Estimation of variance components using Bayesian and frequentist inferences considering simulated data under heterogeneity of variance

Revista Brasileira de Zootecnia

Torres, Robledo de Almeida¹  Carneiro Júnior, José Marques²  Assis, Giselle Mariano Lessa de²  Lopes, Paulo Sávio¹  Euclydes, Ricardo Frederico¹ 

[1] Universidade Federal de Viçosa, Viçosa;Embrapa Acre, Rio Branco

关键词: animal breeding;    a priori information;    genetic parameters;    Gibbs sampling;    heterocedasticity;    simulation     IntroduçãoO principal objetivo do melhoramento animal é o aumento da freqüência dos genes favoráveis de determinada característica dentro da população;   

DOI  :  10.1590/S1516-35982007000700012

学科分类：兽医学

来源: Universidade Federal de Vicosa

【 摘 要 】

A genome of 3.000 centimorgans was simulated for a single quantitative trait governed by 800 loci with two alleles per locus. According to the genomic structure proposed, 1,500 males and 1,500 females constituted the base population which was used to form two (small and large) initial populations. Two types (only additive genetic variance and both additive genetic and environmental variances) and three variability levels (high, medium and low) of heterogeneity of variances were inserted in the initial populations. Variance components were estimated by Bayesian inference via Gibbs Sampling using three different levels of priors (non-informative, slightly informative and informative) and by REML. The best estimates of variance components were obtained with large populations. In small populations, the individual analyses for different variability levels presented problems related to variance components estimation due the small size of subpopulations. Both methods presented similar results for variance components when non-informative priors were used in Bayesian inference. Increasing the level of a priori information improved the estimates of variance components by Bayesian inference, especially in small populations.

【 授权许可】

CC BY-NC