|Van Tassell, Curtis - Curt|
|Schroeder, Steven - Steve|
Submitted to: Congress of the Brazilian Animal Breeding Society
Publication Type: Abstract Only
Publication Acceptance Date: 7/1/2008
Publication Date: 8/1/2008
Citation: Silva, M.V., Van Tassell, C.P., Sonstegard, T.S., Matkumalli, L., Schroeder, S.G., Van Raden, P.M., Wiggans, G.R. 2008. Prediction of total genetic value using genome-wide dense marker in Holstein breed by Bayesian method. Congress of the Brazilian Animal Breeding Society.
Technical Abstract: Marker-assisted selection (MAS) can be based on molecular markers in linkage equilibrium with quantitative trait loci, molecular markers in linkage disequilibrium with QTL, or on selection of the actual mutations causing the QTL effect. However, one problem in relation to all of them is that only a limited proportion of the total genetic variance is captured by the markers. An alternative is the use of genomic selection that is a form of marker-assisted selection in which genetic markers covering the whole genome are used, in which all quantitative trait loci (QTL) are in linkage disequilibrium with at least one marker. The genomic selection approach has become feasible thanks to the large number of single nucleotide polymorphisms (SNP) discovered by genome sequencing and to new methods to efficiently genotype large number of SNPs as such the new Illumina iSelect Bovine 50K Chip. With genomic selection, selection can be based on the sum of estimates of effects for all markers across the genome, fitted as random effects. The objective of this paper was to estimate the genomic breeding value of 474 Holstein young bulls, born after 2000, predicted from a large number of estimated single marker effects across the entire genome using the PTA milk estimate from 2,378 bulls born before 2000. Marker effects were estimated by Bayesian method using scripts in R software and Fortran. Results indicate that Bayesian methods that assumed a prior distribution for the variance associated with each chromosome segment give more accurate predictions of breeding values.