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Estimation of genetic parameters and trends for growth traits in Hays Converter cattle using multiple-trait and random regression models
Livestock Science ( IF 1.8 ) Pub Date : 2020-09-03 , DOI: 10.1016/j.livsci.2020.104245
R. Khorshidi , M.D. MacNeil , D.P. Hays , M.K. Abo-Ismail , J.J. Crowley , E.C. Akanno , Z. Wang , G. Plastow

Hays Converter (HC), the first registered Canadian beef breed, was developed by Harry Hays in the 1950’s as a composite with contributions from Holstein, Hereford, Brown Swiss and with a later introgression of Angus. The breed is well adapted to the Western Canadian climate. Calves reach market weight early, convert feed to gain efficiently, and have excellent carcass quality and yield. The edited data consisted of 21,612 weight records taken at ages 1 to 474 days, from 8,850 animals born at Red Bow Ranching Ltd., Calgary, Alberta between 1970 and 2016. This study aimed to evaluate the genetic parameters and trends in birth, weaning and yearling weights of HC through comparison of different multiple trait models (MTM) with a random regression model (RRM). In MTM scenarios, both adjusted and unadjusted data were examined besides considering contemporary groups (CG) as fixed or random. Estimates of variance components, heritability and genetic correlations from the two approaches were not substantially different and showed similar changes along the growth trajectory. Although there was a considerable reduction in genetic trends from 2004 to 2008, due to weak sire selection, trends generally increased through 2016. Overall, both models performed similarly and fixed CG were preferred.



中文翻译:

使用多特征和随机回归模型估算海斯转基因牛的遗传参数和生长性状趋势

Hays Converter(HC)是加拿大第一个注册的牛肉品种,由Harry Hays在1950年代开发,是Holstein,Hereford,Brown Swiss和后来的Angus渗入的复合材料。该品种非常适合加拿大西部的气候。犊牛尽早达到市场重量,将饲料转化为有效的增产肉,并具有出色的car体质量和产量。编辑后的数据包括1970年至2016年之间在艾伯塔省卡尔加里市Red Bow Ranching Ltd.出生的8,850头动物中1到474天的21612体重记录。该研究旨在评估出生,断奶和繁殖的遗传参数和趋势。通过比较不同的多特征模型(MTM)和随机回归模型(RRM)获得HC的一岁权重。在MTM方案中,除了考虑当代群体(CG)是固定的还是随机的以外,还检查了调整后和未调整后的数据。两种方法的方差成分,遗传性和遗传相关性的估计值没有显着差异,并且沿着生长轨迹显示出相似的变化。尽管由于weak选不力,从2004年到2008年,遗传趋势显着减少,但到2016年,趋势总体上有所增加。总体而言,两个模型的表现相似,首选固定CG。

更新日期:2020-09-03
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