Fernández-González Javier, Isidro Y Sánchez Julio
Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain.
Theor Appl Genet. 2025 Mar 18;138(4):78. doi: 10.1007/s00122-025-04861-8.
We developed an improved variance estimation that incorporates prediction error variance as a correction factor, alongside a novel generalized effective sample size to enhance simulations. This approach enables precise control of variance components, accommodating for more flexible and accurate simulations. Phenotypic variation in field trials results from genetic and environmental factors, and understanding this variation is critical for breeding program simulations. Additive genetic variance, a key component, is often estimated using linear mixed models (LMM), but can be biased due to improper scaling of the genomic relationship matrix. Here, we show that this bias can be minimized by incorporating prediction error variance (PEV) as a correction factor. Our results demonstrate that the PEV-based estimation of additive variance significantly improves accuracy, with root mean square errors orders of magnitude lower than traditional methods. This improved accuracy enables more realistic simulations, and we introduce a novel generalized effective sample size (ESS) to further refine simulations by accounting for sampling variation. Our method outperforms standard simulation approaches, allowing flexibility to include complex interactions such as genotype by environment effects. These findings provide a robust framework for variance estimation and simulation in genetic studies, with broad applicability to breeding programs.
我们开发了一种改进的方差估计方法，该方法将预测误差方差作为校正因子，并结合了一种新颖的广义有效样本量来增强模拟。这种方法能够精确控制方差分量，从而实现更灵活、准确的模拟。田间试验中的表型变异源于遗传和环境因素，了解这种变异对于育种计划模拟至关重要。加性遗传方差是一个关键组成部分，通常使用线性混合模型（LMM）进行估计，但由于基因组关系矩阵的缩放不当可能会产生偏差。在这里，我们表明通过将预测误差方差（PEV）作为校正因子，可以将这种偏差最小化。我们的结果表明，基于PEV的加性方差估计显著提高了准确性，其均方根误差比传统方法低几个数量级。这种提高的准确性使得模拟更加真实，并且我们引入了一种新颖的广义有效样本量（ESS），通过考虑抽样变异来进一步优化模拟。我们的方法优于标准模拟方法，能够灵活地纳入复杂的相互作用，如基因型与环境的效应。这些发现为遗传研究中的方差估计和模拟提供了一个强大的框架，在育种计划中具有广泛的适用性。
