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 de...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 genotypes.However,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 challenges.Here,we first review innovative technologies for predictive breeding.We 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 construction.We 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)scales.Finally,we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives.We call for coordinated efforts in smart breeding through iGEP,institutional partnerships,and innovative technological support.展开更多
Genotyping platforms,as critical supports for genomics,genetics,and molecular breeding,have been well implemented at national institutions/universities in developed countries and multinational seed companies that poss...Genotyping platforms,as critical supports for genomics,genetics,and molecular breeding,have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput,automatic,large-scale,and shared facilities.In this study,we integrated an improved genotyping by target sequencing(GBTS)system with capture-in-solution(liquid chip)technology to develop a multiple single-nucleotide polymorphism(mSNP)approach in which mSNPs can be captured from a single amplicon.From one 40K maize mSNP panel,we developed three types of markers(40K mSNPs,251K SNPs,and 690K haplotypes),and generated multiple panels with various marker densities(1K–40K mSNPs)by sequencing at different depths.Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs.Compared with the one-amplicon-one-SNP system,mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection,linkage disequilibrium decay analysis,and genome-wide association studies.The technologies,protocols,and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals,plants,and microorganisms.展开更多
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 product...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.展开更多
基金National Key Research and Development Program of China(2016YFD0101803)Central Public-interest Scientific Institution Basal Research Fund(Y2020PT20)+5 种基金Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS-XTCX2016009)Shijiazhuang Science and Technology Incubation Program(191540089A)Hebei Innovation Capability Enhancement Project(19962911D)Project of Hainan Yazhou Bay Seed Laboratory(B21HJ0223)Department of Science and Technology of Ninxia Project(NXNYYZ202001)Research activities at CIMMYT were supported by the Bill and Melinda Gates Foundation and the CGIAR Research Program MAIZE.
文摘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 genotypes.However,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 challenges.Here,we first review innovative technologies for predictive breeding.We 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 construction.We 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)scales.Finally,we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives.We call for coordinated efforts in smart breeding through iGEP,institutional partnerships,and innovative technological support.
基金This research is supported by the National Key Research and Development Program of China(2016YFD0101803 and 2017YFD0101201)the Central Public-interest Scientific Institution Basal Research Fund(Y2020PT20)+4 种基金the Agricultural Science and Technology Innovation Program(ASTIP)of the Chinese Academy of Agricultural Sciences(CAAS)(CAAS-XTCX2016009)the Key Research Area and Development Program of Guangdong Province(2018B020202008)the Shijiazhuang Science and Technology Incubation Program(191540089A)the Hebei Innovation Capability Enhancement Project(19962911D)Research activities at CIMMYT were supported by the Bill and Melinda Gates Foundation and the CGIAR Research Program MAIZE.
文摘Genotyping platforms,as critical supports for genomics,genetics,and molecular breeding,have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput,automatic,large-scale,and shared facilities.In this study,we integrated an improved genotyping by target sequencing(GBTS)system with capture-in-solution(liquid chip)technology to develop a multiple single-nucleotide polymorphism(mSNP)approach in which mSNPs can be captured from a single amplicon.From one 40K maize mSNP panel,we developed three types of markers(40K mSNPs,251K SNPs,and 690K haplotypes),and generated multiple panels with various marker densities(1K–40K mSNPs)by sequencing at different depths.Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs.Compared with the one-amplicon-one-SNP system,mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection,linkage disequilibrium decay analysis,and genome-wide association studies.The technologies,protocols,and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals,plants,and microorganisms.
基金The research involved in this report was supported by the National Key Research and Development Program of China(2016YFD0101803)the National Key Basic Research Program of China(2014 CB138206)+1 种基金the Agricultural Science and Technology Innovation Program of CAAS,and Fundamental Research Funds for Central Non-Profit of Institute of Crop Sciences,CAAS(1610092016124)Research activities of CIMMYT staff have been supported by the Bill and Melinda Gates Foundation and the CGIAR Research Program MAIZE.
文摘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.
