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.

Animal morphological diversity is shaped both by adaptation and by developmental constraints. Here we have tested Darwin's "selection opportunity" hypothesis, according to which high evolutionary divergence in late development is due to strong positive selection. We contrasted it to a "developmental constraint" hypothesis, according to which late development is under relaxed negative selection. Indeed, the highest divergence, both at the morphological and molecular levels, is observed late there is a consistent signal that positive selection mainly affects genes and pathways expressed in late embryonic development and in adult. Our results imply that the evolution of embryogenesis is mostly conservative, with most adaptive evolution affecting post-embryonic gene expression, and thus post-embryonic phenotypes. This is consistent with the diversity of environmental challenges to which juveniles and adults are exposed.

A long standing hypothesis is that divergence between humans and chimpanzees might have been driven more by regulatory level adaptions than by protein sequence adaptations. This has especially been suggested for regulatory adaptions in the evolution of the human brain. There is some support for this hypothesis, but it has been limited by the lack of a reliable and powerful way to detect positive selection on regulatory sequences. We present a new method to detect positive selection on transcription factor binding sites, based on Orr's sign test applied to a machine learning model of binding. Unlike other methods, this requires neither defining a priori neutral sites, nor detecting accelerated evolution, thus removing major sources of bias.The method is validated in flies, mice, and primates, by a McDonald-Kreitman-like measure of polymorphism vs. divergence, by experimental binding site gains and losses, and by changes in expression levels. 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 is consistent with the importance of adaptive evolution on gene regulation in the evolution of the human brain.