Estimation of breeding values from large-sized routine carcass data in Japanese Black cattle using Bayesian analysis

Abstract

Volumes of official data sets have been increasing rapidly in the genetic evaluation using the Japanese Black routine carcass field data. Therefore, an alternative approach with smaller memory requirement to the current one using the restricted maximum likelihood (REML) and the empirical best linear unbiased prediction (EBLUP) is desired. This study applied a Bayesian analysis using Gibbs sampling (GS) to a large data set of the routine carcass field data and practically verified its validity in the estimation of breeding values. A Bayesian analysis like REML-EBLUP was implemented, and the posterior means were calculated using every 10th sample from 90,000 of samples after 10,000 samples discarded. Moment and rank correlations between breeding values estimated by GS and REML-EBLUP were very close to one, and the linear regression coefficients and the intercepts of the GS on the REML-EBLUP estimates were substantially one and zero, respectively, showing a very good agreement between breeding value estimation by the current GS and the REML-EBLUP. The current GS required only one-sixth of the memory space with REML-EBLUP. It is confirmed that the current GS approach with relatively small memory requirement is valid as a genetic evaluation procedure using large routine carcass data.