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Foo JC, Völker MP, Streit F, Frank J, Zacharias N, Zillich L, Sirignano L, Nürnberg P, Wienker TF, Wagner M, Nöthen MM, Nothnagel M, Walter H, Lenz B, Spanagel R, Kiefer F, Winterer G, Rietschel M, Witt SH. Polygenic risk scores for nicotine use and family history of smoking are associated with smoking behaviour. Drug Alcohol Depend 2024; 263:112415. [PMID: 39197361 DOI: 10.1016/j.drugalcdep.2024.112415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 07/22/2024] [Accepted: 08/12/2024] [Indexed: 09/01/2024]
Abstract
INTRODUCTION Formal genetics studies show that smoking is influenced by genetic factors; exploring this on the molecular level can offer deeper insight into the etiology of smoking behaviours. METHODS Summary statistics from the latest wave of the GWAS and Sequencing Consortium of Alcohol and Nicotine (GSCAN) were used to calculate polygenic risk scores (PRS) in a sample of ~2200 individuals who smoke/individuals who never smoked. The associations of smoking status with PRS for Smoking Initiation (i.e., Lifetime Smoking; SI-PRS), and Fagerström Test for Nicotine Dependence (FTND) score with PRS for Cigarettes per Day (CpD-PRS) were examined, as were distinct/additive effects of parental smoking on smoking status. RESULTS SI-PRS explained 10.56% of variance (Nagelkerke-R2) in smoking status (p=6.45x10-30). In individuals who smoke, CpD-PRS was associated with FTND score (R2=5.03%, p=1.88x10-12). Parental smoking alone explained R2=3.06% (p=2.43×10-12) of smoking status, and 0.96% when added to the most informative SI-PRS model (total R²=11.52%). CONCLUSION These results show the potential utility of molecular genetic data for research investigating smoking prevention. The fact that PRS explains more variance than family history highlights progress from formal to molecular genetics; the partial overlap and increased predictive value when using both suggests the importance of combining these approaches.
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Affiliation(s)
- Jerome C Foo
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; College of Health Sciences, Department of Psychiatry, University of Alberta, Edmonton, Canada; Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Maja P Völker
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim/Heidelberg/Ulm, Germany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Norman Zacharias
- Department of Otorhinolaryngology, Head and Neck Surgery, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Benjamin Franklin, Berlin, Germany
| | - Lea Zillich
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim/Heidelberg/Ulm, Germany; Department of Translational Brain Research, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; HITBR Hector Institute for Translational Brain Research gGmbH, Mannheim, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lea Sirignano
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Peter Nürnberg
- Cologne Center for Genomics (CCG), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne 50931, Germany
| | - Thomas F Wienker
- Department of Molecular Human Genetics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Michael Wagner
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Markus M Nöthen
- Institute for Human Genetics, University Hospital Bonn, Bonn, Germany
| | - Michael Nothnagel
- Department of Statistical Genetics and Bioinformatics, Cologne Center for Genomics, University of Cologne, Cologne, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Division of Mind and Brain Research, Charité- Universitätsmedizin Berlin, Berlin, Germany
| | - Bernd Lenz
- Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Rainer Spanagel
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Falk Kiefer
- Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Georg Winterer
- Department of Anesthesiology and Intensive Care Medicine, Campus Virchow-Klinikum and Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany; Pharmaimage Biomarker Solutions GmbH, Berlin, Germany; PI Health Solutions GmbH, Berlin, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim/Heidelberg/Ulm, Germany.
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Fu X, Chen Y, Luo X, Ide JS, Li CSR. Gray matter volumetric correlates of the polygenic risk of depression: A study of the Human Connectome Project data. Eur Neuropsychopharmacol 2024; 87:2-12. [PMID: 38936229 DOI: 10.1016/j.euroneuro.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 06/04/2024] [Accepted: 06/07/2024] [Indexed: 06/29/2024]
Abstract
Genetic factors confer risks for depression. Understanding the neural endophenotypes, including brain morphometrics, of genetic predisposition to depression would help in unraveling the pathophysiology of depression. We employed voxel-based morphometry (VBM) to examine how gray matter volumes (GMVs) were correlated with the polygenic risk score (PRS) for depression in 993 young adults of the Human Connectome Project. The phenotype of depression was quantified with a DSM-oriented scale of the Achenbach Adult Self-Report. The PRS for depression was computed for each subject using the Psychiatric Genomics Association Study as the base sample. In multiple regression with age, sex, race, drinking severity, and total intracranial volume as covariates, regional GMVs in positive correlation with the PRS were observed in bilateral hippocampi and right gyrus rectus. Regional GMVs in negative correlation with the PRS were observed in a wide swath of brain regions, including bilateral frontal and temporal lobes, anterior cingulate cortex, thalamus, lingual gyri, cerebellum, and the left postcentral gyrus, cuneus, and parahippocampal gyrus. We also found sex difference in anterior cingulate volumes in manifesting the genetic risk of depression. In addition, the GMV of the right cerebellum crus I partially mediated the link from PRS to depression severity. These findings add to the literature by highlighting 1) a more diverse pattern of the volumetric markers of depression, with most regions showing lower but others higher GMVs in association with the genetic risks of depression, and 2) the cerebellar GMV as a genetically informed neural phenotype of depression, in neurotypical individuals.
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Affiliation(s)
- Xiaoya Fu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Xingguang Luo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Jaime S Ide
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA; Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT 06520, USA; Wu Tsai Institute, Yale University, New Haven, CT 06520, USA.
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JOENSUU LAURA, WALLER KATJA, KANKAANPÄÄ ANNA, PALVIAINEN TEEMU, KAPRIO JAAKKO, SILLANPÄÄ ELINA. Genetic Liability to Cardiovascular Disease, Physical Activity, and Mortality: Findings from the Finnish Twin Cohort. Med Sci Sports Exerc 2024; 56:1954-1963. [PMID: 38768019 PMCID: PMC11419275 DOI: 10.1249/mss.0000000000003482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
PURPOSE We investigated whether longitudinally assessed physical activity (PA) and adherence specifically to World Health Organization PA guidelines mitigate or moderate mortality risk regardless of genetic liability to cardiovascular disease (CVD). We also estimated the causality of the PA-mortality association. METHODS The study used the older Finnish Twin Cohort with 4897 participants aged 33 to 60 yr (54.3% women). Genetic liability to coronary heart disease and systolic and diastolic blood pressure was estimated with polygenic risk scores (PRS) derived from the Pan-UK Biobank ( N ≈ 400,000; >1,000,000 genetic variants). Leisure-time PA was assessed with validated and structured questionnaires three times during 1975 to 1990. The main effects of adherence to PA guidelines and the PRS × PA interactions were evaluated with Cox proportional hazards models against all-cause and CVD mortality. A cotwin control design with 180 monozygotic twin pairs discordant for meeting the guidelines was used for causal inference. RESULTS During the 17.4-yr (mean) follow-up (85,136 person-years), 1195 participants died, with 389 CVD deaths. PRS (per 1 SD increase) were associated with a 17% to 24% higher CVD mortality risk but not with all-cause mortality except for the PRS for diastolic blood pressure. Adherence to PA guidelines did not show significant independent main effects or interactions with all-cause or CVD mortality. Twins whose activity levels adhered to PA guidelines over a 15-yr period did not have statistically significantly reduced mortality risk compared with their less active identical twin sibling. The findings were similar among high, intermediate, and low genetic risk levels for CVD. CONCLUSIONS The genetically informed Finnish Twin Cohort data could not confirm that adherence to PA guidelines either mitigates or moderates genetic CVD risk or causally reduces mortality risk.
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Affiliation(s)
- LAURA JOENSUU
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND
- Gerontology Research Center, University of Jyväskylä, Jyväskylä, FINLAND
| | - KATJA WALLER
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND
| | - ANNA KANKAANPÄÄ
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND
- Gerontology Research Center, University of Jyväskylä, Jyväskylä, FINLAND
| | - TEEMU PALVIAINEN
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, FINLAND
| | - JAAKKO KAPRIO
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, FINLAND
| | - ELINA SILLANPÄÄ
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND
- Wellbeing Services County of Central Finland, Jyväskylä, FINLAND
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Chen IC, Chan TC, Yang HW, Chen YJ, Chen YM. Interplay between polygenic risk score and solar insolation: Implication for systemic lupus erythematosus diagnosis and pathogenesis. Semin Arthritis Rheum 2024; 68:152531. [PMID: 39154620 DOI: 10.1016/j.semarthrit.2024.152531] [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: 08/09/2023] [Revised: 07/03/2024] [Accepted: 08/07/2024] [Indexed: 08/20/2024]
Abstract
OBJECTIVES This research elucidates the correlation between solar radiation insolation, polygenic risk score (PRS), and systemic lupus erythematosus (SLE) diagnosis, utilizing genomic, environmental, and clinical data. METHODS We included 1,800 SLE participants and 1,800 controls from the Taiwan Precision Medicine Initiative, genotyped via the Affymetrix Genome-Wide TWB 2.0 SNP Array. The study employed a SLE-PRS tailored for individuals of Taiwanese ancestry, comprising 27 single nucleotide polymorphisms (SNPs). QGIS computed solar radiation insolation from participants' residences. We employed logistic regression to investigate the associations between SLE-PRS, solar insolation susceptibility, and SLE. Additive and multiplicative interactions were utilized to assess the interactions between solar insolation and SLE-PRS regarding the risk of SLE. RESULTS SLE patients showed decreased solar insolation (p < 0.001). The highest decile of SLE-PRS exhibited a statistically significant lower solar insolation 1, 3, 6, and 12 months prior to diagnosis as compared to the lowest decile. Specifically, there were significant differences observed at 1 and 12 months (p = 0.025 and p = 0.004, respectively). It suggests that higher SLE-PRS correlated with reduced solar insolation tolerance. We observed an increase in SLE risk across ascending SLE-PRS percentiles exclusively in the high solar insolation group, not in the low solar insolation group. However, the interaction effect of SLE-PRS and solar insolation on SLE risk is not statistically significant. Compared to the lowest decile, the highest SLE-PRS decile showed a 10.98-fold increase in SLE risk (95 % CI, 3.773-31.952, p < 0.001). High SLE-PRS scores in conjunction with high solar insolation contribute to SLE incidence. CONCLUSIONS Our study unveils the intertwined nature of UV insolation and polygenic risks in SLE. Future studies should explore the preventative potential of robust solar radiation protection for high-risk individuals before the disease onset.
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Affiliation(s)
- I-Chieh Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ta-Chien Chan
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei City, Taiwan; Institute of Public Health, School of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Hui-Wen Yang
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung-Hsing University, Taichung, Taiwan
| | - Yen-Ju Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan; Division of Allergy, Immunology and Rheumatology, Taichung Veterans General Hospital, Taichung, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ming Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung-Hsing University, Taichung, Taiwan; Division of Allergy, Immunology and Rheumatology, Taichung Veterans General Hospital, Taichung, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan; Precision Medicine Research Center, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
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Chen PY, Wen SH. Integrating Genome-Wide Polygenic Risk Scores With Nongenetic Models to Predict Surgical Site Infection After Total Knee Arthroplasty Using United Kingdom Biobank Data. J Arthroplasty 2024; 39:2471-2477.e1. [PMID: 38735551 DOI: 10.1016/j.arth.2024.05.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 05/06/2024] [Accepted: 05/06/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Prediction of the risk of developing surgical site infection (SSI) in patients following total knee arthroplasty (TKA) is of clinical importance. Genetic susceptibility is involved in developing TKA-related SSI. Previously reported models for predicting SSI were constructed using nongenetic risk factors without incorporating genetic risk factors. To address this issue, we performed a genome-wide association study (GWAS) using the UK Biobank database. METHODS Adult patients who underwent primary TKA (n = 19,767) were analyzed and divided into SSI (n = 269) and non-SSI (n = 19,498) cohorts. Nongenetic covariates, including demographic data and preoperative comorbidities, were recorded. Genetic variants associated with SSI were identified by GWAS and included to obtain standardized polygenic risk scores (zPRS, an estimate of genetic risk). Prediction models were established through analyses of multivariable logistic regression and the receiver operating characteristic curve. RESULTS There were 4 variants (rs117896641, rs111686424, rs8101598, and rs74648298) achieving genome-wide significance that were identified. The logistic regression analysis revealed 7 significant risk factors: increasing zPRS, decreasing age, men, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, and peripheral vascular disease. The areas under the receiver operating characteristic curve were 0.628 and 0.708 when zPRS (model 1) and nongenetic covariates (model 2) were used as predictors, respectively. The areas under the receiver operating characteristic curve increased to 0.76 when both zPRS and nongenetic covariates (model 3) were used as predictors. A risk-prediction nomogram was constructed based on model 3 to visualize the relative effect of statistically significant covariates on the risk of SSI and predict the probability of developing SSI. Age and zPRS were the top 2 covariates that contributed to the risk, with younger age and higher zPRS associated with higher risks. CONCLUSIONS Our GWAS identified 4 novel variants that were significantly associated with susceptibility to SSI following TKA. Integrating genome-wide zPRS with nongenetic risk factors improved the performance of the model in predicting SSI.
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Affiliation(s)
- Pei-Yu Chen
- Tzu Chi University Center for Health and Welfare Data Science, Ministry of Health and Welfare, Hualien City, Taiwan; Institute of Medical Sciences, Tzu Chi University, Hualien City, Taiwan
| | - Shu-Hui Wen
- Institute of Medical Sciences, Tzu Chi University, Hualien City, Taiwan; Department of Public Health, College of Medicine, Tzu Chi University, Hualien City, Taiwan
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Capalbo A, de Wert G, Mertes H, Klausner L, Coonen E, Spinella F, Van de Velde H, Viville S, Sermon K, Vermeulen N, Lencz T, Carmi S. Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations. Hum Reprod Update 2024; 30:529-557. [PMID: 38805697 PMCID: PMC11369226 DOI: 10.1093/humupd/dmae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. OBJECTIVE AND RATIONALE This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. SEARCH METHODS We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. OUTCOMES The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. WIDER IMPLICATIONS The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.
