Originally published as Genetics Published Articles Ahead of Print on July 20, 2009.

Genetics, Vol. 183, 709-721, October 2009, Copyright © 2009
doi:10.1534/genetics.109.104190

Bayesian Quantitative Trait Locus Mapping Based on Reconstruction of Recent Genetic Histories

Dario Gasbarra*,1, Matti Pirinen*, Mikko J. Sillanpää*,{dagger} and Elja Arjas*,{ddagger}

* Department of Mathematics and Statistics and {dagger} Department of Animal Science, University of Helsinki, FIN-00014 Helsinki, Finland and {ddagger} National Institute for Health and Welfare, FI-00271 Helsinki, Finland

1 Corresponding author: Department of Mathematics and Statistics, P.O. Box 68, University of Helsinki, FIN-00014 Helsinki, Finland.
E-mail: dag{at}rni.helsinki.fi

We assume that quantitative measurements on a considered trait and unphased genotype data at certain marker loci are available on a sample of individuals from a background population. Our goal is to map quantitative trait loci by using a Bayesian model that performs, and makes use of, probabilistic reconstructions of the recent unobserved genealogical history (a pedigree and a gene flow at the marker loci) of the sampled individuals. This work extends variance component-based linkage analysis to settings where the unobserved pedigrees are considered as latent variables. In addition to the measured trait values and unphased genotype data at the marker loci, the method requires as an input estimates of the population allele frequencies and of a marker map, as well as some parameters related to the population size and the mating behavior. Given such data, the posterior distribution of the trait parameters (the number, the locations, and the relative variance contributions of the trait loci) is studied by using the reversible-jump Markov chain Monte Carlo methodology. We also introduce two shortcuts related to the trait parameters that allow us to do analytic integration, instead of stochastic sampling, in some parts of the algorithm. The method is tested on two simulated data sets. Comparisons with traditional variance component linkage analysis and association analysis demonstrate the benefits of our approach in a gene mapping context.