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

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.

Adaptive eQTLs reveal the evolutionary impacts of pleiotropy and tissue-specificity while contributing to health and disease

“… 34 Although many eQTLs are likely to be evolving neutrally, there is a growing body of empirical evidence that regulatory DNA is an important target of selection in humans. 35 , 36 , 37 , 38 …”

Impact of Genetic Variation in Gene Regulatory Sequences: A Population Genomics Perspective

“…This score reflects the decrease of substitutions in an inter-species sequence alignment compared to the neutral expectation. Liu and Robinson-Rechavi (2020) proposed a new method to infer the action of selective forces on TFBSs. This method employs Support-Vector Machines, a machine learning approach, to infer the changes in the binding affinity of the TFBS due to variants and does not necessitate a prior definition of "neutral sites."…”

“…However, changes in the non-constraint regions could also have functional consequences (Ludwig et al, 2000;Dermitzakis and Clark, 2002). Estimating the proportion of genetic variants, within CREs, that contribute to adaptive evolution is challenging, mainly due to a lack of a robust model of neutral versus adaptive evolution, specifically for regulatory regions (Liu and Robinson-Rechavi, 2020).…”

The Cancer Genome: Paradigm or Paradox?

“…Hence, with an only 1% difference in the protein-coding genome between the two species [29], a human may be similar enough to a chimpanzee when it concerns their genotypes, but they are certainly very dissimilar when we consider their phenotypes ( Figure 1). Conversely, even in identical twins DNA may not be identical.…”