The Annals of Applied Statistics

Modeling hybrid traits for comorbidity and genetic studies of alcohol and nicotine co-dependence

Heping Zhang, Dungang Liu, Jiwei Zhao, and Xuan Bi

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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.

Article information

Source
Ann. Appl. Stat., Volume 12, Number 4 (2018), 2359-2378.

Dates
Received: September 2016
Revised: January 2018
First available in Project Euclid: 13 November 2018

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1542078048

Digital Object Identifier
doi:10.1214/18-AOAS1156

Mathematical Reviews number (MathSciNet)
MR3875704

Keywords
Comorbidity association EM algorithm latent variable ordinal outcome

Citation

Zhang, Heping; Liu, Dungang; Zhao, Jiwei; Bi, Xuan. Modeling hybrid traits for comorbidity and genetic studies of alcohol and nicotine co-dependence. Ann. Appl. Stat. 12 (2018), no. 4, 2359--2378. doi:10.1214/18-AOAS1156. https://projecteuclid.org/euclid.aoas/1542078048


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Supplemental materials

  • Supplement to “Modeling hybrid traits for comorbidity and genetic studies of alcohol and nicotine co-dependence”. Supplementary materials provide all the technical details for the model fitting.