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Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models

机译:反应规范模型分析450天体重耐罗尔牛的环境相互作用基因型

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摘要

Genotype by environment interactions (GEI) have attracted increasing attention in tropical breeding programs because of the variety of production systems involved. In this work, we assessed GEI in 450-day adjusted weight (W450) Nelore cattle from 366 Brazilian herds by comparing traditional univariate single-environment model analysis (UM) and random regression first order reaction norm models for six environmental variables: standard deviations of herd-year (RRMw) and herd-year-season-management (RRMw-m) groups for mean W450, standard deviations of herd-year (RRMg) and herd-year-season-management (RRMg-m) groups adjusted for 365-450 days weight gain (G450) averages, and two iterative algorithms using herd-year-season-management group solution estimates from a first RRMw-m and RRMg-m analysis (RRMITw-m and RRMITg-m, respectively). The RRM results showed similar tendencies in the variance components and heritability estimates along environmental gradient. Some of the variation among RRM estimates may have been related to the precision of the predictor and to correlations between environmental variables and the likely components of the weight trait. GEI, which was assessed by estimating the genetic correlation surfaces, had values < 0.5 between extreme environments in all models. Regression analyses showed that the correlation between the expected progeny differences for UM and the corresponding differences estimated by RRM was higher in intermediate and favorable environments than in unfavorable environments (p < 0.0001).
机译:由于所涉及的生产系统多种多样,环境相互作用的基因型(GEI)在热带育种计划中引起了越来越多的关注。在这项工作中,我们通过比较传统的单变量单环境模型分析(UM)和随机回归一阶反应规范模型对六个环境变量的评估,评估了来自366个巴西牛群的450天调整体重(W450)内罗尔牛的GEI。牧群年(RRMw)和牧群年管理(RRMw-m)组的平均W450,牧群年(RRMg)和牧群年季节管理(RRMg-m)组的标准偏差经调整为365 -450天的体重增加(G450)平均值,以及两种使用第一批RRMw-m和RRMg-m分析(分别为RRMITw-m和RRMITg-m)使用牧群-季节-季节管理组解决方案估算的迭代算法。 RRM结果显示,方差分量和遗传力估计值沿环境梯度的趋势相似。 RRM估计值之间的某些变化可能与预测变量的精度以及环境变量与体重特征的可能成分之间的相关性有关。通过估计遗传相关表面来评估的GEI在所有模型的极端环境之间的值均<0.5。回归分析表明,在中间环境和有利环境中,UM的预期后代差异与RRM估计的相应差异之间的相关性高于不利环境中的相关性(p <0.0001)。

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