bioRxivEvolutionary Biology
Adaptive variation in human toll-like receptors is contributed by introgression from both Neandertals and Denisovans
Abstract
Pathogens and the diseases they cause have been among the most important selective forces experienced by humans during their evolutionary history. Although adaptive alleles generally arise by mutation, introgression can also be a valuable source of beneficial alleles. Archaic humans, who lived in Europe and Western Asia for over 200,000 years, were likely well-adapted to the environment and its local pathogens, and it is therefore conceivable that modern humans entering Europe and Western Asia who admixed with them obtained a substantial immune advantage from the introgression of archaic alleles.\n\nHere we document a cluster of three toll-like receptors (TLR6-TLR1-TLR10) in modern humans that carries three distinct archaic haplotypes, indicating repeated introgression from archaic humans. Two of these haplotypes are most similar to Neandertal genome, while the third haplotype is most similar to the Denisovan genome. The toll-like receptors are key components of innate immunity and provide an important first line of immune defense against bacteria, fungi and parasites. The unusually high allele frequencies and unexpected levels of population differentiation indicate that there has been local positive selection on multiple haplotypes at this locus. We show that the introgressed alleles have clear functional effects in modern humans; archaic-like alleles underlie differences in the expression of the TLR genes and are associated with reduced microbial resistance and increased allergic disease in large cohorts. This provides strong evidence for recurrent adaptive introgression at the TLR6-TLR1-TLR10 locus, resulting in differences in disease phenotypes in modern humans.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- approach: genetic perturbation / CRISPR
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