首页|Genomic selection in plant breeding:Key factors shaping two decades of progress

Genomic selection in plant breeding:Key factors shaping two decades of progress

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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 rela-tionship with the breeding population,in determining GP accuracy.Special emphasis was placed on opti-mizing 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-nucle-otide polymorphisms,level of linkage disequilibrium,genetic complexity,trait heritability,statistical ma-chine-learning methods,and non-additive effects are the other vital factors.Using wheat,maize,and po-tato 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.

genomic selectiongenetic gaingenomic prediction optimizationdeep learningtraining population optimization

Admas Alemu、Johanna ?strand、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

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Department of Plant Breeding,Swedish University of Agricultural Sciences,Alnarp,Sweden

Lantmännen Lantbruk,Svalöv,Sweden

Facultad de Telemática,University de Colima,Colima,Colima 28040,Mexico

Centro de Biotecnología y Genómica de Plantas(CBGP,UPM-INIA),Universidad Politécnica de Madrid(UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria(INIA),Campus de Montegancedo-UPM,28223 Madrid,Spain

International Center for Agricultural Research in the Dry Areas(ICARDA),Rabat,Morocco

International Maize and Wheat Improvement Center(CIMMYT),Km 45,Carretera México-Veracruz,Texcoco,México 52640,Mexico

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SLU GrogrundEinar and Inga Nilsson FoundationERDF A way of making EuropeNovo Nordisk FondenSLU's Centre for Biological Control.In additionBeatriz Galindo Program

SLU-LTV.2020.1.1.1-654PID2021-123718OB-I00 funded by MCIN/AEI/10.13039/501 100 011 033CEX2020-0009990074727BEAGAL18/00115

2024

10.1016/j.molp.2024.03.007

分子植物(英文版)
中科院上海生命科学研究院植物生理生态所　中国植物生理学会

分子植物(英文版)

CSTPCD
影响因子：0.659
ISSN：1674-2052
年,卷(期)：2024.17(4)
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