A bivariate quantitative genetic model for a threshold trait and a survival trait

Autor: Inge Riis Korsgaard, Lars Holm Damgaard
Přispěvatelé: Revues Inra, Import
Rok vydání: 2006
Předmět:
lcsh:QH426-470
Bayesian analysis
Bivariate analysis
[SDV.GEN.GA] Life Sciences [q-bio]/Genetics/Animal genetics
Biology
Genetic correlation
ordered categorical trait
symbols.namesake
03 medical and health sciences
Genetic model
Statistics
Credible interval
Genetics
Genetics(clinical)
survival trait
ComputingMilieux_MISCELLANEOUS
Ecology
Evolution
Behavior and Systematics
lcsh:SF1-1100
030304 developmental biology
2. Zero hunger
0303 health sciences
Models
Genetic
Research
bivariate genetic model
0402 animal and dairy science
Bayes Theorem
Conditional probability distribution
Quantitative genetics
04 agricultural and veterinary sciences
General Medicine
Survival Analysis
040201 dairy & animal science
[SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics
lcsh:Genetics
Multivariate Analysis
Trait
symbols
Animal Science and Zoology
lcsh:Animal culture
Gibbs sampling
Zdroj: Genetics Selection Evolution, Vol 38, Iss 6, Pp 565-581 (2006)
Genetics Selection Evolution
Genetics Selection Evolution, BioMed Central, 2006, 38 (6), pp.565-581
Damgaard, L H & Korsgaard, I R 2006, ' A bivariate quantitative genetic model for a threshold trait and a survival trait ', Genetics Selection Evolution, vol. 38, pp. 565-581 . https://doi.org/10.1051/gse:2006022
Genetics, Selection, Evolution : GSE
ISSN: 1297-9686
0999-193X
Popis: Many of the functional traits considered in animal breeding can be analyzed as threshold traits or survival traits with examples including disease traits, conformation scores, calving difficulty and longevity. In this paper we derive and implement a bivariate quantitative genetic model for a threshold character and a survival trait that are genetically and environmentally correlated. For the survival trait, we considered the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling was adopted in which model parameters were augmented with unobserved liabilities associated with the threshold trait. The fully conditional posterior distributions associated with parameters of the threshold trait reduced to well known distributions. For the survival trait the two baseline Weibull parameters were updated jointly by a Metropolis-Hastings step. The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling. The Gibbs sampler was tested in a simulation study and illustrated in a joint analysis of calving difficulty and longevity of dairy cattle. The simulation study showed that the estimated marginal posterior distributions covered well and placed high density to the true values used in the simulation of data. The data analysis of calving difficulty and longevity showed that genetic variation exists for both traits. The additive genetic correlation was moderately favorable with marginal posterior mean equal to 0.37 and 95% central posterior credibility interval ranging between 0.11 and 0.61. Therefore, this study suggests that selection for improving one of the two traits will be beneficial for the other trait as well.
Databáze: OpenAIRE
Externí odkaz: