Specific goal: Public availability of reference maps and identified linked molecular markers for traits/genes of interest in ICRISAT's crop species

Outputs:

1. Reference maps, populations and seed for each species
2. Linked molecular markers for traits/genes of interest
3. Global, public database with map and population information
4. Large phenotyping platform for complex traits such as roots

A major application of genomics is the use of molecular markers as indirect selection tools in a breeding program. Essentially, the goal is a better prediction of a particular phenotype from a determined genotype. To accomplish this, it is necessary to determine molecular markers that have a high predictive value for a particular phenotype or trait. Development of such markers requires:

• Priority traits to be identified,
• Phenotyping methods well established to accurately quantify the traits,
• Dissecting the physiological mechanisms underlying complex traits
• Contrasting germplasm sources available,
• Molecular markers available,
• Segregating populations produced, and
• Statistical methods available.

Priority traits have been identified in collaboration with the Global Theme on Crop Improvement. In most cases, adequate phenotyping methods are available, although for some traits such as drought and salinity, these will be firmly established by 2007. In the case of drought, the priority will be given to component traits of drought tolerance, namely transpiration efficiency to contribute to intermittent drought, and root traits to contribute to terminal drought tolerance. The dissection of the physiological mechanisms involved in the target traits will help progress toward the identification of the genes involved in that specific trait. Contrasting germplasm for use in developing segregating populations are available and in many instances, the required populations at various levels of inbreeding are available. Statistical packages for analyzing the molecular and phenotypic data are available and are being incorporated into a bioinformatics system (iMAS) to allow efficient handling of these datasets.

For further information, contact Rajeev Varshney, Dave Hoisington , Tom Hash