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Prioritizing autoimmunity risk variants for functional analyses by fine-mapping mutations under natural selection

Author

Listed:
  • Vasili Pankratov

    (University of Tartu, Institute of Genomics, Centre for Genomics, Evolution and Medicine)

  • Milyausha Yunusbaeva

    (ITMO University, SCAMT Institute)

  • Sergei Ryakhovsky

    (ITMO University, SCAMT Institute)

  • Maksym Zarodniuk

    (University of Tartu, Institute of Bio- and Translational Medicine)

  • Bayazit Yunusbayev

    (University of Tartu, Institute of Genomics, Centre for Genomics, Evolution and Medicine
    ITMO University, SCAMT Institute)

Abstract

Pathogen-driven selection shaped adaptive mutations in immunity genes, including those contributing to inflammatory disorders. Functional characterization of such adaptive variants can shed light on disease biology and past adaptations. This popular idea, however, was difficult to test due to challenges in pinpointing adaptive mutations in selection footprints. In this study, using a local-tree-based approach, we show that 28% of risk loci (153/535) in 21 inflammatory disorders bear footprints of moderate and weak selection, and part of them are population specific. Weak selection footprints allow partial fine-mapping, and we show that in 19% (29/153) of the risk loci under selection, candidate disease variants are hitchhikers, and only in 39% of cases they are likely selection targets. We predict function for a subset of these selected SNPs and highlight examples of antagonistic pleiotropy. We conclude by offering disease variants under selection that can be tested functionally using infectious agents and other stressors to decipher the poorly understood link between environmental stressors and genetic risk in inflammatory conditions.

Suggested Citation

  • Vasili Pankratov & Milyausha Yunusbaeva & Sergei Ryakhovsky & Maksym Zarodniuk & Bayazit Yunusbayev, 2022. "Prioritizing autoimmunity risk variants for functional analyses by fine-mapping mutations under natural selection," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34461-9
    DOI: 10.1038/s41467-022-34461-9
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    References listed on IDEAS

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    2. Lauren Alpert Sugden & Elizabeth G. Atkinson & Annie P. Fischer & Stephen Rong & Brenna M. Henn & Sohini Ramachandran, 2018. "Localization of adaptive variants in human genomes using averaged one-dependence estimation," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    3. Jerome Kelleher & Alison M Etheridge & Gilean McVean, 2016. "Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-22, May.
    4. Aaron J Stern & Peter R Wilton & Rasmus Nielsen, 2019. "An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data," PLOS Genetics, Public Library of Science, vol. 15(9), pages 1-32, September.
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