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Genomic breeding value prediction and QTL mapping of QTLMAS2010 data using Bayesian Methods by Xiaochen Sun; David Habier; Rohan L Fernando; Dorian J Garrick; Jack CM Dekkers is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Background Bayesian methods allow prediction of genomic breeding values (GEBVs) using high-density single nucleotide polymorphisms (SNPs) covering the whole genome with effective shrinkage of SNP effects using appropriate priors. In this study we applied a modification of the well-known BayesA and BayesB methods to estimate the proportion of SNPs with zero effects (π) and a common variance for non-zero effects. The method, termed BayesCπ, was used to predict the GEBVs of the last generation of the QTLMAS2010 data. The accuracy of GEBVs from various methods was estimated by the correlation with phenotypes in the last generation. The methods were BayesCPi and BayesB with different π values, both with and without polygenic effects, and best linear unbiased prediction using an animal model with a genomic or numerator relationship matrix. Positions of quantitative trait loci (QTLs) were identified based on the variances of GEBVs for windows of 10 consecutive SNPs. We also proposed a novel approach to set significance thresholds for claiming QTL in this specific case by using pedigree-based simulation of genotypes. All analyses were focused on detecting and evaluating QTL with additiv

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Author

Xiaochen Sun; David Habier; Rohan L Fernando; Dorian J Garrick; Jack CM Dekkers

Publisher

BioMed Central; Springer (Biomed Central Ltd.); London: BioMed Central, 2007-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1753-6561)

Published

2011

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)