From trees to traits: A review of advances in PhyloG2P methods and future directions

TL;DR

This review discusses recent advances in PhyloG2P methods for linking genotypes to phenotypes, emphasizing methodological improvements, trait measurement considerations, and future integration of diverse biological data.

Contribution

It provides a comprehensive overview of PhyloG2P methods, highlighting new approaches, their strengths and limitations, and future directions for integrating additional biological information.

Findings

Methods based on amino acid substitutions are effective.

Explicit modeling of evolutionary processes improves accuracy.

Combining multiple methods enhances detection of genotype-phenotype associations.

Abstract

Mapping genotypes to phenotypes (G2P) is a fundamental goal in biology. So called PhyloG2P methods are a relatively new set of tools that leverage replicated evolution in phylogenetically independent lineages to identify genomic regions associated with traits of interest. Here, we review recent developments in PhyloG2P methods, focusing on three key areas: methods based on replicated amino acid substitutions, methods detecting changes in evolutionary rates, and methods analysing gene duplication and loss. We discuss how the definition and measurement of traits impacts the utility of these methods, arguing that focusing on simple rather than compound traits will lead to more meaningful genotype-phenotype associations. We advocate for the use of methods that work with continuous traits directly rather than collapsing them to binary representations. We examine the strengths and limitations of different approaches to modeling genetic replication, highlighting the importance of explicit modeling of evolutionary processes. Finally, we outline promising future directions, including the integration of population-level variation, as well as epigenetic and environmental information. No one method is likely to identify all genomic regions of interest, so we encourage users to apply multiple methods that are capable of detecting a wide range of associations. The overall aim of this review is to provide practitioners a roadmap for understanding and applying PhyloG2P methods.