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

作　　者：Admas Alemu Johanna Astrand Osval A.Montesinos-López Julio Isidro y Sanchez Javier Fernandez-Gónzalez Wuletaw Tadesse Ramesh R.Vetukuri Anders S.Carlsson Alf Ceplitis JoséCrossa Rodomiro Ortiz Aakash Chawade 

机构地区：[1]Department of Plant Breeding,Swedish University of Agricultural Sciences,Alnarp,Sweden [2]Lantmannen Lantbruk,Svalöv,Sweden [3]Facultad de Telematica,University de Colima,Colima,Colima 28040,Mexico [4]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 [5]International Center for Agricultural Research in the Dry Areas(ICARDA),Rabat,Morocco [6]International Maize and Wheat Improvement Center(CIMMYT),Km 45,Carretera México-Veracruz,Texcoco,México 52640,Mexico

出　　处：《Molecular Plant》2024年第4期552-578,共27页分子植物（英文版）

基　　金：supported by SLU Grogrund(#SLU-LTV.2020.1.1.1-654);an Einar and Inga Nilsson Foundation grant.J.I.y.S.was supported by grant PID2021-123718OB-I00;funded by MCIN/AEI/10.13039/501100011033;by“ERDF A way of making Europe,”CEX2020-000999-S.R.R.V.;supported by Novo Nordisk Fonden(0074727);SLU’s Centre for Biological Control;In addition,J.I.y.S.and J.F.-G.were supported by the Beatriz Galindo Program BEAGAL 18/00115.

摘　　要：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.

关 键 词：genomic selection genetic gain genomic prediction optimization deep learning training population optimization 

分 类 号：Q94[生物学—植物学]