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Song Y, Li L, Jiang Y, Peng B, Jiang H, Chao Z, Chang X. Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians. Schizophr Bull 2024:sbae145. [PMID: 39190819 DOI: 10.1093/schbul/sbae145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
BACKGROUND AND HYPOTHESIS While genetic correlations, pleiotropic loci, and shared genetic mechanisms of psychiatric disorders have been extensively studied in European populations, the investigation of these factors in East Asian populations has been relatively limited. STUDY DESIGN To identify novel pleiotropic risk loci for depression and schizophrenia (SCZ) in East Asians. We utilized the most comprehensive dataset available for East Asians and quantified the genetic overlap between depression, SCZ, and their related traits via a multitrait genome-wide association study. Global and local genetic correlations were estimated by LDSC and ρ-HESS. Pleiotropic loci were identified by the multitrait analysis of GWAS (MTAG). STUDY RESULTS Besides the significant correlation between depression and SCZ, our analysis revealed genetic correlations between depression and obesity-related traits, such as weight, BMI, T2D, and HDL. In SCZ, significant correlations were detected with HDL, heart diseases and use of various medications. Conventional meta-analysis of depression and SCZ identified a novel locus at 1q25.2 in East Asians. Further multitrait analysis of depression, SCZ and related traits identified ten novel pleiotropic loci for depression, and four for SCZ. CONCLUSIONS Our findings demonstrate shared genetic underpinnings between depression and SCZ in East Asians, as well as their associated traits, providing novel candidate genes for the identification and prioritization of therapeutic targets specific to this population.
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Affiliation(s)
- Yingchao Song
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Linzehao Li
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Yue Jiang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Bichen Peng
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Hengxuan Jiang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Zhen Chao
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Xiao Chang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
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Qi G, Chhetri SB, Ray D, Dutta D, Battle A, Bhattacharjee S, Chatterjee N. Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants. Nat Commun 2024; 15:6985. [PMID: 39143063 PMCID: PMC11324957 DOI: 10.1038/s41467-024-51075-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/29/2024] [Indexed: 08/16/2024] Open
Abstract
Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with ≥ 2 traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value < 2.2 × 10 - 16 ). Follow-up analyzes of 21 trait-specific variants indicate their link to the expression in trait-related tissues for a small number of genes involved in relevant biological processes. Our findings provide deeper insight into the nature of pleiotropy and leads to identification of highly trait-specific susceptibility variants.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Surya B Chhetri
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Diptavo Dutta
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Samsiddhi Bhattacharjee
- Biotechnology Research and Innovation Council-National Institute of Biomedical Genomics (BRIC-NIBMG), Kalyani, India.
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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3
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability. HGG ADVANCES 2024; 5:100319. [PMID: 38872309 PMCID: PMC11260573 DOI: 10.1016/j.xhgg.2024.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France; Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France.
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4
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Xiu Z, Sun L, Liu K, Cao H, Qu HQ, Glessner JT, Ding Z, Zheng G, Wang N, Xia Q, Li J, Li MJ, Hakonarson H, Liu W, Li J. Shared molecular mechanisms and transdiagnostic potential of neurodevelopmental disorders and immune disorders. Brain Behav Immun 2024; 119:767-780. [PMID: 38677625 DOI: 10.1016/j.bbi.2024.04.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/27/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024] Open
Abstract
The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta-analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta-analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders.
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Affiliation(s)
- Zhanjie Xiu
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China; Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Ling Sun
- Department of Child and Adolescent Psychology, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China
| | - Kunlun Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Haiyan Cao
- Department of Child and Adolescent Psychology, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China
| | - Hui-Qi Qu
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Joseph T Glessner
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, United States; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Zhiyong Ding
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd., Jinan, China
| | - Gang Zheng
- National Supercomputer Center in Tianjin (NSCC-TJ), Tianjin, China
| | - Nan Wang
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd., Jinan, China
| | - Qianghua Xia
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China; Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jie Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China
| | - Mulin Jun Li
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, United States; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
| | - Wei Liu
- Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China; Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, China.
| | - Jin Li
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China; Department of Rheumatology and Immunology, Tianjin Medical University General Hospital, Tianjin, China.
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5
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Johnson EC, Austin-Zimmerman I, Thorpe HHA, Levey DF, Baranger DAA, Colbert SMC, Demontis D, Khokhar JY, Davis LK, Edenberg HJ, Di Forti M, Sanchez-Roige S, Gelernter J, Agrawal A. Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking. Neuropsychopharmacology 2024:10.1038/s41386-024-01886-3. [PMID: 38906991 DOI: 10.1038/s41386-024-01886-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/22/2024] [Accepted: 05/06/2024] [Indexed: 06/23/2024]
Abstract
Individuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz; European ancestry N = 161,405; African ancestry N = 15,846), cannabis use disorder (CanUD; European ancestry N = 886,025; African ancestry N = 120,208), and ever-regular tobacco smoking (Smk; European ancestry N = 805,431; African ancestry N = 24,278) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17-0.62). Genetic instrumental variable analyses suggested the presence of shared heritable factors, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for these shared genetic factors. We identified 327 pleiotropic loci with 439 lead SNPs in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both shared genetic factors and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
| | - Isabelle Austin-Zimmerman
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Hayley H A Thorpe
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - David A A Baranger
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO, USA
| | - Sarah M C Colbert
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ditte Demontis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Department of Biomedicine and Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Jibran Y Khokhar
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Marta Di Forti
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry, UC San Diego School of Medicine, La Jolla, CA, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
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Li X, Zhou Z, Ma Y, Ding K, Xiao H, Chen D, Liu N. Shared Genetic Architectures between Coronary Artery Disease and Type 2 Diabetes Mellitus in East Asian and European Populations. Biomedicines 2024; 12:1243. [PMID: 38927450 PMCID: PMC11201280 DOI: 10.3390/biomedicines12061243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/23/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Coronary artery disease (CAD) is a common comorbidity of type 2 diabetes mellitus (T2DM). However, the pathophysiology connecting these two phenotypes remains to be further understood. Combined analysis in multi-ethnic populations can help contribute to deepening our understanding of biological mechanisms caused by shared genetic loci. We applied genetic correlation analysis and then performed conditional and joint association analyses in Chinese, Japanese, and European populations to identify the genetic variants jointly associated with CAD and T2DM. Next, the associations between genes and the two traits were also explored. Finally, fine-mapping and functional enrichment analysis were employed to identify the potential causal variants and pathways. Genetic correlation results indicated significant genetic overlap between CAD and T2DM in the three populations. Over 10,000 shared signals were identified, and 587 were shared by East Asian and European populations. Fifty-six novel shared genes were found to have significant effects on both CAD and T2DM. Most loci were fine-mapped to plausible causal variant sets. Several similarities and differences of the involved genes in GO terms and KEGG pathways were revealed across East Asian and European populations. These findings highlight the importance of immunoregulation, neuroregulation, heart development, and the regulation of glucose metabolism in shared etiological mechanisms between CAD and T2DM.
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Affiliation(s)
- Xiaoyi Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Zechen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Yujia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Kexin Ding
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Han Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Dafang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Na Liu
- Department of Neurology, Peking University Third Hospital, Beijing 100191, China
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Jung ES, Ellinghaus D, Degenhardt F, Meguro A, Khor SS, Mucha S, Wendorff M, Juzenas S, Mizuki N, Tokunaga K, Kim SW, Lee MG, Schreiber S, Kim WH, Franke A, Cheon JH. Genome-wide association analysis reveals the associations of NPHP4, TYW1-AUTS2 and SEMA6D for Behçet's disease and HLA-B*46:01 for its intestinal involvement. Dig Liver Dis 2024; 56:994-1001. [PMID: 37977914 DOI: 10.1016/j.dld.2023.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Intestinal involvement in Behçet's disease (BD) is associated with poor prognosis and is more prevalent in East Asian than in Mediterranean populations. Identifying the genetic causes of intestinal BD is important for understanding the pathogenesis and for appropriate treatment of BD patients. METHODS We performed genome-wide association studies (GWAS) and imputation/replication genotyping of human leukocyte antigen (HLA) alleles for 1,689 Korean and Turkish patients with BD (including 379 patients with intestinal BD) and 2,327 healthy controls, followed by replication using 593 Japanese patients with BD (101 patients with intestinal BD) and 737 healthy controls. Stratified cross-phenotype analyses were performed for 1) overall BD, 2) intestinal BD, and 3) intestinal BD without association of overall BD. RESULTS We identified three novel genome-wide significant susceptibility loci including NPHP4 (rs74566205; P=1.36 × 10-8), TYW1-AUTS2 (rs60021986; P=1.14 × 10-9), and SEMA6D (rs4143322; P=5.54 × 10-9) for overall BD, and a new association with HLA-B*46:01 for intestinal BD (P=1.67 × 10-8) but not for BD without intestinal involvement. HLA peptide binding analysis revealed that Mycobacterial peptides, have a stronger binding affinity to HLA-B*46:01 compared to the known risk allele HLA-B*51:01. CONCLUSIONS HLA-B*46:01 is associated with the development of intestinal BD; NPHP4, TYW1-AUTS2, and SEMA6D are susceptibility loci for overall BD.
