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Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

molecular Plant 2022年第11期15卷 1664-1695页

作者： Yunbi Xu Xingping Zhang Huihui Li Hongjian Zheng Jianan Zhang michael S.Olsen Rajeev K.Varshney boddupalli m.prasanna Qian Qian Institute of Crop Sciences CIMMYT-ChinaChinese Academy of Agricultural SciencesBeijing 100081China CImmYT-China Tropical maize Research Center School of Food Science and EngineeringFoshan UniversityFoshanGuangdong 528231China Peking University Institute of Advanced Agricultural Sciences WeifangShandong 261325China CImmYT-China Specialty maize Research Center Shanghai Academy of Agricultural SciencesShanghai 201400China molBreeding Biotechnology Co. Ltd.ShijiazhuangHebei 050035China National Nanfan Research Institute(Sanya) Chinese Academy of Agricultural SciencesSanyaHainan 572024China CImmYT(International maize and Wheat Improvement Center) ICRAF CampusUnited Nations AvenueNairobiKenya State Agricultural Biotechnology Centre Centre for Crop and Food InnovationFood Futures InstituteMurdoch UniversityMurdochAustralia

The first paradigm of plant breeding involves direct selection-based phenotypic observation,followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and,more recently,by incorporation of molecular marker ***,plant performance or phenotype(P)is determined by the combined effects of genotype(G),envirotype(E),and genotype by environment interaction(GEI).Phenotypes can be predicted more precisely by training a model using data collected from multiple sources,including spatiotemporal omics(genomics,phenomics,and enviromics across time and space).Integration of 3D information profiles(G-P-E),each with multidimensionality,provides predictive breeding with both tremendous opportunities and great ***,we first review innovative technologies for predictive *** then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy,particularly envirotypic data,which have largely been neglected in data collection and are nearly untouched in model *** propose a smart breeding scheme,integrated genomic-enviromic prediction(iGEP),as an extension of genomic prediction,using integrated multiomics information,big data technology,and artificial intelligence(mainly focused on machine and deep learning).We discuss how to implement iGEP,including spatiotemporal models,environmental indices,factorial and spatiotemporal structure of plant breeding data,and cross-species prediction.A strategy is then proposed for prediction-based crop redesign at both the macro(individual,population,and species)and micro(gene,metabolism,and network)***,we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding *** call for coordinated efforts in smart breeding through iGEP,institutional partnerships,and innovative technological support.

关键词： smart breeding genomic selection integrated genomic-enviromic selection spatiotemporal omics crop design machine and deep learning big data artificial intelligence

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Genome-wide association study and genomic prediction of Fusarium ear rot resistance in tropical maize germplasm

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The Crop Journal 2021年第2期9卷 325-341页

作者： Yubo Liu Guanghui Hu Ao Zhang Alexander Loladze Yingxiong Hu Hui Wang Jingtao Qu Xuecai Zhang michael Olsen Felix San Vicente Jose Crossa Feng Lin boddupalli m.prasanna College of Agronomy Shenyang Agricultural UniversityShenyang 110866LiaoningChina International maize and Wheat Improvement Center(CImmYT) El BatanTexcocoMexico College of Biological Science and Technology Shenyang Agricultural UniversityShenyang 110866LiaoningChina maize Research Institute Heilongjiang Academy of Agricultural SciencesHarbin 150086HeilongjiangChina CImmYT–China Specialty maize Research Center Shanghai Academy of Agricultural SciencesShanghai 200063China Crop Breeding and Cultivation Research Institute Shanghai Academy of Agricultural SciencesShanghai 200063China maize Research Institute Sichuan Agricultural UniversityWenjiang 611130SichuanChina International maize and Wheat Improvement Center(CImmYT) P.O.Box 1041Village MarketNairobi 00621Kenya

