Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, Jiangsu 225009, China.
College of Agronomy, Hebei Agricultural University, Baoding, Hebei 071001, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae385.
Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP).
基因组选择 (GS) 已成为一种有效的技术，通过在表型收集之前进行早期选择，加速作物杂交育种。基因组最佳线性无偏预测 (GBLUP) 是一种强大的方法，已在 GS 育种计划中常规使用。然而，GBLUP 假设标记对等贡献于总遗传方差，这可能不是事实。在这项研究中，我们开发了一种称为 GA-GBLUP 的新型 GS 方法，该方法利用遗传算法 (GA) 选择与目标性状相关的标记。我们定义了四个适应度函数进行优化，包括 AIC、BIC、R2 和 HAT，以提高预测能力，并根据连锁不平衡原理对相邻标记进行分组，以减少模型维度。结果表明，GA-GBLUP 模型配备了 R2 和 HAT 适应度函数，对于水稻和玉米数据集的大多数性状，特别是对于遗传力较低的性状，比 GBLUP 产生了更高的可预测性。此外，我们还开发了一个用于 GS 的用户友好的 R 包 GAGBLUP，该包可在 CRAN（https://CRAN.R-project.org/package=GAGBLUP）上免费获得。
