A0704
Title: Modeling hybrid traits for comorbidity
Authors: Heping Zhang - Yale University (United States) [presenting]
Dungang Liu - University of Cincinnati (United States)
Jiwei Zhao - University of Wisconsin-Madison (United States)
Xuan Bi - Yale University (United States)
Abstract: A novel multivariate model for analyzing hybrid traits and identifying genetic factors for comorbid conditions. Comorbidity is common phenomenon in mental health that an individual suffers from multiple disorders simultaneously. 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. Rank-based methods do not inform the strength or direction of effects. Parametric frameworks have been proposed in theory, but 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. We develop a model fitting algorithm for the full likelihood. Our 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. These advantages remain even if the distribution of the latent variables is misspecified. For the SAGE data, we identify three genetic variants that are significantly associated with the comorbidity of alcohol and nicotine addiction at the chromosome-wide level.
