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Title: USING CHICKEN GENOME SEQUENCES TO SEARCH FOR NEW MICROSATELLITE BIOMARKERS

Author
item Kuo, Alice
item FULTON, JANET - HY-LINE INTERNATIONAL
item ASHWELL, CHRISTOPHER - NORTH CAROLINA STATE

Submitted to: Plant and Animal Genome VX Conference Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 10/1/2004
Publication Date: 1/15/2005
Citation: Kuo, A.Y., Fulton, J.E., Ashwell, C.M. 2005. Using chicken genome sequences to search for new microsatellite biomarkers [abstract]. Plant and Animal Genome XIII. 206.

Interpretive Summary:

Technical Abstract: Microsatellite loci have been used to evaluate associations with economically important traits in commercial poultry populations, but these analyses have been limited by the number of available markers. Specific chromosomal regions located on chicken GGA3, GGAZ, and GGA27 were queried for new microsatellite repeats within a 10 centi-Morgan (cM) interval as defined by the genetic consensus map (www.thearkdb.org). The chicken genome sequence annotations available from the Ensembl and UCSC Genome Browsers were used to determine 50 new putative microsatellite markers per region. Primers were designed for these microsatellites, and amplified by PCR in 8 chicken DNA samples from diverse genetic lines to screen for polymorphism. Ten loci per region were selected for further characterization and re-screened using fluorescent primers to confirm polymorphism. The number of base pairs per cM in each of the regions evaluated is 440238, 167419, and 45000, and includes an average of 12, 8, and 3 microsatellites per cM for GGA3, GGAZ and GGA27, respectively. We identified 22, 31, and 9 polymorphisms present in the 10 cM intervals of GGA3, GGAZ, and GGA27, respectively, indicating greater polymorphism content in the macrochromosomes GGA3 and GGAZ than in the microchromosome GGA27. Using the chicken genome to increase marker density can significantly decrease the time to develop new informative microsatellites and allows for ease in targeting specific chromosomal regions. This focused approach will be extremely useful in fine-mapping genome regions where QTL have been detected.