1
|
Arena G, Landoulsi Z, Grossmann D, Payne T, Vitali A, Delcambre S, Baron A, Antony P, Boussaad I, Bobbili DR, Sreelatha AAK, Pavelka L, J Diederich N, Klein C, Seibler P, Glaab E, Foltynie T, Bandmann O, Sharma M, Krüger R, May P, Grünewald A. Polygenic Risk Scores Validated in Patient-Derived Cells Stratify for Mitochondrial Subtypes of Parkinson's Disease. Ann Neurol 2024; 96:133-149. [PMID: 38767023 DOI: 10.1002/ana.26949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 04/25/2024] [Accepted: 04/28/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE The aim of our study is to better understand the genetic architecture and pathological mechanisms underlying neurodegeneration in idiopathic Parkinson's disease (iPD). We hypothesized that a fraction of iPD patients may harbor a combination of common variants in nuclear-encoded mitochondrial genes ultimately resulting in neurodegeneration. METHODS We used mitochondria-specific polygenic risk scores (mitoPRSs) and created pathway-specific mitoPRSs using genotype data from different iPD case-control datasets worldwide, including the Luxembourg Parkinson's Study (412 iPD patients and 576 healthy controls) and COURAGE-PD cohorts (7,270 iPD cases and 6,819 healthy controls). Cellular models from individuals stratified according to the most significant mitoPRS were subsequently used to characterize different aspects of mitochondrial function. RESULTS Common variants in genes regulating Oxidative Phosphorylation (OXPHOS-PRS) were significantly associated with a higher PD risk in independent cohorts (Luxembourg Parkinson's Study odds ratio, OR = 1.31[1.14-1.50], p-value = 5.4e-04; COURAGE-PD OR = 1.23[1.18-1.27], p-value = 1.5e-29). Functional analyses in fibroblasts and induced pluripotent stem cells-derived neuronal progenitors revealed significant differences in mitochondrial respiration between iPD patients with high or low OXPHOS-PRS (p-values < 0.05). Clinically, iPD patients with high OXPHOS-PRS have a significantly earlier age at disease onset compared to low-risk patients (false discovery rate [FDR]-adj p-value = 0.015), similar to prototypic monogenic forms of PD. Finally, iPD patients with high OXPHOS-PRS responded more effectively to treatment with mitochondrially active ursodeoxycholic acid. INTERPRETATION OXPHOS-PRS may provide a precision medicine tool to stratify iPD patients into a pathogenic subgroup genetically defined by specific mitochondrial impairment, making these individuals eligible for future intelligent clinical trial designs. ANN NEUROL 2024;96:133-149.
Collapse
Affiliation(s)
- Giuseppe Arena
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Zied Landoulsi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Dajana Grossmann
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Translational Neurodegeneration Section "Albrecht-Kossel", Department of Neurology, University Medical Center Rostock, University of Rostock, Rostock, Germany
| | - Thomas Payne
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Armelle Vitali
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Sylvie Delcambre
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexandre Baron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Paul Antony
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ibrahim Boussaad
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Dheeraj Reddy Bobbili
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ashwin Ashok Kumar Sreelatha
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| | - Lukas Pavelka
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier du Luxembourg, Luxembourg, Luxembourg
| | - Nico J Diederich
- Department of Neurosciences, Centre Hospitalier de Luxembourg, Strassen, Luxembourg
| | - Christine Klein
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Philip Seibler
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, London, UK
| | - Oliver Bandmann
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Manu Sharma
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| | - Rejko Krüger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier du Luxembourg, Luxembourg, Luxembourg
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anne Grünewald
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| |
Collapse
|
2
|
Pandey KN. Genetic and Epigenetic Mechanisms Regulating Blood Pressure and Kidney Dysfunction. Hypertension 2024; 81:1424-1437. [PMID: 38545780 PMCID: PMC11168895 DOI: 10.1161/hypertensionaha.124.22072] [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: 04/20/2024]
Abstract
The pioneering work of Dr Lewis K. Dahl established a relationship between kidney, salt, and high blood pressure (BP), which led to the major genetic-based experimental model of hypertension. BP, a heritable quantitative trait affected by numerous biological and environmental stimuli, is a major cause of morbidity and mortality worldwide and is considered to be a primary modifiable factor in renal, cardiovascular, and cerebrovascular diseases. Genome-wide association studies have identified monogenic and polygenic variants affecting BP in humans. Single nucleotide polymorphisms identified in genome-wide association studies have quantified the heritability of BP and the effect of genetics on hypertensive phenotype. Changes in the transcriptional program of genes may represent consequential determinants of BP, so understanding the mechanisms of the disease process has become a priority in the field. At the molecular level, the onset of hypertension is associated with reprogramming of gene expression influenced by epigenomics. This review highlights the specific genetic variants, mutations, and epigenetic factors associated with high BP and how these mechanisms affect the regulation of hypertension and kidney dysfunction.
Collapse
Affiliation(s)
- Kailash N. Pandey
- Department of Physiology, Tulane University Health Sciences Center, School of Medicine, New Orleans, LA
| |
Collapse
|
3
|
Carrasco-Zanini J, Pietzner M, Koprulu M, Wheeler E, Kerrison ND, Wareham NJ, Langenberg C. Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study. Lancet Digit Health 2024; 6:e470-e479. [PMID: 38906612 DOI: 10.1016/s2589-7500(24)00087-6] [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: 03/31/2023] [Revised: 04/03/2024] [Accepted: 04/19/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes. METHODS We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins. FINDINGS Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures. INTERPRETATION We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings. FUNDING Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.
Collapse
Affiliation(s)
- Julia Carrasco-Zanini
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
| |
Collapse
|
4
|
Katz AE, Gupte T, Ganesh SK. From Atherosclerosis to Spontaneous Coronary Artery Dissection: Defining a Clinical and Genetic Risk Spectrum for Myocardial Infarction. Curr Atheroscler Rep 2024; 26:331-340. [PMID: 38761354 DOI: 10.1007/s11883-024-01208-4] [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] [Accepted: 05/02/2024] [Indexed: 05/20/2024]
Abstract
PURPOSE OF REVIEW Spontaneous coronary artery dissection (SCAD) has been increasingly recognized as a significant cause of acute myocardial infarction (AMI) in young and middle-aged women and arises through mechanisms independent of atherosclerosis. SCAD has a multifactorial etiology that includes environmental, individual, and genetic factors distinct from those typically associated with coronary artery disease. Here, we summarize the current understanding of the genetic factors contributing to the development of SCAD and highlight those factors which differentiate SCAD from atherosclerotic coronary artery disease. RECENT FINDINGS Recent studies have revealed several associated variants with varying effect sizes for SCAD, giving rise to a complex genetic architecture. Associated genes highlight an important role for arterial cells and their extracellular matrix in the pathogenesis of SCAD, as well as notable genetic overlap between SCAD and other systemic arteriopathies such as fibromuscular dysplasia and vascular connective tissue diseases. Further investigation of individual variants (including in the associated gene PHACTR1) along with polygenic score analysis have demonstrated an inverse genetic relationship between SCAD and atherosclerosis as distinct causes of AMI. SCAD represents an increasingly recognized cause of AMI with opposing clinical and genetic risk factors from that of AMI due to atherosclerosis, and it is often associated with complex underlying genetic conditions. Genetic study of SCAD on a larger scale and with more diverse cohorts will not only further our evolving understanding of a newly defined genetic spectrum for AMI, but it will also inform the clinical utility of integrating genetic testing in AMI prevention and management moving forward.
Collapse
Affiliation(s)
- Alexander E Katz
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Trisha Gupte
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Santhi K Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
5
|
Kaiser B, Uberoi D, Raven-Adams MC, Cheung K, Bruns A, Chandrasekharan S, Otlowski M, Prince AER, Tiller J, Ahmed A, Bombard Y, Dupras C, Moreno PG, Ryan R, Valderrama-Aguirre A, Joly Y. A proposal for an inclusive working definition of genetic discrimination to promote a more coherent debate. Nat Genet 2024:10.1038/s41588-024-01786-8. [PMID: 38914718 DOI: 10.1038/s41588-024-01786-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 05/03/2024] [Indexed: 06/26/2024]
Abstract
Genetic discrimination is an evolving phenomenon that impacts fundamental human rights such as dignity, justice and equity. Although, in the past, various definitions to better conceptualize genetic discrimination have been proposed, these have been unable to capture several key facets of the phenomenon. In this Perspective, we explore definitions of genetic discrimination across disciplines, consider criticisms of such definitions and show how other forms of discrimination and stigmatization can compound genetic discrimination in a way that affects individuals, groups and systems. We propose a nuanced and inclusive definition of genetic discrimination, which reflects its multifaceted impact that should remain relevant in the face of an evolving social context and advancing science. We argue that our definition should be adopted as a guiding academic framework to facilitate scientific and policy discussions about genetic discrimination and support the development of laws and industry policies seeking to address the phenomenon.
Collapse
Affiliation(s)
- Beatrice Kaiser
- Centre of Genomics and Policy, McGill University, Montreal, Quebec, Canada
| | - Diya Uberoi
- Centre of Genomics and Policy, McGill University, Montreal, Quebec, Canada
| | | | - Katherine Cheung
- Centre of Genomics and Policy, McGill University, Montreal, Quebec, Canada
| | - Andreas Bruns
- The German Human Genome-Phenome Archive, University Hospital, Heidelberg, Germany
| | | | - Margaret Otlowski
- Centre for Health, Law and Emerging Technologies, University of Oxford, Oxford, UK
| | | | - Jane Tiller
- Monash University, Parkville, Victoria, Australia
| | | | - Yvonne Bombard
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Genomics Health Services Research Program, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada
| | | | | | | | | | - Yann Joly
- Centre of Genomics and Policy, McGill University, Montreal, Quebec, Canada.
| |
Collapse
|
6
|
Vabalas A, Hartonen T, Vartiainen P, Jukarainen S, Viippola E, Rodosthenous RS, Liu A, Hägg S, Perola M, Ganna A. Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker. NATURE AGING 2024:10.1038/s43587-024-00657-5. [PMID: 38914859 DOI: 10.1038/s43587-024-00657-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 05/27/2024] [Indexed: 06/26/2024]
Abstract
Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging. Previous age clocks focused on predicting either chronological age or longer-term mortality. Aging clocks predicting short-term mortality are lacking and their algorithmic fairness remains unexamined. We developed a deep learning model to predict 1-year mortality using nationwide longitudinal data from the Finnish population (FinRegistry; n = 5.4 million), incorporating more than 8,000 features spanning up to 50 years. We achieved an area under the curve (AUC) of 0.944, outperforming a baseline model that included only age and sex (AUC = 0.897). The model generalized well to different causes of death (AUC > 0.800 for 45 of 50 causes), including coronavirus disease 2019, which was absent in the training data. Performance varied among demographics, with young females exhibiting the best and older males the worst results. Extensive prediction fairness analyses highlighted disparities among disadvantaged groups, posing challenges to equitable integration into public health interventions. Our model accurately identified short-term mortality risk, potentially serving as a population-wide aging marker.
