PT  - JOURNAL ARTICLE
AU  - Wei Zhou
AU  - Jonas B. Nielsen
AU  - Lars G. Fritsche
AU  - Rounak Dey
AU  - Maiken B. Elvestad
AU  - Brooke N. Wolford
AU  - Jonathon LeFaive
AU  - Peter VandeHaar
AU  - Aliya Gifford
AU  - Lisa A. Bastarache
AU  - Wei-Qi Wei
AU  - Joshua C. Denny
AU  - Maoxuan Lin
AU  - Kristian Hveem
AU  - Hyun Min Kang
AU  - Goncalo R. Abecasis
AU  - Cristen J. Wilier
AU  - Seunggeun Lee
TI  - Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies
AID  - 10.1101/212357
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 212357
4099  - http://biorxiv.org/content/early/2017/11/24/212357.short
4100  - http://biorxiv.org/content/early/2017/11/24/212357.full
AB  - In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly -- producing large type I error rates -- in the analysis of phenotypes with unbalanced case-control ratios. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation (SPA) to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational time and memory cost of generalized mixed model. The computation cost linearly depends on sample size, and hence can be applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK-Biobank data of 408,961 white British European-ancestry samples, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.