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

Molecular Plant 2024年第4期17卷 552-578页

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 Department of Plant Breeding Swedish University of Agricultural SciencesAlnarpSweden Lantmannen Lantbruk SvalövSweden Facultad de Telematica University de ColimaColimaColima 28040Mexico 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-UPM28223 MadridSpain International Center for Agricultural Research in the Dry Areas(ICARDA) RabatMorocco International Maize and Wheat Improvement Center(CIMMYT) Km 45Carretera México-VeracruzTexcocoMéxico 52640Mexico

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

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genomic selection of orange-spotted grouper(Epinephelus coioides)based on multiplex PCR enrichment capture sequencing

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Aquaculture and Fisheries 2023年第6期8卷 681-688页

作者： Xinxin Shan Xinhui Zhang Zhiqiang Ruan Jieming Chen Qiong Shi Junmin Xu Xinxin You Shenzhen Key Lab of Marine genomics Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic AnimalsBGI Academy of Marine SciencesBGI MarineBGIShenzhen518083China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) State Key Laboratory of BiocontrolSchool of Marine SciencesSun Yat-Sen UniversityGuangzhou510275China College of Life Sciences University of Chinese Academy of SciencesBeijing100049China

Orange spotted grouper(Epinephelus coioides)is an important mariculture fish,and genomic breeding of this grouper species has been hindered due to lack of efficient genotyping tools.Here,we developed a single nucleotide polymorphism(SNP)genotyping technology based on multiplex PCR enrichment capture sequencing,which mainly aims at target area for high-throughput sequencing,and 741 SNPs were designed for genomic selection(GS)of growth and ammonia tolerance traits at the same time.The multiplex PCR enrichment capture sequencing assay showed that the genotyping efficiency was more than 99%in the orange-spotted grouper and the predictive accuracy of body weight and ammonia tolerance traits was 82%and 96%,respectively.More importantly,the average identity of the sequences with these SNPs aligned to the genomes of giant grouper(E.lanceolatus)and brown-marbled grouper(E.fuscoguttatus)were both over 96%.Test data showed that the SNP genotyping efficiency was more than 94%in both giant grouper and brown-marbled grouper.In summary,these results indicated that the development of SNP loci and genotyping approach based on the multiple PCR enrichment capture sequencing are suitable for GS of growth and ammonia tolerance traits in various grouper species,and it would provide technical support for practical grouper breeding.

关键词： Grouper SNP genotyping Approach Multiple PCR enrichment Capture sequencing genomic selection

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genomic selection: A breakthrough technology in rice breeding

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The Crop Journal 2021年第3期9卷 669-677页

作者： Yang Xu Kexin Ma Yue Zhao Xin Wang Kai Zhou Guangning Yu Cheng Li Pengcheng Li Zefeng Yang Chenwu Xu Shizhong Xu 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 AgricultureYangzhou UniversityYangzhou 225009JiangsuChina Department of Botany and Plant Sciences University of CaliforniaRiversideCA 92507USA

Rice(Oryza sativa)provides a staple food source for more than half the world population.However,the current pace of rice breeding in yield growth is insufficient to meet the food demand of the everincreasing global population.genomic selection(GS)holds a great potential to accelerate breeding progress and is cost-effective via early selection before phenotypes are measured.Previous simulation and experimental studies have demonstrated the usefulness of GS in rice breeding.However,several affecting factors and limitations require careful consideration when performing GS.In this review,we summarize the major genetics and statistical factors affecting predictive performance as well as current progress in the application of GS to rice breeding.We also highlight effective strategies to increase the predictive ability of various models,including GS models incorporating functional markers,genotype by environment interactions,multiple traits,selection index,and multiple omic data.Finally,we envision that integrating GS with other advanced breeding technologies such as unmanned aerial vehicles and open-source breeding platforms will further improve the efficiency and reduce the cost of breeding.