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Affiliation(s)
- Eun Suk Jung
- Department of Internal Medicine and Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, South Korea; Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany.
| | - Frauke Degenhardt
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Akira Meguro
- Department of Ophthalmology and Visual Science, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Seik-Soon Khor
- Genome Medical Science Project, National Center for Global Health and Medicine, Tokyo, Japan
| | - Sören Mucha
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Mareike Wendorff
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Simonas Juzenas
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany; Institute of Biotechnology, Life Science Centre, Vilnius University, Vilnius, Lithuania
| | - Nobuhisa Mizuki
- Department of Ophthalmology and Visual Science, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project, National Center for Global Health and Medicine, Tokyo, Japan
| | - Seung Won Kim
- Department of Internal Medicine and Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, South Korea
| | - Min Goo Lee
- Department of Pharmacology, Brain Korea 21 PLUS Project for Medical Sciences, Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Won Ho Kim
- Department of Internal Medicine and Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, South Korea
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Jae Hee Cheon
- Department of Internal Medicine and Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, South Korea.
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8
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Khan Y, Davis CN, Jinwala Z, Feuer KL, Toikumo S, Hartwell EE, Sanchez-Roige S, Peterson RE, Hatoum AS, Kranzler HR, Kember RL. Combining Transdiagnostic and Disorder-Level GWAS Enhances Precision of Psychiatric Genetic Risk Profiles in a Multi-Ancestry Sample. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.09.24307111. [PMID: 38766259 PMCID: PMC11100926 DOI: 10.1101/2024.05.09.24307111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The etiology of substance use disorders (SUDs) and psychiatric disorders reflects a combination of both transdiagnostic (i.e., common) and disorder-level (i.e., independent) genetic risk factors. We applied genomic structural equation modeling to examine these genetic factors across SUDs, psychotic, mood, and anxiety disorders using genome-wide association studies (GWAS) of European- (EUR) and African-ancestry (AFR) individuals. In EUR individuals, transdiagnostic genetic factors represented SUDs (143 lead single nucleotide polymorphisms [SNPs]), psychotic (162 lead SNPs), and mood/anxiety disorders (112 lead SNPs). We identified two novel SNPs for mood/anxiety disorders that have probable regulatory roles on FOXP1, NECTIN3, and BTLA genes. In AFR individuals, genetic factors represented SUDs (1 lead SNP) and psychiatric disorders (no significant SNPs). The SUD factor lead SNP, although previously significant in EUR- and cross-ancestry GWAS, is a novel finding in AFR individuals. Shared genetic variance accounted for overlap between SUDs and their psychiatric comorbidities, with second-order GWAS identifying up to 12 SNPs not significantly associated with either first-order factor in EUR individuals. Finally, common and independent genetic effects showed different associations with psychiatric, sociodemographic, and medical phenotypes. For example, the independent components of schizophrenia and bipolar disorder had distinct associations with affective and risk-taking behaviors, and phenome-wide association studies identified medical conditions associated with tobacco use disorder independent of the broader SUDs factor. Thus, combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for psychiatric disorders that demonstrate considerable symptom and etiological overlap.
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Affiliation(s)
- Yousef Khan
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Christal N. Davis
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Kyra L. Feuer
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Sylvanus Toikumo
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Emily E. Hartwell
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, United States
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37235, United States
- Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Roseann E. Peterson
- Institute for Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, United States
| | - Alexander S. Hatoum
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Henry R. Kranzler
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Rachel L. Kember
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
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9
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Bhattacharyya U, John J, Lencz T, Lam M. Dissecting Schizophrenia Biology Using Pleiotropy with Cognitive Genomics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.16.24305885. [PMID: 38699340 PMCID: PMC11065000 DOI: 10.1101/2024.04.16.24305885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Given the increasingly large number of loci discovered by psychiatric GWAS, specification of the key biological pathways underlying these loci has become a priority for the field. We have previously leveraged the pleiotropic genetic relationships between schizophrenia and two cognitive phenotypes (educational attainment and cognitive task performance) to differentiate two subsets of illness-relevant SNPs: (1) those with "concordant" alleles, which are associated with reduced cognitive ability/education and increased schizophrenia risk; and (2) those with "discordant" alleles linked to reduced educational and/or cognitive levels but lower schizophrenia susceptibility. In the present study, we extend our prior work, utilizing larger input GWAS datasets and a more powerful statistical approach to pleiotropic meta-analysis, the Pleiotropic Locus Exploration and Interpretation using Optimal test (PLEIO). Our pleiotropic meta-analysis of schizophrenia and the two cognitive phenotypes revealed 768 significant loci (159 novel). Among these, 347 loci harbored concordant SNPs, 270 encompassed discordant SNPs, and 151 "dual" loci contained concordant and discordant SNPs. Competitive gene-set analysis using MAGMA related concordant SNP loci with neurodevelopmental pathways (e.g., neurogenesis), whereas discordant loci were associated with mature neuronal synaptic functions. These distinctions were also observed in BrainSpan analysis of temporal enrichment patterns across developmental periods, with concordant loci containing more prenatally expressed genes than discordant loci. Dual loci were enriched for genes related to mRNA translation initiation, representing a novel finding in the schizophrenia literature.
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10
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Guo X, Chatterjee N, Dutta D. Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability. HGG ADVANCES 2024; 5:100283. [PMID: 38491773 PMCID: PMC10999697 DOI: 10.1016/j.xhgg.2024.100283] [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: 10/12/2023] [Revised: 03/09/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024] Open
Abstract
Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is "activated." For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.
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Affiliation(s)
- Xinyu Guo
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90007, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Diptavo Dutta
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD 20850, USA.
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11
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Sun X, Qian Y, Cheng W, Ye D, Liu B, Zhou D, Wen C, Andreassen OA, Mao Y. Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases. BMC Med 2024; 22:152. [PMID: 38589871 PMCID: PMC11003061 DOI: 10.1186/s12916-024-03376-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 03/26/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Despite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD. METHODS Based on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets. RESULTS Compared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15-1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure. CONCLUSIONS This study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.
