Abstract
Abstract: The objective of this work was to evaluate the effects of genomic information on the genetic evaluation of hip height in Brahman cattle using different matrices built from genomic and pedigree data. Hip height measurements from 1,695 animals, genotyped with high-density SNP chip or imputed from 50 K high-density SNP chip, were used. The numerator relationship matrix (NRM) was compared with the H matrix, which incorporated the NRM and genomic relationship (G) matrix simultaneously. The genotypes were used to estimate three versions of G: observed allele frequency (HGOF), average minor allele frequency (HGMF), and frequency of 0.5 for all markers (HG50). For matrix comparisons, animal data were either used in full or divided into calibration (80% older animals) and validation (20% younger animals) datasets. The accuracy values for the NRM, HGOF, and HG50 were 0.776, 0.813, and 0.594, respectively. The NRM and HGOF showed similar minor variances for diagonal and off-diagonal elements, as well as for estimated breeding values. The use of genomic information resulted in relationship estimates similar to those obtained based on pedigree; however, HGOF is the best option for estimating the genomic relationship matrix and results in a higher prediction accuracy. The ranking of the top 20% animals was very similar for all matrices, but the ranking within them varies depending on the method used.
Highlights
Animal selection studies target traits of interest and use the phenotype of individuals and information of kinship derived from pedigree records, which is the basis for building the numerator relationship matrix (NRM) (Henderson, 1975)
The objective of this work was to evaluate the effects of genomic information on the genetic evaluation of hip height in Brahman cattle using different matrices built from genomic and pedigree data
The use of genomic information resulted in relationship estimates similar to those obtained based on pedigree; HGOF is the best option for estimating the genomic relationship matrix and results in a higher prediction accuracy
Summary
Animal selection studies target traits of interest and use the phenotype of individuals and information of kinship derived from pedigree records, which is the basis for building the numerator relationship matrix (NRM) (Henderson, 1975) This animal breeding and selection method is efficient, but the process can be slow, especially for traits measured only in one sex, such as milk production; measured after the slaughter of animals, including meat quality; or measured late in life, as, for example, longevity (Goddard & Hayes, 2009) and hip height. The advancement of technology and the opportunity of genotyping a high number of individuals for numerous alleles make it possible to use information on alleles that can be shared through common ancestors in the pedigree, including ancestors that may be missing from the pedigree or not genotyped This makes the use of a genomic relationship matrix (G) feasible (VanRaden, 2008), allowing to increase the accuracy of predicted breeding values in genetic evaluations. This has been observed in the dairy system, some practical barriers are still faced in its implementation in the beef sector, primarily due to the lack of short-term return on investment for most producers and to incremental and relatively small genetic gains (Rolf et al, 2014)
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