| Title: | A resource-efficient tool for mixed model association analysis of large-scale data |
| Journal: | Nature Genetics |
| Published: | 25 Nov 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31768069/ |
| DOI: | https://doi.org/10.1038/s41588-019-0530-8 |
| URL: | https://www.biorxiv.org/content/biorxiv/early/2019/09/21/598110.full.pdf |
| Citations: | 367 (158 in last 2 years) as of 8 Aug 2024 |
Publication 3678
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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 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.7 Authors
- Longda Jiang
- Zhili Zheng
- Ting Qi
- Kathryn E. Kemper
- Naomi R. Wray
- Peter M. Visscher
- Jian Yang
1 Application
| Application ID | Title |
|---|---|
| 12514 | The limits of predicting complex traits and diseases from genetic data |
Enabling scientific discoveries that improve human health