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Foo JC, Völker MP, Streit F, Frank J, Zacharias N, Zillich L, Sirignano L, Nürnberg P, Wienker TF, Wagner M, Nöthen MM, Nothnagel M, Walter H, Lenz B, Spanagel R, Kiefer F, Winterer G, Rietschel M, Witt SH. Polygenic risk scores for nicotine use and family history of smoking are associated with smoking behaviour. Drug Alcohol Depend 2024; 263:112415. [PMID: 39197361 DOI: 10.1016/j.drugalcdep.2024.112415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 07/22/2024] [Accepted: 08/12/2024] [Indexed: 09/01/2024]
Abstract
INTRODUCTION Formal genetics studies show that smoking is influenced by genetic factors; exploring this on the molecular level can offer deeper insight into the etiology of smoking behaviours. METHODS Summary statistics from the latest wave of the GWAS and Sequencing Consortium of Alcohol and Nicotine (GSCAN) were used to calculate polygenic risk scores (PRS) in a sample of ~2200 individuals who smoke/individuals who never smoked. The associations of smoking status with PRS for Smoking Initiation (i.e., Lifetime Smoking; SI-PRS), and Fagerström Test for Nicotine Dependence (FTND) score with PRS for Cigarettes per Day (CpD-PRS) were examined, as were distinct/additive effects of parental smoking on smoking status. RESULTS SI-PRS explained 10.56% of variance (Nagelkerke-R2) in smoking status (p=6.45x10-30). In individuals who smoke, CpD-PRS was associated with FTND score (R2=5.03%, p=1.88x10-12). Parental smoking alone explained R2=3.06% (p=2.43×10-12) of smoking status, and 0.96% when added to the most informative SI-PRS model (total R²=11.52%). CONCLUSION These results show the potential utility of molecular genetic data for research investigating smoking prevention. The fact that PRS explains more variance than family history highlights progress from formal to molecular genetics; the partial overlap and increased predictive value when using both suggests the importance of combining these approaches.
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Affiliation(s)
- Jerome C Foo
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; College of Health Sciences, Department of Psychiatry, University of Alberta, Edmonton, Canada; Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Maja P Völker
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim/Heidelberg/Ulm, Germany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Norman Zacharias
- Department of Otorhinolaryngology, Head and Neck Surgery, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Benjamin Franklin, Berlin, Germany
| | - Lea Zillich
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim/Heidelberg/Ulm, Germany; Department of Translational Brain Research, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; HITBR Hector Institute for Translational Brain Research gGmbH, Mannheim, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lea Sirignano
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Peter Nürnberg
- Cologne Center for Genomics (CCG), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne 50931, Germany
| | - Thomas F Wienker
- Department of Molecular Human Genetics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Michael Wagner
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Markus M Nöthen
- Institute for Human Genetics, University Hospital Bonn, Bonn, Germany
| | - Michael Nothnagel
- Department of Statistical Genetics and Bioinformatics, Cologne Center for Genomics, University of Cologne, Cologne, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Division of Mind and Brain Research, Charité- Universitätsmedizin Berlin, Berlin, Germany
| | - Bernd Lenz
- Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Rainer Spanagel
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Falk Kiefer
- Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Georg Winterer
- Department of Anesthesiology and Intensive Care Medicine, Campus Virchow-Klinikum and Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany; Pharmaimage Biomarker Solutions GmbH, Berlin, Germany; PI Health Solutions GmbH, Berlin, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim/Heidelberg/Ulm, Germany.
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Capalbo A, de Wert G, Mertes H, Klausner L, Coonen E, Spinella F, Van de Velde H, Viville S, Sermon K, Vermeulen N, Lencz T, Carmi S. Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations. Hum Reprod Update 2024; 30:529-557. [PMID: 38805697 PMCID: PMC11369226 DOI: 10.1093/humupd/dmae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. OBJECTIVE AND RATIONALE This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. SEARCH METHODS We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. OUTCOMES The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. WIDER IMPLICATIONS The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.
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Affiliation(s)
- Antonio Capalbo
- Juno Genetics, Department of Reproductive Genetics, Rome, Italy
- Center for Advanced Studies and Technology (CAST), Department of Medical Genetics, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Guido de Wert
- Department of Health, Ethics & Society, CAPHRI-School for Public Health and Primary Care and GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Heidi Mertes
- Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Liraz Klausner
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Edith Coonen
- Departments of Clinical Genetics and Reproductive Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- School for Oncology and Developmental Biology, GROW, Maastricht University, Maastricht, The Netherlands
| | - Francesca Spinella
- Eurofins GENOMA Group Srl, Molecular Genetics Laboratories, Department of Scientific Communication, Rome, Italy
| | - Hilde Van de Velde
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
- Brussels IVF, UZ Brussel, Brussel, Belgium
| | - Stephane Viville
- Laboratoire de Génétique Médicale LGM, Institut de Génétique Médicale d’Alsace IGMA, INSERM UMR 1112, Université de Strasbourg, France
- Laboratoire de Diagnostic Génétique, Unité de Génétique de l’infertilité (UF3472), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Karen Sermon
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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Tábuas-Pereira M, Bernardes C, Durães J, Lima M, Nogueira AR, Saraiva J, Tábuas T, Coelho M, Paquette K, Westra K, Kun-Rodrigues C, Almeida MR, Baldeiras I, Brás J, Guerreiro R, Santana I. Exploring first-degree family history in a cohort of Portuguese Alzheimer's disease patients: population evidence for X-chromosome linked and recessive inheritance of risk factors. J Neurol 2024:10.1007/s00415-024-12673-x. [PMID: 39235525 DOI: 10.1007/s00415-024-12673-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 08/11/2024] [Accepted: 08/26/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) heritability is estimated to be around 70-80%. Yet, much of it remains to be explained. Studying transmission patterns may help in understanding other factors contributing to the development of AD. OBJECTIVE In this study, we aimed to search for evidence of autosomal recessive or X- and Y-linked inheritance of risk factors in a large cohort of Portuguese AD patients. METHODS We collected family history from patients with AD and cognitively healthy controls over 75 years of age. We compared the proportions of maternal and paternal history in male and female patients and controls (to search for evidence of X-linked and Y-linked inherited risk factors). We compared the risk of developing AD depending on parents' birthplace (same vs. different), as a proxy of remote consanguinity. We performed linear regressions to study the association of these variables with different endophenotypes. RESULTS We included 3090 participants, 2183 cognitively healthy controls and 907 patients with AD. Men whose mother had dementia have increased odds of developing AD comparing to women whose mother had dementia. In female patients with a CSF biomarker-supported diagnosis of AD, paternal history of dementia is associated with increased CSF phosphorylated Tau levels. People whose parents are from the same town have higher risk of dementia. In multivariate analysis, this proxy is associated with a lower age of onset and higher CSF phosphorylated tau. CONCLUSIONS Our study gives evidence supporting an increased risk of developing AD associated with an X-linked inheritance pattern and remote consanguinity.
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Affiliation(s)
- Miguel Tábuas-Pereira
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
- Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.
- Centre for Innovative Biomedicine and Biotechnology (CIBB), Universidade de Coimbra, Coimbra, Portugal.
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Praceta Prof. Mota Pinto, 3004-561, Coimbra, Portugal.
| | - Catarina Bernardes
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Centre for Innovative Biomedicine and Biotechnology (CIBB), Universidade de Coimbra, Coimbra, Portugal
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Praceta Prof. Mota Pinto, 3004-561, Coimbra, Portugal
| | - João Durães
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Centre for Innovative Biomedicine and Biotechnology (CIBB), Universidade de Coimbra, Coimbra, Portugal
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Praceta Prof. Mota Pinto, 3004-561, Coimbra, Portugal
| | - Marisa Lima
- Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | | | - Jorge Saraiva
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Centre for Innovative Biomedicine and Biotechnology (CIBB), Universidade de Coimbra, Coimbra, Portugal
| | - Teresa Tábuas
- Instituto Politécnico de Bragança, Bragança, Portugal
| | - Mariana Coelho
- Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Praceta Prof. Mota Pinto, 3004-561, Coimbra, Portugal
| | - Kimberly Paquette
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Kaitlyn Westra
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Célia Kun-Rodrigues
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Maria Rosário Almeida
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Centre for Innovative Biomedicine and Biotechnology (CIBB), Universidade de Coimbra, Coimbra, Portugal
| | - Inês Baldeiras
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Centre for Innovative Biomedicine and Biotechnology (CIBB), Universidade de Coimbra, Coimbra, Portugal
| | - José Brás
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Rita Guerreiro
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Isabel Santana
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Centre for Innovative Biomedicine and Biotechnology (CIBB), Universidade de Coimbra, Coimbra, Portugal
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Praceta Prof. Mota Pinto, 3004-561, Coimbra, Portugal
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Herrera-Luis E, Martin-Almeida M, Pino-Yanes M. Asthma-Genomic Advances Toward Risk Prediction. Clin Chest Med 2024; 45:599-610. [PMID: 39069324 PMCID: PMC11284279 DOI: 10.1016/j.ccm.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe Street, Baltimore, MD 21205, USA.
| | - Mario Martin-Almeida
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid 28029, Spain; Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), San Cristóbal de La Laguna 38200, Tenerife, Spain
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Ritchie SC, Taylor HJ, Liang Y, Manikpurage HD, Pennells L, Foguet C, Abraham G, Gibson JT, Jiang X, Liu Y, Xu Y, Kim LG, Mahajan A, McCarthy MI, Kaptoge S, Lambert SA, Wood A, Sim X, Collins FS, Denny JC, Danesh J, Butterworth AS, Di Angelantonio E, Inouye M. Integrated clinical risk prediction of type 2 diabetes with a multifactorial polygenic risk score. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.22.24312440. [PMID: 39228710 PMCID: PMC11370520 DOI: 10.1101/2024.08.22.24312440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Combining information from multiple GWASs for a disease and its risk factors has proven a powerful approach for development of polygenic risk scores (PRSs). This may be particularly useful for type 2 diabetes (T2D), a highly polygenic and heterogeneous disease where the additional predictive value of a PRS is unclear. Here, we use a meta-scoring approach to develop a metaPRS for T2D that incorporated genome-wide associations from both European and non-European genetic ancestries and T2D risk factors. We evaluated the performance of this metaPRS and benchmarked it against existing genome-wide PRS in 620,059 participants and 50,572 T2D cases amongst six diverse genetic ancestries from UK Biobank, INTERVAL, the All of Us Research Program, and the Singapore Multi-Ethnic Cohort. We show that our metaPRS was the most powerful PRS for predicting T2D in European population-based cohorts and had comparable performance to the top ancestry-specific PRS, highlighting its transferability. In UK Biobank, we show the metaPRS had stronger predictive power for 10-year risk than all individual risk factors apart from BMI and biomarkers of dysglycemia. The metaPRS modestly improved T2D risk stratification of QDiabetes risk scores for 10-year risk prediction, particularly when prioritising individuals for blood tests of dysglycemia. Overall, we present a highly predictive and transferrable PRS for T2D and demonstrate that the potential for PRS to incrementally improve T2D risk prediction when incorporated into UK guideline-recommended screening and risk prediction with a clinical risk score.
