Brazilian beef cattle are raised predominantly on pasture in a wide range of environments. In this scenario, genotype by environment (G×E) interaction is an important source of phenotypic variation in the reproductive traits. Hence, the evaluation of G×E interactions for heifer's early pregnancy (HP) and scrotal circumference (SC) traits in Nellore cattle, belonging to three breeding programs, was carried out to determine the animal's sensitivity to the environmental conditions (EC). The dataset consisted of 85 874 records for HP and 151 553 records for SC, from which 1800 heifers and 3343 young bulls were genotyped with the BovineHD BeadChip. Genotypic information for 826 sires was also used in the analyses. EC levels were based on the contemporary group solutions for yearling body weight. Linear reaction norm models (RNM), using pedigree information (RNM_A) or pedigree and genomic information (RNM_H), were used to infer G×E interactions. Two validation schemes were used to assess the predictive ability, with the following training populations: (a) forward scheme-dataset was split based on year of birth from 2008 for HP and from 2011 for SC; and (b) environment-specific scheme-low EC (-3.0 and -1.5) and high EC (1.5 and 3.0). The inclusion of the H matrix in RNM increased the genetic variance of the intercept and slope by 18.55 and 23.00% on average respectively, and provided genetic parameter estimates that were more accurate than those considering pedigree only. The same trend was observed for heritability estimates, which were 0.28-0.56 for SC and 0.26-0.49 for HP, using RNM_H, and 0.26-0.52 for SC and 0.22-0.45 for HP, using RNM_A. The lowest correlation observed between unfavorable (-3.0) and favorable (3.0) EC levels were 0.30 for HP and -0.12 for SC, indicating the presence of G×E interaction. The G×E interaction effect implied differences in animals' genetic merit and re-ranking of animals on different environmental conditions. SNP marker-environment interaction was detected for Nellore sexual precocity indicator traits with changes in effect and variance across EC levels. The RNM_H captured G×E interaction effects better than RNM_A and improved the predictive ability by around 14.04% for SC and 21.31% for HP. Using the forward scheme increased the overall predictive ability for SC (20.55%) and HP (11.06%) compared with the environment-specific scheme. The results suggest that the inclusion of genomic information combined with the pedigree to assess the G×E interaction leads to more accurate variance components and genetic parameter estimates.

中文翻译：

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利用内洛尔牛性早熟指标性状的基因型×环境相互作用的基因组反应规范模型。

巴西肉牛主要在各种环境中的牧场上饲养。在这种情况下，环境之间的基因型（G×E）相互作用是生殖性状表型变异的重要来源。因此，对属于三个育种计划的内罗尔牛的小母牛的早孕（HP）和阴囊周（SC）性状进行了G×E相互作用的评估，以确定该动物对环境条件（EC）的敏感性。该数据集由HP的85 874条记录和SC的151 553条记录组成，其中使用BovineHD BeadChip对1800头小母牛和3343头小公牛进行了基因分型。分析中还使用了826个父本的基因型信息。EC水平是基于当代的一岁体重组解决方案。线性反应范数模型（RNM），使用谱系信息（RNM_A）或谱系和基因组信息（RNM_H）来推断G×E相互作用。采用了两种验证方案来评估预测能力，并对以下人群进行了训练：（a）前向方案数据集基于HP的2008年和SC的2011年的出生年份进行划分；（b）特定于环境的方案-低EC（-3.0和-1.5）和高EC（1.5和3.0）。在RNM中包含H矩阵平均分别使截距和斜率的遗传方差平均增加18.55％和23.00％，并且提供的遗传参数估计比仅考虑系谱的更为准确。对于遗传力估计，观察到了相同的趋势，使用RNM_H时，SC的估计值是0.28-0.56，HP时是0.26-0.49，使用RNM_A的SC值是0.26-0.52，HP时0.22-0.45。EC的不利（-3.0）和EC的有利（3.0）水平之间的最低相关性是HP为0.30，SC为-0.12，表明存在G×E相互作用。G×E相互作用效应暗示了动物的遗传价值差异以及在不同环境条件下动物的排名。检测了Nellore性早熟指标性状的SNP标记-环境相互作用，并在EC水平上产生了影响和方差变化。RNM_H捕获的G×E交互作用优于RNM_A，SC的预测能力提高了约14.04％，HP的预测能力提高了21.31％。与特定于环境的方案相比，使用前向方案增加了SC（20.55％）和HP（11.06％）的总体预测能力。