Powerful rare variant association testing in a copula‐based joint analysis of multiple phenotypes. Issue 1 (15th November 2019)

• Record Type: Journal Article
• Authors: Konigorski, Stefan; Yilmaz, Yildiz E.; Janke, Jürgen; Bergmann, Manuela M.; Boeing, Heiner; Pischon, Tobias
• Is Part Of: Genetic epidemiology. Volume 44:Issue 1(2020)
• Page Start: 26
• Page End: 40
• Publication Date: 2019-11-15
• Subjects: adipokines -- adiponectin -- copula models -- genetic association study -- joint modeling -- multiple phenotypes -- obesity -- rare variant analysis; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22265
• Languages: English
• ISSNs: 0741-0395

Abstract

Abstract: In genetic association studies of rare variants, the low power of association tests is one of the main challenges. In this study, we propose a new single‐marker association test called C‐JAMP (Copula‐based Joint Analysis of Multiple Phenotypes), which is based on a joint model of multiple phenotypes given genetic markers and other covariates. We evaluated its performance and compared its empirical type I error and power with existing univariate and multivariate single‐marker and multi‐marker rare‐variant tests in extensive simulation studies. C‐JAMP yielded unbiased genetic effect estimates and valid type I errors with an adjusted test statistic. When strongly dependent traits were jointly analyzed, C‐JAMP had the highest power in all scenarios except when a high percentage of variants were causal with moderate/small effect sizes. When traits with weak or moderate dependence were analyzed, whether C‐JAMP or competing approaches had higher power depended on the effect size. When C‐JAMP was applied with a misspecified copula function, it still achieved high power in some of the scenarios considered. In a real‐data application, we analyzed sequencing data using C‐JAMP and performed the first genome‐wide association studies of high‐molecular‐weight and medium‐molecular‐weight adiponectin plasma concentrations. C‐JAMP identified 20 rare variants with p ‐values smaller than 10 −5, while all other tests resulted in the identification of fewer variants with higher p ‐values. In summary, the results indicate that C‐JAMP is a powerful, flexible, and robust method for association studies, and we identified novel candidate markers for adiponectin. C‐JAMP is implemented as an R package and freely available from https://cran.r‐project.org/package=CJAMP .