Wombat

Wombat performs quantitative genetic analyses of continuous traits by fitting linear mixed models with restricted maximum likelihood (REML) to estimate covariance components and genetic parameters.

Key Features:

• Linear mixed models: Fits linear mixed models for analysis of continuous traits.
• REML covariance estimation: Uses restricted maximum likelihood to estimate variance and covariance components and derive genetic parameters.
• Multi-trait and bivariate analyses: Supports multiple-trait and bivariate analyses to estimate genetic correlations between traits.
• Fixed and random effects: Accommodates multiple fixed and random effects in model specification.
• Genetic covariance structures: Allows selected genetic covariance structures including reduced-rank estimation.
• Random regression models: Implements random regression models for modeling trait trajectories or reaction norms.
• Numerical and computational efficiency: Employs advanced numerical methods and efficient compilers to improve computation speed.

Scientific Applications:

• Genetic parameter estimation: Estimates genetic variances, covariances and other genetic parameters for continuous traits.
• Genetic correlation analysis: Performs bivariate analyses to quantify genetic correlations between traits.
• Complex architecture dissection: Models complex genetic architectures using multiple fixed and random effects and selected covariance structures.
• Longitudinal and reaction-norm analysis: Applies random regression and reduced-rank models to analyze longitudinal data or reaction norms.

Methodology:

Fits linear mixed models using restricted maximum likelihood (REML) to estimate covariance components and genetic parameters, with options for random regression, reduced-rank estimation, and selected genetic covariance structures, and employs advanced numerical methods for computation.