genetics, 2019

483 citations recorded by the source corpus.

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 hence to 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 develop an MLM-based tool (fastGWA) that controls for population stratification by principal components and for relatedness by a sparse genetic relationship matrix for GWA analyses of biobank-scale data. We demonstrate by extensive simulations that fastGWA is reliable, robust and highly resource-efficient. We then apply fastGWA to 2,173 traits on array-genotyped and imputed samples from 456,422 individuals and to 2,048 traits on whole-exome-sequenced samples from 46,191 individuals in the UKB. fastGWA is a mixed linear model–based approach for performing genome-wide association analyses at biobank scale, while controlling for population stratification and relatedness.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/f8ad8110fe5519f0eeb08ac58144360abef70bd2

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Citations in the corpus

Computationally efficient whole-genome regression for quantitative and binary traits (2020, methodology): “It will be easy to expand the functionality to include tests such as SNP × covariate interactions 16 , variance tests 23 and a whole range of gene-based tests 24–26 .”
A generalized linear mixed model association tool for biobank-scale data (2021, methodology): “In our previous work, we developed an LMM-based GWA tool, fastGWA, that is orders of magnitude faster than other LMM-based tools, mainly owing to the use of a sparse genomic relationship matrix (GRM) to capture pedigree relatedness among individuals 10 .”