Joint oligogenic segregation and linkage analysis using bayesian Markov chain Monte Carlo methods.
One of the most challenging areas in human genetics is the dissection of quantitative traits. In this context, the efficient use of available data is important, including, when possible, use of large pedigrees and many markers for gene mapping. In addition, methods that jointly perform linkage analysis and estimation of the trait model are appealing because they combine the advantages of a model-based analysis with the advantages of methods that do not require prespecification of model parameters for linkage analysis. Here we review a Markov chain Monte Carlo approach for such joint linkage and segregation analysis, which allows analysis of oligogenic traits in the context of multipoint linkage analysis of large pedigrees. We provide an outline for practitioners of the salient features of the method, interpretation of the results, effect of violation of assumptions, and an example analysis of a two-locus trait to illustrate the method.
Wijsman
Ellen M
EM
Division of Medical Genetics, Department of Biostatistics, University of Washington, Box 357720, Seattle, WA 98195-7720, USA. wijsman@u.washington.edu
Yu
Dongmei
D
D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D040641:Quantitative Trait Loci