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Affiliation(s)
- Antonio Capalbo
- Juno Genetics, Department of Reproductive Genetics, Rome, Italy
- Center for Advanced Studies and Technology (CAST), Department of Medical Genetics, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Guido de Wert
- Department of Health, Ethics & Society, CAPHRI-School for Public Health and Primary Care and GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Heidi Mertes
- Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Liraz Klausner
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Edith Coonen
- Departments of Clinical Genetics and Reproductive Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- School for Oncology and Developmental Biology, GROW, Maastricht University, Maastricht, The Netherlands
| | - Francesca Spinella
- Eurofins GENOMA Group Srl, Molecular Genetics Laboratories, Department of Scientific Communication, Rome, Italy
| | - Hilde Van de Velde
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
- Brussels IVF, UZ Brussel, Brussel, Belgium
| | - Stephane Viville
- Laboratoire de Génétique Médicale LGM, Institut de Génétique Médicale d’Alsace IGMA, INSERM UMR 1112, Université de Strasbourg, France
- Laboratoire de Diagnostic Génétique, Unité de Génétique de l’infertilité (UF3472), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Karen Sermon
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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Sung HL, Lin WY. Causal effects of cardiovascular health on five epigenetic clocks. Clin Epigenetics 2024; 16:134. [PMID: 39334501 DOI: 10.1186/s13148-024-01752-5] [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: 08/03/2024] [Accepted: 09/25/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND This work delves into the relationship between cardiovascular health (CVH) and aging. Previous studies have shown an association of ideal CVH with a slower aging rate, measured by epigenetic age acceleration (EAA). However, the causal relationship between CVH and EAA has remained unexplored. METHODS AND RESULTS We performed genome-wide association studies (GWAS) on the (12-point) CVH score and its components using the Taiwan Biobank data, in which weighted genetic risk scores were treated as instrumental variables. Subsequently, we conducted a one-sample Mendelian Randomization (MR) analysis with the two-stage least-squares method on 2383 participants to examine the causal relationship between the (12-point) CVH score and EAA. As a result, we observed a significant causal effect of the CVH score on GrimAge acceleration (GrimEAA) (β [SE]: - 0.993 [0.363] year; p = 0.0063) and DNA methylation-based plasminogen activator inhibitor-1 (DNAmPAI-1) (β [SE]: - 0.294 [0.099] standard deviation (sd) of DNAmPAI-1; p = 0.0030). Digging individual CVH components in depth, the ideal total cholesterol score (0 [poor], 1 [intermediate], or 2 [ideal]) was causally associated with DNAmPAI-1 (β [SE]: - 0.452 [0.150] sd of DNAmPAI-1; false discovery rate [FDR] q = 0.0102). The ideal body mass index (BMI) score was causally associated with GrimEAA (β [SE]: - 2.382 [0.952] years; FDR q = 0.0498) and DunedinPACE (β [SE]: - 0.097 [0.030]; FDR q = 0.0044). We also performed a two-sample MR analysis using the summary statistics from European GWAS. We observed that the (12-point) CVH score exhibits a significant causal effect on Horvath's intrinsic epigenetic age acceleration (β [SE]: - 0.389 [0.186] years; p = 0.036) and GrimEAA (β [SE]: - 0.526 [0.244] years; p = 0.031). Furthermore, we detected causal effects of BMI (β [SE]: 0.599 [0.081] years; q = 2.91E-12), never smoking (β [SE]: - 2.981 [0.524] years; q = 1.63E-7), walking (β [SE]: - 4.313 [1.236] years; q = 0.004), and dried fruit intake (β [SE]: - 1.523 [0.504] years; q = 0.013) on GrimEAA in the European population. CONCLUSIONS Our research confirms the causal link between maintaining an ideal CVH and epigenetic age. It provides a tangible pathway for individuals to improve their health and potentially slow aging.
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Affiliation(s)
- Hsien-Liang Sung
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Room 501, No. 17, Xu-Zhou Road, Taipei, 100, Taiwan
| | - Wan-Yu Lin
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Room 501, No. 17, Xu-Zhou Road, Taipei, 100, Taiwan.
- Master of Public Health Degree Program, College of Public Health, National Taiwan University, Taipei, Taiwan.
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Kwak S, Wang C, Usyk M, Wu F, Freedman ND, Huang WY, McCullough ML, Um CY, Shrubsole MJ, Cai Q, Li H, Ahn J, Hayes RB. Oral Microbiome and Subsequent Risk of Head and Neck Squamous Cell Cancer. JAMA Oncol 2024:2824198. [PMID: 39325441 PMCID: PMC11428028 DOI: 10.1001/jamaoncol.2024.4006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2024]
Abstract
Importance The oral microbiota may be involved in development of head and neck squamous cell cancer (HNSCC), yet current evidence is largely limited to bacterial 16S amplicon sequencing or small retrospective case-control studies. Objective To test whether oral bacterial and fungal microbiomes are associated with subsequent risk of HNSCC development. Design, Setting, and Participants Prospective nested case-control study among participants providing oral samples in 3 epidemiological cohorts, the American Cancer Society Cancer Prevention Study II Nutrition Cohort, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, and the Southern Community Cohort Study. Two hundred thirty-six patients who prospectively developed HNSCC were identified during a mean (SD) of 5.1 (3.6) years of follow-up. Control participants who remained HNSCC free were selected by 2:1 frequency matching on cohort, age, sex, race and ethnicity, and time since oral sample collection. Data analysis was conducted in 2023. Exposures Characterization of the oral bacterial microbiome using whole-genome shotgun sequencing and the oral fungal microbiome using internal transcribed spacer sequencing. Association of bacterial and fungal taxa with HNSCC was assessed by analysis of compositions of microbiomes with bias correction. Association with red and orange oral pathogen complexes was tested by logistic regression. A microbial risk score for HNSCC risk was calculated from risk-associated microbiota. Main Outcomes and Measures The primary outcome was HNSCC incidence. Results The study included 236 HNSCC case participants with a mean (SD) age of 60.9 (9.5) years and 24.6% women during a mean of 5.1 (3.6) years of follow-up, and 485 matched control participants. Overall microbiome diversity at baseline was not related to subsequent HNSCC risk; however 13 oral bacterial species were found to be differentially associated with development of HNSCC. The species included the newly identified Prevotella salivae, Streptococcus sanguinis, and Leptotrichia species, as well as several species belonging to beta and gamma Proteobacteria. The red/orange periodontal pathogen complex was moderately associated with HNSCC risk (odds ratio, 1.06 per 1 SD; 95% CI, 1.00-1.12). A 1-SD increase in microbial risk score (created based on 22 bacteria) was associated with a 50% increase in HNSCC risk (multivariate odds ratio, 1.50; 95% CI, 1.21-1.85). No fungal taxa associated with HNSCC risk were identified. Conclusions and Relevance This case-control study yielded compelling evidence that oral bacteria are a risk factor for HNSCC development. The identified bacteria and bacterial complexes hold promise, along with other risk factors, to identify high-risk individuals for personalized prevention of HNSCC.
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Affiliation(s)
- Soyoung Kwak
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
- NYU Laura and Isaac Perlmutter Cancer Center, New York, New York
| | - Chan Wang
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
- NYU Laura and Isaac Perlmutter Cancer Center, New York, New York
| | - Mykhaylo Usyk
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
- NYU Laura and Isaac Perlmutter Cancer Center, New York, New York
| | - Feng Wu
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
- NYU Laura and Isaac Perlmutter Cancer Center, New York, New York
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | | | - Caroline Y Um
- Department of Population Science, American Cancer Society, Atlanta, Georgia
| | - Martha J Shrubsole
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Qiuyin Cai
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Huilin Li
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
- NYU Laura and Isaac Perlmutter Cancer Center, New York, New York
| | - Jiyoung Ahn
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
- NYU Laura and Isaac Perlmutter Cancer Center, New York, New York
| | - Richard B Hayes
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
- NYU Laura and Isaac Perlmutter Cancer Center, New York, New York
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9
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Rumker L, Sakaue S, Reshef Y, Kang JB, Yazar S, Alquicira-Hernandez J, Valencia C, Lagattuta KA, Mah-Som A, Nathan A, Powell JE, Loh PR, Raychaudhuri S. Identifying genetic variants that influence the abundance of cell states in single-cell data. Nat Genet 2024:10.1038/s41588-024-01909-1. [PMID: 39327486 DOI: 10.1038/s41588-024-01909-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 08/14/2024] [Indexed: 09/28/2024]
Abstract
Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.
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Affiliation(s)
- Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yakir Reshef
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joyce B Kang
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seyhan Yazar
- Translational Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Jose Alquicira-Hernandez
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Translational Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Cristian Valencia
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Annelise Mah-Som
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph E Powell
- Translational Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Po-Ru Loh
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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10
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Rosenthal EA, Hsu L, Thomas M, Peters U, Kachulis C, Patterson K, Jarvik GP. Comparing Ancestry Standardization Approaches for a Transancestry Colorectal Cancer Polygenic Risk Score. Genet Epidemiol 2024. [PMID: 39315597 DOI: 10.1002/gepi.22590] [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: 05/15/2024] [Revised: 08/01/2024] [Accepted: 09/03/2024] [Indexed: 09/25/2024]
Abstract
Colorectal cancer (CRC) is a complex disease with monogenic, polygenic and environmental risk factors. Polygenic risk scores (PRSs) aim to identify high polygenic risk individuals. Due to differences in genetic background, PRS distributions vary by ancestry, necessitating standardization. We compared four post-hoc methods using the All of Us Research Program Whole Genome Sequence data for a transancestry CRC PRS. We contrasted results from linear models trained on A. the entire data or an ancestrally diverse subset AND B. covariates including principal components of ancestry or admixture. Standardization with the training subset also adjusted the variance. All methods performed similarly within ancestry, OR (95% C.I.) per s.d. change in PRS: African 1.5 (1.02, 2.08), Admixed American 2.2 (1.27, 3.85), European 1.6 (1.43, 1.89), and Middle Eastern 1.1 (0.71, 1.63). Using admixture and an ancestrally diverse training set provided distributions closest to standard Normal. Training a model on ancestrally diverse participants, adjusting both the mean and variance using admixture as covariates, created standard Normal z-scores, which can be used to identify patients at high polygenic risk. These scores can be incorporated into comprehensive risk calculation including other known risk factors, allowing for more precise risk estimates.
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Affiliation(s)
- Elisabeth A Rosenthal
- Division Medical Genetics, School of Medicine, University of Washington, Seattle, Washington, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | | | - Karynne Patterson
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Gail P Jarvik
- Division Medical Genetics, School of Medicine, University of Washington, Seattle, Washington, USA
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
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11
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Naito T, Inoue K, Namba S, Sonehara K, Suzuki K, Matsuda K, Kondo N, Toda T, Yamauchi T, Kadowaki T, Okada Y. Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores. COMMUNICATIONS MEDICINE 2024; 4:181. [PMID: 39304733 PMCID: PMC11415376 DOI: 10.1038/s43856-024-00596-7] [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: 10/12/2023] [Accepted: 08/22/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND Although polygenic risk scores (PRSs) are expected to be helpful in precision medicine, it remains unclear whether high-PRS groups are more likely to benefit from preventive interventions for diseases. Recent methodological advancements enable us to predict treatment effects at the individual level. METHODS We employed causal forest to explore the relationship between PRSs and individual risk of diseases associated with certain environmental factors. Following simulations illustrating its performance, we applied our approach to investigate the individual risk of cardiometabolic diseases, including coronary artery diseases (CAD) and type 2 diabetes (T2D), associated with obesity and smoking among individuals from UK Biobank (UKB; n = 369,942) and BioBank Japan (BBJ; n = 149,421). RESULTS Here we find the heterogeneous association of obesity and smoking with diseases across PRS values, complicated by the multi-dimensional combination of individual characteristics such as age and sex. The highest positive correlations of PRSs and the exposure-related disease risks are observed between obesity and T2D in UKB and between smoking and CAD in BBJ (Spearman's ρ = 0.61 and 0.32, respectively). However, most relationships are weak or negative, suggesting that high-PRS groups will not necessarily benefit most from environmental factor prevention. CONCLUSIONS Our study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine.
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Affiliation(s)
- Tatsuhiko Naito
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.
- Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama City, Kanagawa, Japan.
| | - Kosuke Inoue
- Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Hakubi Center, Kyoto University, Kyoto, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama City, Kanagawa, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama City, Kanagawa, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ken Suzuki
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Naoki Kondo
- Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tatsushi Toda
- Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama City, Kanagawa, Japan.
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan.
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Osaka, Japan.
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12
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Mehta UM, Roy N, Bahuguna A, Kotambail A, Arunachal G, Venkatasubramanian G, Thirthalli J. Incremental predictive value of genetic risk and functional brain connectivity in determining antipsychotic response in schizophrenia. Psychiatry Res 2024; 342:116201. [PMID: 39321637 DOI: 10.1016/j.psychres.2024.116201] [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: 05/18/2024] [Revised: 09/11/2024] [Accepted: 09/15/2024] [Indexed: 09/27/2024]
Abstract
We aimed to assess the incremental value of schizophrenia polygenic risk score (PgRS) and resting-state functional brain connectivity (rsFC) when added to clinical data in predicting the six-week response to oral risperidone (Risperdal) in schizophrenia. Fifty-seven, 54, and 43 individuals in a group of never-before-treated first-episode schizophrenia had good quality whole-genome sequencing (10x), rsFC, and both genomic and rsFC data, respectively, at baseline. Symptom severity ratings were obtained at baseline and six-weeks of oral risperidone (Risperdal) treatment. The primary outcome was the percentage change in the Positive and Negative Syndrome Scale Total scores after risperidone (Risperdal) treatment. Clinical, PgRS, and rsFC determinants of treatment response were first evaluated independently. Subsequently, three blocks of hierarchical multiple regression analyses with leave-one-out cross-validation (n = 43), were implemented to study clinical, clinical + PgRS and clinical + PgRS + rsFC determinants of treatment response. While the combined clinical variables did not show a statistically significant prediction of treatment response, adding PgRS (9 % R2 change) and rsFC (26 % R2 change) in hierarchical steps, significantly improved the overall proportion of variance explained in treatment response. This proof-of-concept investigation underscores the incremental benefits offered by genetic and neuroimaging metrics over clinical measures in determining prospectively-ascertained short-term treatment response in first-episode schizophrenia.
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Affiliation(s)
- Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore 560029, India.
| | - Neelabja Roy
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore 560029, India
| | - Ashutosh Bahuguna
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore 560029, India
| | - Ananthapadmanabha Kotambail
- Department of Human Genetics, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore 560029, India
| | - Gautham Arunachal
- Department of Human Genetics, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore 560029, India
| | - Ganesan Venkatasubramanian
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore 560029, India
| | - Jagadisha Thirthalli
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
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13
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Hafeman D, Uher R, Merranko J, Zwicker A, Goldstein B, Goldstein T, Axelson D, Monk K, Sakolsky D, Iyengar S, Diler R, Nimgaonkar V, Birmaher B. Person-level contributions of bipolar polygenic risk score to the prediction of new-onset bipolar disorder in at-risk offspring. J Affect Disord 2024; 368:S0165-0327(24)01599-4. [PMID: 39299598 DOI: 10.1016/j.jad.2024.09.107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Revised: 09/12/2024] [Accepted: 09/14/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND Previous work indicates that polygenic risk scores (PRS) for bipolar disorder (BD) are elevated in adults and youth with BD, but whether BD-PRS can inform person-level diagnostic prediction is unknown. Here, we test whether BD-PRS improves performance of a previously published risk calculator (RC) for BD. METHODS 156 parents with BD-I/II and their offspring ages 6-18 were recruited and evaluated with standardized diagnostic assessments every two years for >12 years. DNA was extracted from saliva samples, genotyping performed, and BD-PRS calculated based on a 2021 meta-analysis. Using a bootstrapped and cross-validated penalized Cox regression, we assessed whether BD-PRS (alone and interacting with clinical variables) improved RC performance. RESULTS Of 227 offspring, 38 developed BD during follow-up. The penalized regression selected BD-PRS and interactions between BD-PRS and parental age at mood disorder onset (AAO), depression, and anxiety. The resulting RC discriminated offspring who developed BD (vs. those that did not) with good accuracy (AUC = 0.81); removing BD-PRS and its interaction terms was associated with a significant decrement to the AUC (decrement = 0.07, p = 0.039). Further exploration of selected interaction terms indicated that all were significant (p-values<0.02), indicating that BD-PRS has a larger effect on the outcome in offspring with depression and anxiety, whose affected parent had a younger AAO. CONCLUSIONS The addition of BD-PRS to clinical/demographic predictors in the RC significantly improved its accuracy. BD-PRS predicted BD on the person-level, particularly in offspring of parents with earlier AAO who already had symptoms of anxiety and depression at intake.
