GSEVM v.2: MCMC software to analyze genetically structured environmental variance models
- 14 May 2010
- journal article
- Published by Wiley in Journal of Animal Breeding and Genetics
- Vol. 127 (3) , 249-251
Abstract
Summary: This note provides a description of software that allows to fit Bayesian genetically structured variance models using Markov chain Monte Carlo (MCMC). The gsevm v.2 program was written in Fortran 90. The DOS and Unix executable programs, the user’s guide, and some example files are freely available for research purposes at http://www.bdporc.irta.es/estudis.jsp. The main feature of the program is to compute Monte Carlo estimates of marginal posterior distributions of parameters of interest. The program is quite flexible, allowing the user to fit a variety of linear models at the level of the mean and the logvariance.Keywords
This publication has 9 references indexed in Scilit:
- Selection for Environmental Variation: A Statistical Analysis and Power Calculations to Detect ResponseGenetics, 2008
- Genetic parameters related to environmental variability of weight traits in a selection experiment for weight gain in mice; signs of correlated canalised responseGenetics Selection Evolution, 2008
- A comparison of strategies for Markov chain Monte Carlo computation in quantitative geneticsGenetics Selection Evolution, 2008
- Genetic parameters for canalisation analysis of litter size and litter weight traits at birth in miceGenetics Selection Evolution, 2006
- Normal linear models with genetically structured residual variance heterogeneity: a case studyGenetics Research, 2003
- Bayesian Measures of Model Complexity and FitJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics2002
- Prediction of the response to a selection for canalisation of a continuous trait in animal breedingGenetics Selection Evolution, 1998
- A Simple Method for Computing the Inverse of a Numerator Relationship Matrix Used in Prediction of Breeding ValuesBiometrics, 1976