Linking Rice, Arabidopsis and the Grasses to Explore Natural Genetic Variation

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To date, the map-based cloning and characterization of genes from crop species has largely been limited to monogenic traits, such as those providing disease resistance, that are controlled by single genes causing clear phenotypic effects. Most agronomically-important traits, such as yield and yield components, however, are polygenic traits controlled by multiple genes and influenced by the environment. Over the past decade, the application of molecular markers and genetic linkage maps has allowed specific regions of the genome controlling polygenic traits, called quantitative trait loci (QTLs), to be identified in all of the major crop species. In spite of the wealth of positional information for these regions, however, few genes underlying quantitative traits have actually been cloned. As a result, many questions concerning the nature and function of these genes, as well as the molecular mechanisms behind the genetic variation of agronomically-important traits, remain unanswered. The genes underlying QTLs derived from a wild rice ancestor (O. rufipogon) that are associated with natural variation for flowering time, grain weight, and plant height in elite cultivated material will be identified and characterized. Three QTLs that map to rice chromosome 1, one of the first rice chromosomes to be completely sequenced, will be targeted. The specific objectives are to fine-map major QTLs for flowering time, grain weight, and plant height, then to isolate candidate genes that are located within those regions and, finally, to demonstrate phenotypic complementation via transformation with genes underlying the QTLs. The sequence variation from orthologs of the genes underlying the target QTLs in wild and cultivated Oryza, other grasses, and Arabidopsis will be compared to gain an evolutionary perspective and insight into functionally-important conserved domains. This work will generate significant new information about the identity and functional significance of genes associated with agronomically-important traits in the grasses and will test a multi-faceted strategy for rapid identification of genes underlying QTLs when genomic sequence is available in the target region.