Dissecting genomic determinants of positive selection with an evolution-guided regression model

Abstract

In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as the expression level of a gene, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald-Kreitman test, are available to examine the association between genomic features and positive selection, we currently lack a statistical approach to disentangle the direct effects of genomic features from the indirect effects mediated by confounding factors. To address this problem, we present a novel statistical model, the MK regression, which augments the McDonald-Kreitman test with a generalized linear model. Analogous to the classic multiple regression model, the MK regression can analyze multiple genomic features simultaneously to distinguish between direct and indirect effects on positive selection. Using the MK regression, we identify numerous genomic features responsible for positive selection in chimpanzees, including local mutation rate, residue exposure level, gene expression level, tissue specificity, and metabolic genes. In particular, we show that highly expressed genes have a higher rate of adaptation than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection on protein sequences. Also, we observe that metabolic genes tend to have a higher rate of adaptation than their non-metabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.Significance StatementThe McDonald-Kreitman test is a powerful method to detect genomic features associated with positive selection, but it cannot disentangle the direct effects of genomic features from the indirect effects mediated by confounding factors. Here we introduce the MK regression that augments the McDonald-Kreitman test with a generalized linear model. Analogous to the classic multiple regression model, the MK regression enables quantification of the direct effects of genomic features by controlling for potential confounders. It is particularly useful to analyze data with multiple correlated features and species with low levels of polymorphism. Using the MK regression, we identify numerous genomic features responsible for positive selection in chimpanzees, which provides novel insights into the adaptive evolution of our closest relatives.

Testing the adaptive walk model of gene evolution

“…We further jointly estimated the combined effect of the potential confounding factors by applying a recently developed method that extends the MK test with a generalized linear model [57].…”

“…To jointly estimate the effect of the potential confounding factors, we applied a recently developed method that extends the MK test with a generalized linear model [57]. This approach disentangles the effects of each factor on the rate of adaptive substitutions per nucleotide site.However, this method does not model the distribution of fitness effects and hence cannot account for segregating slightly deleterious mutations, which can bias estimates of the rate of adaptive substitutions [58].…”

“…To jointly estimate the effect of the potential confounding factors, we applied a recently developed method that extends the MK test with a generalized linear model [57]. This approach disentangles the effects of each factor on the rate of adaptive substitutions per nucleotide site.…”

Mixture Density Regression reveals frequent recent adaptation in the human genome

“…However, this factor does not seems to be a determinant of positive selection when other genomic factors are simultaneously considered using a MK regression approach (Huang 2021). Given the inconsistency with previous evidence and the low strength of the association, the weak negative relationship between selection and the number of PPIs found in this study should be taken with caution.…”

Transcription factor binding sites are frequently under accelerated evolution in primates

“…In the future, it is of great interest to develop a rigorous method for inferring accelerated evolution in overlapping TFBSs. Motivated by the recent success of evolution-based regression models 34,[78][79][80] , we propose that unifying our pooling-based methods and generalized linear models may be a promising direction to disentangle causal from correlational relationships in the analysis of accelerated evolution.…”