Collapse
Affiliation(s)
- Andrius Vabalas
- 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
| | - Pekka Vartiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Pediatric Research Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sakari Jukarainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Essi Viippola
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Aoxing Liu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Markus Perola
- The Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
| |
Collapse
|
7
|
Baumann A, Ruckert C, Meier C, Hutschenreiter T, Remy R, Schnur B, Döbel M, Fankep RCN, Skowronek D, Kutz O, Arnold N, Katzke AL, Forster M, Kobiela AL, Thiedig K, Zimmer A, Ritter J, Weber BHF, Honisch E, Hackmann K, Schmidt G, Sturm M, Ernst C. Limitations in next-generation sequencing-based genotyping of breast cancer polygenic risk score loci. Eur J Hum Genet 2024:10.1038/s41431-024-01647-2. [PMID: 38907004 DOI: 10.1038/s41431-024-01647-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/17/2024] [Accepted: 06/10/2024] [Indexed: 06/23/2024] Open
Abstract
Considering polygenic risk scores (PRSs) in individual risk prediction is increasingly implemented in genetic testing for hereditary breast cancer (BC) based on next-generation sequencing (NGS). To calculate individual BC risks, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) with the inclusion of the BCAC 313 or the BRIDGES 306 BC PRS is commonly used. The PRS calculation depends on accurately reproducing the variant allele frequencies (AFs) and, consequently, the distribution of PRS values anticipated by the algorithm. Here, the 324 loci of the BCAC 313 and the BRIDGES 306 BC PRS were examined in population-specific database gnomAD and in real-world data sets of five centers of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), to determine whether these expected AFs can be reproduced by NGS-based genotyping. Four PRS loci were non-existent in gnomAD v3.1.2 non-Finnish Europeans, further 24 loci showed noticeably deviating AFs. In real-world data, between 11 and 23 loci were reported with noticeably deviating AFs, and were shown to have effects on final risk prediction. Deviations depended on the sequencing approach, variant caller and calling mode (forced versus unforced) employed. Therefore, this study demonstrates the necessity to apply quality assurance not only in terms of sequencing coverage but also observed AFs in a sufficiently large cohort, when implementing PRSs in a routine diagnostic setting. Furthermore, future PRS design should be guided by the technical reproducibility of expected AFs across commonly used genotyping methods, especially NGS, in addition to the observed effect sizes.
Collapse
Affiliation(s)
- Alexandra Baumann
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Christian Ruckert
- Department of Medical Genetics, University Hospital Münster, Münster, Germany
| | - Christoph Meier
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
| | - Tim Hutschenreiter
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Robert Remy
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University of Cologne and University Hospital Cologne, Cologne, Germany
| | - Benedikt Schnur
- Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany
| | - Marvin Döbel
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Rudel Christian Nkouamedjo Fankep
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University of Cologne and University Hospital Cologne, Cologne, Germany
| | - Dariush Skowronek
- Department of Human Genetics, University Medicine Greifswald and Interfaculty Institute of Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Oliver Kutz
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- Department of Gynecology and Obstetrics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
| | - Norbert Arnold
- Department of Gynecology and Obstetrics, Institute of Clinical Chemistry Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Anna-Lena Katzke
- Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany
| | - Michael Forster
- Department of Gynecology and Obstetrics, Institute of Clinical Chemistry Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Anna-Lena Kobiela
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University of Cologne and University Hospital Cologne, Cologne, Germany
| | - Katharina Thiedig
- Division of Gynaecology and Obstetrics, Klinikum rechts der Isar der Technischen Universität München, München, Germany
| | - Andreas Zimmer
- Institute for Human Genetics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Julia Ritter
- Department of Human Genetics, Labor Berlin - Charité Vivantes GmbH, Berlin, Germany
| | - Bernhard H F Weber
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
- Institute of Clinical Human Genetics, University Hospital Regensburg, Regensburg, Germany
| | - Ellen Honisch
- Department of Gynaecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Karl Hackmann
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at TUD Dresden University of Technology and Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between German Cancer Research Center (DKFZ), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Gunnar Schmidt
- Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany
| | - Marc Sturm
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Corinna Ernst
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University of Cologne and University Hospital Cologne, Cologne, Germany.
| |
Collapse
|
8
|
Chen T, Pham G, Fox L, Adler N, Wang X, Zhang J, Byun J, Han Y, Saunders GRB, Liu D, Bray MJ, Ramsey AT, McKay J, Bierut L, Amos CI, Hung RJ, Lin X, Zhang H, Chen LS. Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304556. [PMID: 38562690 PMCID: PMC10984046 DOI: 10.1101/2024.03.19.24304556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies. Polygenic risk scores (PRSs) are powerful tools for patient risk stratification but have not yet been widely used in primary care for lung cancer, particularly in diverse patient populations. Methods We propose the GREAT care paradigm, which employs PRSs to stratify disease risk and personalize interventions. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardized PRS distributions across all ancestries. We applied our PRSs to 796 individuals from the GISC Trial, 350,154 from UK Biobank (UKBB), and 210,826 from All of Us Research Program (AoU), totaling 561,776 individuals of diverse ancestry. Results Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58 - 2.18) in UKBB and 2.39 (95% CI: 1.93 - 2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32 - 1.41) in UKBB and 1.32 (95% CI: 1.28 - 1.36) in AoU. Conclusion Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations. This model will be evaluated in two cluster-randomized clinical trials aimed at motivating health behavior changes in high-risk patients of diverse ancestry. This pioneering approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.
Collapse
|
9
|
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:S0002-9297(24)00209-X. [PMID: 38908374 DOI: 10.1016/j.ajhg.2024.06.003] [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: 11/20/2023] [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.
Collapse
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.
| |
Collapse
|
10
|
Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 PMCID: PMC11149372 DOI: 10.1186/s13073-024-01345-0] [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: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
Collapse
Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| |
Collapse
|
11
|
Dowrick A, Ziebland S, Rai T, Friedemann Smith C, Nicholson BD. A manifesto for improving cancer detection: four key considerations when implementing innovations across the interface of primary and secondary care. Lancet Oncol 2024:S1470-2045(24)00102-5. [PMID: 38848741 DOI: 10.1016/s1470-2045(24)00102-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/09/2024] [Accepted: 02/15/2024] [Indexed: 06/09/2024]
Abstract
Improving cancer outcomes through innovative cancer detection initiatives in primary care is an international policy priority. There are unique implementation challenges to the roll-out and scale-up of different innovations, requiring synchronisation between national policy levers and local implementation strategies. We draw on implementation science to highlight key considerations when seeking to sustainably embed cancer detection initiatives within health systems and clinical practice. Points of action include considering the implications of change on the current configuration of responsibility for detecting cancer; investing in understanding how to adapt systems to support innovations; developing strategies to address inequity when planning innovation implementation; and anticipating and making efforts to mitigate the unintended consequences of innovation. We draw on examples of contemporary cancer detection issues to illustrate how to apply these recommendations to practice.
Collapse
Affiliation(s)
- Anna Dowrick
- Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, UK.
| | - Sue Ziebland
- Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, UK
| | - Tanvi Rai
- Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, UK
| | | | - Brian D Nicholson
- Nuffield Department of Primary Care Health Science, University of Oxford, Oxford, UK
| |
Collapse
|
12
|
Topriceanu CC, Chaturvedi N, Mathur R, Garfield V. Validity of European-centric cardiometabolic polygenic scores in multi-ancestry populations. Eur J Hum Genet 2024; 32:697-707. [PMID: 38182743 PMCID: PMC11153583 DOI: 10.1038/s41431-023-01517-3] [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: 06/10/2023] [Revised: 10/29/2023] [Accepted: 11/28/2023] [Indexed: 01/07/2024] Open
Abstract
Polygenic scores (PGSs) provide an individual level estimate of genetic risk for any given disease. Since most PGSs have been derived from genome wide association studies (GWASs) conducted in populations of White European ancestry, their validity in other ancestry groups remains unconfirmed. This is especially relevant for cardiometabolic diseases which are known to disproportionately affect people of non-European ancestry. Thus, we aimed to evaluate the performance of PGSs for glycaemic traits (glycated haemoglobin, and type 1 and type 2 diabetes mellitus), cardiometabolic risk factors (body mass index, hypertension, high- and low-density lipoproteins, and total cholesterol and triglycerides) and cardiovascular diseases (including stroke and coronary artery disease) in people of White European, South Asian, and African Caribbean ethnicity in the UK Biobank. Whilst PGSs incorporated some GWAS data from multi-ethnic populations, the vast majority originated from White Europeans. For most outcomes, PGSs derived mostly from European populations had an overall better performance in White Europeans compared to South Asians and African Caribbeans. Thus, multi-ancestry GWAS data are needed to derive ancestry stratified PGSs to tackle health inequalities.
Collapse
Affiliation(s)
- Constantin-Cristian Topriceanu
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK.
- MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
| | - Nish Chaturvedi
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK
- MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| | - Rohini Mathur
- Centre for Primary Care, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Victoria Garfield
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK
- MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
| |
Collapse
|
13
|
Busse E, Lee B, Nagamani SCS. Genetic Evaluation for Monogenic Disorders of Low Bone Mass and Increased Bone Fragility: What Clinicians Need to Know. Curr Osteoporos Rep 2024; 22:308-317. [PMID: 38600318 DOI: 10.1007/s11914-024-00870-6] [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] [Accepted: 03/23/2024] [Indexed: 04/12/2024]
Abstract
PURPOSE OF REVIEW The purpose of this review is to outline the principles of clinical genetic testing and to provide practical guidance to clinicians in navigating genetic testing for patients with suspected monogenic forms of osteoporosis. RECENT FINDINGS Heritability assessments and genome-wide association studies have clearly shown the significant contributions of genetic variations to the pathogenesis of osteoporosis. Currently, over 50 monogenic disorders that present primarily with low bone mass and increased risk of fractures have been described. The widespread availability of clinical genetic testing offers a valuable opportunity to correctly diagnose individuals with monogenic forms of osteoporosis, thus instituting appropriate surveillance and treatment. Clinical genetic testing may identify the appropriate diagnosis in a subset of patients with low bone mass, multiple or unusual fractures, and severe or early-onset osteoporosis, and thus clinicians should be aware of how to incorporate such testing into their clinical practices.
Collapse
Affiliation(s)
- Emily Busse
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
- Texas Children's Hospital, Houston, TX, USA.
| | - Sandesh C S Nagamani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
| |
Collapse
|
14
|
Peña E, Mas-Bermejo P, Lecube A, Ciudin A, Arenas C, Simó R, Rigla M, Caixàs A, Rosa A. Use of polygenic risk scores to assess weight loss after bariatric surgery: a 5-year follow-up study. J Gastrointest Surg 2024:S1091-255X(24)00485-2. [PMID: 38821212 DOI: 10.1016/j.gassur.2024.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/19/2024] [Accepted: 05/27/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUND Bariatric surgery (BS) is currently the most effective long-term treatment of severe obesity. However, the interindividual variability observed in surgical outcomes suggests a moderating effect of several factors, including individual genetic background. This study aimed to investigate the contribution of the genetic architecture of body mass index (BMI) to the variability in weight loss outcomes after BS. METHODS A total of 106 patients with severe obesity who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy were followed up for 5 years. Changes in BMI (BMIchange) and percentage of total weight loss (%TWL) were evaluated during the postoperative period. Polygenic risk scores (PRSs), including 50 genetic variants, were calculated for each participant to determine their genetic risk of high BMI based on a previous genome-wide association study. Generalized estimating equation models were used to study the role of the individual's polygenic score and other factors on BMIchange and %TWL in the long term after surgery. RESULTS This study found an effect of the polygenic score on %TWL and BMIchange, in which patients with lower scores had better outcomes after surgery than those with higher scores. Furthermore, when analyzing only patients who underwent RYGB, the results were replicated, showing greater weight loss after surgery for patients with lower polygenic scores. DISCUSSION Our results indicate that genetic background assessed with PRSs, along with other individual factors, such as biological sex, age, and preoperative BMI, has an effect on BS outcomes and could represent a useful tool for estimating surgical outcomes in advance.