Fusarium ear rot(FER)is a destructive maize fungal disease *** this study,three tropical maize populations consisting of 874 inbred lines were used to perform genomewide association study(GWAS)and genomic prediction(GP)analyses of FER *** phenotypic variation and high heritability for FER were observed,although it was highly influenced by large genotype-by-environment *** the 874 inbred lines,GWAS with general linear model(GLm)identified 3034 single-nucleotide polymorphisms(SNPs)significantly associated with FER resistance at the P-value threshold of 1×10^(-5),the average phenotypic variation explained(PVE)by these associations was 3%with a range from 2.33%to 6.92%,and 49 of these associations had PVE values greater than 5%.The GWAS analysis with mixed linear model(mLm)identified 19 significantly associated SNPs at the P-value threshold of 1×10^(-4),the average PVE of these associations was 1.60%with a range from 1.39%to 2.04%.Within each of the three populations,the number of significantly associated SNPs identified by GLm and mLm ranged from 25 to 41,and from 5 to 22,*** SNP associations across populations were rare.A few stable genomic regions conferring FER resistance were identified,which located in bins 3.04/05,7.02/04,9.00/01,9.04,9.06/07,and 10.03/*** genomic regions in bins 9.00/01 and 9.04 are *** produced moderate accuracies with genome-wide markers,and relatively high accuracies with SNP associations detected from *** prediction accuracies were observed when the training and validation sets were closely *** results implied that FER resistance in maize is controlled by minor QTL with small effects,and highly influenced by the genetic background of the populations *** selection(GS)by incorporating SNP associations detected from GWAS is a promising tool for improving FER resistance in maize.

关键词： maize Fusarium ear rot Genome-wide association study Genomic prediction Genomic selection

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Factors affecting genomic selection revealed by empirical evidence in maize

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The Crop Journal 2018年第4期6卷 341-352页

作者： Xiaogang Liu Hongwu Wang Hui Wang Zifeng Guo Xiaojie Xu Jiacheng Liu Shanhong Wang Wen-Xue Li Cheng Zou boddupalli m.prasanna michael S.Olsen Changling Huang Yunbi Xu Institute of Crop Science National Key Facility of Crop Gene Resources and Genetic Improvement Chinese Academy of Agricultural Sciences International maize and Wheat Improvement Center (CImmYT) ICRAF campus International maize and Wheat Improvement Center (CImmYT)

Genomic selection(GS) as a promising molecular breeding strategy has been widely implemented and evaluated for plant breeding, because it has remarkable superiority in enhancing genetic gain, reducing breeding time and expenditure, and accelerating the breeding process. In this study the factors affecting prediction accuracy(rmG) in GS were evaluated systematically, using six agronomic traits(plant height, ear height, ear length, ear diameter,grain yield per plant and hundred-kernel weight) evaluated in one natural and two biparental populations. The factors examined included marker density, population size, heritability,statistical model, population relationships and the ratio of population size between the training and testing sets, the last being revealed by resampling individuals in different proportions from a population. Prediction accuracy continuously increased as marker density and population size increased and was positively correlated with heritability; rmGshowed a slight gain when the training set increased to three times as large as the testing set. Low predictive performance between unrelated populations could be attributed to different allele frequencies, and predictive ability and prediction accuracy could be improved by including more related lines in the training population. Among the seven statistical models examined, including ridge regression best linear unbiased prediction(RR-BLUP), genomic BLUP(GBLUP), Bayes A, Bayes B, Bayes C, Bayesian least absolute shrinkage and selection operator(Bayesian LASSO), and reproducing kernel Hilbert space(RKHS), the RKHS and additive-dominance model(Add + Dom model) showed credible ability for capturing non-additive effects, particularly for complex traits with low heritability. Empirical evidence generated in this study for GS-relevant factors will help plant breeders to develop GS-assisted breeding strategies for more efficient development of varieties.

关键词： marker density Population size Population relationship Zea mays

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Genomic prediction of the performance of hybrids and the combining abilities for line by tester trials in maize

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The Crop Journal 2022年第1期10卷 109-116页

作者： Ao Zhang Paulino Pérez-Rodríguez Felix San Vicente Natalia Palacios-Rojas Thanda Dhliwayo Yubo Liu Zhenhai Cui Yuan Guan Hui Wang Hongjian Zheng michael Olsen boddupalli m.prasanna Yanye Ruan Jose Crossa Xuecai Zhang College of Biological Science and Technology Shenyang Agricultural UniversityShenyang 110866LiaoningChina b International maize and Wheat Improvement Center(CImmYT) El BatanTexcoco 56237Mexico CImmYT-China Specialty maize Research Center Shanghai Academy of Agricultural SciencesShanghai 200063China Colegio de Postgraduados Estado De MéxicoMexico Crop Breeding and Cultivation Research Institute Shanghai Academy of Agricultural SciencesShanghai 200063China International maize and Wheat Improvement Center(CImmYT) P.O.Box 1041Village MarketNairobi 00621Kenya

