This manuscript delves into the intersection of genomics and phenotypic prediction, focusing on the statistical innovation required to navigate the complexities introduced by noisy covariates and confounders. The primary emphasis is on the development of advanced robust statistical models tailored for genomic prediction from single nucleotide polymorphism data in plant and animal breeding and multi‐field trials. The manuscript highlights the significance of incorporating all estimated effects of marker loci into the statistical framework and aiming to reduce the high dimensionality of data while preserving critical information. This paper introduces a new robust statistical framework for genomic prediction, employing one‐stage and two‐stage linear mixed model analyses along with utilizing the popular robust minimum density power divergence estimator (MDPDE) to estimate genetic effects on phenotypic traits. The study illustrates the superior performance of the proposed MDPDE‐based genomic prediction and associated heritability estimation procedures over existing competitors through extensive empirical experiments on artificial datasets and application to a real‐life maize breeding dataset. The results showcase the robustness and accuracy of the proposed MDPDE‐based approaches, especially in the presence of data contamination, emphasizing their potential applications in improving breeding programs and advancing genomic prediction of phenotyping traits.

中文翻译：

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使用密度功率散度进行稳健的基因组预测和遗传力估计


本手稿深入探讨了基因组学和表型预测的交叉点，重点关注驾驭嘈杂协变量和混杂因素引入的复杂性所需的统计创新。主要重点是开发先进的稳健统计模型，用于从动植物育种和多田试验中的单核苷酸多态性数据进行基因组预测。该手稿强调了将标记位点的所有估计效应纳入统计框架的重要性，并旨在减少数据的高维性，同时保留关键信息。本文介绍了一种新的稳健的基因组预测统计框架，采用一阶段和两阶段线性混合模型分析，并利用流行的稳健最小密度功率散度估计器 （MDPDE） 来估计对表型性状的遗传影响。该研究通过在人工数据集上进行广泛的实证实验并应用于现实生活中的玉米育种数据集，说明了所提出的基于 MDPDE 的基因组预测和相关遗传力估计程序优于现有竞争对手的性能。结果展示了所提出的基于 MDPDE 的方法的稳健性和准确性，尤其是在存在数据污染的情况下，强调了它们在改进育种计划和推进表型性状基因组预测方面的潜在应用。