UK-Biobank Whole Exome Sequence Binary Phenome Analysis with Robust Region-based Rare Variant Test

Abstract

In biobank data analysis, most binary phenotypes have unbalanced case-control ratios, which can cause inflation of 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 for associations of such phenotypes. For gene- or region-based multiple variant tests, a few methods exist which adjust for unbalanced case-control ratios; however, these methods are either less accurate when case-control ratios are extremely unbalanced or not scalable for large data analyses. To address these problems, we propose SKAT/SKAT-O type region-based tests, where the single-variant score statistic is calibrated based on SPA and Efficient Resampling (ER). Through simulation studies, we show that the proposed method provides well-calibrated p-values. In contrast, the unadjusted approach has greatly inflated type I error rates (90 times of exome-wide =2.5x10-6) when the case-control ratio is 1:99. Additionally, the proposed method has similar computation time as the unadjusted approaches and is scalable for large sample data. Our UK Biobank whole exome sequence data analysis of 45,596 unrelated European samples and 791 PheCode phenotypes identified 10 rare variant associations with p-value < 10-7, including the associations between JAK2 and myeloproliferative disease, TNC and large cell lymphoma and F11 and congenital coagulation defects. All analysis summary results are publicly available through a web-based visual server.