IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-14561-0.html
   My bibliography  Save this article

Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants

Author

Listed:
  • Margaret K. R. Donovan

    (University of California, San Diego
    University of California, San Diego)

  • Agnieszka D’Antonio-Chronowska

    (University of California, San Diego)

  • Matteo D’Antonio

    (University of California, San Diego)

  • Kelly A. Frazer

    (University of California, San Diego
    University of California, San Diego)

Abstract

The Genotype-Tissue Expression (GTEx) resource has provided insights into the regulatory impact of genetic variation on gene expression across human tissues; however, thus far has not considered how variation acts at the resolution of the different cell types. Here, using gene expression signatures obtained from mouse cell types, we deconvolute bulk RNA-seq samples from 28 GTEx tissues to quantify cellular composition, which reveals striking heterogeneity across these samples. Conducting eQTL analyses for GTEx liver and skin samples using cell composition estimates as interaction terms, we identify thousands of genetic associations that are cell-type-associated. The skin cell-type associated eQTLs colocalize with skin diseases, indicating that variants which influence gene expression in distinct skin cell types play important roles in traits and disease. Our study provides a framework to estimate the cellular composition of GTEx tissues enabling the functional characterization of human genetic variation that impacts gene expression in cell-type-specific manners.

Suggested Citation

  • Margaret K. R. Donovan & Agnieszka D’Antonio-Chronowska & Matteo D’Antonio & Kelly A. Frazer, 2020. "Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14561-0
    DOI: 10.1038/s41467-020-14561-0
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-14561-0
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-020-14561-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ryo Yamamoto & Ryan Chung & Juan Manuel Vazquez & Huanjie Sheng & Philippa L. Steinberg & Nilah M. Ioannidis & Peter H. Sudmant, 2022. "Tissue-specific impacts of aging and genetics on gene expression patterns in humans," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Jorge Vaquero-Garcia & Joseph K. Aicher & San Jewell & Matthew R. Gazzara & Caleb M. Radens & Anupama Jha & Scott S. Norton & Nicholas F. Lahens & Gregory R. Grant & Yoseph Barash, 2023. "RNA splicing analysis using heterogeneous and large RNA-seq datasets," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    3. Matteo D’Antonio & Jennifer P. Nguyen & Timothy D. Arthur & Hiroko Matsui & Agnieszka D’Antonio-Chronowska & Kelly A. Frazer, 2023. "Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    4. Xiaoyu Song & Jiayi Ji & Joseph H. Rothstein & Stacey E. Alexeeff & Lori C. Sakoda & Adriana Sistig & Ninah Achacoso & Eric Jorgenson & Alice S. Whittemore & Robert J. Klein & Laurel A. Habel & Pei Wa, 2023. "MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    5. Paul Little & Si Liu & Vasyl Zhabotynsky & Yun Li & Dan-Yu Lin & Wei Sun, 2023. "A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14561-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.