We developed two resource-efficient tools,fastGWAandfastGWA-GLMM, for mixed model-based genome-wide association analysis in large-scale data (Jiang et al. 2019 and 2021 Nat Genet). We first applied fastGWA to 2,173 traits on 456,422 array-genotyped and 49,960 whole-exome-sequenced individuals of European ancestry in the UK Biobank (UKB) (Jiang et al. 2019 Nat Genet). We then applied fastGWA-GLMM to 2,989 binary traits on 456,348 array-genotyped individuals of European ancestry in the UKB (Jiang et al. 2021 Nat Genet). Seehttps://yanglab.westlake.edu.cn/software/gcta/index.html#DataResourcefor detailed instructions to query and download the data. See “Tutorial” page for instructions to use this online portal.
Zhili Zheng(online tool, data analysis and documentation),Longda Jiang(data analysis and documentation),Hailing Fang(data transfer and maintenance),Jian Yang(overseeing, documentation, and maintenance). The online tool was developed based on the source code modified from PheWeb. We thank Alibaba Cloud - Australia and New Zealand and the Westlake University High Performance Computing Centre for hosting the online tool and the summary data.
If you have any question, please send an email to Jian Yang atjian.yang@westlake.edu.cn.
Jiang L, Zheng Z, Qi T, Kemper KE, Wray NR, Visscher PM, Yang J. (2019) A resource-efficient tool for mixed model association analysis of large-scale data.Nature Genetics, 51:1749–1755.
Jiang L, Zheng Z, Fang H, Yang J. (2021) A generalized linear mixed model association tool for biobank-scale data.Nature Genetics, 53:1616-1621.