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ARS Home » Southeast Area » Raleigh, North Carolina » Plant Science Research » Research » Publications at this Location » Publication #134162


item Whitt, Sherry
item Buckler, Edward - Ed

Submitted to: Methods in Molecular Biology
Publication Type: Other
Publication Acceptance Date: 8/20/2002
Publication Date: N/A
Citation: N/A

Interpretive Summary: Many genes control most characteristics of crop plants, but until recently it has been very difficult to identify the specific genes that control these traits. We have recently developed a methodology for rapidly dissecting these traits. We have used this method to evaluate maize (corn) genes of agronomic importance related to flowering time, plant height, and starch and protein kernel content. This book chapter provides many of the methodological details needed to carry out this approach. Specifically we outline the procedures our laboratory has used successfully to choose genes for study, find appropriate samples for testing, measure phenotypes, perform molecular analyses, organize and relate large amounts of data, and implement statistical tests. This chapter will allow a wide array of researchers to implement these novel approaches to dissect agronomic traits.

Technical Abstract: We describe a methodology used to identify statistical associations between candidate genes and phenotypic traits of agronomic importance. Association tests have received little attention in the plant genetics community, as there was no way to deal with population structure. We have developed a method for dealing with population structure that now allows these rapid high-resolution approaches to be applied to plants. Our methods have an advantage in that we can resolve an association to a single nucleotide in approximately six months to a year, whereas other commonly used techniques require years to achieve this level of resolution. This chapter provides the details on how to: 1) Select candidate genes, 2) Choose germplasm, 3) Obtain phenotypic measures, 4) Perform molecular analyses, 5) Organize mass amounts of sequence and phenotypic data, 6) Implement statistical analyses which account for population structure. These methodological details will allow this approach to be implemented by a wider audience.