The Impact of Recessive Deleterious Variation on Signals of Adaptive Introgression in Human Populations
Open accessPublished version of a preprint
Abstract
Admixture with archaic hominins has altered the landscape of genomic variation in modern human populations. Several gene regions have been identified previously as candidates of adaptive introgression (AI) that facilitated human adaptation to specific environments. However, simulation-based studies have suggested that population genetic processes other than adaptive mutations, such as heterosis from recessive deleterious variants private to populations before admixture, can also lead to patterns in genomic data that resemble AI. The extent to which the presence of deleterious variants affect the false-positive rate and the power of current methods to detect AI has not been fully assessed. Here, we used extensive simulations under parameters relevant for human evolution to show that recessive deleterious mutations can increase the false positive rates of tests for AI compared to models without deleterious variants, especially when the recombination rates are low. We next examined candidates of AI in modern humans identified from previous studies, and show that 24 out of 26 candidate regions remain significant, even when deleterious variants are included in the null model. However, two AI candidate genes, HYAL2 and HLA, are particularly susceptible to high false positive signals of AI due to recessive deleterious mutations. These genes are located in regions of the human genome with high exon density together with low recombination rate, factors that we show increase the rate of false-positives due to recessive deleterious mutations. Although the combination of such parameters is rare in the human genome, caution is warranted in such regions, as well as in other species with more compact genomes and/or lower recombination rates. In sum, our results suggest that recessive deleterious mutations cannot account for the signals of AI in most, but not all, of the top candidates for AI in humans, suggesting they may be genuine signals of adaptation.
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References 83 · Cited by 35
- Authors
- Zhang, X., Kim, B., Lohmueller, K. E., & Huerta-Sánchez, E.
- Affiliations
- University of California, Los Angeles (US)
- Stanford University (US)
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 02912
- Corresponding author
- Lohmueller, Kirk E
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-07-01
- Volume
- 215
- Issue
- 3
- Pages
- 799-812
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- PMC
- PMC7337073
- OpenAlex
- W3032299993
- Cited by
- 35
- References
- 83
- Citations by year
- 2025: 6 · 2024: 5 · 2023: 5 · 2022: 5 · 2021: 13 · 2020: 1
- FWCI
- 22.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NIH (1R35GM128946-01)
- Topics
- Social Sciences › Anthropology › Pleistocene-Era Hominins and Archaeology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- adaptive introgression, recessive deleterious mutations, heterosis, false positive rate, recombination rate, HYAL2, HLA, human adaptation
- MeSH terms
- Genetic Introgression, Adaptation, Physiological, Computer Simulation, Genes, Recessive, HLA Antigens, Humans, Hyaluronoglucosaminidase, Models, Genetic, Mutation, Population, Cell Adhesion Molecules, Genome, Human, Evolution, Molecular, GPI-Linked Proteins
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