bioRxivGenomicsPreprint
Adaptive loss of function accelerated the evolution of ancient and modern human cognition
Alexander L. StarriD, Gabriella M Cale, Leslie MagtanongiD, Michael E. Palmer, Hunter B. FraseriD
Abstract
Methods to detect accelerated evolution have identified many genomic regions with unexpectedly rapid evolution in the human lineage-significantly more than in chimpanzees, our closest living relatives. However, these methods focus on accelerated sequence evolution of short non-coding regions, leaving open the questions of how to identify accelerated evolution of molecular function, as opposed to sequence, and whether accelerated evolution has shaped the human genome more broadly. Here, we introduce a new approach to detect accelerated evolution: Function Aware Statistical Test for Evolutionary Rates (FASTER). In contrast to previous methods, FASTER can detect not only accelerated evolution of sequence, but also of predicted function, and can be applied to any set of genomic regions. Applying this method to humans and chimpanzees, we identified protein-coding, untranslated (UTR), and non-coding regions with accelerated evolution of function. Across all these genomic levels, we consistently found more rapid evolution in conserved sites in the human lineage compared to chimpanzee, many of which are predicted to reduce protein stability or chromatin accessibility. Multiple lines of evidence suggest this human-acceleration was driven in part by positive selection on brain development and cognition which has continued to shape human evolution even in the past several thousand years. Collectively, these results demonstrate the power of genome-wide scans for the evolution of predicted function and specifically suggest that a higher rate of reduction in function-including widespread decreases in cis-regulatory activity-may have been an important driver of both ancient and recent human evolution.
- organism: human
- organism: mouse
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- research focus: database / resource
- approach: statistical model
- Citations
- 0
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