Publication Date

Spring 5-16-2026

Abstract

Gaussian mixed-models (GMMs) show promise as a tool for modeling polygenic trait evolution for multiple taxa with established phylogenetic comparative methods (PCMs). When phenotypic traits are influenced by more than one gene, neither a gene tree nor a species tree may be completely adequate to model specific cross-taxa dependencies. In such cases common solutions include using trees inferred from concatenated DNA sequences [35, 95] and consensus gene trees [35]. The GMM-based model, first proposed by Jiang in 2017 [55] allows traits to evolve on more than one tree with distinct topologies. This approach provides a framework for trait evolutionary modeling when a small, targeted number of genes should be considered, but the relative contributions of each is unknown. In this work we investigate the method with Bayesian and maximum likelihood procedures under Brownian motion. Initial findings suggest potential for improved estimates of evolutionary rate over currently available methods, provided challenges posed by non-identifiability of parameters in GMMs can be mediated. The model is explored with both simulated and empirical data.

Degree Name

Statistics

Level of Degree

Masters

Department Name

Mathematics & Statistics

First Committee Member (Chair)

James Degnan

Second Committee Member

Fletcher Christensen

Third Committee Member

Brent Wagner

Language

English

Keywords

Continuous polygenic trait evolution, phylogenetic trees, Brownian motion, Gaussian Mixture Models, evolutionary rate, phylogenetic generalized least squares

Document Type

Thesis

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