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Affiliation(s)
- Xiaohui Sun
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yu Qian
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
- School of Life Sciences, Westlake University, Hangzhou, 310024, China
| | - Weiqiu Cheng
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, 0407, Norway
| | - Ding Ye
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Bin Liu
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengping Wen
- College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, 0407, Norway.
| | - Yingying Mao
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
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12
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Jiang Y, Li H, Li Z, Du S, Zhang R, Zhao Y, Christiani DC, Shen S, Chen F. A cross-trait study of lung cancer and its related respiratory diseases based on large-scale exome sequencing population. Transl Lung Cancer Res 2024; 13:512-525. [PMID: 38601445 PMCID: PMC11002514 DOI: 10.21037/tlcr-24-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/27/2024] [Indexed: 04/12/2024]
Abstract
Background Genome-wide association studies (GWASs) explain the genetic susceptibility between diseases and common variants. Nevertheless, with the appearance of large-scale sequencing profiles, we could explore the rare coding variants in disease pathogenesis. Methods We estimated the genetic correlation of nine respiratory diseases and lung cancer in the UK Biobank (UKB) by linkage disequilibrium score regression (LDSC). Then, we performed exome-wide association studies at single-variant level and gene-level for lung cancer and lung cancer-related respiratory diseases using the whole-exome sequencing (WES) data of 427,934 European participants. Cross-trait meta-analysis was conducted by association analysis based on subsets (ASSET) to identify the pleiotropic variants, while in-silico functional analysis was performed to explore their function. Causal mediation analysis was used to explore whether these pleiotropic variants lead to lung cancer is mediated by affecting the chronic respiratory diseases. Results Five respiratory diseases [emphysema, pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and fibrosis] were genetically correlated with lung cancer. We identified 102 significant independent variants at single-variant levels for lung cancer and five lung cancer-related diseases. 15:78590583:G>A (missense variant in CHRNA5) was shared in lung cancer, emphysema, and COPD. Meanwhile, 14 significant genes and 87 suggestive genes were identified in gene-based association tests, including HSD3B7 (lung cancer), SRSF2 (pneumonia), TNXB (asthma), TERT (fibrosis), MOSPD3 (emphysema). Based on the cross-trait meta-analysis, we detected 145 independent pleiotropic variants. We further identified abundant pathways with significant enrichment effects, demonstrating that these pleiotropic genes were functional. Meanwhile, the proportion of mediation effects of these variants ranged from 6 to 23 (emphysema: 23%; COPD: 20%; pneumonia: 20%; fibrosis: 7%; asthma: 6%) through these five respiratory diseases to the incidence of lung cancer. Conclusions The identified shared genetic variants, genes, biological pathways, and potential intermediate causal pathways provide a basis for further exploration of the relationship between lung cancer and respiratory diseases.
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Affiliation(s)
- Yunke Jiang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Hongru Li
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zaiming Li
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Sha Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Ruyang Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Yang Zhao
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Biomedical Big Data of Nanjing Medical University, Nanjing, China
| | - David C. Christiani
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Pulmonary and Critical Care Division, Massachusetts General Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Sipeng Shen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Biomedical Big Data of Nanjing Medical University, Nanjing, China
| | - Feng Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Biomedical Big Data of Nanjing Medical University, Nanjing, China
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13
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Dai J, Chen K, Zhu Y, Xia L, Wang T, Yuan Z, Zeng P. Identifying risk loci for obsessive-compulsive disorder and shared genetic component with schizophrenia: A large-scale multi-trait association analysis with summary statistics. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110906. [PMID: 38043635 DOI: 10.1016/j.pnpbp.2023.110906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/26/2023] [Accepted: 11/28/2023] [Indexed: 12/05/2023]
Abstract
Due to limited samples, no genetic loci have been identified for obsessive-compulsive disorder (OCD) in genome-wide association studies. Additionally, although co-morbidities between OCD and schizophrenia (SCZ) were observed, their common genetic etiology was not completely known. Here, we conducted a comprehensive investigation regarding the genetic architecture of OCD and the common genetic foundation shared by OCD and SCZ using summary statistics data (2688 cases and 7037 controls for OCD; 53,386 cases and 77,258 controls for SCZ). We discovered significant genetic correlation between OCD and SCZ (r̂g=0.296, P = 2.82 × 10-11). We then performed two multi-trait association analyses to detect OCD-associated loci and colocalization analysis to detect causal variants. Parallel gene-level analyses were also implemented. We identified 323 OCD-relevant variants located within 12 loci, with four loci shared the same causal variants between OCD and SCZ. Further, the gene-level analyses discovered 8 OCD-associated genes. Finally, multiple functional analyses at both SNP and gene levels showed that these genetic association signals had significant enrichments in the regions of left ventricle and anterior cingulate cortex, and suggested an important role of pathways involving regulation of telomere maintenance, histone phosphorylation, and GnRH secretion. Overall, this study identified new genetic loci for OCD and provided substantial evidence supporting common genetic foundation underlying OCD and SCZ. The findings advanced our understanding of genetic architecture and pathophysiology of OCD as well as shedding light on shared genetic etiology of the two disorders.
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Affiliation(s)
- Jing Dai
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Keying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yiyang Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Lei Xia
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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14
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Chen K, Gao T, Liu Y, Zhu K, Wang T, Zeng P. Identifying risk loci for FTD and shared genetic component with ALS: A large-scale multitrait association analysis. Neurobiol Aging 2024; 134:28-39. [PMID: 37979250 DOI: 10.1016/j.neurobiolaging.2023.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 11/20/2023]
Abstract
Current genome-wide association studies of frontotemporal dementia (FTD) are underpowered due to limited samples. Further, common genetic etiologies between FTD and amyotrophic lateral sclerosis (ALS) remain unknown. Using the largest summary statistics of FTD (3526 cases and 9402 controls) and ALS (27,205 cases and 110,881 controls), we found a significant genetic correlation between them (rˆg = 0.637, P = 0.032) and identified 190 FTD-related variants within 5 loci (3p22.1, 5q35.1, 9p21.2, 19p13.11, and 20q13.13). Among these, ALS and FTD had causal variants in 9p21.2 and 19p13.11. Moreover, MOBP (3p22.1), C9orf72 (9p21.2), MOB3B (9p21.2), UNC13A (19p13.11), SLC9A8 (20q13.13), SNAI1 (20q13.13), and SPATA2 (20q13.13) were discovered by both SNP- and gene-level analyses, which together discovered 15 FTD-associated genes, with 10 not detected before (IFNK, RNF114, SLC9A8, SPATA2, SNAI1, SCFD1, POLDIP2, TMEM97, G2E3, and PIGW). Functional analyses showed these genes were enriched in heart left ventricle, kidney cortex, and some brain regions. Overall, this study provides insights into genetic determinants of FTD and shared genetic etiology underlying FTD and ALS.
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Affiliation(s)
- Keying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Tongyu Gao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ying Liu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Kexuan Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Biological Data Mining and Healthcare Transformation Innovation Engineering Research Center, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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15
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Johnson EC, Austin-Zimmerman I, Thorpe HH, Levey DF, Baranger DA, Colbert SM, Demontis D, Khokhar JY, Davis LK, Edenberg HJ, Forti MD, Sanchez-Roige S, Gelernter J, Agrawal A. Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.17.24301430. [PMID: 38293235 PMCID: PMC10827265 DOI: 10.1101/2024.01.17.24301430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Individuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz), cannabis use disorder (CanUD), and ever-regular tobacco smoking (Smk) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17 - 0.62). Causal inference analyses suggested the presence of horizontal pleiotropy, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for horizontal pleiotropy. We identified 439 pleiotropic loci in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both horizontal pleiotropy and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
| | - Isabelle Austin-Zimmerman
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Hayley Ha Thorpe
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - David Aa Baranger
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO USA
| | - Sarah Mc Colbert
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Ditte Demontis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Department of Biomedicine and Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Jibran Y Khokhar
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Marta Di Forti
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry, UC San Diego School of Medicine, La Jolla, CA, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
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16
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Kim EE, Jang CS, Kim H, Han B. PASTRY: achieving balanced power for detecting risk and protective minor alleles in meta-analysis of association studies with overlapping subjects. BMC Bioinformatics 2024; 25:24. [PMID: 38216869 PMCID: PMC10790263 DOI: 10.1186/s12859-023-05627-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 12/20/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND Meta-analysis is a statistical method that combines the results of multiple studies to increase statistical power. When multiple studies participating in a meta-analysis utilize the same public dataset as controls, the summary statistics from these studies become correlated. To solve this challenge, Lin and Sullivan proposed a method to provide an optimal test statistic adjusted for the correlation. This method quickly became the standard practice. However, we identified an unexpected power asymmetry phenomenon in this standard framework. This can lead to unbalanced power for detecting protective minor alleles and risk minor alleles. RESULTS We found that the power asymmetry of the current framework is mainly due to the errors in approximating the correlation term. We then developed a meta-analysis method based on an accurate correlation estimator, called PASTRY (A method to avoid Power ASymmeTRY). PASTRY outperformed the standard method on both simulated and real datasets in terms of the power symmetry. CONCLUSIONS Our findings suggest that PASTRY can help to alleviate the power asymmetry problem. PASTRY is available at https://github.com/hanlab-SNU/PASTRY .
