Ozlem Senol-Cosar1,2, Ryan J Schmidt3, Emily Qian4, Derick Hoskinson1, Heather Mason-Suares1,2, Birgit Funke5,6,7, Matthew S Lebo8,9. 1. Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA. 2. Department of Pathology, Harvard Medical School/Brigham and Women's Hospital, Boston, MA, USA. 3. Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Keck School of Medicine of USC, Los Angeles, CA, USA. 4. Veritas Genetics, Cambridge, MA, USA. 5. Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA. bfunke@veritasgenetics.com. 6. Veritas Genetics, Cambridge, MA, USA. bfunke@veritasgenetics.com. 7. Department of Pathology, Harvard Medical School/Massachusetts General Hospital, Boston, MA, USA. bfunke@veritasgenetics.com. 8. Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA. mlebo@bwh.harvard.edu. 9. Department of Pathology, Harvard Medical School/Brigham and Women's Hospital, Boston, MA, USA. mlebo@bwh.harvard.edu.
Abstract
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.
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.
Authors: Xi Luo; Simone Feurstein; Shruthi Mohan; Christopher C Porter; Sarah A Jackson; Sioban Keel; Michael Chicka; Anna L Brown; Chimene Kesserwan; Anupriya Agarwal; Minjie Luo; Zejuan Li; Justyne E Ross; Panagiotis Baliakas; Daniel Pineda-Alvarez; Courtney D DiNardo; Alison A Bertuch; Nikita Mehta; Tom Vulliamy; Ying Wang; Kim E Nichols; Luca Malcovati; Michael F Walsh; Lesley H Rawlings; Shannon K McWeeney; Jean Soulier; Anna Raimbault; Mark J Routbort; Liying Zhang; Gabriella Ryan; Nancy A Speck; Sharon E Plon; David Wu; Lucy A Godley Journal: Blood Adv Date: 2019-10-22
Authors: Jason L Vassy; Matthew S Lebo; Limin Hao; Peter Kraft; Gabriel F Berriz; Elizabeth D Hynes; Christopher Koch; Prathik Korategere V Kumar; Shruti S Parpattedar; Marcie Steeves; Wanfeng Yu; Ashley A Antwi; Charles A Brunette; Morgan Danowski; Manish K Gala; Robert C Green; Natalie E Jones; Anna C F Lewis; Steven A Lubitz; Pradeep Natarajan Journal: Nat Med Date: 2022-04-18 Impact factor: 87.241
Authors: Megan H Hawley; Naif Almontashiri; Leslie G Biesecker; Natalie Berger; Wendy K Chung; John Garcia; Theresa A Grebe; Melissa A Kelly; Matthew S Lebo; Daniela Macaya; Hui Mei; Julia Platt; Gabi Richard; Ashley Ryan; Kate L Thomson; Matteo Vatta; Roddy Walsh; James S Ware; Matthew Wheeler; Hana Zouk; Heather Mason-Suares; Birgit Funke Journal: Hum Mutat Date: 2020-06-24 Impact factor: 4.700