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Decoding the Genomics of Abdominal Aortic Aneurysm

Authors :
Sai Zhang
Joshua M. Spin
Philip S. Tsao
Michael Snyder
Jingjing Li
Lawrence L.K. Leung
Ronald L. Dalman
Cuiping Pan
Alicia Deng
Source :
Cell. 174:1361-1372.e10
Publication Year :
2018
Publisher :
Elsevier BV, 2018.

Abstract

Summary A key aspect of genomic medicine is to make individualized clinical decisions from personal genomes. We developed a machine-learning framework to integrate personal genomes and electronic health record (EHR) data and used this framework to study abdominal aortic aneurysm (AAA), a prevalent irreversible cardiovascular disease with unclear etiology. Performing whole-genome sequencing on AAA patients and controls, we demonstrated its predictive precision solely from personal genomes. By modeling personal genomes with EHRs, this framework quantitatively assessed the effectiveness of adjusting personal lifestyles given personal genome baselines, demonstrating its utility as a personal health management tool. We showed that this new framework agnostically identified genetic components involved in AAA, which were subsequently validated in human aortic tissues and in murine models. Our study presents a new framework for disease genome analysis, which can be used for both health management and understanding the biological architecture of complex diseases. Video Abstract Download : Download video (25MB)

Details

ISSN :
00928674
Volume :
174
Database :
OpenAIRE
Journal :
Cell
Accession number :
edsair.doi.dedup.....90d7ed883c4effc5c798063aec4dc8e0
Full Text :
https://doi.org/10.1016/j.cell.2018.07.021