We are developing a platform to rapidly dissect complex traits in maize by utilizing both association and linkage based approaches. To conduct these analyses, we must develop linkage and association populations that capture much of the natural variation inherent in the maize genome. Extensive phenotyping and surveys of tens of thousands of candidate gene sequences will then be employed. The development and adaptation of novel statistical genetic approaches is also required to study these diverse mapping populations. This approach should allow the rapid dissection of complex traits down to the gene level.

A full range of genomic and field genetic approaches are being used to identify alleles involved in improved nitrogen efficiency, aluminum tolerance, and kernel quality. Targeted alleles are those that can reduce the environmental impact of maize agriculture and provide a more nutritional plant. RNA microarrays, metabolite profiling and other genomic approaches are being applied to dissect these traits.

Making the connection between genomics and plant breeding remains a formidable challenge for current bioinformatics tools. We are developing improved bioinformatics tools that integrate public databases with genomic diversity data and agronomic data.