期刊
NATURE GENETICS
卷 51, 期 12, 页码 1749-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41588-019-0530-8
关键词
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资金
- Australian Research Council [DP160101343, DP160101056, FT180100186, FL180100072]
- Australian National Health and Medical Research Council [1078037, 1078901, 1113400, 1107258]
- Sylvia & Charles Viertel Charitable Foundation
- UK Biobank [12514]
- MRC [MC_PC_12028] Funding Source: UKRI
- Australian Research Council [FT180100186, FL180100072] Funding Source: Australian Research Council
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.
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