Implementing a QTL Detection Study (GWAS) Using Genomic Prediction Methodology
Genomic prediction exploits historical genotypic and phenotypic data to predict performance on selection candidates based only on their genotypes. It achieves this by a process known as training that derives the values of all the chromosome fragments that can be characterized by regressing the historical phenotypes on some or all of the genotyped loci. A genome-wide association study (GWAS) involves a genome-wide search for chromosome fragments with significant association with phenotype. One Bayesian approach to GWAS makes inferences using samples from the posterior distribution of genotypic effects obtained in the traini...
Source: Springer protocols feed by Bioinformatics - January 1, 2013 Category: Bioinformatics Source Type: news

Bayesian Methods Applied to GWAS
Bayesian multiple-regression methods are being successfully used for genomic prediction and selection. These regression models simultaneously fit many more markers than the number of observations available for the analysis. Thus, the Bayes theorem is used to combine prior beliefs of marker effects, which are expressed in terms of prior distributions, with information from data for inference. Often, the analyses are too complex for closed-form solutions and Markov chain Monte Carlo (MCMC) sampling is used to draw inferences from posterior distributions. This chapter describes how these Bayesian multiple-regression analyses ...
Source: Springer protocols feed by Bioinformatics - January 1, 2013 Category: Bioinformatics Source Type: news