title: An Effective Method to Identify Heritable Components from Multivariate Phenotypes.
abstract: Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach.
authors.affiliation: Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, United States of America.
authors.forename: Jiangwen
authors.initials: J
authors.lastname: Sun
authors.affiliation: Treatment Research Center, University of Pennsylvania Perelman School of Medicine and Philadelphia VAMC, Philadelphia, Pennsylvania, United States of America.
authors.forename: Henry R
authors.initials: HR
authors.lastname: Kranzler
authors.affiliation: Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, United States of America.
authors.forename: Jinbo
authors.initials: J
authors.lastname: Bi
mesh_terms: D000465:Algorithms; D019970:Cocaine-Related Disorders / Q000235:genetics*; D003198:Computer Simulation; D005260:Female; D056726:Genetic Association Studies / Q000706:statistics & numerical data*; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation / Q000235:genetics; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008957:Models, Genetic; D010641:Phenotype; D019655:Quantitative Trait, Heritable
chemical_list: D005819:Genetic Markers