Summary: A new method has identified an extensive set of gene regulatory regions in the human brain that show evidence of positive selection during our evolution.
Source: Swiss Institute of Bioinformatics
With only about a 1% difference in protein-coding DNA, the human and chimpanzee genomes are remarkably similar. Discovering which biological changes underlie what makes us human is a central, often debated question in evolutionary biology. Researchers at the SIB Swiss Institute of Bioinformatics and the University of Lausanne have developed a new approach to identify, for the first time at scale, adaptive human-specific changes in gene regulation in the brain.
These findings open new avenues for research into human evolution, brain development, and neuroscience by highlighting regulatory elements that likely contributed to the evolution of our cognitive traits.
The study is published in Science Advances.
Gene expression changes rather than protein sequence changes
For decades, scientists have proposed that differences between humans and other great apes may arise more from changes in gene regulation—when, where, and how strongly genes are expressed—than from changes to protein sequences themselves. Identifying the precise regulatory elements that act as “gene dimmers” and that have been subject to positive selection has been difficult because these elements are short and statistically challenging to analyze.
Marc Robinson-Rechavi, Group Leader at SIB and co-author of the study, explains: “To address these compelling evolutionary questions we need precise methods to identify genomic regions that experienced positive selection. Such discoveries not only inform evolutionary theory but also improve our mechanistic understanding of gene function, with potential long-term biomedical relevance.”
High proportion of brain regulatory elements under positive selection
Using a novel computational framework, the research team identified a large collection of regulatory regions in the human brain that show signatures of positive selection across human evolution. The approach combines machine learning models of transcription factor binding with experimental binding data, enabling robust inference of functional changes in regulatory sequences.
Jialin Liu, postdoctoral researcher and lead author, says: “We demonstrate that regulatory regions active in the human brain show a particularly high level of positive selection compared with other tissues such as the stomach or heart. That is important, because it gives us a way to pinpoint genomic regions that may have contributed to the evolution of human cognitive abilities.”
The study examined transcription factor binding sites across multiple tissues using machine learning models to predict how sequence changes alter binding affinity. By comparing these predicted functional changes among human, chimpanzee, and gorilla sequences, the team was able to infer where adaptive changes in regulation occurred specifically along the human lineage.
“We now have a catalog of positively selected regulatory regions that control gene expression in the human brain,” adds Robinson-Rechavi. “As we link these regions to the genes they regulate, our understanding of cognition and brain evolution becomes clearer and opens new possibilities for follow-up research.”
Box – Positive selection: why accelerated change matters
Most genetic mutations are neutral and accumulate at a steady rate, reflecting the time since two species last shared a common ancestor. However, an accelerated rate of change in a specific genomic region can indicate positive selection: a mutation that provided a fitness advantage became more frequent in the population and spread across generations. Regulatory elements are often short—only a few nucleotides long—which makes estimating their rate of change and detecting selection statistically challenging. The new method addresses these challenges by focusing on predicted changes in binding affinity rather than relying solely on acceleration metrics or predefined neutral sites.
Source: Swiss Institute of Bioinformatics
Contact: Press Office – Swiss Institute of Bioinformatics
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Original Research: Closed access. “Robust inference of positive selection on regulatory sequences in the human brain” by Jialin Liu and Marc Robinson-Rechavi. Science Advances
Abstract
Robust inference of positive selection on regulatory sequences in the human brain
A longstanding hypothesis is that divergence between humans and chimpanzees might have been driven more by regulatory-level adaptations than by protein sequence adaptations, particularly in the evolution of the human brain. The authors present a new method to detect positive selection on transcription factor binding sites by measuring predicted affinity changes using a machine learning model of binding. Unlike earlier approaches, this method does not require defining neutral sites in advance nor detecting accelerated evolution, thereby reducing major sources of bias. Scanning CTCF binding sites across 29 human and 11 mouse tissues or cell types, the analysis reveals that human brain–related cell types have the highest proportion of positive selection. These results support the view that adaptive evolution of gene regulation has played an important role in the evolution of the human brain.