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Affiliation(s)
- Danella Hafeman
- University of Pittsburgh School of Medicine, Department of Psychiatry, Western Psychiatric Hospital, University of Pittsburgh Medical Center, United States of America.
| | - Rudolf Uher
- Dalhousie University, Department of Psychiatry, United States of America
| | - John Merranko
- University of Pittsburgh School of Medicine, Department of Psychiatry, Western Psychiatric Hospital, University of Pittsburgh Medical Center, United States of America
| | - Alyson Zwicker
- Dalhousie University, Department of Psychiatry, United States of America
| | - Benjamin Goldstein
- Center for Addiction and Mental Health, University of Toronto Faculty of Medicine, United States of America
| | - Tina Goldstein
- University of Pittsburgh School of Medicine, Department of Psychiatry, Western Psychiatric Hospital, University of Pittsburgh Medical Center, United States of America
| | - David Axelson
- Nationwide Children's Hospital and The Ohio State College of Medicine, United States of America
| | - Kelly Monk
- University of Pittsburgh School of Medicine, Department of Psychiatry, Western Psychiatric Hospital, University of Pittsburgh Medical Center, United States of America
| | - Dara Sakolsky
- University of Pittsburgh School of Medicine, Department of Psychiatry, Western Psychiatric Hospital, University of Pittsburgh Medical Center, United States of America
| | - Satish Iyengar
- University of Pittsburgh, Department of Statistics, United States of America
| | - Rasim Diler
- University of Pittsburgh School of Medicine, Department of Psychiatry, Western Psychiatric Hospital, University of Pittsburgh Medical Center, United States of America
| | - Vishwajit Nimgaonkar
- University of Pittsburgh School of Medicine, Department of Psychiatry, Western Psychiatric Hospital, University of Pittsburgh Medical Center, United States of America
| | - Boris Birmaher
- University of Pittsburgh School of Medicine, Department of Psychiatry, Western Psychiatric Hospital, University of Pittsburgh Medical Center, United States of America
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14
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Vilkaite G, Vogel J, Mattsson-Carlgren N. Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer's disease. Cell Rep Med 2024; 5:101735. [PMID: 39293391 DOI: 10.1016/j.xcrm.2024.101735] [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: 04/30/2024] [Revised: 07/28/2024] [Accepted: 08/20/2024] [Indexed: 09/20/2024]
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by the aggregation of β-amyloid (Aβ) and tau in the brain. Breakthroughs in disease-modifying treatments targeting Aβ bring new hope for the management of AD. But to effectively modify and someday even prevent AD, a better understanding is needed of the biological mechanisms that underlie and link Aβ and tau in AD. Developments of high-throughput omics, including genomics, proteomics, and transcriptomics, together with molecular imaging of Aβ and tau with positron emission tomography (PET), allow us to discover and understand the biological pathways that regulate the aggregation and spread of Aβ and tau in living humans. The field of integrated omics and PET studies of Aβ and tau in AD is growing rapidly. We here provide an update of this field, both in terms of biological insights and in terms of future clinical implications of integrated omics-molecular imaging studies.
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Affiliation(s)
- Gabriele Vilkaite
- Department of Clinical Sciences Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Jacob Vogel
- Department of Clinical Sciences Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden; Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden; Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden.
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15
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Khan A, Kiryluk K. Polygenic scores and their applications in kidney disease. Nat Rev Nephrol 2024:10.1038/s41581-024-00886-2. [PMID: 39271761 DOI: 10.1038/s41581-024-00886-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/06/2024] [Indexed: 09/15/2024]
Abstract
Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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Jin W, Boss J, Bakulski KM, Goutman SA, Feldman EL, Fritsche LG, Mukherjee B. Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank. J Neurol 2024:10.1007/s00415-024-12644-2. [PMID: 39249108 DOI: 10.1007/s00415-024-12644-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 08/12/2024] [Accepted: 08/16/2024] [Indexed: 09/10/2024]
Abstract
BACKGROUND AND OBJECTIVES Amyotrophic lateral sclerosis (ALS) causes profound impairments in neurological function, and a cure for this devastating disease remains elusive. This study aimed to identify pre-disposing genetic, phenotypic, and exposure-related factors for amyotrophic lateral sclerosis using multi-modal data and assess their joint predictive potential. METHODS Utilizing data from the UK (United Kingdom) Biobank, we analyzed an unrelated set of 292 ALS cases and 408,831 controls of European descent. Two polygenic risk scores (PRS) are constructed: "GWAS Hits PRS" and "PRS-CS," reflecting oligogenic and polygenic ALS risk profiles, respectively. Time-restricted phenome-wide association studies (PheWAS) were performed to identify pre-existing conditions increasing ALS risk, integrated into phenotypic risk scores (PheRS). A poly-exposure score ("PXS") captures the influence of environmental exposures measured through survey questionnaires. We evaluate the performance of these scores for predicting ALS incidence and stratifying risk, adjusting for baseline demographic covariates. RESULTS Both PRSs modestly predicted ALS diagnosis but with increased predictive power when combined (covariate-adjusted receiver operating characteristic [AAUC] = 0.584 [0.525, 0.639]). PheRS incorporated diagnoses 1 year before ALS onset (PheRS1) modestly discriminated cases from controls (AAUC = 0.515 [0.472, 0.564]). The "PXS" did not significantly predict ALS. However, a model incorporating PRSs and PheRS1 improved the prediction of ALS (AAUC = 0.604 [0.547, 0.667]), outperforming a model combining all risk scores. This combined risk score identified the top 10% of risk score distribution with a fourfold higher ALS risk (95% CI [2.04, 7.73]) versus those in the 40%-60% range. DISCUSSION By leveraging UK Biobank data, our study uncovers pre-disposing ALS factors, highlighting the improved effectiveness of multi-factorial prediction models to identify individuals at highest risk for ALS.
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Affiliation(s)
- Weijia Jin
- Department of Biostatistics, University of Florida, Gainesville, FL, 32603, USA
| | - Jonathan Boss
- Department of Biostatistics, University of Michigan, University of Michigan, Ann Arbor, MI, 48109, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kelly M Bakulski
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Stephen A Goutman
- Department of Neurology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Eva L Feldman
- Department of Neurology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lars G Fritsche
- Department of Biostatistics, University of Michigan, University of Michigan, Ann Arbor, MI, 48109, USA.
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, University of Michigan, Ann Arbor, MI, 48109, USA.
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA.
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA.
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17
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Lukusa MT, Yang CY, Tsai MC. Mendelian randomization analysis on the impacts of age at menarche on adult height: A Taiwanese population study. Pediatr Neonatol 2024:S1875-9572(24)00158-X. [PMID: 39278795 DOI: 10.1016/j.pedneo.2024.04.012] [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: 10/05/2023] [Revised: 03/30/2024] [Accepted: 04/29/2024] [Indexed: 09/18/2024] Open
Abstract
BACKGROUNDS Ample evidence supports potential influence of age at menarche (AM) on adult height (AH), but multiple confounders may affect causal estimates. To address this issue, the Mendelian randomization (MR) analysis was used to explore the causal impacts of AM on AH. METHODS Using data (n = 57,349) from the publicly accessible Taiwan Biobank and randomly splitting them into 2 equal-size subsets, we identified single nucleotide polymorphisms (SNPs) significantly associated with AM in the exploration subset and used these SNPs as instrumental variables to estimate the effects of instruments on AH in the validation subset based on two stage least squares (2SLS) regression. In addition, three more summary statistics-based approaches, namely inverse variance weighted (IVW), MR-Egger, and weighted median (WM) analyses, were used to verify the findings. We also performed heterogeneity and sensitivity analyses to evaluate the robustness of the results. RESULTS We identified 4 leading SNPs associated with AM at the genome-wide significant level, whereas rs9409082 may exert some pleiotropic effects on AH. After eliminating rs9409082, the 2SLS analysis indicated that one year delay in genetically determined AM predicted 1.5 cm height gain in adulthood (β = 1.508, 95% confidence interval [0.852, 2.163]). The causal relationship was also supported by WM (β = 1.183, [0.329, 2.038]) and IVW (β = 1.493, [0.523, 2.463]) methods. CONCLUSIONS Evidence from the present MR study supports a causal relationship between later AM and taller AH.
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Affiliation(s)
- Martin Tshishimbi Lukusa
- Institute of Data Science, College of Management, National Cheng Kung University, Tainan, Taiwan; Department of Statistics, College of Management, National Cheng Kung University, Tainan, Taiwan; Department of Statistics, College of Business, Feng Chia University, Taichung, Taiwan
| | - Cheng-Yi Yang
- Department of Statistics, College of Management, National Cheng Kung University, Tainan, Taiwan; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Meng-Che Tsai
- Division of Genetics, Endocrinology, and Metabolism, Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Genomic Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Medical Humanities and Social Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
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18
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Karasov TL, Neumann M, Leventhal L, Symeonidi E, Shirsekar G, Hawks A, Monroe G, Exposito-Alonso M, Bergelson J, Weigel D, Schwab R. Continental-scale associations of Arabidopsis thaliana phyllosphere members with host genotype and drought. Nat Microbiol 2024:10.1038/s41564-024-01773-z. [PMID: 39242816 DOI: 10.1038/s41564-024-01773-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/02/2024] [Indexed: 09/09/2024]
Abstract
Plants are colonized by distinct pathogenic and commensal microbiomes across different regions of the globe, but the factors driving their geographic variation are largely unknown. Here, using 16S ribosomal DNA and shotgun sequencing, we characterized the associations of the Arabidopsis thaliana leaf microbiome with host genetics and climate variables from 267 populations in the species' native range across Europe. Comparing the distribution of the 575 major bacterial amplicon variants (phylotypes), we discovered that microbiome composition in A. thaliana segregates along a latitudinal gradient. The latitudinal clines in microbiome composition are predicted by metrics of drought, but also by the spatial genetics of the host. To validate the relative effects of drought and host genotype we conducted a common garden field study, finding 10% of the core bacteria to be affected directly by drought and 20% to be affected by host genetic associations with drought. These data provide a valuable resource for the plant microbiome field, with the identified associations suggesting that drought can directly and indirectly shape genetic variation in A. thaliana via the leaf microbiome.
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Affiliation(s)
- Talia L Karasov
- School of Biological Sciences, University of Utah, Salt Lake City, UT, USA.
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany.
| | - Manuela Neumann
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Robert Bosch GmbH, Renningen, Germany
| | - Laura Leventhal
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Plant Biology, Carnegie Institution for Plant Science, Stanford, CA, USA
| | - Efthymia Symeonidi
- School of Biological Sciences, University of Utah, Salt Lake City, UT, USA
| | - Gautam Shirsekar
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Department of Entomology and Plant Pathology, Institute of Agriculture, University of Tennessee, Knoxville, TN, USA
| | - Aubrey Hawks
- School of Biological Sciences, University of Utah, Salt Lake City, UT, USA
| | - Grey Monroe
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Department of Plant Sciences, University of California Davis, Davis, CA, USA
| | - Moisés Exposito-Alonso
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Plant Biology, Carnegie Institution for Plant Science, Stanford, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California Berkeley, Berkeley, CA, USA
| | - Joy Bergelson
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
| | - Detlef Weigel
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany.
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.
| | - Rebecca Schwab
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany
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19
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Burra P, Zanetto A, Schnabl B, Reiberger T, Montano-Loza AJ, Asselta R, Karlsen TH, Tacke F. Hepatic immune regulation and sex disparities. Nat Rev Gastroenterol Hepatol 2024:10.1038/s41575-024-00974-5. [PMID: 39237606 DOI: 10.1038/s41575-024-00974-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/25/2024] [Indexed: 09/07/2024]
Abstract
Chronic liver disease is a major cause of morbidity and mortality worldwide. Epidemiology, clinical phenotype and response to therapies for gastrointestinal and liver diseases are commonly different between women and men due to sex-specific hormonal, genetic and immune-related factors. The hepatic immune system has unique regulatory functions that promote the induction of intrahepatic tolerance, which is key for maintaining liver health and homeostasis. In liver diseases, hepatic immune alterations are increasingly recognized as a main cofactor responsible for the development and progression of chronic liver injury and fibrosis. In this Review, we discuss the basic mechanisms of sex disparity in hepatic immune regulation and how these mechanisms influence and modify the development of autoimmune liver diseases, genetic liver diseases, portal hypertension and inflammation in chronic liver disease. Alterations in gut microbiota and their crosstalk with the hepatic immune system might affect the progression of liver disease in a sex-specific manner, creating potential opportunities for novel diagnostic and therapeutic approaches to be evaluated in clinical trials. Finally, we identify and propose areas for future basic, translational and clinical research that will advance our understanding of sex disparities in hepatic immunity and liver disease.
