More on the Best Evolutionary Rate for Phylogenetic Analysis

Abstract

The accumulation of genome-scale molecular data sets for nonmodel taxa brings us ever closer to resolving the tree of life of all living organisms. However, despite the depth of data available, a number of studies that each used thousands of genes have reported conflicting results. The focus of phylogenomic projects must thus shift to more careful experimental design. Even though we still have a limited understanding of what are the best predictors of the phylogenetic informativeness of a gene, there is wide agreement that one key factor is its evolutionary rate; but there is no consensus as to whether the rates derived as optimal in various analytical, empirical, and simulation approaches have any general applicability. We here use simulations to infer optimal rates in a set of realistic phylogenetic scenarios with varying tree sizes, numbers of terminals, and tree shapes. Furthermore, we study the relationship between the optimal rate and rate variation among sites and among lineages. Finally, we examine how well the predictions made by a range of experimental design methods correlate with the observed performance in our simulations.We find that the optimal level of divergence is surprisingly robust to differences in taxon sampling and even to among-site and among-lineage rate variation as often encountered in empirical data sets. This finding encourages the use of methods that rely on a single optimal rate to predict a gene’s utility. Focusing on correct recovery either of the most basal node in the phylogeny or of the entire topology, the optimal rate is about 0.45 substitutions from root to tip in average Yule trees and about 0.2 in difficult trees with short basal and long-apical branches, but all rates leading to divergence levels between about 0.1 and 0.5 perform reasonably well.Testing the performance of six methods that can be used to predict a gene’s utility against our simulation results, we find that the probability of resolution, signal-noise analysis, and Fisher information are good predictors of phylogenetic informativeness, but they require specification of at least part of a model tree. Likelihood quartet mapping also shows very good performance but only requires sequence alignments and is thus applicable without making assumptions about the phylogeny. Despite them being the most commonly used methods for experimental design, geometric quartet mapping and the integration of phylogenetic informativeness curves perform rather poorly in our comparison. Instead of derived predictors of phylogenetic informativeness, we suggest that the number of sites in a gene that evolve at near-optimal rates (as inferred here) could be used directly to prioritize genes for phylogenetic inference. In combination with measures of model fit, especially with respect to compositional biases and among-site and among-lineage rate variation, such an approach has the potential to greatly improve marker choice and should be tested on empirical data.

Mismatch of the morphology model is mostly unproblematic in total-evidence dating: insights from an extensive simulation study

“…For all extant taxa, we simulated nucleotide sequences of length 1,000 bp under the Jukes-Cantor model, a strict clock, and with a clock rate of 0.0025 substitutions per Myr for all sites. This rate translates into 0.25 expected substitutions between root and tip on our tree, which is 100 Myr deep, a rate that has been found optimal for topology inference in a recent simulation study (Klopfstein, et al 2017). For the nucleotide data, we always used the same model for simulation as for analysis, ensuring straightforward recovery of the relationships among the extant taxa; the molecular data thus in fact almost acts as a full constraint on the topology and molecular branch lengths (see results section).…”

“…2a). To examine the impact of saturation, we simulated morphological characters evolving at different evolutionary rates, which gave very similar results as a recent simulation study on phylogenetic informativeness (Klopfstein, et al 2017): rates between 0.05 and 0.75 substitutions between root and tip performed well, as long as the characters were variable (Fig. 2b).…”

Optimizing Phylogenomics with Rapidly Evolving Long Exons: Comparison with Anchored Hybrid Enrichment and Ultraconserved Elements

“…In contrast, a distinct advantage of using protein-coding DNA sequences for phylogenetics is that nucleotide evolution can be modeled more precisely and the strength of selection can be quantified by comparing rates of synonymous and nonsynonymous mutations (McDonald and Kreitman 1991). Rate differences among 1 st , 2 nd , and 3 rd codon positions, for example, are a wellunderstood bi-product of selection acting more strongly against non-synonymous versus synonymous mutations (Jackman et al 1999) and this among-site rate variation in coding sequences may be beneficial to phylogenetic reconstruction (Klopfstein et al 2017). Translations to amino acid sequences are also commonly used for phylogenetic analysis (Fig.…”

Phylogenetic Synecdoche Demonstrates Optimality of Subsampling and Improves Recovery of the Blaberoidea Phylogeny

“…In this study, we combine two approaches, data targeting and phylogenetic subsampling, aimed at improving phylogenomic inference by limiting confounding signal. Data targeting seeks to only utilize positively informative data, which requires identifying features of genetic loci that are efficient in recovering reasonable phylogenetic relationships with confidence (e.g., Townsend 2007;Borowiec et al 2015;Gilbert et al 2015;Tan et al 2015;Brown and Thomson 2017;Klopfstein et al 2017;Reddy et al 2017;Molloy and Warnow 2018). Phylogenetic subsampling allows assessing the presence of confounding signal (Edwards 2016;Simon et al 2018).…”

“…The relationship between the features and phylogenetic utility of molecular data have long been studied (e.g., see Blaxter 2004). Perhaps the prime suspect in investigating a locus' quality for phylogenetic 80 inference is its evolutionary rate (Klopfstein et al 2017). The optimal mean rate of a locus for resolving difficult tree shapes (Steel and Leuenberger 2017;Dornburg et al 2018) is very conservative (Klopfstein et al 2017;Steel and Leuenberger 2017).…”