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doi:10.1534/genetics.106.057653
A more recent version of this article appeared on August 1, 2006.
REGULAR RESEARCH PAPERS |
Simultaneous fine mapping of multiple closely linked quantitative trait loci using combined linkage disequilibrium and linkage with a general pedigree
Sang Hong Lee 1* and Julius H.J. Van der Werf 1
1 UNE
* To whom correspondence should be addressed. E-mail: slee7{at}metz.une.edu.au.
Submitted on February 26, 2006
Revised on May 9, 2006
Accepted on 26 May 2006
Within a small region (e.g. < 10 cM), there can be multiple quantitative trait loci (QTL) underlying phenotypes of a trait. Simultaneous fine mapping of closely linked QTL needs an efficient tool to remove confounded shade effects among QTL within such a small region. We propose a variance component method using combined linkage disequilibrium (LD) and linkage information and a reversible jump Markov chain Monte Carlo (MCMC) sampling for model selection. QTL identity by descent (IBD) coefficients between individuals are estimated by a hybrid MCMC combining the random walk and the meiosis Gibbs sampler. These coefficients are used in a mixed linear model and an empirical Bayesian procedure combines residual maximum likelihood (REML) to estimate QTL effects and a reversible jump MCMC which samples the number of QTL and the posterior QTL intensities across the tested region. Note that there are two MCMC processes used, i.e. an (internal) MCMC for IBD estimation and an (external) MCMC for model selection. In a simulation study, the use of the multiple QTL model clearly removes the shade effects between three closely linked QTL located at 1.125, 3.875 and 7.875 cM across the region of 10 cM, using 40 markers at 0.25 cM intervals. It is shown that the use of combined LD and linkage information gives much more useful information compared to using linkage information alone for both single and multiple QTL analyses. When using a lower marker density (11 markers at 1 cM intervals), the signal of the second QTL can disappear. Extreme values of past effective size (resulting in extreme levels of LD) decrease the mapping accuracy.
Key Words: dominance, fine-mapping, linkage disequilibrium, multiple quantitative trait loci, reversible jump Markov chain Monte Carlo