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Suri P, Elgaeva EE, Williams FMK, Freidin MB, Zaytseva OO, Aulchenko YS, Tsepilov YA. Evidence of causal effects of blood pressure on back pain and back pain on type II diabetes provided by a bidirectional Mendelian randomization study. Spine J 2023; 23:1161-1171. [PMID: 37061135 DOI: 10.1016/j.spinee.2023.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/01/2023] [Accepted: 04/07/2023] [Indexed: 04/17/2023]
Abstract
BACKGROUND CONTEXT Cardiovascular risk factors (hypertension, dyslipidemia, and type II diabetes) have been proposed as risk factors for back pain. However, few longitudinal studies have found significant associations between cardiovascular risk factors and back pain, and these may be explained by confounding or reverse causation. PURPOSE To examine potential causal effects of cardiovascular risk factors on back pain, and vice versa. STUDY DESIGN Bidirectional Mendelian randomization (MR) study. PATIENT SAMPLES Genome-wide association studies (GWAS) with sample sizes between 173,082 and 1,028,947 participants. OUTCOME MEASURES Outcomes included (1) back pain associated with health care use (BP-HC) in the forward MR; and (2) seven cardiovascular phenotypes in the reverse MR, including 2 measurements used for the evaluation of hypertension (diastolic blood pressure and systolic blood pressure), 4 phenotypes related to dyslipidemia (LDL cholesterol, HDL cholesterol, total cholesterol, and triglycerides), and type II diabetes. METHODS We used summary statistics from large, publicly available GWAS for BP-HC and the 7 cardiovascular phenotypes to obtain genetic instrumental variables. We examined MR evidence for causal associations using inverse-variance weighted (IVW) analysis, Causal Analysis Using Summary Effect (CAUSE), and sensitivity analyses. RESULTS In forward MR analyses of seven cardiovascular phenotypes, diastolic blood pressure was associated with BP-HC across all analyses (IVW estimate: OR = 1.10 per 10.5 mm Hg increase [1.04-1.17], p-value = .001), and significant associations of systolic blood pressure with BP-HC were also found (IVW estimate: OR = 1.09 per 19.3 mm Hg increase [1.04-1.15], p-value = .0006). In reverse MR analyses, only type II diabetes was associated with BP-HC across all analyses (IVW estimate: OR = 1.40 [1.13-1.73], p-value = .002). CONCLUSIONS These findings from analyses of large, population-based samples indicate that higher blood pressure increases the risk of BP-HC, and BP-HC itself increases the risk of type II diabetes.
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Affiliation(s)
- Pradeep Suri
- Division of Rehabilitation Care Services, VA Puget Sound Health Care System, 1660 S. Columbian Way, 98108, Seattle, USA; Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, 1660 S. Columbian Way, 98108, Seattle, USA; Department of Rehabilitation Medicine, University of Washington, 325 Ninth Avenue, 98104, Seattle, USA; Clinical Learning, Evidence, and Research (CLEAR) Center, University of Washington, 325 Ninth Avenue, 98104, Seattle, USA.
| | - Elizaveta E Elgaeva
- Department of Natural Sciences, Novosibirsk State University, Pirogova Street 2, 630090,Novosibirsk, Russia; Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, 630090, Novosibirsk, Russia
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, Westminster Bridge Road, London, UK
| | - Maxim B Freidin
- Department of Biology, School of Biological and Behavioural Sciences, Queen Mary University of London, Fogg Buliding, Mile End Road, London, UK
| | - Olga O Zaytseva
- Genos Glycoscience Research Laboratory, Borongajska cesta 83H, 10000, Zagreb, Croatia
| | - Yurii S Aulchenko
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, 630090, Novosibirsk, Russia; PolyOmica, Het Vlaggeschip 61, 5237 PA, 's-Hertogenbosch, the Netherlands
| | - Yakov A Tsepilov
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, 630090, Novosibirsk, Russia; Kurchatov Genomics Center, Institute of Cytology & Genetics, 630090, Novosibirsk, Russia
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Chen S, Cong R, Chen Y, Li X, Zhang X, Wang G, Pang X, Xing W, Wang Y, Xu X. Effects of Polyunsaturated Fatty Acid Serum Levels on Vascular Dementia: A Two-Sample Mendelian Randomization Study. Cerebrovasc Dis 2023; 53:261-269. [PMID: 37482052 DOI: 10.1159/000531861] [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: 04/05/2023] [Accepted: 06/27/2023] [Indexed: 07/25/2023] Open
Abstract
INTRODUCTION Several observational studies have indicated that polyunsaturated fatty acid serum levels (PUFAs) are associated with vascular dementia (VaD), but their causal relationships remain elusive. Therefore, we attempted to evaluate the causal effect of PUFAs on VaD in a two-sample Mendelian randomization (MR) analysis by using summary statistics from aggregated genome-wide association studies. METHODS The inverse-variance weighted (IVW) method was performed as the primary analysis. Sensitivity analyses (MR-Egger regression, weighted median, penalized weighted median, and MR pleiotropy residual sum and outlier methods) were also implemented to estimate the effects of potential violations of MR hypotheses. RESULTS No causality was found for PUFAs (odds ratio, 1.14; 95% confidence interval, 0.91-1.42; p = 0.25) on VaD in the IVW model. The results were consistent in sensitivity analyses. There was no notable horizontal pleiotropy or heterogeneity. CONCLUSION In this two-sample MR analysis, our findings did not support the assumption that PUFAs play causal role in the occurrence or development of VaD.
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Affiliation(s)
- Shanshan Chen
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ruyi Cong
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yongbo Chen
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiaoliang Li
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiaoyu Zhang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Guohua Wang
- The Second Affiliated Hospital of Shandong First Medical University, Taian, China
| | - Xiuyu Pang
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Weijia Xing
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Youxin Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
- Centre for Precision Medicine, Edith Cowan University, Perth, Washington, Australia
| | - Xizhu Xu
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- The Second Affiliated Hospital of Shandong First Medical University, Taian, China
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53
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Cheng S, Xu Z, Bian S, Chen X, Shi Y, Li Y, Duan Y, Liu Y, Lin J, Jiang Y, Jing J, Li Z, Wang Y, Meng X, Liu Y, Fang M, Jin X, Xu X, Wang J, Wang C, Li H, Liu S, Wang Y. The STROMICS genome study: deep whole-genome sequencing and analysis of 10K Chinese patients with ischemic stroke reveal complex genetic and phenotypic interplay. Cell Discov 2023; 9:75. [PMID: 37479695 PMCID: PMC10362040 DOI: 10.1038/s41421-023-00582-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 06/21/2023] [Indexed: 07/23/2023] Open
Abstract
Ischemic stroke is a leading cause of global mortality and long-term disability. However, there is a paucity of whole-genome sequencing studies on ischemic stroke, resulting in limited knowledge of the interplay between genomic and phenotypic variations among affected patients. Here, we outline the STROMICS design and present the first whole-genome analysis on ischemic stroke by deeply sequencing and analyzing 10,241 stroke patients from China. We identified 135.59 million variants, > 42% of which were novel. Notable disparities in allele frequency were observed between Chinese and other populations for 89 variants associated with stroke risk and 10 variants linked to response to stroke medications. We investigated the population structure of the participants, generating a map of genetic selection consisting of 31 adaptive signals. The adaption of the MTHFR rs1801133-G allele, which links to genetically evaluated VB9 (folate acid) in southern Chinese patients, suggests a gene-specific folate supplement strategy. Through genome-wide association analysis of 18 stroke-related traits, we discovered 10 novel genetic-phenotypic associations and extensive cross-trait pleiotropy at 6 lipid-trait loci of therapeutic relevance. Additionally, we found that the set of loss-of-function and cysteine-altering variants present in the causal gene NOTCH3 for the autosomal dominant stroke disorder CADASIL displayed a broad neuro-imaging spectrum. These findings deepen our understanding of the relationship between the population and individual genetic layout and clinical phenotype among stroke patients, and provide a foundation for future efforts to utilize human genetic knowledge to investigate mechanisms underlying ischemic stroke outcomes, discover novel therapeutic targets, and advance precision medicine.
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Affiliation(s)
- Si Cheng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Changping Laboratory, Beijing, China
- Clinical Center for Precision Medicine in Stroke, Capital Medical University, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhe Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shengzhe Bian
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Xi Chen
- BGI-Tianjin, BGI-Shenzhen, Tianjin, China
| | - Yanfeng Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yanran Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yang Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jinxi Lin
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yong Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jing Jing
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Tiantan Neuroimaging Center of Excellence, Beijing, China
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | - Xin Jin
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, Guangdong, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen, Guangdong, China
- James D. Watson Institute of Genome Sciences, Hangzhou, Zhejiang, China
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Siyang Liu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China.
- BGI-Shenzhen, Shenzhen, Guangdong, China.
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- China National Clinical Research Center for Neurological Diseases, Beijing, China.
- Changping Laboratory, Beijing, China.
- Clinical Center for Precision Medicine in Stroke, Capital Medical University, Beijing, China.
