This project makes use of the wealth of sequence information available for rice to bridge the study of molecular genetic variation with that of whole plant phenotypic variation. We will develop an association mapping platform that will provide new insights into the relationship between molecular and phenotypic diversity, leading to a more efficient utilization of natural variation in crop improvement. By developing a custom, high-resolution Rice Genotyping Array (SNP chip) that can be used to explore the genetic diversity of rice and its wild progenitor, we aim to better understand how allelic variation is distributed among the different sub-populations of O. sativa and O. rufipogon. Based on a rigorous phenotyping effort using controlled vocabularies, morphological, developmental and biochemical variation in rice can be compared with similar variation in other plant species, facilitating the identification of candidate genes and functional polymorphisms associated with these phenotypes.

The project also takes advantage of the unique evolutionary history of Oryza to explore the genetic architecture and combining ability of a series of ancient and highly self-fertile sub-populations that make rice unique among the grasses. New genetic resources will be constructed that will provide a systematic set of tools for exploring the genetic basis of transgressive variation in this inbreeding species. In this context, we will address the following questions:

1. Can we predict the occurrence of transgressive phenotypes based on the particular subpopulations that are combined?
2. Can we identify specific regions of the genome and specific alleles/haplotypes that, when combined, are likely to confer transgressive variation?
3. Can we make predictions about how a specific gene or allele will interact with a given genetic background in the context of a gene network?

It is of particular interest to compare the population structure and extent of linkage disequlibrium (LD) of rice with that of maize, barley, wheat and other species because these crop species have fundamentally different mating habits, histories of domestication, ploidy levels and breeding contexts. Integration of knowledge about evolution and natural population structure of domesticated species with emerging sequence and functional genomics informationwill lead to better management and utilization of natural variation for crop improvement. We will also take advantage of numerous opportunities for training and education of diverse groups of students in an interdisciplinary setting as part of this project.