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Affiliation(s)
- Patrizia Burra
- Gastroenterology and Multivisceral Transplant Unit, Department of Surgery, Oncology, and Gastroenterology, Padua University Hospital, Padua, Italy.
| | - Alberto Zanetto
- Gastroenterology and Multivisceral Transplant Unit, Department of Surgery, Oncology, and Gastroenterology, Padua University Hospital, Padua, Italy
| | - Bernd Schnabl
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, VA San Diego Healthcare System, San Diego, CA, USA
| | - Thomas Reiberger
- Division of Gastroenterology and Hepatology, Department of Medicine III, Medical University of Vienna, Vienna, Austria
| | - Aldo J Montano-Loza
- Division of Gastroenterology and Liver Unit, Department of Medicine, University of Alberta Hospital, Edmonton, Alberta, Canada
| | - Rosanna Asselta
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Tom Hemming Karlsen
- Department of Transplantation Medicine, Clinic of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital and University of Oslo, Oslo, Norway
- Research Institute of Internal Medicine, Clinic of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Frank Tacke
- Department of Hepatology and Gastroenterology, Charité - Universitätsmedizin Berlin, Campus Virchow-Klinikum (CVK) and Campus Charité Mitte (CCM), Berlin, Germany
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20
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Ellis CA, Oliver KL, Harris RV, Ottman R, Scheffer IE, Mefford HC, Epstein MP, Berkovic SF, Bahlo M. Inflation of polygenic risk scores caused by sample overlap and relatedness: Examples of a major risk of bias. Am J Hum Genet 2024; 111:1805-1809. [PMID: 39168121 PMCID: PMC11393675 DOI: 10.1016/j.ajhg.2024.07.014] [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: 04/24/2024] [Revised: 07/18/2024] [Accepted: 07/19/2024] [Indexed: 08/23/2024] Open
Abstract
Polygenic risk scores (PRSs) are an important tool for understanding the role of common genetic variants in human disease. Standard best practices recommend that PRSs be analyzed in cohorts that are independent of the genome-wide association study (GWAS) used to derive the scores without sample overlap or relatedness between the two cohorts. However, identifying sample overlap and relatedness can be challenging in an era of GWASs performed by large biobanks and international research consortia. Although most genomics researchers are aware of best practices and theoretical concerns about sample overlap and relatedness between GWAS and PRS cohorts, the prevailing assumption is that the risk of bias is small for very large GWASs. Here, we present two real-world examples demonstrating that sample overlap and relatedness is not a minor or theoretical concern but an important potential source of bias in PRS studies. Using a recently developed statistical adjustment tool, we found that excluding overlapping and related samples was equal to or more powerful than adjusting for overlap bias. Our goal is to make genomics researchers aware of the magnitude of risk of bias from sample overlap and relatedness and to highlight the need for mitigation tools, including independent validation cohorts in PRS studies, continued development of statistical adjustment methods, and tools for researchers to test their cohorts for overlap and relatedness with GWAS cohorts without sharing individual-level data.
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Affiliation(s)
- Colin A Ellis
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Karen L Oliver
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, VIC 3084, Australia; Population Health and Immunity Division, the Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia; Department of Medical Biology, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Rebekah V Harris
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, VIC 3084, Australia
| | - Ruth Ottman
- Departments of Neurology and Epidemiology, and the Gertrude H. Sergievsky Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Division of Translational Epidemiology and Mental Health Equity, New York State Psychiatric Institute, New York, NY 10032, USA
| | - Ingrid E Scheffer
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, VIC 3084, Australia; Department of Paediatrics, Royal Children's Hospital, University of Melbourne, Parkville, VIC 3052, Australia; The Florey Institute and Murdoch Children's Research Institute, Parkville, VIC 3052, Australia
| | - Heather C Mefford
- Center for Pediatric Neurological Disease Research, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Michael P Epstein
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30307, USA
| | - Samuel F Berkovic
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, VIC 3084, Australia
| | - Melanie Bahlo
- Population Health and Immunity Division, the Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia; Department of Medical Biology, University of Melbourne, Melbourne, VIC 3052, Australia.
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21
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Akgun B, Feliciano-Astacio BE, Hamilton-Nelson KL, Scott K, Rivero J, Adams LD, Sanchez JJ, Valladares GS, Tejada S, Bussies PL, Silva-Vergara C, Rodriguez VC, Mena PR, Celis K, Whitehead PG, Prough M, Kosanovic C, Van Booven DJ, Schmidt MA, Acosta H, Griswold AJ, Dalgard CL, McInerney KF, Beecham GW, Cuccaro ML, Vance JM, Pericak-Vance MA, Rajabli F. Genome-wide association analysis and admixture mapping in a Puerto Rican cohort supports an Alzheimer disease risk locus on chromosome 12. Front Aging Neurosci 2024; 16:1459796. [PMID: 39295643 PMCID: PMC11408238 DOI: 10.3389/fnagi.2024.1459796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Accepted: 08/26/2024] [Indexed: 09/21/2024] Open
Abstract
Introduction Hispanic/Latino populations are underrepresented in Alzheimer Disease (AD) genetic studies. Puerto Ricans (PR), a three-way admixed (European, African, and Amerindian) population is the second-largest Hispanic group in the continental US. We aimed to conduct a genome-wide association study (GWAS) and comprehensive analyses to identify novel AD susceptibility loci and characterize known AD genetic risk loci in the PR population. Materials and methods Our study included Whole Genome Sequencing (WGS) and phenotype data from 648 PR individuals (345 AD, 303 cognitively unimpaired). We used a generalized linear-mixed model adjusting for sex, age, population substructure, and genetic relationship matrix. To infer local ancestry, we merged the dataset with the HGDP/1000G reference panel. Subsequently, we conducted univariate admixture mapping (AM) analysis. Results We identified suggestive signals within the SLC38A1 and SCN8A genes on chromosome 12q13. This region overlaps with an area of linkage of AD in previous studies (12q13) in independent data sets further supporting. Univariate African AM analysis identified one suggestive ancestral block (p = 7.2×10-6) located in the same region. The ancestry-aware approach showed that this region has both European and African ancestral backgrounds and both contributing to the risk in this region. We also replicated 11 different known AD loci -including APOE- identified in mostly European studies, which is likely due to the high European background of the PR population. Conclusion PR GWAS and AM analysis identified a suggestive AD risk locus on chromosome 12, which includes the SLC38A1 and SCN8A genes. Our findings demonstrate the importance of designing GWAS and ancestry-aware approaches and including underrepresented populations in genetic studies of AD.
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Affiliation(s)
- Bilcag Akgun
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | | | - Kara L Hamilton-Nelson
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Kyle Scott
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Joe Rivero
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Larry D Adams
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Jose J Sanchez
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Glenies S Valladares
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Sergio Tejada
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Parker L Bussies
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Concepcion Silva-Vergara
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Vanessa C Rodriguez
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Pedro R Mena
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Katrina Celis
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Patrice G Whitehead
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Michael Prough
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Christina Kosanovic
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Derek J Van Booven
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Michael A Schmidt
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | | | - Anthony J Griswold
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Clifton L Dalgard
- Department of Anatomy, Physiology, and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
| | - Katalina F McInerney
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Gary W Beecham
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Michael L Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Jeffery M Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, United States
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Margaret A Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, United States
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Farid Rajabli
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, United States
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22
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Huang R, Kong X, Geng R, Wu J, Chen T, Li J, Li C, Wu Y, You D, Zhao Y, Zhong Z, Ni S, Bai J. Joint and interactive associations of body mass index and genetic factors with cardiovascular disease: a prospective study in UK Biobank. BMC Public Health 2024; 24:2371. [PMID: 39223569 PMCID: PMC11367834 DOI: 10.1186/s12889-024-19916-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 08/28/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Both body mass index (BMI) and genetic factors independently contribute to cardiovascular disease (CVD). However, it is unclear whether genetic risk modifies the association between BMI and the risk of incident CVD. This study aimed to investigate whether BMI categories and genetic risk jointly and interactively contribute to incident CVD events, including hypertension (HTN), atrial fibrillation (AF), coronary heart disease (CHD), stroke, and heart failure (HF). METHODS A total of 496,851 participants from the UK Biobank with one or more new-onset CVD events were included in the analyses. BMI was categorized as normal weight (< 25.0 kg/m2), overweight (25.0-29.9 kg/m2), and obesity (≥ 30.0 kg/m2). Genetic risk for each outcome was defined as low (lowest tertile), intermediate (second tertile), and high (highest tertile) using polygenic risk score. The joint associations of BMI categories and genetic risk with incident CVD were investigated using Cox proportional hazard models. Additionally, additive interactions were evaluated. RESULTS Among the 496,851 participants, 270,726 (54.5%) were female, with a mean (SD) age was 56.5 (8.1) years. Over a median follow-up (IQR) of 12.4 (11.5-13.1) years, 102,131 (22.9%) participants developed HTN, 26,301 (5.4%) developed AF, 32,222 (6.9%) developed CHD, 10,684 (2.2%) developed stroke, and 13,304 (2.7%) developed HF. Compared with the normal weight with low genetic risk, the obesity with high genetic risk had the highest risk of CVD: HTN (HR: 3.96; 95%CI: 3.84-4.09), AF (HR: 3.60; 95%CI: 3.38-3.83), CHD (HR: 2.76; 95%CI: 2.61-2.91), stroke (HR: 1.44; 95%CI: 1.31-1.57), and HF (HR: 2.47; 95%CI: 2.27-2.69). There were significant additive interactions between BMI categories and genetic risk for HTN, AF, and CHD, with relative excess risk of 0.53 (95%CI: 0.43-0.62), 0.67 (95%CI: 0.51-0.83), and 0.37 (95%CI: 0.25-0.49), respectively. CONCLUSIONS BMI and genetic factors jointly and interactively contribute to incident CVD, especially among participants with high genetic risk. These findings have public health implications for identifying populations more likely to have cardiovascular benefit from weight loss interventions.
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Affiliation(s)
- Ruyu Huang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xinxin Kong
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Rui Geng
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jingwei Wu
- Department of Epidemiology and Biostatistics, College of Public Health, Temple University, Philadelphia, PA, 19122, USA
| | - Tao Chen
- Center for Health Economics, University of York, York, YO105DD, UK
| | - Jiong Li
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Chunjian Li
- Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yaqian Wu
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Dongfang You
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Zihang Zhong
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Senmiao Ni
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Jianling Bai
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
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23
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Lang PLM, Erberich JM, Lopez L, Weiß CL, Amador G, Fung HF, Latorre SM, Lasky JR, Burbano HA, Expósito-Alonso M, Bergmann DC. Century-long timelines of herbarium genomes predict plant stomatal response to climate change. Nat Ecol Evol 2024; 8:1641-1653. [PMID: 39117952 PMCID: PMC11383800 DOI: 10.1038/s41559-024-02481-x] [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: 12/06/2022] [Accepted: 06/21/2024] [Indexed: 08/10/2024]
Abstract
Dissecting plant responses to the environment is key to understanding whether and how plants adapt to anthropogenic climate change. Stomata, plants' pores for gas exchange, are expected to decrease in density following increased CO2 concentrations, a trend already observed in multiple plant species. However, it is unclear whether such responses are based on genetic changes and evolutionary adaptation. Here we make use of extensive knowledge of 43 genes in the stomatal development pathway and newly generated genome information of 191 Arabidopsis thaliana historical herbarium specimens collected over 193 years to directly link genetic variation with climate change. While we find that the essential transcription factors SPCH, MUTE and FAMA, central to stomatal development, are under strong evolutionary constraints, several regulators of stomatal development show signs of local adaptation in contemporary samples from different geographic regions. We then develop a functional score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the past centuries, suggesting a genetic component contributing to this change. This approach combining historical genomics with functional experimental knowledge could allow further investigations of how different, even in historical samples unmeasurable, cellular plant phenotypes may have already responded to climate change through adaptive evolution.
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Affiliation(s)
- Patricia L M Lang
- Department of Biology, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA.
| | - Joel M Erberich
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Lua Lopez
- Department of Biological Sciences, California State University San Bernardino, San Bernardino, CA, USA
- Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Clemens L Weiß
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Gabriel Amador
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Hannah F Fung
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Sergio M Latorre
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, UK
- Research Group for Ancient Genomics and Evolution, Department of Molecular Biology, Max Planck Institute for Biology, Tübingen, Germany
| | - Jesse R Lasky
- Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Hernán A Burbano
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, UK
- Research Group for Ancient Genomics and Evolution, Department of Molecular Biology, Max Planck Institute for Biology, Tübingen, Germany
| | - Moisés Expósito-Alonso
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA
- Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
- Department of Integrative Biology, University of California, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA, USA
| | - Dominique C Bergmann
- Department of Biology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
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24
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Piazza GG, Allegrini AG, Eley TC, Epskamp S, Fried E, Isvoranu AM, Roiser JP, Pingault JB. Polygenic Scores and Networks of Psychopathology Symptoms. JAMA Psychiatry 2024; 81:902-910. [PMID: 38865107 PMCID: PMC11170456 DOI: 10.1001/jamapsychiatry.2024.1403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/19/2024] [Indexed: 06/13/2024]
Abstract
Importance Studies on polygenic risk for psychiatric traits commonly use a disorder-level approach to phenotyping, implicitly considering disorders as homogeneous constructs; however, symptom heterogeneity is ubiquitous, with many possible combinations of symptoms falling under the same disorder umbrella. Focusing on individual symptoms may shed light on the role of polygenic risk in psychopathology. Objective To determine whether polygenic scores are associated with all symptoms of psychiatric disorders or with a subset of indicators and whether polygenic scores are associated with comorbid phenotypes via specific sets of relevant symptoms. Design, Setting, and Participants Data from 2 population-based cohort studies were used in this cross-sectional study. Data from children in the Avon Longitudinal Study of Parents and Children (ALSPAC) were included in the primary analysis, and data from children in the Twins Early Development Study (TEDS) were included in confirmatory analyses. Data analysis was conducted from October 2021 to January 2024. Pregnant women based in the Southwest of England due to deliver in 1991 to 1992 were recruited in ALSPAC. Twins born in 1994 to 1996 were recruited in TEDS from population-based records. Participants with available genetic data and whose mothers completed the Short Mood and Feelings Questionnaire and the Strength and Difficulties Questionnaire when children were 11 years of age were included. Main Outcomes and Measures Psychopathology relevant symptoms, such as hyperactivity, prosociality, depression, anxiety, and peer and conduct problems at age 11 years. Psychological networks were constructed including individual symptoms and polygenic scores for depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), body mass index (BMI), and educational attainment in ALSPAC. Following a preregistered confirmatory analysis, network models were cross-validated in TEDS. Results Included were 5521 participants from ALSPAC (mean [SD] age, 11.8 [0.14] years; 2777 [50.3%] female) and 4625 participants from TEDS (mean [SD] age, 11.27 [0.69] years; 2460 [53.2%] female). Polygenic scores were preferentially associated with restricted subsets of core symptoms and indirectly associated with other, more distal symptoms of psychopathology (network edges ranged between r = -0.074 and r = 0.073). Psychiatric polygenic scores were associated with specific cross-disorder symptoms, and nonpsychiatric polygenic scores were associated with a variety of indicators across disorders, suggesting a potential contribution of nonpsychiatric traits to comorbidity. For example, the polygenic score for ADHD was associated with a core ADHD symptom, being easily distracted (r = 0.07), and the polygenic score for BMI was associated with symptoms across disorders, including being bullied (r = 0.053) and not thinking things out (r = 0.041). Conclusions and Relevance Genetic associations observed at the disorder level may hide symptom-level heterogeneity. A symptom-level approach may enable a better understanding of the role of polygenic risk in shaping psychopathology and comorbidity.
