title: Multivariate linkage analysis using the electrophysiological phenotypes in the COGA alcoholism data.
abstract: Multivariate linkage analysis using several correlated traits may provide greater statistical power to detect susceptibility genes in loci whose effects are too small to be detected in univariate analysis. In this analysis, we apply a new approach and perform a linkage analysis of several electrophysiological phenotypes of the Collaborative Study on the Genetics of Alcoholism data of the Genetic Analysis Workshop 14. Our approach is based on a variance-component model to map candidate genes using repeated or longitudinal measurements. It can take into account covariate effects and time-dependent genetic effects in general pedigree data. We compare our results with the ones obtained by SOLAR using single measurement data. Our multivariate linkage analysis found linkage evidence on two regions on chromosome 4: around marker GABRB1 at 51.4 cM and marker FABP2 at 116.8 cM (unadjusted p-value = 0.00006).
authors.affiliation: Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut, CT 06520-8034, USA. Heping.Zhang@yale.edu
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Zhong
authors.forename: Yuanqing
authors.initials: Y
authors.lastname: Ye
mesh_terms: D000437:Alcoholism / Q000235:genetics*; D002874:Chromosome Mapping; D003299:Cooperative Behavior; D030541:Databases, Genetic; D055724:Electrophysiological Phenomena / Q000235:genetics*; D006801:Humans; D015999:Multivariate Analysis; D010641:Phenotype