Inferring selective constraint from population genomic data suggests recent regulatory turnover in the human brain

TL;DR

This study uses human population genomic variation data and machine learning to identify human-specific regulatory elements, revealing recent regulatory turnover in the human brain that is not detectable by traditional conservation-based methods.

Contribution

It introduces a supervised machine learning approach leveraging allele frequency data to detect human-specific purifying selection, uncovering novel regulatory elements linked to brain development.

Findings

Identifies known and novel human-specific regulatory regions.

Shows regulatory turnover plays a role in human brain evolution.

Most of the genome is currently unconstrained by natural selection.

Abstract

The comparative genomics revolution of the past decade has enabled the discovery of functional elements in the human genome via sequence comparison. While that is so, an important class of elements, those specific to humans, is entirely missed by searching for sequence conservation across species. Here we present an analysis based on variation data among human genomes that utilizes a supervised machine learning approach for the identification of human specific purifying selection in the genome. Using only allele frequency information from the complete low coverage 1000 Genomes Project dataset in conjunction with a support vector machine trained from known functional and non-functional portions of the genome, we are able to accurately identify portions of the genome constrained by purifying selection. Our method identifies previously known human-specific gains or losses of function and uncovers many novel candidates. Candidate targets for gain and loss of function along the human lineage include numerous putative regulatory regions of genes essential for normal development of the central nervous system, including a significant enrichment of gain of function events near neurotransmitter receptor genes. These results are consistent with regulatory turnover being a key mechanism in the evolution of human-specific characteristics of brain development. Finally, we show that the majority of the genome is unconstrained by natural selection currently, in agreement with what has been estimated from phylogenetic methods but in sharp contrast to estimates based on transcriptomics or other high-throughput functional methods.