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Journal article
Clinical Genetics Lacks Standard Definitions and Protocols for the Collection and Use of Diversity Measures
American journal of human genetics, v 107(1), pp 72-82
02 Jul 2020
PMID: 32504544
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Genetics researchers and clinical professionals rely on diversity measures such as race, ethnicity, and ancestry (REA) to stratify study participants and patients for a variety of applications in research and precision medicine. However, there are no comprehensive, widely accepted standards or guidelines for collecting and using such data in clinical genetics practice. Two NIH-funded research consortia, the Clinical Genome Resource (ClinGen) and Clinical Sequencing Evidence-generating Research (CSER), have partnered to address this issue and report how REA are currently collected, conceptualized, and used. Surveying clinical genetics professionals and researchers (n = 448), we found heterogeneity in the way REA are perceived, defined, and measured, with variation in the perceived importance of REA in both clinical and research settings. The majority of respondents (>55%) felt that REA are at least somewhat important for clinical variant interpretation, ordering genetic tests, and communicating results to patients. However, there was no consensus on the relevance of REA, including how each of these measures should be used in different scenarios and what information they can convey in the context of human genetics. A lack of common definitions and applications of REA across the precision medicine pipeline may contribute to inconsistencies in data collection, missing or inaccurate classifications, and misleading or inconclusive results. Thus, our findings support the need for standardization and harmonization of REA data collection and use in clinical genetics and precision health research.
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Details
- Title
- Clinical Genetics Lacks Standard Definitions and Protocols for the Collection and Use of Diversity Measures
- Creators
- Alice B. Popejoy - Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USAKristy R. Crooks - University of Colorado DenverStephanie M. Fullerton - University of WashingtonLucia A. Hindorff - National Human Genome Research InstituteGillian W. Hooker - Concert PharmaceuticalsBarbara A. Koenig - University of California SystemNatalie Pino - National Human Genome Research InstituteErin M. Ramos - National Human Genome Research InstituteDeborah I. Ritter - Baylor College of MedicineHannah Wand - Stanford Health CareMatt W. Wright - Stanford University School of MedicineMichael Yudell - Drexel University, Center for Science, Technology, and SocietyJames Y. Zou - Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USASharon E. Plon - Baylor College of MedicineCarlos D. Bustamante - Stanford UniversityKelly E. Ormond - Stanford University
- Publication Details
- American journal of human genetics, v 107(1), pp 72-82
- Publisher
- Elsevier
- Number of pages
- 11
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Center for Science, Technology, and Society
- Web of Science ID
- WOS:000546685300007
- Scopus ID
- 2-s2.0-85085942401
- Other Identifier
- 991021876910904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Genetics & Heredity