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Affiliation(s)
- Giulia G. Piazza
- Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
| | - Andrea G. Allegrini
- Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
- Social Genetic and Developmental Psychiatry, King’s College London, London, United Kingdom
| | - Thalia C. Eley
- Social Genetic and Developmental Psychiatry, King’s College London, London, United Kingdom
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore, Singapore
| | - Eiko Fried
- Department of Clinical Psychology, Leiden University, Leiden, the Netherlands
| | | | - Jonathan P. Roiser
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Jean-Baptiste Pingault
- Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
- Social Genetic and Developmental Psychiatry, King’s College London, London, United Kingdom
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25
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Blose BA, Silverstein SM, Stuart KV, Keane PA, Khawaja AP, Wagner SK. Association between polygenic risk for schizophrenia and retinal morphology: A cross-sectional analysis of the United Kingdom Biobank. Psychiatry Res 2024; 339:116106. [PMID: 39079374 DOI: 10.1016/j.psychres.2024.116106] [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: 03/22/2024] [Revised: 07/17/2024] [Accepted: 07/24/2024] [Indexed: 08/15/2024]
Abstract
We examined the relationship between genetic risk for schizophrenia (SZ), using polygenic risk scores (PRSs), and retinal morphological alterations. Retinal structural and vascular indices derived from optical coherence tomography (OCT) and color fundus photography (CFP) and PRSs for SZ were analyzed in N = 35,024 individuals from the prospective cohort study, United Kingdom Biobank (UKB). Results indicated that macular ganglion cell-inner plexiform layer (mGC-IPL) thickness was significantly inversely related to PRS for SZ, and this relationship was strongest within higher PRS quintiles and independent of potential confounders and age. PRS, however, was unrelated to retinal vascular characteristics, with the exception of venular tortuosity, and other retinal structural indices (macular retinal nerve fiber layer [mRNFL], inner nuclear layer [INL], cup-to-disc ratio [CDR]). Additionally, the association between greater PRS and reduced mGC-IPL thickness was only significant for participants in the 40-49 and 50-59 age groups, not those in the 60-69 age group. These findings suggest that mGC-IPL thinning is associated with a genetic predisposition to SZ and may reflect neurodevelopmental and/or neurodegenerative processes inherent to SZ. Retinal microvasculature alterations, however, may be secondary consequences of SZ and do not appear to be associated with a genetic predisposition to SZ.
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Affiliation(s)
- Brittany A Blose
- Department of Psychology, University of Rochester, Rochester, NY, United States; Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, United States
| | - Steven M Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, United States; Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York, United States; Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, United States; Center for Visual Science, University of Rochester, Rochester, New York, United States.
| | - Kelsey V Stuart
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Pearse A Keane
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Anthony P Khawaja
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Siegfried K Wagner
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
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26
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de Kat AC, Roelofs F, Slagboom PE, Broekmans FJM, Beekman M, Berg NVD. Late reproduction is associated with extended female survival but not with familial longevity. Reprod Biomed Online 2024; 49:104073. [PMID: 38964280 DOI: 10.1016/j.rbmo.2024.104073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 04/03/2024] [Accepted: 04/09/2024] [Indexed: 07/06/2024]
Abstract
RESEARCH QUESTION Are age at last childbirth and number of children, as facets of female reproductive health, related to individual lifespan or familial longevity? DESIGN This observational study included 10,255 female participants from a multigenerational historical cohort, the LINKing System for historical family reconstruction (LINKS), and 1258 female participants from 651 long-lived families in the Leiden Longevity Study (LLS). Age at last childbirth and number of children, as outcomes of reproductive success, were compared with individual and familial longevity using the LINKS dataset. In addition, the genetic predisposition in the form of a polygenic risk score (PRS) for age at menopause was studied in relation to familial longevity using the LLS dataset. RESULTS For each year increase in the age of the birth of the last child, a woman's lifespan increased by 0.06 years (22 days; P = 0.002). The yearly risk for having a last child was 9% lower in women who survived to the oldest 10% of their birth cohort (hazard ratio 0.91, 95% CI 0.86-0.95). Women who came from long-living families did not have a higher mean age of last childbirth. There was no significant association between familial longevity and genetic predisposition to age at menopause. CONCLUSIONS Female reproductive health associates with a longer lifespan. Familial longevity does not associate to extended reproductive health. Other factors in somatic maintenance that support a longer lifespan are likely to have an impact on reproductive health.
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Affiliation(s)
- Annelien C de Kat
- Department of Reproductive Medicine and Gynecology, University Medical Center Utrecht, Utrecht, The Netherlands..
| | - Femke Roelofs
- Department of Reproductive Medicine and Gynecology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Frank J M Broekmans
- Department of Reproductive Medicine and Gynecology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marian Beekman
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Niels van den Berg
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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27
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Ahmad F, Muhmood T. Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions. Colloids Surf B Biointerfaces 2024; 241:114041. [PMID: 38897022 DOI: 10.1016/j.colsurfb.2024.114041] [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: 02/01/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/21/2024]
Abstract
Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.
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Affiliation(s)
- Farooq Ahmad
- State Key Laboratory of Chemistry and Utilization of Carbon Based Energy Resources, College of Chemistry, Xinjiang University, Urumqi 830017, China.
| | - Tahir Muhmood
- International Iberian Nanotechnology Laboratory (INL), Avenida Mestre José Veiga, Braga 4715-330, Portugal.
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28
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Sun Y, McDonald T, Baur A, Xu H, Bateman NB, Shen Y, Li C, Ye K. Fish oil supplementation modifies the associations between genetically predicted and observed concentrations of blood lipids: a cross-sectional gene-diet interaction study in UK Biobank. Am J Clin Nutr 2024; 120:540-549. [PMID: 39019260 PMCID: PMC11393395 DOI: 10.1016/j.ajcnut.2024.07.009] [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: 09/07/2023] [Revised: 07/07/2024] [Accepted: 07/11/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND Dyslipidemia is a well-known risk factor for cardiovascular disease, the leading cause of mortality worldwide. Although habitual intake of fish oil is associated with cardioprotective effects through triglyceride reduction, the interactions of fish oil with the genetic predisposition to dysregulated lipids remain elusive. OBJECTIVES We examined whether fish oil supplementation modifies the association between genetically predicted and observed concentrations of total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides. METHODS A total of 441,985 participants with complete genetic and phenotypic data from the UK Biobank were included. Polygenic scores (PGS) of the 4 lipids were calculated in participants of diverse ancestries. For each lipid, multivariable linear regression models were used to assess if fish oil supplementation modified the association between PGS and the observed circulating concentration, with adjustment for relevant covariates. RESULTS Fish oil supplementation attenuates the associations between genetically predicted and observed circulating concentrations of total cholesterol, LDL cholesterol, and triglycerides while accentuating the corresponding association for HDL cholesterol among 424,090 participants of European ancestry. Consistent significant findings were obtained using PGS calculated based on multiple genome-wide association studies or alternative PGS methods. For triglycerides, each standard deviation (SD) increment in PGS is associated with 0.254 [95% confidence interval (CI): 0.248, 0.259] SD increase in the observed concentration among European-ancestry participants who reported fish oil usage. In contrast, a stronger association was observed in nonusers (0.267; 95% CI: 0.263, 0.270). Consistently, we showed that fish oil significantly attenuates the association between genetically predicted and observed concentrations of triglycerides in African-ancestry participants. CONCLUSIONS Fish oil supplementation attenuates the association between genetically predicted and observed circulating concentrations of total cholesterol, LDL cholesterol, and triglycerides while accentuating the corresponding association for HDL cholesterol in individuals of European ancestry. Further research is needed to understand the clinical implications of these findings.
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Affiliation(s)
- Yitang Sun
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, United States
| | - Tryggvi McDonald
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, United States
| | - Abigail Baur
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, United States
| | - Huifang Xu
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, United States
| | - Naveen Brahman Bateman
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, United States
| | - Ye Shen
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
| | - Changwei Li
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Kaixiong Ye
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, United States; Institute of Bioinformatics, University of Georgia, Athens, GA, United States.
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29
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Fujii R, Hishida A, Nakatochi M, Okumiyama H, Takashima N, Tsuboi Y, Suzuki K, Ikezaki H, Shimanoe C, Kato Y, Tamura T, Ito H, Michihata N, Tanoue S, Suzuki S, Kuriki K, Kadota A, Watanabe T, Momozawa Y, Wakai K, Matsuo K. Polygenic risk score for blood pressure and lifestyle factors with overall and CVD mortality: a prospective cohort study in a Japanese population. Hypertens Res 2024; 47:2284-2294. [PMID: 38961281 DOI: 10.1038/s41440-024-01766-9] [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: 01/09/2024] [Revised: 05/29/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
Although previous polygenic risk score (PRS) studies for cardiovascular disease (CVD) focused on incidence, few studies addressed CVD mortality and quantified risks by environmental exposures in different genetic liability groups. This prospective study aimed to examine the associations of blood pressure PRS with all-cause and CVD mortality and to quantify the attributable risk by modifiable lifestyles across different PRS strata. 9,296 participants in the Japan Multi-Institutional Collaborative Cohort Study without hypertension at baseline were analyzed in this analysis. PRS for systolic blood pressure and diastolic blood pressure (PRSSBP and PRSDBP) were developed using publicly available Biobank Japan GWAS summary statistics. CVD-related mortality was defined by the International Classification of Diseases 10th version (I00-I99). Cox-proportional hazard model was used to examine associations of PRSs and lifestyle variables (smoking, drinking, and dietary sodium intake) with mortality. During a median 12.6-year follow-up period, we observed 273 all-cause and 41 CVD mortality cases. Compared to the middle PRS group (20-80th percentile), adjusted hazard ratios for CVD mortality at the top PRS group ( > 90th percentile) were 3.67 for PRSSBP and 2.92 for PRSDBP. Attributable risks of CVD mortality by modifiable lifestyles were higher in the high PRS group ( > 80th percentile) compared with the low PRS group (0-80th percentile). In summary, blood pressure PRS is associated with CVD mortality in the general Japanese population. Our study implies that integrating PRS with lifestyle could contribute to identify target populations for lifestyle intervention even though improvement of discriminatory ability by PRS alone is limited.
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Affiliation(s)
- Ryosuke Fujii
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan.
- Institute for Biomedicine, Eurac Research, Via A.Volta 21, Bolzano/Bozen, 39100, Italy.
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
| | - Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daikominami, Higashi-ku, Nagoya, 461-8673, Japan
| | - Hiroshi Okumiyama
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan
| | - Naoyuki Takashima
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto, 602-8566, Japan
- NCD Epidemiology Research Center, Shiga University of Medical Science, Tsukiwacho, Seta, Otsu, 520-2192, Japan
| | - Yoshiki Tsuboi
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan
| | - Koji Suzuki
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan
| | - Hiroaki Ikezaki
- Department of Comprehensive General Internal Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
- Department of General Internal Medicine, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Chisato Shimanoe
- Department of Pharmacy, Saga University Hospital, 5-1-1 Nabeshima, Saga, 849-8501, Japan
| | - Yasufumi Kato
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Takashi Tamura
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Hidemi Ito
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan
- Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Nobuaki Michihata
- Cancer Prevention Center, Chiba Cancer Center Research Institute, 666-2 Nitona-cho, Chuo-ku, Chiba, 260-8717, Japan
| | - Shiroh Tanoue
- Department of Epidemiology and Preventive Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Sadao Suzuki
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, 467-8601, Japan
| | - Kiyonori Kuriki
- Laboratory of Public Health, Division of Nutritional Sciences, School of Food and Nutritional Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka, 422-8526, Japan
| | - Aya Kadota
- NCD Epidemiology Research Center, Shiga University of Medical Science, Tsukiwacho, Seta, Otsu, 520-2192, Japan
- Faculty of Nursing Science, Tsuruga Nursing University, 78-2 Kizaki, Tsuruga, 914-0814, Japan
| | - Takeshi Watanabe
- Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Keitaro Matsuo
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan
- Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
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Stadler M, Zhao SS, Bowes J. A review of the advances in understanding the genetic basis of spondylarthritis and emerging clinical benefit. Best Pract Res Clin Rheumatol 2024:101982. [PMID: 39223061 DOI: 10.1016/j.berh.2024.101982] [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: 05/30/2024] [Revised: 07/12/2024] [Accepted: 07/17/2024] [Indexed: 09/04/2024]
Abstract
Spondyloarthropathies (SpA), including ankylosing spondylitis (AS) and psoriatic arthritis (PsA), have been shown to have a substantial genetic predisposition based on heritability estimates derived from family studies and genome-wide association studies (GWAS). GWAS have uncovered numerous genetic loci associated with susceptibility to SpA, with significant associations to human leukocyte antigen (HLA) genes, which are major genetic risk factors for both AS and PsA. Specific loci differentiating PsA from cutaneous-only psoriasis have been identified, though these remain limited. Further research with larger sample sizes is necessary to identify more PsA-specific genetic markers. Current research focuses on translating these genetic insights into clinical applications. For example, polygenic risk scores are showing promise for the classification of disease risk and diagnosis and future research should focus on refining these risk assessment tools to improve clinical outcomes for individuals with SpA. Addressing these challenges will help integrate genetic testing into patients care and impact clinical practice.
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Affiliation(s)
- Michael Stadler
- The Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Sizheng Steven Zhao
- The Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - John Bowes
- The Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
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Jensen M, Smolen C, Tyryshkina A, Pizzo L, Banerjee D, Oetjens M, Shimelis H, Taylor CM, Pounraja VK, Song H, Rohan L, Huber E, El Khattabi L, van de Laar I, Tadros R, Bezzina C, van Slegtenhorst M, Kammeraad J, Prontera P, Caberg JH, Fraser H, Banka S, Van Dijck A, Schwartz C, Voorhoeve E, Callier P, Mosca-Boidron AL, Marle N, Lefebvre M, Pope K, Snell P, Boys A, Lockhart PJ, Ashfaq M, McCready E, Nowacyzk M, Castiglia L, Galesi O, Avola E, Mattina T, Fichera M, Bruccheri MG, Mandarà GML, Mari F, Privitera F, Longo I, Curró A, Renieri A, Keren B, Charles P, Cuinat S, Nizon M, Pichon O, Bénéteau C, Stoeva R, Martin-Coignard D, Blesson S, Le Caignec C, Mercier S, Vincent M, Martin C, Mannik K, Reymond A, Faivre L, Sistermans E, Kooy RF, Amor DJ, Romano C, Andrieux J, Girirajan S. Genetic modifiers and ascertainment drive variable expressivity of complex disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.27.24312158. [PMID: 39252907 PMCID: PMC11383473 DOI: 10.1101/2024.08.27.24312158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Variable expressivity of disease-associated variants implies a role for secondary variants that modify clinical features. We assessed the effects of modifier variants towards clinical outcomes of 2,252 individuals with primary variants. Among 132 families with the 16p12.1 deletion, distinct rare and common variant classes conferred risk for specific developmental features, including short tandem repeats for neurological defects and SNVs for microcephaly, while additional disease-associated variants conferred multiple genetic diagnoses. Within disease and population cohorts of 773 individuals with the 16p12.1 deletion, we found opposing effects of secondary variants towards clinical features across ascertainments. Additional analysis of 1,479 probands with other primary variants, such as 16p11.2 deletion and CHD8 variants, and 1,084 without primary variants, showed that phenotypic associations differed by primary variant context and were influenced by synergistic interactions between primary and secondary variants. Our study provides a paradigm to dissect the genomic architecture of complex disorders towards personalized treatment.