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Affiliation(s)
- Emma E Kim
- Department of Chemistry, Seoul National University, Seoul, 03080, Korea
| | - Chloe Soohyun Jang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Hakin Kim
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, 03080, Korea
| | - Buhm Han
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080, Korea.
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, 03080, Korea.
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17
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Li R, Benz L, Duan R, Denny JC, Hakonarson H, Mosley JD, Smoller JW, Wei WQ, Ritchie MD, Moore JH, Chen Y. mixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301073. [PMID: 38260403 PMCID: PMC10802662 DOI: 10.1101/2024.01.09.24301073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Genome-wide association studies (GWAS) have been instrumental in identifying genetic associations for various diseases and traits. However, uncovering genetic underpinnings among traits beyond univariate phenotype associations remains a challenge. Multi-phenotype associations (MPA), or genetic pleiotropy, offer important insights into shared genes and pathways among traits, enhancing our understanding of genetic architectures of complex diseases. GWAS of biobank-linked electronic health record (EHR) data are increasingly being utilized to identify MPA among various traits and diseases. However, methodologies that can efficiently take advantage of distributed EHR to detect MPA are still lacking. Here, we introduce mixWAS, a novel algorithm that efficiently and losslessly integrates multiple EHRs via summary statistics, allowing the detection of MPA among mixed phenotypes while accounting for heterogeneities across EHRs. Simulations demonstrate that mixWAS outperforms the widely used MPA detection method, Phenome-wide association study (PheWAS), across diverse scenarios. Applying mixWAS to data from seven EHRs in the US, we identified 4,534 MPA among blood lipids, BMI, and circulatory diseases. Validation in an independent EHR data from UK confirmed 97.7% of the associations. mixWAS fundamentally improves the detection of MPA and is available as a free, open-source software.
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Affiliation(s)
- Ruowang Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Luke Benz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health
| | - Hakon Hakonarson
- Division of Human Genetics, Children's Hospital of Philadelphia
- Center for Applied Genomics, Children's Hospital of Philadelphia
- Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
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18
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Cao R, Olawsky E, McFowland E, Marcotte E, Spector L, Yang T. Subset scanning for multi-trait analysis using GWAS summary statistics. Bioinformatics 2024; 40:btad777. [PMID: 38191683 PMCID: PMC11087659 DOI: 10.1093/bioinformatics/btad777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 11/23/2023] [Accepted: 01/05/2024] [Indexed: 01/10/2024] Open
Abstract
MOTIVATION Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge. RESULTS To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression. AVAILABILITY AND IMPLEMENTATION Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan.
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Affiliation(s)
- Rui Cao
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States
| | - Evan Olawsky
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States
| | - Edward McFowland
- Technology and Operations Management, Harvard Business School, Harvard University, Boston, MA 02163, United States
| | - Erin Marcotte
- Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Logan Spector
- Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Tianzhong Yang
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States
- Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
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19
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Bhattacharya A, Vo DD, Jops C, Kim M, Wen C, Hervoso JL, Pasaniuc B, Gandal MJ. Isoform-level transcriptome-wide association uncovers genetic risk mechanisms for neuropsychiatric disorders in the human brain. Nat Genet 2023; 55:2117-2128. [PMID: 38036788 PMCID: PMC10703692 DOI: 10.1038/s41588-023-01560-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/05/2023] [Indexed: 12/02/2023]
Abstract
Methods integrating genetics with transcriptomic reference panels prioritize risk genes and mechanisms at only a fraction of trait-associated genetic loci, due in part to an overreliance on total gene expression as a molecular outcome measure. This challenge is particularly relevant for the brain, in which extensive splicing generates multiple distinct transcript-isoforms per gene. Due to complex correlation structures, isoform-level modeling from cis-window variants requires methodological innovation. Here we introduce isoTWAS, a multivariate, stepwise framework integrating genetics, isoform-level expression and phenotypic associations. Compared to gene-level methods, isoTWAS improves both isoform and gene expression prediction, yielding more testable genes, and increased power for discovery of trait associations within genome-wide association study loci across 15 neuropsychiatric traits. We illustrate multiple isoTWAS associations undetectable at the gene-level, prioritizing isoforms of AKT3, CUL3 and HSPD1 in schizophrenia and PCLO with multiple disorders. Results highlight the importance of incorporating isoform-level resolution within integrative approaches to increase discovery of trait associations, especially for brain-relevant traits.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Daniel D Vo
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Connor Jops
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Minsoo Kim
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Cindy Wen
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
| | - Jonatan L Hervoso
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael J Gandal
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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20
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Jiang Y, Zhang Y, Ju C, Zhang R, Li H, Chen F, Zhu Y, Shen S, Wei Y. A cross-disorder study to identify causal relationships, shared genetic variants, and genes across 21 digestive disorders. iScience 2023; 26:108238. [PMID: 37965154 PMCID: PMC10641500 DOI: 10.1016/j.isci.2023.108238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/26/2023] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Digestive disorders are a significant contributor to the global burden of disease and seriously affect human quality of life. Research has already confirmed the presence of pleiotropic genetic loci among digestive disorders, and studies have explored shared genetic factors among pan-cancers, including various malignant digestive disorders. However, most cross-phenotype studies within the digestive tract system have been limited to a few traits, with no systematic coverage of common benign and malignant digestive disorders. Here, we analyzed data from the UK Biobank to investigate 21 digestive disorders, exploring the genetic correlations and causal relationships between diseases, as well as the common genetic factors and potential biological pathways driving these relationships. Our findings confirmed the extensive genetic correlation and causal relationship between digestive disorders, providing important insights into the genetic etiology, causality, disease prevention, and clinical treatment of diseases.
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Affiliation(s)
- Yue Jiang
- Clinical Stem Cell Center, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Yihong Zhang
- Laboratory Medicine Center, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China
| | - Can Ju
- Department of Biostatistics, School of Public Health, Center of Global Health, Nanjing Medical University, Nanjing 211166, China
| | - Ruyang Zhang
- Department of Biostatistics, School of Public Health, Center of Global Health, Nanjing Medical University, Nanjing 211166, China
| | - Hui Li
- Department of Gastroenterology, The Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210028, China
| | - Feng Chen
- Department of Biostatistics, School of Public Health, Center of Global Health, Nanjing Medical University, Nanjing 211166, China
| | - Yefei Zhu
- Laboratory Medicine Center, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China
| | - Sipeng Shen
- Department of Biostatistics, School of Public Health, Center of Global Health, Nanjing Medical University, Nanjing 211166, China
| | - Yongyue Wei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
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21
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Guo Y, Chung W, Shan Z, Zhu Z, Costenbader KH, Liang L. Genome-Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases. J Am Heart Assoc 2023; 12:e030211. [PMID: 37947095 PMCID: PMC10727280 DOI: 10.1161/jaha.123.030211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 10/16/2023] [Indexed: 11/12/2023]
Abstract
Background Patients with rheumatoid arthritis (RA) have a 2- to 10-fold increased risk of cardiovascular disease (CVD), but the biological mechanisms and existence of causality underlying such associations remain to be investigated. We aimed to investigate the genetic associations and underlying mechanisms between RA and CVD by leveraging large-scale genomic data and genetic cross-trait analytic approaches. Methods and Results Within UK Biobank data, we examined the genetic correlation, shared genetics, and potential causality between RA (Ncases=6754, Ncontrols=452 384) and cardiovascular diseases (CVD, Ncases=44 238, Ncontrols=414 900) using linkage disequilibrium score regression, cross-trait meta-analysis, and Mendelian randomization. We observed significant genetic correlations of RA with myocardial infarction (rg:0.40 [95% CI, 0.24-0.56), angina (rg:0.42 [95% CI, 0.28-0.56]), coronary heart diseases (rg:0.41 [95% CI, 0.27-0.55]), and CVD (rg:0.43 [95% CI, 0.29-0.57]) after correcting for multiple testing (P<0.05/5). When stratified by frequent use of analgesics, we found increased genetic correlation between RA and CVD among participants without aspirin usage (rg:0.54 [95% CI, 0.30-0.78] for angina; Pvalue=6.69×10-6) and among participants with paracetamol usage (rg:0.75 [95% CI, 0.20-1.29] for myocardial infarction; Pvalue=8.90×10-3), whereas others remained similar. Cross-trait meta-analysis identified 9 independent shared loci between RA and CVD, including PTPN22 at chr1p13.2, BCL2L11 at chr2q13, and CCR3 at chr3p21.31 (Psingle trait<1×10-3 and Pmeta<5×10-8), highlighting potential shared pathogenesis including accelerating atherosclerosis, upregulating oxidative stress, and vascular permeability. Finally, Mendelian randomization estimates showed limited evidence of causality between RA and CVD. Conclusions Our results supported shared genetic pathogenesis rather than causality in explaining the observed association between RA and CVD. The identified shared genetic factors provided insights into potential novel therapeutic target for RA-CVD comorbidities.