关键词： genomic selection Rice Hybrid Predictive ability Model

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Genetic dissection of and genomic selection for seed weight, pod length,and pod width in soybean

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The Crop Journal 2023年第3期11卷 832-841页

作者： Yijie Chen Yajun Xiong Huilong Hong Gang Li Jie Gao Qingyuan Guo Rujian Sun Honglei Ren Fan Zhang Jun Wang Jian Song Lijuan Qiu College of Agriculture Yangtze UniversityJingzhou 434025HubeiChina College of Life Sciences Yangtze UniversityJingzhou 434025HubeiChina National Key Facility for Gene Resources and Genetic Improvement/Institute of Crop Sciences Chinese Academy of Agricultural SciencesBeijing 100081China Soybean Research Institute Heilongjiang Academy of Agricultural SciencesHarbin 150086HeilongjiangChina Engineering Research Center of Ecology and Agricultural Use of Wetland Ministry of EducationJingzhou 434025HubeiChina Hulunbuir Institute of Agriculture and Animal Husbandry Hulunbuir 021000Inner MongoliaChina

A biparental soybean population of 364 recombinant inbred lines(RILs)derived from Zhongdou 41×ZYD02.878 was used to identify quantitative trait loci(QTL)associated with hundred-seed weight(100-SW),pod length(PL),and pod width(PW).100-SW,PL,and PW showed moderate correlations among one another,and 100-SW was correlated most strongly with PW(0.64–0.74).Respectively 74,70,75 and19 QTL accounting for 38.7%–78.8%of total phenotypic variance were identified by inclusive composite interval mapping,restricted two-stage multi-locus genome-wide association analysis,3 variancecomponent multi-locus random-SNP-effect mixed linear model analysis,and conditional genome-wide association analysis.Of these QTL,189 were novel,and 24 were detected by multiple methods.Six loci were associated with 100-SW,PL,and PW and may be pleiotropic loci.A total of 284 candidate genes were identified in colocalizing QTL regions,including the verified gene Seed thickness 1(ST1).Eleven genes with functions involved in pectin biosynthesis,phytohormone,ubiquitin-protein,and photosynthesis pathways were prioritized by examining single nucleotide polymorphism(SNP)variation,calculating genetic differentiation index,and inquiring gene expression.The prediction accuracies of genomic selection(GS)for 100-SW,PL,and PW based on single trait-associated markers reached 0.82,0.76,and 0.86 respectively,but selection index(SI)-assisted GS strategy did not increase GS efficiency and inclusion of trait-associated markers as fixed effects reduced prediction accuracy.These results shed light on the genetic basis of 100-SW,PL,and PW and provide GS models for these traits with potential application in breeding programs.

关键词： Soybean Seed weight Pod size Genetic analysis genomic selection

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A global alfalfa diversity panel reveals genomic selection signatures in Chinese varieties and genomic associations with root development

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Journal of Integrative Plant Biology 2021年第11期63卷 1937-1951页

作者： Lin Chen Fei He Ruicai Long Fan Zhang Mingna Li Zhen Wang Junmei Kang Qingchuan Yang Institute of Animal Science Chinese Academy of Agricultural SciencesBeijing 100193China

Alfalfa(Medicago sativa L.) is an important forage crop worldwide. However, little is known about the effects of breeding status and different geographical populations on alfalfa improvement. Here, we sequenced 220 alfalfa core germplasms and determined that Chinese alfalfa cultivars form an independent group, as evidenced by comparisons of FSTvalues between different subgroups, suggesting that geographical origin plays an important role in group differentiation. By tracing the influence of geographical regions on the genetic diversity of alfalfa varieties in China, we identified 350 common candidate genetic regions and 548 genes under selection. We also defined 165 loci associated with 24 important traits from genome-wide association studies. Of those, 17 genomic regions closely associated with a given phenotype were under selection, with the underlying haplotypes showing significant differences between subgroups of distinct geographical origins. Based on results from expression analysis and association mapping,we propose that 6-phosphogluconolactonase(MsPGL) and a gene encoding a protein with NHL domains(MsNHL) are critical candidate genes for root growth. In conclusion, our results provide valuable information for alfalfa improvement via molecular breeding.

