The aim of this study was to perform a Bayesian genome-wide association study (GWAS) to identify genomic regions associated with growth and adaptation traits in Hereford and Braford cattle, and to select Tag-SNPs to represent these regions in low-density panels useful for genomic predictions. Phenotypic data from 126,290 animals from the Conexão Delta G genetic breeding program and a set of 3,545 animals and 131 sires genotyped with the Illumina BovineSNP50 and HD chips, respectively, were used. Using BayesB (π=0.995) method, Tag-SNPs were selected according to parameters such as window variance, model frequency, t-like statistic, linkage disequilibrium and minor allele frequency to compose low-density panels. Estimated cross-validation accuracies for growth traits were obtained by calculating genetic correlations between observed phenotypes and direct genomic values. Based on BayesA and Tag-SNPs, these accuracy values ranged from 0.13 to 0.30 for k-means and 0.36 to 0.65 for random clustering of animals to compose validation groups. For adaptation traits, observed genetic correlations ranged from 0.18 to 0.42 and 0.33 to 0.61 for k-means clustering and random, respectively. Although genomic prediction accuracies were higher with the full marker panel, predictions with low-density panels retained on average 76% of the accuracy obtained with BayesB for growth traits and 64% for adaptation traits. The proposed Tag-SNP panels may be useful for future functional enrichment and fine mapping studies and for lower-cost commercial genomic predictions. Keywords: beef cattle, genomic prediction, gwas, low-density panel
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Electronic Poster Session - Methods and Tools - Prediction 2, , 484, 2018
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