Fine-scale mapping of disease genes with multiple mutations via spatial clustering techniques.
We present a method to perform fine mapping by placing haplotypes into clusters on the basis of risk. Each cluster has a haplotype "center." Cluster allocation is defined according to haplotype centers, with each haplotype assigned to the cluster with the "closest" center. The closeness of two haplotypes is determined by a similarity metric that measures the length of the shared segment around the location of a putative functional mutation for the particular cluster. Our method allows for missing marker information but still estimates the risks of complete haplotypes without resorting to a one-marker-at-a-time analysis. The dimensionality issues that can occur in haplotype analyses are removed by sampling over the haplotype space, allowing for estimation of haplotype risks without explicitly assigning a parameter to each haplotype to be estimated. In this way, we are able to handle haplotypes of arbitrary size. Furthermore, our clustering approach has the potential to allow us to detect the presence of multiple functional mutations.
Molitor
John
J
Department of Preventive Medicine, University of Southern California, Los Angeles, Los Angeles, CA, 90089, USA. jmolitor@usc.edu
Marjoram
Paul
P
Thomas
Duncan
D
D000465:Algorithms; D001259:Ataxia / Q000235:genetics; D001499:Bayes Theorem; D016000:Cluster Analysis; D003550:Cystic Fibrosis / Q000235:genetics; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D009154:Mutation / Q000235:genetics