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On September 7th, the Crop Molecular Breeding Technology and Application Innovation Team of the Institute of Crop Science of the Chinese Academy of Agricultural Sciences proposed and advocated a new strategy of intelligent breeding driven by big data and artificial intelligence - genome-environment group integration prediction
.
The proposal of this new strategy will change the breeding mode of mainly using genotype and phenotype selection in the past, so that future breeding can accurately predict the performance of specific genotypes in specific environments under the support of all-round information of genotype-phenotype-environmental type, and realize targeted breeding
that adapts to climate change and adapts to specific environments in the true sense.
The results were published online in Molecular Plant
.
.
The proposal of this new strategy will change the breeding mode of mainly using genotype and phenotype selection in the past, so that future breeding can accurately predict the performance of specific genotypes in specific environments under the support of all-round information of genotype-phenotype-environmental type, and realize targeted breeding
that adapts to climate change and adapts to specific environments in the true sense.
The results were published online in Molecular Plant
.
A plant phenotype is the result of a combination of its genotype and the
environment.
Most of the genome selection techniques used in current breeding predict phenotypes based on the genotype of individuals, and little consideration is given to the influence of the individual's environment on
phenotypic prediction.
By placing an individual whose genotype is fully understood, it is theoretically possible to accurately predict
its phenotype.
environment.
Most of the genome selection techniques used in current breeding predict phenotypes based on the genotype of individuals, and little consideration is given to the influence of the individual's environment on
phenotypic prediction.
By placing an individual whose genotype is fully understood, it is theoretically possible to accurately predict
its phenotype.
This paper comprehensively introduces the challenges of introducing environmental-type variation in different spatios-spaces and the big data generated by data integration with genotypes and phenotypes on artificial intelligence, and discusses the concepts related to genome-environment-group integration prediction and the construction, optimization and implementation
of models.
Information about genes, metabolic pathways, and networks can be translated into new one-dimensional genotype data and integrated into predictive models
.
Prediction-based crop redesign can target genes, metabolism and networks at the microscopic level and individuals, populations and species
at the macroscopic level.
This paper looks forward to how to use intelligent breeding to enhance the genetic gain of plant improvement, including the integration and utilization of various breeding technologies and methods, through the sharing of platforms, technologies, facilities, groups, data, models, and even breeding materials, to maximize resource utilization and breeding efficiency, and effectively carry out intelligent breeding with the support of open source breeding and service platforms
.
New strategies for genome-environmental integration prediction will drive significant changes
in areas such as smart breeding and crop cultivation physiology.
of models.
Information about genes, metabolic pathways, and networks can be translated into new one-dimensional genotype data and integrated into predictive models
.
Prediction-based crop redesign can target genes, metabolism and networks at the microscopic level and individuals, populations and species
at the macroscopic level.
This paper looks forward to how to use intelligent breeding to enhance the genetic gain of plant improvement, including the integration and utilization of various breeding technologies and methods, through the sharing of platforms, technologies, facilities, groups, data, models, and even breeding materials, to maximize resource utilization and breeding efficiency, and effectively carry out intelligent breeding with the support of open source breeding and service platforms
.
New strategies for genome-environmental integration prediction will drive significant changes
in areas such as smart breeding and crop cultivation physiology.
This article is led by the Institute of Composition Science of the Chinese Academy of Agricultural Sciences, with the joint participation
of the Institute of Modern Agriculture of Peking University, Foshan College of Science and Technology, Shanghai Academy of Agricultural Sciences, Shijiazhuang Boreddy Biotechnology Co.
, Ltd.
, International Maize and Wheat Improvement Center, and Murdoch University in Australia.
of the Institute of Modern Agriculture of Peking University, Foshan College of Science and Technology, Shanghai Academy of Agricultural Sciences, Shijiazhuang Boreddy Biotechnology Co.
, Ltd.
, International Maize and Wheat Improvement Center, and Murdoch University in Australia.
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