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Considerations for clinical curation, classification, and reporting of low-penetrance and low effect size variants associated with disease risk.
Senol-Cosar, Ozlem; Schmidt, Ryan J; Qian, Emily; Hoskinson, Derick; Mason-Suares, Heather; Funke, Birgit; Lebo, Matthew S.
Afiliación
  • Senol-Cosar O; Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA.
  • Schmidt RJ; Department of Pathology, Harvard Medical School/Brigham and Women's Hospital, Boston, MA, USA.
  • Qian E; Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Keck School of Medicine of USC, Los Angeles, CA, USA.
  • Hoskinson D; Veritas Genetics, Cambridge, MA, USA.
  • Mason-Suares H; Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA.
  • Funke B; Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA.
  • Lebo MS; Department of Pathology, Harvard Medical School/Brigham and Women's Hospital, Boston, MA, USA.
Genet Med ; 21(12): 2765-2773, 2019 12.
Article en En | MEDLINE | ID: mdl-31147632
PURPOSE: Clinically relevant variants exhibit a wide range of penetrance. Medical practice has traditionally focused on highly penetrant variants with large effect sizes and, consequently, classification and clinical reporting frameworks are tailored to that variant type. At the other end of the penetrance spectrum, where variants are often referred to as "risk alleles," traditional frameworks are no longer appropriate. This has led to inconsistency in how such variants are interpreted and classified. Here, we describe a conceptual framework to begin addressing this gap. METHODS: We used a set of risk alleles to define data elements that can characterize the validity of reported disease associations. We assigned weight to these data elements and established classification categories expressing confidence levels. This framework was then expanded to develop criteria for inclusion of risk alleles on clinical reports. RESULTS: Foundational data elements include cohort size, quality of phenotyping, statistical significance, and replication of results. Criteria for determining inclusion of risk alleles on clinical reports include presence of clinical management guidelines, effect size, severity of the associated phenotype, and effectiveness of intervention. CONCLUSION: This framework represents an approach for classifying risk alleles and can serve as a foundation to catalyze community efforts for refinement.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Medición de Riesgo / Susceptibilidad a Enfermedades / Curaduría de Datos Tipo de estudio: Etiology_studies / Guideline / Risk_factors_studies Límite: Humans Idioma: En Revista: Genet Med Asunto de la revista: GENETICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Medición de Riesgo / Susceptibilidad a Enfermedades / Curaduría de Datos Tipo de estudio: Etiology_studies / Guideline / Risk_factors_studies Límite: Humans Idioma: En Revista: Genet Med Asunto de la revista: GENETICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos