Understanding Barriers to Data Sharing in ClinVar: A Qualitative Study
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Authors
Gaonkar, Soham H
Sanders, Lauren
Issue Date
2025-05-01
Type
thesis_open
Language
Keywords
ClinVar , ClinVar submissions , data sharing , variant data , data sharing barriers , variant classification , Genetics
Health Information Technology
Laboratory Medicine
Medical Genetics
Alternative Title
Human Genetics Theses
Abstract
Genetic variant interpretation and classification plays a critical role in genomic medicine, yet challenges persist that affect the amount and type of shared variant data used to refine and improve variant classification. Public databases, such as ClinVar, facilitate variant data sharing and collaboration between researchers, commercial laboratories, and clinical laboratories. However, significant inconsistencies remain among ClinVar submissions, contributing to discordant classifications with potential implications for patient care. This study explores the factors influencing ClinVar data submission practices among the top ClinVar-contributing laboratories through qualitative interviews with 18 laboratory personnel, including variant curators, genetic counselors, and laboratory directors. Participants identified significant barriers to the timeliness, quantity, and quality of ClinVar submissions, such as difficulties with the ClinVar submission template, quality control barriers, bioinformatic resource constraints, and patient privacy concerns. Variability in laboratory policies, lack of standardization in ClinVar guidelines, and institutional privacy protections also impede broad variant data sharing. While some laboratories use automated bioinformatic pipelines to streamline submissions, resource disparities among laboratories lead to inconsistencies in data quality and sufficiency. Addressing these systemic barriers is essential for improving ClinVar’s effectiveness as a reliable resource for variant classification. Our findings suggest that enhanced automation, clearer submission guidelines and templates, and standardized privacy policies could improve data-sharing practices, ultimately strengthening the accuracy, quantity and clinical utility of submissions to ClinVar.
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