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AlMail A, Jamjoom A, Pan A, Feng MY, Chau V, D'Gama AM, Howell K, Liang NSY, McTague A, Poduri A, Wiltrout K, Bassett AS, Christodoulou J, Dupuis L, Gill P, Levy T, Siper P, Stark Z, Vorstman JAS, Diskin C, Jewitt N, Baribeau D, Costain G. Consensus reporting guidelines to address gaps in descriptions of ultra-rare genetic conditions. NPJ Genom Med 2024; 9:27. [PMID: 38582909 PMCID: PMC10998895 DOI: 10.1038/s41525-024-00408-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 02/27/2024] [Indexed: 04/08/2024] Open
Abstract
Genome-wide sequencing and genetic matchmaker services are propelling a new era of genotype-driven ascertainment of novel genetic conditions. The degree to which reported phenotype data in discovery-focused studies address informational priorities for clinicians and families is unclear. We identified reports published from 2017 to 2021 in 10 genetics journals of novel Mendelian disorders. We adjudicated the quality and detail of the phenotype data via 46 questions pertaining to six priority domains: (I) Development, cognition, and mental health; (II) Feeding and growth; (III) Medication use and treatment history; (IV) Pain, sleep, and quality of life; (V) Adulthood; and (VI) Epilepsy. For a subset of articles, all subsequent published follow-up case descriptions were identified and assessed in a similar manner. A modified Delphi approach was used to develop consensus reporting guidelines, with input from content experts across four countries. In total, 200 of 3243 screened publications met inclusion criteria. Relevant phenotypic details across each of the 6 domains were rated superficial or deficient in >87% of papers. For example, less than 10% of publications provided details regarding neuropsychiatric diagnoses and "behavioural issues", or about the type/nature of feeding problems. Follow-up reports (n = 95) rarely contributed this additional phenotype data. In summary, phenotype information relevant to clinical management, genetic counselling, and the stated priorities of patients and families is lacking for many newly described genetic diseases. The PHELIX (PHEnotype LIsting fiX) reporting guideline checklists were developed to improve phenotype reporting in the genomic era.
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Affiliation(s)
- Ali AlMail
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Program in Genetics & Genome Biology, SickKids Research Institute, Toronto, ON, Canada
| | - Ahmed Jamjoom
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
- Department of Pediatrics, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Amy Pan
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Min Yi Feng
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Vann Chau
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
- Division of Neurology, Hospital for Sick Children, Toronto, ON, Canada
| | - Alissa M D'Gama
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Katherine Howell
- Department of Neurology, Royal Children's Hospital, Melbourne, VIC, Australia
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Nicole S Y Liang
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Division of Clinical and Metabolic Genetics, Hospital for Sick Children, Toronto, ON, Canada
| | - Amy McTague
- Department of Neurology, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Annapurna Poduri
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Kimberly Wiltrout
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Anne S Bassett
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | | | - Lucie Dupuis
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Division of Clinical and Metabolic Genetics, Hospital for Sick Children, Toronto, ON, Canada
| | - Peter Gill
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | - Tess Levy
- Division of Psychiatry, Ichan School of Medicine at Mount Sinai, New York City, NY, USA
| | - Paige Siper
- Division of Psychiatry, Ichan School of Medicine at Mount Sinai, New York City, NY, USA
| | - Zornitza Stark
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
- Victorian Clinical Genetics Service, Melbourne, VIC, Australia
| | - Jacob A S Vorstman
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Hospital for Sick Children, Toronto, ON, Canada
| | - Catherine Diskin
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | - Natalie Jewitt
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | - Danielle Baribeau
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, Hospital for Sick Children, Toronto, ON, Canada.
- Autism Research Centre, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.
| | - Gregory Costain
- Program in Genetics & Genome Biology, SickKids Research Institute, Toronto, ON, Canada.
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Division of Clinical and Metabolic Genetics, Hospital for Sick Children, Toronto, ON, Canada.
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Phuong J, Riches NO, Madlock‐Brown C, Duran D, Calzoni L, Espinoza JC, Datta G, Kavuluru R, Weiskopf NG, Ward‐Caviness CK, Lin AY. Social Determinants of Health Factors for Gene-Environment COVID-19 Research: Challenges and Opportunities. ADVANCED GENETICS (HOBOKEN, N.J.) 2022; 3:2100056. [PMID: 35574521 PMCID: PMC9087427 DOI: 10.1002/ggn2.202100056] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Indexed: 01/25/2023]
Abstract
The characteristics of a person's health status are often guided by how they live, grow, learn, their genetics, as well as their access to health care. Yet, all too often, studies examining the relationship between social determinants of health (behavioral, sociocultural, and physical environmental factors), the role of demographics, and health outcomes poorly represent these relationships, leading to misinterpretations, limited study reproducibility, and datasets with limited representativeness and secondary research use capacity. This is a profound hurdle in what questions can or cannot be rigorously studied about COVID-19. In practice, gene-environment interactions studies have paved the way for including these factors into research. Similarly, our understanding of social determinants of health continues to expand with diverse data collection modalities as health systems, patients, and community health engagement aim to fill the knowledge gaps toward promoting health and wellness. Here, a conceptual framework is proposed, adapted from the population health framework, socioecological model, and causal modeling in gene-environment interaction studies to integrate the core constructs from each domain with practical considerations needed for multidisciplinary science.