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Vaher H, Kingo K, Kolberg P, Pook M, Raam L, Laanesoo A, Remm A, Tenson T, Alasoo K, Mrowietz U, Weidinger S, Kingo K, Rebane A. Skin Colonization with S. aureus Can Lead to Increased NLRP1 Inflammasome Activation in Patients with Atopic Dermatitis. J Invest Dermatol 2023; 143:1268-1278.e8. [PMID: 36736455 DOI: 10.1016/j.jid.2023.01.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/16/2022] [Accepted: 01/04/2023] [Indexed: 02/04/2023]
Abstract
The role of NLRP1 inflammasome activation and subsequent production of IL-1 family cytokines in the development of atopic dermatitis (AD) is not clearly understood. Staphylococcus aureus is known to be associated with increased mRNA levels of IL1 family cytokines in the skin and more severe AD. In this study, the altered expression of IL-1 family cytokines and inflammasome-related genes was confirmed, and a positive relationship between mRNA levels of inflammasome sensor NLRP1 and IL1B or IL18 was determined. Enhanced expression of the NLRP1 and PYCARD proteins and increased caspase-1 activity were detected in the skin of patients with AD. The genetic association of IL18R1 and IL18RAP with AD was confirmed, and the involvement of various immune cell types was predicted using published GWAS and expression quantitative trait loci datasets. In keratinocytes, the inoculation with S. aureus led to the increased secretion of IL-1β and IL-18, whereas small interfering RNA silencing of NLRP1 inhibited the production of these cytokines. Our results suggest that skin colonization with S. aureus may cause the activation of the NLRP1 inflammasome in keratinocytes, which leads to the secretion of IL-1β and IL-18 and thereby may contribute to the pathogenesis of AD, particularly in the presence of genetic variations in the IL-18 pathway.
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Affiliation(s)
- Helen Vaher
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Kristiina Kingo
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Peep Kolberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Martin Pook
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Liisi Raam
- Dermatology Clinic, Tartu University Hospital, Tartu, Estonia; Department of Dermatology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - Anet Laanesoo
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Anu Remm
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Tanel Tenson
- Institute of Technology, University of Tartu, Tartu, Estonia
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Ulrich Mrowietz
- Department of Dermatology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Stephan Weidinger
- Department of Dermatology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Külli Kingo
- Dermatology Clinic, Tartu University Hospital, Tartu, Estonia; Department of Dermatology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - Ana Rebane
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.
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Häder A, Schäuble S, Gehlen J, Thielemann N, Buerfent BC, Schüller V, Hess T, Wolf T, Schröder J, Weber M, Hünniger K, Löffler J, Vylkova S, Panagiotou G, Schumacher J, Kurzai O. Pathogen-specific innate immune response patterns are distinctly affected by genetic diversity. Nat Commun 2023; 14:3239. [PMID: 37277347 DOI: 10.1038/s41467-023-38994-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 05/25/2023] [Indexed: 06/07/2023] Open
Abstract
Innate immune responses vary by pathogen and host genetics. We analyze quantitative trait loci (eQTLs) and transcriptomes of monocytes from 215 individuals stimulated by fungal, Gram-negative or Gram-positive bacterial pathogens. We identify conserved monocyte responses to bacterial pathogens and a distinct antifungal response. These include 745 response eQTLs (reQTLs) and corresponding genes with pathogen-specific effects, which we find first in samples of male donors and subsequently confirm for selected reQTLs in females. reQTLs affect predominantly upregulated genes that regulate immune response via e.g., NOD-like, C-type lectin, Toll-like and complement receptor-signaling pathways. Hence, reQTLs provide a functional explanation for individual differences in innate response patterns. Our identified reQTLs are also associated with cancer, autoimmunity, inflammatory and infectious diseases as shown by external genome-wide association studies. Thus, reQTLs help to explain interindividual variation in immune response to infection and provide candidate genes for variants associated with a range of diseases.
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Affiliation(s)
- Antje Häder
- Research Group Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
| | - Sascha Schäuble
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
| | - Jan Gehlen
- Institute of Human Genetics, Philipps University of Marburg, 35033, Marburg, Germany
| | - Nadja Thielemann
- Institute for Hygiene and Microbiology, Julius Maximilians University of Wuerzburg, 97080, Wuerzburg, Germany
| | - Benedikt C Buerfent
- Institute of Human Genetics, Philipps University of Marburg, 35033, Marburg, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany
| | - Vitalia Schüller
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany
| | - Timo Hess
- Institute of Human Genetics, Philipps University of Marburg, 35033, Marburg, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany
| | - Thomas Wolf
- Systems Biology and Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
| | - Julia Schröder
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany
| | - Michael Weber
- Research Group Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
- Systems Biology and Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
- Institute of Molecular Pathogenesis, Friedrich-Loeffler-Institute, 07743, Jena, Germany
| | - Kerstin Hünniger
- Research Group Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
- Institute for Hygiene and Microbiology, Julius Maximilians University of Wuerzburg, 97080, Wuerzburg, Germany
| | - Jürgen Löffler
- Department of Internal Medicine II, University Hospital Wuerzburg, Josef-Schneider-Strasse 2 /C11, 97080, Wuerzburg, Germany
| | - Slavena Vylkova
- Research Group Host Fungal Interfaces, Septomics Research Center and Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
| | - Gianni Panagiotou
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
- Faculty of Biological Sciences, Friedrich Schiller University, 07743, Jena, Germany
- Department of Medicine and State Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Hong Kong SAR, China
| | - Johannes Schumacher
- Institute of Human Genetics, Philipps University of Marburg, 35033, Marburg, Germany.
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany.
| | - Oliver Kurzai
- Research Group Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany.
- Institute for Hygiene and Microbiology, Julius Maximilians University of Wuerzburg, 97080, Wuerzburg, Germany.
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Lei J, Luo D, Xiong J, Li M. Using Mendelian randomization analysis to determine the causal connection between unpleasant emotions and coronary atherosclerosis. Front Cardiovasc Med 2023; 10:1126157. [PMID: 37283573 PMCID: PMC10239874 DOI: 10.3389/fcvm.2023.1126157] [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: 12/17/2022] [Accepted: 05/02/2023] [Indexed: 06/08/2023] Open
Abstract
Objective Observational studies have shown a correlation between unpleasant emotions and coronary atherosclerosis, but the underlying causal linkages are still uncertain. We conducted a Mendelian randomization (MR) investigation on two samples for this purpose. Methods In genome-wide association studies in the UK Biobank (total = 459,561), we selected 40 distinct single-nucleotide polymorphisms (SNPs) related to unpleasant emotions as genome-wide statistically significant instrumental variables. FinnGen consortium provided summary-level data on coronary atherosclerosis for 211,203 individuals of Finnish descent. MR-Egger regression, the inverse variance weighted technique (IVW), and the weighted median method were used in the process of conducting data analysis. Results There was sufficient evidence to establish a causal connection between unpleasant emotions and coronary atherosclerosis risk. For each unit increase in the log-odds ratio of unpleasant feelings, the odds ratios were 3.61 (95% CI: 1.64-7.95; P = 0.001). The outcomes of sensitivity analyses were comparable. There was no indication of heterogeneity or directional pleiotropy. Conclusion Our findings provide causal evidence for the effects of unpleasant emotions on coronary atherosclerosis.
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Affiliation(s)
- Jiyong Lei
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Da Luo
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jiarui Xiong
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mingjiang Li
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
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Zhu S, Fan Y, Hu X, Shao M. Insights into risk factors for urolithiasis: a mendelian randomization study. BMC Urol 2023; 23:76. [PMID: 37118729 PMCID: PMC10148447 DOI: 10.1186/s12894-023-01243-4] [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: 07/23/2022] [Accepted: 04/12/2023] [Indexed: 04/30/2023] Open
Abstract
BACKGROUND Risk factors for urolithiasis have not been identified. Here, we aimed to identify potentially causal risk factors driving the risk of urolithiasis. METHODS Two sets of instrumental variables were used for analysis, derived from publicly available databases. Summary-level statistical data for urolithiasis were obtained from the MRC-IEU Consortium and UK biobank (Neale Lab). Mendelian randomization (MR) was conducted to identify causal risk of urolithiasis. Finally, the results of the two databases were combined and a meta-analysis was performed. RESULTS In the MRC-IEU consortium, the odds of urolithiasis increased per 1-SD increase of body mass index (BMI) (OR = 1.0016, 95% CI:1.0004-1.0029, p = 0.010), triglycerides (OR = 1.0016, 95% CI:1.0003-1.0029, p = 0.017), adiponectin (OR = 1.0027, 95% CI:1.0003-1.0050, p = 0.024), and body fat percentage (OR = 1.008, 95% CI:1.0001-1.0161, p = 0.047). In addition, alcohol intake also increased the incidence of urolithiasis (OR = 1.0030, 95% CI:1.0009-1.0051, p = 0.005). In the UK biobank, the odds of urolithiasis increased per 1-SD increase of waist circumference (OR = 1.0215, 95% CI:1.0061-1.0372, p = 0.008) and body fat percentage (OR = 1.0239, 95% CI:1.0043-1.0440, p = 0.020). Surprisingly, we found that the risk of urolithiasis decreased with increasing hip circumference (OR = 0.9954, 95% CI:0.9915-0.9992, p = 0.017). In a meta-analysis of MR results, higher BMI (OR = 1.0016, 95% CI:1.0004-1.0027, p = 0.009), waist circumference (OR = 1.0073, 95% CI:1.0020-1.0126, p = 0.007), adiponectin (OR = 1.0026, 95% CI:1.0008-1.0043, p = 0.004), triglycerides (OR = 1.0015, 95% CI:1.0004-1.0026, p = 0.008) and body fat percentage (OR = 1.0104, 95% CI:1.0030-1.0178, p = 0.006) increased the risk of urolithiasis. Furthermore, alcohol intake also increased the incidence of urolithiasis (OR = 1.0033, 95% CI:1.0012-1.0053, p = 0.002). CONCLUSIONS Our MR study found that higher BMI, triglycerides, waist circumference, adiponectin, body fat percentage, and alcohol intake increased the risk of urolithiasis.