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Tsoulos N, Papadopoulou E, Agiannitopoulos K, Grigoriadis D, Tsaousis GN, Bouzarelou D, Gogas H, Troupis T, Venizelos V, Fountzilas E, Theochari M, Ziogas DC, Giassas S, Koumarianou A, Christopoulou A, Busby G, Nasioulas G, Markopoulos C. Polygenic Risk Score (PRS) Combined with NGS Panel Testing Increases Accuracy in Hereditary Breast Cancer Risk Estimation. Diagnostics (Basel) 2024; 14:1826. [PMID: 39202314 PMCID: PMC11353636 DOI: 10.3390/diagnostics14161826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/21/2024] [Accepted: 08/15/2024] [Indexed: 09/03/2024] Open
Abstract
Breast cancer (BC) is the most prominent tumor type among women, accounting for 32% of newly diagnosed cancer cases. BC risk factors include inherited germline pathogenic gene variants and family history of disease. However, the etiology of the disease remains occult in most cases. Therefore, in the absence of high-risk factors, a polygenic basis has been suggested to contribute to susceptibility. This information is utilized to calculate the Polygenic Risk Score (PRS) which is indicative of BC risk. This study aimed to evaluate retrospectively the clinical usefulness of PRS integration in BC risk calculation, utilizing a group of patients who have already been diagnosed with BC. The study comprised 105 breast cancer patients with hereditary genetic analysis results obtained by NGS. The selection included all testing results: high-risk gene-positive, intermediate/low-risk gene-positive, and negative. PRS results were obtained from an external laboratory (Allelica). PRS-based BC risk was computed both with and without considering additional risk factors, including gene status and family history. A significantly different PRS percentile distribution consistent with higher BC risk was observed in our cohort compared to the general population. Higher PRS-based BC risks were detected in younger patients and in those with FH of cancers. Among patients with a pathogenic germline variant detected, reduced PRS values were observed, while the BC risk was mainly determined by a monogenic etiology. Upon comprehensive analysis encompassing FH, gene status, and PRS, it was determined that 41.90% (44/105) of the patients demonstrated an elevated susceptibility for BC. Moreover, 63.63% of the patients with FH of BC and without an inherited pathogenic genetic variant detected showed increased BC risk by incorporating the PRS result. Our results indicate a major utility of PRS calculation in women with FH in the absence of a monogenic etiology detected by NGS. By combining high-risk strategies, such as inherited disease analysis, with low-risk screening strategies, such as FH and PRS, breast cancer risk stratification can be improved. This would facilitate the development of more effective preventive measures and optimize the allocation of healthcare resources.
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Affiliation(s)
- Nikolaos Tsoulos
- Genekor Medical S.A., 15344 Athens, Greece; (N.T.); (E.P.); (D.G.); (G.N.T.); (D.B.); (G.N.)
| | - Eirini Papadopoulou
- Genekor Medical S.A., 15344 Athens, Greece; (N.T.); (E.P.); (D.G.); (G.N.T.); (D.B.); (G.N.)
| | | | - Dimitrios Grigoriadis
- Genekor Medical S.A., 15344 Athens, Greece; (N.T.); (E.P.); (D.G.); (G.N.T.); (D.B.); (G.N.)
| | - Georgios N. Tsaousis
- Genekor Medical S.A., 15344 Athens, Greece; (N.T.); (E.P.); (D.G.); (G.N.T.); (D.B.); (G.N.)
| | - Dimitra Bouzarelou
- Genekor Medical S.A., 15344 Athens, Greece; (N.T.); (E.P.); (D.G.); (G.N.T.); (D.B.); (G.N.)
| | - Helen Gogas
- First Department of Internal Medicine, Laikon General Hospital, School of Medicine, National Kapodistrian University of Athens, 11527 Athens, Greece; (H.G.); (D.C.Z.)
| | - Theodore Troupis
- School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece; (T.T.); (C.M.)
| | | | - Elena Fountzilas
- Second Department of Medical Oncology, Euromedica General Clinic, 54645 Thessaloniki, Greece;
| | - Maria Theochari
- Oncology Unit, “Hippokrateion” General Hospital of Athens, 11527 Athens, Greece;
| | - Dimitrios C. Ziogas
- First Department of Internal Medicine, Laikon General Hospital, School of Medicine, National Kapodistrian University of Athens, 11527 Athens, Greece; (H.G.); (D.C.Z.)
| | - Stylianos Giassas
- Second Oncology Clinic IASO, General Maternity and Gynecology Clinic, 15123 Athens, Greece;
| | - Anna Koumarianou
- Hematology Oncology Unit, 4th Department of Internal Medicine, School of Medicine, National and Kapodistrian University of Athens, Attikon University Hospital, 12462 Athens, Greece;
| | | | - George Busby
- Allelica Inc., 447 Broadway, New York, NY 10013, USA;
| | - George Nasioulas
- Genekor Medical S.A., 15344 Athens, Greece; (N.T.); (E.P.); (D.G.); (G.N.T.); (D.B.); (G.N.)
| | - Christos Markopoulos
- School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece; (T.T.); (C.M.)
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Kolovos A, Hassall MM, Siggs OM, Souzeau E, Craig JE. Polygenic Risk Scores Driving Clinical Change in Glaucoma. Annu Rev Genomics Hum Genet 2024; 25:287-308. [PMID: 38599222 DOI: 10.1146/annurev-genom-121222-105817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Glaucoma is a clinically heterogeneous disease and the world's leading cause of irreversible blindness. Therapeutic intervention can prevent blindness but relies on early diagnosis, and current clinical risk factors are limited in their ability to predict who will develop sight-threatening glaucoma. The high heritability of glaucoma makes it an ideal substrate for genetic risk prediction, with the bulk of risk being polygenic in nature. Here, we summarize the foundations of glaucoma genetic risk, the development of polygenic risk prediction instruments, and emerging opportunities for genetic risk stratification. Although challenges remain, genetic risk stratification will significantly improve glaucoma screening and management.
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Affiliation(s)
- Antonia Kolovos
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
| | - Mark M Hassall
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
| | - Owen M Siggs
- Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia;
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
| | - Emmanuelle Souzeau
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
| | - Jamie E Craig
- Department of Ophthalmology, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia; , , ,
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8
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Jones AC, Patki A, Srinivasasainagendra V, Tiwari HK, Armstrong ND, Chaudhary NS, Limdi NA, Hidalgo BA, Davis B, Cimino JJ, Khan A, Kiryluk K, Lange LA, Lange EM, Arnett DK, Young BA, Diamantidis CJ, Franceschini N, Wassertheil-Smoller S, Rich SS, Rotter JI, Mychaleckyj JC, Kramer HJ, Chen YDI, Psaty BM, Brody JA, de Boer IH, Bansal N, Bis JC, Irvin MR. Single-Ancestry versus Multi-Ancestry Polygenic Risk Scores for CKD in Black American Populations. J Am Soc Nephrol 2024:00001751-990000000-00377. [PMID: 39073889 DOI: 10.1681/asn.0000000000000437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/28/2024] [Indexed: 07/31/2024] Open
Abstract
Key Points
The predictive performance of an African ancestry–specific polygenic risk score (PRS) was comparable to a European ancestry–derived PRS for kidney traits.However, multi-ancestry PRSs outperform single-ancestry PRSs in Black American populations.Predictive accuracy of PRSs for CKD was improved with the use of race-free eGFR.
Background
CKD is a risk factor of cardiovascular disease and early death. Recently, polygenic risk scores (PRSs) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European ancestry–derived PRSs demonstrate diminished predictive performance in African ancestry populations.
Methods
This study aimed to develop a PRS for CKD in Black American populations. We obtained score weights from a meta-analysis of genome-wide association studies for eGFR in the Million Veteran Program and Reasons for Geographic and Racial Differences in Stroke Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of participants from the Hypertension Genetic Epidemiology Network. Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine Consortium and Genetics of Hypertension Associated Treatment Study.
Results
The prevalence of CKD—defined as stage 3 or higher—was associated with the PRS as a continuous predictor (odds ratio [95% confidence interval]: 1.35 [1.08 to 1.68]) and in a threshold-dependent manner. Furthermore, including APOL1 risk status—a putative variant for CKD with higher prevalence among those of sub-Saharan African descent—improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable with a European ancestry–derived PRS for kidney traits. However, single-ancestry PRSs were less predictive than multi-ancestry–derived PRSs.
Conclusions
In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.
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Affiliation(s)
- Alana C Jones
- Medical Scientist Training Program, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Vinodh Srinivasasainagendra
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nicole D Armstrong
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ninad S Chaudhary
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nita A Limdi
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Bertha A Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Brittney Davis
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - James J Cimino
- Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Ethan M Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Donna K Arnett
- Office of the Provost, University of South Carolina, Columbia, South Carolina
| | - Bessie A Young
- Division of Nephrology, University of Washington, Seattle, Washington
| | | | - Nora Franceschini
- Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sylvia Wassertheil-Smoller
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, New York
| | - Stephen S Rich
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Josyf C Mychaleckyj
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Holly J Kramer
- Departments of Public Health Sciences and Medicine, Loyola University Medical Center, Taywood, Illinois
| | - Yii-Der I Chen
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Bruce M Psaty
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Ian H de Boer
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Nisha Bansal
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
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9
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Huang T, Kianersi S, Wang H, Potts KS, Noordam R, Sofer T, Rutter MK, Redline S. Sleep duration irregularity and risk for incident cardiovascular disease in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.26.24311090. [PMID: 39108534 PMCID: PMC11302714 DOI: 10.1101/2024.07.26.24311090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/12/2024]
Abstract
Background Emerging evidence supports a link between circadian disruption as measured by higher night-to-night variation in sleep duration and increased risk of cardiovascular disease (CVD). It remains unclear whether this association varies by CVD types or may be modified by average sleep duration and genetic risk for CVD. Methods Our prospective analysis included 86,219 UK Biobank participants who were free from CVD when completing 7 days of accelerometer measurement in 2013-2016. Sleep irregularity was evaluated by the standard deviation (SD) of accelerometer-measured sleep duration over 7 days. Incident major CVD events, defined as fatal or nonfatal myocardial infarction (MI) and stroke, were identified through linkage to Hospital Episode Statistics data until May 31, 2022. Multivariable-adjusted Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% CIs for associations of sleep duration SD with risk for major CVD events overall and for MI and stroke separately. Results We documented 2,310 incident cases of major CVD events (MI: 1,183, stroke: 1,175) over 636,258 person-years of follow-up. After adjusting for sociodemographic factors and family history of CVD, the HR (95% CI) associated with a 1-hour increase in sleep duration SD was 1.19 (1.10, 1.27) for CVD (p-trend<0.0001), 1.23 (1.11, 1.35) for MI (p-trend<0.0001), and 1.17 (1.05, 1.29) for stroke (p-trend=0.003). Additional adjustment for lifestyle factors, co-morbidities and sleep-related factors modestly attenuated these associations. Higher sleep irregularity was associated with higher CVD risk irrespective of genetic risk (p-interaction=0.43), but this association was stronger among individuals with longer average sleep duration >8 hours (p-interaction=0.006). Conclusions Higher night-to-night variation in accelerometer-measured sleep duration was associated with consistently higher risks for major CVD events. The association did not seem to be modified by genetic risk for CVD and was more pronounced in long sleepers.
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Affiliation(s)
- Tianyi Huang
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Sina Kianersi
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaitlin S. Potts
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Martin K. Rutter
- Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, University of Manchester, Manchester, UK
- Diabetes, Endocrinology and Metabolism Centre, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester, UK
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
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10
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Heyne HO, Pajuste FD, Wanner J, Daniel Onwuchekwa JI, Mägi R, Palotie A, Kälviainen R, Daly MJ. Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events. Nat Commun 2024; 15:6277. [PMID: 39054313 PMCID: PMC11272783 DOI: 10.1038/s41467-024-50295-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 07/04/2024] [Indexed: 07/27/2024] Open
Abstract
A diagnosis of epilepsy has significant consequences for an individual but is often challenging in clinical practice. Novel biomarkers are thus greatly needed. Here, we investigated how common genetic factors (epilepsy polygenic risk scores, [PRSs]) influence epilepsy risk in detailed longitudinal electronic health records (EHRs) of > 700k Finns and Estonians. We found that a high genetic generalized epilepsy PRS (PRSGGE) increased risk for genetic generalized epilepsy (GGE) (hazard ratio [HR] 1.73 per PRSGGE standard deviation [SD]) across lifetime and within 10 years after an unspecified seizure event. The effect of PRSGGE was significantly larger on idiopathic generalized epilepsies, in females and for earlier epilepsy onset. Analogously, we found significant but more modest focal epilepsy PRS burden associated with non-acquired focal epilepsy (NAFE). Here, we outline the potential of epilepsy specific PRSs to serve as biomarkers after a first seizure event.
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Affiliation(s)
- Henrike O Heyne
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.
- Hasso Plattner Institute, Mount Sinai School of Medicine, New York, NY, US.
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Julian Wanner
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jennifer I Daniel Onwuchekwa
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Faculty of Life Sciences, University of Siegen, Siegen, Germany
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Reetta Kälviainen
- Kuopio Epilepsy Center, Neurocenter, Kuopio University Hospital, Member of ERN EpiCARE, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
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11
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Monti R, Eick L, Hudjashov G, Läll K, Kanoni S, Wolford BN, Wingfield B, Pain O, Wharrie S, Jermy B, McMahon A, Hartonen T, Heyne H, Mars N, Lambert S, Hveem K, Inouye M, van Heel DA, Mägi R, Marttinen P, Ripatti S, Ganna A, Lippert C. Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning. Am J Hum Genet 2024; 111:1431-1447. [PMID: 38908374 PMCID: PMC11267524 DOI: 10.1016/j.ajhg.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/24/2024] Open
Abstract
Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.
