Searching for alcoholism susceptibility genes using Markov chain Monte Carlo methods

Markov chain Monte Carlo (MCMC) methods offer a rapid parametric approach t hat can test for linkage throughout the entire genome. It has an advantage similar to nonparametric methods in that the model does not have to be comp letely specified a priori. However, unlike nonparametric methods, there are no limitations on pedigree size and MCMC methods can also handle relativel y complex pedigree structures. In addition MCMC methods can be used to carr y segregation analysis in order to answer questions on the genetic componen ts of a disease phenotype. Segregation analysis gave evidence for between t wo and eight alcoholism susceptibility loci, each having a modest effect on the phenotype. MCMC methods were used to map alcoholism loci using the phe notypes ALDX1 (DSM-III-R and Feighner criteria) and ALDX2 (World Health Org anization diagnosis ICD-10 criteria). There was mild evidence for quantitat ive trait loci on chromosomes 2, 10, and 11. (C) 1999 Wiley-Liss, Inc.