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Affiliation(s)
- Shusheng Zhu
- Department of Urology, Jining No. 1 People's Hospital, Jining, 272000, Shandong, China
| | - Yanpeng Fan
- Department of Urology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Xia Hu
- Department of Geriatrics, Jining No. 1 People's Hospital, Jining, Shandong, China
| | - Mingming Shao
- Department of Urology, Jining No. 1 People's Hospital, Jining, 272000, Shandong, China.
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Cifello J, Kuksa PP, Saravanan N, Valladares O, Leung YY, Wang LS. hipFG: High-throughput harmonization and integration pipeline for functional genomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.21.537695. [PMID: 37162864 PMCID: PMC10168270 DOI: 10.1101/2023.04.21.537695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Preparing functional genomic (FG) data with diverse assay types and file formats for integration into analysis workflows that interpret genome-wide association and other studies is a significant and time-consuming challenge. Here we introduce hipFG, an automatically customized pipeline for efficient and scalable normalization of heterogenous FG data collections into standardized, indexed, rapidly searchable analysis-ready datasets while accounting for FG datatypes (e.g., chromatin interactions, genomic intervals, quantitative trait loci).
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Affiliation(s)
- Jeffrey Cifello
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania
| | - Pavel P. Kuksa
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania
| | - Naveensri Saravanan
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania
| | - Otto Valladares
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania
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Lin Z, Huang J, Xie S, Zheng Z, Tang K, Li S, Chen R. The Association Between Insulin Use and Asthma: An Epidemiological Observational Analysis and Mendelian Randomization Study. Lung 2023; 201:189-199. [PMID: 36971839 DOI: 10.1007/s00408-023-00611-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 03/09/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND Asthma is a common respiratory disease caused by genetic and environmental factors, but the contribution of insulin use to the risk of asthma remains unclear. This study aimed to investigate the association between insulin use and asthma in a large population-based cohort, and further explore their causal relationship by Mendelian randomization (MR) analysis. METHODS An epidemiological study including 85,887 participants from the National Health and Nutrition Examination Survey (NHANES) 2001-2018 was performed to evaluate the association between insulin use and asthma. Based on the inverse-variance weighted approach, MR analysis were conducted to estimate the causal effect of insulin use on asthma from the UKB and FinnGen datasets, respectively. RESULTS In the NHANES cohort, we found that insulin use was associated with an increased risk of asthma [odd ratio (OR) 1.38; 95% CI 1.16-1.64; p < 0.001]. For the MR analysis, we found a causal relationship between insulin use and a higher risk of asthma in both Finn (OR 1.10; p < 0.001) and UK Biobank cohorts (OR 1.18; p < 0.001). Meanwhile, there was no causal association between diabetes and asthma. After multivariable adjustment for diabetes in UKB cohort, the insulin use remained significantly associated with an increased risk of asthma (OR 1.17, p < 0.001). CONCLUSIONS An association between insulin use and an increased risk of asthma was found via the real-world data from the NHANES. In addition, the current study identified a causal effect and provided a genetic evidence of insulin use and asthma. More studies are needed to elucidate the mechanisms underlying the association between insulin use and asthma.
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Affiliation(s)
- Zikai Lin
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
- Nanshan School of Medical, Guangzhou Medical University, Guangzhou, China
| | - Junfeng Huang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Shuojia Xie
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
- Nanshan School of Medical, Guangzhou Medical University, Guangzhou, China
| | - Ziwen Zheng
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Kailun Tang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
- Department of Allergy and Clinical Immunology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Clinical Medical College of Henan University, Kaifeng, China
| | - Shiyue Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
| | - Ruchong Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
- Department of Allergy and Clinical Immunology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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60
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de Klein N, Tsai EA, Vochteloo M, Baird D, Huang Y, Chen CY, van Dam S, Oelen R, Deelen P, Bakker OB, El Garwany O, Ouyang Z, Marshall EE, Zavodszky MI, van Rheenen W, Bakker MK, Veldink J, Gaunt TR, Runz H, Franke L, Westra HJ. Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. Nat Genet 2023; 55:377-388. [PMID: 36823318 PMCID: PMC10011140 DOI: 10.1038/s41588-023-01300-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 01/17/2023] [Indexed: 02/25/2023]
Abstract
Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.
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Affiliation(s)
- Niek de Klein
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Wellcome Sanger Institute, Hinxton, UK
| | - Ellen A Tsai
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Martijn Vochteloo
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Institute for Life Science and Technology, Hanze University of Applied Sciences, Groningen, The Netherlands
- Oncode Institute, Groningen, The Netherlands
| | - Denis Baird
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Yunfeng Huang
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Chia-Yen Chen
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Sipko van Dam
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Ancora Health, Groningen, The Netherlands
| | - Roy Oelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Groningen, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Groningen, The Netherlands
| | - Olivier B Bakker
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Wellcome Sanger Institute, Hinxton, UK
| | - Omar El Garwany
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Wellcome Sanger Institute, Hinxton, UK
| | | | - Eric E Marshall
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Maria I Zavodszky
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Wouter van Rheenen
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mark K Bakker
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jan Veldink
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Heiko Runz
- Translational Biology, Research and Development, Biogen Inc., Cambridge, MA, USA.
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Groningen, The Netherlands.
| | - Harm-Jan Westra
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Groningen, The Netherlands.
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Guo H, Cao W, Zhu Y, Li T, Hu B. A genome-wide cross-cancer meta-analysis highlights the shared genetic links of five solid cancers. Front Microbiol 2023; 14:1116592. [PMID: 36819030 PMCID: PMC9935838 DOI: 10.3389/fmicb.2023.1116592] [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: 12/05/2022] [Accepted: 01/06/2023] [Indexed: 02/05/2023] Open
Abstract
Breast, ovarian, prostate, lung, and head/neck cancers are five solid cancers with complex interrelationships. However, the shared genetic factors of the five cancers were often revealed either by the combination of individual genome-wide association study (GWAS) approach or by the fixed-effect model-based meta-analysis approach with practically impossible assumptions. Here, we presented a random-effect model-based cross-cancer meta-analysis framework for identifying the genetic variants jointly influencing the five solid cancers. A comprehensive genetic correlation analysis (genome-wide, partitioned, and local) approach was performed by using GWAS summary statistics of the five cancers, and we observed three cancer pairs with significant genetic correlation: breast-ovarian cancer (r g = 0.221, p = 0.0003), breast-lung cancer (r g = 0.234, p = 7.6 × 10-6), and lung-head/neck cancer (r g = 0.652, p = 0.010). Furthermore, a random-effect model-based cross-trait meta-analysis was conducted for each significant cancer pair, and we found 27 shared genetic loci between breast and ovarian cancers, 18 loci between breast and lung cancers, and three loci between lung and head/neck cancers. Functional analysis indicates that the shared genes are enriched in human T-cell leukemia virus 1 infection (HTLV-1) and antigen processing and presentation (APP) pathways. Our study investigates the shared genetic links across five solid cancers and will help to reveal their potential molecular mechanisms.
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Affiliation(s)
- Hongping Guo
- School of Mathematics and Statistics, Hubei Normal University, Huangshi, China,*Correspondence: Hongping Guo ✉
| | - Wenhao Cao
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, United States
| | - Yiran Zhu
- School of Mathematics and Statistics, Hubei Normal University, Huangshi, China
| | - Tong Li
- School of Mathematics and Statistics, Hubei Normal University, Huangshi, China
| | - Boheng Hu
- School of Mathematics and Statistics, Hubei Normal University, Huangshi, China
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Xin J, Gu D, Chen S, Ben S, Li H, Zhang Z, Du M, Wang M. SUMMER: a Mendelian randomization interactive server to systematically evaluate the causal effects of risk factors and circulating biomarkers on pan-cancer survival. Nucleic Acids Res 2023; 51:D1160-D1167. [PMID: 35947748 PMCID: PMC9825440 DOI: 10.1093/nar/gkac677] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/18/2022] [Accepted: 07/26/2022] [Indexed: 01/30/2023] Open
Abstract
Genome-wide association studies (GWASs) underlying case-control design have uncovered hundreds of genetic loci involved in tumorigenesis and provided rich resources for identifying risk factors and biomarkers associated with cancer susceptibility. However, the application of GWAS in determining the genetic architecture of cancer survival remains unestablished. Here, we systematically evaluated genetic effects at the genome-wide level on cancer survival that included overall survival (OS) and cancer-specific survival (CSS), leveraging data deposited in the UK Biobank cohort of a total of 19 628 incident patients across 17 cancer types. Furthermore, we assessed the causal effects of risk factors and circulating biomarkers on cancer prognosis via a Mendelian randomization (MR) analytic framework, which integrated cancer survival GWAS dataset, along with phenome-wide association study (PheWAS) and blood genome-wide gene expression/DNA methylation quantitative trait loci (eQTL/meQTL) datasets. On average, more than 10 traits, 700 genes, and 4,500 CpG sites were prone to cancer prognosis. Finally, we developed a user-friendly online database, SUrvival related cancer Multi-omics database via MEndelian Randomization (SUMMER; http://njmu-edu.cn:3838/SUMMER/), to help users query, browse, and download cancer survival results. In conclusion, SUMMER provides an important resource to assist the research community in understanding the genetic mechanisms of cancer survival.