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Affiliation(s)
- Yanjun Guo
- Department of Occupational and Environmental Health, School of Public HealthTongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
- Division of Preventive MedicineBrigham and Women’s HospitalBostonMA
- Harvard Medical SchoolBostonMA
| | - Wonil Chung
- Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
- Department of Statistics and Actuarial ScienceSoongsil UniversitySeoulKorea
| | - Zhilei Shan
- Department of NutritionHarvard T.H. Chan School of Public HealthBostonMA
| | - Zhaozhong Zhu
- Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
| | - Karen H. Costenbader
- Division of Preventive MedicineBrigham and Women’s HospitalBostonMA
- Division of Rheumatology, Inflammation and Immunity, Department of MedicineBrigham and Women’s HospitalBostonMA
| | - Liming Liang
- Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMA
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22
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Bjornsdottir G, Chalmer MA, Stefansdottir L, Skuladottir AT, Einarsson G, Andresdottir M, Beyter D, Ferkingstad E, Gretarsdottir S, Halldorsson BV, Halldorsson GH, Helgadottir A, Helgason H, Hjorleifsson Eldjarn G, Jonasdottir A, Jonasdottir A, Jonsdottir I, Knowlton KU, Nadauld LD, Lund SH, Magnusson OT, Melsted P, Moore KHS, Oddsson A, Olason PI, Sigurdsson A, Stefansson OA, Saemundsdottir J, Sveinbjornsson G, Tragante V, Unnsteinsdottir U, Walters GB, Zink F, Rødevand L, Andreassen OA, Igland J, Lie RT, Haavik J, Banasik K, Brunak S, Didriksen M, T Bruun M, Erikstrup C, Kogelman LJA, Nielsen KR, Sørensen E, Pedersen OB, Ullum H, Masson G, Thorsteinsdottir U, Olesen J, Ludvigsson P, Thorarensen O, Bjornsdottir A, Sigurdardottir GR, Sveinsson OA, Ostrowski SR, Holm H, Gudbjartsson DF, Thorleifsson G, Sulem P, Stefansson H, Thorgeirsson TE, Hansen TF, Stefansson K. Rare variants with large effects provide functional insights into the pathology of migraine subtypes, with and without aura. Nat Genet 2023; 55:1843-1853. [PMID: 37884687 PMCID: PMC10632135 DOI: 10.1038/s41588-023-01538-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 09/18/2023] [Indexed: 10/28/2023]
Abstract
Migraine is a complex neurovascular disease with a range of severity and symptoms, yet mostly studied as one phenotype in genome-wide association studies (GWAS). Here we combine large GWAS datasets from six European populations to study the main migraine subtypes, migraine with aura (MA) and migraine without aura (MO). We identified four new MA-associated variants (in PRRT2, PALMD, ABO and LRRK2) and classified 13 MO-associated variants. Rare variants with large effects highlight three genes. A rare frameshift variant in brain-expressed PRRT2 confers large risk of MA and epilepsy, but not MO. A burden test of rare loss-of-function variants in SCN11A, encoding a neuron-expressed sodium channel with a key role in pain sensation, shows strong protection against migraine. Finally, a rare variant with cis-regulatory effects on KCNK5 confers large protection against migraine and brain aneurysms. Our findings offer new insights with therapeutic potential into the complex biology of migraine and its subtypes.
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Affiliation(s)
| | - Mona A Chalmer
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Rigshospitalet-Glostrup, Copenhagen, Denmark
| | | | | | | | | | | | | | | | - Bjarni V Halldorsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Reykjavik University, School of Technology, Reykjavik, Iceland
| | - Gisli H Halldorsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Hannes Helgason
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Ingileif Jonsdottir
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | - Sigrun H Lund
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Physical Sciences, School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Pall Melsted
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | | | | | | | | | | | | | | | | | - Linn Rødevand
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jannicke Igland
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
- Department of Health and Social Science, Centre for Evidence-Based Practice, Western Norway University of Applied Science, Bergen, Norway
| | - Rolv T Lie
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maria Didriksen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Mie T Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine Health, Aarhus University, Aarhus, Denmark
| | - Lisette J A Kogelman
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Rigshospitalet-Glostrup, Copenhagen, Denmark
| | - Kaspar R Nielsen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Ole B Pedersen
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Jes Olesen
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Rigshospitalet-Glostrup, Copenhagen, Denmark
| | - Petur Ludvigsson
- Department of Pediatrics, Landspitali University Hostpital, Reykjavik, Iceland
| | - Olafur Thorarensen
- Department of Pediatrics, Landspitali University Hostpital, Reykjavik, Iceland
| | | | | | - Olafur A Sveinsson
- Laeknasetrid Clinic, Reykjavik, Iceland
- Department of Neurology, Landspitali University Hospital, Reykjavik, Iceland
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hilma Holm
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | | | - Thomas F Hansen
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Rigshospitalet-Glostrup, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kari Stefansson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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23
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564319. [PMID: 37961722 PMCID: PMC10634875 DOI: 10.1101/2023.10.27.564319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
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Stevelink R, Campbell C, Chen S, Abou-Khalil B, Adesoji OM, Afawi Z, Amadori E, Anderson A, Anderson J, Andrade DM, Annesi G, Auce P, Avbersek A, Bahlo M, Baker MD, Balagura G, Balestrini S, Barba C, Barboza K, Bartolomei F, Bast T, Baum L, Baumgartner T, Baykan B, Bebek N, Becker AJ, Becker F, Bennett CA, Berghuis B, Berkovic SF, Beydoun A, Bianchini C, Bisulli F, Blatt I, Bobbili DR, Borggraefe I, Bosselmann C, Braatz V, Bradfield JP, Brockmann K, Brody LC, Buono RJ, Busch RM, Caglayan H, Campbell E, Canafoglia L, Canavati C, Cascino GD, Castellotti B, Catarino CB, Cavalleri GL, Cerrato F, Chassoux F, Cherny SS, Cheung CL, Chinthapalli K, Chou IJ, Chung SK, Churchhouse C, Clark PO, Cole AJ, Compston A, Coppola A, Cosico M, Cossette P, Craig JJ, Cusick C, Daly MJ, Davis LK, de Haan GJ, Delanty N, Depondt C, Derambure P, Devinsky O, Di Vito L, Dlugos DJ, Doccini V, Doherty CP, El-Naggar H, Elger CE, Ellis CA, Eriksson JG, Faucon A, Feng YCA, Ferguson L, Ferraro TN, Ferri L, Feucht M, Fitzgerald M, Fonferko-Shadrach B, Fortunato F, Franceschetti S, Franke A, French JA, Freri E, Gagliardi M, Gambardella A, Geller EB, Giangregorio T, Gjerstad L, Glauser T, Goldberg E, Goldman A, Granata T, Greenberg DA, Guerrini R, Gupta N, Haas KF, Hakonarson H, Hallmann K, Hassanin E, Hegde M, Heinzen EL, Helbig I, Hengsbach C, Heyne HO, Hirose S, Hirsch E, Hjalgrim H, Howrigan DP, Hucks D, Hung PC, Iacomino M, Imbach LL, Inoue Y, Ishii A, Jamnadas-Khoda J, Jehi L, Johnson MR, Kälviäinen R, Kamatani Y, Kanaan M, Kanai