关键词： alfalfa genome-wide association studies genomic selection geographical origin population structure root development

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An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops

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Molecular Plant 2021年第6期14卷 874-887页

作者： Xianran Li Tingting Guo Jinyu Wang Wubishet ABekele Sivakumar Sukumaran Adam EVanous James PMcNellie Laura Tibbs Cortes Marta SLopes Kendall RLamkey Mark EWestgate John KMcKay Sotirios VArchontoulis Matthew PReynolds Nicholas ATinker Patrick SSchnable Jianming Yu Department of Agronomy Iowa State UniversityAmesIA 50011USA Ottawa Research and Development Centre Agriculture and Agri-Food CanadaOttawaONCanada International Maize and Wheat Improvement Center(CIMMYT) Mexico CityMexico Department of Bioagricultural Sciences and Pest Management Colorado State UniversityFort CollinsCO 80523USA

Identifying mechanisms and pathways involved in gene–environment interplay and phenotypic plasticity is a long-standing challenge.It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction.A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments.With extensive field-observed complex traits,environmental profiles,and genome-wide single nucleotide polymorphisms for three major crops(maize,wheat,and oat),we demonstrated that identifying such an environmental index(i.e.,a combination of environmental parameter and growth window)enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension.Interestingly,genes identified for two reaction-norm parameters(i.e.,intercept and slope)derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels,agreeing with the different diversity levels and genetic constitutions of the panels.In addition,we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments.This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits,enhanced performance prediction in breeding for future climates,and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.

关键词： phenotypic plasticity genotype by environment interaction gene-environment interplay genomewide association studies genomic selection flowering time reaction norm

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Development of a 50K SNP Array for Japanese Flounder and Its Application in genomic selection for Disease Resistance

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Engineering 2021年第3期7卷 406-411页

作者： Qian Zhou Ya-dong Chen Sheng Lu Yang Liu Wen-teng Xu Yang-zhen Li Lei Wang Na Wang Ying-ming Yang Song-lin Chen Key Laboratory for Sustinabe Devebspment of Marine Fisheries Ministry of AgrirulurYelow Ssat Fidheris Rsearth listuteChinese cademy of Frtsery scintesQingdao 266071China Laboratory for Marine Fisheries Sidentce and Food Producton Procasses Pitot National Laboratory for Marine Science and Tehnobg(Qingdao)Qingdao 266373China

Single nucleotide polymorphism(SNP)armays are a powerful genotyping tool used in genetic research and genomic breeding programs.Japanese flounder(Paralichthys olivaceus)is an economically-important aquaculture flatfish in many countries.However,the lack of high-efficient genotyping tools has impeded the genomic breeding programs for Japanese flounder.We developed a 50K Japanese flounder SNP array,"Yuxin No.1,"and report its utility in genomic selection(GS)for disease resistance to bacterial pathogens.We screened more than 42,.2 million SNPs from the whole-genome resequencing data of 1099 individuals and selected 48697 SNPs that were evenly distributed across the genome to anchor the array with Affymetrix Axiom genotyping technology.Evaluation of the array performance with 168 fishs howed that 74.7%of the loci were successfully genotyped with high call rates(>98%)and that the poly-morphic SNPs had good cluster separations.More than 85%of the SNPs were concordant with SNPs obtained from the whole-genome resequencing data.To validate"Yuxin No.1"for GS,the arrayed geno-typing data of 27 individuals from a candidate population and 931 individuals from a reference popula-tion were used to calculate the genomic estimated breeding values(GEBVs)for disease resistance toEdwardsiella tarda.There was a 21.2%relative increase in the accuracy of GEBV using the weighted geno-mic best linear unpiased prediction(wGBLUJP),compared to traditional pedigree-based best linear unbi-ased prediction(ABLUP),suggesting good performance of the'Yuxin No.1"SNP array for GS.In summary,we developed the"Yuxin No.1"50K SNP array,which provides a useful platform for high-quality geno-typing that may be beneficial to the genomic selective breeding of Japanese flounder.

关键词： Paralichthys olivaceus Single nucleotide polymorphism SNP array Disease resistance genomic selection

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Enhancing Genetic Gain through genomic selection: From Livestock to Plants

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Plant Communications 2020年第1期1卷 4-24页

作者： Yunbi Xu Xiaogang Liu Junjie Fu Hongwu Wang Jiankang Wang Changling Huang Boddupalli MPrasanna Michael SOlsen Guoying Wang Aimin Zhang Institute of Crop Science/CIMMYT-China Chinese Academy of Agricultural SciencesBeijing 100081China CIMMYT-China Tropical Maize Research Center Foshan UniversityFoshan 528231China CIMMYT-China Specialty Maize Research Center Shanghai Academy of Agricultural SciencesShanghai 201400China Institute of Genetics and Developmental Biology Chinese Academy of SciencesBeijing 100101China CIMMYT(International Maize and Wheat Improvement Center) ICRAF CampusUnited Nations AvenueNairobiKenya

Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies,the rate of genetic gain needs to be accelerated to meet humanity’s demand for agricultural products.In this regard,genomic selection(GS)has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects.Livestock scientists pioneered GS application largely due to livestock’s significantly higher individual values and the greater reduction in generation interval that can be achieved in GS.Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects,along with significant cost reduction.Moreover,it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain.In addition,establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small-and medium-sized enterprises and agricultural research systems in developing countries.New strategies centered on GS for enhancing genetic gain need to be developed.