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Affiliation(s)
- Remo Monti
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Eick
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Brooke N Wolford
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Benjamin Wingfield
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Oliver Pain
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience; Institute of Psychiatry, Psychology and Neuroscience; King's College London, London, UK
| | - Sophie Wharrie
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Bradley Jermy
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Henrike Heyne
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Nina Mars
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel Lambert
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | | | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pekka Marttinen
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Massachusetts General Hospital and Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christoph Lippert
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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12
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Mabey B, Hughes E, Kucera M, Simmons T, Hullinger B, Pederson HJ, Yehia L, Eng C, Garber J, Gary M, Gordon O, Klemp JR, Mukherjee S, Vijai J, Offit K, Olopade OI, Pruthi S, Kurian A, Robson ME, Whitworth PW, Pal T, Ratzel S, Wagner S, Lanchbury JS, Taber KJ, Slavin TP, Gutin A. Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors. Genet Med 2024; 26:101128. [PMID: 38829299 DOI: 10.1016/j.gim.2024.101128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 06/05/2024] Open
Abstract
PURPOSE We previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort. METHODS This study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs. RESULTS Three hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC. CONCLUSION CRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Joseph Vijai
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kenneth Offit
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Mark E Robson
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Tuya Pal
- Vanderbilt University Medical Center, Nashville, TN
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13
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Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Matsuda K, Izumi Y, Kinoshita K, Tamiya G, Hozawa A, Yamamoto M. Genetic Risk, Healthy Lifestyle Adherence, and Risk of Developing Diabetes in the Japanese Population. J Atheroscler Thromb 2024:64906. [PMID: 38910120 DOI: 10.5551/jat.64906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
AIM This study examined the relationship between genetic risk, healthy lifestyle, and risk of developing diabetes. METHODS This prospective cohort study included 11,014 diabetes-free individuals ≥ 20 years old from the Tohoku Medical Megabank Community-based cohort study. Lifestyle scores, including the body mass index, smoking, physical activity, and gamma-glutamyl transferase (marker of alcohol consumption), were assigned, and participants were categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score (PRS) was constructed based on the type 2 diabetes loci from the BioBank Japan study. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and diabetes incidence and to calculate the area under the receiver operating characteristic curve (AUROC). RESULT Of the 11,014 adults included (67.8% women; mean age [standard deviation], 59.1 [11.3] years old), 297 (2.7%) developed diabetes during a mean 4.3 (0.8) years of follow-up. Genetic and lifestyle score is independently associated with the development of diabetes. Compared with the low genetic risk and ideal lifestyle groups, the odds ratio was 3.31 for the low genetic risk and poor lifestyle group. When the PRS was integrated into a model including the lifestyle and family history, the AUROC significantly improved to 0.719 (95% confidence interval [95% CI]: 0.692-0.747) compared to a model including only the lifestyle and family history (0.703 [95% CI, 0.674-0.732]). CONCLUSION Our findings indicate that adherence to a healthy lifestyle is important for preventing diabetes, regardless of genetic risk. In addition, genetic risk might provide information beyond lifestyle and family history to stratify individuals at high risk of developing diabetes.
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Affiliation(s)
| | - Naoki Nakaya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Tomohiro Nakamura
- Tohoku Medical Megabank Organization, Tohoku University
- Kyoto Women's University
| | - Mana Kogure
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Rieko Hatanaka
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kumi Nakaya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Ippei Chiba
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Ikumi Kanno
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kotaro Nochioka
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
| | - Naho Tsuchiya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Takumi Hirata
- Tohoku Medical Megabank Organization, Tohoku University
- Human Care Research Team, Tokyo metropolitan Institute for Geriatrics and Gerontology
| | - Akira Narita
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Taku Obara
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Mami Ishikuro
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University
| | - Tomoko Kobayashi
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
| | - Eiichi N Kodama
- Tohoku Medical Megabank Organization, Tohoku University
- International Research Institute of Disaster Science, Tohoku University
| | | | - Masatsugu Orui
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Soichi Ogishima
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Satoshi Nagaie
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Nobuo Fuse
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Junichi Sugawara
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
- Suzuki Memorial Hospital
| | - Shinichi Kuriyama
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- International Research Institute of Disaster Science, Tohoku University
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo
| | - Yoko Izumi
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kengo Kinoshita
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- RIKEN Center for Advanced Intelligence Project
| | - Atsushi Hozawa
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
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14
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Schendel D, Ejlskov L, Overgaard M, Jinwala Z, Kim V, Parner E, Kalkbrenner AE, Acosta CL, Fallin MD, Xie S, Mortensen PB, Lee BK. 3-generation family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions associated with autism: an open-source catalogue of findings. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.03.23298042. [PMID: 37961212 PMCID: PMC10635276 DOI: 10.1101/2023.11.03.23298042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The relatively few conditions and family members investigated in autism family health history limits etiologic understanding. For more comprehensive understanding and hypothesis-generation we produced an open-source catalogue of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980-2012, of Denmark-born parents (1,697,231 births), and their 3-generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex-specific co-occurrence of each disorder. We obtained 6,462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co-occurrence aHRS. Results are catalogued in interactive heat maps and down-loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/views/ASDPlots_16918786403110/e-Figure5. While primarily for reference material or use in other studies (e.g., meta-analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and non-genetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity.
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Affiliation(s)
- Diana Schendel
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus BSS, Aarhus University, Aarhus, Denmark
| | - Linda Ejlskov
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus BSS, Aarhus University, Aarhus, Denmark
| | | | - Zeal Jinwala
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Viktor Kim
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
| | - Erik Parner
- Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Amy E Kalkbrenner
- University of Wisconsin Milwaukee, Joseph J Zilber College of Public Health, Milwaukee, WI, USA
| | - Christine Ladd Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - M Danielle Fallin
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Current affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA USA
| | - Sherlly Xie
- Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA
- Medtronic, Mounds View, Minnesota, USA
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus BSS, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Brian K Lee
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA, USA
- Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
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15
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Jermy B, Läll K, Wolford BN, Wang Y, Zguro K, Cheng Y, Kanai M, Kanoni S, Yang Z, Hartonen T, Monti R, Wanner J, Youssef O, Lippert C, van Heel D, Okada Y, McCartney DL, Hayward C, Marioni RE, Furini S, Renieri A, Martin AR, Neale BM, Hveem K, Mägi R, Palotie A, Heyne H, Mars N, Ganna A, Ripatti S. A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk. Nat Commun 2024; 15:5007. [PMID: 38866767 PMCID: PMC11169548 DOI: 10.1038/s41467-024-48938-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 05/17/2024] [Indexed: 06/14/2024] Open
Abstract
Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.
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Affiliation(s)
- Bradley Jermy
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Brooke N Wolford
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kristina Zguro
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Remo Monti
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Julian Wanner
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Omar Youssef
- Helsinki Biobank, Hospital District of Helsinki and Uusimaa (HUS), Helsinki, Finland
- Pathology Department, University of Helsinki, Helsinki, Finland
| | - Christoph Lippert
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Simone Furini
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy
| | - Alessandra Renieri
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Medical Genetics, University of Siena, Siena, Italy
- Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Henrike Heyne
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Massachusetts General Hospital, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Massachusetts General Hospital, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Public Health, University of Helsinki, Helsinki, Finland.
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16
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Huang S, Joshi A, Shi Z, Wei J, Tran H, Zheng SL, Duggan D, Ashworth A, Billings L, Helfand BT, Qamar A, Bulwa Z, Tafur A, Xu J. Combined polygenic scores for ischemic stroke risk factors aid risk assessment of ischemic stroke. Int J Cardiol 2024; 404:131990. [PMID: 38521508 DOI: 10.1016/j.ijcard.2024.131990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 03/01/2024] [Accepted: 03/20/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Current risk assessment for ischemic stroke (IS) is limited to clinical variables. We hypothesize that polygenic scores (PGS) of IS (PGSIS) and IS-associated diseases such as atrial fibrillation (AF), venous thromboembolism (VTE), coronary artery disease (CAD), hypertension (HTN), and Type 2 diabetes (T2D) may improve the performance of IS risk assessment. METHODS Incident IS was followed for 479,476 participants in the UK Biobank who did not have an IS diagnosis prior to the recruitment. Lifestyle variables (obesity, smoking and alcohol) at the time of study recruitment, clinical diagnoses of IS-associated diseases, PGSIS, and five PGSs for IS-associated diseases were tested using the Cox proportional-hazards model. Predictive performance was assessed using the C-statistic and net reclassification index (NRI). RESULTS During a median average 12.5-year follow-up, 8374 subjects were diagnosed with IS. Known clinical variables (age, gender, clinical diagnoses of IS-associated diseases, obesity, and smoking) and PGSIS were all independently associated with IS (P < 0.001). In addition, PGSIS and each PGS for IS-associated diseases was also independently associated with IS (P < 0.001). Compared to the clinical model, a joint clinical/PGS model improved the C-statistic for predicting IS from 0.71 to 0.73 (P < 0.001) and significantly reclassified IS risk (NRI = 0.017, P < 0.001), and 6.48% of subjects were upgraded from low to high risk. CONCLUSIONS Adding PGSs of IS and IS-associated diseases to known clinical risk factors statistically improved risk assessment for IS, demonstrating the supplementary value of inherited susceptibility measurement . However, its clinical utility is likely limited due to modest improvements in predictive values.
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Affiliation(s)
- Sarah Huang
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Abhishek Joshi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Jun Wei
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Huy Tran
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - S Lilly Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - David Duggan
- Affiliate of City of Hope, Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Annabelle Ashworth
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Liana Billings
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL, USA; University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | - Brian T Helfand
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA; University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | - Arman Qamar
- Cardiovascular Institute, NorthShore University HealthSystem, Evanston, IL, USA
| | - Zachary Bulwa
- Department of Neurology, NorthShore University HealthSystem, Evanston, IL, USA
| | - Alfonso Tafur
- Cardiovascular Institute, NorthShore University HealthSystem, Evanston, IL, USA
| | - Jianfeng Xu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA; University of Chicago Pritzker School of Medicine, Chicago, IL, USA.
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17
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Gibson JT, Rudd JHF. Polygenic risk scores in atrial fibrillation: Associations and clinical utility in disease prediction. Heart Rhythm 2024; 21:913-918. [PMID: 38336192 DOI: 10.1016/j.hrthm.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 02/02/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024]
Abstract
Atrial fibrillation (AF) is a common heart arrhythmia and a major cause of cardioembolic stroke. Therefore, accurate prediction is desirable to allow high-risk individuals to be identified early and their risk lowered before complications arise. Polygenic risk scores (PRSs) have become a popular method of quantifying aggregated genetic risk from common variants, but their clinical value in AF remains uncertain. This literature review summarizes the associations between PRS and AF risk and discusses the evidence for the clinical utility of PRS for AF prediction. Stroke risk in patients with AF is also considered. Despite consistent associations between PRS and AF risk, the performance of PRS as a stand-alone tool for AF prediction was poor. However, addition of PRS to the existing AF prediction models commonly improved the predictive performance above that of the clinical models alone, including in cohorts with comorbid cardiovascular disease. Associations between PRS and cardioembolic stroke risk in patients with AF have also been reported, but improvements in stroke prediction models from PRS have been minimal. PRS are likely to add value to the existing clinical AF prediction models; however, standardization of PRS across studies and populations will likely be required before they can be meaningfully adopted into routine clinical practice.
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Affiliation(s)
- Joel T Gibson
- Department of Medical Genetics, University of Cambridge, Lv 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - James H F Rudd
- Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom.
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18
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Vilhjálmsson BJ. Towards fair and clinically relevant polygenic predictions. Trends Genet 2024; 40:379-380. [PMID: 38643035 DOI: 10.1016/j.tig.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 04/03/2024] [Indexed: 04/22/2024]
Abstract
Lennon et al. recently proposed a clinical polygenic score (PGS) pipeline as part of the Electronic Medical Records and Genomics (eMERGE) network initiative. In this spotlight article we discuss the broader context for the use of PGS in preventive medicine and highlight key limitations and challenges facing their inclusion in prediction models.
