Robust Region-Based Rare Variant Tests in Large Biobanks and Cohorts

• Activity Number: 42 - Statistical Genetics I – New Approaches for Association Mapping
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Zhangchen Zhao* and Wei Zhou and Seunggeun Lee
• Companies: University of Michigan and Broad Institute of Harvard and MIT and University of Michigan
• Keywords: rare-variant test; unbalanced case-control; saddlepoint approximation; efficient resampling ; generalized mixed model; PheWAS

Abstract

In biobank data, most binary phenotypes have unbalanced case-control ratios, which can cause inflated type I error rates. Recently, a saddlepoint approximation (SPA) based single variant test has been developed to provide an accurate and scalable method to test such associations. For region-based tests, a few methods exist that can adjust for unbalanced case-control ratios; however, these methods are either less accurate or not scalable for large data analyses. To address these issues, we develop a robust method, where the single-variant score statistic is calibrated based on SPA and Efficient Resampling (ER). Through simulation studies and UK Biobank whole exome sequence analysis, the proposed method provides well-calibrated p-values. It also has similar computation time as unadjusted approaches and is scalable for large samples. We further extend robust methods and propose a scalable generalized mixed model region-based test(SAIGE-GENE) to adjust sample relatedness. Through the analysis of the HUNT study of 69716 Norwegian samples and the UK Biobank data of 408910 White British samples, SAIGE-GENE can efficiently analyze large sample data with type I error rates well controlled.