Rxivist logo

Predicting tissue-specific gene expression from whole blood transcriptome

By Mahashweta Basu, Kun Wang, Eytan Ruppin, Sridhar Hannenhalli

Posted 11 May 2020
bioRxiv DOI: 10.1101/2020.05.10.086942

Complex diseases are systemic, largely mediated via transcriptional dysregulation in multiple tissues. Thus, knowledge of tissue-specific transcriptome in an individual can provide important information about an individual's health. Unfortunately, with a few exceptions such as blood, skin, and muscle, an individual's tissue specific transcriptome is not accessible through non-invasive means. However, due to shared genetics and regulatory programs between tissues, the transcriptome in blood may be predictive of those in other tissues, at least to some extent. Here, based on GTEx data, we address this question in a rigorous, systematic manner, for the first time. We find that an individual's whole blood gene expression and splicing profile can predict tissue-specific expression levels in a significant manner (beyond demographic variables) for many genes. On average, across 32 tissues, the expression of about 60% of the genes is predictable from blood expression in a significant manner, with a maximum of 81% of the genes for the musculoskeletal tissue. Remarkably, 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 predictors built directly from the blood transcriptome. The code for our pipeline for tissue-specific gene expression prediction-TEEBoT, is provided, enabling others to study its potential translational value in other indications. ### Competing Interest Statement The authors have declared no competing interest.

Download data

  • Downloaded 751 times
  • Download rankings, all-time:
    • Site-wide: 44,896
    • In genetics: 1,905
  • Year to date:
    • Site-wide: 106,794
  • Since beginning of last month:
    • Site-wide: 88,950

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide