Sparse precision matrix estimation in phenotypic trait evolution models

TL;DR

This paper introduces a Bayesian method using Gaussian graphical models with G-Wishart priors to estimate sparse precision matrices, revealing direct trait associations in phenotypic evolution models, and demonstrates improved accuracy through simulations and real data applications.

Contribution

It develops a novel Bayesian approach for sparse precision matrix estimation in phenotypic trait evolution, incorporating G-Wishart priors and enabling direct association testing.

Findings

Accurate graph structure estimation in simulated data.

Lower estimation errors for sparse precision matrices.

Effective identification of trait associations in biological data.

Abstract

Phylogenetic trait evolution models allow for the estimation of evolutionary correlations between a set of traits observed in a sample of related organisms. By directly modeling the evolution of the traits along an estimable phylogenetic tree, the model's structure effectively controls for shared evolutionary history. In these models, relevant correlations are usually assessed through the high posterior density interval of their marginal distributions. However, the selected correlations alone may not provide the full picture regarding trait relationships. Their association structure, expressed through a graph that encodes partial correlations, can in contrast highlight sparsity patterns featuring direct associations between traits. In order to develop a model-based method to identify this association structure we explore the use of Gaussian graphical models (GGM) for covariance selection. We model the precision matrix with a G-Wishart conjugate prior, which results in sparse precision estimates. Furthermore the model naturally allows for Bayes Factor tests of association between the traits, with no additional computation required. We evaluate our approach through Monte Carlo simulations and applications that examine the association structure and evolutionary correlations of phenotypic traits in Darwin's finches and genomic and phenotypic traits in prokaryotes. Our approach provides accurate graph estimates and lower errors for the precision and correlation parameter estimates, particularly for conditionally independent traits, which are the target for sparsity in GGMs.