|Yu, Jing -|
|Smith, C. Wayne -|
Submitted to: Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 3, 2010
Publication Date: February 18, 2010
Citation: Yu, J., Kohel, R.J., Smith, C. 2010. The construction of a tetraploid cotton genome wide comprehensive reference map. Genomics. 95:230-240. Interpretive Summary: Genetic markers and maps serve to guide researchers toward genes of interest. A genetic map is a map based on genetic distances, the frequencies of recombination between markers. The more dense markers are on a genetic map, the more likely researchers can quickly identify the gene(s) of interest. Many individual cotton genetic maps have been constructed. The goal of this study is to combine individual genetic maps into a single map with a high density of genetic markers. However, there are potential limitations in individual genetic linkage maps because different genetic populations and different markers were used. To overcome these problems and to synthesize a single, merged comprehensive reference map (CRM) a unique mathematical algorithm was used that converted genetic distances to genetic marker order that enabled combining of multiple genetic maps. It was possible to combine data from 28 cotton AD-genome individual maps (number of markers per map from few hundreds to nearly two thousands) into a linear CRM construction. The current output CRM contains 7,424 markers and represents over 93% of the combined mapping information of 28 AD-genome genetic maps. The CRM is publicly available, and an electronic workflow or pipeline was made to allow dynamically updated with additional cotton genetic mapping studies through CottonDB.
Technical Abstract: Integration of multiple genomic maps provides a higher density of markers and greater genome coverage, which not only facilitates the identification and positioning of QTLs and candidate genes, but it also provides a basic structure for the genome sequence assembly. However, the diversity in markers and populations used in individual mapping studies limits the ability to fully integrate the available data. By concentrating on marker orders rather than marker distances, published map data could be used to produce a comprehensive reference map (CRM) that includes a majority of known markers with optimally estimated order of those markers across the genome. In this study, a tetraploid cotton genome-wide CRM was constructed from 28 public cotton genetic maps. The initial CRM contained 7,424 markers and represented over 93% of the combined mapping information from the 28 individual maps. The current output is stored and displayed through CottonDB (http://www.cottondb.org), the public cotton genome database.