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Ann Appl Stat. 2018 Dec;12(4):2359-2378. doi: 10.1214/18-AOAS1156. Epub 2018 Nov 13.

Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence.

Author information

1
Heping Zhang is Susan Dwight Bliss Professor (heping.zhang@yale.edu), Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520; Dungang Liu is Assistant Professor (dungang.liu@uc.edu), Department of Operations, Business Analytics and Information Systems, University of Cincinnati Lindner College of Business, Cincinnati, OH 45221; Jiwei Zhao is Assistant Professor (zhaoj@buffalo.edu), 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.

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.

KEYWORDS:

EM algorithm; association; comorbidity; latent variable; ordinal outcome

PMID:
30666272
PMCID:
PMC6338437
[Available on 2019-12-01]
DOI:
10.1214/18-AOAS1156

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