Inclusion of genomic information in estimation of genetic parameters for body weights and visual scores in Nelore cattle

Rafael Nakamura Watanabe,Danísio Prado Munari,Guilherme Batista Do Nascimento,Priscila Arrigucci Bernardes,Raysildo Barbosa Lôbo,Fernando Baldi,Rebeka Magalhães Da Costa

doi:10.37496/rbz5020200077

Abstract

ABSTRACT The aim of this study was to evaluate genomic information inclusion in genetic parameter estimation of standardized body weight at birth and at 240, 365, and 450 days of age, and visual scores for body structure, precocity, and body muscularity, measured as yearlings in Nelore cattle. We compared genetic parameters, (co)variance components (estimated from Bayesian inference and Gibbs sampling), breeding value accuracies, genetic trends, and principal component analysis (PCA) obtained through traditional GBLUP and ssGBLUP methods. For all traits [...]

Highlights

The use of genomic information for evaluating production animals makes it possible to obtain genetic gains more rapidly compared with the use of information obtained only through phenotype records (Meuwissen et al, 2016)
Part of the phenotypic variation was explained by the additive genetic effect, indicating the capacity of traits to respond to the selection process
Heritability estimates for the traits evaluated in single-step genomic best linear unbiased prediction (ssGBLUP) and best linear unbiased prediction (BLUP) ranged from 0.31±0.04 to 0.81±0.01 and 0.31±0.02 to 0.82±0.01, respectively

Summary

Introduction

The use of genomic information for evaluating production animals makes it possible to obtain genetic gains more rapidly compared with the use of information obtained only through phenotype records (Meuwissen et al, 2016). Given that the amount of genotype data available for Nelore breed commercial cattle herds is relatively small in comparison with the volume of pedigree data, the best manner for using the genomic data on these animals is to combine genomic, pedigree, and phenotype information, through single-step genomic best linear unbiased prediction (ssGBLUP) (Haile-Mariam et al, 2013). This methodology consists of using genomic information to correct the pedigree relationship matrix and ascertain the genetic breeding. These traits tend to be used as a selection criterion in genetic improvement programs because of its favorable genetic correlation with several traits of economic interest (Araújo et al, 2014), especially for visual score traits (Abreu et al, 2018)

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