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Affiliation(s)
- Matthew Jensen
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | - Corrine Smolen
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | - Anastasia Tyryshkina
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Lucilla Pizzo
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Deepro Banerjee
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Matthew Oetjens
- Autism & Developmental Medicine Institute, Geisinger, Lewisburg, PA 17837, USA
| | - Hermela Shimelis
- Autism & Developmental Medicine Institute, Geisinger, Lewisburg, PA 17837, USA
| | - Cora M Taylor
- Autism & Developmental Medicine Institute, Geisinger, Lewisburg, PA 17837, USA
| | - Vijay Kumar Pounraja
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | - Hyebin Song
- Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA
| | - Laura Rohan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Emily Huber
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Laila El Khattabi
- Institut Cochin, Inserm U1016, CNRS UMR8104, Université Paris Cité, CARPEM, Paris, France
| | - Ingrid van de Laar
- Department of Clinical Genetics, Erasmus MC, Univ. Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rafik Tadros
- Department of Clinical Genetics, Erasmus MC, Univ. Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Connie Bezzina
- Department of Clinical Genetics, Erasmus MC, Univ. Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marjon van Slegtenhorst
- Department of Clinical Genetics, Erasmus MC, Univ. Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Janneke Kammeraad
- Department of Clinical Genetics, Erasmus MC, Univ. Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Paolo Prontera
- Medical Genetics Unit, Hospital Santa Maria della Misericordia, Perugia, Italy
| | - Jean-Hubert Caberg
- Centre Hospitalier Universitaire de Liège. Domaine Universitaire du Sart Tilman, Liège, Belgium
| | - Harry Fraser
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Siddhartha Banka
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Centre for Genomic Medicine, St. Mary's Hospital, Central Manchester University Hospitals, NHS Foundation Trust Manchester Academic Health Sciences Centre, Manchester, UK
| | - Anke Van Dijck
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | | | - Els Voorhoeve
- Department of Clinical Genetics, Amsterdam UMC, Amsterdam, The Netherlands
| | - Patrick Callier
- Center for Rare Diseases and Reference Developmental Anomalies and Malformation Syndromes, CHU Dijon, Dijon, France
| | - Anne-Laure Mosca-Boidron
- Center for Rare Diseases and Reference Developmental Anomalies and Malformation Syndromes, CHU Dijon, Dijon, France
| | - Nathalie Marle
- Center for Rare Diseases and Reference Developmental Anomalies and Malformation Syndromes, CHU Dijon, Dijon, France
| | - Mathilde Lefebvre
- Laboratoire de Genetique Chromosomique et Moleculaire, CHU Dijon, France
| | - Kate Pope
- Bruce Lefroy Centre, Murdoch Children's Research Institute, Melbourne, Australia
| | - Penny Snell
- Bruce Lefroy Centre, Murdoch Children's Research Institute, Melbourne, Australia
| | - Amber Boys
- Bruce Lefroy Centre, Murdoch Children's Research Institute, Melbourne, Australia
| | - Paul J Lockhart
- Bruce Lefroy Centre, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Myla Ashfaq
- Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77030, USA
| | - Elizabeth McCready
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Margaret Nowacyzk
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Lucia Castiglia
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, Troina, Italy
| | - Ornella Galesi
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, Troina, Italy
| | - Emanuela Avola
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, Troina, Italy
| | - Teresa Mattina
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, Troina, Italy
| | - Marco Fichera
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, Troina, Italy
- Section of Clinical Biochemistry and Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania School of Medicine, Catania, Italy
| | - Maria Grazia Bruccheri
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, Troina, Italy
| | | | - Francesca Mari
- Laboratory of Clinical Molecular Genetics and Cytogenetics, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Flavia Privitera
- Laboratory of Clinical Molecular Genetics and Cytogenetics, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Ilaria Longo
- Laboratory of Clinical Molecular Genetics and Cytogenetics, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Aurora Curró
- Laboratory of Clinical Molecular Genetics and Cytogenetics, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandra Renieri
- Laboratory of Clinical Molecular Genetics and Cytogenetics, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Boris Keren
- Département de Génétique, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Université, 75019 Paris, France
| | - Perrine Charles
- Département de Génétique, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Université, 75019 Paris, France
| | | | | | | | | | - Radka Stoeva
- CHU Nantes, Medical Genetics Department, Nantes, France
| | | | - Sophia Blesson
- Department of Genetics, Bretonneau University Hospital, Tours, France
| | - Cedric Le Caignec
- CHU Toulouse, Department of Medical Genetics, Toulouse, France
- Toulouse Neuro Imaging, Center, Inserm, UPS, Université de Toulouse, Toulouse, France
| | - Sandra Mercier
- Department of Genetics, Bretonneau University Hospital, Tours, France
| | - Marie Vincent
- Department of Genetics, Bretonneau University Hospital, Tours, France
| | - Christa Martin
- Autism & Developmental Medicine Institute, Geisinger, Lewisburg, PA 17837, USA
| | - Katrin Mannik
- Institute of Genomics, University of Tartu, Estonia
- Health2030 Genome Center, Fondation Campus Biotech, Geneva, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, Switzerland
| | - Laurence Faivre
- Center for Rare Diseases and Reference Developmental Anomalies and Malformation Syndromes, CHU Dijon, Dijon, France
- Laboratoire de Genetique Chromosomique et Moleculaire, CHU Dijon, France
| | - Erik Sistermans
- Department of Clinical Genetics, Amsterdam UMC, Amsterdam, The Netherlands
| | - R Frank Kooy
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | - David J Amor
- Department of Clinical Genetics, Amsterdam UMC, Amsterdam, The Netherlands
| | - Corrado Romano
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, Troina, Italy
- Section of Clinical Biochemistry and Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania School of Medicine, Catania, Italy
| | - Joris Andrieux
- Institut de Genetique Medicale, Hopital Jeanne de Flandre, CHRU de Lille, Lille, France
| | - Santhosh Girirajan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
- Department of Anthropology, Pennsylvania State University, University Park, PA 16802, USA
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Jabalameli MR, Lin JR, Zhang Q, Wang Z, Mitra J, Nguyen N, Gao T, Khusidman M, Sathyan S, Atzmon G, Milman S, Vijg J, Barzilai N, Zhang ZD. Polygenic prediction of human longevity on the supposition of pervasive pleiotropy. Sci Rep 2024; 14:19981. [PMID: 39198552 PMCID: PMC11358495 DOI: 10.1038/s41598-024-69069-0] [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: 06/21/2023] [Accepted: 07/31/2024] [Indexed: 09/01/2024] Open
Abstract
The highly polygenic nature of human longevity renders pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between aging-related traits (ARTs), we aimed to model the additive variance in lifespan as a function of the cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores (iLGSs) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts (the Scripps Wellderly cohort and the Medical Genome Reference Bank (MRGB)) and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of iLGS, we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at a higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with iLGS highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.
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Affiliation(s)
- M Reza Jabalameli
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Jhih-Rong Lin
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Quanwei Zhang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Zhen Wang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Joydeep Mitra
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Tina Gao
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Mark Khusidman
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Sanish Sathyan
- Department of Neurology, Albert Einstein College of Medicine, New York, NY, USA
| | - Gil Atzmon
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
- Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Sofiya Milman
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Nir Barzilai
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
- Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, USA
| | - Zhengdong D Zhang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA.
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Chang D, Gupta VK, Hur B, Cobo-López S, Cunningham KY, Han NS, Lee I, Kronzer VL, Teigen LM, Karnatovskaia LV, Longbrake EE, Davis JM, Nelson H, Sung J. Gut Microbiome Wellness Index 2 enhances health status prediction from gut microbiome taxonomic profiles. Nat Commun 2024; 15:7447. [PMID: 39198444 PMCID: PMC11358288 DOI: 10.1038/s41467-024-51651-9] [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: 09/25/2023] [Accepted: 08/09/2024] [Indexed: 09/01/2024] Open
Abstract
Recent advancements in translational gut microbiome research have revealed its crucial role in shaping predictive healthcare applications. Herein, we introduce the Gut Microbiome Wellness Index 2 (GMWI2), an enhanced version of our original GMWI prototype, designed as a standardized disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involves pooling existing 8069 stool shotgun metagenomes from 54 published studies across a global demographic landscape (spanning 26 countries and six continents) to identify gut taxonomic signals linked to disease presence or absence. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). This performance exceeds that of the original GMWI model and traditional species-level α-diversity indices, indicating a more robust gut microbiome signature for differentiating between healthy and non-healthy phenotypes across multiple diseases. When assessed through inter-study validation and external validation cohorts, GMWI2 maintains an average accuracy of nearly 75%. Furthermore, by reevaluating previously published datasets, GMWI2 offers new insights into the effects of diet, antibiotic exposure, and fecal microbiota transplantation on gut health. Available as an open-source command-line tool, GMWI2 represents a timely, pivotal resource for evaluating health using an individual's unique gut microbial composition.
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Affiliation(s)
- Daniel Chang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Vinod K Gupta
- Microbiomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Benjamin Hur
- Microbiomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Sergio Cobo-López
- Viral Information Institute, San Diego State University, San Diego, CA, USA
| | - Kevin Y Cunningham
- Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA
| | - Nam Soo Han
- Brain Korea 21 Center for Bio-Health Industry, Department of Food Science and Biotechnology, Chungbuk National University, Cheongju, South Korea
| | - Insuk Lee
- Department of Biotechnology, Yonsei University, Seoul, South Korea
| | - Vanessa L Kronzer
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Levi M Teigen
- Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN, USA
| | | | | | - John M Davis
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Heidi Nelson
- Emeritus, Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Jaeyun Sung
- Microbiomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
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Yamamoto Y, Shirai Y, Edahiro R, Kumanogoh A, Okada Y. Large-scale cross-trait genetic analysis highlights shared genetic backgrounds of autoimmune diseases. Immunol Med 2024:1-10. [PMID: 39171621 DOI: 10.1080/25785826.2024.2394258] [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: 06/25/2024] [Accepted: 08/15/2024] [Indexed: 08/23/2024] Open
Abstract
Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.
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Affiliation(s)
- Yuji Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Japan
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan
- Japan Agency for Medical Research and Development, Core Research for Evolutional Science and Technology (AMED-CREST), Tokyo, Japan
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan
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Hennion V, Scott J, Martinot V, Godin O, Marie-Claire C, Bellivier F, Jamain S, Etain B. Polygenic risk scores for mood disorders and actigraphy estimates of sleep and circadian rhythms: A preliminary study in bipolar disorders. J Sleep Res 2024:e14307. [PMID: 39168480 DOI: 10.1111/jsr.14307] [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: 04/09/2024] [Revised: 07/23/2024] [Accepted: 07/26/2024] [Indexed: 08/23/2024]
Abstract
In bipolar disorders, abnormalities of sleep patterns and of circadian rhythms of activity are observed during mood episodes, but also persist during euthymia. Shared vulnerabilities between mood disorders and abnormalities of sleep patterns and circadian rhythms of activity have been suggested. This exploratory study investigated the association between polygenic risk scores for bipolar disorder and major depressive disorder, actigraphy estimates of sleep patterns, and circadian rhythms of activity in a sample of 62 euthymic individuals with bipolar disorder. The polygenic risk score - bipolar disorder and polygenic risk score - major depressive disorder were calculated for three stringent thresholds of significance. Data reduction was applied to aggregate actigraphy measures into dimensions using principal component analysis. A higher polygenic risk score - major depressive disorder was associated with more fragmented sleep, while a higher polygenic risk score - bipolar disorder was associated with a later peak of circadian rhythms of activity. These results remained significant after adjustment for age, sex, bipolar disorder subtype, body mass index, current depressive symptoms, current tobacco use, and medications prescribed at inclusion, but not after correction for multiple testing. In conclusion, the genetic vulnerabilities to major depression and to bipolar disorder might be associated with different abnormalities of sleep patterns and circadian rhythms of activity. The results should be replicated in larger and independent samples.
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Affiliation(s)
- Vincent Hennion
- Optimisation Thérapeutique en Neuropsychopharmacologie, INSERM U1144, Université Paris Cité, Paris, France
- Département de Psychiatrie et de Médecine Addictologique, AP-HP Nord, GH Saint-Louis-Lariboisière-Fernand-Widal, DMU Neurosciences, Paris, France
- Université Paris Cité, Paris, France
| | - Jan Scott
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Victoire Martinot
- Département de Psychiatrie et de Médecine Addictologique, AP-HP Nord, GH Saint-Louis-Lariboisière-Fernand-Widal, DMU Neurosciences, Paris, France
- Université Paris Cité, Paris, France
| | - Ophélia Godin
- Université Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Créteil, France
| | - Cynthia Marie-Claire
- Optimisation Thérapeutique en Neuropsychopharmacologie, INSERM U1144, Université Paris Cité, Paris, France
| | - Frank Bellivier
- Optimisation Thérapeutique en Neuropsychopharmacologie, INSERM U1144, Université Paris Cité, Paris, France
- Département de Psychiatrie et de Médecine Addictologique, AP-HP Nord, GH Saint-Louis-Lariboisière-Fernand-Widal, DMU Neurosciences, Paris, France
- Université Paris Cité, Paris, France
| | - Stéphane Jamain
- Université Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Créteil, France
| | - Bruno Etain
- Optimisation Thérapeutique en Neuropsychopharmacologie, INSERM U1144, Université Paris Cité, Paris, France
- Département de Psychiatrie et de Médecine Addictologique, AP-HP Nord, GH Saint-Louis-Lariboisière-Fernand-Widal, DMU Neurosciences, Paris, France
- Université Paris Cité, Paris, France
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36
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Petrovska J, Coynel D, Freytag V, de Quervain DJF, Papassotiropoulos A. Polygenic susceptibility for multiple sclerosis is associated with working memory in low-performing young adults. J Neurol Sci 2024; 463:123138. [PMID: 39059048 DOI: 10.1016/j.jns.2024.123138] [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: 02/12/2024] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND Multiple sclerosis (MS) is a complex disease with substantial heritability estimates. Besides typical clinical manifestations such as motor and sensory deficits, MS is characterized by structural and functional brain abnormalities, and by cognitive impairment such as decreased working memory (WM) performance. OBJECTIVES We investigated the possible link between the polygenic risk for MS and WM performance in healthy adults (18-35 years). Additionally, we addressed the relationship between polygenic risk for MS and white matter fractional anisotropy (FA). METHODS We generated a polygenic risk score (PRS) of MS susceptibility and investigated its association with WM performance in 3282 healthy adults (two subsamples, N1 = 1803, N2 = 1479). The association between MS-PRS and FA was studied in the second subsample. MS severity PRS associations were also investigated for the WM and FA measurements. RESULTS MS-PRS was significantly associated with WM performance within the 10% lowest WM-performing individuals (p = 0.001; pFDR = 0.018). It was not significantly associated with any of the investigated FA measurements. MS severity PRS was significantly associated with brain-wide mean FA (p = 0.041) and showed suggestive associations with additional FA measurements. CONCLUSIONS By identifying a genetic link between MS and WM performance this study contributes to the understanding of the genetic complexity of MS, and hopefully to the possible identification of molecular pathways linked to cognitive deficits in MS. It also contributes to the understanding of genetic associations with MS severity, as these associations seem to involve distinct biological pathways compared to genetic variants linked to the overall risk of developing MS.
