Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

Background: The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. Results: This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution o...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Ross K. Shepherd, Theo H. E. Meuwissen, John A. Woolliams
• Keywords: BMCBI 2010, Dense Snp, EM Algorithm, SNP Panels