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Affiliation(s)
- Bjarni Jóhann Vilhjálmsson
- National Centre for Register-based Research, Aarhus BSS, Aarhus University, Aarhus, Denmark; Bioinformatics Research Centre, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark; Novo Nordisk Foundation Centre for Genomics Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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19
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Mars N, Kerminen S, Tamlander M, Pirinen M, Jakkula E, Aaltonen K, Meretoja T, Heinävaara S, Widén E, Ripatti S. Comprehensive Inherited Risk Estimation for Risk-Based Breast Cancer Screening in Women. J Clin Oncol 2024; 42:1477-1487. [PMID: 38422475 PMCID: PMC11095905 DOI: 10.1200/jco.23.00295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 11/24/2023] [Accepted: 12/20/2023] [Indexed: 03/02/2024] Open
Abstract
PURPOSE Family history (FH) and pathogenic variants (PVs) are used for guiding risk surveillance in selected high-risk women but little is known about their impact for breast cancer screening on population level. In addition, polygenic risk scores (PRSs) have been shown to efficiently stratify breast cancer risk through combining information about common genetic factors into one measure. METHODS In longitudinal real-life data, we evaluate PRS, FH, and PVs for stratified screening. Using FinnGen (N = 117,252), linked to the Mass Screening Registry for breast cancer (1992-2019; nationwide organized biennial screening for age 50-69 years), we assessed the screening performance of a breast cancer PRS and compared its performance with FH of breast cancer and PVs in moderate- (CHEK2)- to high-risk (PALB2) susceptibility genes. RESULTS Effect sizes for FH, PVs, and high PRS (>90th percentile) were comparable in screening-aged women, with similar implications for shifting age at screening onset. A high PRS identified women more likely to be diagnosed with breast cancer after a positive screening finding (positive predictive value [PPV], 39.5% [95% CI, 37.6 to 41.5]). Combinations of risk factors increased the PPVs up to 45% to 50%. A high PRS conferred an elevated risk of interval breast cancer (hazard ratio [HR], 2.78 [95% CI, 2.00 to 3.86] at age 50 years; HR, 2.48 [95% CI, 1.67 to 3.70] at age 60 years), and women with a low PRS (<10th percentile) had a low risk for both interval- and screen-detected breast cancers. CONCLUSION Using real-life screening data, this study demonstrates the effectiveness of a breast cancer PRS for risk stratification, alone and combined with FH and PVs. Further research is required to evaluate their impact in a prospective risk-stratified screening program, including cost-effectiveness.
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Affiliation(s)
- Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Sini Kerminen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Max Tamlander
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Eveliina Jakkula
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Medical Genetics, University of Helsinki, Helsinki, Finland
| | - Kirsimari Aaltonen
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Medical Genetics, University of Helsinki, Helsinki, Finland
| | - Tuomo Meretoja
- Breast Surgery Unit, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
- University of Helsinki, Helsinki, Finland
| | - Sirpa Heinävaara
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Finnish Cancer Registry, Cancer Society of Finland, Helsinki, Finland
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Public Health, University of Helsinki, Helsinki, Finland
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20
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Mandla R, Schroeder P, Porneala B, Florez JC, Meigs JB, Mercader JM, Leong A. Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study. Genome Med 2024; 16:63. [PMID: 38671457 PMCID: PMC11046943 DOI: 10.1186/s13073-024-01337-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic scores (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. METHODS We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): (1) age and sex; (2) age, sex, body mass index (BMI), systolic blood pressure, and family history of T2D; (3) all variables in (2) and random glucose; and (4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. RESULTS PGS was associated with incident T2D in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of the top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk ((PGS-CRS interaction p = 0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)). CONCLUSIONS Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.
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Affiliation(s)
- Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Philip Schroeder
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, 100 Cambridge St. Fl. 16, Boston, MA, 02114, USA
| | - Jose C Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - James B Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, 100 Cambridge St. Fl. 16, Boston, MA, 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Josep M Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Aaron Leong
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, 100 Cambridge St. Fl. 16, Boston, MA, 02114, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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21
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Cheng Y, Wu L, Xin J, Ben S, Chen S, Li H, Zhao L, Wang M, Cheng G, Du M. An early-onset specific polygenic risk score optimizes age-based risk estimate and stratification of prostate cancer: population-based cohort study. J Transl Med 2024; 22:366. [PMID: 38632662 PMCID: PMC11025178 DOI: 10.1186/s12967-024-05190-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/11/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Early-onset prostate cancer (EOPC, ≤ 55 years) has a unique clinical entity harboring high genetic risk, but the majority of EOPC patients still substantial opportunity to be early-detected thus suffering an unfavorable prognosis. A refined understanding of age-based polygenic risk score (PRS) for prostate cancer (PCa) would be essential for personalized risk stratification. METHODS We included 167,517 male participants [4882 cases including 205 EOPC and 4677 late-onset PCa (LOPC)] from UK Biobank. A General-, an EOPC- and an LOPC-PRS were derived from age-specific genome-wide association studies. Weighted Cox proportional hazard models were applied to estimate the risk of PCa associated with PRSs. The discriminatory capability of PRSs were validated using time-dependent receiver operating characteristic (ROC) curves with additional 4238 males from PLCO and TCGA. Phenome-wide association studies underlying Mendelian Randomization were conducted to discover EOPC linking phenotypes. RESULTS The 269-PRS calculated via well-established risk variants was more strongly associated with risk of EOPC [hazard ratio (HR) = 2.35, 95% confidence interval (CI) 1.99-2.78] than LOPC (HR = 1.95, 95% CI 1.89-2.01; I2 = 79%). EOPC-PRS was dramatically related to EOPC risk (HR = 4.70, 95% CI 3.98-5.54) but not to LOPC (HR = 0.98, 95% CI 0.96-1.01), while LOPC-PRS had similar risk estimates for EOPC and LOPC (I2 = 0%). Particularly, EOPC-PRS performed optimal discriminatory capability for EOPC (area under the ROC = 0.613). Among the phenomic factors to PCa deposited in the platform of ProAP (Prostate cancer Age-based PheWAS; https://mulongdu.shinyapps.io/proap ), EOPC was preferentially associated with PCa family history while LOPC was prone to environmental and lifestyles exposures. CONCLUSIONS This study comprehensively profiled the distinct genetic and phenotypic architecture of EOPC. The EOPC-PRS may optimize risk estimate of PCa in young males, particularly those without family history, thus providing guidance for precision population stratification.
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Affiliation(s)
- Yifei Cheng
- Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Department of Environmental Genomics, School of Public Health, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- The Key Laboratory of Modern Toxicology of Ministry of Education, Department of Genetic Toxicology, School of Public Health, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Junyi Xin
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Shuai Ben
- Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Department of Environmental Genomics, School of Public Health, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- The Key Laboratory of Modern Toxicology of Ministry of Education, Department of Genetic Toxicology, School of Public Health, Center for Global Health, Nanjing Medical University, Nanjing, China
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Silu Chen
- Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Department of Environmental Genomics, School of Public Health, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- The Key Laboratory of Modern Toxicology of Ministry of Education, Department of Genetic Toxicology, School of Public Health, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Huiqin Li
- Department of Biostatistics, School of Public Health, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Lingyan Zhao
- Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Department of Environmental Genomics, School of Public Health, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- The Key Laboratory of Modern Toxicology of Ministry of Education, Department of Genetic Toxicology, School of Public Health, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Meilin Wang
- Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Department of Environmental Genomics, School of Public Health, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- The Key Laboratory of Modern Toxicology of Ministry of Education, Department of Genetic Toxicology, School of Public Health, Center for Global Health, Nanjing Medical University, Nanjing, China
- Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Nanjing Medical University, Suzhou, China
| | - Gong Cheng
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University & Jiangsu Province People's Hospital, 300 Guangzhou Road, Nanjing, 210029, China.
| | - Mulong Du
- Department of Biostatistics, School of Public Health, Center for Global Health, Nanjing Medical University, Nanjing, China.
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, 655 Huntington Avenue, Boston, MA, 02115, USA.
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22
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Spears C, Xu M, Shoben A, Dason S, Toland AE, Byrne L. Clinical features of prostate cancer by polygenic risk score. Fam Cancer 2024:10.1007/s10689-024-00369-0. [PMID: 38619781 DOI: 10.1007/s10689-024-00369-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 02/25/2024] [Indexed: 04/16/2024]
Abstract
Genome-wide association studies have identified more than 290 single nucleotide variants (SNVs) associated with prostate cancer. These SNVs can be combined to generate a Polygenic Risk Score (PRS), which estimates an individual's risk to develop prostate cancer. Identifying individuals at higher risk for prostate cancer using PRS could allow for personalized screening recommendations, improve current screening tools, and potentially result in improved survival rates, but more research is needed before incorporating them into clinical use. Our study aimed to investigate associations between PRS and clinical factors in affected individuals, including age of diagnosis, metastases, histology, International Society of Urological Pathology (ISUP) Grade Group (GG) and family history of prostate cancer, while taking into account germline genetic testing in known prostate cancer related genes. To evaluate the relationship between these clinical factors and PRS, a quantitative retrospective chart review of 250 individuals of European ancestry diagnosed with prostate cancer who received genetic counseling services at The Ohio State University's Genitourinary Cancer Genetics Clinic and a 72-SNV PRS through Ambry Genetics, was performed. We found significant associations between higher PRS and younger age of diagnosis (p = 0.002), lower frequency of metastases (p = 0.006), and having a first-degree relative diagnosed with prostate cancer (p = 0.024). We did not observe significant associations between PRS and ISUP GG, histology or a having a second-degree relative with prostate cancer. These findings provide insights into features associated with higher PRS, but larger multi-ancestral studies using PRS that are informative across populations are needed to understand its clinical utility.
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Affiliation(s)
- Christina Spears
- Division of Human Genetics, Department of Internal Medicine, College of Medicine, The Ohio State University, 2012 Kenny Road, Columbus, OH, 43212, USA.
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
| | - Menglin Xu
- Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Abigail Shoben
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Shawn Dason
- Division of Urologic Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Amanda Ewart Toland
- Division of Human Genetics, Department of Internal Medicine, College of Medicine, The Ohio State University, 2012 Kenny Road, Columbus, OH, 43212, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Lindsey Byrne
- Division of Human Genetics, Department of Internal Medicine, College of Medicine, The Ohio State University, 2012 Kenny Road, Columbus, OH, 43212, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
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23
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Tokutomi T, Yoshida A, Fukushima A, Yamamoto K, Ishigaki Y, Kawame H, Fuse N, Nagami F, Suzuki Y, Sakurai-Yageta M, Uruno A, Suzuki K, Tanno K, Ohmomo H, Shimizu A, Yamamoto M, Sasaki M. The Health History of First-Degree Relatives' Dyslipidemia Can Affect Preferences and Intentions following the Return of Genomic Results for Monogenic Familial Hypercholesterolemia. Genes (Basel) 2024; 15:384. [PMID: 38540442 PMCID: PMC10970353 DOI: 10.3390/genes15030384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 06/14/2024] Open
Abstract
Genetic testing is key in modern healthcare, particularly for monogenic disorders such as familial hypercholesterolemia. This Tohoku Medical Megabank Project study explored the impact of first-degree relatives' dyslipidemia history on individual responses to familial hypercholesterolemia genomic results. Involving 214 participants and using Japan's 3.5KJPN genome reference panel, the study assessed preferences and intentions regarding familial hypercholesterolemia genetic testing results. The data revealed a significant inclination among participants with a family history of dyslipidemia to share their genetic test results, with more than 80% of participants intending to share positive results with their partners and children and 98.1% acknowledging the usefulness of positive results for personal health management. The study underscores the importance of family health history in genetic-testing perceptions, highlighting the need for family-centered approaches in genetic counseling and healthcare. Notable study limitations include the regional scope and reliance on questionnaire data. The study results emphasize the association between family health history and genetic-testing attitudes and decisions.