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Affiliation(s)
- Junyi Xin
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Dongying Gu
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Silu Chen
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.,Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuai Ben
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.,Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Huiqin Li
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.,Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.,Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Mulong Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.,Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.,The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
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Kim M, Vo DD, Kumagai ME, Jops CT, Gandal MJ. GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data. Bioinformatics 2023; 39:6887175. [PMID: 36495218 PMCID: PMC9825774 DOI: 10.1093/bioinformatics/btac786] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 10/29/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
SUMMARY With the continued deluge of results from genome-wide association and functional genomic studies, it has become increasingly imperative to quickly combine and visualize different layers of genetic and genomic data within a given locus to facilitate exploratory and integrative data analyses. While several tools have been developed to visualize locus-level genetic results, the limited speed, scalability and flexibility of current approaches remain a significant bottleneck. Here, we present a Julia package for high-performance genetics and genomics-related data visualization that enables fast, simultaneous plotting of hundreds of association results along with multiple relevant genomic annotations. Leveraging the powerful plotting and layout utilities from Makie.jl facilitates the customization and extensibility of every component of a plot, enabling generation of publication-ready figures. AVAILABILITY AND IMPLEMENTATION The GeneticsMakie.jl package is open source and distributed under the MIT license via GitHub (https://github.com/mmkim1210/GeneticsMakie.jl). The GitHub repository contains installation instructions as well as examples and documentation for built-in functions. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Minsoo Kim
- To whom correspondence should be addressed. or
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Zhang X, Geng T, Li N, Wu L, Wang Y, Zheng D, Guo B, Wang B. Associations of Lipids and Lipid-Lowering Drugs with Risk of Vascular Dementia: A Mendelian Randomization Study. Nutrients 2022; 15:nu15010069. [PMID: 36615727 PMCID: PMC9824558 DOI: 10.3390/nu15010069] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Accumulating observational studies suggested that hypercholesterolemia is associated with vascular dementia (VaD); however, the causality between them remains unclear. Hence, the aim of this study is to infer causal associations of circulating lipid-related traits [including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), apolipoprotein A-I (apoA-I), and apolipoprotein B (apoB)] with VaD jointly using univariable MR (uvMR), multivariable MR (mvMR) and bidirectional two-sample MR methods. Then, the summary-data-based MR (SMR) and two-sample MR analysis were conducted to investigate the association of lipid-lowering drugs target genes expression (including HMGCR, PCSK9, NPC1L1, and APOB) and LDL-C level mediated by these target genes with VaD. The results of forward MR analyses found that genetically predicted HDL-C, LDL-C, TG, apoA-I, and apoB concentrations were not significantly associated with the risk of VaD (all p > 0.05). Notably, there was suggestive evidence for a causal effect of genetically predicted VaD on HDL-C via reverse MR analysis [odds ratio (OR), 0.997; 95% confidence interval (CI), 0.994−0.999; p = 0.022]. On the contrary, the MR results showed no significant relationship between VaD with LDL-C, TG, apoA-I, and apoB. The results for the SMR method found that there was no evidence of association for expression of HMGCR, PCSK9, NPC1L1, and APOB gene with risk of VaD. Furthermore, the result of MR analysis provided evidence for the decreased LDL-C level mediated by gene HMGCR reduced the risk of VaD (OR, 18.381; 95% CI, 2.092−161.474; p = 0.009). Oppositely, none of the IVW methods indicated any causal effects for the other three genes. Using genetic data, this study provides evidence that the VaD risk may cause a reduction of HDL-C level. Additionally, the finding supports the hypothesis that lowering LDL-C levels using statins may be an effective prevention strategy for VaD risk, which requires clinical trials to confirm this result in the future.
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Affiliation(s)
- Xiaoyu Zhang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Tao Geng
- Geriatric Department, Emergency General Hospital, Beijing 100028, China
| | - Ning Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Lijuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Youxin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Deqiang Zheng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Bo Guo
- Department of Hematology, The Second Medical Centre & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China
- Correspondence: (B.G.); (B.W.); Tel.: +86-1066876227 (B.G.); +86-1062856765 (B.W.)
| | - Baoguo Wang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
- Correspondence: (B.G.); (B.W.); Tel.: +86-1066876227 (B.G.); +86-1062856765 (B.W.)
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Mendelian randomization and genetic colocalization infer the effects of the multi-tissue proteome on 211 complex disease-related phenotypes. Genome Med 2022; 14:140. [PMID: 36510323 PMCID: PMC9746220 DOI: 10.1186/s13073-022-01140-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 11/10/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Human proteins are widely used as drug targets. Integration of large-scale protein-level genome-wide association studies (GWAS) and disease-related GWAS has thus connected genetic variation to disease mechanisms via protein. Previous proteome-by-phenome-wide Mendelian randomization (MR) studies have been mainly focused on plasma proteomes. Previous MR studies using the brain proteome only reported protein effects on a set of pre-selected tissue-specific diseases. No studies, however, have used high-throughput proteomics from multiple tissues to perform MR on hundreds of phenotypes. METHODS Here, we performed MR and colocalization analysis using multi-tissue (cerebrospinal fluid (CSF), plasma, and brain from pre- and post-meta-analysis of several disease-focus cohorts including Alzheimer disease (AD)) protein quantitative trait loci (pQTLs) as instrumental variables to infer protein effects on 211 phenotypes, covering seven broad categories: biological traits, blood traits, cancer types, neurological diseases, other diseases, personality traits, and other risk factors. We first implemented these analyses with cis pQTLs, as cis pQTLs are known for being less prone to horizontal pleiotropy. Next, we included both cis and trans conditionally independent pQTLs that passed the genome-wide significance threshold keeping only variants associated with fewer than five proteins to minimize pleiotropic effects. We compared the tissue-specific protein effects on phenotypes across different categories. Finally, we integrated the MR-prioritized proteins with the druggable genome to identify new potential targets. RESULTS In the MR and colocalization analysis including study-wide significant cis pQTLs as instrumental variables, we identified 33 CSF, 13 plasma, and five brain proteins to be putative causal for 37, 18, and eight phenotypes, respectively. After expanding the instrumental variables by including genome-wide significant cis and trans pQTLs, we identified a total of 58 CSF, 32 plasma, and nine brain proteins associated with 58, 44, and 16 phenotypes, respectively. For those protein-phenotype associations that were found in more than one tissue, the directions of the associations for 13 (87%) pairs were consistent across tissues. As we were unable to use methods correcting for horizontal pleiotropy given most of the proteins were only associated with one valid instrumental variable after clumping, we found that the observations of protein-phenotype associations were consistent with a causal role or horizontal pleiotropy. Between 66.7 and 86.3% of the disease-causing proteins overlapped with the druggable genome. Finally, between one and three proteins, depending on the tissue, were connected with at least one drug compound for one phenotype from both DrugBank and ChEMBL databases. CONCLUSIONS Integrating multi-tissue pQTLs with MR and the druggable genome may open doors to pinpoint novel interventions for complex traits with no effective treatments, such as ovarian and lung cancers.
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No Evidence to Support a Causal Relationship between Circulating Adiponectin Levels and Ankylosing Spondylitis: A Bidirectional Two-Sample Mendelian Randomization Study. Genes (Basel) 2022; 13:genes13122270. [PMID: 36553537 PMCID: PMC9778096 DOI: 10.3390/genes13122270] [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: 10/14/2022] [Revised: 11/27/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022] Open
Abstract
Based on previous observational studies, the causal association between circulating adiponectin (CA) levels and ankylosing spondylitis (AS) risk remains unclear. Therefore, this study aims to investigate whether CA levels are related to the risk of AS. We carried out a bidirectional two-sample Mendelian randomization (MR) analysis to examine the causal correlation between CA levels and AS via published genome-wide association study (GWAS) datasets. Single-nucleotide polymorphisms (SNPs) related to CA levels were derived from a large GWAS that included 39,883 individuals of European descent. SNPs related to AS were obtained from the FinnGen consortium (2252 cases and 227,338 controls). The random-effects inverse variance weighted (IVW) method was the primary method utilized in our research. We also used four complementary approaches to improve the dependability of this study (MR-Egger regression, Weighted median, Weighted mode, and Simple mode). Random-effects IVW (odds ratio [OR], 1.00; 95% confidence interval [CI], 0.79-1.27, p = 0.984) and four complementary methods all indicated that genetically predicted CA levels were not causally related to the risk of AS. In reverse MR analysis, there is little evidence to support the genetic causality between the risk of AS and CA levels.
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67
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Matushyn M, Bose M, Mahmoud AA, Cuthbertson L, Tello C, Bircan KO, Terpolovsky A, Bamunusinghe V, Khan U, Novković B, Grabherr MG, Yazdi PG. SumStatsRehab: an efficient algorithm for GWAS summary statistics assessment and restoration. BMC Bioinformatics 2022; 23:443. [PMID: 36284273 PMCID: PMC9594936 DOI: 10.1186/s12859-022-04920-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 06/06/2022] [Indexed: 11/23/2022] Open
Abstract
Background Generating polygenic risk scores for diseases and complex traits requires high quality GWAS summary statistic files. Often, these files can be difficult to acquire either as a result of unshared or incomplete data. To date, bioinformatics tools which focus on restoring missing columns containing identification and association data are limited, which has the potential to increase the number of usable GWAS summary statistics files. Results SumStatsRehab was able to restore rsID, effect/other alleles, chromosome, base pair position, effect allele frequencies, beta, standard error, and p-values to a better extent than any other currently available tool, with minimal loss. Conclusions SumStatsRehab offers a unique tool utilizing both functional programming and pipeline-like architecture, allowing users to generate accurate data restorations for incomplete summary statistics files. This in turn, increases the number of usable GWAS summary statistics files, which may be invaluable for less researched health traits.