关键词： genomic selection genetic gain open-source breeding genomic prediction molecular marker livestock breeding

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genomic selection using Bayes Cπ and GBLUP for resistance against Edwardsiella tarda in Japanese flounder（Paralichthys olivaceus）

Genomic selection using Bayes Cπ and GBLUP for resistance a...

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第三届中国畜牧基因组产业转化高峰论坛

作者： Sheng Lu Yang Liu Feng Liu Changwei Shao Qian Zhou Na Wang Yangzhen Li Yingming Yang Yingping Zhang Hejun Sun Weiwei Zheng Songlin Chen Yellow Sea Fisheries Research Institute Chinese Academy of Fishery Sciences (CAFS) Key Laboratory for Sustainable Development of Marine Fisheries Ministry of Agriculture Laboratory for Marine Fisheries Science and Food Production Processes Qingdao National Laboratory for Marine Science and Technology College of Marine Life Science Ocean University of China Wuxi Fisheries College Nanjing Agricultural University Marine and Fishery Institute of Zhejiang Ocean University

Japanese flounder is one of the most widely farmed economic flatfish species throughout eastern Asia including China,Korea,and Japan.Edwardsiella tarda is a major species of pathogenic bacteria that causes ascites disease and consequently,a huge economy loss for Japanese flounder farming.After generations selection,traditional breeding methods can hardly improve the E.tarda resistance effectively.genomic selection is an effective way to predict the breeding potential of parents but has rarely been used in aquatic breeding.In this study,we chose931 individuals from 90 families,challenged by E.tarda from 2013-2015 as a reference population and 71 parents of these families as selection candidates.1,934,475 markers were detected via genome sequencing and applied in this study.Two different algorithms,the Bayes Cπand GBLUP were used for genomic selection calculation.In the reference population,two algorithms let to the same result（0.904）and Pearson’s correlation results between EBV andGEBV of Bayes Cπand GBLUP were 0.706 and 0.795,respectively.In selection candidates,GEBV from two algorithms were extremely similar（0.957）.Comparing GEBV with the survival rate of families structured by selection candidates,the correlation was 0.682 and 0.665,respectively.GEBV of different groups divided by survival rate revealed significant differences,and the group with the higher survival rate had higher mean GEBV.Our study established genomic selection method for the Japanese flounder and for the first time applied these to E.tarda resistance breeding.Here,we provide a new breeding technique for aquatic breeding.

关键词： genomic selection Bayes Cπ GBLUP Edwardsiella tarda Japanese flounder

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High-throughput Phenotyping and genomic selection:The Frontiers of Crop Breeding Converge

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Journal of Integrative Plant Biology 2012年第5期54卷 312-320页

作者： Lloren Cabrera-Bosquet Jos Crossa Jarislav von Zitzewitz María Dolors Serret Jos Luis Araus French National Institute for Agricultural Research (INRA UMR759)Ecophysiology Laboratory of Plants under Environmental StressMontpellier F-34060France International Maize and Wheat Improvement Center (CIMMYT) El BatnTexcoco CP 56130Mexico National Research Program on Rainfed Crops National Institute for Agricultural ResearchEst.Exp.La EstanzuelaColonia 70000Uruguay Unit of Plant Physiology Faculty of BiologyUniversity of BarcelonaAv.Diagonal643Barcelona 08028Spain

genomic selection （GS） and high-throughput phenotyping have recently been captivating the interest of the crop breeding com- munity from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and compa- rable to genomic selection. Despite the fact that the two method- ological approaches represent the extremes of what is understood as the breeding process （phenotype versus genome）, they both consider the targeted traits （e.g. grain yield, growth, phenology, plant adaptation to stress） as a black box instead of dissectingthem as a set of secondary traits （i.e. physiological） putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy （NIRS） and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.

关键词： genomic selection high-throughput phenotyping NIRS quantitative traits SNPs.

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