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Searching for alcoholism susceptibility genes using markov chain monte carlo methods
Author(s) -
Leal Suzanne M.,
Heath Simon C.
Publication year - 1999
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.1370170737
Subject(s) - markov chain monte carlo , nonparametric statistics , markov chain , linkage (software) , computational biology , parametric statistics , trait , computer science , phenotype , a priori and a posteriori , monte carlo method , genetics , biology , statistics , gene , mathematics , machine learning , philosophy , epistemology , programming language
Markov chain Monte Carlo (MCMC) methods offer a rapid parametric approach that 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 completely specified a priori . However, unlike nonparametric methods, there are no limitations on pedigree size and MCMC methods can also handle relatively complex pedigree structures. In addition MCMC methods can be used to carry segregation analysis in order to answer questions on the genetic components of a disease phenotype. Segregation analysis gave evidence for between two and eight alcoholism susceptibility loci, each having a modest effect on the phenotype. MCMC methods were used to map alcoholism loci using the phenotypes ALDX1 (DSM‐III‐R and Feighner criteria) and ALDX2 (World Health Organization diagnosis ICD‐10 criteria). There was mild evidence for quantitative trait loci on chromosomes 2,10, and 11.