A Bayesian Approach to Detect Quantitative
Trait Loci using Markov Chain Monte Carlo
by
Markov chain Monte Carlo (MCMC) techniques are applied to
simultaneously identify multiple quantitative trait loci (QTL) and the
magnitude of their effects. Using a Bayesian approach a multi-locus
model is fit to quantitative trait and molecular marker data, instead
of fitting one locus at a time. The phenotypic trait is modeled as a
linear function of the additive and dominance effects of the unknown
QTL genotypes. Inference summaries for the locations of the QTL and
their effects are derived from the corresponding marginal posterior
densities obtained by integrating the likelihood, rather than by
optimizing the joint likelihood surface. This is done using MCMC by
treating the unknown QTL genotypes, and any missing marker genotypes,
as augmented data and then by including these unknowns in the Markov
chain cycle along with the unknown parameters. Parameter estimates
are obtained as means of the corresponding marginal posterior
densities. High posterior density regions of the marginal densities
are obtained as confidence regions. We examine flowering time data
from double haploid progeny of Brassica napus, to illustrate the
proposed method.
Click to get
MCMC code
(gzip of TAR file)
Manuscript no longer available via FTP due to copyright restrictions.
TR # 925r, August 1995 (rev. April 1996)
[Original title: Markov Chain Monte Carlo Approach
to Detect Polygene Loci for Complex Traits]