Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits

BMC Medical Genomics - Tập 12 - Trang 1-16 - 2019
Benjamin S. Glicksberg1,2,3, Letizia Amadori1,4, Nicholas K. Akers1, Katyayani Sukhavasi5, Oscar Franzén1,6,7, Li Li1,2, Gillian M. Belbin1,8, Kristin L. Akers1,9, Khader Shameer1,2, Marcus A. Badgeley1,2, Kipp W. Johnson1,2, Ben Readhead1,2, Bruce J. Darrow4, Eimear E. Kenny8,10, Christer Betsholtz11, Raili Ermel12, Josefin Skogsberg13, Arno Ruusalepp6,11, Eric E. Schadt1,2,6,9, Joel T. Dudley1,2,14, Hongxia Ren15, Jason C. Kovacic4, Chiara Giannarelli1,4, Shuyu D. Li1,9, Johan L. M. Björkegren1,5,6,13, Rong Chen1,9
1Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
2The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, USA
3Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, USA
4Cardiovascular Research Center and Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, USA
5Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Tartu, Estonia
6Clinical Gene Networks AB, Stockholm, Sweden
7Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, NOVUM, Huddinge, Sweden
8Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
9Sema4, a Mount Sinai Venture, Stamford, USA
10Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
11Department of Immunology, Genetics and Pathology, Uppsala, Sweden
12Department of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia
13Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset Huddinge, Stockholm, Sweden
14Department of Health Policy and Research, Icahn School of Medicine at Mount Sinai, New York, USA
15Department of Pediatrics, Herman B Wells Center for Pediatric Research, Center for Diabetes and Metabolic Diseases, Stark Neurosciences Research Institute, Indiana University, Indianapolis, USA

Tóm tắt

Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene. We identified LoFs in 433 genes significantly associated with at least one of 10 major CVD traits. Next, we used RNA-sequence data from the STARNET study to validate 115 of the 433 LoF harboring-genes in that their expression levels were concordantly associated with corresponding CVD traits. Together with the documented hepatic lipid-lowering gene, APOC3, the expression levels of six additional liver LoF-genes were positively associated with levels of plasma lipids in STARNET. Candidate LoF-genes were subjected to gene silencing in HepG2 cells with marked overall effects on cellular LDLR, levels of triglycerides and on secreted APOB100 and PCSK9. In addition, we identified novel LoFs in DGAT2 associated with lower plasma cholesterol and glucose levels in BioMe that were also confirmed in STARNET, and showed a selective DGAT2-inhibitor in C57BL/6 mice not only significantly lowered fasting glucose levels but also affected body weight. In sum, by integrating genetic and electronic medical record data, and leveraging one of the world’s largest human RNA-sequence datasets (STARNET), we identified known and novel CVD-trait related genes that may serve as targets for CVD therapeutics and as such merit further investigation.

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