Application of Bayesian spatial statistical methods to analysis of haplotypes effects and gene mapping.
We propose a method to analyze haplotype effects using ideas derived from Bayesian spatial statistics. We assume that two haplotypes that are similar to one another in structure are likely to have similar risks, and define a distance metric to specify the appropriate level of closeness between the two haplotypes. Through the choice of distance metric, varying levels of population genetics theory can be incorporated into the modeling process, including some that allow estimation of the location of the disease causing mutation(s). This location can be estimated, along with the other parameters of the model, using Markov chain Monte Carlo (MCMC) estimation methods. We demonstrate the effectiveness of the model on two real datasets, a well-known dataset used to fine-map the gene for cystic fibrosis, and one used to localize the gene for Friedreich's ataxia.
Molitor
John
J
Department of Preventive Medicine, University of Southern California, Los Angeles, 900089-9011, USA. jmolitor@usc.edu
Marjoram
Paul
P
Thomas
Duncan
D
D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D003550:Cystic Fibrosis / Q000235:genetics; D005621:Friedreich Ataxia / Q000235:genetics; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method