Avaliação Genômica da pigmentação ocular, pelame e caracterização racial nas raças Hereford e Braford

Abstract

The present study aimed to estimate genetic parameters for a threshold animal model using traditional (pedigree and phenotype) and genomic (single-step, Bayes Cπ, Bayes B and Bayes A) methodologies, and measuring accuracy gains for breeding value predictions with the incorporation of genomic information from complete SNPs panels or reduced panels containing most representative SNPs for eye pigmentation (PO), hair coat at weaning (PelD) and at yearling (PelS) and breed characterization (CR) traitsin Hereford and Braford cattle. Phenotypic data from animals born between 1991 and 2014 from the breeding program Connection Delta Gwere used, totaling 73,615 observations for PO, 81,043 for PelD, 41,390 for PelS and 28,186 for CR, along with pedigree data of 169 839 animals and genotypes of 3,954 animals and 41,011 SNP markers. Estimated genetic and residual variance values made possible to obtain heritabilities of medium to high magnitude for PO (0,46±0,02; 0,46±0,02), PelD (0,44±0,03; 0,44±0,03)PelS (0.42 ± 0.02 ; 0.41 ± 0.02) and CR (0.33 ± 0.02, 0.32 ± 0.02) through traditional and genomic methods, respectively. Genetic correlations for all traits were of low magnitude (-0.24<rg<0.30), except between PelD and PelS (0.62). The use of genomic information provided higher accuracy levels for PO (0,28 and 0,14), PelD (0,13 and 0,07), PelS (0,16 and 0,30) and CR (0,10 and 0,04) traits considering k-means and random grouping strategies, respectively. Moreover, using Genome-Wide Association (GWAS) approach, it was possible the detection of chromosome segments and SNP markers statically associated to these traits. Thus, very low-density SNP markers panels were proposed containing 159, 88, 122 and 40 markers for PO, PelD, PelS and CR, respectively. Such panels provided accuracy levels of 0.89 and 0.87, 0.77 and 0.87, 0.80 and 0.88, and, finally, 0.35 and 0.38, considering k-means and random strategies, respectively. Thus, it is possible to obtain gains by genetic selection for the characteristics of studies and genetic prediction values is performed more accurately with the use of genomic information, particularly when used in panels of reduced SNPs identified as the most representative.