Robust methods for detecting convergent shifts in evolutionary rates

Abstract

Identifying genomic elements underlying phenotypic adaptations is an important problem in evolutionary biology. Comparative analyses learning from convergent evolution of traits are gaining momentum in accurately detecting such elements. We previously developed a method for predicting phenotypic associations of genetic elements by contrasting patterns of sequence evolution in species showing a phenotype with those that do not. Using this method, we successfully demonstrated convergent evolutionary rate shifts in genetic elements associated with two phenotypic adaptations, namely the independent subterranean and marine transitions of terrestrial mammalian lineages. Our method calculates gene-specific rates of evolution on branches of phylogenetic trees using linear regression. These rates represent the extent of sequence divergence on a branch after removing the expected divergence on the branch due to background factors. The rates calculated using this regression analysis exhibit an important statistical limitation, namely heteroscedasticity. We observe that the rates on branches that are longer on average show higher variance, and describe how this problem adversely affects the confidence with which we can make inferences about rate shifts. Using a combination of data transformation and weighted regression, we have developed an updated method that corrects this heteroscedasticity in the rates. We additionally illustrate the improved performance offered by the updated method at robust detection of convergent rate shifts in phylogenetic trees of protein-coding genes across mammals, as well as using simulated tree datasets. Overall, we present an important extension to our evolutionary-rates-based method that performs more robustly and consistently at detecting convergent shifts in evolutionary rates.

Complementary evolution of coding and noncoding sequence underlies mammalian hairlessness

“…The RERconverge package in R was used to generate phylogenetic trees for each gene and noncoding region in which branch length represented the amount of evolutionary change, or the number of nonsynonymous substitutions, that occurred along that branch as described in several previous publications (Kowalczyk et al, 2019(Kowalczyk et al, , 2020Partha et al, 2019). Alignments for 19,149 genes in 62 mammal species were obtained from the UCSC 100-way alignment (Blanchette et al, 2004;Harris, 2007;Kent et al, 2002).…”

“…Alignments were used to generate evolutionary rate trees based on a well-established topology of the mammalian phylogeny in the Phylogenetic Analysis by Maximum Likelihood (PAML) program (Meyer et al, 2018;Yang, 2007). Briefly, RERconverge was used to convert evolutionary rate information from each gene-or noncoding element-specific tree by correcting for the mean-variance relationship among branch lengths and normalizing each branch for the average evolutionary rate along that branch such that the final branch length was relative to the expectation for that branch (Partha et al, 2019).…”

“…We used RERconverge, an established computational pipeline, to link convergently evolving genes and noncoding regions to convergent evolution of mammalian hairlessness. Previous work using RERconverge (Kowalczyk et al, 2019) to detect convergent evolutionary rate shifts in genes and noncoding elements associated with convergently evolving traits has identified the putative genetic basis of the marine phenotype in mammals (Chikina et al, 2016), the fossorial phenotype in subterranean mammals (Partha et al, 2017(Partha et al, , 2019, and extreme longevity in mammals (Kowalczyk et al, 2020). Those studies revealed trends that are not species-specific, but instead represent relevant genetic changes that occurred phylogenywide.…”

Cancer control is a key functionality underlying evolution of extended lifespan in mammals

“…Having defined the 3L and ELL phenotypes, we compute the association between these phenotypes and protein-specific relative evolutionary rates using the RERconverge method 23,24 (Fig. 1C).…”