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Affiliation(s)
- Tomoharu Tokutomi
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Morioka 020-8505, Japan
| | - Akiko Yoshida
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Morioka 020-8505, Japan
| | - Akimune Fukushima
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Morioka 020-8505, Japan
| | - Kayono Yamamoto
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Morioka 020-8505, Japan
| | - Yasushi Ishigaki
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
| | - Hiroshi Kawame
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Nobuo Fuse
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Fuji Nagami
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Yoichi Suzuki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Mika Sakurai-Yageta
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Kichiya Suzuki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Kozo Tanno
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
| | - Hideki Ohmomo
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
| | - Atsushi Shimizu
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Makoto Sasaki
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
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24
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Hopper JL, Li S, MacInnis RJ, Dowty JG, Nguyen TL, Bui M, Dite GS, Esser VFC, Ye Z, Makalic E, Schmidt DF, Goudey B, Alpen K, Kapuscinski M, Win AK, Dugué PA, Milne RL, Jayasekara H, Brooks JD, Malta S, Calais-Ferreira L, Campbell AC, Young JT, Nguyen-Dumont T, Sung J, Giles GG, Buchanan D, Winship I, Terry MB, Southey MC, Jenkins MA. Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies. Genet Epidemiol 2024. [PMID: 38504141 DOI: 10.1002/gepi.22555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/29/2024] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Karen Alpen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Miroslaw Kapuscinski
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
- Genetic Medicine, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Harindra Jayasekara
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Jennifer D Brooks
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Sue Malta
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Lucas Calais-Ferreira
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Alexander C Campbell
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Jesse T Young
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Centre for Adolescent Health, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
- Justice Health Group, Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, South Korea
- Genome Medicine Institute, Seoul National University, Seoul, South Korea
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel Buchanan
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ingrid Winship
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
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25
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Li S, Dite GS, MacInnis RJ, Bui M, Nguyen TL, Esser VFC, Ye Z, Dowty JG, Makalic E, Sung J, Giles GG, Southey MC, Hopper JL. Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses. Genet Epidemiol 2024. [PMID: 38472646 DOI: 10.1002/gepi.22556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 02/23/2024] [Indexed: 03/14/2024]
Abstract
A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS-disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first-degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
- Genomic Medicine Institute, Seoul National University, Euigwahakgwan #402, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul, South Korea
- Institute of Health and Environment, Seoul National University, 1st GwanakRo, Seoul, South Korea
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
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26
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Andreoli L, Peeters H, Van Steen K, Dierickx K. Taking the risk. A systematic review of ethical reasons and moral arguments in the clinical use of polygenic risk scores. Am J Med Genet A 2024:e63584. [PMID: 38450933 DOI: 10.1002/ajmg.a.63584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/08/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024]
Abstract
Debates about the prospective clinical use of polygenic risk scores (PRS) have grown considerably in the last years. The potential benefits of PRS to improve patient care at individual and population levels have been extensively underlined. Nonetheless, the use of PRS in clinical contexts presents a number of unresolved ethical challenges and consequent normative gaps that hinder their optimal implementation. Here, we conducted a systematic review of reasons of the normative literature discussing ethical issues and moral arguments related to the use of PRS for the prevention and treatment of common complex diseases. In total, we have included and analyzed 34 records, spanning from 2013 to 2023. The findings have been organized in three major themes: in the first theme, we consider the potential harms of PRS to individuals and their kin. In the theme "Threats to health equity," we consider ethical concerns of social relevance, with a focus on justice issues. Finally, the theme "Towards best practices" collects a series of research priorities and provisional recommendations to be considered for an optimal clinical translation of PRS. We conclude that the use of PRS in clinical care reinvigorates old debates in matters of health justice; however, open questions, regarding best practices in clinical counseling, suggest that the ethical considerations applicable in monogenic settings will not be sufficient to face PRS emerging challenges.
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Affiliation(s)
- Lara Andreoli
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Kris Dierickx
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
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27
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Tamlander M, Jermy B, Seppälä TT, Färkkilä M, Widén E, Ripatti S, Mars N. Genome-wide polygenic risk scores for colorectal cancer have implications for risk-based screening. Br J Cancer 2024; 130:651-659. [PMID: 38172535 PMCID: PMC10876651 DOI: 10.1038/s41416-023-02536-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/27/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Hereditary factors, including single genetic variants and family history, can be used for targeting colorectal cancer (CRC) screening, but limited data exist on the impact of polygenic risk scores (PRS) on risk-based CRC screening. METHODS Using longitudinal health and genomics data on 453,733 Finnish individuals including 8801 CRC cases, we estimated the impact of a genome-wide CRC PRS on CRC screening initiation age through population-calibrated incidence estimation over the life course in men and women. RESULTS Compared to the cumulative incidence of CRC at age 60 in Finland (the current age for starting screening in Finland), a comparable cumulative incidence was reached 5 and 11 years earlier in persons with high PRS (80-99% and >99%, respectively), while those with a low PRS (< 20%) reached comparable incidence 7 years later. The PRS was associated with increased risk of post-colonoscopy CRC after negative colonoscopy (hazard ratio 1.76 per PRS SD, 95% CI 1.54-2.01). Moreover, the PRS predicted colorectal adenoma incidence and improved incident CRC risk prediction over non-genetic risk factors. CONCLUSIONS Our findings demonstrate that a CRC PRS can be used for risk stratification of CRC, with further research needed to optimally integrate the PRS into risk-based screening.
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Affiliation(s)
- Max Tamlander
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Bradley Jermy
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Toni T Seppälä
- Faculty of Medicine and Health Technology, University of Tampere and TAYS Cancer Centre, Tampere, Finland
- Department of Gastroenterology and Alimentary Tract Surgery, Tampere University Hospital, Tampere, Finland
- Applied Tumor Genomics Research Program, University of Helsinki, Helsinki, Finland
- Abdominal Center, Helsinki University Hospital, Helsinki University, Helsinki, Finland
| | - Martti Färkkilä
- Abdominal Center, Helsinki University Hospital, Helsinki University, Helsinki, Finland
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Xiang R, Kelemen M, Xu Y, Harris LW, Parkinson H, Inouye M, Lambert SA. Recent advances in polygenic scores: translation, equitability, methods and FAIR tools. Genome Med 2024; 16:33. [PMID: 38373998 PMCID: PMC10875792 DOI: 10.1186/s13073-024-01304-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 02/21/2024] Open
Abstract
Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.
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Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin Kelemen
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Laura W Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
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Lennon NJ, Kottyan LC, Kachulis C, Abul-Husn NS, Arias J, Belbin G, Below JE, Berndt SI, Chung WK, Cimino JJ, Clayton EW, Connolly JJ, Crosslin DR, Dikilitas O, Velez Edwards DR, Feng Q, Fisher M, Freimuth RR, Ge T, Glessner JT, Gordon AS, Patterson C, Hakonarson H, Harden M, Harr M, Hirschhorn JN, Hoggart C, Hsu L, Irvin MR, Jarvik GP, Karlson EW, Khan A, Khera A, Kiryluk K, Kullo I, Larkin K, Limdi N, Linder JE, Loos RJF, Luo Y, Malolepsza E, Manolio TA, Martin LJ, McCarthy L, McNally EM, Meigs JB, Mersha TB, Mosley JD, Musick A, Namjou B, Pai N, Pesce LL, Peters U, Peterson JF, Prows CA, Puckelwartz MJ, Rehm HL, Roden DM, Rosenthal EA, Rowley R, Sawicki KT, Schaid DJ, Smit RAJ, Smith JL, Smoller JW, Thomas M, Tiwari H, Toledo DM, Vaitinadin NS, Veenstra D, Walunas TL, Wang Z, Wei WQ, Weng C, Wiesner GL, Yin X, Kenny EE. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. Nat Med 2024; 30:480-487. [PMID: 38374346 PMCID: PMC10878968 DOI: 10.1038/s41591-024-02796-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.
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Affiliation(s)
| | - Leah C Kottyan
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Josh Arias
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Gillian Belbin
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Sonja I Berndt
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - James J Cimino
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | - David R Crosslin
- Tulane University, New Orleans, LA, USA
- University of Washington, Seattle, WA, USA
| | | | | | - QiPing Feng
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Tian Ge
- Mass General Brigham, Boston, MA, USA
| | | | | | | | | | - Maegan Harden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Margaret Harr
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Clive Hoggart
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Li Hsu
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | | | | | | | - Amit Khera
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Katie Larkin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nita Limdi
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuan Luo
- Northwestern University, Evanston, IL, USA
| | | | - Teri A Manolio
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lisa J Martin
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Li McCarthy
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Tesfaye B Mersha
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Nihal Pai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Cynthia A Prows
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dan M Roden
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Robb Rowley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | | | | | | | - Hemant Tiwari
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | | | | | - Zhe Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
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30
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Park M, Lee YG. Association of Family History and Polygenic Risk Score With Longitudinal Prognosis in Parkinson Disease. Neurol Genet 2024; 10:e200115. [PMID: 38169864 PMCID: PMC10759146 DOI: 10.1212/nxg.0000000000200115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/02/2023] [Indexed: 01/05/2024]
Abstract
Background and Objectives Evidence suggests that either family history or polygenic risk score (PRS) is associated with developing Parkinson disease (PD). However, little is known about the longitudinal prognosis of PD according to family history and higher PRS. Methods From the Parkinson's Progression Markers Initiative database, 395 patients with PD who followed up for more than 2 years were grouped into those with family history within first-degree, second-degree, and third-degree relatives (N = 127 [32.2%]) vs those without (N = 268 [67.8%]). The PRS of 386 patients was computed using whole-genome sequencing data. Longitudinal assessment of motor, cognition, and imaging based on dopaminergic degeneration was conducted during the regular follow-up period. Effects of family history, PRS, or both on longitudinal changes of cognition, motor severity, and nigrostriatal degeneration were tested using a linear mixed model. The risk of freezing of gait (FOG) according to family history was assessed using the Kaplan-Meier analysis and Cox regression models. Results During a median follow-up of 9.1 years, PD with positive family history showed a slower decline of caudate dopamine transporter uptake (β estimate of family history × time = 0.02, 95% CI = 0.002-0.036, p = 0.027). Family history of PD and higher PRS were independently associated with a slower decline of Montreal Cognitive Assessment (β estimate of family history × time = 0.12, 95% CI = 0.02-0.22, p = 0.017; β estimate of PRS × time = 0.09, 95% CI = 0.03-0.16, p = 0.006). In those 364 patients without FOG at baseline, PD with positive family history had a lower risk of FOG (hazard ratio of family history = 0.57, 95% CI = 0.38-0.84, p = 0.005). Discussion Having a family history of PD predicts slower progression of cognitive decline and caudate dopaminergic degeneration, and less FOG compared with those without a family history independent of PRS. Taken together, information on family history could be used as a proxy for the clinical heterogeneity of PD. Trial Registration Information The study was registered at clinicaltrials.gov (NCT01141023), and the enrollment began June 1, 2010.
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Affiliation(s)
- Mincheol Park
- From the Department of Neurology (M.P.), Gwangmyeong Hospital, Chung-Ang University College of Medicine; and Department of Neurology (Y.L.), Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
| | - Young-Gun Lee
- From the Department of Neurology (M.P.), Gwangmyeong Hospital, Chung-Ang University College of Medicine; and Department of Neurology (Y.L.), Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
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31
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Brīvība M, Atava I, Pečulis R, Elbere I, Ansone L, Rozenberga M, Silamiķelis I, Kloviņš J. Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population. Int J Mol Sci 2024; 25:1151. [PMID: 38256224 PMCID: PMC10817091 DOI: 10.3390/ijms25021151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Numerous type 2 diabetes (T2D) polygenic risk scores (PGSs) have been developed to predict individuals' predisposition to the disease. An independent assessment and verification of the best-performing PGS are warranted to allow for a rapid application of developed models. To date, only 3% of T2D PGSs have been evaluated. In this study, we assessed all (n = 102) presently published T2D PGSs in an independent cohort of 3718 individuals, which has not been included in the construction or fine-tuning of any T2D PGS so far. We further chose the best-performing PGS, assessed its performance across major population principal component analysis (PCA) clusters, and compared it with newly developed population-specific T2D PGS. Our findings revealed that 88% of the published PGSs were significantly associated with T2D; however, their performance was lower than what had been previously reported. We found a positive association of PGS improvement over the years (p-value = 8.01 × 10-4 with PGS002771 currently showing the best discriminatory power (area under the receiver operating characteristic (AUROC) = 0.669) and PGS003443 exhibiting the strongest association PGS003443 (odds ratio (OR) = 1.899). Further investigation revealed no difference in PGS performance across major population PCA clusters and when compared with newly developed population-specific PGS. Our findings revealed a positive trend in T2D PGS performance, consistently identifying high-T2D-risk individuals in an independent European population.
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Affiliation(s)
- Monta Brīvība
- Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia; (I.A.); (I.E.); (L.A.); (J.K.)
