Maternal body composition in seedstock herds. 3. Multivariate analysis using factor analytic models and cluster analysis
J. De Faveri B F , A. P. Verbyla C E , S. J. Lee D and W. S. Pitchford D
+ Author Affiliations
- Author Affiliations
A Cooperative Research Centre for Beef Genetic Technologies.
B Qld Department of Agriculture and Fisheries, PO Box 1054, Mareeba, Qld 4880, Australia.
C CSIRO Data61 and School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, SA 5064, Australia.
D School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy Campus, SA 5371, Australia.
E Present address: CSIRO Atherton, Qld 4883, Australia.
F Corresponding author. Email: joanne.defaveri@daf.qld.gov.au
Animal Production Science 58(1) 135-144 https://doi.org/10.1071/AN15465
Submitted: 5 May 2014 Accepted: 26 June 2017 Published: 28 September 2017
Abstract
Considerable information exists on genetic relationships of body composition and carcass quality of young and finished beef cattle. However, there is a dearth of information on genetic relationships of cow body composition over time and, also, relationships with young-animal body-composition measures. The aim of the present study is to understand genetic relationships among various cow body-composition traits of Angus cows over time, from yearling to weaning of a second calf at ~3.5 years. To determine genetic correlations among various composition traits over time, a multi-trait–multi-time analysis is required. For the Maternal Productivity Project, this necessitates modelling of five traits (namely weight and ultrasound measure for loin eye muscle area (EMA), rib fat, P8 rump fat and intramuscular fat) by five time combinations (recordings at yearling then pre-calving and weaning in first and second parity). The approach was based on including all 25 trait-by-time combinations in an analysis using factor analytic models to approximate the genetic covariance matrix. Various models for the residual covariance structure were investigated. The analyses yielded correlations that could be compared with those of past studies reported in the literature and, also, to a set of bivariate analyses. Clustering of the genetic multi-trait–multi-time correlation structure resulted in a separation of traits (weight and EMA, and the fat traits) and also of time effects into early (heifer = before first lactation) and late (cow = post-first lactation) measurements.
Additional keywords: genetic correlations, mixed models, multi-trait–multi-time.
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