Biology subjects

Tao, Q.

Publications and source records attributed to Tao, Q..

3 recordsLinked to original sources

bioRxiv2018

Pervasive correlation of molecular evolutionary rates in the tree of life

New species arise from pre-existing species and inherit similar genomes and environments. This predicts greater similarity of mutation rates and the tempo of molecular evolution between direct ancestors and descendants, resulting in autocorrelation of evolutionary rates within lineages in the tree of life. Surprisingly, molecular sequence data have not confirmed this expectation, possibly because available methods lack power to detect autocorrelated rates. Here we present a machine learning method to detect the presence evolutionary rate autocorrelation in large phylogenies. The new method is computationally efficient and performs better than the available state-of-the-art methods. Application of the new method reveals extensive rate autocorrelation in DNA and amino acid sequence evolution of mammals, birds, insects, metazoans, plants, fungi, and prokaryotes. Therefore, rate autocorrelation is a common phenomenon throughout the tree of life. These findings suggest concordance between molecular and non-molecular evolutionary patterns and will foster unbiased and precise dating of the tree of life.

evolutionary biology↗

bioRxiv2017

RelTime relaxes the strict molecular clock throughout the phylogeny

The RelTime method estimates divergence times when evolutionary rates vary among lineages. Theoretical analyses show that RelTime relaxes the strict molecular clock throughout a molecular phylogeny, and it performs well in the analysis of empirical and computer simulated datasets in which evolutionary rates are variable. Lozano-Fernandez et al. (2017) found that the application of RelTime to one metazoan dataset (Erwin et al. 2011) produced equal rates for several ancient lineages, which led them to speculate that RelTime imposes a strict molecular clock for deep animal divergences. RelTime does not impose a strict molecular clock. The pattern observed by Lozano-Fernandez et al. (2017) was a result of the use of an option to assign the same rate to lineages in RelTime when the rates are not statistically significantly different. The median rate difference was 5% for many deep metazoan lineages for Erwin et al. (2011) dataset, so the rate equality was not rejected. In fact, RelTime analysis with and without the option to test rate differences produced very similar time estimates. We found that the Bayesian time estimates vary widely depending on the root priors assigned, and that the use of less restrictive priors produce Bayesian divergence times that are concordant with those from RelTime for Erwin et al. (2011) dataset. Therefore, it is prudent to discuss Bayesian estimates obtained under a range of priors in any discourse about molecular dating, including method comparisons.

evolutionary biology↗

bioRxiv2017

Theoretical foundation of the RelTime method for estimating divergence times from variable evolutionary rates

RelTime estimates divergence times by relaxing the assumption of a strict molecular clock in a phylogeny. It showed excellent performance in estimating divergence times for both simulated and empirical molecular sequence datasets in which evolutionary rates varied extensively throughout the tree. RelTime is computationally efficient and scales well with increasing size of datasets. Until now, however, RelTime has not had a formal mathematical foundation. Here, we show that the basis of the RelTime approach is a relative rate framework (RRF) that combines comparisons of evolutionary rates in sister lineages with the principle of minimum rate change between an evolutionary lineage and its descendants. We present analytical solutions for estimating relative lineage rates and divergence times under RRF. We also discuss the relationship of RRF with other approaches, including the Bayesian framework. We conclude that RelTime will be also useful for phylogenies with branch lengths derived not only from molecular data, but also morphological and biochemical traits.

evolutionary biology↗