Abstract
Methods for detecting Quantitative Trait Loci (QTL) without markers have generally used iterative peeling algorithms for determining genotype probabilities. These algorithms have considerable shortcomings in complex pedigrees. A Monte Carlo Markov chain (MCMC) method which samples the pedigree of the whole population jointly is described. Simultaneous sampling of the pedigree was achieved by sampling descent graphs using the Metropolis-Hastings algorithm. A descent graph describes the inheritance state of each allele and provides pedigrees guaranteed to be consistent with Mendelian sampling. Sampling descent graphs overcomes most, if not all, of the limitations incurred by iterative peeling algorithms. The algorithm was able to find the QTL in most of the simulated populations. However, when the QTL was not modeled or found then its effect was ascribed to the polygenic component. No QTL were detected when they were not simulated.
Highlights
Considerable research has been directed at locating quantitative trait loci (QTL) among continuously distributed traits in the genome of various species using designed experiments and marker information
This paper describes an Monte Carlo Markov chain (MCMC) method for finding the effect of a postulated QTL with two alleles among quantitative variation, and the probabilities for all individuals in the population having 0, 1 or 2 copies of the gene, without any genotypic information
Records were generated by adding the appropriate genetic effects – breeding values and QTL effects – for the model and residuals drawn from the normal distribution N(0,3)
Summary
Considerable research has been directed at locating quantitative trait loci (QTL) among continuously distributed traits in the genome of various species using designed experiments and marker information. The heritability (h2) of the trait is required as an input for FINDGENE With this algorithm, genotypic probabilities (T) are determined using the method of Fernando et al [3] with the data adjusted for the current estimate (denoted) of the other effects in the model (y-Xb -Za), a penetrance function and the pedigree. Sorensen [20] describes an implementation of the Gibbs Sampler which improves the method of Janss et al [13] by simultaneously sampling b and a effects by first permuting y appropriately In both these algorithms, genotypes are sampled individually using iterative peeling. This paper describes an MCMC method for finding the effect of a postulated QTL with two alleles among quantitative variation, and the probabilities for all individuals in the population having 0, 1 or 2 copies of the gene, without any genotypic information. The benefits of this method over previously published methods are discussed and some obvious applications and extensions identified
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