M, Kantanen AM, Kara B, Kariuki SM, Kasperavičiūte D, Kasteleijn-Nolst Trenite D, Kato M, Kegele J, Kesim Y, Khoueiry-Zgheib N, King C, Kirsch HE, Klein KM, Kluger G, Knake S, Knowlton RC, Koeleman BPC, Korczyn AD, Koupparis A, Kousiappa I, Krause R, Krenn M, Krestel H, Krey I, Kunz WS, Kurki MI, Kurlemann G, Kuzniecky R, Kwan P, Labate A, Lacey A, Lal D, Landoulsi Z, Lau YL, Lauxmann S, Leech SL, Lehesjoki AE, Lemke JR, Lerche H, Lesca G, Leu C, Lewin N, Lewis-Smith D, Li GHY, Li QS, Licchetta L, Lin KL, Lindhout D, Linnankivi T, Lopes-Cendes I, Lowenstein DH, Lui CHT, Madia F, Magnusson S, Marson AG, May P, McGraw CM, Mei D, Mills JL, Minardi R, Mirza N, Møller RS, Molloy AM, Montomoli M, Mostacci B, Muccioli L, Muhle H, Müller-Schlüter K, Najm IM, Nasreddine W, Neale BM, Neubauer B, Newton CRJC, Nöthen MM, Nothnagel M, Nürnberg P, O’Brien TJ, Okada Y, Ólafsson E, Oliver KL, Özkara C, Palotie A, Pangilinan F, Papacostas SS, Parrini E, Pato CN, Pato MT, Pendziwiat M, Petrovski S, Pickrell WO, Pinsky R, Pippucci T, Poduri A, Pondrelli F, Powell RHW, Privitera M, Rademacher A, Radtke R, Ragona F, Rau S, Rees MI, Regan BM, Reif PS, Rhelms S, Riva A, Rosenow F, Ryvlin P, Saarela A, Sadleir LG, Sander JW, Sander T, Scala M, Scattergood T, Schachter SC, Schankin CJ, Scheffer IE, Schmitz B, Schoch S, Schubert-Bast S, Schulze-Bonhage A, Scudieri P, Sham P, Sheidley BR, Shih JJ, Sills GJ, Sisodiya SM, Smith MC, Smith PE, Sonsma ACM, Speed D, Sperling MR, Stefansson H, Stefansson K, Steinhoff BJ, Stephani U, Stewart WC, Stipa C, Striano P, Stroink H, Strzelczyk A, Surges R, Suzuki T, Tan KM, Taneja RS, Tanteles GA, Taubøll E, Thio LL, Thomas GN, Thomas RH, Timonen O, Tinuper P, Todaro M, Topaloğlu P, Tozzi R, Tsai MH, Tumiene B, Turkdogan D, Unnsteinsdóttir U, Utkus A, Vaidiswaran P, Valton L, van Baalen A, Vetro A, Vining EPG, Visscher F, von Brauchitsch S, von Wrede R, Wagner RG, Weber YG, Weckhuysen S, Weisenberg J, Weller M, Widdess-Walsh P, Wolff M, Wolking S, Wu D, Yamakawa K, Yang W, Yapıcı Z, Yücesan E, Zagaglia S, Zahnert F, Zara F, Zhou W, Zimprich F, Zsurka G, Zulfiqar Ali Q. GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture. Nat Genet 2023; 55:1471-1482. [PMID: 37653029 PMCID: PMC10484785 DOI: 10.1038/s41588-023-01485-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 07/21/2023] [Indexed: 09/02/2023]
Abstract
Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.
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Asgari Y, Sugier PE, Baghfalaki T, Lucotte E, Karimi M, Sedki M, Ngo A, Liquet B, Truong T. GCPBayes pipeline: a tool for exploring pleiotropy at the gene level. NAR Genom Bioinform 2023; 5:lqad065. [PMID: 37416786 PMCID: PMC10320750 DOI: 10.1093/nargab/lqad065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/16/2023] [Accepted: 06/16/2023] [Indexed: 07/08/2023] Open
Abstract
Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.
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Affiliation(s)
- Yazdan Asgari
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Pierre-Emmanuel Sugier
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64000 Pau, France
| | | | - Elise Lucotte
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Mojgan Karimi
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Mohammed Sedki
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Psychiatrie du développement et trajectoires, 94807 Villejuif, France
| | - Amélie Ngo
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Benoit Liquet
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64000 Pau, France
- School of Mathematical and Physical Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Thérèse Truong
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
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Zhou Y, Lin Z, Xie S, Gao Y, Zhou H, Chen F, Fu Y, Yang C, Ke C. Interplay of chronic obstructive pulmonary disease and colorectal cancer development: unravelling the mediating role of fatty acids through a comprehensive multi-omics analysis. J Transl Med 2023; 21:587. [PMID: 37658368 PMCID: PMC10474711 DOI: 10.1186/s12967-023-04278-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/14/2023] [Indexed: 09/03/2023] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) patients often exhibit gastrointestinal symptoms, A potential association between COPD and Colorectal Cancer (CRC) has been indicated, warranting further examination. METHODS In this study, we collected COPD and CRC data from the National Health and Nutrition Examination Survey, genome-wide association studies, and RNA sequence for a comprehensive analysis. We used weighted logistic regression to explore the association between COPD and CRC incidence risk. Mendelian randomization analysis was performed to assess the causal relationship between COPD and CRC, and cross-phenotype meta-analysis was conducted to pinpoint crucial loci. Multivariable mendelian randomization was used to uncover mediating factors connecting the two diseases. Our results were validated using both NHANES and GEO databases. RESULTS In our analysis of the NHANES dataset, we identified COPD as a significant contributing factor to CRC development. MR analysis revealed that COPD increased the risk of CRC onset and progression (OR: 1.16, 95% CI 1.01-1.36). Cross-phenotype meta-analysis identified four critical genes associated with both CRC and COPD. Multivariable Mendelian randomization suggested body fat percentage, omega-3, omega-6, and the omega-3 to omega-6 ratio as potential mediating factors for both diseases, a finding consistent with the NHANES dataset. Further, the interrelation between fatty acid-related modules in COPD and CRC was demonstrated via weighted gene co-expression network analysis and Kyoto Encyclopedia of Genes and Genomes enrichment results using RNA expression data. CONCLUSIONS This study provides novel insights into the interplay between COPD and CRC, highlighting the potential impact of COPD on the development of CRC. The identification of shared genes and mediating factors related to fatty acid metabolism deepens our understanding of the underlying mechanisms connecting these two diseases.
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Affiliation(s)
- Youtao Zhou
- The First Clinical Medical School, Guangzhou Medical University, Guangzhou, China
| | - Zikai Lin
- Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Shuojia Xie
- Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Yuan Gao
- The First Clinical Medical School, Guangzhou Medical University, Guangzhou, China
| | - Haobin Zhou
- The First Clinical Medical School, Guangzhou Medical University, Guangzhou, China
| | - Fengzhen Chen
- The First Clinical Medical School, Guangzhou Medical University, Guangzhou, China
| | - Yuewu Fu
- Department of General Surgery, School of Medicine, The First Affiliated Hospital, Ji'nan University, Guangzhou, China
| | - Cuiyan Yang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Chuanfeng Ke
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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Zhang Y, Jiang X, Mentzer AJ, McVean G, Lunter G. Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank. CELL GENOMICS 2023; 3:100371. [PMID: 37601973 PMCID: PMC10435382 DOI: 10.1016/j.xgen.2023.100371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 05/04/2023] [Accepted: 07/07/2023] [Indexed: 08/22/2023]
Abstract
Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.
