TAILIEUCHUNG - Báo cáo sinh học: " Bayesian analysis of genetic change due to selection using Gibbs sampling"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Bayesian analysis of genetic change due to selection using Gibbs sampling | 333 Genet Sei Evol 1994 26 333-360 Elsevier INRA Original article Bayesian analysis of genetic change due to selection using Gibbs sampling DA Sorensen1 cs Wang2 J Jensen1 D Gianola2 1 National Institute of Animal Science Research Center Foulum PB 39 DK8830 Tjele Denmark 2 Department of Meat and Animal Science University of Wisconsin-Madison Madison WI 53706-1284 USA Received 7 June 1993 accepted 10 February 1994 Summary - A method of analysing response to selection using a Bayesian perspective is presented. The following measures of response to selection were analysed 1 total response in terms of the difference in additive genetic means between last and first generations 2 the slope through the origin of the regression of mean additive genetic value on generation 3 the linear regression slope of mean additive genetic value on generation. Inferences are based on marginal posterior distributions of the above-defined measures of genetic response and uncertainties about fixed effects and variance components are taken into account. The marginal posterior distributions were estimated using the Gibbs sampler. Two simulated data sets with heritability levels and having 5 cycles of selection were used to illustrate the method. Two analyses were carried out for each data set with partial data generations 0-2 and with the whole data. The Bayesian analysis differed from a traditional analysis based on best linear unbiased predictors BLUP with an animal model when the amount of information in the data was small. Inferences about selection response were similar with both methods at high heritability values and using all the data for the analysis. The Bayesian approach correctly assessed the degree of uncertainty associated with insufficient information in the data. A Bayesian analysis using 2 different sets of prior distributions for the variance components showed that inferences differed only when the relative amount of information contributed by the data was small. .

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