Genetic and quantitative study of survival characteristics, Gaussian linear and ordered categorical traits in Nellore cattle

• Grant number: 07/01285-8
• Support Opportunities: Scholarships abroad - Research
• Start date: September 03, 2007
• End date: February 02, 2008
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Marcelo Hessel van Melis
• Grantee: Marcelo Hessel van Melis
• Host Investigator: Luís Varona Aguado
• Host Institution: Pessoa Física
• Institution abroad: Investigación y Tecnología Agroalimentarias, Lleida (IRTA), Spain

Abstract

Applications of the bivariate models in livestock contribute to our understanding of the genetic correlations between economically important traits related to e.g. production and fertility. Papers involving multivariate analyses with one linear and several categorical traits have been found in literature, but survival analysis methodology is theoretically superior to threshold model approaches because it allows proper treatment of censored observations and inclusion of time-dependent covariates. These statistical properties alone justify consideration of this methodology for genetic evaluation of economically relevant traits. The overall objective of this post-doctoral are derive and implement bivariate quantitative genetic models for a linear Gaussian and a survival traits and an ordered categorical and a survival traits which are genetically and environmentally correlated. Heritability and genetic and residual correlation coefficients will be estimated. The bivariate model will allow for a more accurate genetic evaluation of animals for correlated traits owing to the shared information between traits. For the survival trait, we will consider the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling will be adopted. The model parameters will be augmented with unobserved liabilities associated with the threshold character. Model parameters will be inferred from their marginal posterior distributions. The required fully conditional posterior distributions will be derived and issues on implementation discussed. The two Weibull baseline parameters will be updated jointly using a Metropolis-Hasting step. The remaining model parameters with non-normalized fully conditional distributions will be updated univariately using adaptive rejection sampling. The traits considered in this study are weaning weight at 210 days (WW210), postweaning weight gain (PWG), scrotal circumference at 550 days (SC18), muscling (Musc), heifer pregnancy (HP) and age at first conception (AFC), and stayability at five years of age (Stay) and length of productive life (LPL). The WW210, PWG and SC18 are linear Gaussian traits. The Musc, HP and Stay are ordered categorical traits. The AFC and LPL are survival traits. (AU)