Collapse
Affiliation(s)
- Elionora Peña
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Secció de Zoologia i Antropologia Biòlogica, Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Patricia Mas-Bermejo
- Secció de Zoologia i Antropologia Biòlogica, Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona, Barcelona, Spain
| | - Albert Lecube
- Department of Endocrinology and Nutrition, Arnau de Vilanova University Hospital, Institut de Recerca Biomèdica de Lleida, Universitat de Lleida, Lleida, Spain
| | - Andreea Ciudin
- Diabetes and Metabolism Research Unit, Department of Endocrinology and Nutrition, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain
| | - Concepción Arenas
- Statistics Section of the Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, Spain
| | - Rafael Simó
- Diabetes and Metabolism Research Unit, Department of Endocrinology and Nutrition, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain
| | - Mercedes Rigla
- Department of Endocrinology and Nutrition, Institut d'Investigació i Innovació, Parc Taulí Hospital Universitari, Sabadell, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Assumpta Caixàs
- Department of Endocrinology and Nutrition, Institut d'Investigació i Innovació, Parc Taulí Hospital Universitari, Sabadell, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Araceli Rosa
- Secció de Zoologia i Antropologia Biòlogica, Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica En Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain.
| |
Collapse
|
15
|
Gao PY, Ma LZ, Wang XJ, Wu BS, Huang YM, Wang ZB, Fu Y, Ou YN, Feng JF, Cheng W, Tan L, Yu JT. Physical frailty, genetic predisposition, and incident dementia: a large prospective cohort study. Transl Psychiatry 2024; 14:212. [PMID: 38802408 PMCID: PMC11130190 DOI: 10.1038/s41398-024-02927-7] [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: 08/04/2023] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Physical frailty and genetic factors are both risk factors for increased dementia; nevertheless, the joint effect remains unclear. This study aimed to investigated the long-term relationship between physical frailty, genetic risk, and dementia incidence. A total of 274,194 participants from the UK Biobank were included. We applied Cox proportional hazards regression models to estimate the association between physical frailty and genetic and dementia risks. Among the participants (146,574 females [53.45%]; mean age, 57.24 years), 3,353 (1.22%) new-onset dementia events were recorded. Compared to non-frailty, the hazard ratio (HR) for dementia incidence in prefrailty and frailty was 1.396 (95% confidence interval [CI], 1.294-1.506, P < 0.001) and 2.304 (95% CI, 2.030-2.616, P < 0.001), respectively. Compared to non-frailty and low polygenic risk score (PRS), the HR for dementia risk was 3.908 (95% CI, 3.051-5.006, P < 0.001) for frailty and high PRS. Furthermore, among the participants, slow walking speed (HR, 1.817; 95% CI, 1.640-2.014, P < 0.001), low physical activity (HR, 1.719; 95% CI, 1.545-1.912, P < 0.001), exhaustion (HR, 1.670; 95% CI, 1.502-1.856, P < 0.001), low grip strength (HR, 1.606; 95% CI, 1.479-1.744, P < 0.001), and weight loss (HR, 1.464; 95% CI, 1.328-1.615, P < 0.001) were independently associated with dementia risk compared to non-frailty. Particularly, precise modulation for different dementia genetic risk populations can also be identified due to differences in dementia risk resulting from the constitutive pattern of frailty in different genetic risk populations. In conclusion, both physical frailty and high genetic risk are significantly associated with higher dementia risk. Early intervention to modify frailty is beneficial for achieving primary and precise prevention of dementia, especially in those at high genetic risk.
Collapse
Affiliation(s)
- Pei-Yang Gao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ling-Zhi Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Xue-Jie Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Ming Huang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Zhi-Bo Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| |
Collapse
|
16
|
Dimitriou M, Moulos P, Kalafati IP, Saranti G, Rallidis LS, Dedoussis GV. Evaluation of Polygenic Risk Scores for Prediction of Coronary Artery Disease in a Greek Case-Control Study. J Pers Med 2024; 14:565. [PMID: 38929788 PMCID: PMC11204902 DOI: 10.3390/jpm14060565] [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: 04/29/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
Coronary artery disease (CAD) stands as the most predominant type of cardiovascular disease (CVD). Polygenic risk scores (PRSs) have become essential tools for quantifying genetic susceptibility, and researchers endeavor to improve their predictive precision. The aim of the present work is to assess the performance and the relative contribution of PRSs developed for CVD or CAD within a Greek population. The sample under study comprised 924 Greek individuals (390 cases with CAD and 534 controls) from the THISEAS study. Nine PRSs drawn from the PGS catalog were replicated and tested for CAD risk prediction. PRSs computations were performed in the R language, and snpStats was used to process genotypic data. Descriptive characteristics of the study were analyzed using the statistical software IBM SPSS Statistics v21.0. The effectiveness of each PRS was assessed using the PRS R2 metric provided by PRSice2. Among nine PRSs, PGS000747 greatly increased the predictive value of primary CAD risk factors by 21.6% (p-value = 2.63 × 10-25). PGS000012 was associated with a modest increase in CAD risk by 2.2% (p-value = 9.58 × 10-4). The remarkable risk discrimination capability of PGS000747 stands out as the most noteworthy outcome of our study.
Collapse
Affiliation(s)
- Maria Dimitriou
- Department of Nutritional Science and Dietetics, School of Health Science, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
| | - Panagiotis Moulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center ‘Alexander Fleming’, 16672 Vari, Greece
| | - Ioanna Panagiota Kalafati
- Department of Nutrition and Dietetics, School of Physical Education, Sport Science and Dietetics, University of Thessaly, 42132 Trikala, Greece;
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 17671 Athens, Greece; (G.S.); (G.V.D.)
| | - Georgia Saranti
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 17671 Athens, Greece; (G.S.); (G.V.D.)
| | - Loukianos S. Rallidis
- Second Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Attikon Hospital, 12462 Athens, Greece;
| | - George V. Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 17671 Athens, Greece; (G.S.); (G.V.D.)
| |
Collapse
|
17
|
Staerk C, Klinkhammer H, Wistuba T, Maj C, Mayr A. Generalizability of polygenic prediction models: how is the R 2 defined on test data? BMC Med Genomics 2024; 17:132. [PMID: 38755654 PMCID: PMC11100126 DOI: 10.1186/s12920-024-01905-8] [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: 09/01/2023] [Accepted: 05/08/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) quantify an individual's genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the R 2 is a commonly used measure to evaluate prediction accuracy. While the R 2 is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. METHODS Based on large-scale genotype data from the UK Biobank, we compare three definitions of the R 2 on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoprotein A, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. RESULTS Our analysis shows that the choice of the R 2 definition can lead to considerably different results on test data, making the comparison of R 2 values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the R 2 based on the mean squared prediction error (MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis - whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of R 2 can provide valuable complementary information. CONCLUSIONS Awareness of the different definitions of the R 2 on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting R 2 values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.
Collapse
Affiliation(s)
- Christian Staerk
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany.
- Institute of Statistics, RWTH Aachen University, Aachen, Germany.
| | - Hannah Klinkhammer
- Department of 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
| | - Tobias Wistuba
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Carlo Maj
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Andreas Mayr
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
| |
Collapse
|
18
|
Yang DW, Miller JA, Xue WQ, Tang M, Lei L, Zheng Y, Diao H, Wang TM, Liao Y, Wu YX, Zheng XH, Zhou T, Li XZ, Zhang PF, Chen XY, Yu X, Li F, Ji M, Sun Y, He YQ, Jia WH. Polygenic risk-stratified screening for nasopharyngeal carcinoma in high-risk endemic areas of China: a cost-effectiveness study. Front Public Health 2024; 12:1375533. [PMID: 38756891 PMCID: PMC11097958 DOI: 10.3389/fpubh.2024.1375533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024] Open
Abstract
Background Nasopharyngeal carcinoma (NPC) has an extremely high incidence rate in Southern China, resulting in a severe disease burden for the local population. Current EBV serologic screening is limited by false positives, and there is opportunity to integrate polygenic risk scores for personalized screening which may enhance cost-effectiveness and resource utilization. Methods A Markov model was developed based on epidemiological and genetic data specific to endemic areas of China, and further compared polygenic risk-stratified screening [subjects with a 10-year absolute risk (AR) greater than a threshold risk underwent EBV serological screening] to age-based screening (EBV serological screening for all subjects). For each initial screening age (30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, and 65-69 years), a modeled cohort of 100,000 participants was screened until age 69, and then followed until age 79. Results Among subjects aged 30 to 54 years, polygenic risk-stratified screening strategies were more cost-effective than age-based screening strategies, and almost comprised the cost-effectiveness efficiency frontier. For men, screening strategies with a 1-year frequency and a 10-year absolute risk (AR) threshold of 0.7% or higher were cost-effective, with an incremental cost-effectiveness ratio (ICER) below the willingness to pay (¥203,810, twice the local per capita GDP). Specifically, the strategies with a 10-year AR threshold of 0.7% or 0.8% are the most cost-effective strategies, with an ICER ranging from ¥159,752 to ¥201,738 compared to lower-cost non-dominated strategies on the cost-effectiveness frontiers. The optimal strategies have a higher probability (29.4-35.8%) of being cost-effective compared to other strategies on the frontier. Additionally, they reduce the need for nasopharyngoscopies by 5.1-27.7% compared to optimal age-based strategies. Likewise, for women aged 30-54 years, the optimal strategy with a 0.3% threshold showed similar results. Among subjects aged 55 to 69 years, age-based screening strategies were more cost-effective for men, while no screening may be preferred for women. Conclusion Our economic evaluation found that the polygenic risk-stratified screening could improve the cost-effectiveness among individuals aged 30-54, providing valuable guidance for NPC prevention and control policies in endemic areas of China.
Collapse
Affiliation(s)
- Da-Wei Yang
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jacob A. Miller
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, United States
| | - Wen-Qiong Xue
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | | | - Lin Lei
- Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Yuming Zheng
- Wuzhou Red Cross Hospital, Wuzhou, Guangxi, China
| | - Hua Diao
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Tong-Min Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ying Liao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yan-Xia Wu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiao-Hui Zheng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ting Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xi-Zhao Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Pei-Fen Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xue-Yin Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xia Yu
- Cancer Research Institute of Zhongshan City, Zhongshan Hospital of Sun Yat-sen University, Zhongshan, China
| | - Fugui Li
- Cancer Research Institute of Zhongshan City, Zhongshan Hospital of Sun Yat-sen University, Zhongshan, China
| | - Mingfang Ji
- Cancer Research Institute of Zhongshan City, Zhongshan Hospital of Sun Yat-sen University, Zhongshan, China
| | - Ying Sun
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yong-Qiao He
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wei-Hua Jia
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| |
Collapse
|
19
|
Zheng Z, Liu S, Sidorenko J, Wang Y, Lin T, Yengo L, Turley P, Ani A, Wang R, Nolte IM, Snieder H, Yang J, Wray NR, Goddard ME, Visscher PM, Zeng J. Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Nat Genet 2024; 56:767-777. [PMID: 38689000 PMCID: PMC11096109 DOI: 10.1038/s41588-024-01704-y] [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: 10/01/2022] [Accepted: 03/05/2024] [Indexed: 05/02/2024]
Abstract
We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.