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Affiliation(s)
- Mykyta Matushyn
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | - Madhuchanda Bose
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | | | - Lewis Cuthbertson
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | - Carlos Tello
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | - Karatuğ Ozan Bircan
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | - Andrew Terpolovsky
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | - Varuna Bamunusinghe
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | - Umar Khan
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | - Biljana Novković
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | - Manfred G Grabherr
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA
| | - Puya G Yazdi
- SelfDecode.Com, 1031 Ives Dairy Road Suite 228 - 1047, Miami, FL, 33179, USA.
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Shared Genetic Regulatory Networks Contribute to Neuropathic and Inflammatory Pain: Multi-Omics Systems Analysis. Biomolecules 2022; 12:biom12101454. [PMID: 36291662 PMCID: PMC9599593 DOI: 10.3390/biom12101454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 09/27/2022] [Accepted: 10/07/2022] [Indexed: 11/17/2022] Open
Abstract
The mechanisms of chronic pain are complex, and genetic factors play an essential role in the development of chronic pain. Neuropathic pain (NP) and inflammatory pain (IP) are two primary components of chronic pain. Previous studies have uncovered some common biological processes in NP and IP. However, the shared genetic mechanisms remained poorly studied. We utilized multi-omics systematic analyses to investigate the shared genetic mechanisms of NP and IP. First, by integrating several genome-wide association studies (GWASs) with multi-omics data, we revealed the significant overlap of the gene co-expression modules in NP and IP. Further, we uncovered the shared biological pathways, including the previously reported mitochondrial electron transport and ATP metabolism, and stressed the role of genetic factors in chronic pain with neurodegenerative diseases. Second, we identified 24 conservative key drivers (KDs) contributing to NP and IP, containing two well-established pain genes, IL1B and OPRM1, and some novel potential pain genes, such as C5AR1 and SERPINE1. The subnetwork of those KDs highlighted the processes involving the immune system. Finally, gene expression analysis of the KDs in mouse models underlined two of the KDs, SLC6A15 and KCNQ5, with unidirectional regulatory functions in NP and IP. Our study provides strong evidence to support the current understanding of the shared genetic regulatory networks underlying NP and IP and potentially benefit the future common therapeutic avenues for chronic pain.
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Zhang RQ, Kuo K, Liu FT, Chen SD, Yang YX, Guo Y, Dong Q, Tan L, Zhang C, Yu JT. Shared polygenic risk and causal inferences in Parkinson's disease. BRAIN DISORDERS 2022. [DOI: 10.1016/j.dscb.2022.100048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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70
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Wang Z, Emmerich A, Pillon NJ, Moore T, Hemerich D, Cornelis MC, Mazzaferro E, Broos S, Ahluwalia TS, Bartz TM, Bentley AR, Bielak LF, Chong M, Chu AY, Berry D, Dorajoo R, Dueker ND, Kasbohm E, Feenstra B, Feitosa MF, Gieger C, Graff M, Hall LM, Haller T, Hartwig FP, Hillis DA, Huikari V, Heard-Costa N, Holzapfel C, Jackson AU, Johansson Å, Jørgensen AM, Kaakinen MA, Karlsson R, Kerr KF, Kim B, Koolhaas CM, Kutalik Z, Lagou V, Lind PA, Lorentzon M, Lyytikäinen LP, Mangino M, Metzendorf C, Monroe KR, Pacolet A, Pérusse L, Pool R, Richmond RC, Rivera NV, Robiou-du-Pont S, Schraut KE, Schulz CA, Stringham HM, Tanaka T, Teumer A, Turman C, van der Most PJ, Vanmunster M, van Rooij FJA, van Vliet-Ostaptchouk JV, Zhang X, Zhao JH, Zhao W, Balkhiyarova Z, Balslev-Harder MN, Baumeister SE, Beilby J, Blangero J, Boomsma DI, Brage S, Braund PS, Brody JA, Bruinenberg M, Ekelund U, Liu CT, Cole JW, Collins FS, Cupples LA, Esko T, Enroth S, Faul JD, Fernandez-Rhodes L, Fohner AE, Franco OH, Galesloot TE, Gordon SD, Grarup N, Hartman CA, Heiss G, Hui J, Illig T, Jago R, James A, Joshi PK, Jung T, Kähönen M, Kilpeläinen TO, Koh WP, Kolcic I, Kraft PP, Kuusisto J, Launer LJ, Li A, Linneberg A, Luan J, Vidal PM, Medland SE, Milaneschi Y, Moscati A, Musk B, Nelson CP, Nolte IM, Pedersen NL, Peters A, Peyser PA, Power C, Raitakari OT, Reedik M, Reiner AP, Ridker PM, Rudan I, Ryan K, Sarzynski MA, Scott LJ, Scott RA, Sidney S, Siggeirsdottir K, Smith AV, Smith JA, Sonestedt E, Strøm M, Tai ES, Teo KK, Thorand B, Tönjes A, Tremblay A, Uitterlinden AG, Vangipurapu J, van Schoor N, Völker U, Willemsen G, Williams K, Wong Q, Xu H, Young KL, Yuan JM, Zillikens MC, Zonderman AB, Ameur A, Bandinelli S, Bis JC, Boehnke M, Bouchard C, Chasman DI, Smith GD, de Geus EJC, Deldicque L, Dörr M, Evans MK, Ferrucci L, Fornage M, Fox C, Garland T, Gudnason V, Gyllensten U, Hansen T, Hayward C, Horta BL, Hyppönen E, Jarvelin MR, Johnson WC, Kardia SLR, Kiemeney LA, Laakso M, Langenberg C, Lehtimäki T, Marchand LL, Magnusson PKE, Martin NG, Melbye M, Metspalu A, Meyre D, North KE, Ohlsson C, Oldehinkel AJ, Orho-Melander M, Pare G, Park T, Pedersen O, Penninx BWJH, Pers TH, Polasek O, Prokopenko I, Rotimi CN, Samani NJ, Sim X, Snieder H, Sørensen TIA, Spector TD, Timpson NJ, van Dam RM, van der Velde N, van Duijn CM, Vollenweider P, Völzke H, Voortman T, Waeber G, Wareham NJ, Weir DR, Wichmann HE, Wilson JF, Hevener AL, Krook A, Zierath JR, Thomis MAI, Loos RJF, Hoed MD. Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention. Nat Genet 2022; 54:1332-1344. [PMID: 36071172 PMCID: PMC9470530 DOI: 10.1038/s41588-022-01165-1] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 07/18/2022] [Indexed: 02/02/2023]
Abstract
Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.