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Affiliation(s)
- J Petrovska
- Division of Molecular Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland.
| | - D Coynel
- Division of Cognitive Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland
| | - V Freytag
- Division of Molecular Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland
| | - D J-F de Quervain
- Division of Cognitive Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Psychiatric University Clinics, University of Basel, CH-4055 Basel, Switzerland
| | - A Papassotiropoulos
- Division of Molecular Neuroscience, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, CH-4055 Basel, Switzerland; Psychiatric University Clinics, University of Basel, CH-4055 Basel, Switzerland
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Wu C, Yang F, Zhong H, Hong J, Lin H, Zong M, Ren H, Zhao S, Chen Y, Shi Z, Wang X, Shen J, Wang Q, Ni M, Chen B, Cai Z, Zhang M, Cao Z, Wu K, Gao A, Li J, Liu C, Xiao M, Li Y, Shi J, Zhang Y, Xu X, Gu W, Bi Y, Ning G, Wang W, Wang J, Liu R. Obesity-enriched gut microbe degrades myo-inositol and promotes lipid absorption. Cell Host Microbe 2024; 32:1301-1314.e9. [PMID: 38996548 DOI: 10.1016/j.chom.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 04/29/2024] [Accepted: 06/14/2024] [Indexed: 07/14/2024]
Abstract
Numerous studies have reported critical roles for the gut microbiota in obesity. However, the specific microbes that causally contribute to obesity and the underlying mechanisms remain undetermined. Here, we conducted shotgun metagenomic sequencing in a Chinese cohort of 631 obese subjects and 374 normal-weight controls and identified a Megamonas-dominated, enterotype-like cluster enriched in obese subjects. Among this cohort, the presence of Megamonas and polygenic risk exhibited an additive impact on obesity. Megamonas rupellensis possessed genes for myo-inositol degradation, as demonstrated in vitro and in vivo, and the addition of myo-inositol effectively inhibited fatty acid absorption in intestinal organoids. Furthermore, mice colonized with M. rupellensis or E. coli heterologously expressing the myo-inositol-degrading iolG gene exhibited enhanced intestinal lipid absorption, thereby leading to obesity. Altogether, our findings uncover roles for M. rupellensis as a myo-inositol degrader that enhances lipid absorption and obesity, suggesting potential strategies for future obesity management.
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Affiliation(s)
- Chao Wu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fangming Yang
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China
| | - Huanzi Zhong
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China
| | - Jie Hong
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huibin Lin
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mingxi Zong
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huahui Ren
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China
| | - Shaoqian Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yufei Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhun Shi
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China
| | - Xingyu Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juan Shen
- BGI Research, Shenzhen 518083, China
| | - Qiaoling Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengshan Ni
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Banru Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhongle Cai
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minchun Zhang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiwen Cao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kui Wu
- BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China; Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, BGI Research, Shenzhen 518083, China
| | - Aibo Gao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junhua Li
- BGI Research, Shenzhen 518083, China
| | - Cong Liu
- BGI Research, Shenzhen 518083, China
| | | | - Yan Li
- BGI Research, Shenzhen 518083, China
| | - Juan Shi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yifei Zhang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xun Xu
- BGI Research, Shenzhen 518083, China
| | - Weiqiong Gu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Jiqiu Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Ruixin Liu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Vinueza Veloz MF, Bhatta L, Jones PR, Tesli M, Smith GD, Davies NM, Brumpton BM, Næss ØE. Educational attainment and mental health conditions: a within-sibship Mendelian randomization study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.10.24311789. [PMID: 39148847 PMCID: PMC11326327 DOI: 10.1101/2024.08.10.24311789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Importance Observational studies have demonstrated consistent protective effects of higher educational attainment (EA) on the risk of suffering mental health conditions (MHC). Determining whether these beneficial effects are causal is challenging given the potential role of dynastic effects and demographic factors (assortative mating and population structure) in this association. Objective To evaluate to what extent the relationship between EA and various MHC is independent from dynastic effects and demographic factors. Design Within-sibship Mendelian randomization (MR) study. Setting One-sample MR analyses included participants' data from the Trøndelag Health Study (HUNT, Norway) and UK Biobank (United Kingdom). For two-sample MR analyses we used summary statistics from publicly available genome-wide-association-studies. Participants 61 880 siblings (27 507 sibships). Exposure Years of education. Main outcomes Scores for symptoms of anxiety, depression and neuroticism using the Hospital Anxiety Depression Scale (HADS), the 7-item Generalized Anxiety Disorder Scale (GAD-7), the 9-item Patient Health Questionnaire (PHQ-9), and the Eysenck Personality Questionnaire, as well as self-reported consumption of psychotropic medication. Results One standard deviation (SD) unit increase in years of education was associated with a lower symptom score of anxiety (-0.20 SD [95%CI: -0.26, -0.14]), depression (-0.11 SD [-0.43, 0.22]), neuroticism (-0.30 SD [-0.53, -0.06]), and lower odds of psychotropic medication consumption (OR: 0.60 [0.52, 0.69]). Estimates from the within-sibship MR analyses showed some attenuation, which however were suggestive of a causal association (anxiety: -0.17 SD [-0.33, -0.00]; depression: -0.18 SD [-1.26, 0.89]; neuroticism: -0.29 SD [-0.43, -0.15]); psychotropic medication consumption: OR, 0.52 [0.34, 0.82]). Conclusions and Relevance Associations between EA and MHC in adulthood, although to some extend explained by dynastic effects and demographic factors, overall remain robust, indicative of a causal effect. However, larger studies are warranted to improve statistical power and further validate our conclusions.
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Affiliation(s)
- María Fernanda Vinueza Veloz
- Department of Community Medicine and Global Health, Institute of Health and Society, Faculty of Medicine, University of Oslo, Post box 1130, 0318 Oslo, Norway
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology – NTNU, Post box 8905, 7491 Trondheim, Norway
- FIU-PH, Division of Mental Health Care, St Olavs Hospital, Post box 3250 Torgarden, 7006 Trondheim, Norway
| | - Paul Remy Jones
- Department of Community Medicine and Global Health, Institute of Health and Society, Faculty of Medicine, University of Oslo, Post box 1130, 0318 Oslo, Norway
| | - Martin Tesli
- Department of Mental Disorders, Norwegian Institute of Public Health, Post box 222 Skøyen, N-0214 Oslo, Norway
- Centre for Research and Education in Forensic Psychiatry, Department of Mental Health and Addiction, Oslo University Hospital, PO Box 4956 Nydalen, 0424 Oslo, Norway
| | - George Davey Smith
- MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, Oakfield House, Oakfield Grove, BS8 2BN Bristol, United Kingdom
| | - Neil Martin Davies
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology – NTNU, Post box 8905, 7491 Trondheim, Norway
- MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, Oakfield House, Oakfield Grove, BS8 2BN Bristol, United Kingdom
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, W1T 7NF London, United Kingdom
- Department of Statistical Sciences, University College London, Gower Street, WC1E 6BT London, United Kingdom
| | - Ben M. Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology – NTNU, Post box 8905, 7491 Trondheim, Norway
- HUNT Research Center, Department of Public and Nursing, Norwegian University of Science and Technology – NTNU, Post box 8905, 7491 Trondheim, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Postboks 3250 Torgarden, 7006 Trondheim, Norway
| | - Øyvind Erik Næss
- Department of Community Medicine and Global Health, Institute of Health and Society, Faculty of Medicine, University of Oslo, Post box 1130, 0318 Oslo, Norway
- Department Chronic diseases, Norwegian Institute of Public Health, Post box 222 Skøyen, N-0213 Oslo, Norway
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Anazco D, Acosta A. Precision medicine for obesity: current evidence and insights for personalization of obesity pharmacotherapy. Int J Obes (Lond) 2024:10.1038/s41366-024-01599-z. [PMID: 39127792 DOI: 10.1038/s41366-024-01599-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 06/17/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
Obesity is a chronic and complex disease associated with increased morbidity, mortality, and financial burden. It is expected that by 2030 one of two people in the United States will have obesity. The backbone for obesity management continues to be lifestyle interventions, consisting of calorie deficit diets and increased physical activity levels, however, these interventions are often insufficient to achieve sufficient and maintained weight loss. As a result, multiple patients require additional interventions such as antiobesity medications or bariatric interventions in order to achieve clinically significant weight loss and improvement or resolution of obesity-associated comorbidities. Despite the recent advances in the field of obesity pharmacotherapy that have resulted in never-before-seen weight loss outcomes, comorbidity improvement, and even reduction in cardiovascular mortality, there is still a significant interindividual variability in terms of response to antiobesity medications, with a subset of patients not achieving a clinically significant weight loss. Currently, the trial-and-error paradigm for the selection of antiobesity medications results in increased costs and risks for developing side effects, while also reduces engagement in weight management programs for patients with obesity. The implementation of a precision medicine framework to the selection of antiobesity medications might help reduce heterogeneity and optimize weight loss outcomes by identifying unique subsets of patients, or phenotypes, that have a better response to a specific intervention. The detailed study of energy balance regulation holds promise, as actionable behavioral and physiologic traits could help guide antiobesity medication selection based on previous mechanistic studies. Moreover, the rapid advances in genotyping, multi-omics, and big data analysis might hold the key to discover additional signatures or phenotypes that might respond better to a certain intervention and might permit the widespread adoption of a precision medicine approach for obesity management.
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Affiliation(s)
- Diego Anazco
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Andres Acosta
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
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40
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Sharew NT, Clark SR, Schubert KO, Amare AT. Pharmacogenomic scores in psychiatry: systematic review of current evidence. Transl Psychiatry 2024; 14:322. [PMID: 39107294 PMCID: PMC11303815 DOI: 10.1038/s41398-024-02998-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 06/21/2024] [Accepted: 06/27/2024] [Indexed: 08/10/2024] Open
Abstract
In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.
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Affiliation(s)
- Nigussie T Sharew
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
- Asrat Woldeyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia
| | - Scott R Clark
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
| | - K Oliver Schubert
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
- Division of Mental Health, Northern Adelaide Local Health Network, SA Health, Adelaide, Australia
- Headspace Adelaide Early Psychosis - Sonder, Adelaide, SA, Australia
| | - Azmeraw T Amare
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.
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Petzl AM, Jabbour G, Cadrin-Tourigny J, Pürerfellner H, Macle L, Khairy P, Avram R, Tadros R. Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice? Europace 2024; 26:euae201. [PMID: 39073570 PMCID: PMC11332604 DOI: 10.1093/europace/euae201] [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: 07/02/2024] [Accepted: 07/22/2024] [Indexed: 07/30/2024] Open
Abstract
Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.
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Affiliation(s)
- Adrian M Petzl
- Electrophysiology Service, Department of Medicine, Montreal Heart Institute, Université de Montréal, 5000 rue Bélanger, Montreal, QC H1T 1C8, Canada
- Cardiovascular Genetics Center, Montreal Heart Institute, Université de Montréal, 5000 rue Bélanger, Montreal, QC H1T 1C8, Canada
| | - Gilbert Jabbour
- Electrophysiology Service, Department of Medicine, Montreal Heart Institute, Université de Montréal, 5000 rue Bélanger, Montreal, QC H1T 1C8, Canada
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, Canada
| | - Julia Cadrin-Tourigny
- Electrophysiology Service, Department of Medicine, Montreal Heart Institute, Université de Montréal, 5000 rue Bélanger, Montreal, QC H1T 1C8, Canada
- Cardiovascular Genetics Center, Montreal Heart Institute, Université de Montréal, 5000 rue Bélanger, Montreal, QC H1T 1C8, Canada
| | - Helmut Pürerfellner
- Department of Internal Medicine 2/Cardiology, Ordensklinikum Linz Elisabethinen, Linz, Austria
| | - Laurent Macle
- Electrophysiology Service, Department of Medicine, Montreal Heart Institute, Université de Montréal, 5000 rue Bélanger, Montreal, QC H1T 1C8, Canada
| | - Paul Khairy
- Electrophysiology Service, Department of Medicine, Montreal Heart Institute, Université de Montréal, 5000 rue Bélanger, Montreal, QC H1T 1C8, Canada
| | - Robert Avram
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, Canada
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, Canada
| | - Rafik Tadros
- Electrophysiology Service, Department of Medicine, Montreal Heart Institute, Université de Montréal, 5000 rue Bélanger, Montreal, QC H1T 1C8, Canada
- Cardiovascular Genetics Center, Montreal Heart Institute, Université de Montréal, 5000 rue Bélanger, Montreal, QC H1T 1C8, Canada
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Talwar JV, Klie A, Pagadala MS, Carter H. GRIEVOUS: your command-line general for resolving cross-dataset genotype inconsistencies. Bioinformatics 2024; 40:btae489. [PMID: 39078222 PMCID: PMC11322043 DOI: 10.1093/bioinformatics/btae489] [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: 02/01/2024] [Revised: 07/19/2024] [Accepted: 07/29/2024] [Indexed: 07/31/2024] Open
Abstract
SUMMARY Harmonizing variant indexing and allele assignments across datasets is crucial for data integrity in cross-dataset studies such as multi-cohort genome-wide association studies, meta-analyses, and the development, validation, and application of polygenic risk scores. Ensuring this indexing and allele consistency is a laborious, time-consuming, and error-prone process requiring a certain degree of computational proficiency. Here, we introduce GRIEVOUS, a command-line tool for cross-dataset variant homogenization. By means of an internal database and a custom indexing methodology, GRIEVOUS identifies, formats, and aligns all biallelic single nucleotide polymorphisms (SNPs) across all summary statistic and genotype files of interest. Upon completion of dataset harmonization, GRIEVOUS can also be used to extract the maximal set of biallelic SNPs common to all datasets. AVAILABILITY AND IMPLEMENTATION GRIEVOUS and all supporting documentation and tutorials can be found at https://github.com/jvtalwar/GRIEVOUS. It is freely and publicly available under the MIT license and can be installed via pip.
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Affiliation(s)
- James V Talwar
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, United States
| | - Adam Klie
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, United States
| | - Meghana S Pagadala
- Biomedical Science Program, University of California San Diego, La Jolla, CA 92093, United States
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, United States
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, United States
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Rajabli F, Emekci A. Addressing overlapping sample challenges in genome-wide association studies: Meta-reductive approach. PLoS One 2024; 19:e0296207. [PMID: 39088468 PMCID: PMC11293628 DOI: 10.1371/journal.pone.0296207] [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: 12/06/2023] [Accepted: 06/10/2024] [Indexed: 08/03/2024] Open
Abstract
Polygenic risk scores (PRS) are instrumental in genetics, offering insights into an individual level genetic risk to a range of diseases based on accumulated genetic variations. These scores rely on Genome-Wide Association Studies (GWAS). However, precision in PRS is often challenged by the requirement of extensive sample sizes and the potential for overlapping datasets that can inflate PRS calculations. In this study, we present a novel methodology, Meta-Reductive Approach (MRA), that was derived algebraically to adjust GWAS results, aiming to neutralize the influence of select cohorts. Our approach recalibrates summary statistics using algebraic derivations. Validating our technique with datasets from Alzheimer disease studies, we showed that the summary statistics of the MRA and those derived from individual-level data yielded the exact same values. This innovative method offers a promising avenue for enhancing the accuracy of PRS, especially when derived from meta-analyzed GWAS data.
