BACKGROUND Genomic prediction (GP) is used in animal and plant breeding to help identify the best genotypes for selection. One of the most important measures of the effectiveness and reliability of GP in plant breeding is predictive accuracy. An accurate estimate of this measure is thus central to GP. Moreover, regression models are the models of choice for analyzing field trial data in plant breeding. However, models that use the classical likelihood typically perform poorly, often resulting in biased parameter estimates, when their underlying assumptions are violated. This typically happens when data are contaminated with outliers. These biases often translate into inaccurate estimates of heritability and predictive accuracy, compromising the performance of GP. Since phenotypic data are susceptible to contamination, improving the methods for estimating heritability and predictive accuracy can enhance the performance of GP. Robust statistical methods provide an intuitively appealing and a theoretically well justified framework for overcoming some of the drawbacks of classical regression, most notably the departure from the normality assumption. We compare the performance of robust and classical approaches to two recently published methods for estimating heritability and predictive accuracy of GP using simulation of several plausible scenarios of random and block data contamination with outliers and commercial maize and rye breeding datasets. RESULTS The robust approach generally performed as good as or better than the classical approach in phenotypic data analysis and in estimating the predictive accuracy of heritability and genomic prediction under both the random and block contamination scenarios. Notably, it consistently outperformed the classical approach under the random contamination scenario. Analyses of the empirical maize and rye datasets further reinforce the stability and reliability of the robust approach in the presence of outliers or missing data. CONCLUSIONS The proposed robust approach enhances the predictive accuracy of heritability and genomic prediction by minimizing the deleterious effects of outliers for a broad range of simulation scenarios and empirical breeding datasets. Accordingly, plant breeders should seriously consider regularly using the robust alongside the classical approach and increasing the number of replicates to three or more, to further enhance the accuracy of the robust approach.

中文翻译：

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可靠地估算植物育种的遗传力和预测准确性：使用模拟和经验数据进行评估。

背景技术基因组预测（GP）被用于动植物育种中，以帮助鉴定最佳基因型以供选择。预测准确性是GP在植物育种中有效性和可靠性的最重要措施之一。因此，此度量的准确估算对于GP至关重要。而且，回归模型是分析植物育种中田间试验数据的首选模型。但是，使用经典似然性的模型通常会表现不佳，通常会在违反其基本假设时导致参数估计有偏差。当数据被异常值污染时，通常会发生这种情况。这些偏见通常会转化为对遗传力和预测准确性的不准确估计，从而损害了GP的性能。由于表型数据容易受到污染，改进估计遗传力和预测准确性的方法可以增强GP的性能。健壮的统计方法为克服经典回归的某些缺陷（最明显的是偏离正态性假设）提供了直观上有吸引力且理论上合理的框架。我们通过使用离群值和商业玉米和黑麦育种数据集模拟几种可能的随机和块数据污染情景，将鲁棒和经典方法的性能与两种最新发表的方法来估计GP的遗传力和预测准确性进行比较。结果在表型数据分析以及在随机和块污染情况下，遗传力的预测准确性和基因组预测的预测准确性方面，鲁棒性方法的性能通常优于或优于经典方法。值得注意的是，在随机污染的情况下，其性能始终优于传统方法。对玉米和黑麦经验数据集的分析进一步增强了在存在异常值或数据缺失的情况下稳健方法的稳定性和可靠性。结论所提出的鲁棒方法通过最小化离群值对广泛的模拟场景和经验育种数据集的有害影响，提高了遗传力和基因组预测的预测准确性。因此，