Multipoint linkage analysis via Metropolis jumping kernels.
Multipoint linkage analysis is being performed routinely in medical genetic studies to localize disease genes. This likelihood-based method is very computationally intensive. Exact computations are thus formidable for problems with large number of genetic markers and complex pedigrees. This paper proposes a Monte Carlo method to estimate the required likelihoods. The space of multilocus genotypes is sampled using a hybrid algorithm with a mixture of Gibbs samplers and Metropolis jumping kernels. These samples are essentially realizations of a Markov chain, and are distributed approximately according to the conditional genotype distribution given the observed phenotypic data. We present a simulation study with several eight-point analyses to demonstrate the feasibility of the current method.
Lin
S
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Department of Statistics, Ohio State University, Columbus 43210, USA.
D000465:Algorithms; D000483:Alleles; D001699:Biometry / Q000379:methods*; D005260:Female; D008040:Genetic Linkage; D005826:Genetics, Medical / Q000706:statistics & numerical data; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D008126:Lod Score; D008297:Male; D008390:Markov Chains; D009010:Monte Carlo Method; D010641:Phenotype