Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: A simple algorithm to estimate genetic variance in an animal threshold model using Bayesian inference | 0degảrd et al. Genetics Selection Evolution 2010 42 29 http content 42 1 29 Ge n et i cs Selection Evolution RESEARCH Open Access A simple algorithm to estimate genetic variance in an animal threshold model using Bayesian inference J0rgen 0degard1 2 Theo HE Meuwissen2 Bjorg Heringstad2 3 Per Madsen4 Abstract Background In the genetic analysis of binary traits with one observation per animal animal threshold models frequently give biased heritability estimates. In some cases this problem can be circumvented by fitting sire- or sire-dam models. However these models are not appropriate in cases where individual records exist on parents. Therefore the aim of our study was to develop a new Gibbs sampling algorithm for a proper estimation of genetic co variance components within an animal threshold model framework. Methods In the proposed algorithm individuals are classified as either informative or non-informative with respect to genetic co variance components. The non-informative individuals are characterized by their Mendelian sampling deviations deviance from the mid-parent mean being completely confounded with a single residual on the underlying liability scale. For threshold models residual variance on the underlying scale is not identifiable. Hence variance of fully confounded Mendelian sampling deviations cannot be identified either but can be inferred from the between-family variation. In the new algorithm breeding values are sampled as in a standard animal model using the full relationship matrix but genetic co variance components are inferred from the sampled breeding values and relationships between informative individuals usually parents only. The latter is analogous to a sire-dam model in cases with no individual records on the parents . Results When applied to simulated data sets the standard animal threshold model failed to produce useful results since samples of genetic variance always drifted towards infinity while the new algorithm .
