Zanella R.,Embrapa Swine and Poultry National Research Center |
Zanella R.,University Of Passo Fundo |
Peixoto J.O.,Embrapa Swine and Poultry National Research Center |
Cardoso F.F.,Embrapa Southern Region Animal Husbandry |
And 10 more authors.
Genetics Selection Evolution
Background: Genetic improvement in livestock populations can be achieved without significantly affecting genetic diversity if mating systems and selection decisions take genetic relationships among individuals into consideration. The objective of this study was to examine the genetic diversity of two commercial breeds of pigs. Genotypes from 1168 Landrace (LA) and 1094 Large White (LW) animals from a commercial breeding program in Brazil were obtained using the Illumina PorcineSNP60 Beadchip. Inbreeding estimates based on pedigree (Fx) and genomic information using runs of homozygosity (FROH) and the single nucleotide polymorphisms (SNP) by SNP inbreeding coefficient (FSNP) were obtained. Linkage disequilibrium (LD), correlation of linkage phase (r) and effective population size (Ne) were also estimated. Results: Estimates of inbreeding obtained with pedigree information were lower than those obtained with genomic data in both breeds. We observed that the extent of LD was slightly larger at shorter distances between SNPs in the LW population than in the LA population, which indicates that the LW population was derived from a smaller Ne. Estimates of Ne based on genomic data were equal to 53 and 40 for the current populations of LA and LW, respectively. The correlation of linkage phase between the two breeds was equal to 0.77 at distances up to 50 kb, which suggests that genome-wide association and selection should be performed within breed. Although selection intensities have been stronger in the LA breed than in the LW breed, levels of genomic and pedigree inbreeding were lower for the LA than for the LW breed. Conclusions: The use of genomic data to evaluate population diversity in livestock animals can provide new and more precise insights about the effects of intense selection for production traits. Resulting information and knowledge can be used to effectively increase response to selection by appropriately managing the rate of inbreeding, minimizing negative effects of inbreeding depression and therefore maintaining desirable levels of genetic diversity. © 2016 Zanella et al. Source
Freitas M.S.,BRF SA |
Freitas L.S.,BRF SA |
Weber T.,BRF SA |
Yamaki M.,BRF SA |
And 3 more authors.
Journal of Animal Science
The effects of modified single-step genomic best linear unbiased prediction (ssGBLUP) iterations on GEBV and SNP were investigated using 85,388 age at 100 kg phenotypes from the BRF SA breeding program Landrace pure line animals, off-tested between 2002 and 2013. Pedigree data comprised animals born between 1999 and 2013. A total of 1,068 animals were assigned to the training population, in which all of them had genotypes, original and corrected age at 100 kg phenotypes, and weighted deregressed proof records. A total of 100 genotyped animals, with high accuracy age at 100 kg estimated breeding values, were assigned to the validation population. After applying the quality control workflow, a set of 41,042 SNP was used for the analysis. Standard and modified ssGBLUP, BayesCπ, and Bayesian Lasso were compared, and their predictive abilities were accessed by approximate true and GEBV correlations. Modified ssGBLUP iteration effects on SNP estimates and GEBV were relevant, in which assigned differential weights and shrinkage caused important losses on ssGBLUP predictive ability for age at 100 kg GEBV. Even though ssGBLUP accuracy can be equal or better than the compared Bayesian methods, additional gains can be obtained by correctly identifying the number of iterations required for best ssGBLUP performance. © 2015 American Society of Animal Science. All rights reserved. Source