Use of a local approximation to the ancestral recombination graph for fine mapping disease genes

Abstract

We describe a novel coalescent-based method for estimating the location of a disease susceptibility locus. This is designed for the situation where we have genotype data from a sample of cases and controls, in a region that is believed to contain a disease mutation. For a given position on the marker-map, we use the marker information of both cases and controls to reconstruct local approximations of the ancestral recombination graph using Markov Chain Monte Carlo. From this, we can compute the likelihood of the phenotype data assuming a susceptibility gene at this position; the procedure is repeated at a series of locations across the region to estimate the posterior density.