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Predicting tissue-specific gene expression from whole blood transcriptome

  • Mahashweta Basu
  • , Kun Wang
  • , Eytan Ruppin*
  • , Sridhar Hannenhalli*
  • *Corresponding author for this work
  • University of Maryland, Baltimore
  • National Institutes of Health

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

Complex diseases are mediated via transcriptional dysregulation in multiple tissues. Thus, knowing an individual’s tissue-specific gene expression can provide critical information about her health. Unfortunately, for most tissues, the transcriptome cannot be obtained without invasive procedures. Could we, however, infer an individual’s tissue-specific expression from her whole blood transcriptome? Here, we rigorously address this question. We find that an individual’s whole blood transcriptome can significantly predict tissue-specific expression levels for ~60% of the genes on average across 32 tissues, with up to 81% of the genes in skeletal muscle. The tissue-specific expression inferred from the blood transcriptome is almost as good as the actual measured tissue expression in predicting disease state for six different complex disorders, including hypertension and type 2 diabetes, substantially surpassing the blood transcriptome. The code for tissue-specific gene expression prediction, TEEBoT, is provided, enabling others to study its potential translational value in other indications.

Original languageEnglish
Article numbereabd6991
JournalScience advances
Volume7
Issue number14
DOIs
StatePublished - Mar 2021
Externally publishedYes

Funding

FundersFunder number
National Cancer InstituteZIABC011802

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

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