The two most important activities in maize breeding are the development of inbred lines with high values of general combining ability(GCA)and specific combining ability(SCA),and the identification of hybrids with high yield *** selection(GS)is a promising genomic tool to perform selection on the untested breeding material based on the genomic estimated breeding values estimated from the genomic prediction(GP).In this study,GP analyses were carried out to estimate the performance of hybrids,GCA,and SCA for grain yield(GY)in three maize line-by-tester trials,where all the material was phenotyped in 10 to 11 multiple-location trials and genotyped with a mid-density molecular marker *** showed that the prediction abilities for the performance of hybrids ranged from 0.59 to0.81 across all trials in the model including the additive effect of lines and *** the model including both additive and non-additive effects,the prediction abilities for the performance of hybrids were improved and ranged from 0.64 to 0.86 across all *** prediction abilities of the GCA for GY were low,ranging between-0.14 and 0.13 across all trials in the model including only inbred lines;the prediction abilities of the GCA for GY were improved and ranged from 0.49 to 0.55 across all trials in the model including both inbred lines and testers,while the prediction abilities of the SCA for GY were negative across all *** prediction abilities for GY between testers varied from-0.66 to 0.82;the performance of hybrids between testers is difficult to *** offers the opportunity to predict the performance of new hybrids and the GCA of new inbred lines based on the molecular marker information,the total breeding cost could be reduced dramatically by phenotyping fewer multiple-location trials.

关键词： maize Genomic selection Line-By-Tester General combining ability Specific combining ability

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Heat-tolerant maize for rainfed hot,dry environments in the lowland tropics:From breeding to improved seed delivery

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The Crop Journal 2023年第4期11卷 986-1000页

作者： Pervez H.Zaidi madhumal Thayil Vinayan Sudha K.Nair Prakash H.Kuchanur Ramesh Kumar Shyam Bir Singh mahendra Prasad Tripathi Ayyanagouda Patil Salahuddin Ahmed Aamir Hussain Atul Prabhakar Kulkarni Passang Wangmo mitchell R.Tuinstra boddupalli m.prasanna International maize and Wheat Improvement Center(CImmYT) Hyderabad 502324India University of Agricultural Sciences Raichur 584104KarnatakaIndia ICAR-Indian Institute of maize Research Ludhiana 141001BiharIndia National maize Research Program RampurChitwan 44200Nepal Bangladesh Wheat and maize Research Institute Dinajpur-5201Bangladesh maize and millet Research Institute YusafwalaSahiwal 57000Pakistan Agriculture Research and Development Centre Wengkhar 43001Bhutan Institute of Plant Sciences Purdue UniversityWest LafayetteIN 47906USA International maize and Wheat Improvement Center(CImmYT) NairobiKenya

Climate change-induced heat stress combines two challenges:high day-and nighttime temperatures,and physiological water deficit due to demand-side drought caused by increase in vapor-pressure *** is one of the major factors in low productivity of maize in rainfed stress-prone environments in South Asia,affecting a large population of smallholder farmers who depend on maize for their sustenance and *** International maize and Wheat Improvement Center(CImmYT)maize program in Asia,in partnership with public-sector maize research institutes and private-sector seed companies in South Asian countries,is implementing an intensive initiative for developing and deploying heat-tolerant maize that combines high yield potential with resilience to heat and drought *** the integration of novel breeding tools and methods,including genomics-assisted breeding,doubled haploidy,fieldbased precision phenotyping,and trait-based selection,new maize germplasm with increased tolerance to heat stress is being developed for the South Asian *** a decade of concerted effort has resulted in the successful development and release of 20 high-yielding heat-tolerant maize hybrids in CImmYT genetic *** public–private partnerships,eight hybrids are presently being deployed on over 50,000 ha in South Asian countries,including Bangladesh,Bhutan,India,Nepal,and Pakistan.

