Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle

• Ano de defesa: 2015
• Autor(a) principal: Somavilla, Adriana Luiza [UNESP]
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Bovino; Teoria bayesiana de decisão estatistica; Nelore (Zebu); Crescimento; Genômica; Genetica animal; Genomics
• Link de acesso: http://hdl.handle.net/11449/128158; http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/18-09-2015/000848601.pdf

Selection for fast growth rates using number of days to achieve specific weights or average weight gain would result in shorter production periods. Maintaining the rate of productivity increasing will demand, among other factors, genetically improved animals in both pasture and feedlot systems. Besides, genomic information could be used to predict genomic-enabled breeding values (GEBVs) earlier in animals' life, which would reduce generation intervals and increase productivity gains. Numerous studies have been conducted in order to identify appropriate methodologies to specific breeds and traits, which will result in more accurate GEBVs. The aim of this study was to compare the prediction accuracy of GEBVs and the ability to identify genomic regions and genes related to average weight daily gain in Nelore cattle, by applying different regression models and genotypes densities datasets. Genomic and phenotypic information of 804 steers born in three season, offspring of 34 bulls, were used to predict GEBVs through three models (Bayesian GBLUP, BayesA and BayesC ), four genotypic densities (Illumina BovineHD BeadChip, TagSNPs, GeneSeek Genomic Profiler High (HDi) and Low (LDi) density indicus) and two adjusted phenotypes. Family structure was accounted by using principal component analysis. Animals were assigned either to training (seasons 1 and 2) or testing (season 3) subsets to perform the cross-validation analysis. Estimates of Pearson correlation, regression coefficients and mean squared errors were used to access accuracy, inflation and bias of the estimated GEBVs, respectively. Genome-wide association study (GWAS) was also performed on above datasets, however, results were compared based on ...