The genetic underpinnings of calf mortality can be partly polygenic and partly due to deleterious effects of recessive lethal alleles. Prediction of the genetic merits of selection candidates should thus take into account both genetic components contributing to calf mortality. However, simultaneously modeling polygenic risk and recessive lethal allele effects in genomic prediction is challenging due to effects that behave differently. In this study, we present a novel approach where mortality risk probabilities from polygenic and lethal allele components are predicted separately to compute the total risk probability of an individual for its future offspring as a basis for selection. We present methods for transforming genomic estimated breeding values of polygenic effect into risk probabilities using normal density and cumulative distribution functions and show computations of risk probability from recessive lethal alleles given sire genotypes and population recessive allele frequencies. Simulated data were used to test the novel approach as implemented in probit, logit, and linear models. In the simulation study, the accuracy of predicted risk probabilities was computed as the correlation between predicted mortality probabilities and observed calf mortality for validation sires. The results indicate that our novel approach can greatly increase the accuracy of selection for mortality traits compared with the accuracy of predictions obtained without distinguishing polygenic and lethal gene effects.

中文翻译：

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整合隐性致死基因信息的新方法提高了死亡率特征基因组预测的准确性

小牛死亡率的遗传基础可以部分是多基因的，部分是由于隐性致死等位基因的有害影响。因此，选择候选者的遗传优点的预测应考虑导致小牛死亡率的两个遗传成分。然而，由于效应表现不同，在基因组预测中同时模拟多基因风险和隐性致死等位基因效应具有挑战性。在这项研究中，我们提出了一种新方法，其中分别预测来自多基因和致死等位基因成分的死亡风险概率，以计算个体对其未来后代的总风险概率，作为选择的基础。我们提出了使用正态密度和累积分布函数将多基因效应的基因组估计育种值转换为风险概率的方法，并显示给定父系基因型和种群隐性等位基因频率的隐性致死等位基因的风险概率计算。模拟数据用于测试在 probit、logit 和线性模型中实施的新方法。在模拟研究中，预测风险概率的准确性计算为预测死亡率概率与验证公牛观察到的犊牛死亡率之间的相关性。结果表明，与在不区分多基因和致死基因效应的情况下获得的预测准确性相比，我们的新方法可以大大提高死亡率性状选择的准确性。