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Decoding the Genomics of Abdominal Aortic Aneurysm
- 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)
- Subjects :
- 0301 basic medicine
Genomics
Computational biology
Disease
030204 cardiovascular system & hematology
Biology
Polymorphism, Single Nucleotide
Genome
General Biochemistry, Genetics and Molecular Biology
Machine Learning
Mice
03 medical and health sciences
0302 clinical medicine
Electronic health record
medicine
Animals
Humans
Gene Regulatory Networks
Protein Interaction Maps
Whole Genome Sequencing
Health management system
Precision medicine
medicine.disease
Abdominal aortic aneurysm
Disease Models, Animal
030104 developmental biology
Gene Expression Regulation
ROC Curve
Area Under Curve
Aortic Aneurysm, Abdominal
Genome-Wide Association Study
Personal genomics
Subjects
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