Robust inference of positive selection on regulatory sequences in the human brain.

Details

Serval ID
serval:BIB_805F3C22323C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Robust inference of positive selection on regulatory sequences in the human brain.
Journal
Science advances
Author(s)
Liu J., Robinson-Rechavi M.
ISSN
2375-2548 (Electronic)
ISSN-L
2375-2548
Publication state
Published
Issued date
11/2020
Peer-reviewed
Oui
Volume
6
Number
48
Pages
eabc9863
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
A longstanding hypothesis is that divergence between humans and chimpanzees might have been driven more by regulatory level adaptations than by protein sequence adaptations. This has especially been suggested for regulatory adaptations in the evolution of the human brain. We present a new method to detect positive selection on transcription factor binding sites on the basis of measuring predicted affinity change with a machine learning model of binding. Unlike other methods, this approach requires neither defining a priori neutral sites nor detecting accelerated evolution, thus removing major sources of bias. We scanned the signals of positive selection for CTCF binding sites in 29 human and 11 mouse tissues or cell types. We found that human brain-related cell types have the highest proportion of positive selection. This result is consistent with the view that adaptive evolution to gene regulation has played an important role in evolution of the human brain.
Pubmed
Open Access
Yes
Create date
07/12/2020 15:09
Last modification date
16/12/2020 6:26
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