Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts.

ABSTRACT: With very large sample sizes, biobanks provide an exciting opportunity to identify genetic components of complex traits. To analyze rare variants, region-based multiple-variant aggregate tests are commonly used to increase power for association tests. However, because of the substantial computational cost, existing region-based tests cannot analyze hundreds of thousands of samples while accounting for confounders such as population stratification and sample relatedness. Here we propose a scalable generalized mixed-model region-based association test, SAIGE-GENE, that is applicable to exome-wide and genome-wide region-based analysis for hundreds of thousands of samples and can account for unbalanced case-control ratios for binary traits. Through extensive simulation studies and analysis of

SUBMITTER: Zhou W 

PROVIDER: S-EPMC7871731 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature