Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviro...Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and management.To support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops.Already,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and terrain.This wealth of information,coupled with freely available climate data,fuels innovative enviromics research.While enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research institutions.Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.展开更多
The expression of quantitative traits of a line of a crop depends on its genetics,the environment where it is sown and the interaction between the genetic information and the environment known as GxE.Thus to maximize ...The expression of quantitative traits of a line of a crop depends on its genetics,the environment where it is sown and the interaction between the genetic information and the environment known as GxE.Thus to maximize food production,new varieties are developed by selecting superior lines of seeds suitable for a specific environment.Genomic selection is a computational technique for developing a new variety that uses whole genome molecular markers to identify top lines of a crop.A large number of statistical and machine learning models are employed for single environment trials,where it is assumed that the environment does not have any effect on the quantitative traits.However,it is essential to consider both genomic and environmental data to develop a new variety,as these strong assumptions may lead to failing to select top lines for an environment.Here we devised three novel deep learning frameworks incorporating GxE within the deep learning model and predicted line-specific yield for an environment.In the process,we also developed a new technique for identifying environmentspecific markers that can be useful in many applications of environment-specific genomic selection.The result demonstrates that our best framework obtains 1.75 to 1.95 times better correlation coefficients than other deep learning models that incorporate environmental data depending on the test scenario.Furthermore,the feature importance analysis shows that environmental information,followed by genomic information,is the driving factor in predicting environment-specific yield for a line.We also demonstrate a way to extend our framework for new data types,such as text or soil data.The extended model also shows the potential to be useful in genomic selection.展开更多
Neurological and psychiatric disorders collectively constitute the greatest burden of disease. However, the human brain is the most complex of biological systems and therefore accurately modeling brain disorders pres...Neurological and psychiatric disorders collectively constitute the greatest burden of disease. However, the human brain is the most complex of biological systems and therefore accurately modeling brain disorders presents enormous challenges. A large range of therapeutic approaches across a diverse collection of brain disorders have been found to show great promise in preclinical testing and then failed during clinical trials. There are a variety of potential reasons for such failures, on both the preclinical and clinical sides of the equation. In this article, I wi l l focus on the key issues of validity in animal models. I wi l l discuss two forms of construct validity,‘genetic construct validity’ and ‘environmental construct val idity’,which model specific aspects of the genome and ‘envirome’ relevant to the disorder in question. The generation of new gene-edited animal models has been facilitated by new technologies, the most notable of which are CRISPR-Cas systems. These and other technologies can be used to enhance contruct validity. Finally, I wi l l discuss how face validity can be optimized, via more sophisticated cognitive, affective and motor behavioural tests, translational tools and the integration of molecular, cellular and systems data. Predictive validity cannot yet be assessed for the many preclinical models where we currently lack effective clinical interventions, however this wi l l change as the translational pipeline is honed to deliver therapies for a range of devastating disorders.展开更多
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
基金R.T.R.,L.L.P.,and G.E.M.thank the Brazilian agencies Coordenac¸ao de Aperfeic¸oamento de Pessoal de Nıvel Superior(CAPES)and Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico(CNPq)for the financial support,which was instrumental in the successful execution of this project.L.H.was supported through an ARC Future Fellowship(FT220100350)from the Australian Research Council.C.H.A.was supported by The University of Colorado Boulder Grand ChallengeCIRES Earth Lab.Y.X.was supported by the Agricultural Science and Technology Innovation Program(ASTIP)of the Chinese Academy of Agricultural Sciences,Shenzhen Science and Technology Program(KQTD202303010928390070)Hebei Science and Technology Program(215A7612D),and the Provincial Technology Innovation Program of Shandong,China.
文摘Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and management.To support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops.Already,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and terrain.This wealth of information,coupled with freely available climate data,fuels innovative enviromics research.While enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research institutions.Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
文摘The expression of quantitative traits of a line of a crop depends on its genetics,the environment where it is sown and the interaction between the genetic information and the environment known as GxE.Thus to maximize food production,new varieties are developed by selecting superior lines of seeds suitable for a specific environment.Genomic selection is a computational technique for developing a new variety that uses whole genome molecular markers to identify top lines of a crop.A large number of statistical and machine learning models are employed for single environment trials,where it is assumed that the environment does not have any effect on the quantitative traits.However,it is essential to consider both genomic and environmental data to develop a new variety,as these strong assumptions may lead to failing to select top lines for an environment.Here we devised three novel deep learning frameworks incorporating GxE within the deep learning model and predicted line-specific yield for an environment.In the process,we also developed a new technique for identifying environmentspecific markers that can be useful in many applications of environment-specific genomic selection.The result demonstrates that our best framework obtains 1.75 to 1.95 times better correlation coefficients than other deep learning models that incorporate environmental data depending on the test scenario.Furthermore,the feature importance analysis shows that environmental information,followed by genomic information,is the driving factor in predicting environment-specific yield for a line.We also demonstrate a way to extend our framework for new data types,such as text or soil data.The extended model also shows the potential to be useful in genomic selection.
文摘Neurological and psychiatric disorders collectively constitute the greatest burden of disease. However, the human brain is the most complex of biological systems and therefore accurately modeling brain disorders presents enormous challenges. A large range of therapeutic approaches across a diverse collection of brain disorders have been found to show great promise in preclinical testing and then failed during clinical trials. There are a variety of potential reasons for such failures, on both the preclinical and clinical sides of the equation. In this article, I wi l l focus on the key issues of validity in animal models. I wi l l discuss two forms of construct validity,‘genetic construct validity’ and ‘environmental construct val idity’,which model specific aspects of the genome and ‘envirome’ relevant to the disorder in question. The generation of new gene-edited animal models has been facilitated by new technologies, the most notable of which are CRISPR-Cas systems. These and other technologies can be used to enhance contruct validity. Finally, I wi l l discuss how face validity can be optimized, via more sophisticated cognitive, affective and motor behavioural tests, translational tools and the integration of molecular, cellular and systems data. Predictive validity cannot yet be assessed for the many preclinical models where we currently lack effective clinical interventions, however this wi l l change as the translational pipeline is honed to deliver therapies for a range of devastating disorders.
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.