Collapse
Affiliation(s)
- Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Shouye Liu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Ying Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - Alireza Ani
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Rujia Wang
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia
- Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
| |
Collapse
|
20
|
Hibler EA, Szymaniak B, Abbass MA. Colorectal Cancer Risk between Mendelian and Non-Mendelian Inheritance. Clin Colon Rectal Surg 2024; 37:140-145. [PMID: 38606051 PMCID: PMC11006447 DOI: 10.1055/s-0043-1770382] [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: 04/13/2024]
Abstract
Hereditary colorectal cancer has been an area of focus for research and public health practitioners due to our ability to quantify risk and then act based on such results by enrolling patients in surveillance programs. The wide access to genetic testing and whole-genome sequencing has resulted in identifying many low/moderate penetrance genes. Above all, our understanding of the family component of colorectal cancer has been improving. Polygenic scores are becoming part of the risk assessment for many cancers, and the data about polygenic risk scores for colorectal cancer is promising. The challenge is determining how we incorporate this data in clinical care.
Collapse
Affiliation(s)
- Elizabeth A. Hibler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Brittany Szymaniak
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Mohammad Ali Abbass
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| |
Collapse
|
21
|
Øvretveit K, Ingeström EML, Spitieris M, Tragante V, Wade KH, Thomas LF, Wolford BN, Wisløff U, Gudbjartsson DF, Holm H, Stefansson K, Brumpton BM, Hveem K. Polygenic risk scores associate with blood pressure traits across the lifespan. Eur J Prev Cardiol 2024; 31:644-654. [PMID: 38007706 PMCID: PMC11025038 DOI: 10.1093/eurjpc/zwad365] [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: 10/16/2023] [Revised: 10/18/2023] [Accepted: 11/02/2023] [Indexed: 11/28/2023]
Abstract
AIMS Hypertension is a major modifiable cause of morbidity and mortality that affects over 1 billion people worldwide. Blood pressure (BP) traits have a strong genetic component that can be quantified with polygenic risk scores (PRSs). To date, the performance of BP PRSs has mainly been assessed in adults, and less is known about polygenic hypertension risk in childhood. METHODS AND RESULTS Multiple PRSs for systolic BP (SBP), diastolic BP (DBP), and pulse pressure were developed using either genome-wide significant weights, pruning and thresholding, or Bayesian regression. Among 87 total PRSs, the top performer for each trait was applied in independent cohorts of children and adult to assess genotype-phenotype associations and disease risk across the lifespan. Differences between those with low (1st decile), average (2nd-9th decile), and high (10th decile) PRS emerge in the first years of life and are maintained throughout adulthood. These diverging BP trajectories also seem to affect cardiovascular and renal disease risk, with increased risk observed among those in the top decile and reduced risk among those in the bottom decile of the polygenic risk distribution compared with the rest of the population. CONCLUSION Genetic risk factors are associated with BP traits across the lifespan, beginning in the first years of life. Given the importance of exposure time in disease pathogenesis and the early rise in BP levels among those genetically susceptible, PRSs may help identify high-risk individuals prior to hypertension onset, facilitate primordial prevention, and reduce the burden of this public health challenge.
Collapse
Affiliation(s)
- Karsten Øvretveit
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
| | - Emma M L Ingeström
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Michail Spitieris
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Kaitlin H Wade
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, Bristol BS8 1TH, UK
- Avon Longitudinal Study of Parents and Children, Bristol BS8 1TH, UK
| | - Laurent F Thomas
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Brooke N Wolford
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
| | - Ulrik Wisløff
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Ben M Brumpton
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Kristian Hveem
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Innovation and Research, St. Olavs Hospital, Trondheim, Norway
| |
Collapse
|
22
|
Xiang R, Liu Y, Ben-Eghan C, Ritchie S, Lambert SA, Xu Y, Takeuchi F, Inouye M. Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305830. [PMID: 38699308 PMCID: PMC11065006 DOI: 10.1101/2024.04.15.24305830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N~408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N~40,466), where genetically less variable individuals (low vPGS) had increased conventional PGS accuracy (by ~19%) than genetically more variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.
Collapse
Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC, 3086, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, VIC, 3010, Australia
| | - Yang Liu
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, 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
| | - Chief Ben-Eghan
- 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
| | - Scott Ritchie
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, 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, Baker Heart and Diabetes Institute, Melbourne, Victoria, 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
| | - 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
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Fumihiko Takeuchi
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, 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
| |
Collapse
|
23
|
Gerussi A, Cappadona C, Bernasconi DP, Cristoferi L, Valsecchi MG, Carbone M, Invernizzi P, Asselta R. Improving predictive accuracy in primary biliary cholangitis: A new genetic risk score. Liver Int 2024. [PMID: 38619000 DOI: 10.1111/liv.15916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/05/2024] [Accepted: 03/11/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND AND AIMS Genetic variants influence primary biliary cholangitis (PBC) risk. We established and tested an accurate polygenic risk score (PRS) using these variants. METHODS Data from two Italian cohorts (OldIT 444 cases, 901 controls; NewIT 255 cases, 579 controls) were analysed. The latest international genome-wide meta-analysis provided effect size estimates. The PRS, together with human leukocyte antigen (HLA) status and sex, was included in an integrated risk model. RESULTS Starting from 46 non-HLA genes, 22 variants were selected. PBC patients in the OldIT cohort showed a higher risk score than controls: -.014 (interquartile range, IQR, -.023, .005) versus -.022 (IQR -.030, -.013) (p < 2.2 × 10-16). For genetic-based prediction, the area under the curve (AUC) was .72; adding sex increased the AUC to .82. Validation in the NewIT cohort confirmed the model's accuracy (.71 without sex, .81 with sex). Individuals in the top group, representing the highest 25%, had a PBC risk approximately 14 times higher than that of the reference group (lowest 25%; p < 10-6). CONCLUSION The combination of sex and a novel PRS accurately discriminated between PBC cases and controls. The model identified a subset of individuals at increased risk of PBC who might benefit from tailored monitoring.
Collapse
Affiliation(s)
- Alessio Gerussi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Claudio Cappadona
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Humanitas Research Hospital, Milan, Italy
| | - Davide Paolo Bernasconi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Laura Cristoferi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Maria Grazia Valsecchi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- Biostatistics and Clinical Epidemiology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - Marco Carbone
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Rosanna Asselta
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Humanitas Research Hospital, Milan, Italy
| |
Collapse
|
24
|
Stein MB, Jain S, Papini S, Campbell-Sills L, Choi KW, Martis B, Sun X, He F, Ware EB, Naifeh JA, Aliaga PA, Ge T, Smoller JW, Gelernter J, Kessler RC, Ursano RJ. Polygenic risk for suicide attempt is associated with lifetime suicide attempt in US soldiers independent of parental risk. J Affect Disord 2024; 351:671-682. [PMID: 38309480 DOI: 10.1016/j.jad.2024.01.254] [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: 09/03/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Suicide is a leading cause of death worldwide. Whereas some studies have suggested that a direct measure of common genetic liability for suicide attempts (SA), captured by a polygenic risk score for SA (SA-PRS), explains risk independent of parental history, further confirmation would be useful. Even more unsettled is the extent to which SA-PRS is associated with lifetime non-suicidal self-injury (NSSI). METHODS We used summary statistics from the largest available GWAS study of SA to generate SA-PRS for two non-overlapping cohorts of soldiers of European ancestry. These were tested in multivariable models that included parental major depressive disorder (MDD) and parental SA. RESULTS In the first cohort, 417 (6.3 %) of 6573 soldiers reported lifetime SA and 1195 (18.2 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.26, 95%CI:1.13-1.39, p < 0.001] per standardized unit SA-PRS]. In the second cohort, 204 (4.2 %) of 4900 soldiers reported lifetime SA, and 299 (6.1 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.20, 95%CI:1.04-1.38, p = 0.014]. A combined analysis of both cohorts yielded similar results. In neither cohort or in the combined analysis was SA-PRS significantly associated with NSSI. CONCLUSIONS PRS for SA conveys information about likelihood of lifetime SA (but not NSSI, demonstrating specificity), independent of self-reported parental history of MDD and parental history of SA. LIMITATIONS At present, the magnitude of effects is small and would not be immediately useful for clinical decision-making or risk-stratified prevention initiatives, but this may be expected to improve with further iterations. Also critical will be the extension of these findings to more diverse populations.
Collapse
Affiliation(s)
- Murray B Stein
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; VA San Diego Healthcare System, San Diego, CA, USA; Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA.
| | - Sonia Jain
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | - Santiago Papini
- Department of Psychology, University of Hawai'i at Mānoa, Honolulu, HI, USA
| | - Laura Campbell-Sills
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Karmel W Choi
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Brian Martis
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; VA San Diego Healthcare System, San Diego, CA, USA
| | - Xiaoying Sun
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | - Feng He
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | - Erin B Ware
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - James A Naifeh
- Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Pablo A Aliaga
- Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Joel Gelernter
- Departments of Psychiatry, Genetics, and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Robert J Ursano
- Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| |
Collapse
|
25
|
Xin J, Gu D, Li S, Qian S, Cheng Y, Shao W, Ben S, Chen S, Zhu L, Jin M, Chen K, Hu Z, Zhang Z, Du M, Shen H, Wang M. Integration of pathologic characteristics, genetic risk and lifestyle exposure for colorectal cancer survival assessment. Nat Commun 2024; 15:3042. [PMID: 38589358 PMCID: PMC11002003 DOI: 10.1038/s41467-024-47204-9] [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: 08/31/2023] [Accepted: 03/20/2024] [Indexed: 04/10/2024] Open
Abstract
The development of an effective survival prediction tool is key for reducing colorectal cancer mortality. Here, we apply a three-stage study to devise a polygenic prognostic score (PPS) for stratifying colorectal cancer overall survival. Leveraging two cohorts of 3703 patients, we first perform a genome-wide survival association analysis to develop eight candidate PPSs. Further using an independent cohort with 470 patients, we identify the 287 variants-derived PPS (i.e., PPS287) achieving an optimal prediction performance [hazard ratio (HR) per SD = 1.99, P = 1.76 × 10-8], accompanied by additional tests in two external cohorts, with HRs per SD of 1.90 (P = 3.21 × 10-14; 543 patients) and 1.80 (P = 1.11 × 10-9; 713 patients). Notably, the detrimental impact of pathologic characteristics and genetic risk could be attenuated by a healthy lifestyle, yielding a 7.62% improvement in the 5-year overall survival rate. Therefore, our findings demonstrate the integrated contribution of pathologic characteristics, germline variants, and lifestyle exposure to the prognosis of colorectal cancer patients.
