Genetic evaluation of growth traits in beef cattle using random regression models

Jb Van Wyk,Fwc Neser,P Lubout,Md Fair

doi:10.4314/sajas.v42i5.6

Abstract

Direct- and maternal heritabilities were estimated for weight traits in Brangus cattle using random regression models. After editing, 54 924 records, from birth- (BW) to mature weight (MW) from 21 673 animals were selected for analysis. The data, which covered a period of 8 generations (1985 to 2010), were transformed to a log scale to accommodate the wide range of weights  being studied (15 to 850 kg). Traits included in the analysis were birth- (BW), weaning- (WW), yearling- (YW), eighteen month- (FW) and three measurements of mature weight (MW). Linear polynomials with intercepts were fitted using random regression models. The direct heritability estimates were moderate and ranged from 0.13 to 0.25 while maternal heritability estimates were low ranging from 0.05 to 0.06.Keywords: Heritability estimates, South African Brangus cattle

Highlights

Random regression models (RRM) have become the method of choice to analyze longitudinal data or repeated measurements (Meyer, 2004; 2005; Schaeffer & Jamrozik, 2008). This technology has been used in dairy production studies for the genetic analysis of test day models (Legarra et al, 2004), its application for growth traits in beef cattle is obvious as an animal is weighed several times during its lifetime, sometimes repeatedly for the same trait
The maximum heritability estimate of 0.25 corresponds to results obtained in a recent study on Brangus cattle where multivariate combined with repeatability models were used (Neser et al, 2012), the rest of the values were substantially lower
This is in contrast to results obtained by Meyer (2005) who found, in a study on Angus cattle that heritability estimates from random regression models were higher than results obtained from univariate analysis using the same data

Summary

Introduction

Random regression models (RRM) have become the method of choice to analyze longitudinal data or repeated measurements (Meyer, 2004; 2005; Schaeffer & Jamrozik, 2008). This technology has been used in dairy production studies for the genetic analysis of test day models (Legarra et al, 2004), its application for growth traits in beef cattle is obvious as an animal is weighed several times during its lifetime, sometimes repeatedly for the same trait. Misztal et al (2000) found that parameters estimated with RRM using Legendry polynomials are prone to contain artifacts due to data distribution This is especially true where estimates of direct and maternal covariances are obtained. An alternative to Legendry polynomials are splines. Meyer (2005) found that due to a locality of fit for each parameter, models using splines have potentially better numerical properties than polynomials. Bohmanova et al (2005) reported no difference in the accuracies of RRM using Legendry polynomials, RRM using splines and MT

Objectives

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Results