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32
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Carrascosa-Carrillo JM, Aterido A, Li T, Guillén Y, Martinez S, Marsal S, Julià A. Toward Precision Medicine in Atopic Dermatitis Using Molecular-Based Approaches. ACTAS DERMO-SIFILIOGRAFICAS 2024; 115:66-75. [PMID: 37652096 DOI: 10.1016/j.ad.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023] Open
Abstract
Atopic dermatitis is the most common chronic inflammatory skin disorder, affecting up to 20% of children and 10% of adults in developed countries. The pathophysiology of atopic dermatitis is complex and involves a strong genetic predisposition and T-cell driven inflammation. Although our understanding of the pathology and drivers of this disease has improved in recent years, there are still knowledge gaps in the immune pathways involved. Therefore, advances in new omics technologies in atopic dermatitis will play a key role in understanding the pathogenesis of this burden disease and could develop preventive strategies and personalized treatment strategies. In this review, we discuss the latest developments in genetics, transcriptomics, epigenomics, proteomics, and metagenomics and understand how integrating multiple omics datasets will identify potential biomarkers and uncover nets of associations between several molecular levels.
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Affiliation(s)
- J M Carrascosa-Carrillo
- Dermatology Department, Hospital Germans Trias i Pujol, UAB, IGTP, Badalona, Barcelona, Spain
| | - A Aterido
- IMIDomics, Inc., Barcelona, Spain; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, Spain
| | - T Li
- IMIDomics, Inc., Barcelona, Spain
| | | | | | - S Marsal
- IMIDomics, Inc., Barcelona, Spain; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, Spain.
| | - A Julià
- IMIDomics, Inc., Barcelona, Spain; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, Spain
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33
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Carrascosa-Carrillo JM, Aterido A, Li T, Guillén Y, Martinez S, Marsal S, Julià A. Toward Precision Medicine in Atopic Dermatitis Using Molecular-Based Approaches. ACTAS DERMO-SIFILIOGRAFICAS 2024; 115:T66-T75. [PMID: 37923065 DOI: 10.1016/j.ad.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 11/07/2023] Open
Abstract
Atopic dermatitis is the most common chronic inflammatory skin disorder, affecting up to 20% of children and 10% of adults in developed countries. The pathophysiology of atopic dermatitis is complex and involves a strong genetic predisposition and T-cell driven inflammation. Although our understanding of the pathology and drivers of this disease has improved in recent years, there are still knowledge gaps in the immune pathways involved. Therefore, advances in new omics technologies in atopic dermatitis will play a key role in understanding the pathogenesis of this burden disease and could develop preventive strategies and personalized treatment strategies. In this review, we discuss the latest developments in genetics, transcriptomics, epigenomics, proteomics, and metagenomics and understand how integrating multiple omics datasets will identify potential biomarkers and uncover nets of associations between several molecular levels.
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Affiliation(s)
- J M Carrascosa-Carrillo
- Dermatology Department, Hospital Germans Trias i Pujol, UAB, IGTP, Badalona, Barcelona, España
| | - A Aterido
- IMIDomics, Inc., Barcelona, España; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, España
| | - T Li
- IMIDomics, Inc., Barcelona, España
| | | | | | - S Marsal
- IMIDomics, Inc., Barcelona, España; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, España.
| | - A Julià
- IMIDomics, Inc., Barcelona, España; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, España
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Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B, Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 2024; 25:8-25. [PMID: 37620596 PMCID: PMC10961971 DOI: 10.1038/s41576-023-00637-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jibril Hirbo
- Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iman Martin
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Hurtado H, Hansen M, Strack J, Vainik U, Decker AL, Khundrakpam B, Duncan K, Finn AS, Mabbott DJ, Merz EC. Polygenic risk for depression and anterior and posterior hippocampal volume in children and adolescents. J Affect Disord 2024; 344:619-627. [PMID: 37858734 PMCID: PMC10842073 DOI: 10.1016/j.jad.2023.10.068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/25/2023] [Accepted: 10/09/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND Depression has frequently been associated with smaller hippocampal volume. The hippocampus varies in function along its anterior-posterior axis, with the anterior hippocampus more strongly associated with stress and emotion processing. The goals of this study were to examine the associations among parental history of anxiety/depression, polygenic risk scores for depression (PGS-DEP), and anterior and posterior hippocampal volumes in children and adolescents. To examine specificity to PGS-DEP, we examined associations of educational attainment polygenic scores (PGS-EA) with anterior and posterior hippocampal volume. METHODS Participants were 350 3- to 21-year-olds (46 % female). PGS-DEP and PGS-EA were computed based on recent, large-scale genome-wide association studies. High-resolution, T1-weighted magnetic resonance imaging (MRI) data were acquired, and a semi-automated approach was used to segment the hippocampus into anterior and posterior subregions. RESULTS Children and adolescents with higher polygenic risk for depression were more likely to have a parent with a history of anxiety/depression. Higher polygenic risk for depression was significantly associated with smaller anterior but not posterior hippocampal volume. PGS-EA was not associated with anterior or posterior hippocampal volumes. LIMITATIONS Participants in these analyses were all of European ancestry. CONCLUSIONS Polygenic risk for depression may lead to smaller anterior but not posterior hippocampal volume in children and adolescents, and there may be specificity of these effects to PGS-DEP rather than PGS-EA. These findings may inform the earlier identification of those in need of support and the design of more effective, personalized treatment strategies. DECLARATIONS OF INTEREST none. DECLARATIONS OF INTEREST None.
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Affiliation(s)
- Hailee Hurtado
- Department of Psychology, Colorado State University, Fort Collins, CO, USA
| | - Melissa Hansen
- Department of Psychology, Colorado State University, Fort Collins, CO, USA
| | - Jordan Strack
- Department of Psychology, Colorado State University, Fort Collins, CO, USA
| | - Uku Vainik
- University of Tartu, Tartu, Estonia; Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Alexandra L Decker
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Katherine Duncan
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Amy S Finn
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Donald J Mabbott
- Department of Psychology, University of Toronto, Toronto, ON, Canada.; Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada.; Department of Psychology, Hospital for Sick Children, Toronto, ON, Canada
| | - Emily C Merz
- Department of Psychology, Colorado State University, Fort Collins, CO, USA.
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Lyall DM, Kormilitzin A, Lancaster C, Sousa J, Petermann‐Rocha F, Buckley C, Harshfield EL, Iveson MH, Madan CR, McArdle R, Newby D, Orgeta V, Tang E, Tamburin S, Thakur LS, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia-Applied models and digital health. Alzheimers Dement 2023; 19:5872-5884. [PMID: 37496259 PMCID: PMC10955778 DOI: 10.1002/alz.13391] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 05/19/2023] [Accepted: 05/26/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. METHODS This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. RESULTS This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).
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Affiliation(s)
- Donald M. Lyall
- School of Health and WellbeingCollege of Medical and Veterinary Sciences, University of GlasgowGlasgowUK
| | | | | | - Jose Sousa
- Personal Health Data ScienceSANO‐Centre for Computational Personalised MedicineKrakowPoland
- Faculty of MedicineHealth and Life Science, Queen's University BelfastBelfastUK
| | - Fanny Petermann‐Rocha
- School of Health and WellbeingCollege of Medical and Veterinary Sciences, University of GlasgowGlasgowUK
- Centro de Investigación BiomédicaFacultad de Medicina, Universidad Diego PortalesSantiagoChile
| | - Christopher Buckley
- Department of SportExercise and Rehabilitation, Northumbria UniversityNewcastle upon TyneUK
| | - Eric L. Harshfield
- Stroke Research Group, Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Matthew H. Iveson
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | | | - Ríona McArdle
- Translational and Clinical Research InstituteFaculty of Medical Sciences, Newcastle UniversityNewcastle upon TyneUK
| | | | | | - Eugene Tang
- Translational and Clinical Research InstituteFaculty of Medical Sciences, Newcastle UniversityNewcastle upon TyneUK
| | - Stefano Tamburin
- Department of NeurosciencesBiomedicine and Movement Sciences, University of VeronaVeronaItaly
| | | | | | | | - David J. Llewellyn
- University of Exeter Medical SchoolExeterUK
- Alan Turing InstituteLondonUK
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Wang W, Wang H. Understanding the complex genetics and molecular mechanisms underlying glaucoma. Mol Aspects Med 2023; 94:101220. [PMID: 37856931 DOI: 10.1016/j.mam.2023.101220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 10/21/2023]
Abstract
Glaucoma is the leading cause of irreversible blindness worldwide. Currently the only effective treatment for glaucoma is to reduce the intraocular pressure, which can halt the progression of the disease. Highlighting the importance of identifying individuals at risk of developing glaucoma and those with early-stage glaucoma will help patients receive treatment before sight loss. However, some cases of glaucoma do not have raised intraocular pressure. In fact, glaucoma is caused by a variety of different mechanisms and has a wide range of different subtypes. Understanding other risk factors, the underlying mechanisms, and the pathology of glaucoma might lead to novel treatments and treatment of underlying diseases. In this review we present the latest research into glaucoma including the genetics and molecular basis of the disease.
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Affiliation(s)
- Weiwei Wang
- Shaanxi Eye Hospital, Xi'an People's Hospital (Xi'an Fourth Hospital), Affiliated People's Hospital, Northwest University, Xi'an, 710004, Shaanxi Province, China.
| | - Huaizhou Wang
- Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
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Vaskimo LM, Gomon G, Naamane N, Cordell HJ, Pratt A, Knevel R. The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective. Genes (Basel) 2023; 14:2167. [PMID: 38136989 PMCID: PMC10743278 DOI: 10.3390/genes14122167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.
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Affiliation(s)
- Lotta M. Vaskimo
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Georgy Gomon
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Najib Naamane
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Heather J. Cordell
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Arthur Pratt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Department of Rheumatology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
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Heyne HO, Pajuste FD, Wanner J, Onwuchekwa JID, Mägi R, Palotie A, Kälviainen R, Daly MJ. Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.27.23297542. [PMID: 38076931 PMCID: PMC10705659 DOI: 10.1101/2023.11.27.23297542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
A diagnosis of epilepsy has significant consequences for an individual but is often challenging in clinical practice. Novel biomarkers are thus greatly needed. Here, we investigated how common genetic factors (epilepsy polygenic risk scores, [PRSs]) influence epilepsy risk in detailed longitudinal electronic health records (EHRs) of > 360k Finns spanning up to 50 years of individuals' lifetimes. Individuals with a high genetic generalized epilepsy PRS (PRSGGE) in FinnGen had an increased risk for genetic generalized epilepsy (GGE) (hazard ratio [HR] 1.55 per PRSGGE standard deviation [SD]) across their lifetime and after unspecified seizure events. Effect sizes of epilepsy PRSs were comparable to effect sizes in clinically curated data supporting our EHR-derived epilepsy diagnoses. Within 10 years after an unspecified seizure, the GGE rate was 37% when PRSGGE > 2 SD compared to 5.6% when PRSGGE < -2 SD. The effect of PRSGGE was even larger on GGE subtypes of idiopathic generalized epilepsy (IGE) (HR 2.1 per SD PRSGGE). We further report significantly larger effects of PRSGGE on epilepsy in females and in younger age groups. Analogously, we found significant but more modest focal epilepsy PRS burden associated with non-acquired focal epilepsy (NAFE). We found PRSGGE specifically associated with GGE in comparison with >2000 independent diseases while PRSNAFE was also associated with other diseases than NAFE such as back pain. Here, we show that epilepsy specific PRSs have good discriminative ability after a first seizure event i.e. in circumstances where the prior probability of epilepsy is high outlining a potential to serve as biomarkers for an epilepsy diagnosis.
