184-9 Unbalanced Genetic Data Analyses: Genetic Model Evaluations and Applications.

See more from this Division: C01 Crop Breeding & Genetics
See more from this Session: Tools for Evaluating and/or Enhancing Genetic Progress
Tuesday, November 2, 2010: 10:15 AM
Long Beach Convention Center, Room 101A, First Floor
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Jixiang Wu1, Johnie Jenkins2, Jack McCarty2, Karl Glover1 and William Berzonsky1, (1)South Dakota State University, Brookings, SD
(2)Genetics & Precision Ag Research Unit, USDA-ARS, Mississippi State, MS
Fully utilizing genetic information is important for crop improvement and relies on appropriate data analyses; however, genetic data structures collected from different breeding programs may often be unbalanced due to a large of number parents employed, constraints of seed supply, and/or unpredictable factors. Traditional methods are challenged by such unbalanced data structures and thus valuable genetic information may be under-utilized. Therefore, it is valuable to assess genetic analyses for unbalanced data structures by utilizing new methodologies. Using mixed linear model approaches, several commonly used genetic models, including additive-dominance model and their extensions regarding their statistical properties were investigated by Monte Carlo simulations. As for demonstration, actual unbalanced breeding data will be analyzed. Detailed results from model evaluations and actual data will be presented.
See more from this Division: C01 Crop Breeding & Genetics
See more from this Session: Tools for Evaluating and/or Enhancing Genetic Progress