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Affiliation(s)
- Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Andrew Emmerich
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Nicolas J Pillon
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Tim Moore
- Division of Cardiology, Department of Medicine, University of California, Los Angeles, CA, USA
| | - Daiane Hemerich
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marilyn C Cornelis
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Eugenia Mazzaferro
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Siacia Broos
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Exercise Physiology Research Group, KU Leuven, Leuven, Belgium
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Physical Activity, Sports & Health Research Group, KU Leuven, Leuven, Belgium
| | - Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Mike Chong
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Audrey Y Chu
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- GlaxoSmithKline, Cambridge, MA, USA
| | - Diane Berry
- Division of Population, Policy and Practice, Great Ormond Street Hospital Institute for Child Health, University College London, London, UK
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Nicole D Dueker
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
- Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Elisa Kasbohm
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- Institute of Mathematics and Computer Science, University of Greifswald, Greifswald, Germany
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Leanne M Hall
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Toomas Haller
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fernando P Hartwig
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
- MRC Integrative Epidemiology Unit, NIHR Bristol Biomedical Research Center, University of Bristol, Bristol, UK
| | - David A Hillis
- Genetics, Genomics, and Bioinformatics Graduate Program, University of California, Riverside, CA, USA
| | - Ville Huikari
- Institute of Health Sciences, University of Oulu, Oulu, Finland
| | - Nancy Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Christina Holzapfel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
- Institute for Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Anja Moltke Jørgensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marika A Kaakinen
- Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kathleen F Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Boram Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Chantal M Koolhaas
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Zoltan Kutalik
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | | | - Penelope A Lind
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Biomedical Science, Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Mattias Lorentzon
- Geriatric Medicine, Institute of Medicine, University of Gothenburg and Sahlgrenska University Hospital Mölndal, Gothenburg, Sweden
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Christoph Metzendorf
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Kristine R Monroe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alexander Pacolet
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Exercise Physiology Research Group, KU Leuven, Leuven, Belgium
| | - Louis Pérusse
- Department of Kinesiology, Université Laval, Quebec, Quebec, Canada
- Centre Nutrition Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Quebec, Canada
| | - Rene Pool
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit and Avon Longitudinal Study of Parents and Children, University of Bristol Medical School, Population Health Sciences and Avon Longitudinal Study of Parents and Children, University of Bristol, Bristol, UK
| | - Natalia V Rivera
- Respiratory Division, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Rheumatology Division, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Center of Molecular Medicine (CMM), Karolinska Institutet, Stockholm, Sweden
| | - Sebastien Robiou-du-Pont
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Katharina E Schraut
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Christina-Alexandra Schulz
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, Germany
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Mathias Vanmunster
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Exercise Physiology Research Group, KU Leuven, Leuven, Belgium
| | - Frank J A van Rooij
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jana V van Vliet-Ostaptchouk
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Xiaoshuai Zhang
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- School of Public Health, Department of Biostatistics, Shandong University, Jinan, China
| | - Jing-Hua Zhao
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Zhanna Balkhiyarova
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Clinical and Experimental Medicine, University of Surrey, Guilford, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guilford, UK
| | - Marie N Balslev-Harder
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian E Baumeister
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- University of Münster, Münster, Germany
| | - John Beilby
- Diagnostic Genomics, PathWest Laboratory Medicine WA, Perth, Western Australia, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Peter S Braund
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | | | - Ulf Ekelund
- Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - John W Cole
- Vascular Neurology, Department of Neurology, University of Maryland School of Medicine and the Baltimore VAMC, Baltimore, MD, USA
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD, USA
| | - L Adrienne Cupples
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stefan Enroth
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Lindsay Fernandez-Rhodes
- Department of Biobehavioral Health, College of Health and Human Development, Pennsylvania State University, University Park, PA, USA
| | - Alison E Fohner
- Department of Epidemiology, Institute of Public Health Genetics, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Oscar H Franco
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Tessel E Galesloot
- Radboud Institute for Health Sciences, Department for Health Evidence, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Scott D Gordon
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Catharina A Hartman
- Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jennie Hui
- Diagnostic Genomics, PathWest Laboratory Medicine WA, Perth, Western Australia, Australia
- School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
- Busselton Population Medical Research Institute, Busselton, Western Australia, Australia
| | - Thomas Illig
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Russell Jago
- Centre for Exercise Nutrition & Health Sciences, School for Policy Studies, University of Bristol, Bristol, UK
| | - Alan James
- Department of Pulmonary Physiology and Sleep Medicine, Sir Charles Gairdner Hospital, Western Australia, Perth, Australia
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Humanity Inc, Boston, MA, USA
| | - Taeyeong Jung
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Mika Kähönen
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Woon-Puay Koh
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore, Singapore
| | - Ivana Kolcic
- Department of Public Health, University of Split School of Medicine, Split, Croatia
| | - Peter P Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institutes of Health, Baltimore, MD, USA
| | - Aihua Li
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Pedro Marques Vidal
- Division of Internal Medicine, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sarah E Medland
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Psychology and Faculty of Medicine, University of Queensland, St Lucia, Queensland, Australia
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bill Musk
- Busselton Population Medical Research Institute, Busselton, Western Australia, Australia
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Christine Power
- Division of Population, Policy and Practice, Great Ormond Street Hospital Institute for Child Health, University College London, London, UK
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Mägi Reedik
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Alex P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Kathy Ryan
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Stephen Sidney
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | | | - Albert V Smith
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Icelandic Heart Association, Kópavogur, Iceland
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Emily Sonestedt
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Marin Strøm
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- Faculty of Health Sciences, University of the Faroe Islands, Tórshavn, Faroe Islands
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Koon K Teo
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Angelo Tremblay
- Department of Kinesiology, Université Laval, Quebec, Quebec, Canada
- Centre Nutrition Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Quebec, Canada
| | - Andre G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jagadish Vangipurapu
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Natasja van Schoor
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, the Netherlands
| | - Uwe Völker
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Kayleen Williams
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin L Young
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jian Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - M Carola Zillikens
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Science, National Instiute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Adam Ameur
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, NIHR Bristol Biomedical Research Center, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, NIHR Bristol Biomedical Research Center, University of Bristol, Bristol, UK
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Louise Deldicque
- Faculty of Movement and Rehabilitation Sciences, Institute of Neuroscience, UC Louvain, Louvain-la-Neuve, Belgium
| | - Marcus Dörr
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Michele K Evans
- Laboratory of Epidemiology and Population Science, National Instiute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Caroline Fox
- Genetics and Pharmacogenomics (GpGx), Merck Research Labs, Boston, MA, USA
| | - Theodore Garland
- Department of Evolution, Ecology, and Organismal Biology, University of California, Riverside, Riverside, CA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kópavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Ulf Gyllensten
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Bernardo L Horta
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Elina Hyppönen
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Population, Policy and Practice, Great Ormond Street Hospital Institute for Child Health, University College London, London, UK
| | - Marjo-Riitta Jarvelin
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics and HPA-MRC Center, School of Public Health, Imperial College London, London, UK
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lambertus A Kiemeney
- Radboud Institute for Health Sciences, Department for Health Evidence, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Nicholas G Martin
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Mads Melbye
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- K.G.Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Center for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - David Meyre
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Albertine J Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Guillaume Pare
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Tune H Pers
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ozren Polasek
- University of Split School of Medicine, Split, Croatia
| | - Inga Prokopenko
- Department of Clinical and Experimental Medicine, University of Surrey, Guilford, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guilford, UK
- UMR 8199 - EGID, Institut Pasteur de Lille, CNRS, University of Lille, Lille, France
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol Medical School, University of Bristol, Bristol, UK
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | - Nathalie van der Velde
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
- Section of Geriatrics, Department of Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Aging and Later Life, Amsterdam, the Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Peter Vollenweider
- Division of Internal Medicine, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Gérard Waeber
- Division of Internal Medicine, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Heinz-Erich Wichmann
- Institute of Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Andrea L Hevener
- Division of Endocrinology, Department of Medicine, University of California, Los Angeles, CA, USA
| | - Anna Krook
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Juleen R Zierath
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Martine A I Thomis
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Physical Activity, Sports & Health Research Group, KU Leuven, Leuven, Belgium
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marcel den Hoed
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden.
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Ma M, Guo L, Liu X, Zheng Y, Gu C, Li B. Genetic correlation between female infertility and mental health and lifestyle factors: A linkage disequilibrium score regression study. Health Sci Rep 2022; 5:e797. [PMID: 36090619 PMCID: PMC9436294 DOI: 10.1002/hsr2.797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 12/04/2022] Open
Abstract
Background and Aims Female fertility is a heterogeneous condition of complete psychosocial and physical well‐being. Observational studies have revealed that women with infertility have varying degrees of poor mental status and lifestyle choices in varying degrees. However, the genetic contribution to female infertility remains elusive. Our study aimed to explore the genetic correlations between female infertility and mental health and lifestyle factors. Methods The genome‐wide association study (GWAS) data sets of characteristics related to mental health and lifestyle were obtained from the IEU OpenGWAS database. The GWAS data sets of female infertility were derived from the Finggen database. Linkage disequilibrium score regression was performed to systematically estimate the pairwise genetic correlations between female infertility and a set of mental health‐ and lifestyle‐related traits. Results The genetic correlation analysis revealed a moderate and positive genetic correlation between depressive symptoms, major depressive disorder, and female infertility. Similarly, worry and the personality trait of neuroticism displayed a moderate positive genetic correlation with female infertility. Adversely, a negative and moderate genetic correlation was observed between strenuous sports or exercises and female infertility. Conclusion The study demonstrated genetic correlations between female infertility and mental health status, including depression, worry, and neuroticism. Additionally, we observed that females with better physical activity may have reduced risks of female infertility. These findings would serve as a fundamental resource for understanding the genetic mechanisms of the effects of mental health and lifestyle factors on female infertility.
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Affiliation(s)
- Miao Ma
- Department of Gynecology and Obstetrics Obstetrics and Gynecology Hospital of Fudan University Shanghai China
| | - Lu Guo
- Department of Gynecology and Obstetrics Obstetrics and Gynecology Hospital of Fudan University Shanghai China
| | - Xiaocheng Liu
- Department of Gynecology and Obstetrics Obstetrics and Gynecology Hospital of Fudan University Shanghai China
| | - Yingxin Zheng
- Department of Gynecology and Obstetrics Obstetrics and Gynecology Hospital of Fudan University Shanghai China
| | - Chao Gu
- Department of Gynecology and Obstetrics Obstetrics and Gynecology Hospital of Fudan University Shanghai China
| | - Bin Li
- Department of Gynecology and Obstetrics Obstetrics and Gynecology Hospital of Fudan University Shanghai China
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Walker VM, Zheng J, Gaunt TR, Smith GD. Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond. Annu Rev Biomed Data Sci 2022; 5:1-17. [PMID: 35363507 PMCID: PMC7614231 DOI: 10.1146/annurev-biodatasci-122120-024910] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.
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Affiliation(s)
- Venexia M. Walker
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - George Davey Smith
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
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Zhang X, Cong R, Geng T, Zhang J, Liu D, Tian Q, Meng X, Song M, Wu L, Zheng D, Wang W, Wang B, Wang Y. Assessment of the Causal Effects of IgG N-Glycosylation Level on Risk of Dementia: A 2-Sample Mendelian Randomization Study. J Alzheimers Dis 2022; 88:1435-1441. [DOI: 10.3233/jad-220074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Previous prospective studies highlighted aberrant immunoglobulin G (IgG) N-glycosylation as a risk factor for dementia [such as Alzheimer’s disease (AD) and vascular dementia (VaD)]. It is unclear whether this association is causal or explained by confounding or reverse causation. Objective: The aim is to examine the association of genetically predicted IgG N-glycosylation with dementia using 2-sample Mendelian randomization (MR). Methods: Independent genetic variants for IgG N-glycosylation traits were selected as instrument variables from published genome-wide association studies (GWAS) among individuals of European ancestry. We extracted their corresponding summary statistics from large-scale clinically diagnosed AD GWAS dataset and FinnGen biobank VaD GWAS dataset. The inverse variance weighted (IVW) was performed to calculate the effect estimates. Meanwhile, multiple sensitivity analyses were used to assess horizontal pleiotropy and outliers. Results: There were no associations of genetically predicted IgG N-glycosylation traits with the risk of AD and VaD using the IVW method (all p > 0.0013). These estimates of four additional sensitivity analyses methods were consistent with the IVW estimates in terms of direction and magnitude. Additionally, the MR-PRESSO global test and the intercept of MR-Egger regression indicated no evidence of horizontal pleiotropy. Meanwhile, the heterogeneity test showed no significant heterogeneity using the Cochran Q statistic. The leave-one-out sensitivity analyses also did not detect any significant change. Conclusion: Our MR study did not support evidence for the hypothesis that IgG N-glycosylation level may be causally associated with the risk of dementia.
