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Maternal body composition in seedstock herds. 3. Multivariate analysis using factor analytic models and cluster analysis

De Faveri, J. and Verbyla, A. P. and Lee, S. J. and Pitchford, W. S. (2017) Maternal body composition in seedstock herds. 3. Multivariate analysis using factor analytic models and cluster analysis. Animal Production Science . pp. 135-144.

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Article Link(s): https://doi.org/10.1071/AN15465

Publisher URL: http://www.publish.csiro.au/paper/AN15465

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.

Item Type:Article
Business groups:Horticulture and Forestry Science
Keywords:genetic correlations, mixed models, multi-trait–multi-time.
Subjects:Science > Biology > Genetics
Science > Zoology > Anatomy
Animal culture > Cattle > Meat production
Deposited On:11 Jan 2018 06:04
Last Modified:11 Jan 2018 06:04

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