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Affiliation(s)
- Jimmy Phuong
- Division of Biomedical and Health InformaticsUniversity of WashingtonSeattleWA98195USA
- Harborview Injury Prevention Research CenterUniversity of WashingtonSeattleWA98104USA
| | - Naomi O. Riches
- Department of Biomedical InformaticsUniversity of Utah School of MedicineSalt Lake CityUT84108‐3514USA
| | - Charisse Madlock‐Brown
- Health Informatics and Information ManagementUniversity of Tennessee Health Science CenterMemphisTN38163USA
| | - Deborah Duran
- National Institute on Minority Health and Health Disparities (NIMHD)National Institutes of HealthBethesdaMD20892‐5465USA
| | - Luca Calzoni
- National Institute on Minority Health and Health Disparities (NIMHD)National Institutes of HealthBethesdaMD20892‐5465USA
- Department of Biomedical InformaticsUniversity of PittsburghPittsburghPA15206USA
| | - Juan C. Espinoza
- Department of PediatricsChildren's Hospital Los AngelesLos AngelesCA90015USA
| | - Gora Datta
- Department of Civil and Environmental EngineeringUniversity of California at BerkeleyBerkeleyCA94720USA
| | - Ramakanth Kavuluru
- Division of Biomedical InformaticsDepartment of Internal MedicineUniversity of KentuckyLexingtonKY40506USA
| | - Nicole G. Weiskopf
- Department of Medical Informatics & Clinical EpidemiologyOregon Health & Science UniversityPortlandOR97239USA
| | - Cavin K. Ward‐Caviness
- Center for Public Health and Environmental AssessmentUS Environmental Protection AgencyChapel HillNC27514USA
| | - Asiyah Yu Lin
- National Human Genome Research Institute (NHGRI)National Institutes of HealthBethesdaMD20892‐2152USA
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Ayton SG, Pavlicova M, Robles-Espinoza CD, Tamez Peña JG, Treviño V. Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis. Genet Med 2021; 24:15-25. [PMID: 34906494 DOI: 10.1016/j.gim.2021.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 05/20/2021] [Accepted: 09/10/2021] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Multiomics cancer subtyping is becoming increasingly popular for directing state-of-the-art therapeutics. However, these methods have never been systematically assessed for their ability to capture cancer prognosis for identified subtypes, which is essential to effectively treat patients. METHODS We systematically searched PubMed, The Cancer Genome Atlas, and Pan-Cancer Atlas for multiomics cancer subtyping studies from 2010 through 2019. Studies comprising at least 50 patients and examining survival were included. Pooled Cox and logistic mixed-effects models were used to compare the ability of multiomics subtyping methods to identify clinically prognostic subtypes, and a structural equation model was used to examine causal paths underlying subtyping method and mortality. RESULTS A total of 31 studies comprising 10,848 unique patients across 32 cancers were analyzed. Latent-variable subtyping was significantly associated with overall survival (adjusted hazard ratio, 2.81; 95% CI, 1.16-6.83; P = .023) and vital status (1 year adjusted odds ratio, 4.71; 95% CI, 1.34-16.49; P = .015; 5 year adjusted odds ratio, 7.69; 95% CI, 1.83-32.29; P = .005); latent-variable-identified subtypes had greater associations with mortality across models (adjusted hazard ratio, 1.19; 95% CI, 1.01-1.42; P = .050). Our structural equation model confirmed the path from subtyping method through multiomics subtype (βˆ = 0.66; P = .048) on survival (βˆ = 0.37; P = .008). CONCLUSION Multiomics methods have different abilities to define clinically prognostic cancer subtypes, which should be considered before administration of personalized therapy; preliminary evidence suggests that latent-variable methods better identify clinically prognostic biomarkers and subtypes.
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Affiliation(s)
- Sarah G Ayton
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico
| | - Martina Pavlicova
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY
| | - Carla Daniela Robles-Espinoza
- Laboratorio Internacional de Investigación sobre el Genoma Humano (LIIGH), Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - José G Tamez Peña
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico
| | - Víctor Treviño
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico.
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