Crossa José, Martini Johannes W R, Vitale Paolo, Pérez-Rodríguez Paulino, Costa-Neto Germano, Fritsche-Neto Roberto, Runcie Daniel, Cuevas Jaime, Toledo Fernando, Li H, De Vita Pasquale, Gerard Guillermo, Dreisigacker Susanne, Crespo-Herrera Leonardo, Saint Pierre Carolina, Bentley Alison, Lillemo Morten, Ortiz Rodomiro, Montesinos-López Osval A, Montesinos-López Abelardo
International Maize and Wheat Improvement Center (CIMMYT), Carretera México - Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico; Colegio de Postgraduados, Montecillos, Edo. de México CP 56230, Mexico.
Aardevo B.V., Nagele, The Netherlands.
Trends Plant Sci. 2025 Jul;30(7):756-774. doi: 10.1016/j.tplants.2024.12.009. Epub 2025 Jan 30.
With growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP). Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology.
随着越来越多的证据表明基因组选择（GS）可提高植物育种中的遗传增益，现在是时候回顾一下提高其效率的关键因素了。在这篇专题综述中，我们重点关注正在使GS方法民主化的统计机器学习（ML）方法和软件。我们概述了基因组辅助预测的原理，并讨论了统计ML工具如何利用大数据提高GS效率。此外，我们研究了近年来开发的各种统计ML工具，用于预测连续、二元、分类和计数表型的性状。我们强调了基因组预测（GP）中使用的深度学习（DL）模型的独特优势。最后，我们回顾了为使GP模型的使用民主化而开发的软件以及支持采用GS方法的最新数据管理工具。
