Rxivist logo

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

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

Posted 11 Apr 2019
bioRxiv DOI: 10.1101/598110 (published DOI: 10.1038/s41588-019-0530-8)

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 2,173 traits on 456,422 array-genotyped and imputed individuals and 2,048 traits on 46,191 whole-exome-sequenced individuals in the UKB.

Download data

  • Downloaded 2,236 times
  • Download rankings, all-time:
    • Site-wide: 2,855 out of 84,782
    • In genetics: 227 out of 4,448
  • Year to date:
    • Site-wide: 4,031 out of 84,782
  • Since beginning of last month:
    • Site-wide: 8,047 out of 84,782

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News