Collapse
Affiliation(s)
- Junyi Xin
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dongying Gu
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Shuwei Li
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Sangni Qian
- Department of Epidemiology and Biostatistics at School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yifei Cheng
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wei Shao
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuai Ben
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Silu Chen
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Linjun Zhu
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mingjuan Jin
- Department of Epidemiology and Biostatistics at School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kun Chen
- Department of Epidemiology and Biostatistics at School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Mulong Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
- The Affiliated Suzhou Hospital of, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China.
| |
Collapse
|
26
|
Youssef O, Loukola A, Zidi-Mouaffak YHS, Tamlander M, Ruotsalainen S, Kilpeläinen E, Mars N, Ripatti S, Palotie A, Donner K, Carpén O. High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases. J Transl Med 2024; 104:100325. [PMID: 38220043 DOI: 10.1016/j.labinv.2024.100325] [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] [Revised: 12/11/2023] [Accepted: 01/05/2024] [Indexed: 01/16/2024] Open
Abstract
Formalin-fixed paraffin-embedded (FFPE) tissues stored in biobanks and pathology archives are a vast but underutilized source for molecular studies on different diseases. Beyond being the "gold standard" for preservation of diagnostic human tissues, FFPE samples retain similar genetic information as matching blood samples, which could make FFPE samples an ideal resource for genomic analysis. However, research on this resource has been hindered by the perception that DNA extracted from FFPE samples is of poor quality. Here, we show that germline disease-predisposing variants and polygenic risk scores (PRS) can be identified from FFPE normal tissue (FFPE-NT) DNA with high accuracy. We optimized the performance of FFPE-NT DNA on a genome-wide array containing 657,675 variants. Via a series of testing and validation phases, we established a protocol for FFPE-NT genotyping with results comparable with blood genotyping. The median call rate of FFPE-NT samples in the validation phase was 99.85% (range 98.26%-99.94%) and median concordance with matching blood samples was 99.79% (range 98.85%-99.9%). We also demonstrated that a rare pathogenic PALB2 genetic variant predisposing to cancer can be correctly identified in FFPE-NT samples. We further imputed the FFPE-NT genotype data and calculated the FFPE-NT genome-wide PRS in 3 diseases and 4 disease risk variables. In all cases, FFPE-NT and matching blood PRS were highly concordant (all Pearson's r > 0.95). The ability to precisely genotype FFPE-NT on a genome-wide array enables translational genomics applications of archived FFPE-NT samples with the possibility to link to corresponding phenotypes and longitudinal health data.
Collapse
Affiliation(s)
- Omar Youssef
- Department of Pathology, University of Helsinki, Helsinki, Finland; Clinical and Chemical Pathology Department, National Cancer Institute, Cairo University, Cairo, Egypt; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Anu Loukola
- Helsinki Biobank, Helsinki University Hospital (HUS), Helsinki, Finland
| | - Yossra H S Zidi-Mouaffak
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Helsinki Biobank, Helsinki University Hospital (HUS), Helsinki, Finland
| | - Max Tamlander
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sanni Ruotsalainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Elina Kilpeläinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Nina Mars
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Kati Donner
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Olli Carpén
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Helsinki Biobank, Helsinki University Hospital (HUS), Helsinki, Finland
| |
Collapse
|
27
|
Savage SA. Telomere length and cancer risk: finding Goldilocks. Biogerontology 2024; 25:265-278. [PMID: 38109000 DOI: 10.1007/s10522-023-10080-9] [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: 08/22/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023]
Abstract
Telomeres are the nucleoprotein complex at chromosome ends essential in genomic stability. Baseline telomere length (TL) is determined by rare and common germline genetic variants but shortens with age and is susceptible to certain environmental exposures. Cellular senescence or apoptosis are normally triggered when telomeres reach a critically short length, but cancer cells overcome these protective mechanisms and continue to divide despite chromosomal instability. Rare germline variants in telomere maintenance genes cause exceedingly short telomeres for age (< 1st percentile) and the telomere biology disorders, which are associated with elevated risks of bone marrow failure, myelodysplastic syndrome, acute myeloid leukemia, and squamous cell carcinoma of the head/neck and anogenital regions. Long telomeres due to rare germline variants in the same or different telomere maintenance genes are associated with elevated risks of other cancers, such as chronic lymphocytic leukemia or sarcoma. Early epidemiology studies of TL in the general population lacked reproducibility but new methods, including creation of a TL polygenic score using common variants, have found longer telomeres associated with excess risks of renal cell carcinoma, glioma, lung cancer, and others. It has become clear that when it comes to TL and cancer etiology, not too short, not too long, but "just right" telomeres are important in minimizing cancer risk.
Collapse
Affiliation(s)
- Sharon A Savage
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, 6E456, Bethesda, MD, 20892-6772, USA.
| |
Collapse
|
28
|
Singh RK, Zhao Y, Elze T, Fingert J, Gordon M, Kass MA, Luo Y, Pasquale LR, Scheetz T, Segrè AV, Wiggs JL, Zebardast N. Polygenic Risk Scores for Glaucoma Onset in the Ocular Hypertension Treatment Study. JAMA Ophthalmol 2024; 142:356-363. [PMID: 38483402 PMCID: PMC10941023 DOI: 10.1001/jamaophthalmol.2024.0151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/14/2024] [Indexed: 03/17/2024]
Abstract
Importance Primary open-angle glaucoma (POAG) is a highly heritable disease, with 127 identified risk loci to date. Polygenic risk score (PRS) may provide a clinically useful measure of aggregate genetic burden and improve patient risk stratification. Objective To assess whether a PRS improves prediction of POAG onset in patients with ocular hypertension. Design, Setting, and Participants This was a post hoc analysis of the Ocular Hypertension Treatment Study. Data were collected from 22 US sites with a mean (SD) follow-up of 14.0 (6.9) years. A total of 1636 participants were followed up from February 1994 to December 2008; 1077 participants were enrolled in an ancillary genetics study, of which 1009 met criteria for this analysis. PRS was calculated using summary statistics from the largest cross-ancestry POAG meta-analysis, with weights trained using 8 813 496 variants from 449 186 cross-ancestry participants in the UK Biobank. Data were analyzed from July 2022 to December 2023. Exposures From February 1994 to June 2002, participants were randomized to either topical intraocular pressure-lowering medication or close observation. After June 2002, both groups received medication. Main Outcomes and Measures Outcome measures were hazard ratios for POAG onset. Concordance index and time-dependent areas under the receiver operating characteristic curve were used to compare the predictive performance of multivariable Cox proportional hazards models. Results Of 1009 included participants, 562 (55.7%) were female, and the mean (SD) age was 55.9 (9.3) years. The mean (SD) PRS was significantly higher for 350 POAG converters (0.24 [0.95]) compared with 659 nonconverters (-0.12 [1.00]) (P < .001). POAG risk increased 1.36% (95% CI, 1.08-1.64) with each higher PRS decile, with conversion ranging from 9.52% (95% CI, 7.09-11.95) in the lowest PRS decile to 21.81% (95% CI, 19.37-24.25) in the highest decile. Comparison of low-risk and high-risk PRS tertiles showed a 2.0-fold increase in 20-year POAG risk for participants of European and African ancestries. In the subgroup randomized to delayed treatment, each increase in PRS decile was associated with a 0.52-year (95% CI, 0.01-1.03) decrease in age at diagnosis (P = .047). No significant linear association between PRS and age at POAG diagnosis was present in the early treatment group. Prediction models significantly improved with the addition of PRS as a covariate (C index = 0.77) compared with the Ocular Hypertension Treatment Study baseline model (C index = 0.75) (P < .001). Each 1-SD higher PRS conferred a mean hazard ratio of 1.25 (95% CI, 1.13-1.44) for POAG onset. Conclusions and Relevance Higher PRS was associated with increased risk for POAG in patients with ocular hypertension. The inclusion of a PRS improved the prediction of POAG onset. Trial Registration ClinicalTrials.gov Identifier: NCT00000125.
Collapse
Affiliation(s)
- Rishabh K. Singh
- Department of Ophthalmology, Columbia University Medical Center, New York, New York
- Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts
| | - Yan Zhao
- Massachusetts Eye and Ear, Harvard Medical School, Boston
| | - Tobias Elze
- Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts
| | - John Fingert
- Carver College of Medicine, University of Iowa, Iowa City
| | - Mae Gordon
- Washington University School of Medicine, St Louis, Missouri
| | - Michael A. Kass
- Washington University School of Medicine, St Louis, Missouri
| | - Yuyang Luo
- Massachusetts Eye and Ear, Harvard Medical School, Boston
| | | | - Todd Scheetz
- Carver College of Medicine, University of Iowa, Iowa City
| | - Ayellet V. Segrè
- Massachusetts Eye and Ear, Harvard Medical School, Boston
- Ocular Genomics Institute, Massachusetts Eye and Ear, Boston
| | - Janey L. Wiggs
- Massachusetts Eye and Ear, Harvard Medical School, Boston
- Ocular Genomics Institute, Massachusetts Eye and Ear, Boston
| | | |
Collapse
|
29
|
Liu Y, Ritchie SC, Teo SM, Ruuskanen MO, Kambur O, Zhu Q, Sanders J, Vázquez-Baeza Y, Verspoor K, Jousilahti P, Lahti L, Niiranen T, Salomaa V, Havulinna AS, Knight R, Méric G, Inouye M. Integration of polygenic and gut metagenomic risk prediction for common diseases. NATURE AGING 2024; 4:584-594. [PMID: 38528230 PMCID: PMC11031402 DOI: 10.1038/s43587-024-00590-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/13/2024] [Indexed: 03/27/2024]
Abstract
Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.
Collapse
Affiliation(s)
- Yang Liu
- 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, Victoria, Australia.
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Scott C Ritchie
- 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, Victoria, Australia
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- 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
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Shu Mei Teo
- 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, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Matti O Ruuskanen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Computing, University of Turku, Turku, Finland
| | - Oleg Kambur
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Jon Sanders
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne, Victoria, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Teemu Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Division of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Aki S Havulinna
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM-HiLIFE, University of Helsinki, Helsinki, Finland
| | - Rob Knight
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Guillaume Méric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
- Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia
- Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden
| | - 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, Victoria, Australia.
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- 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.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- The Alan Turing Institute, London, UK.
| |
Collapse
|
30
|
Liu X, Littlejohns TJ, Bešević J, Bragg F, Clifton L, Collister JA, Trichia E, Gray LJ, Khunti K, Hunter DJ. Incorporating polygenic risk into the Leicester Risk Assessment score for 10-year risk prediction of type 2 diabetes. Diabetes Metab Syndr 2024; 18:102996. [PMID: 38608567 DOI: 10.1016/j.dsx.2024.102996] [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: 09/05/2023] [Revised: 02/22/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024]
Abstract
AIMS We evaluated whether incorporating information on ethnic background and polygenic risk enhanced the Leicester Risk Assessment (LRA) score for predicting 10-year risk of type 2 diabetes. METHODS The sample included 202,529 UK Biobank participants aged 40-69 years. We computed the LRA score, and developed two new risk scores using training data (80% sample): LRArev, which incorporated additional information on ethnic background, and LRAprs, which incorporated polygenic risk for type 2 diabetes. We assessed discriminative and reclassification performance in a test set (20% sample). Type 2 diabetes was ascertained using primary care, hospital inpatient and death registry records. RESULTS Over 10 years, 7,476 participants developed type 2 diabetes. The Harrell's C indexes were 0.796 (95% Confidence Interval [CI] 0.785, 0.806), 0.802 (95% CI 0.792, 0.813), and 0.829 (95% CI 0.820, 0.839) for the LRA, LRArev and LRAprs scores, respectively. The LRAprs score significantly improved the overall reclassification compared to the LRA (net reclassification index [NRI] = 0.033, 95% CI 0.015, 0.049) and LRArev (NRI = 0.040, 95% CI 0.024, 0.055) scores. CONCLUSIONS Polygenic risk moderately improved the performance of the existing LRA score for 10-year risk prediction of type 2 diabetes.
