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Efficient Methods to Compute Genomic Predictions

Permanent URL:
http://handle.nal.usda.gov/10113/27183
File:
Download [PDF File]
Abstract:
Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
Author(s):
VanRaden, P.M.
Subject(s):
dairy cattle , genetic variation , genome , breeding value , genetic markers , computer software , simulation models , data analysis , inbreeding coefficient , alleles , gene frequency , loci , selection index , equations , genetic improvement , linkage disequilibrium , phenotype , animal breeding , artificial selection
Format:
p. 4414-4423.
Note:
Includes references
Source:
Journal of dairy science 2008 Nov., v. 91, no. 11
Language:
English
Publisher:
American Dairy Science Association
Year:
2008
Collection:
Journal Articles, USDA Authors, Peer-Reviewed
Rights:
Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.