WITHDRAWN: Sampling Ancestries at a Genomic Location Conditional on Data from Surrounding Genetic Markers

• Author(s): Kelly M. Burkett and Brad McNeney and Jinko Graham
• Keywords: statistical genetics ; coalescent process ; genetic association ; Markov Chain Monte Carlo

Abstract

For understanding genetic associations with disease outcomes, it can be useful to model the latent ancestries that give rise to the sample's genetic variability. Though the true ancestry of genetic sequences sampled from unrelated individuals is unknown, we can model the distribution of the ancestry conditional on the observed sequence data. However, incorporating ancestry into genetic association methodology requires Monte Carlo methods to sample from this distribution. We describe our implementation of a Markov Chain Monte Carlo sampler for the topology, node times and data at internal nodes of the ancestral tree at a genomic location, conditional on observed genotype data at surrounding markers. We apply the sampler to data from a case-control study to illustrate the approach and discuss extensions of the algorithm.