J Anim Breed Genet. 2019;00:1–19. wileyonlinelibrary.com/journal/jbg | 1 © 2019 Blackwell Verlag GmbH 1 | INTRODUCTION The selection for growth traits using phenotypes and pedigree data with best linear unbiased prediction (BLUP) (Henderson, 1975) has been effective over the years. However, this can be improved if DNA polymorphisms affecting growth traits in beef cattle are determined (Snelling et al., 2010). Genome- Wide Association Studies (GWAS) enable the identification of SNPs associated with the quantitative trait loci (QTL) of interest and/or genes related to the phenotypic expression of the trait (Utsunomiya et al., 2013). The assumption in GWAS is that association arise because at least one single nucleotide Received: 6 August 2019 | Revised: 3 November 2019 | Accepted: 5 November 2019 DOI: 10.1111/jbg.12458 ORIGINAL ARTICLE Tag-SNP selection using Bayesian genomewide association study for growth traits in Hereford and Braford cattle Gabriel Soares Campos 1 | Bruna Pena Sollero 2 | Fernando Antonio Reimann 1 | Vinicius Silva Junqueira 3 | Leandro Lunardini Cardoso 1,2 | Marcos Jun Iti Yokoo 2 | Arione Augusti Boligon 1 | José Braccini 4 | Fernando Flores Cardoso 1,2 1 Departamento de Zootecnia, Universidade Federal de Pelotas, Pelotas, Brazil 2 Embrapa Pecuária Sul, Bagé, Brazil 3 Departamento de Zootecnia, Universidade Federal de Viçosa, Viçosa, Brazil 4 Departamento de Zootecnia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil Correspondence Gabriel Soares Campos, Universidade Federal de Pelotas, Departamento de Zootecnia, Pelotas, RS, 96010-900, Brazil. Email: gabrielsoarescampos@hotmail.com Funding information Conselho Nacional de Desenvolvimento Científico e Tecnológico, Grant/Award Number 305102/2018-4; Empresa Brasileira de Pesquisa Agropecuária, Grant/ Award Number 12.13.14.014.00. Abstract The aim of this study was to perform a Bayesian genomewide association study (GWAS) to identify genomic regions associated with growth traits in Hereford and Braford cattle, and to select Tag-SNPs to represent these regions in low-density pan- els useful for genomic predictions. In addition, we propose candidate genes through functional enrichment analysis associated with growth traits using Medical Subject Headings (MeSH). Phenotypic data from 126,290 animals and genotypes for 131 sires and 3,545 animals were used. The Tag-SNPs were selected with BayesB (π = 0.995) method to compose low-density panels. The number of Tag-single nucleotide poly- morphism (SNP) ranged between 79 and 103 SNP for the growth traits at weaning and between 78 and 100 SNP for the yearling growth traits. The average proportion of variance explained by Tag-SNP with BayesA was 0.29, 0.23, 0.32 and 0.19 for birthweight (BW), weaning weight (WW205), yearling weight (YW550) and post- weaning gain (PWG345), respectively. For Tag-SNP with BayesA method accuracy values ranged from 0.13 to 0.30 for k-means and from 0.30 to 0.65 for random clus- tering of animals to compose reference and validation groups. 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 with full markers for growth traits. The MeSH analysis was able to translate genomic information providing biological meanings of more specific gene products related to the growth traits. The proposed Tag-SNP panels may be useful for future fine map- ping studies and for lower-cost commercial genomic prediction applications. KEYWORDS beef cattle, genomic prediction, GWAS, low-density panel