Structured antedependence models for genetic analysis of repeated measures on multiple quantitative traits

Area of Science:

• Quantitative Genetics
• Statistical Genetics
• Animal Breeding

Background:

• Analyzing correlated traits changing over time (longitudinal data) is crucial in genetic analyses.
• Existing methods like random regression and character process models have limitations, especially for multivariate trait analysis.

Purpose of the Study:

• To propose and derive methods for the genetic analysis of two or more correlated function-valued traits using a novel approach.
• To introduce structured antedependence models as a flexible alternative for multivariate longitudinal genetic analysis.

Main Methods:

• Development of structured antedependence (SAD) models for multivariate function-valued traits.
• Application of SAD models to analyze correlated traits in genetic studies.
• Comparison of SAD models with existing multivariate random regression models.

Main Results:

• SAD models provide substantial flexibility in modeling complex, non-stationary correlation structures.
• The proposed methodology performs better than multivariate random regression models.
• SAD models achieve this improved performance with fewer parameters.

Conclusions:

• Structured antedependence models offer a powerful and parsimonious approach for the genetic analysis of multiple correlated longitudinal traits.
• This methodology overcomes limitations of previous models in handling multivariate genetic data with time-dependent correlations.
• The findings have implications for genetic improvement programs in livestock and other species with complex trait dynamics.