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We present a Bayesian, Markov-chain Monte Carlo method for fine-scale linkage-disequilibrium gene mapping using high-density marker maps. The method explicitly models the genealogy underlying a sample of case chromosomes in the vicinity of a putative disease locus, in contrast with the assumption of a star-shaped tree made by many existing multipoint methods. Within this modeling framework, we can allow for missing marker information and for uncertainty about the true underlying genealogy and the makeup of ancestral marker haplotypes. A crucial advantage of our method is the incorporation of the shattered coalescent model for genealogies, allowing for multiple founding mutations at the disease locus and for sporadic cases of disease. Output from the method includes approximate posterior distributions of the location of the disease locus and population-marker haplotype proportions. In addition, output from the algorithm is used to construct a cladogram to represent genetic heterogeneity at the disease locus, highlighting clusters of case chromosomes sharing the same mutation. We present detailed simulations to provide evidence of improvements over existing methodology. Furthermore, inferences about the location of the disease locus are shown to remain robust to modeling assumptions.

Original publication

DOI

10.1086/339271

Type

Journal article

Journal

Am J Hum Genet

Publication Date

03/2002

Volume

70

Pages

686 - 707

Keywords

Algorithms, Alleles, Bayes Theorem, Bias, Case-Control Studies, Chromosome Mapping, Computer Simulation, Cystic Fibrosis, Cystic Fibrosis Transmembrane Conductance Regulator, Female, Genetic Heterogeneity, Genetic Markers, Haplotypes, Humans, Linkage Disequilibrium, Male, Markov Chains, Models, Genetic, Monte Carlo Method, Mutation, Pedigree, Phylogeny, Probability, Recombination, Genetic, Sequence Deletion