TAILIEUCHUNG - Báo cáo sinh học: "A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models | 585 Genet Sei. Evol. 35 2003 585-604 INRA EDP Sciences 2003 DOI gse 2003041 Original article a comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models Lìvìu R. Totira Rohan L. Fernandoa b Jack . Dekkersa b Soledad A. FernÁndezc Bernt Guldbrandtsend a Department of Animal Science Iowa State UniversitY Ames lA 50011-3150 USA b Lawrence H. Baker Center for Bio-informatics and Biological Statistics Iowa State UniversitY Ames IA 50011-3150 USA c Department of Statistics The Ohio State UniversitY Columbus OH 43210 USA d Danish Institute of Animal Science Foulum Denmark Received 27 FebruarY 2002 accepted 5 MaY 2003 Abstract - An increased availability of genotypes at marker loci has prompted the development of models that include the effect of individual genes. Selection based on these models is known as marker-assisted selection MAS . MAS is known to be efficient especially for traits that have low heritability and non-additive gene action. BLUP methodology under non-additive gene action is not feasible for large inbred or crossbred pedigrees. It is easy to incorporate nonadditive gene action in a finite locus model. Under such a model the unobservable genotypic values can be predicted using the conditional mean of the genotypic values given the data. To compute this conditional mean conditional genotype probabilities must be computed. In this study these probabilities were computed using iterative peeling and three Markov chain Monte Carlo MCMC methods scalar Gibbs blocking Gibbs and a sampler that combines the Elston Stewart algorithm with iterative peeling ESIP . The performance of these four methods was assessed using simulated data. For pedigrees with loops iterative peeling fails to provide accurate genotype probability estimates for some pedigree members. Also computing time is exponentially related to the number of loci in the model. For MCMC methods a linear relationship can be .

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