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Affiliation(s)
- Henrike O Heyne
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Germany
- Hasso Plattner Institute, Mount Sinai School of Medicine, NY, US
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Julian Wanner
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Germany
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
| | - Jennifer I Daniel Onwuchekwa
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Germany
- Faculty of Life Sciences, University of Siegen, Germany
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Reetta Kälviainen
- Kuopio Epilepsy Center, Neurocenter, Kuopio University Hospital, Member of ERN EpiCARE, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mark J Daly
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
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Law JH, Sultan N, Finer S, Fudge N. Advancing the communication of genetic risk for cardiometabolic diseases: a critical interpretive synthesis. BMC Med 2023; 21:432. [PMID: 37953248 PMCID: PMC10641935 DOI: 10.1186/s12916-023-03150-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Genetics play an important role in risk for cardiometabolic diseases-including type 2 diabetes, cardiovascular disease and obesity. Existing research has explored the clinical utility of genetic risk tools such as polygenic risk scores-and whether interventions communicating genetic risk information using these tools can impact on individuals' cognitive appraisals of disease risk and/or preventative health behaviours. Previous systematic reviews suggest mixed results. To expand current understanding and address knowledge gaps, we undertook an interpretive, reflexive method of evidence synthesis-questioning the theoretical basis behind current interventions that communicate genetic risk information and exploring how the effects of genetic risk tools can be fully harnessed for cardiometabolic diseases. METHODS We obtained 189 records from a combination of database, website and grey literature searches-supplemented with reference chaining and expert subject knowledge within the review team. Using pre-defined critical interpretive synthesis methods, quantitative and qualitative evidence was synthesised and critiqued alongside theoretical understanding from surrounding fields of behavioural and social sciences. FINDINGS Existing interventions communicating genetic risk information focus predominantly on the "self", targeting individual-level cognitive appraisals, such as perceived risk and perceived behavioural control. This approach risks neglecting the role of contextual factors and upstream determinants that can reinforce individuals' interpretations of risk. It also assumes target populations to embody an "ascetic subject of compliance"-the idea of a patient who strives to comply diligently with professional medical advice, logically and rationally adopting any recommended lifestyle changes. We developed a synthesising argument-"beyond the ascetic subject of compliance"-grounded in three major limitations of this perspective: (1) difficulty applying existing theories/models to diverse populations, (2) the role of familial variables and (3) the need for a life course perspective. CONCLUSIONS Interventions communicating genetic risk information should account for wider influences that can affect individuals' responses to risk at different levels-including through interactions with their family systems, socio-cultural environments and wider health provision. PROTOCOL REGISTRATION PROSPERO CRD42021289269.
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Affiliation(s)
- Jing Hui Law
- Centre for Primary Care, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Najia Sultan
- Centre for Primary Care, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Sarah Finer
- Centre for Primary Care, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Barts Health NHS Trust, London, UK
| | - Nina Fudge
- Centre for Primary Care, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
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Dueñas N, Klinkhammer H, Bonifaci N, Spier I, Mayr A, Hassanin E, Diez-Villanueva A, Moreno V, Pineda M, Maj C, Capellà G, Aretz S, Brunet J. Ability of a polygenic risk score to refine colorectal cancer risk in Lynch syndrome. J Med Genet 2023; 60:1044-1051. [PMID: 37321833 DOI: 10.1136/jmg-2023-109344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/12/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND Polygenic risk scores (PRSs) have been used to stratify colorectal cancer (CRC) risk in the general population, whereas its role in Lynch syndrome (LS), the most common type of hereditary CRC, is still conflicting. We aimed to assess the ability of PRS to refine CRC risk prediction in European-descendant individuals with LS. METHODS 1465 individuals with LS (557 MLH1, 517 MSH2/EPCAM, 299 MSH6 and 92 PMS2) and 5656 CRC-free population-based controls from two independent cohorts were included. A 91-SNP PRS was applied. A Cox proportional hazard regression model with 'family' as a random effect and a logistic regression analysis, followed by a meta-analysis combining both cohorts were conducted. RESULTS Overall, we did not observe a statistically significant association between PRS and CRC risk in the entire cohort. Nevertheless, PRS was significantly associated with a slightly increased risk of CRC or advanced adenoma (AA), in those with CRC diagnosed <50 years and in individuals with multiple CRCs or AAs diagnosed <60 years. CONCLUSION The PRS may slightly influence CRC risk in individuals with LS in particular in more extreme phenotypes such as early-onset disease. However, the study design and recruitment strategy strongly influence the results of PRS studies. A separate analysis by genes and its combination with other genetic and non-genetic risk factors will help refine its role as a risk modifier in LS.
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Affiliation(s)
- Nuria Dueñas
- Hereditary Cancer Program, Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain
- Hereditary Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
- Biomedical Research Centre Network for Oncology (CIBERONC), Instituto Salud Carlos III, Madrid, Spain
- European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS), Nijmegen, Netherlands
| | - Hannah Klinkhammer
- Institute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany
| | - Nuria Bonifaci
- Hereditary Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
- Biomedical Research Centre Network for Oncology (CIBERONC), Instituto Salud Carlos III, Madrid, Spain
| | - Isabel Spier
- European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS), Nijmegen, Netherlands
- Institute of Human Genetics, Medical Faculty, University of Bonn, Bonn, Germany
- National Center for Hereditary Tumor Syndromes, University of Bonn, Bonn, Germany
| | - Andreas Mayr
- Institute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Emadeldin Hassanin
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Diez-Villanueva
- Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain
- Colorectal Cancer Group (ONCOBELL), Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Instituto Salud Carlos III, Madrid, Spain
| | - Victor Moreno
- Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain
- Colorectal Cancer Group (ONCOBELL), Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Instituto Salud Carlos III, Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona, Spain
| | - Marta Pineda
- Hereditary Cancer Program, Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain
- Hereditary Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
- Biomedical Research Centre Network for Oncology (CIBERONC), Instituto Salud Carlos III, Madrid, Spain
- European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS), Nijmegen, Netherlands
| | - Carlo Maj
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany
| | - Gabriel Capellà
- Hereditary Cancer Program, Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain
- Hereditary Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
- Biomedical Research Centre Network for Oncology (CIBERONC), Instituto Salud Carlos III, Madrid, Spain
- European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS), Nijmegen, Netherlands
| | - Stefan Aretz
- European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS), Nijmegen, Netherlands
- Institute of Human Genetics, Medical Faculty, University of Bonn, Bonn, Germany
- National Center for Hereditary Tumor Syndromes, University of Bonn, Bonn, Germany
| | - Joan Brunet
- Hereditary Cancer Program, Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain
- Hereditary Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain
- Biomedical Research Centre Network for Oncology (CIBERONC), Instituto Salud Carlos III, Madrid, Spain
- European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS), Nijmegen, Netherlands
- Hereditary Cancer Program, Catalan Institute of Oncology - ICO, Girona, Spain
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Mayerhofer E, Parodi L, Narasimhalu K, Harloff A, Georgakis MK, Rosand J, Anderson CD. Genetic and Nongenetic Components of Stroke Family History: A Population Study of Adopted and Nonadopted Individuals. J Am Heart Assoc 2023; 12:e031566. [PMID: 37830349 PMCID: PMC10757525 DOI: 10.1161/jaha.123.031566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023]
Abstract
Background Genetic and nongenetic factors account for the association of family history with disease risk. Comparing adopted and nonadopted individuals provides an opportunity to disentangle those factors. Methods and Results We examined associations between family history of stroke and heart disease with incident stroke and myocardial infarction (MI) in 495 640 UK Biobank participants (mean age, 56.5 years; 55% women) stratified by childhood adoption status (5747 adoptees). We estimated hazard ratios (HRs) per affected family member, and for polygenic risk scores in Cox models adjusted for baseline age and sex. A total of 12 518 strokes and 23 923 MIs occurred over a 13-year follow-up. In nonadoptees, family history of stroke and heart disease was associated with increased stroke and MI risk, with the strongest association of family history of stroke for incident stroke (HR, 1.16 [95% CI, 1.12-1.19]) and family history of heart disease for incident MI (HR, 1.48 [95% CI, 1.45-1.50]). In adoptees, family history of stroke associated with incident stroke (HR, 1.41 [95% CI, 1.06-1.86]), but family history of heart disease was not associated with incident MI (P>0.5). Polygenic risk scores showed strong disease-specific associations in both groups. In nonadoptees, the stroke polygenic risk score mediated 6% risk between family history of stroke and incident stroke, and the MI polygenic risk score mediated 13% risk between family history of heart disease and incident MI. Conclusions Family history of stroke and heart disease increases risk for their respective conditions. Family history of stroke contains substantial potentially modifiable nongenetic risk, indicating a need for novel prevention strategies, whereas family history of heart disease represents predominantly genetic risk.
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Affiliation(s)
- Ernst Mayerhofer
- Center for Genomic MedicineMassachusetts General HospitalBostonMA
- Program in Medical and Population GeneticsBroad Institute of Harvard and the Massachusetts Institute of TechnologyCambridgeMA
- McCance Center for Brain HealthMassachusetts General HospitalBostonMA
| | - Livia Parodi
- Center for Genomic MedicineMassachusetts General HospitalBostonMA
- Program in Medical and Population GeneticsBroad Institute of Harvard and the Massachusetts Institute of TechnologyCambridgeMA
- McCance Center for Brain HealthMassachusetts General HospitalBostonMA
- Department of NeurologyBrigham and Women’s HospitalBostonMA
| | - Kaavya Narasimhalu
- Center for Genomic MedicineMassachusetts General HospitalBostonMA
- Program in Medical and Population GeneticsBroad Institute of Harvard and the Massachusetts Institute of TechnologyCambridgeMA
- McCance Center for Brain HealthMassachusetts General HospitalBostonMA
| | - Andreas Harloff
- Department of Neurology and Neurophysiology, Medical Center–University of Freiburg, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Marios K. Georgakis
- Center for Genomic MedicineMassachusetts General HospitalBostonMA
- Program in Medical and Population GeneticsBroad Institute of Harvard and the Massachusetts Institute of TechnologyCambridgeMA
- McCance Center for Brain HealthMassachusetts General HospitalBostonMA
- Institute for Stroke and Dementia ResearchUniversity Hospital, Ludwig‐Maximilians‐University MunichMunichGermany
| | - Jonathan Rosand
- Center for Genomic MedicineMassachusetts General HospitalBostonMA
- Program in Medical and Population GeneticsBroad Institute of Harvard and the Massachusetts Institute of TechnologyCambridgeMA
- McCance Center for Brain HealthMassachusetts General HospitalBostonMA
| | - Christopher D. Anderson
- Center for Genomic MedicineMassachusetts General HospitalBostonMA
- Program in Medical and Population GeneticsBroad Institute of Harvard and the Massachusetts Institute of TechnologyCambridgeMA
- McCance Center for Brain HealthMassachusetts General HospitalBostonMA
- Department of NeurologyBrigham and Women’s HospitalBostonMA
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Szalai C. Arguments for and against the whole-genome sequencing of newborns. Am J Transl Res 2023; 15:6255-6263. [PMID: 37969196 PMCID: PMC10641337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/11/2023] [Indexed: 11/17/2023]
Abstract
Recent decades have brought enormous progress in both genetics and genomics, as well as in information technology (IT). The sequence of the human genome is now known, and although our knowledge is far from complete, great progress has been made in understanding how the genome works. With the developments in storage capacity, artificial intelligence, and learning algorithms, we are now able to learn and interpret complex systems such as the human genome in a very short time. Perhaps the most important goal of learning about the human genome is to understand diseases better: how they develop; how their processes can be prevented or slowed down; and after diseases have developed, how they can be cured or their symptoms alleviated. The vast majority of diseases have a genetic background, i.e., genes, sequence variations, and gene-gene interactions play a role in most diseases to a greater or lesser extent. Accordingly, the first step is to discover which genes, or genomic variants, cause or contribute to the development of a particular disease in a given patient. Given that an individual's genome remains virtually unchanged throughout their life (with one or two exceptions, such as in the case of cancer, which is caused by somatic mutations), it might be considered advantageous to sequence the genome of every person at birth. In this paper, we set out to show the possible benefits of sequencing the entire genome of every human being at birth, while also discussing the main arguments against it.