关键词： Climate change Drought Heat stress Lowland tropics maize Vapor pressure deficit

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Performance and yield stability of maize hybrids in stress-prone environments in eastern Africa

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The Crop Journal 2020年第1期8卷 107-118页

作者： Wender Santos Rezende Yoseph Beyene Stephen mugo Eric Ndou manje Gowda Julius Pyton Sserumaga Godfrey Asea Ismail Ngolinda mcDonald Jumbo Sylvester O.Oikeh michael Olsen Aluízio Borém Cosme Damião Cruz boddupalli m.prasanna Federal University of Viçosa(UFV) P.O.Box 36570000ViçosaBrazil International maize and Wheat Improvement Center(CImmYT) P.O.Box 1041-00621NairobiKenya Bayer Crop Science P.O.Box 1512PetitSouth Africa National Crops Resources Research Institute(NaCRRI) P.O.Box 7084KampalaUganda Ilonga Agricultural Research Institute(ARI-Ilonga) P.O.Box 66KilosaTanzania African Agricultural Technology Foundation(AATF) P.O.Box 30709-00100NairobiKenya

Identification and deployment of high-yielding and stress-tolerant maize hybrids adapted to stress-prone agro-ecologies is important for improving the food security and livelihoods of smallholder farmers in eastern *** objectives of this study were to(i)assess the performance of maize hybrids under well-watered and drought stress conditions;(ii)evaluate grain yield stability of 65 intermediate-maturing and 55 early-maturing hybrids in 24 well-watered locations and seven drought stress locations;and(iii)identify representative and/or discriminative testing locations for increasing genetic gains for the target *** were significant differences for grain yield among early-and intermediatematuring hybrids tested under well-watered and drought stress *** the early-maturing hybrids,the top 10 hybrids produced 46.8%–73.9%and 31.2%–42.1%higher mean grain yields than the best commercial check under drought and well-watered conditions,*** the intermediate-maturing hybrids,the top 10 hybrids produced 25.2%–47.7%and 8.5%–13.5%higher grain yield than commercial checks under drought stress and well-watered conditions,respectively,suggesting improvement in the levels of drought tolerance in both early-and intermediate-maturing *** biplot analysis and a bi-segmented regression linear method identified specific early-maturing and intermediate-maturing hybrids that performed well under both well-watered and drought stress *** hybrids could be recommended for commercial production in eastern *** in Kenya was found to be the most representative and highly discriminating site among well-watered testing locations,while Kabuku in Tanzania was the least representative of test *** testing under drought stress conditions,Kiboko in Kenya was identified as the most representative *** information could be useful for allocating resources and streamlining CImmYT maize hybrid testing in eastern Africa

关键词： Drought stress Eastern Africa Genotype by environment interaction Yield stability Zea mays L.

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Genetic dissection of maternal influence on in vivo haploid induction in maize

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The Crop Journal 2020年第2期8卷 287-298页

作者： Sudha K.Nair Vijay Chaikam manje Gowda Vemuri Hindu Albrecht E.melchinger prasanna m.boddupalli International maize and Wheat Improvement Center(CImmYT) ICRISAT CampusPatancheruGreater Hyderabad 502324India International maize and Wheat Improvement Center(CImmYT) ICRAF CampusUN AvenueGigiriP.O.Box 1041-00621NairobiKenya Institute of Plant Breeding Seed Science and Population GeneticsUniversity of HohenheimD-70593 StuttgartGermany

In vivo haploid induction based on maternal haploid inducers is the first step in deriving completely homozygous maize doubled haploid(DH) lines. Haploid induction rate(HIR) is influenced by both pollen parent inducing haploidy and the maternal source germplasm used in induction crosses. This study was aimed at analyzing the influence of source germplasm on HIR using 671 tropical inbred lines organized in two association mapping panels. These two association mapping panels(AmP1 and AmP2) were crossed to two different Tropically Adapted Inducer Lines(TAILs). For HIR assessment, seeds from induction crosses were planted in the field and ploidy status of each surviving plant was assessed using a gold standard classification based on visual differences between the haploid and diploid plants. The analysis revealed significant variation for HIR and led to identification of several tropical inbred lines that respond very positively to haploid induction. Use of HIR data in a genome wide association study(GWAS) led to identification of twenty-seven and two SNPs that were significantly associated with HIR in AmP1 and AmP2,respectively. meta-analysis of AmP1 and AmP2 GWAS led to identification of 52 SNPs with significant effect on HIR across both studies. Genome-wide prediction revealed moderate to high prediction accuracy within AmPs using random SNPs. Inclusion of the SNPs detected in GWAS into the prediction model led to improvement in prediction accuracy. Overall, the study revealed that the maternal influence on HIR is controlled by a few moderate and many small effect QTL.

关键词： planted prediction vivo

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