Collapse
Affiliation(s)
- Xiaonan Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Jelena Bešević
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK; MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lei Clifton
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Eirini Trichia
- Nuffield Department of Population Health, University of Oxford, Oxford, UK; MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Laura J Gray
- Department of Population Health Sciences, University of Leicester, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - David J Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, UK; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
| |
Collapse
|
31
|
Yanes T, Tiller J, Haining CM, Wallingford C, Otlowski M, Keogh L, McInerney-Leo A, Lacaze P. Future implications of polygenic risk scores for life insurance underwriting. NPJ Genom Med 2024; 9:25. [PMID: 38555372 PMCID: PMC10981684 DOI: 10.1038/s41525-024-00407-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 03/08/2024] [Indexed: 04/02/2024] Open
Affiliation(s)
- Tatiane Yanes
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia.
| | - Jane Tiller
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Casey M Haining
- Centre for Health Equity, Melbourne School of Population and Global Health, University of Melbourne, Victoria, Australia
| | - Courtney Wallingford
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
| | - Margaret Otlowski
- Centre for Law and Genetics, Faculty of Law, University of Tasmania, Churchill Avenue, Hobart, Tasmania, Australia
| | - Louise Keogh
- Centre for Health Equity, Melbourne School of Population and Global Health, University of Melbourne, Victoria, Australia
| | - Aideen McInerney-Leo
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
| | - Paul Lacaze
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| |
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
Kikuchi M, Miyashita A, Hara N, Kasuga K, Saito Y, Murayama S, Kakita A, Akatsu H, Ozaki K, Niida S, Kuwano R, Iwatsubo T, Nakaya A, Ikeuchi T. Polygenic effects on the risk of Alzheimer's disease in the Japanese population. Alzheimers Res Ther 2024; 16:45. [PMID: 38414085 PMCID: PMC10898021 DOI: 10.1186/s13195-024-01414-x] [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: 08/10/2023] [Accepted: 02/11/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer's disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans. METHODS In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD. RESULTS The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results. CONCLUSIONS We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.
Collapse
Affiliation(s)
- Masataka Kikuchi
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Science, The University of Tokyo, 6-2-3 Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan.
- Department of Medical Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan.
| | - Akinori Miyashita
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan
| | - Norikazu Hara
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan
| | - Kensaku Kasuga
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan
| | - Yuko Saito
- Brain Bank for Aging Research (Department of Neuropathology), Tokyo Metropolitan Institute of Geriatrics and Gerontology, Tokyo, Japan
| | - Shigeo Murayama
- Brain Bank for Aging Research (Department of Neuropathology), Tokyo Metropolitan Institute of Geriatrics and Gerontology, Tokyo, Japan
- Brain Bank for Neurodevelopmental, Neurological and Psychiatric Disorders, United Graduate School of Child Development, Osaka University, Osaka, Japan
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Hiroyasu Akatsu
- Department of General Medicine & General Internal Medicine, Nagoya City University Graduate School of Medicine, Nagoya, Japan
| | - Kouichi Ozaki
- Medical Genome Center, National Center for Geriatrics and Gerontology, Research Institute, Aichi, Japan
- RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Shumpei Niida
- Core Facility Administration, National Center for Geriatrics and Gerontology, Research Institute, Aichi, Japan
| | - Ryozo Kuwano
- Social Welfare Corporation Asahigawaso, Asahigawaso Research Institute, Okayama, Japan
| | - Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Akihiro Nakaya
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Science, The University of Tokyo, 6-2-3 Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan.
| |
Collapse
|
34
|
Laza C, Niño de Guzmán E, Gea M, Plazas M, Posso M, Rué M, Castells X, Román M. "For and against" factors influencing participation in personalized breast cancer screening programs: a qualitative systematic review until March 2022. Arch Public Health 2024; 82:23. [PMID: 38389068 PMCID: PMC10882761 DOI: 10.1186/s13690-024-01248-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: 11/09/2023] [Accepted: 02/05/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND Personalized breast cancer screening is a novel strategy that estimates individual risk based on age, breast density, family history of breast cancer, personal history of benign breast lesions, and polygenic risk. Its goal is to propose personalized early detection recommendations for women in the target population based on their individual risk. Our aim was to synthesize the factors that influence women's decision to participate in personalized breast cancer screening, from the perspective of women and health care professionals. METHODS Systematic review of qualitative evidence on factors influencing participation in personalized Breast Cancer Screening. We searched in Medline, Web of science, Scopus, EMBASE, CINAHL and PsycINFO for qualitative and mixed methods studies published up to March 2022. Two reviewers conducted study selection and extracted main findings. We applied the best-fit framework synthesis and adopted the Multilevel influences on the cancer care continuum model for analysis. After organizing initial codes into the seven levels of the selected model, we followed thematic analysis and developed descriptive and analytical themes. We assessed the methodological quality with the Critical Appraisal Skills Program tool. RESULTS We identified 18 studies published between 2017 and 2022, conducted in developed countries. Nine studies were focused on women (n = 478) and in four studies women had participated in a personalized screening program. Nine studies focused in health care professionals (n = 162) and were conducted in primary care and breast cancer screening program settings. Factors influencing women's decision to participate relate to the women themselves, the type of program (personalized breast cancer screening) and perspective of health care professionals. Factors that determined women participation included persistent beliefs and insufficient knowledge about breast cancer and personalized screening, variable psychological reactions, and negative attitudes towards breast cancer risk estimates. Other factors against participation were insufficient health care professionals knowledge on genetics related to breast cancer and personalized screening process. The factors that were favourable included the women's perceived benefits for themselves and the positive impact on health systems. CONCLUSION We identified the main factors influencing women's decisions to participate in personalized breast cancer screening. Factors related to women, were the most relevant negative factors. A future implementation requires improving health literacy for women and health care professionals, as well as raising awareness of the strategy in society.
Collapse
Affiliation(s)
- Celmira Laza
- Department of Nursing and Physiotherapy, University of Lleida, Lleida, Spain
- Biomedical Research Institute of Lleida Fundació Dr. Pifarré (IRBLleida), Lleida, Spain
| | - Ena Niño de Guzmán
- Cancer Prevention and Control Program, Institut Català d' Oncologia, Barcelona, Spain
| | - Montserrat Gea
- Department of Nursing and Physiotherapy, University of Lleida, Lleida, Spain
- Biomedical Research Institute of Lleida Fundació Dr. Pifarré (IRBLleida), Lleida, Spain
| | - Merideidy Plazas
- Cochrane Associated Center- University Foundation of Health Sciences, Bogotá, Colombia
| | - Margarita Posso
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain
| | - Montserrat Rué
- Biomedical Research Institute of Lleida Fundació Dr. Pifarré (IRBLleida), Lleida, Spain
- Basic Medical Sciences, University of Lleida, Lleida, Spain
| | - Xavier Castells
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain
| | - Marta Román
- Department of Epidemiology and Evaluation, Hospital del Mar Research Institute, Barcelona, Spain.
| |
Collapse
|
35
|
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.
Collapse
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
| |
Collapse
|
36
|
Jiang X, Zai CC, Dimick MK, Kennedy JL, Young LT, Birmaher B, Goldstein BI. Psychiatric Polygenic Risk Scores Across Youth With Bipolar Disorder, Youth at High Risk for Bipolar Disorder, and Controls. J Am Acad Child Adolesc Psychiatry 2024:S0890-8567(24)00062-5. [PMID: 38340895 DOI: 10.1016/j.jaac.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/23/2023] [Accepted: 02/01/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVE There is a pronounced gap in knowledge regarding polygenic underpinnings of youth bipolar disorder (BD). This study aimed to compare polygenic risk scores (PRSs) in youth with BD, youth at high clinical and/or familial risk for BD (HR), and controls. METHOD Participants were 344 youths of European ancestry (13-20 years old), including 136 youths with BD, 121 HR youths, and 87 controls. PRSs for BD, schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were constructed using independent genome-wide summary statistics from adult cohorts. Multinomial logistic regression was used to examine the association between each PRS and diagnostic status (BD vs HR vs controls). All genetic analyses controlled for age, sex, and 2 genetic principal components. RESULTS The BD group showed significantly higher BD-PRS than the control group (odds ratio = 1.54, 95% CI = 1.13-2.10, p = .006), with the HR group numerically intermediate. BD-PRS explained 7.9% of phenotypic variance. PRSs for schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were not significantly different among groups. In the BD group, BD-PRS did not significantly differ in relation to BD subtype, age of onset, psychosis, or family history of BD. CONCLUSION BD-PRS derived from adult genome-wide summary statistics is elevated in youth with BD. Absence of significant between-group differences in PRSs for other psychiatric disorders supports the specificity of BD-PRS in youth. These findings add to the biological validation of BD in youth and could have implications for early identification and diagnosis. To enhance clinical utility, future genome-wide association studies that focus specifically on early-onset BD are warranted, as are studies integrating additional genetic and environmental factors. DIVERSITY & INCLUSION STATEMENT We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.
Collapse
Affiliation(s)
- Xinyue Jiang
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada
| | - Clement C Zai
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada; Tanenbaum Centre for Pharmacogenetics, Psychiatric Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Mikaela K Dimick
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - James L Kennedy
- University of Toronto, Toronto, Ontario, Canada; Tanenbaum Centre for Pharmacogenetics, Psychiatric Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - L Trevor Young
- University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Boris Birmaher
- Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada.
| |
Collapse
|
37
|
Liao K, Zöllner S. A Stacking Framework for Polygenic Risk Prediction in Admixed Individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.31.24302103. [PMID: 38434717 PMCID: PMC10907988 DOI: 10.1101/2024.01.31.24302103] [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: 03/05/2024]
Abstract
Polygenic risk scores (PRS) are summaries of an individual's personalized genetic risk for a trait or disease. However, PRS often perform poorly for phenotype prediction when the ancestry of the target population does not match the population in which GWAS effect sizes were estimated. For many populations this can be addressed by performing GWAS in the target population. However, admixed individuals (whose genomes can be traced to multiple ancestral populations) lie on an ancestry continuum and are not easily represented as a discrete population. Here, we propose slaPRS (stacking local ancestry PRS), which incorporates multiple ancestry GWAS to alleviate the ancestry dependence of PRS in admixed samples. slaPRS uses ensemble learning (stacking) to combine local population specific PRS in regions across the genome. We compare slaPRS to single population PRS and a method that combines single population PRS globally. In simulations, slaPRS outperformed existing approaches and reduced the ancestry dependence of PRS in African Americans. In lipid traits from African British individuals (UK Biobank), slaPRS again improved on single population PRS while performing comparably to the globally combined PRS. slaPRS provides a data-driven and flexible framework to incorporate multiple population-specific GWAS and local ancestry in samples of admixed ancestry.
