Literature DB >> 28899996

Prediction and Subtyping of Hypertension from Pan-Tissue Transcriptomic and Genetic Analyses.

Mahashweta Basu1, Mahfuza Sharmin1, Avinash Das1, Nishanth Ulhas Nair1, Kun Wang1, Joo Sang Lee1, Yen-Pei Christy Chang2, Eytan Ruppin3, Sridhar Hannenhalli3.   

Abstract

Hypertension (HT) is a complex systemic disease involving transcriptional changes in multiple organs. Here we systematically investigate the pan-tissue transcriptional and genetic landscape of HT spanning dozens of tissues in hundreds of individuals. We find that in several tissues, previously identified HT-linked genes are dysregulated and the gene expression profile is predictive of HT. Importantly, many expression quantitative trait loci (eQTL) SNPs associated with the population variance of the dysregulated genes are linked with blood pressure in an independent genome-wide association study, suggesting that the functional effect of HT-associated SNPs may be mediated through tissue-specific transcriptional dysregulation. Analyses of pan-tissue transcriptional dysregulation profile, as well as eQTL SNPs underlying the dysregulated genes, reveals substantial heterogeneity among the HT patients, revealing two broad groupings - a Diffused group where several tissues exhibit HT-associated molecular alterations and a Localized group where such alterations are localized to very few tissues. These two patient subgroups differ in several clinical phenotypes including respiratory, cerebrovascular, diabetes, and heart disease. These findings suggest that the Diffused and Localized subgroups may be driven by different molecular mechanisms and have different genetic underpinning.
Copyright © 2017 by the Genetics Society of America.

Entities:  

Keywords:  GTEx; complex disease; differential expression; eQTL; hypertension; pan-tissue; systemic disease

Mesh:

Year:  2017        PMID: 28899996      PMCID: PMC5676229          DOI: 10.1534/genetics.117.300280

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


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Review 1.  Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension.

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