A generalized linear mixed model association tool for biobank-scale data

By Longda Jiang, Zhili Zheng, Hailing Fang, Jian Yang. Nature Genetics, 2021

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Compared with linear mixed model-based genome-wide association (GWA) methods, generalized linear mixed model (GLMM)-based methods have better statistical properties when applied to binary traits but are computationally much slower. In the present study, leveraging efficient sparse matrix-based algorithms, we developed a GLMM-based GWA tool, fastGWA-GLMM, that is severalfold to orders of magnitude faster than the state-of-the-art tools when applied to the UK Biobank (UKB) data and scalable to cohorts with millions of individuals. We show by simulation that the fastGWA-GLMM test statistics of both common and rare variants are well calibrated under the null, even for traits with extreme case–control ratios. We applied fastGWA-GLMM to the UKB data of 456,348 individuals, 11,842,647 variants and 2,989 binary traits (full summary statistics available at http://fastgwa.info/ukbimpbin), and identified 259 rare variants associated with 75 traits, demonstrating the use of imputed genotype data in a large cohort to discover rare variants for binary complex traits. FastGWA-GLMM is a fast, scalable generalized linear mixed model method for genetic association testing for binary traits in large cohorts that is robust to variant frequency and case–control imbalance.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/f55ab71294ba86fa8627b117f008f7cb7f005e1f