Collapse
Affiliation(s)
- Kevin Liao
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Sebastian Zöllner
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
- University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA
| |
Collapse
|
38
|
Hanson H, Astiazaran-Symonds E, Amendola LM, Balmaña J, Foulkes WD, James P, Klugman S, Ngeow J, Schmutzler R, Voian N, Wick MJ, Pal T, Tischkowitz M, Stewart DR. Response to Stern. Genet Med 2024; 26:101030. [PMID: 38156990 DOI: 10.1016/j.gim.2023.101030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024] Open
Affiliation(s)
- Helen Hanson
- Peninsula Clinical Genetics Service, Royal Devon University Healthcare NHS Foundation Trust, Exeter, United Kingdom; Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Esteban Astiazaran-Symonds
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD; Department of Medicine, College of Medicine-Tucson, University of Arizona, Tucson, AZ
| | | | - Judith Balmaña
- Hereditary Cancer Genetics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain; Medical Oncology Department, Hospital Universitari Vall d'Hebron, Vall d'Hebron Hospital Campus, Barcelona, Spain
| | - William D Foulkes
- Departments of Human Genetics, Oncology and Medicine, McGill University, Montréal, Québec, Canada
| | - Paul James
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia; Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Susan Klugman
- Division of Reproductive & Medical Genetics, Department of Obstetrics & Gynecology and Women's Health, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY
| | - Joanne Ngeow
- Genomic Medicine, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Rita Schmutzler
- Center of Integrated Oncology (CIO), University of Cologne, Cologne, Germany; Center for Hereditary Breast and Ovarian Cancer, University Hospital of Cologne, Cologne, Germany
| | - Nicoleta Voian
- Providence Genetic Risk Clinic, Providence Cancer Institute, Portland, OR
| | - Myra J Wick
- Departments of Obstetrics and Gynecology and Clinical Genomics, Mayo Clinic, Rochester, MN
| | - Tuya Pal
- Department of Medicine, Vanderbilt University Medical Center/Vanderbilt-Ingram Cancer Center, Nashville, TN
| | - Marc Tischkowitz
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Douglas R Stewart
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| |
Collapse
|
39
|
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: 1] [Impact Index Per Article: 1.0] [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.
Collapse
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
| |
Collapse
|
40
|
Zhang J, Wu Y, Chen S, Luo Q, Xi H, Li J, Qin X, Peng Y, Ma N, Yang B, Qiu X, Lu W, Chen Y, Jiang Y, Chen P, Liu Y, Zhang C, Zhang Z, Xiong Y, Shen J, Liang H, Ren Y, Ying C, Dong M, Li X, Xu C, Wang H, Zhang D, Xu C, Huang H. Prospective prenatal cell-free DNA screening for genetic conditions of heterogenous etiologies. Nat Med 2024; 30:470-479. [PMID: 38253798 DOI: 10.1038/s41591-023-02774-x] [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: 02/14/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Abstract
Prenatal cell-free DNA (cfDNA) screening uses extracellular fetal DNA circulating in the peripheral blood of pregnant women to detect prevalent fetal chromosomal anomalies. However, numerous severe conditions with underlying single-gene defects are not included in current prenatal cfDNA screening. In this prospective, multicenter and observational study, pregnant women at elevated risk for fetal genetic conditions were enrolled for a cfDNA screening test based on coordinative allele-aware target enrichment sequencing. This test encompasses the following three of the most frequent pathogenic genetic variations: aneuploidies, microdeletions and monogenic variants. The cfDNA screening results were compared to invasive prenatal or postnatal diagnostic test results for 1,090 qualified participants. The comprehensive cfDNA screening detected a genetic alteration in 135 pregnancies with 98.5% sensitivity and 99.3% specificity relative to standard diagnostics. Of 876 fetuses with suspected structural anomalies on ultrasound examination, comprehensive cfDNA screening identified 55 (56.1%) aneuploidies, 6 (6.1%) microdeletions and 37 (37.8%) single-gene pathogenic variants. The inclusion of targeted monogenic conditions alongside chromosomal aberrations led to a 60.7% increase (from 61 to 98) in the detection rate. Overall, these data provide preliminary evidence that a comprehensive cfDNA screening test can accurately identify fetal pathogenic variants at both the chromosome and single-gene levels in high-risk pregnancies through a noninvasive approach, which has the potential to improve prenatal evaluation of fetal risks for severe genetic conditions arising from heterogenous molecular etiologies. ClinicalTrials.gov registration: ChiCTR2100045739 .
Collapse
Affiliation(s)
- Jinglan Zhang
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China.
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- Beijing BioBiggen Technology Co., Ltd, Beijing, China.
| | - Yanting Wu
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Songchang Chen
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
| | - Qiong Luo
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Women's Reproductive Health of Zhejiang Province, and Zhejiang Provincial Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Hui Xi
- National Health Commission (NHC) Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, China
| | - Jianli Li
- Beijing BioBiggen Technology Co., Ltd, Beijing, China
| | - Xiaomei Qin
- Beijing BioBiggen Technology Co., Ltd, Beijing, China
| | - Ying Peng
- National Health Commission (NHC) Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, China
| | - Na Ma
- National Health Commission (NHC) Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, China
| | - Bingxin Yang
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiang Qiu
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Weiliang Lu
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Yuan Chen
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Women's Reproductive Health of Zhejiang Province, and Zhejiang Provincial Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Ying Jiang
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Women's Reproductive Health of Zhejiang Province, and Zhejiang Provincial Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Panpan Chen
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Women's Reproductive Health of Zhejiang Province, and Zhejiang Provincial Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Yifeng Liu
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Women's Reproductive Health of Zhejiang Province, and Zhejiang Provincial Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Chen Zhang
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
| | - Zhiwei Zhang
- Beijing BioBiggen Technology Co., Ltd, Beijing, China
| | - Yu Xiong
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Jie Shen
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Huan Liang
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Yunyun Ren
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Chunmei Ying
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Minyue Dong
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Women's Reproductive Health of Zhejiang Province, and Zhejiang Provincial Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaotian Li
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Congjian Xu
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Hua Wang
- National Health Commission (NHC) Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, China.
- NHC Key Laboratory of Birth Defects Research, Prevention and Treatment, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, China.
| | - Dan Zhang
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Key Laboratory of Women's Reproductive Health of Zhejiang Province, and Zhejiang Provincial Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China.
| | - Chenming Xu
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China.
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
| | - Hefeng Huang
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China.
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China.
- Shanghai Frontiers Science Research Center of Reproduction and Development, Shanghai, China.
| |
Collapse
|
41
|
Considerations for clinical implementation of polygenic risk scores in diverse US populations. Nat Med 2024; 30:354-355. [PMID: 38374345 DOI: 10.1038/s41591-024-02801-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
|
42
|
Ding P, Du Y, Jiang X, Chen H, Huang L. Establishment and analysis of a novel diagnostic model for systemic juvenile idiopathic arthritis based on machine learning. Pediatr Rheumatol Online J 2024; 22:18. [PMID: 38243323 PMCID: PMC10797915 DOI: 10.1186/s12969-023-00949-x] [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: 10/31/2023] [Accepted: 12/21/2023] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND Systemic juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis with clinical features such as fever, lymphadenopathy, arthritis, rash, and serositis. It seriously affects the growth and development of children and has a high rate of disability and mortality. SJIA may result from genetic, infectious, or autoimmune factors since the precise source of the disease is unknown. Our study aims to develop a genetic-based diagnostic model to explore the identification of SJIA at the genetic level. METHODS The gene expression dataset of peripheral blood mononuclear cell (PBMC) samples from SJIA was collected from the Gene Expression Omnibus (GEO) database. Then, three GEO datasets (GSE11907-GPL96, GSE8650-GPL96 and GSE13501) were merged and used as a training dataset, which included 125 SJIA samples and 92 health samples. GSE7753 was used as a validation dataset. The limma method was used to screen differentially expressed genes (DEGs). Feature selection was performed using Lasso, random forest (RF)-recursive feature elimination (RFE) and RF classifier. RESULTS We finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples. And we combined the 4 key genes and performed a grid search as well as 10-fold cross-validation with 5 repetitions to finally identify the RF model with optimal mtry. The mean area under the curve (AUC) value for 5-fold cross-validation was greater than 0.95. The model's performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained. All of the above AUC values demonstrated the strong robustness of the SJIA diagnostic model. CONCLUSIONS We successfully developed a new SJIA diagnostic model that can be used for a novel aid in the identification of SJIA. In addition, the identification of 4 key genes that may serve as potential biomarkers for SJIA provides new insights to further understand the mechanisms of SJIA.
Collapse
Affiliation(s)
- Pan Ding
- Department of Medical Record Statistics, Wenzhou People's Hospital, Wenzhou, China
| | - Yi Du
- Lianyungang Maternal and Child Health Hospital, Lianyungang, China
| | - Xinyue Jiang
- Zhoushan Center for Disease Control and Prevention, Zhoushan, China
| | - Huajian Chen
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| | - Li Huang
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| |
Collapse
|
43
|
Hodonsky CJ, Turner AW, Khan MD, Barrientos NB, Methorst R, Ma L, Lopez NG, Mosquera JV, Auguste G, Farber E, Ma WF, Wong D, Onengut-Gumuscu S, Kavousi M, Peyser PA, van der Laan SW, Leeper NJ, Kovacic JC, Björkegren JLM, Miller CL. Multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci. CELL GENOMICS 2024; 4:100465. [PMID: 38190101 PMCID: PMC10794848 DOI: 10.1016/j.xgen.2023.100465] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/07/2023] [Accepted: 11/19/2023] [Indexed: 01/09/2024]
Abstract
Genome-wide association studies (GWASs) have identified hundreds of risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWASs, and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotypes to identify quantitative trait loci for expression (eQTLs) and splicing (sQTLs) in coronary arteries from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary artery; 19% exhibited cell-type-specific expression. Colocalization revealed subgroups of eGenes unique to CAD and blood pressure GWAS. Fine-mapping highlighted additional eGenes, including TBX20 and IL5. We also identified sQTLs for 1,690 genes, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing to accurately identify disease-relevant isoform expression. Our work provides a patient-derived coronary artery eQTL resource and exemplifies the need for diverse study populations and multifaceted approaches to characterize gene regulation in disease processes.
