Thomas R Austin1,2, Caitlin P McHugh3, Jennifer A Brody4,5, Joshua C Bis4,5, Colleen M Sitlani4,5, Traci M Bartz4,5,6, Mary L Biggs4,6, Nisha Bansal7, Petra Buzkova6, Steven A Carr8, Christopher R deFilippi9, Mitchell S V Elkind10, Howard A Fink11, James S Floyd4,12,5, Alison E Fohner4,12,13, Robert E Gerszten14, Susan R Heckbert4,12, Daniel H Katz14, Jorge R Kizer15,16,17,18, Rozenn N Lemaitre4,5, W T Longstreth12,19, Barbara McKnight5, Hao Mei20, Kenneth J Mukamal21, Anne B Newman22, Debby Ngo21, Michelle C Odden23, Ramachandran S Vasan24,25, Ali Shojaie6, Noah Simon6, George Davey Smith26, Neil M Davies26,27,28, David S Siscovick29, Nona Sotoodehnia4,30, Russell P Tracy31, Kerri L Wiggins4,5, Jie Zheng26, Bruce M Psaty4,12,5,32. 1. Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA. austintr@uw.edu. 2. Department of Epidemiology, University of Washington, Seattle, WA, USA. austintr@uw.edu. 3. Alzheimer's Disease Data Initiative, Kirkland, WA, USA. 4. Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA. 5. Department of Medicine, University of Washington, Seattle, WA, USA. 6. Department of Biostatistics, University of Washington, Seattle, WA, USA. 7. Division of Nephrology, University of Washington, Seattle, WA, USA. 8. Broad Institute of MIT and Harvard, Boston, MA, USA. 9. Inova Heart and Vascular Institute, Falls Church, VA, USA. 10. Department of Neurology, Columbia University, New York, NY, USA. 11. Geriatric Research Education & Clinical Center, Minneapolis VA Healthcare System, Minneapolis, MN, USA. 12. Department of Epidemiology, University of Washington, Seattle, WA, USA. 13. Institute of Public Health Genetics, University of Washington, Seattle, WA, USA. 14. Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. 15. Cardiology Section, San Francisco VA Health Care System, San Francisco, CA, USA. 16. Department of Biostatistics, University of California San Francisco, San Francisco, CA, USA. 17. Department of Epidemology, University of California San Francisco, San Francisco, CA, USA. 18. Department of Medicine, University of California San Francisco, San Francisco, CA, USA. 19. Department of Neurology, University of Washington, Seattle, WA, USA. 20. Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA. 21. Beth Israel Deaconess Medical Center, Boston, MA, USA. 22. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA. 23. Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA. 24. Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA. 25. Department of Medicine, Boston University School of Medicine, Boston, MA, USA. 26. MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK. 27. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norwegian, Norway. 28. Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK. 29. The New York Academy of Medicine, New York, NY, USA. 30. Division of Cardiology, University of Washington, Seattle, WA, USA. 31. Departments of Pathology & Laboratory Medicine, and Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, USA. 32. Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
Abstract
BACKGROUND: In the last decade, genomic studies have identified and replicated thousands of genetic associations with measures of health and disease and contributed to the understanding of the etiology of a variety of health conditions. Proteins are key biomarkers in clinical medicine and often drug-therapy targets. Like genomics, proteomics can advance our understanding of biology. METHODS AND RESULTS: In the setting of the Cardiovascular Health Study (CHS), a cohort study of older adults, an aptamer-based method that has high sensitivity for low-abundance proteins was used to assay 4979 proteins in frozen, stored plasma from 3188 participants (61% women, mean age 74 years). CHS provides active support, including central analysis, for seven phenotype-specific working groups (WGs). Each CHS WG is led by one or two senior investigators and includes 10 to 20 early or mid-career scientists. In this setting of mentored access, the proteomic data and analytic methods are widely shared with the WGs and investigators so that they may evaluate associations between baseline levels of circulating proteins and the incidence of a variety of health outcomes in prospective cohort analyses. We describe the design of CHS, the CHS Proteomics Study, characteristics of participants, quality control measures, and structural characteristics of the data provided to CHS WGs. We additionally highlight plans for validation and replication of novel proteomic associations. CONCLUSION: The CHS Proteomics Study offers an opportunity for collaborative data sharing to improve our understanding of the etiology of a variety of health conditions in older adults.