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Affiliation(s)
- Csaba Szalai
- Department of Genetics, Cell and Immunobiology, Semmelweis University1089 Budapest, Hungary
- Heim Pál Children’s Hospital1089 Budapest, Hungary
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Jackson L, Weedon MN, Green HD, Mallabar-Rimmer B, Harrison JW, Wood AR, Ruth KS, Tyrrell J, Wright CF. Influence of family history on penetrance of hereditary cancers in a population setting. EClinicalMedicine 2023; 64:102159. [PMID: 37936660 PMCID: PMC10626157 DOI: 10.1016/j.eclinm.2023.102159] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 11/09/2023] Open
Abstract
Background We sought to investigate how penetrance of familial cancer syndromes varies with family history using a population-based cohort. Methods We analysed 454,712 UK Biobank participants with exome sequence and clinical data (data collected between March 2006 and June 2021). We identified participants with a self-reported family history of breast or colorectal cancer and a pathogenic/likely pathogenic variant in the major genes responsible for hereditary breast cancer or Lynch syndrome. We calculated survival to cancer diagnosis (controlled for sex, death, recruitment centre, screening and prophylactic surgery). Findings Women with a pathogenic BRCA1 or BRCA2 variant had an increased risk of breast cancer that was higher in those with a first-degree family history (relative hazard 10.3 and 7.8, respectively) than those without (7.2 and 4.7). Penetrance to age 60 was also higher in those with a family history (44.7%, CI 32.2-59.3 and 24.1%, CI 17.5-32.6) versus those without (22.8%, CI 15.9-32.0 and 17.9%, CI 13.8-23.0). A similar pattern was seen in Lynch syndrome: individuals with a pathogenic MLH1, MSH2 or MSH6 variant had an increased risk of colorectal cancer that was significantly higher in those with a family history (relative hazard 35.6, 48.0 and 9.9) than those without (13.0, 15.4 and 7.2). Penetrance to age 60 was also higher for carriers of a pathogenic MLH1 or MSH2 variant in those with a family history (30.9%, CI 18.1-49.3 and 38.3%, CI 21.5-61.8) versus those without (20.5% CI 9.6-40.5 and 8.3% CI 2.1-30.4), but not for MSH6 (6.5% CI 2.7-15.1 with family history versus 8.3%, CI 5.1-13.2). Relative risk increases were also observed both within and across conditions. Interpretation Individuals with pathogenic cancer syndrome variants may be at a less elevated risk of cancer in the absence of a first-degree family history, so in the context of results return, family history should be considered when counselling patients on the risks and benefits of potential follow-up care. Funding The current work is supported by the MRC (grant no MR/T00200X/1). The MRC had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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Affiliation(s)
- Leigh Jackson
- Institute of Biomedical and Clinical Science, University of Exeter College of Medicine and Health, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Michael N. Weedon
- Institute of Biomedical and Clinical Science, University of Exeter College of Medicine and Health, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Harry D. Green
- Institute of Biomedical and Clinical Science, University of Exeter College of Medicine and Health, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Bethan Mallabar-Rimmer
- Institute of Biomedical and Clinical Science, University of Exeter College of Medicine and Health, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Jamie W. Harrison
- Institute of Biomedical and Clinical Science, University of Exeter College of Medicine and Health, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Andy R. Wood
- Institute of Biomedical and Clinical Science, University of Exeter College of Medicine and Health, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Kate S. Ruth
- Institute of Biomedical and Clinical Science, University of Exeter College of Medicine and Health, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Jess Tyrrell
- Institute of Biomedical and Clinical Science, University of Exeter College of Medicine and Health, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - Caroline F. Wright
- Institute of Biomedical and Clinical Science, University of Exeter College of Medicine and Health, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
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Hassanin E, Maj C, Klinkhammer H, Krawitz P, May P, Bobbili DR. Assessing the performance of European-derived cardiometabolic polygenic risk scores in South-Asians and their interplay with family history. BMC Med Genomics 2023; 16:164. [PMID: 37438803 PMCID: PMC10339617 DOI: 10.1186/s12920-023-01598-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/01/2023] [Indexed: 07/14/2023] Open
Abstract
BACKGROUND & AIMS We aimed to assess the performance of European-derived polygenic risk scores (PRSs) for common metabolic diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D) in the South Asian (SAS) individuals in the UK Biobank. Additionally, we studied the interaction between PRS and family history (FH) in the same population. METHODS To calculate the PRS, we used a previously published model derived from the EUR population and applied it to the individuals of SAS ancestry from the UKB study. Each PRS was adjusted according to an individual's genotype location in the principal components (PC) space to derive an ancestry adjusted PRS (aPRS). We calculated the percentiles based on aPRS and stratified individuals into three aPRS categories: low, intermediate, and high. Considering the intermediate-aPRS percentile as a reference, we compared the low and high aPRS categories and generated the odds ratio (OR) estimates. Further, we measured the combined role of aPRS and first-degree family history (FH) in the SAS population. RESULTS The risk of developing severe obesity for SAS individuals was almost twofold higher for individuals with high aPRS than for those with intermediate aPRS, with an OR of 1.95 (95% CI = 1.71-2.23, P < 0.01). At the same time, the risk of severe obesity was lower in the low-aPRS group (OR = 0.60, CI = 0.53-0.67, P < 0.01). Results in the same direction were found in the EUR data, where the low-PRS group had an OR of 0.53 (95% CI = 0.51-0.56, P < 0.01) and the high-PRS group had an OR of 2.06 (95% CI = 2.00-2.12, P < 0.01). We observed similar results for CAD and T2D. Further, we show that SAS individuals with a familial history of CAD and T2D with high-aPRS are associated with a higher risk of these diseases, implying a greater genetic predisposition. CONCLUSION Our findings suggest that CAD, obesity, and T2D GWAS summary statistics generated predominantly from the EUR population can be potentially used to derive aPRS in SAS individuals for risk stratification. With future GWAS recruiting more SAS participants and tailoring the PRSs towards SAS ancestry, the predictive power of PRS is likely to improve further.
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Affiliation(s)
- Emadeldin Hassanin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux, L-4367, Luxembourg
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Carlo Maj
- Centre for Human Genetics, University of Marburg, Marburg, Germany
| | - Hannah Klinkhammer
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
- Medical Faculty, Institute for Medical Biometry, Informatics and Epidemiology, University Bonn, Bonn, Germany
| | - Peter Krawitz
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux, L-4367, Luxembourg
| | - Dheeraj Reddy Bobbili
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux, L-4367, Luxembourg.
- Wellytics Technologies Pvt Ltd, Hyderabad, India.
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Mars N, Lindbohm JV, Della Briotta Parolo P, Widén E, Kaprio J, Palotie A, Ripatti S. Response to Li and Hopper. Am J Hum Genet 2023; 110:1224-1225. [PMID: 37419095 PMCID: PMC10357468 DOI: 10.1016/j.ajhg.2023.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 05/30/2023] [Accepted: 05/30/2023] [Indexed: 07/09/2023] Open
Affiliation(s)
- Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joni V Lindbohm
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Epidemiology and Public Health, University College London, London, UK; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Elisabeth Widén
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Li S, Hopper JL. Implications of family history and polygenic risk scores for causation. Am J Hum Genet 2023; 110:1221-1223. [PMID: 37419094 PMCID: PMC10357467 DOI: 10.1016/j.ajhg.2023.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 04/19/2023] [Accepted: 05/30/2023] [Indexed: 07/09/2023] Open
Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC 3053, Australia; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3051, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC 3053, Australia.
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Harsunen M, Haukka J, Harjutsalo V, Mars N, Syreeni A, Härkönen T, Käräjämäki A, Ilonen J, Knip M, Sandholm N, Miettinen PJ, Groop PH, Tuomi T. Residual insulin secretion in individuals with type 1 diabetes in Finland: longitudinal and cross-sectional analyses. Lancet Diabetes Endocrinol 2023; 11:465-473. [PMID: 37290465 DOI: 10.1016/s2213-8587(23)00123-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/18/2023] [Accepted: 04/18/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND Contrary to the presumption that type 1 diabetes leads to an absolute insulin deficiency, many individuals with type 1 diabetes have circulating C-peptide years after the diagnosis. We studied factors affecting random serum C-peptide concentration in individuals with type 1 diabetes and the association with diabetic complications. METHODS Our longitudinal analysis included individuals newly diagnosed with type 1 diabetes from Helsinki University Hospital (Helsinki, Finland) with repeated random serum C-peptide and concomitant glucose measurements from within 3 months of diagnosis and at least once later. The long-term cross-sectional analysis included data from participants from 57 centres in Finland who had type 1 diabetes diagnosed after 5 years of age, initiation of insulin treatment within 1 year from diagnosis, and a C-peptide concentration of less than 1·0 nmol/L (FinnDiane study) and patients with type 1 diabetes from the DIREVA study. We tested the association of random serum C-peptide concentrations and polygenic risk scores with one-way ANOVA, and association of random serum C-peptide concentrations, polygenic risk scores, and clinical factors with logistic regression. FINDINGS The longitudinal analysis included 847 participants younger than 16 years and 110 aged 16 years or older. In the longitudinal analysis, age at diagnosis strongly correlated with the decline in C-peptide secretion. The cross-sectional analysis included 3984 participants from FinnDiane and 645 from DIREVA. In the cross-sectional analysis, at a median duration of 21·6 years (IQR 12·5-31·2), 776 (19·4%) of 3984 FinnDiane participants had residual random serum C-peptide secretion (>0·02 nmol/L), which was associated with lower type 1 diabetes polygenic risk compared with participants without random serum C-peptide (p<0·0001). Random serum C-peptide was inversely associated with hypertension, HbA1c, and cholesterol, but also independently with microvascular complications (adjusted OR 0·61 [95% CI 0·38-0·96], p=0·033, for nephropathy; 0·55 [0·34-0·89], p=0·014, for retinopathy). INTERPRETATION Although children with multiple autoantibodies and HLA risk genotypes progressed to absolute insulin deficiency rapidly, many adolescents and adults had residual random serum C-peptide decades after the diagnosis. Polygenic risk of type 1 and type 2 diabetes affected residual random serum C-peptide. Even low residual random serum C-peptide concentrations seemed to be associated with a beneficial complications profile. FUNDING Folkhälsan Research Foundation; Academy of Finland; University of Helsinki and Helsinki University Hospital; Medical Society of Finland; the Sigrid Juselius Foundation; the "Liv and Hälsa" Society; Novo Nordisk Foundation; and State Research Funding via the Helsinki University Hospital, the Vasa Hospital District, Turku University Hospital, Vasa Central Hospital, Jakobstadsnejdens Heart Foundation, and the Medical Foundation of Vaasa.
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Affiliation(s)
- Minna Harsunen
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; New Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jani Haukka
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Valma Harjutsalo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Nina Mars
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anna Syreeni
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Taina Härkönen
- Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
| | - Annemari Käräjämäki
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Diabetes unit of Ostrobothnia, Wellbeing Services County of Ostrobothnia, Vaasa, Finland
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Mikael Knip
- Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; Tampere Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Päivi Johanna Miettinen
- Translational Stem Cell Biology and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland; New Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Diabetes, Central Medical School, Monash University, Melbourne, VIC, Australia
| | - Tiinamaija Tuomi
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland; Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Abdominal Center, Endocrinology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Lund, Sweden.
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Patel S, Wei J, Shi Z, Rifkin AS, Zheng SL, Gelfman E, Duggan D, Helfand BT, Hulick PJ, Xu J. Refining Risk for Alzheimer's Disease Among Heterozygous APOEɛ4 Carriers. J Alzheimers Dis 2023:JAD230156. [PMID: 37334598 DOI: 10.3233/jad-230156] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
In a large population-based cohort, we show not all heterozygous APOEɛ4 carriers are at increased risk for Alzheimer's disease (AD); a significantly higher AD proportion was only found for ɛ3/ɛ4, not ɛ2/ɛ4. Among ɛ3/ɛ4 carriers (24% in the cohort), the AD proportion differed considerably by polygenic risk score (PRS). In particular, the AD proportion was lower than the entire cohort for subjects in the bottom 20-percentile PRS and was higher than that of homozygous ɛ4 carriers for subjects at the top 5th-percentile PRS. Family history was no longer a significant predictor of AD risk after adjusting APOE and PRS.
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Affiliation(s)
- Smita Patel
- Department of Neurology, NorthShore University HealthSystem, Evanston, IL, USA
| | - Jun Wei
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Andrew S Rifkin
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - S Lilly Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | | | - David Duggan
- Translational Genomics Research Institute, Part of City of Hope, Phoenix, AZ, USA
| | - Brian T Helfand
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
- University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | - Peter J Hulick
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, USA
| | - Jianfeng Xu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
- University of Chicago Pritzker School of Medicine, Chicago, IL, USA
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Li C, Pan Y, Zhang R, Huang Z, Li D, Han Y, Larkin C, Rao V, Sun X, Kelly TN. Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment. Circ Res 2023; 132:1628-1647. [PMID: 37289909 PMCID: PMC10328558 DOI: 10.1161/circresaha.123.321999] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.
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Affiliation(s)
- Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Davey Li
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Yunan Han
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Claire Larkin
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Varun Rao
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
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