A critical step in studying biological features (e.g., genetic variants, gene families, metabolic capabilities, or taxa) underlying traits or outcomes of interest is assessing their diversity and distribution. Accurate assessments of these patterns are essential for linking features to traits or outcomes and understanding their functional impact. Consequently, it is of crucial importance that the metrics employed for quantifying feature diversity can perform robustly under any evolutionary scenario. However, the standard metrics used for quantifying and comparing the distribution of features, such as prevalence, phylogenetic diversity, and related approaches, either do not take into consideration evolutionary history, or assume strictly vertical patterns of inheritance. Consequently, these approaches cannot accurately assess diversity for features that have undergone recombination or horizontal transfer. To address this issue, we have devised RecPD, a novel recombination-aware phylogenetic-diversity metric for measuring the distribution and diversity of features under all evolutionary scenarios. RecPD utilizes ancestral-state reconstruction to map the presence / absence of features onto ancestral nodes in a species tree, and then identifies potential recombination events in the evolutionary history of the feature. We also derive a number of related metrics from RecPD that can be used to assess and quantify evolutionary dynamics and correlation of feature evolutionary histories. We used simulation studies to show that RecPD reliably identifies evolutionary histories under diverse recombination and loss scenarios. We then apply RecPD in a real-world scenario in a preliminary study type III effector protein families secreted by the plant pathogenic bacterium Pseudomonas syringae and demonstrate that prevalence is an inadequate metric that obscures the potential impact of recombination. We believe RecPD will have broad utility for revealing and quantifying complex evolutionary processes for features at any biological level.
Determining the evolutionary history of gene families
