Breeding informatics

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modern rice breeding

The success of modern rice breeding depends on the use of accurate selection criteria derived from multiple sources of information. To implement marker-assisted breeding effectively, we employ an integrated breeding platform that has an efficient information system for managing breeding logistics and information from different sources (phenotypic, genetic, genomic, etc.). The platform also provides efficient analytical pipelines and decision support tools. This can lead to a shortening of the breeding cycle while minimizing resource requirements. An efficient system for genotyping markers tightly linked or diagnostic of trait-controlling genes is also needed.

Phenotypic information needs to be collected from environments that are representative of the target population of environments (TPE) to reveal and explore genotype-by-environment interactions (GEI). Efficient multienvironment testing (MET) networks are needed for determining the stability and adaptability of genotypes and the discrimination power of specific environments. MET networks can also facilitate the exchange of germplasm among breeding programs, which can potentially speed up the development of new varieties while increasing genetic diversity.

working on breeding informatics

An integrated breeding platform with rice-specific marker applications and decision tools

IRIS content will be greatly improved through quality checks, reorganization of existing data sets, and uploading of well-curated historical data sets. Efficient analytical pipelines will be developed for predicting breeding value (genetic merit) using pedigree, marker, and phenotypic data. A suite of decision support tools will be developed to assist in the design of efficient marker-/ genomics-assisted breeding strategies. An efficient genotyping platform for SNPs that are diagnostic for important traits will also be developed and diagnostic SNP markers for key traits will be validated, optimized, and made available for deployment in breeding programs. Decision support tools will be developed through collaboration with the Integrated Plant Breeding Platform (IBP) project of the Generation Challenge Program.

A global rice germplasm information system to support rice breeding

A global rice information system will be developed that integrates phenotypic data with genetic, genomic, and genotypic data with breeding decision support tools to support the implementation of modern rice breeding strategies. Data integration is one of the key components in developing breeding informatics. The system includes data curation tools, a data processing pipeline, Web visualization, and simple data-mining tools. The breeding decision support tools will also be migrated into the integrated data environment. This will add critical value through Web-based data access and use to the key products of the IBP project.

Multienvironment Testing (MET) and International Germplasm Evaluation (INGER)

The new MET system will be a systematic and multistage testing scheme for promising breeding lines developed by GRiSP breeding programs. To be managed by GRiSP, MET will involve public- and private-sector partners at the key locations. This will allow for products to be channeled quickly into the right target environments and markets, while generating valuable feedback from farmers, millers, consumers, and other stakeholders in the public, private, and NGO sector.

Through INGER, NARES can exchange superior materials among themselves for release directly to farmers. Or they could use these in hybridization. Aside from seeds, INGER will facilitate the worldwide exchange of nonseed biological materials and breeding-related information. In Africa, INGER will be embedded in the Africa Rice Breeding Task Force. This task force will be established to regroup scarce human resources devoted to rice breeding in Africa. It will aim to achieve higher rice productivity through (1) the identification of required plant types responding to farmers’ needs and consumers’ preferences in well-characterized target populations of environments; (2) establishment of a regional rice variety testing network using extensive METs and centralized G × E analyses; (3) development of accelerated and regionally accepted varietal release procedures; and (4) development of alternative and effective models for seed production systems.


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