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Affiliation(s)
- Xiaoyu Zhang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Ruyi Cong
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Tao Geng
- Geriatric Department, Emergency General Hospital, Beijing, China
- Department of Medicine & Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong
| | - Jinxia Zhang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Di Liu
- Centre for Biomedical Information Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Qiuyue Tian
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Xiaoni Meng
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Manshu Song
- School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | - Lijuan Wu
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Deqiang Zheng
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Wei Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
- Centre for Precision Health, Edith Cowan University, Perth, Australia
| | - Baoguo Wang
- Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Youxin Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
- School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
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Huang N, Zhuang Z, Liu Z, Huang T. Observational and Genetic Associations of Modifiable Risk Factors with Aortic Valve Stenosis: A Prospective Cohort Study of 0.5 Million Participants. Nutrients 2022; 14:nu14112273. [PMID: 35684074 PMCID: PMC9182826 DOI: 10.3390/nu14112273] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 01/09/2023] Open
Abstract
Background: Observational studies have shown that modifiable risk factors are associated with aortic valve stenosis (AVS). However, the causality behind these associations remains largely unknown. Objectives: To explore the associations of modifiable risk factors, including metabolic factors, biochemical measures, education, and lifestyles with AVS and their potential causal associations. Methods: We enrolled 361,930 British white people with genetic data in the UK biobank. Cox proportional risk regression models were used to estimate the hazard ratios between 28 modifiable risk factors and AVS. We used genetic instruments for modifiable risk factors to determine the potential causal relationships using a one-sample Mendelian randomization (MR) approach. Results: A total of 1602 participants developed AVS during an 8.4-year follow-up. Observational analyses showed higher adiposity, blood pressure, heart rate, low-density lipoprotein, urate, C-reactive protein, creatinine, albumin, and glycated hemoglobin, but lower serum vitamin D, and education, unhealthy lifestyle, and poor sleep quality were related to a higher risk of AVS after adjusting for the Bonferroni correction (p < 0.0013). Genetically predicted 1-SD higher levels of body mass index [HR: 1.09, 95% CI: 1.03 to 1.16], body fat percentage (1.17, 1.03 to 1.33), triglyceride (TG) [1.08, 1.00 to 1.16], low-density lipoprotein (LDL) (1.15, 1.08 to 1.21) and serum total cholesterol (TC) (1.13, 1.02 to 1.25) were associated with a higher risk of AVS, respectively. Genetically determined per category higher insomnia (1.32, 1.13 to 1.55) was also associated with AVS. The abovementioned genetic associations with the incident AVS showed an increasing relationship pattern. Conclusions: This study provides strong evidence for the potential causal roles of cardiometabolic factors in developing AVS, highlighting that an idea of metabolic status through a healthy lifestyle may help prevent AVS.
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Affiliation(s)
- Ninghao Huang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (N.H.); (Z.Z.)
| | - Zhenhuang Zhuang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (N.H.); (Z.Z.)
| | - Zhonghua Liu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong 999077, China;
| | - Tao Huang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (N.H.); (Z.Z.)
- Key Laboratory of Molecular Cardiovascular Sciences, Ministry of Education, Peking University, Beijing 100871, China
- Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing 100871, China
- Correspondence:
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Xue H, Pan W. Robust inference of bi-directional causal relationships in presence of correlated pleiotropy with GWAS summary data. PLoS Genet 2022; 18:e1010205. [PMID: 35576237 PMCID: PMC9135345 DOI: 10.1371/journal.pgen.1010205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 05/26/2022] [Accepted: 04/15/2022] [Indexed: 11/25/2022] Open
Abstract
To infer a causal relationship between two traits, several correlation-based causal direction (CD) methods have been proposed with the use of SNPs as instrumental variables (IVs) based on GWAS summary data for the two traits; however, none of the existing CD methods can deal with SNPs with correlated pleiotropy. Alternatively, reciprocal Mendelian randomization (MR) can be applied, which however may perform poorly in the presence of (unknown) invalid IVs, especially for bi-directional causal relationships. In this paper, first, we propose a CD method that performs better than existing CD methods regardless of the presence of correlated pleiotropy. Second, along with a simple but yet effective IV screening rule, we propose applying a closely related and state-of-the-art MR method in reciprocal MR, showing its almost identical performance to that of the new CD method when their model assumptions hold; however, if the modeling assumptions are violated, the new CD method is expected to better control type I errors. Notably bi-directional causal relationships impose some unique challenges beyond those for uni-directional ones, and thus requiring special treatments. For example, we point out for the first time several scenarios where a bi-directional relationship, but not a uni-directional one, can unexpectedly cause the violation of some weak modeling assumptions commonly required by many robust MR methods. We also offer some numerical support and a modeling justification for the application of our new methods (and more generally MR) to binary traits. Finally we applied the proposed methods to 12 risk factors and 4 common diseases, confirming mostly well-known uni-directional causal relationships, while identifying some novel and plausible bi-directional ones such as between body mass index and type 2 diabetes (T2D), and between diastolic blood pressure and stroke. Inferring causal relationships between two traits based on observational data is one of the most important as well as challenging problems in scientific research. The potential impact of such an application on and beyond genetics/genomics is significant, such as in prioritizing molecular, clinical and behavioral targets for therapeutic and behavioral interventions. However, this problem, especially on a possibly bi-directional causal relationship between a pair of traits (in which the causal direction may be from either trait to the other, and both causal directions may be present at the same time), had been largely neglected until very recently. The increasing availability of large-scale GWAS summary data of various traits has popularized the development and application of Mendelian randomization (MR) methods for such a purpose. We point out some severe limitations with the current methods, mainly due to some new and unique challenges facing inference of bi-directional relationships as compared to that of uni-directional relationships that has been more commonly and exclusively considered in MR. By combining two basic ideas of bidirectional (or reciprocal) MR and Steiger’s correlation-based screening methods, we develop two new approaches based on constrained maximum likelihood (cML) and GWAS summary data to infer causal effects (as in typical MR) and SNP-trait correlations (as in Steiger’s method), called MR-cML and CD-cML respectively, demonstrating their similar effectiveness and more importantly their advantages over existing methods through extensive simulations, real data examples and statistical theory. In particular, our proposed two methods are robust to violations of all three valid IV assumptions, including presence of correlated pleiotropy.
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Affiliation(s)
- Haoran Xue
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
- * E-mail:
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Huang J, Liang ZS, Pallotti S, Ranson JM, Llewellyn DJ, Zheng ZJ, King DA, Zhou Q, Zheng H, Napolioni V. PAGEANT: personal access to genome and analysis of natural traits. Nucleic Acids Res 2021; 50:e39. [PMID: 34928375 PMCID: PMC9023285 DOI: 10.1093/nar/gkab1245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/17/2021] [Accepted: 12/03/2021] [Indexed: 12/02/2022] Open
Abstract
GWASs have identified numerous genetic variants associated with a wide variety of diseases, yet despite the wide availability of genetic testing the insights that would enhance the interpretability of these results are not widely available to members of the public. As a proof of concept and demonstration of technological feasibility, we developed PAGEANT (Personal Access to Genome & Analysis of Natural Traits), usable through Graphical User Interface or command line-based version, aiming to serve as a protocol and prototype that guides the overarching design of genetic reporting tools. PAGEANT is structured across five core modules, summarized by five Qs: (i) quality assurance of the genetic data; (ii) qualitative assessment of genetic characteristics; (iii) quantitative assessment of health risk susceptibility based on polygenic risk scores and population reference; (iv) query of third-party variant databases (e.g. ClinVAR and PharmGKB) and (v) quick Response code of genetic variants of interest. Literature review was conducted to compare PAGEANT with academic and industry tools. For 2504 genomes made publicly available through the 1000 Genomes Project, we derived their genomic characteristics for a suite of qualitative and quantitative traits. One exemplary trait is susceptibility to COVID-19, based on the most up-to-date scientific findings reported.
