Bayesian analysis of multilocus association in quantitative and qualitative traits.

A Bayesian model-based method for multilocus association analysis of quantitative and qualitative (binary) traits is presented. The method selects a trait-associated subset of markers among candidates, and is equally applicable for analyzing wide chromosomal segments (genome scans) and small candidate regions. The method can be applied in situations involving missing genotype data. The number of trait loci, their marker positions, and the magnitudes of their gene effects (strengths of association) are all estimated simultaneously. The inference of parameters is based on their posterior distributions, which are obtained through Markov chain Monte Carlo simulations. The strengths of the approach are: 1) flexible use of oligogenic models with unknown number of loci, 2) performing the estimation of association jointly with model selection, and 3) avoidance of the multiple testing problem, which typically complicates the approaches based on association testing. The performance of the method was tested and compared to the multilocus conditional search procedure by analyzing two simulated data sets. We also applied the method to cystic fibrosis haplotype data (two-locus haplotypes), where gene position has already been identified. The method is implemented as a software package, which is freely available for research purposes under the name BAMA.

Kilpikari R ，Sillanpää MJ 《GENETIC EPIDEMIOLOGY》

Bayesian oligogenic analysis of quantitative and qualitative traits in general pedigrees.

A Bayesian method for multipoint oligogenic analysis of quantitative and qualitative traits is presented. This method can be applied to general pedigrees, which do not necessarily have to be "peelable" and can have large numbers of markers. The number of quantitative/qualitative trait loci (QTL), their map positions in the genome, and phenotypic effects (mode of inheritances) are all estimated simultaneously within the same framework. The summaries of the estimated parameters are based on the marginal posterior distributions that are obtained through Markov chain Monte Carlo (MCMC) methods. The method uses founder alleles together with segregation indicators in order to determine the genotypes of the trait loci of all individuals in the pedigree. To improve mixing properties of the sampler, we propose (1) joint sampling of map position and segregation indicators, (2) omitting data augmentation for untyped or uninformative markers (homozygous parent), and (3) updating several markers jointly within a single block. The performance of the method was tested with two replicate GAW10 data sets (considering two levels of available marker information). The results were concordant and similar to those presented earlier with other methods. These analyses clearly illustrate the utility and wide applicability of the method.

Uimari P ，Sillanpää MJ 《GENETIC EPIDEMIOLOGY》

Bayesian composite model space approach for mapping quantitative trait Loci in variance component model.

Fang M ，Liu S ，Jiang D 《-》

Mapping a quantitative trait locus via the EM algorithm and Bayesian classification.

Mapping a locus controlling a quantitative genetic trait (e.g., blood pressure) to a specific genomic region is of considerable interest. Data on the quantitative trait under consideration and several codominant genetic markers with known genomic locations are collected from members of families and statistically analyzed to draw inferences on the genomic position of the trait locus. The vector of parameters of interest comprises the pairwise recombination fractions, theta, between the putative quantitative trait locus and the marker loci. One of the major complications in estimating theta for a quantitative trait in humans is the lack of haplotype information on members of families. The purpose of this study was to devise a computationally simple and efficient method of estimation of theta in the absence of haplotype information. We have proposed a two-stage estimation procedure using the expectation-maximization (EM) algorithm. In the first stage, parameters of the QTL are estimated based on data of a sample of unrelated individuals. From estimates thus obtained, we have used a Bayes' rule to infer QTL genotypes of parents in families. Finally, in the second stage of the procedure, we have proposed an EM algorithm for obtaining the maximum likelihood estimate of theta based on data of informative families (which are identified upon inferring parental QTL genotypes performed in the first stage). We have shown, using simulated data, that the proposed procedure is cost-effective, computationally simple, and statistically efficient. As expected, analysis of data on multiple markers jointly is more efficient than the analysis based on single markers.

Ghosh S ，Majumder PP 《GENETIC EPIDEMIOLOGY》

Bayesian point estimation of quantitative trait loci.

Sisson SA ，Hurn MA 《BIOMETRICS》