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Affiliation(s)
- Yidong Zhang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100006, China
| | - Xilin Jiang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0SR, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0BB, UK
| | - Alexander J. Mentzer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Gerton Lunter
- MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, the Netherlands
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28
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Chen M, Zhang Y, Adams T, Ji D, Jiang W, Wain LV, Cho M, Kaminski N, Zhao H. Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases. Thorax 2023; 78:792-798. [PMID: 36216496 PMCID: PMC10083187 DOI: 10.1136/thorax-2021-217703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 09/16/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Although genome-wide association studies (GWAS) have identified many genomic regions associated with idiopathic pulmonary fibrosis (IPF), the causal genes and functions remain largely unknown. Many single-cell expression data have become available for IPF, and there is increasing evidence suggesting a shared genetic basis between IPF and other diseases. METHODS We conducted integrative analyses to improve the power of GWAS. First, we calculated global and local genetic correlations to identify IPF genetically associated traits and local regions. Then, we prioritised candidate genes contributing to local genetic correlation. Second, we performed transcriptome-wide association analysis (TWAS) of 44 tissues to identify candidate genes whose genetically predicted expression level is associated with IPF. To replicate our findings and investigate the regulatory role of the transcription factors (TF) in identified candidate genes, we first conducted the heritability enrichment analysis in TF binding sites. Then, we examined the enrichment of the TF target genes in cell-type-specific differentially expressed genes (DEGs) identified from single-cell expression data of IPF and healthy lung samples. FINDINGS We identified 12 candidate genes across 13 genomic regions using local genetic correlation, including the POT1 locus (p value=0.00041), which contained variants with protective effects on lung cancer but increasing IPF risk. We identified another 13 novel genes using TWAS. Two TFs, MAFK and SMAD2, showed significant enrichment in both partitioned heritability and cell-type-specific DEGs. INTERPRETATION Our integrative analysis identified new genes for IPF susceptibility and expanded the understanding of the complex genetic architecture and disease mechanism of IPF.
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Affiliation(s)
- Ming Chen
- Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Yiliang Zhang
- Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Taylor Adams
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Dingjue Ji
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
| | - Wei Jiang
- Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Louise V Wain
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Michael Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Hongyu Zhao
- Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
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Zheng J, Wang X, Li J, Wu Y, Chang J, Xin J, Wang M, Wang T, Wei Q, Wang M, Zhang R. Rare variants confer shared susceptibility to gastrointestinal tract cancer risk. Front Oncol 2023; 13:1161639. [PMID: 37483484 PMCID: PMC10358854 DOI: 10.3389/fonc.2023.1161639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023] Open
Abstract
Background Cancers arising within the gastrointestinal tract are complex disorders involving genetic events that cause the conversion of normal tissue to premalignant lesions and malignancy. Shared genetic features are reported in epithelial-based gastrointestinal cancers which indicate common susceptibility among this group of malignancies. In addition, the contribution of rare variants may constitute parts of genetic susceptibility. Methods A cross-cancer analysis of 38,171 shared rare genetic variants from genome-wide association assays was conducted, which included data from 3,194 cases and 1,455 controls across three cancer sites (esophageal, gastric and colorectal). The SNP-level association was performed by multivariate logistic regression analyses for single cancer, followed by association analysis for SubSETs (ASSET) to adjust the bias of overlapping controls. Gene-level analyses were conducted by SKAT-O, with multiple comparison adjustments by false discovery rate (FDR). Based on the significant genes indicated by SKATO analysis, pathways analysis was conducted using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Results Meta-analysis in three gastrointestinal (GI) cancers identified 13 novel susceptibility loci that reached genome-wide significance (P ASSET< 5×10-8). SKAT-O analysis revealed EXOC6, LRP5L and MIR1263/LINC01324 to be significant genes shared by GI cancers (P adj<0.05, P FDR<0.05). Furthermore, GO pathway analysis identified significant enrichment of synaptic transmission and neuron development pathways shared by all three cancer types. Conclusion Rare variants and the corresponding genes potentially contribute to shared susceptibility in different GI cancer types. The discovery of these novel variants and genes offers new insights for the carcinogenic mechanisms and missing heritability of GI cancers.
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Affiliation(s)
- Ji Zheng
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China
| | - Xin Wang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingrao Li
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China
| | - Yuanna Wu
- Department of Biological Sciences, Dedman College of Humanities and Sciences, Southern Methodist University, Dallas, TX, United States
| | - Jiang Chang
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - 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, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - 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, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Tianpei Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Qingyi Wei
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, United States
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Mengyun Wang
- Yiwu Research Institute of Fudan University, Yiwu, Zhejiang, China
- Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai Medical College, Shanghai, China
| | - Ruoxin Zhang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China
- Yiwu Research Institute of Fudan University, Yiwu, Zhejiang, China
- Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai Medical College, Shanghai, China
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Li Z, Dang W, Hao T, Zhang H, Yao Z, Zhou W, Deng L, Yu H, Wen Y, Liu L. Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis. Front Psychiatry 2023; 14:1144697. [PMID: 37426090 PMCID: PMC10328439 DOI: 10.3389/fpsyt.2023.1144697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 05/31/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction The comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection). Methods In this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trait meta-analysis to identify the pleiotropic genomic SNPs and the genes shared by MDD and COVID-19 outcomes, and then explore the potential bidirectional causal relationships between MDD and COVID-19 outcomes by implementing a bidirectional MR study design. We further conducted functional annotations analyses to obtain biological insight for shared genes from the results of cross-trait meta-analysis. Results We have identified 71 SNPs located on 25 different genes are shared between MDD and COVID-19 outcomes. We have also found that genetic liability to MDD is a causal factor for COVID-19 outcomes. In particular, we found that MDD has causal effect on severe COVID-19 (OR = 1.832, 95% CI = 1.037-3.236) and hospitalized COVID-19 (OR = 1.412, 95% CI = 1.021-1.953). Functional analysis suggested that the shared genes are enriched in Cushing syndrome, neuroactive ligand-receptor interaction. Discussion Our findings provide convincing evidence on shared genetic etiology and causal relationships between MDD and COVID-19 outcomes, which is crucial to prevention, and therapeutic treatment of MDD and COVID-19.
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Affiliation(s)
- Ziqi Li
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Weijia Dang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Tianqi Hao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Hualin Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Ziwei Yao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Wenchao Zhou
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Liufei Deng
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yalu Wen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Long Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
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Sato G, Shirai Y, Namba S, Edahiro R, Sonehara K, Hata T, Uemura M, Matsuda K, Doki Y, Eguchi H, Okada Y. Pan-cancer and cross-population genome-wide association studies dissect shared genetic backgrounds underlying carcinogenesis. Nat Commun 2023; 14:3671. [PMID: 37340002 DOI: 10.1038/s41467-023-39136-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/31/2023] [Indexed: 06/22/2023] Open
Abstract
Integrating genomic data of multiple cancers allows de novo cancer grouping and elucidating the shared genetic basis across cancers. Here, we conduct the pan-cancer and cross-population genome-wide association study (GWAS) meta-analysis and replication studies on 13 cancers including 250,015 East Asians (Biobank Japan) and 377,441 Europeans (UK Biobank). We identify ten cancer risk variants including five pleiotropic associations (e.g., rs2076295 at DSP on 6p24 associated with lung cancer and rs2525548 at TRIM4 on 7q22 nominally associated with six cancers). Quantifying shared heritability among the cancers detects positive genetic correlations between breast and prostate cancer across populations. Common genetic components increase the statistical power, and the large-scale meta-analysis of 277,896 breast/prostate cancer cases and 901,858 controls identifies 91 newly genome-wide significant loci. Enrichment analysis of pathways and cell types reveals shared genetic backgrounds across said cancers. Focusing on genetically correlated cancers can contribute to enhancing our insights into carcinogenesis.
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Affiliation(s)
- Go Sato
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Hata
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Mamoru Uemura
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Yuichiro Doki
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Hidetoshi Eguchi
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
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Lindström S, Wang L, Feng H, Majumdar A, Huo S, Macdonald J, Harrison T, Turman C, Chen H, Mancuso N, Bammler T, Gallinger S, Gruber SB, Gunter MJ, Le Marchand L, Moreno V, Offit K, De Vivo I, O’Mara TA, Spurdle AB, Tomlinson I, Fitzgerald R, Gharahkhani P, Gockel I, Jankowski J, Macgregor S, Schumacher J, Barnholtz-Sloan J, Bondy ML, Houlston RS, Jenkins RB, Melin B, Wrensch M, Brennan P, Christiani DC, Johansson M, Mckay J, Aldrich MC, Amos CI, Landi MT, Tardon A, Bishop DT, Demenais F, Goldstein AM, Iles MM, Kanetsky PA, Law MH, Amundadottir LT, Stolzenberg-Solomon R, Wolpin BM, Klein A, Petersen G, Risch H, Chanock SJ, Purdue MP, Scelo G, Pharoah P, Kar S, Hung RJ, Pasaniuc B, Kraft P. Genome-wide analyses characterize shared heritability among cancers and identify novel cancer susceptibility regions. J Natl Cancer Inst 2023; 115:712-732. [PMID: 36929942 PMCID: PMC10248849 DOI: 10.1093/jnci/djad043] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 11/07/2022] [Accepted: 02/14/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci. METHODS We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci. RESULTS We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci. CONCLUSIONS Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types.
