| Autor: |
Tanika N. Kelly, May E Montasser, Alyna T. Khan, Laura M. Raffield, Carla Wilson, Elizabeth C. Oelsner, Kerri L. Wiggins, Ming-Huei Chen, Gina M. Peloso, Adolfo Correa, Andrew D. Johnson, Donna K. Arnett, Xiuqing Guo, Jai G. Broome, Daniel E. Weeks, Rebecca D. Jackson, Lucia Juarez, Stephen T. McGarvey, Pradeep Natarajan, Braxton D. Mitchell, Kent D. Taylor, Bruce M. Psaty, Santhi K Ganesh, Cathy C. Laurie, Nicola L. Hawley, Leslie S. Emery, Adrienne M. Stilp, Alanna C. Morrison, Jennifer A Smith, Charles Kooperberg, Catherine M. D’Augustine, Jan Graffelman, Paul S. de Vries, Chancellor Hohensee, Sharon L R Kardia, Patricia A Peyser, Wan-Ling Hsu, Erin J Buth, Kathleen C. Barnes, Susan R. Heckbert, Ramachandran S. Vasan, Nathan Pankratz, Karen M. Mutalik, Quenna Wong, Brian E. Cade, Jingmin Liu, Joshua C. Bis, Cecelia A. Laurie, Kari E. North, Fei Fei Wang, Mariza de Andrade, Nancy L. Heard-Costa, William Craig Johnson, L. Adrienne Cupples, Scott T. Weiss, Seyed Mehdi Nouraie, Patrick T. Ellinor, Jerome I. Rotter, Weiniu Gan, Shannon Kelly, Stephen S. Rich, Cashell E. Jaquish, Dongquan Chen, Nora Franceschini, Lisa R. Yanek, Jiwon Lee, Alexander P. Reiner, Megan L. Grove, Stella Aslibekyan, Myriam Fornage, Lawrence F Bielak, Rasika A. Mathias |
| Přispěvatelé: |
Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya. COSDA-UPC - COmpositional and Spatial Data Analysis |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
American Journal of Epidemiology
American journal of epidemiology, vol 190, iss 10 |
| Popis: |
Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948–2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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