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Affiliation(s)
- Jie Huang
- Department of Global Health, Peking University School of Public Health, Beijing, China.,Institute for Global Health and Development, Peking University, Beijing, China.,National Institute of Health Data Science at Peking University, Beijing, China
| | - Zhi-Sheng Liang
- Department of Global Health, Peking University School of Public Health, Beijing, China
| | - Stefano Pallotti
- Genetics and Animal Breeding Group, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Janice M Ranson
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - David J Llewellyn
- College of Medicine and Health, University of Exeter, Exeter, UK.,Alan Turing Institute, London, UK
| | - Zhi-Jie Zheng
- Department of Global Health, Peking University School of Public Health, Beijing, China
| | - Daniel A King
- Northwell Health Cancer Institute and Feinstein Institutes for Research, Lake Success, NY, USA
| | - Qiang Zhou
- Shenzhen Center for Prehospital Care, Shenzhen, China
| | - Houfeng Zheng
- Diseases & Population (DaP) Geninfo Lab., School of Life Sciences, Westlake University, Hangzhou, China
| | - Valerio Napolioni
- Genomic and Molecular Epidemiology (GAME)Lab., School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
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77
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Prince C, Mitchell RE, Richardson TG. Integrative multiomics analysis highlights immune-cell regulatory mechanisms and shared genetic architecture for 14 immune-associated diseases and cancer outcomes. Am J Hum Genet 2021; 108:2259-2270. [PMID: 34741802 PMCID: PMC8715275 DOI: 10.1016/j.ajhg.2021.10.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 10/13/2021] [Indexed: 12/17/2022] Open
Abstract
Developing functional insight into the causal molecular drivers of immunological disease is a critical challenge in genomic medicine. Here, we systematically apply Mendelian randomization (MR), genetic colocalization, immune-cell-type enrichment, and phenome-wide association methods to investigate the effects of genetically predicted gene expression on ten immune-associated diseases and four cancer outcomes. Using whole blood-derived estimates for regulatory variants from the eQTLGen consortium (n = 31,684), we constructed genetic risk scores for 10,104 genes. Applying the inverse-variance-weighted MR method transcriptome wide while accounting for linkage disequilibrium structure identified 664 unique genes with evidence of a genetically predicted effect on at least one disease outcome (p < 4.81 × 10-5). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci by using gene expression data derived from 18 types of immune cells. This highlighted many cell-type-dependent effects, such as PRKCQ expression and asthma risk (posterior probability = 0.998), which was T cell specific. Phenome-wide analyses on 311 complex traits and endpoints allowed us to explore shared genetic architecture and prioritize key drivers of disease risk, such as CASP10, which provided evidence of an effect on seven cancer-related outcomes. Our atlas of results can be used to characterize known and novel loci in immune-associated disease and cancer susceptibility, both in terms of elucidating cell-type-dependent effects as well as dissecting shared disease pathways and pervasive pleiotropy. As an exemplar, we have highlighted several key findings in this study, although similar evaluations can be conducted via our interactive web platform.
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Affiliation(s)
- Claire Prince
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Ruth E Mitchell
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Tom G Richardson
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK; Novo Nordisk Research Centre, Headington, Oxford OX3 7FZ, UK.
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78
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MacArthur JA, Buniello A, Harris LW, Hayhurst J, McMahon A, Sollis E, Cerezo M, Hall P, Lewis E, Whetzel PL, Bahcall OG, Barroso I, Carroll RJ, Inouye M, Manolio TA, Rich SS, Hindorff LA, Wiley K, Parkinson H. Workshop proceedings: GWAS summary statistics standards and sharing. CELL GENOMICS 2021; 1:100004. [PMID: 36082306 PMCID: PMC9451133 DOI: 10.1016/j.xgen.2021.100004] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.
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Affiliation(s)
- Jacqueline A.L. MacArthur
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
- BHF Data Science Centre, Health Data Research UK, London, UK
| | - Annalisa Buniello
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Laura W. Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - James Hayhurst
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Elliot Sollis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Maria Cerezo
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Peggy Hall
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elizabeth Lewis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Patricia L. Whetzel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Orli G. Bahcall
- Cell Genomics, Cell Press, 50 Hampshire St., 5th Floor, Cambridge, MA 02139, USA
| | - Inês Barroso
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Robert J. Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, 75 Commercial Rd., Melbourne 3004, VIC, Australia
- The Alan Turing Institute, London, UK
| | - Teri A. Manolio
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Lucia A. Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ken Wiley
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
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79
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Murphy AE, Schilder BM, Skene NG. MungeSumstats: A Bioconductor package for the standardisation and quality control of many GWAS summary statistics. Bioinformatics 2021; 37:4593-4596. [PMID: 34601555 PMCID: PMC8652100 DOI: 10.1093/bioinformatics/btab665] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/12/2021] [Accepted: 08/29/2021] [Indexed: 11/15/2022] Open
Abstract
Motivation Genome‐wide association studies (GWAS) summary statistics have popularized and accelerated genetic research. However, a lack of standardization of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies. Results To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardization and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardized, tabular summary statistic file, VCF or R native data object. Availability and implementation MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alan E Murphy
- UK Dementia Research Institute at Imperial College London
| | | | - Nathan G Skene
- UK Dementia Research Institute at Imperial College London
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80
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Kerimov N, Hayhurst JD, Peikova K, Manning JR, Walter P, Kolberg L, Samoviča M, Sakthivel MP, Kuzmin I, Trevanion SJ, Burdett T, Jupp S, Parkinson H, Papatheodorou I, Yates AD, Zerbino DR, Alasoo K. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat Genet 2021; 53:1290-1299. [PMID: 34493866 PMCID: PMC8423625 DOI: 10.1038/s41588-021-00924-w] [Citation(s) in RCA: 147] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022]
Abstract
Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue ( https://www.ebi.ac.uk/eqtl ), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.
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Affiliation(s)
- Nurlan Kerimov
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - James D Hayhurst
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Kateryna Peikova
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Jonathan R Manning
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Peter Walter
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Liis Kolberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Marija Samoviča
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Manoj Pandian Sakthivel
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Ivan Kuzmin
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Stephen J Trevanion
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Tony Burdett
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Simon Jupp
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Helen Parkinson
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Irene Papatheodorou
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Andrew D Yates
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Daniel R Zerbino
- Open Targets, Wellcome Genome Campus, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
- Open Targets, Wellcome Genome Campus, Cambridge, UK.
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81
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Novikova G, Andrews SJ, Renton AE, Marcora E. Beyond association: successes and challenges in linking non-coding genetic variation to functional consequences that modulate Alzheimer's disease risk. Mol Neurodegener 2021; 16:27. [PMID: 33882988 PMCID: PMC8061035 DOI: 10.1186/s13024-021-00449-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/13/2021] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia, affecting millions of people worldwide; however, no disease-modifying treatments are currently available. Genome-wide association studies (GWASs) have identified more than 40 loci associated with AD risk. However, most of the disease-associated variants reside in non-coding regions of the genome, making it difficult to elucidate how they affect disease susceptibility. Nonetheless, identification of the regulatory elements, genes, pathways and cell type/tissue(s) impacted by these variants to modulate AD risk is critical to our understanding of disease pathogenesis and ability to develop effective therapeutics. In this review, we provide an overview of the methods and approaches used in the field to identify the functional effects of AD risk variants in the causal path to disease risk modification as well as describe the most recent findings. We first discuss efforts in cell type/tissue prioritization followed by recent progress in candidate causal variant and gene nomination. We discuss statistical methods for fine-mapping as well as approaches that integrate multiple levels of evidence, such as epigenomic and transcriptomic data, to identify causal variants and risk mechanisms of AD-associated loci. Additionally, we discuss experimental approaches and data resources that will be needed to validate and further elucidate the effects of these variants and genes on biological pathways, cellular phenotypes and disease risk. Finally, we discuss future steps that need to be taken to ensure that AD GWAS functional mapping efforts lead to novel findings and bring us closer to finding effective treatments for this devastating disease.
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Affiliation(s)
- Gloriia Novikova
- Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shea J Andrews
- Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alan E Renton
- Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Edoardo Marcora
- Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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82
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Andrews SJ, Fulton-Howard B, O'Reilly P, Marcora E, Goate AM. Causal Associations Between Modifiable Risk Factors and the Alzheimer's Phenome. Ann Neurol 2021; 89:54-65. [PMID: 32996171 PMCID: PMC8088901 DOI: 10.1002/ana.25918] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/22/2020] [Accepted: 09/23/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The purpose of this study was to infer causal relationships between 22 previously reported risk factors for Alzheimer's disease (AD) and the "AD phenome": AD, AD age of onset (AAOS), hippocampal volume, cortical surface area and thickness, cerebrospinal fluid (CSF) levels of amyloid-β (Aβ42 ), tau, and ptau181 , and the neuropathological burden of neuritic plaques, neurofibrillary tangles (NFTs), and vascular brain injury (VBI). METHODS Polygenic risk scores (PRS) for the 22 risk factors were computed in 26,431 AD cases/controls and the association with AD was evaluated using logistic regression. Two-sample Mendelian randomization (MR) was used to infer the causal effect of risk factors on the AD phenome. RESULTS PRS for increased education and diastolic blood pressure were associated with reduced risk for AD. MR indicated that only education was causally associated with reduced risk of AD, delayed AAOS, and increased cortical surface area and thickness. Total- and LDL-cholesterol levels were causally associated with increased neuritic plaque burden, although the effects were driven by single nucleotide polymorphisms (SNPs) within the APOE locus. Diastolic blood pressure and pulse pressure are causally associated with increased risk of VBI. Furthermore, total cholesterol was associated with decreased hippocampal volume; smoking initiation with decreased cortical thickness; type 2 diabetes with an earlier AAOS; and sleep duration with increased cortical thickness. INTERPRETATION Our comprehensive examination of the genetic evidence for the causal relationships between previously reported risk factors in AD using PRS and MR supports a causal role for education, blood pressure, cholesterol levels, smoking, and diabetes with the AD phenome. ANN NEUROL 2021;89:54-65.
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Affiliation(s)
- Shea J Andrews
- 'Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian Fulton-Howard
- 'Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paul O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Edoardo Marcora
- 'Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alison M Goate
- 'Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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