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Genetic mapping of cell type specificity for complex traits

Author

Listed:
  • Kyoko Watanabe

    (VU University Amsterdam)

  • Maša Umićević Mirkov

    (VU University Amsterdam)

  • Christiaan A. Leeuw

    (VU University Amsterdam)

  • Martijn P. Heuvel

    (VU University Amsterdam
    VU Medical Center)

  • Danielle Posthuma

    (VU University Amsterdam
    VU Medical Center)

Abstract

Single-cell RNA sequencing (scRNA-seq) data allows to create cell type specific transcriptome profiles. Such profiles can be aligned with genome-wide association studies (GWASs) to implicate cell type specificity of the traits. Current methods typically rely only on a small subset of available scRNA-seq datasets, and integrating multiple datasets is hampered by complex batch effects. Here we collated 43 publicly available scRNA-seq datasets. We propose a 3-step workflow with conditional analyses within and between datasets, circumventing batch effects, to uncover associations of traits with cell types. Applying this method to 26 traits, we identify independent associations of multiple cell types. These results lead to starting points for follow-up functional studies aimed at gaining a mechanistic understanding of these traits. The proposed framework as well as the curated scRNA-seq datasets are made available via an online platform, FUMA, to facilitate rapid evaluation of cell type specificity by other researchers.

Suggested Citation

  • Kyoko Watanabe & Maša Umićević Mirkov & Christiaan A. Leeuw & Martijn P. Heuvel & Danielle Posthuma, 2019. "Genetic mapping of cell type specificity for complex traits," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11181-1
    DOI: 10.1038/s41467-019-11181-1
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    Cited by:

    1. Andras Gezsi & Sandra Auwera & Hannu Mäkinen & Nora Eszlari & Gabor Hullam & Tamas Nagy & Sarah Bonk & Rubèn González-Colom & Xenia Gonda & Linda Garvert & Teemu Paajanen & Zsofia Gal & Kevin Kirchner, 2024. "Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. Danni A. Gadd & Robert F. Hillary & Daniel L. McCartney & Liu Shi & Aleks Stolicyn & Neil A. Robertson & Rosie M. Walker & Robert I. McGeachan & Archie Campbell & Shen Xueyi & Miruna C. Barbu & Claire, 2022. "Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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