Collapse
Affiliation(s)
- Chani J Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Adam W Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Mohammad Daud Khan
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Nelson B Barrientos
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Ruben Methorst
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nicolas G Lopez
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305, USA
| | - Jose Verdezoto Mosquera
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Gaëlle Auguste
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Emily Farber
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Wei Feng Ma
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Medical Scientist Training Program, Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA
| | - Doris Wong
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, 3000 CA Rotterdam, the Netherlands
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan, Ann Arbor, MI 48019, USA
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Nicholas J Leeper
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305, USA
| | - Jason C Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; St. Vincent's Clinical School, University of New South Wales, Sydney, NSW 2052, Australia
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Huddinge, Karolinska Institutet, 141 52 Huddinge, Sweden
| | - Clint L Miller
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.
| |
Collapse
|
44
|
Kim J, Kim D, Bae HJ, Park BE, Kang TS, Lim SH, Lee SY, Chung YH, Ryu JW, Lee MY, Yang PS, Joung B. Associations of combined polygenic risk score and glycemic status with atrial fibrillation, coronary artery disease and ischemic stroke. Cardiovasc Diabetol 2024; 23:5. [PMID: 38172896 PMCID: PMC10765629 DOI: 10.1186/s12933-023-02021-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/10/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND It is unknown whether high hemoglobin A1c (HbA1c) is associated with increases in the risk of cardiovascular disease among individuals with elevated genetic susceptibility. We aimed to investigate the association between HbA1c and atrial fibrillation (AF), coronary artery disease (CAD), and ischemic stroke according to the polygenic risk score (PRS). METHODS The UK Biobank cohort included 502,442 participants aged 40-70 years who were recruited from 22 assessment centers across the United Kingdom from 2006 to 2010. This study included 305,605 unrelated individuals with available PRS and assessed new-onset AF, CAD, and ischemic stroke. The participants were divided into tertiles based on the validated PRS for each outcome. Within each PRS tertiles, the risks of incident events associated with HbA1c levels were investigated and compared with HbA1c < 5.7% and low PRS. Data were analyzed from November 2022 to May 2023. RESULTS Of 305,605 individuals, 161,605 (52.9%) were female, and the mean (SD) age was 56.6 (8.1) years. During a median follow-up of 11.9 (interquartile range 11.1-12.6) years, the incidences of AF, CAD, and ischemic stroke were 4.6, 2.9 and 1.1 per 100 person-years, respectively. Compared to individuals with HbA1c < 5.7% and low PRS, individuals with HbA1c ≥ 6.5% and high PRS had a 2.67-times higher risk for AF (hazard ratio [HR], 2.67; 95% confidence interval (CI), 2.43-2.94), 5.71-times higher risk for CAD (HR, 5.71; 95% CI, 5.14-6.33) and 2.94-times higher risk for ischemic stroke (HR, 2.94; 95% CI, 2.47-3.50). In the restricted cubic spline models, while a U-shaped trend was observed between HbA1c and the risk of AF, dose-dependent increases were observed between HbA1c and the risk of CAD and ischemic stroke regardless PRS tertile. CONCLUSIONS Our results suggest that the nature of the dose-dependent relationship between HbA1c levels and cardiovascular disease in individuals with different PRS is outcome-specific. This adds to the evidence that PRS may play a role together with glycemic status in the development of cardiovascular disease.
Collapse
Affiliation(s)
- Juntae Kim
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea
| | - Dongmin Kim
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea
| | - Han-Joon Bae
- Department of Cardiology, Daegu Catholic University Medical Center, 33 Duryugongwonro 17- gil, Nam-gu, Daegu, 42472, Republic of Korea
| | - Byoung-Eun Park
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea
| | - Tae Soo Kang
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea
| | - Seong-Hoon Lim
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea
| | - Su Yeon Lee
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea
| | - Young Hak Chung
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea
| | - Ji Wung Ryu
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea
| | - Myung-Yong Lee
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 31116, Republic of Korea
| | - Pil-Sung Yang
- Department of Cardiology, CHA Bundang Medical Center, CHA University, 59, Yatap-ro, Bundang-gu, Seongnam, 13496, Gyeonggi-do, Republic of Korea.
| | - Boyoung Joung
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, 50 Yonseiro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
| |
Collapse
|
45
|
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: 32] [Impact Index Per Article: 32.0] [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.
Collapse
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.
| |
Collapse
|
46
|
Benincasa G, Suades R, Padró T, Badimon L, Napoli C. Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases. EUROPEAN HEART JOURNAL. CARDIOVASCULAR PHARMACOTHERAPY 2023; 9:758-769. [PMID: 37562936 DOI: 10.1093/ehjcvp/pvad059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/19/2023] [Accepted: 08/09/2023] [Indexed: 08/12/2023]
Abstract
Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.
Collapse
Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania 'Luigi Vanvitelli', 80138 Naples, Italy
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
| | - Rosa Suades
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV) Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Teresa Padró
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV) Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Lina Badimon
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV) Instituto de Salud Carlos III, 28029 Madrid, Spain
- Cardiovascular Research Chair, Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania 'Luigi Vanvitelli', 80138 Naples, Italy
| |
Collapse
|
47
|
Bright JK, Rayner C, Freeman Z, Zavos HMS, Ahmadzadeh YI, Viding E, McAdams TA. Using twin-pairs to assess potential bias in polygenic prediction of externalising behaviours across development. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299910. [PMID: 38168304 PMCID: PMC10760293 DOI: 10.1101/2023.12.13.23299910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Prediction from polygenic scores may be confounded sources of passive gene-environment correlation (rGE; e.g. population stratification, assortative mating, and environmentally mediated effects of parental genotype on child phenotype). Using genomic data from 10,000 twin pairs, we asked whether polygenic scores from the recent externalising genome-wide association study predicted conduct problems, ADHD symptomology and callous-unemotional traits, and whether these predictions are biased by rGE. We ran regression models including within-family and between-family polygenic scores, to separate the direct genetic influence on a trait from environmental influences that correlate with genes (indirect genetic effects). Findings suggested that this externalising polygenic score is a good index of direct genetic influence on conduct and ADHD-related symptoms across development, with minimal bias from rGE, although the polygenic score predicted less variance in CU traits. Post-hoc analyses showed some indirect genetic effects acting on a common factor indexing stability of conduct problems across time and contexts.
Collapse
Affiliation(s)
- Joanna K Bright
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, Kings College London
| | - Christopher Rayner
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, Kings College London
| | - Ze Freeman
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, Kings College London
| | - Helena M S Zavos
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, Kings College London
| | - Yasmin I Ahmadzadeh
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, Kings College London
| | - Essi Viding
- Division of Psychology and Language Sciences, University College London
| | - Tom A McAdams
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, Kings College London
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| |
Collapse
|
48
|
Solomon BD. The future of commercial genetic testing. Curr Opin Pediatr 2023; 35:615-619. [PMID: 37218641 PMCID: PMC10667560 DOI: 10.1097/mop.0000000000001260] [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] [Indexed: 05/24/2023]
Abstract
PURPOSE OF REVIEW There are thousands of different clinical genetic tests currently available. Genetic testing and its applications continue to change rapidly for multiple reasons. These reasons include technological advances, accruing evidence about the impact and effects of testing, and many complex financial and regulatory factors. RECENT FINDINGS This article considers a number of key issues and axes related to the current and future state of clinical genetic testing, including targeted versus broad testing, simple/Mendelian versus polygenic and multifactorial testing models, genetic testing for individuals with high suspicion of genetic conditions versus ascertainment through population screening, the rise of artificial intelligence in multiple aspects of the genetic testing process, and how developments such as rapid genetic testing and the growing availability of new therapies for genetic conditions may affect the field. SUMMARY Genetic testing is expanding and evolving, including into new clinical applications. Developments in the field of genetics will likely result in genetic testing becoming increasingly in the purview of a very broad range of clinicians, including general paediatricians as well as paediatric subspecialists.
Collapse
Affiliation(s)
- Benjamin D. Solomon
- Medical Genetics Branch, National Human Genome Research Institute, United States of America
| |
Collapse
|
49
|
Katz AE, Ganesh SK. Advancements in the Genetics of Spontaneous Coronary Artery Dissection. Curr Cardiol Rep 2023; 25:1735-1743. [PMID: 37979122 PMCID: PMC10810930 DOI: 10.1007/s11886-023-01989-1] [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] [Accepted: 10/24/2023] [Indexed: 11/19/2023]
Abstract
PURPOSE OF REVIEW Spontaneous coronary artery dissection (SCAD) is a significant cause of acute myocardial infarction that is increasingly recognized in young and middle-aged women. The etiology of SCAD is likely multifactorial and may include the interaction of environmental and individual factors. Here, we summarize the current understanding of the genetic factors contributing to the development of SCAD. RECENT FINDINGS The molecular findings underlying SCAD have been demonstrated to include a combination of rare DNA sequence variants with large effects, common variants contributing to a complex genetic architecture, and variants with intermediate impact. The genes associated with SCAD highlight the role of arterial cells and their extracellular matrix in the pathogenesis of the disease and shed light on the relationship between SCAD and other disorders, including fibromuscular dysplasia and connective tissue diseases. While up to 10% of affected individuals may harbor a rare variant with large effect, SCAD most often presents as a complex genetic condition. Analyses of larger and more diverse cohorts will continue to improve our understanding of risk susceptibility loci and will also enable consideration of the clinical utility of genetic testing strategies in the management of SCAD.
Collapse
Affiliation(s)
- Alexander E Katz
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, USA
- Department of Human Genetics, University of Michigan, 7220, MSRB III, 1150 West Medical Center Drive, Ann Arbor, MI, 48109-0644, USA
| | - Santhi K Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, USA.
- Department of Human Genetics, University of Michigan, 7220, MSRB III, 1150 West Medical Center Drive, Ann Arbor, MI, 48109-0644, USA.
| |
Collapse
|
50
|
Gallagher JH, Vassy JL, Clayman ML. Navigating the uncertainty of precision cancer screening: The role of shared decision-making. PEC INNOVATION 2023; 2:100127. [PMID: 37214512 PMCID: PMC10194244 DOI: 10.1016/j.pecinn.2023.100127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 05/24/2023]
Abstract
Objective Describe how applying a shared decision making (SDM) lens to the implementation of new technologies can improve patient-centeredness. Methods This paper argues that the emergence of polygenic risk scores (PRS) for cancer screening presents an illustrative opportunity to include SDM when novel technologies enter clinical care. Results PRS are novel tools that indicate an individual's genetic risk of a given disease relative to the population. PRS are anticipated to help identify individuals most and least likely to benefit from screening. However, PRS have several types of uncertainty, including validity across populations, disparate computational methods, and inclusion of different genomic data across laboratories. Conclusion Implementing SDM alongside new technologies could prove useful for their ethical and patient-centered utilization. SDM's importance as an approach to decision-making will not diminish, as evidence, uncertainty, and patient values will remain intrinsic to the art and science of clinical care. Innovation SDM can help providers and patients navigate the considerable uncertainty inherent in implementing new technologies, enabling decision-making based on existing evidence and patient values.
Collapse
Affiliation(s)
- Joseph H. Gallagher
- Virginia Commonwealth University School of Medicine, Richmond, VA, United States of America
| | - Jason L. Vassy
- Center for Healthcare Organization and Implementation Research (CHOIR), Veterans Health Administration, Bedford MA and Boston MA, United States
- Harvard Medical School, Boston, MA United States
- Brigham and Women’s Hospital, Boston, MA, United States
- Population Precision Health, Ariadne Labs, Boston, MA, United States
| | - Marla L. Clayman
- Center for Healthcare Organization and Implementation Research (CHOIR), Veterans Health Administration, Bedford MA and Boston MA, United States
- UMass Chan School of Medicine, Department of Population and Quantitative Health Sciences, Worcester, MA, United States
| |
Collapse
|