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Konigorski S, Yilmaz YE, Janke J, Bergmann MM, Boeing H, Pischon T. Powerful rare variant association testing in a copula-based joint analysis of multiple phenotypes. Genet Epidemiol 2019; 44:26-40. [PMID: 31732979 DOI: 10.1002/gepi.22265] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 08/13/2019] [Accepted: 09/16/2019] [Indexed: 12/16/2022]
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.
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Affiliation(s)
- Stefan Konigorski
- Molecular Epidemiology Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Digital Health and Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
| | - Yildiz E Yilmaz
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL, Canada.,Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada.,Discipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Jürgen Janke
- Molecular Epidemiology Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Manuela M Bergmann
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Charité-Universitätsmedizin Berlin, Berlin, Germany.,DZHK (German Center for Cardiovascular Research), partner site Berlin, Berlin, Germany
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Zhang H, Liu D, Zhao J, Bi X. Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence. Ann Appl Stat 2018; 12:2359-2378. [PMID: 30666272 PMCID: PMC6338437 DOI: 10.1214/18-aoas1156] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
We propose a novel multivariate model for analyzing hybrid traits and identifying genetic factors for comorbid conditions. Comorbidity is a common phenomenon in mental health in which an individual suffers from multiple disorders simultaneously. For example, in the Study of Addiction: Genetics and Environment (SAGE), alcohol and nicotine addiction were recorded through multiple assessments that we refer to as hybrid traits. Statistical inference for studying the genetic basis of hybrid traits has not been well-developed. Recent rank-based methods have been utilized for conducting association analyses of hybrid traits but do not inform the strength or direction of effects. To overcome this limitation, a parametric modeling framework is imperative. Although such parametric frameworks have been proposed in theory, they are neither well-developed nor extensively used in practice due to their reliance on complicated likelihood functions that have high computational complexity. Many existing parametric frameworks tend to instead use pseudo-likelihoods to reduce computational burdens. Here, we develop a model fitting algorithm for the full likelihood. Our extensive simulation studies demonstrate that inference based on the full likelihood can control the type-I error rate, and gains power and improves the effect size estimation when compared with several existing methods for hybrid models. These advantages remain even if the distribution of the latent variables is misspecified. After analyzing the SAGE data, we identify three genetic variants (rs7672861, rs958331, rs879330) that are significantly associated with the comorbidity of alcohol and nicotine addiction at the chromosome-wide level. Moreover, our approach has greater power in this analysis than several existing methods for hybrid traits.Although the analysis of the SAGE data motivated us to develop the model, it can be broadly applied to analyze any hybrid responses.
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Affiliation(s)
- Heping Zhang
- Heping Zhang is Susan Dwight Bliss Professor , Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520; Dungang Liu is Assistant Professor , Department of Operations, Business Analytics and Information Systems, University of Cincinnati Lindner College of Business, Cincinnati, OH 45221; Jiwei Zhao is Assistant Professor , Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214; and Xuan Bi is Postdoctoral Associate, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
| | - Dungang Liu
- Heping Zhang is Susan Dwight Bliss Professor , Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520; Dungang Liu is Assistant Professor , Department of Operations, Business Analytics and Information Systems, University of Cincinnati Lindner College of Business, Cincinnati, OH 45221; Jiwei Zhao is Assistant Professor , Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214; and Xuan Bi is Postdoctoral Associate, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
| | - Jiwei Zhao
- Heping Zhang is Susan Dwight Bliss Professor , Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520; Dungang Liu is Assistant Professor , Department of Operations, Business Analytics and Information Systems, University of Cincinnati Lindner College of Business, Cincinnati, OH 45221; Jiwei Zhao is Assistant Professor , Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214; and Xuan Bi is Postdoctoral Associate, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
| | - Xuan Bi
- Heping Zhang is Susan Dwight Bliss Professor , Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520; Dungang Liu is Assistant Professor , Department of Operations, Business Analytics and Information Systems, University of Cincinnati Lindner College of Business, Cincinnati, OH 45221; Jiwei Zhao is Assistant Professor , Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214; and Xuan Bi is Postdoctoral Associate, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
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