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Affiliation(s)
- Sara Lindström
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Lu Wang
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Helian Feng
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Arunabha Majumdar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Mathematics, Indian Institute of Technology Hyderabad, Kandi, Telangana, India
| | - Sijia Huo
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - James Macdonald
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Tabitha Harrison
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hongjie Chen
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Theo Bammler
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | | | - Steve Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Marc J Gunter
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | | | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
| | - Kenneth Offit
- Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | | | - Immaculata De Vivo
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Radcliffe Institute, Cambridge, MA, USA
| | - Tracy A O’Mara
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Amanda B Spurdle
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ian Tomlinson
- Cancer Research Centre, The University of Edinburgh, Edinburgh, UK
| | | | - Rebecca Fitzgerald
- MRC Cancer Unit, Hutchison-MRC Research Centre, University of Cambridge, Cambridge, UK
| | - Puya Gharahkhani
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Janusz Jankowski
- Institute for Clinical Trials, University College London, Holborn, UK
- University of the South Pacific, Suva, Fiji
| | - Stuart Macgregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | | | - Jill Barnholtz-Sloan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Trans-Divisional Research Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Melissa L Bondy
- Department of Epidemiology and Population Health, Stanford University, Palo Alto, CA, USA
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Robert B Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA
| | - Beatrice Melin
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Margaret Wrensch
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Paul Brennan
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - David C Christiani
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mattias Johansson
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - James Mckay
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Melinda C Aldrich
- Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Adonina Tardon
- University Institute of Oncology of the Principality of Asturias (IUOPA), University of Oviedo and Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Oviedo, Spain
| | | | - D Timothy Bishop
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | - Florence Demenais
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Paris, France
| | - Alisa M Goldstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mark M Iles
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | - Peter A Kanetsky
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia
| | | | - Laufey T Amundadottir
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rachael Stolzenberg-Solomon
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | | | - Alison Klein
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gloria Petersen
- Department of Quantitative Health Science, Mayo Clinic, Rochester, MN, USA
| | - Harvey Risch
- Yale School of Public Health, Chronic Disease Epidemiology, New Haven, CT, USA
| | | | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mark P Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ghislaine Scelo
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Siddhartha Kar
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbuaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Peter Kraft
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Ortiz-Fernández L, Carmona EG, Kerick M, Lyons P, Carmona FD, López Mejías R, Khor CC, Grayson PC, Tombetti E, Jiang L, Direskeneli H, Saruhan-Direskeneli G, Callejas-Rubio JL, Vaglio A, Salvarani C, Hernández-Rodríguez J, Cid MC, Morgan AW, Merkel PA, Burgner D, Smith KG, Gonzalez-Gay MA, Sawalha AH, Martin J, Marquez A. Identification of new risk loci shared across systemic vasculitides points towards potential target genes for drug repurposing. Ann Rheum Dis 2023; 82:837-847. [PMID: 36797040 PMCID: PMC10314028 DOI: 10.1136/ard-2022-223697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/04/2023] [Indexed: 02/18/2023]
Abstract
OBJECTIVES The number of susceptibility loci currently associated with vasculitis is lower than in other immune-mediated diseases due in part to small cohort sizes, a consequence of the low prevalence of vasculitides. This study aimed to identify new genetic risk loci for the main systemic vasculitides through a comprehensive analysis of their genetic overlap. METHODS Genome-wide data from 8467 patients with any of the main forms of vasculitis and 29 795 healthy controls were meta-analysed using ASSET. Pleiotropic variants were functionally annotated and linked to their target genes. Prioritised genes were queried in DrugBank to identify potentially repositionable drugs for the treatment of vasculitis. RESULTS Sixteen variants were independently associated with two or more vasculitides, 15 of them representing new shared risk loci. Two of these pleiotropic signals, located close to CTLA4 and CPLX1, emerged as novel genetic risk loci in vasculitis. Most of these polymorphisms appeared to affect vasculitis by regulating gene expression. In this regard, for some of these common signals, potential causal genes were prioritised based on functional annotation, including CTLA4, RNF145, IL12B, IL5, IRF1, IFNGR1, PTK2B, TRIM35, EGR2 and ETS2, each of which has key roles in inflammation. In addition, drug repositioning analysis showed that several drugs, including abatacept and ustekinumab, could be potentially repurposed in the management of the analysed vasculitides. CONCLUSIONS We identified new shared risk loci with functional impact in vasculitis and pinpointed potential causal genes, some of which could represent promising targets for the treatment of vasculitis.
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Affiliation(s)
| | - Elio G Carmona
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
- Unidad de Enfermedades Autoinmunes Sistémicas, Hospital Clínico San Cecilio, Instituto de Investigación Biosanitaria de Granada ibs.GRANADA, Granada, Spain
| | - Martin Kerick
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
| | - Paul Lyons
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Francisco David Carmona
- Departamento de Genética e Instituto de Biotecnología, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Raquel López Mejías
- Research Group on Genetic Epidemiology and Atherosclerosis in Systemic Diseases and in Metabolic Bone Diseases of the Musculoskeletal System, IDIVAL, Santander, Spain
| | - Chiea Chuen Khor
- Duke-NUS Medical School, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Peter C Grayson
- Systemic Autoimmunity Branch, NIAMS, National Institutes of Health, Bethesda, Maryland, USA
| | - Enrico Tombetti
- Division of Immunology, Transplantation and Infectious Diseases, San Raffaele Hospital, Milan, Italy
- Department of Biomedical and Clinical Sciences L. Sacco, Milan University, Milan, Italy
| | - Lindi Jiang
- Department of Rheumatology, Zhongshan Hospital, Fudan University, Shanghai, China
- Evidence-Based Medicine Center, Fudan University, Shanghai, China
| | - Haner Direskeneli
- Department of Internal Medicine, Division of Rheumatology, Marmara University, Faculty of Medicine, Istanbul, Turkey
| | | | - José-Luis Callejas-Rubio
- Systemic Autoimmune Diseases Unit, San Cecilio University Hospital, Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Augusto Vaglio
- Department of Biomedical Experimental and Clinical Sciences "Mario Serio", University of Florence, Florence, Italy
- Nephrology and Dialysis Unit, Meyer Children's Hospital, Florence, Italy
| | - Carlo Salvarani
- Rheumatology Unit, Azienda USL-IRCCS di Reggio Emilia and Azienda Ospedaliero - Universitaria di Modena, Università di Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Jose Hernández-Rodríguez
- Department of Autoimmune Diseases, Hospital Clinic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Maria Cinta Cid
- Department of Autoimmune Diseases, Hospital Clinic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Ann W Morgan
- School of Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds Medtech and In vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Peter A Merkel
- Division of Rheumatology, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Division of Epidemiology, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David Burgner
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Department of General Medicine, Royal Children's Hospital, Parkville, Victoria, Australia
- Department of Paediatrics, Monash University, Clayton, Victoria, Australia
| | - Kenneth Gc Smith
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Miguel Angel Gonzalez-Gay
- Research Group on Genetic Epidemiology and Atherosclerosis in Systemic Diseases and in Metabolic Bone Diseases of the Musculoskeletal System, IDIVAL, University of Cantabria, Santander, Spain
| | - Amr H Sawalha
- Division of Rheumatology, Department of Pediatrics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Lupus Center of Excellence, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Javier Martin
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
| | - Ana Marquez
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
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Yao X, Yang H, Han H, Kou X, Jiang Y, Luo M, Zhou Y, Wang J, Fan X, