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United States Department of Agriculture

Agricultural Research Service

Title: Predicting Chromosomal Locations of Genetically Mapped Loci in Maize Using the Morgan2mcclintock Translator

Authors
item Lawrence, Carolyn
item Seigfried, Trent
item Anderson, Lorinda - COLORADO STATE UNIVERSITY
item Amarillo, Ina - FLORIDA STATE UNIVERSITY
item Bass, Hank - FLORIDA STATE UNIVERSITY

Submitted to: Maize Genetics Conference Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: January 20, 2006
Publication Date: March 9, 2006
Citation: Lawrence, C.J., Seigfried, T.E., Anderson, L.K., Amarillo, I.E., Bass, H.W. 2006. Predicting chromosomal locations of genetically mapped loci in maize using the Morgan2McClintock translator [abstract]. 48th Annual Maize Genetics Conference Program and Abstracts. p. 55.

Technical Abstract: The Morgan2McClintock Translator (http://www.lawrencelab.org/Morgan2McClintock) permits prediction of meiotic pachytene chromosome map positions from recombination-based linkage data using recombination nodule frequency distributions. Its outputs permit estimation of DNA content between mapped loci and help to create an integrated overview of the maize nuclear genome structure. The alpha version of the Morgan2McClintock Translator converts linkage map locations to predicted cytological positions in an automated fashion, but loci that are near one another (i.e., within approximately 3 cM of each other on a genetic map) cannot be resolved using the existing conversion equations due to the RN data collection procedure (RN frequencies on pachytene synaptonemal complexes were measured at a 0.2 micron length interval resolution). It should be possible to better resolve these positions if the conversion equations are refined. In addition, it should be possible to accomplish the map conversion in the opposite direction (i.e., using cytological map coordinates to predict genetic map positions). Theoretically, this approach is applicable to other organisms with comparable cytological crossover-distribution data such as tomato and mouse, and we plan to develop a set of similar tools for these organisms that should be useful in comparing genetic and chromosomal aspects of genomes in various species.

Last Modified: 4/23/2014
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