Factors That Affect the Rates of Adaptive and Nonadaptive Evolution at the Gene Level in Humans and Chimpanzees. (15th February 2022)

• Record Type: Journal Article
• Authors: Soni, Vivak; Eyre-Walker, Adam
• Editors: Enard, David
• Is Part Of: Genome biology and evolution. Volume 14:Number 2(2022)
• Publication Date: 2022-02-15
• Subjects: adaptive evolution -- humans -- chimpanzees -- recombination rate -- gene age; Genomics -- Periodicals; Genes -- Periodicals; 572.8605
• DOI: 10.1093/gbe/evac028
• Languages: English
• ISSNs: 1759-6653

Abstract

Abstract: The rate of amino acid substitution has been shown to be correlated to a number of factors including the rate of recombination, the age of the gene, the length of the protein, mean expression level, and gene function. However, the extent to which these correlations are due to adaptive and nonadaptive evolution has not been studied in detail, at least not in hominids. We find that the rate of adaptive evolution is significantly positively correlated to the rate of recombination, protein length and gene expression level, and negatively correlated to gene age. These correlations remain significant when each factor is controlled for in turn, except when controlling for expression in an analysis of protein length; and they also generally remain significant when biased gene conversion is taken into account. However, the positive correlations could be an artifact of population size contraction. We also find that the rate of nonadaptive evolution is negatively correlated to each factor, and all these correlations survive controlling for each other and biased gene conversion. Finally, we examine the effect of gene function on rates of adaptive and nonadaptive evolution; we confirm that virus-interacting proteins (VIPs) have higher rates of adaptive and lower rates of nonadaptive evolution, but we also demonstrate that there is significant variation in the rate of adaptive and nonadaptive evolution between GO categories when removing VIPs. We estimate that the VIP/non-VIP axis explains about 5–8 fold more of the variance in evolutionary rate than GO categories.