Genome biologyGenomicsPeer-reviewed journal article
Admixture-enabled selection for rapid adaptive evolution in the Americas
Emily T. NorrisiD, Lavanya RishishwariD, Aroon T. ChandeiD, Andrew B. ConleyiD, Kaixiong YeiD, Augusto Valderrama-AguirreiD, I. King JordaniD
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Abstract
BackgroundAdmixture occurs when previously isolated populations come together and exchange genetic material. We hypothesized that admixture can enable rapid adaptive evolution in human populations by introducing novel genetic variants (haplotypes) at intermediate frequencies, and we tested this hypothesis via the analysis of whole genome sequences sampled from admixed Latin American populations in Colombia, Mexico, Peru, and Puerto Rico.\n\nResultsOur screen for admixture-enabled selection relies on the identification of loci that contain more or less ancestry from a given source population than would be expected given the genome-wide ancestry frequencies. We employed a combined evidence approach to evaluate levels of ancestry enrichment at (1) single loci across multiple populations and (2) multiple loci that function together to encode polygenic traits. We found cross-population signals of African ancestry enrichment at the major histocompatibility locus on chromosome 6, consistent with admixture-enabled selection for enhanced adaptive immune response. Several of the human leukocyte antigen genes at this locus (HLA-A, HLA-DRB51 and HLA-DRB5) showed independent evidence of positive selection prior to admixture, based on extended haplotype homozygosity in African populations. A number of traits related to inflammation, blood metabolites, and both the innate and adaptive immune system showed evidence of admixture-enabled polygenic selection in Latin American populations.\n\nConclusionsThe results reported here, considered together with the ubiquity of admixture in human evolution, suggest that admixture serves as a fundamental mechanism that drives rapid adaptive evolution in human populations.
- organism: human
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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