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Affiliation(s)
- Farid Rajabli
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States of America
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, United States of America
| | - Azra Emekci
- Pioneer High School, San Jose, CA, United States of America
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Bhatt IS, Raygoza Garay JA, Bhagavan SG, Ingalls V, Dias R, Torkamani A. Polygenic Risk Score-Based Association Analysis Identifies Genetic Comorbidities Associated with Age-Related Hearing Difficulty in Two Independent Samples. J Assoc Res Otolaryngol 2024; 25:387-406. [PMID: 38782831 PMCID: PMC11349729 DOI: 10.1007/s10162-024-00947-0] [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: 01/16/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
PURPOSE Age-related hearing loss is the most common form of permanent hearing loss that is associated with various health traits, including Alzheimer's disease, cognitive decline, and depression. The present study aims to identify genetic comorbidities of age-related hearing loss. Past genome-wide association studies identified multiple genomic loci involved in common adult-onset health traits. Polygenic risk scores (PRS) could summarize the polygenic inheritance and quantify the genetic susceptibility of complex traits independent of trait expression. The present study conducted a PRS-based association analysis of age-related hearing difficulty in the UK Biobank sample (N = 425,240), followed by a replication analysis using hearing thresholds (HTs) and distortion-product otoacoustic emissions (DPOAEs) in 242 young adults with self-reported normal hearing. We hypothesized that young adults with genetic comorbidities associated with age-related hearing difficulty would exhibit subclinical decline in HTs and DPOAEs in both ears. METHODS A total of 111,243 participants reported age-related hearing difficulty in the UK Biobank sample (> 40 years). The PRS models were derived from the polygenic risk score catalog to obtain 2627 PRS predictors across the health spectrum. HTs (0.25-16 kHz) and DPOAEs (1-16 kHz, L1/L2 = 65/55 dB SPL, F2/F1 = 1.22) were measured on 242 young adults. Saliva-derived DNA samples were subjected to low-pass whole genome sequencing, followed by genome-wide imputation and PRS calculation. The logistic regression analyses were performed to identify PRS predictors of age-related hearing difficulty in the UK Biobank cohort. The linear mixed model analyses were performed to identify PRS predictors of HTs and DPOAEs. RESULTS The PRS-based association analysis identified 977 PRS predictors across the health spectrum associated with age-related hearing difficulty. Hearing difficulty and hearing aid use PRS predictors revealed the strongest association with the age-related hearing difficulty phenotype. Youth with a higher genetic predisposition to hearing difficulty revealed a subclinical elevation in HTs and a decline in DPOAEs in both ears. PRS predictors associated with age-related hearing difficulty were enriched for mental health, lifestyle, metabolic, sleep, reproductive, digestive, respiratory, hematopoietic, and immune traits. Fifty PRS predictors belonging to various trait categories were replicated for HTs and DPOAEs in both ears. CONCLUSION The study identified genetic comorbidities associated with age-related hearing loss across the health spectrum. Youth with a high genetic predisposition to age-related hearing difficulty and other related complex traits could exhibit sub-clinical decline in HTs and DPOAEs decades before clinically meaningful age-related hearing loss is observed. We posit that effective communication of genetic risk, promoting a healthy lifestyle, and reducing exposure to environmental risk factors at younger ages could help prevent or delay the onset of age-related hearing difficulty at older ages.
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Affiliation(s)
- Ishan Sunilkumar Bhatt
- Department of Communication Sciences & Disorders, University of Iowa, 250 Hawkins Dr, Iowa City, IA, 52242, USA.
| | - Juan Antonio Raygoza Garay
- Department of Communication Sciences & Disorders, University of Iowa, 250 Hawkins Dr, Iowa City, IA, 52242, USA
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, 52242, USA
| | - Srividya Grama Bhagavan
- Department of Communication Sciences & Disorders, University of Iowa, 250 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Valerie Ingalls
- Department of Communication Sciences & Disorders, University of Iowa, 250 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Raquel Dias
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, 32608, USA
| | - Ali Torkamani
- Department of Integrative Structural and Computational Biology, Scripps Science Institute, La Jolla, CA, 92037, USA
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Stikker B, Trap L, Sedaghati-Khayat B, de Bruijn MJW, van Ijcken WFJ, de Roos E, Ikram A, Hendriks RW, Brusselle G, van Rooij J, Stadhouders R. Epigenomic partitioning of a polygenic risk score for asthma reveals distinct genetically driven disease pathways. Eur Respir J 2024; 64:2302059. [PMID: 38901884 PMCID: PMC11358516 DOI: 10.1183/13993003.02059-2023] [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: 11/22/2023] [Accepted: 05/28/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND Individual differences in susceptibility to developing asthma, a heterogeneous chronic inflammatory lung disease, are poorly understood. Whether genetics can predict asthma risk and how genetic variants modulate the complex pathophysiology of asthma are still debated. AIM To build polygenic risk scores for asthma risk prediction and epigenomically link predictive genetic variants to pathophysiological mechanisms. METHODS Restricted polygenic risk scores were constructed using single nucleotide variants derived from genome-wide association studies and validated using data generated in the Rotterdam Study, a Dutch prospective cohort of 14 926 individuals. Outcomes used were asthma, childhood-onset asthma, adulthood-onset asthma, eosinophilic asthma and asthma exacerbations. Genome-wide chromatin analysis data from 19 disease-relevant cell types were used for epigenomic polygenic risk score partitioning. RESULTS The polygenic risk scores obtained predicted asthma and related outcomes, with the strongest associations observed for childhood-onset asthma (2.55 odds ratios per polygenic risk score standard deviation, area under the curve of 0.760). Polygenic risk scores allowed for the classification of individuals into high-risk and low-risk groups. Polygenic risk score partitioning using epigenomic profiles identified five clusters of variants within putative gene regulatory regions linked to specific asthma-relevant cells, genes and biological pathways. CONCLUSIONS Polygenic risk scores were associated with asthma(-related traits) in a Dutch prospective cohort, with substantially higher predictive power observed for childhood-onset than adult-onset asthma. Importantly, polygenic risk score variants could be epigenomically partitioned into clusters of regulatory variants with different pathophysiological association patterns and effect estimates, which likely represent distinct genetically driven disease pathways. Our findings have potential implications for personalised risk mitigation and treatment strategies.
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Affiliation(s)
- Bernard Stikker
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Lianne Trap
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- L. Trap and B. Sedaghati-Khayat made an equal contribution to this study
| | - Bahar Sedaghati-Khayat
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- L. Trap and B. Sedaghati-Khayat made an equal contribution to this study
| | - Marjolein J W de Bruijn
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Wilfred F J van Ijcken
- Center for Biomics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Emmely de Roos
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rudi W Hendriks
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Guy Brusselle
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jeroen van Rooij
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- J. van Rooij and R. Stadhouders contributed equally to this article as lead authors and supervised the work
| | - Ralph Stadhouders
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- J. van Rooij and R. Stadhouders contributed equally to this article as lead authors and supervised the work
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Thiele M, Villesen IF, Niu L, Johansen S, Sulek K, Nishijima S, Espen LV, Keller M, Israelsen M, Suvitaival T, Zawadzki AD, Juel HB, Brol MJ, Stinson SE, Huang Y, Silva MCA, Kuhn M, Anastasiadou E, Leeming DJ, Karsdal M, Matthijnssens J, Arumugam M, Dalgaard LT, Legido-Quigley C, Mann M, Trebicka J, Bork P, Jensen LJ, Hansen T, Krag A. Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases. J Hepatol 2024; 81:345-359. [PMID: 38552880 DOI: 10.1016/j.jhep.2024.03.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/16/2024] [Accepted: 03/19/2024] [Indexed: 07/26/2024]
Abstract
The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.
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Affiliation(s)
- Maja Thiele
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ida Falk Villesen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Stine Johansen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark
| | | | - Suguru Nishijima
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Lore Van Espen
- KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Laboratory of Viral Metagenomics, Leuven, Belgium
| | - Marisa Keller
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Mads Israelsen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | | | - Helene Bæk Juel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Maximilian Joseph Brol
- Medizinische Klinik B (Gastroenterologie, Hepatologie, Endokrinologie, Klinische Infektiologie), Universitätsklinikum Münster Westfälische, Wilhelms-Universität Münster, Germany
| | - Sara Elizabeth Stinson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Yun Huang
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Maria Camilla Alvarez Silva
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Diana Julie Leeming
- Fibrosis, Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Morten Karsdal
- Fibrosis, Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Jelle Matthijnssens
- KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Laboratory of Viral Metagenomics, Leuven, Belgium
| | - Manimozhiyan Arumugam
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Matthias Mann
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Jonel Trebicka
- Medizinische Klinik B (Gastroenterologie, Hepatologie, Endokrinologie, Klinische Infektiologie), Universitätsklinikum Münster Westfälische, Wilhelms-Universität Münster, Germany
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Aleksander Krag
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark.
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Fujii R. En route to conquer the silent killer "hypertension": Integration of polygenic risk score with non-genetic determinants. Hypertens Res 2024:10.1038/s41440-024-01826-0. [PMID: 39090181 DOI: 10.1038/s41440-024-01826-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 07/03/2024] [Indexed: 08/04/2024]
Affiliation(s)
- Ryosuke Fujii
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, Toyoake, 470-1192, Japan.
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48
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Singh S, Stocco G, Theken KN, Dickson A, Feng Q, Karnes JH, Mosley JD, El Rouby N. Pharmacogenomics polygenic risk score: Ready or not for prime time? Clin Transl Sci 2024; 17:e13893. [PMID: 39078255 PMCID: PMC11287822 DOI: 10.1111/cts.13893] [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: 05/12/2024] [Revised: 06/11/2024] [Accepted: 06/25/2024] [Indexed: 07/31/2024] Open
Abstract
Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.
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Affiliation(s)
- Sonal Singh
- Merck & Co., IncSouth San FranciscoCaliforniaUSA
| | - Gabriele Stocco
- Department of Medical, Surgical and Health SciencesUniversity of TriesteTriesteItaly
- Institute for Maternal and Child Health IRCCS Burlo GarofoloTriesteItaly
| | - Katherine N. Theken
- Department of Oral and Maxillofacial Surgery and Pharmacology, School of Dental MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alyson Dickson
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - QiPing Feng
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jason H. Karnes
- Department of Pharmacy Practice and Science, R. Ken Coit College of PharmacyUniversity of ArizonaTucsonArizonaUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jonathan D. Mosley
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Nihal El Rouby
- Division of Pharmacy Practice and Adminstrative Sciences, James L Winkle College of PharmacyUniversity of CincinnatiCincinnatiOhioUSA
- St. Elizabeth HealthcareEdgewoodKentuckyUSA
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Yang X, Wang Z, Li H, Qin W, Liu N, Liu Z, Wang S, Xu J, Wang J. Polygenic Score for Conscientiousness Is a Protective Factor for Reversion from Mild Cognitive Impairment to Normal Cognition. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2309889. [PMID: 38838096 PMCID: PMC11304237 DOI: 10.1002/advs.202309889] [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: 12/16/2023] [Revised: 05/21/2024] [Indexed: 06/07/2024]
Abstract
Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS-C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue-specific enrichment analysis indicate that the protective effect of PGS-C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high-cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS-C.
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Affiliation(s)
- Xuan Yang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
- Department of RadiologyJining No.1 People's HospitalJiningShandong272000P. R. China
| | - Zirui Wang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Haonan Li
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Wen Qin
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Nana Liu
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Zhixuan Liu
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Siqi Wang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Jiayuan Xu
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Junping Wang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
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50
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Sun X, Ma S, Guo Y, Chen C, Pan L, Cui Y, Chen Z, Dijkhuizen RM, Zhou Y, Boltze J, Yu Z, Li P. The association between air pollutant exposure and cerebral small vessel disease imaging markers with modifying effects of PRS-defined genetic susceptibility. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 281:116638. [PMID: 38944013 DOI: 10.1016/j.ecoenv.2024.116638] [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: 04/09/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/01/2024]
Abstract
Studies have highlighted a possible link between air pollution and cerebral small vessel disease (CSVD) imaging markers. However, the exact association and effects of polygenic risk score (PRS) defined genetic susceptibility remains unclear. This cross-sectional study used data from the UK Biobank. Participants aged 40-69 years were recruited between the year 2006 and 2010. The annual average concentrations of NOX, NO2, PM2.5, PM2.5-10, PM2.5 absorbance, and PM10, were estimated, and joint exposure to multiple air pollutants was reflected in the air pollution index (APEX). Air pollutant exposure was classified into the low (T1), intermediate (T2), and high (T3) tertiles. Three CSVD markers were used: white matter hyper-intensity (WMH), mean diffusivity (MD), and fractional anisotropy (FA). The first principal components of the MD and FA measures in the 48 white matter tracts were analysed. The sample consisted of 44,470 participants from the UK Biobank. The median (T1-T3) concentrations of pollutants were as follows: NO2, 25.5 (22.4-28.7) μg/m3; NOx, 41.3 (36.2-46.7) μg/m3; PM10, 15.9 (15.4-16.4) μg/m3; PM2.5, 9.9 (9.5-10.3) μg/m3; PM2.5 absorbance, 1.1 (1.0-1.2) per metre; and PM2.5-10, 6.1 (5.9-6.3) μg/m3. Compared with the low group, the high group's APEX, NOX, and PM2.5 levels were associated with increased WMH volumes, and the estimates (95 %CI) were 0.024 (0.003, 0.044), 0.030 (0.010, 0.050), and 0.032 (0.011, 0.053), respectively, after adjusting for potential confounders. APEX, PM10, PM2.5 absorbance, and PM2.5-10 exposure in the high group were associated with increased FA values compared to that in the low group. Sex-specific analyses revealed associations only in females. Regarding the combined associations of air pollutant exposure and PRS-defined genetic susceptibility with CSVD markers, the associations of NO2, NOX, PM2.5, and PM2.5-10 with WMH were more profound in females with low PRS-defined genetic susceptibility, and the associations of PM10, PM2.5, and PM2.5 absorbance with FA were more profound in females with higher PRS-defined genetic susceptibility. Our study demonstrated that air pollutant exposure may be associated with CSVD imaging markers, with females being more susceptible, and that PRS-defined genetic susceptibility may modify the associations of air pollutants.
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Affiliation(s)
- Xiaowei Sun
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Shiyang Ma
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yunlu Guo
- Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Caiyang Chen
- Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Lijun Pan
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Yidan Cui
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zengai Chen
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Yan Zhou
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Johannes Boltze
- School of Life Sciences, University of Warwick, Coventry, UK.
| | - Zhangsheng Yu
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - Peiying Li
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Outcomes Research Consortium, Cleveland, OH, United States.
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