An Empirical Bayes approach for the study of phenotypic evolution from high-dimensional data

Area of Science:

• Evolutionary biology
• Phylogenetics
• Computational biology

Background:

• Multivariate phylogenetic comparative methods are crucial for analyzing complex traits (e.g., 3D shapes, gene expression).
• Existing methods struggle with high-dimensional datasets (thousands of traits) due to computational intractability.

Purpose of the Study:

• To develop a computationally efficient maximum likelihood approach for high-dimensional phylogenetic comparative analysis.
• To enable robust inference and model comparison for trait evolution in large datasets.
• To extend the phylogenetic toolkit with complex evolutionary models, such as the Ornstein-Uhlenbeck process with multiple optima.

Main Methods:

• Proposed a novel maximum likelihood approach utilizing the Empirical Bayes framework.
• Incorporated complete covariances among species and traits for parameter inference.
• Validated the approach through simulations, comparing performance against existing methods.

Main Results:

• The Empirical Bayes approach accurately estimates parameters even when traits outnumber lineages tenfold.
• Demonstrated significant improvements in speed (at least 10x faster) and memory efficiency compared to current methods.
• Successfully applied the framework to analyze mammalian jaw morphology evolution and dietary adaptation.

Conclusions:

• The Empirical Bayes approach provides an efficient solution for high-dimensional phylogenetic comparative studies.
• This method facilitates the analysis of complex trait evolution and adaptive processes.
• The R package `mvMORPH` implements this framework, making high-dimensional analyses accessible.