Admas Alemu, Johanna Åstrand, Osval A Montesinos-López, Julio Isidro Y Sánchez, Javier Fernández-Gónzalez, Wuletaw Tadesse, Ramesh R Vetukuri, Anders S Carlsson, Alf Ceplitis, José Crossa, Rodomiro Ortiz, Aakash Chawade
{"title":"植物育种中的基因组选择:影响二十年进展的关键因素。","authors":"Admas Alemu, Johanna Åstrand, Osval A Montesinos-López, Julio Isidro Y Sánchez, Javier Fernández-Gónzalez, Wuletaw Tadesse, Ramesh R Vetukuri, Anders S Carlsson, Alf Ceplitis, José Crossa, Rodomiro Ortiz, Aakash Chawade","doi":"10.1016/j.molp.2024.03.007","DOIUrl":null,"url":null,"abstract":"<p><p>Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.</p>","PeriodicalId":19012,"journal":{"name":"Molecular Plant","volume":null,"pages":null},"PeriodicalIF":17.1000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Genomic selection in plant breeding: Key factors shaping two decades of progress.\",\"authors\":\"Admas Alemu, Johanna Åstrand, Osval A Montesinos-López, Julio Isidro Y Sánchez, Javier Fernández-Gónzalez, Wuletaw Tadesse, Ramesh R Vetukuri, Anders S Carlsson, Alf Ceplitis, José Crossa, Rodomiro Ortiz, Aakash Chawade\",\"doi\":\"10.1016/j.molp.2024.03.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.</p>\",\"PeriodicalId\":19012,\"journal\":{\"name\":\"Molecular Plant\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":17.1000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Plant\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.molp.2024.03.007\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/3/12 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Plant","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.molp.2024.03.007","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/12 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
基因组选择,即应用基因组预测(GP)模型选择候选个体,在过去二十年中取得了长足进步,有效加快了植物育种的遗传收益。本文全面概述了这一时期影响植物育种中基因组预测的关键因素。我们深入研究了训练群体大小和遗传多样性的关键作用,以及它们与育种群体的关系,这些因素决定了基因组预测的准确性。我们特别强调了训练群体规模的优化。我们探讨了训练群体规模的益处以及超过最佳规模后的相关收益递减问题。同时,我们还考虑了资源分配与通过当前优化算法最大限度提高预测准确性之间的平衡。单核苷酸多态性(SNP)的密度和分布、连锁不平衡程度、遗传复杂性、性状遗传率、统计机器学习方法和非加成效应是其他重要因素。我们以小麦、玉米和马铃薯为例,总结了这些因素对不同性状 GP 精确度的影响。在 GP 中寻求高准确度(使用皮尔逊相关性作为衡量标准时,理论上可达到 1)是一个活跃的研究领域,但对于各种性状而言,这还远未达到最佳状态。我们假设,如果有超大规模的基因型和表型数据集、有效的训练群体优化方法以及其他全息方法(转录组学、代谢组学和蛋白质组学)的支持,再加上深度学习算法,就能突破目前的限制,实现尽可能高的预测准确率,使基因组学成为植物育种的有效工具。
Genomic selection in plant breeding: Key factors shaping two decades of progress.
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
期刊介绍:
Molecular Plant is dedicated to serving the plant science community by publishing novel and exciting findings with high significance in plant biology. The journal focuses broadly on cellular biology, physiology, biochemistry, molecular biology, genetics, development, plant-microbe interaction, genomics, bioinformatics, and molecular evolution.
Molecular Plant publishes original research articles, reviews, Correspondence, and Spotlights on the most important developments in plant biology.