TAILIEUCHUNG - Báo cáo sinh học: "A study on the minimum number of loci required for genetic evaluation using a finite locus model"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: A study on the minimum number of loci required for genetic evaluation using a finite locus model | Genet. Sel. Evol. 36 2004 395-414 INRA EDP Sciences 2004 DOI gse 2004008 395 Original article A study on the minimum number of loci required for genetic evaluation using a finite locus model Liviu R. ToTiRa Rohan L. FERNANDOa b Jack . DEKKERSa b Soledad A. FernAndezc a Department of Animal Science Iowa State University Ames IA 50011 USA b Lawrence H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50011 USA c Department of Statistics The Ohio State University Columbus OH 43210 USA Received 22 August 2003 accepted 22 March 2004 Abstract - For a finite locus model Markov chain Monte Carlo MCMC methods can be used to estimate the conditional mean of genotypic values given phenotypes which is also known as the best predictor BP . When computationally feasible this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends among other factors on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees genetic evaluations obtained by best linear prediction BLP were compared to genetic evaluations obtained by BP. For BLP evaluation used here as the standard of comparison only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation the gene frequencies and genotypic effects which completely specify the required distribution were derived such that the genotypic mean the additive variance and the dominance variance were the same as in the .

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