BACKGROUND: In the last decade, genomic studies have identified and replicated thousands of genetic associations with measures of health and disease and contributed to the understanding of the etiology of a variety of health conditions. Proteins are key biomarkers in clinical medicine and often drug-therapy targets. Like genomics, proteomics can advance our understanding of biology. METHODS AND RESULTS: In the setting of the Cardiovascular Health Study (CHS), a cohort study of older adults, an aptamer-based method that has high sensitivity for low-abundance proteins was used to assay 4979 proteins in frozen, stored plasma from 3188 participants (61% women, mean age 74 years). CHS provides active support, including central analysis, for seven phenotype-specific working groups (WGs). Each CHS WG is led by one or two senior investigators and includes 10 to 20 early or mid-career scientists. In this setting of mentored access, the proteomic data and analytic methods are widely shared with the WGs and investigators so that they may evaluate associations between baseline levels of circulating proteins and the incidence of a variety of health outcomes in prospective cohort analyses. We describe the design of CHS, the CHS Proteomics Study, characteristics of participants, quality control measures, and structural characteristics of the data provided to CHS WGs. We additionally highlight plans for validation and replication of novel proteomic associations. CONCLUSION: The CHS Proteomics Study offers an opportunity for collaborative data sharing to improve our understanding of the etiology of a variety of health conditions in older adults.
Authors: Moritz F Sinner; Nathan R Tucker; Kathryn L Lunetta; Kouichi Ozaki; J Gustav Smith; Stella Trompet; Joshua C Bis; Honghuang Lin; Mina K Chung; Jonas B Nielsen; Steven A Lubitz; Bouwe P Krijthe; Jared W Magnani; Jiangchuan Ye; Michael H Gollob; Tatsuhiko Tsunoda; Martina Müller-Nurasyid; Peter Lichtner; Annette Peters; Elena Dolmatova; Michiaki Kubo; Jonathan D Smith; Bruce M Psaty; Nicholas L Smith; J Wouter Jukema; Daniel I Chasman; Christine M Albert; Yusuke Ebana; Tetsushi Furukawa; Peter W Macfarlane; Tamara B Harris; Dawood Darbar; Marcus Dörr; Anders G Holst; Jesper H Svendsen; Albert Hofman; Andre G Uitterlinden; Vilmundur Gudnason; Mitsuaki Isobe; Rainer Malik; Martin Dichgans; Jonathan Rosand; David R Van Wagoner; Emelia J Benjamin; David J Milan; Olle Melander; Susan R Heckbert; Ian Ford; Yongmei Liu; John Barnard; Morten S Olesen; Bruno H C Stricker; Toshihiro Tanaka; Stefan Kääb; Patrick T Ellinor Journal: Circulation Date: 2014-08-14 Impact factor: 29.690
Authors: R P Tracy; R N Lemaitre; B M Psaty; D G Ives; R W Evans; M Cushman; E N Meilahn; L H Kuller Journal: Arterioscler Thromb Vasc Biol Date: 1997-06 Impact factor: 8.311
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Authors: Stefano Romeo; Len A Pennacchio; Yunxin Fu; Eric Boerwinkle; Anne Tybjaerg-Hansen; Helen H Hobbs; Jonathan C Cohen Journal: Nat Genet Date: 2007-02-25 Impact factor: 38.330
Authors: Bram P Prins; Ali Abbasi; Anson Wong; Ahmad Vaez; Ilja Nolte; Nora Franceschini; Philip E Stuart; Javier Guterriez Achury; Vanisha Mistry; Jonathan P Bradfield; Ana M Valdes; Jose Bras; Aleksey Shatunov; Chen Lu; Buhm Han; Soumya Raychaudhuri; Steve Bevan; Maureen D Mayes; Lam C Tsoi; Evangelos Evangelou; Rajan P Nair; Struan F A Grant; Constantin Polychronakos; Timothy R D Radstake; David A van Heel; Melanie L Dunstan; Nicholas W Wood; Ammar Al-Chalabi; Abbas Dehghan; Hakon Hakonarson; Hugh S Markus; James T Elder; Jo Knight; Dan E Arking; Timothy D Spector; Bobby P C Koeleman; Cornelia M van Duijn; Javier Martin; Andrew P Morris; Rinse K Weersma; Cisca Wijmenga; Patricia B Munroe; John R B Perry; Jennie G Pouget; Yalda Jamshidi; Harold Snieder; Behrooz Z Alizadeh Journal: PLoS Med Date: 2016-06-21 Impact factor: 11.069
Authors: Amit V Khera; Mark Chaffin; Krishna G Aragam; Mary E Haas; Carolina Roselli; Seung Hoan Choi; Pradeep Natarajan; Eric S Lander; Steven A Lubitz; Patrick T Ellinor; Sekar Kathiresan Journal: Nat Genet Date: 2018-08-13 Impact factor: 38.330