Cell GenomicsGenomicsPeer-reviewed journal article
Intraspecific sequence variation and complete genomes refine the identification of rapidly evolved regions in humans
Yanting LuoiD, Riley J. ManganiD, Seth WeaveriD, Sarah A ZhaoiD, Federica MostiiD, Michael C. ThomasiD, Ravi KarraiD, Debra L. SilveriD, M KellisiD, Craig B. LoweiD
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Abstract
Summary Humans exhibit significant phenotypic differences from other great apes, yet pinpointing the underlying genetic changes has been limited by incomplete reference genomes and a reliance on single assemblies to represent a species. We aligned 20 telomere-to-telomere (T2T) assemblies spanning great ape divergence and variation to define 1,596 Consensus HAQERs (consensus human ancestor quickly evolved regions), regions that diverged rapidly between the human-chimpanzee ancestor and an ancestral node of modern humans. Unlike prior HAQER sets, Consensus HAQERs incorporate population variation, reducing the likelihood of intraspecies variation appearing as interspecies divergence. Consensus HAQERs exhibit signatures of elevated mutation rates, ancient positive selection, bivalent regulatory function, are enriched in disease-linked loci, and often emerged in previously inaccessible repetitive DNA. Through multiplex, single-cell enhancer assays, we identify HAQERs as active enhancers in the developing brain and cardiomyocytes, highlighting their potential contributions to human-specific gene regulation across multiple tissues. Highlights[bullet] Telomere-to-telomere alignments of diverse human and great ape genomes identify 1,596 Consensus HAQERs, regions of rapid sequence divergence separating human ancestors from other great apes. [bullet]Consensus HAQERs exhibit signatures of both elevated mutation rates and ancient positive selection. [bullet]HAQERs are enriched in bivalent regulatory elements and disease-linked loci. [bullet]Multiplex, single-cell gene regulatory assays identify HAQERs as enhancers in the developing heart and brain.
- organism: human
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
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