MAPPING-LINKED QUANTITATIVE TRAIT LOCI USING BAYESIAN-ANALYSIS AND MARKOV-CHAIN MONTE-CARLO ALGORITHMS

A Bayesian method for mapping linked quantitative trait loci (QTL) usi ng multiple linked genetic markers is presented. Parameter estimation and hypothesis testing was implemented via Markov chain Monte Carlo (M CMC) algorithms, Parameters included were allele frequencies and subst itution effects for two biallelic QTL, map positions of the QTL and ma rkers, allele frequencies of the markers, and polygenic and residual v ariances. Missing data were polygenic effects and multi-locus marker-Q TL genotypes. Three different MCMC schemes for testing the presence of a single or two linked QTL on the chromosome were compared. The first approach includes a model indicator variable representing two unlinke d QTL affecting the trait, one linked and one unlinked QTL, or both QT L linked with the markers. The second approach incorporates an indicat or variable for each QTL into the model for phenotype, allowing or not allowing for a substitution effect of a QTL on phenotype, and the thi rd approach is based on model determination by reversible jump MCMC. M ethods were evaluated empirically by analyzing simulated granddaughter designs. All methods identified correctly a second, linked QTL and di d not reject the one-QTL model when there was only a single QTL and no additional or an unlinked QTL.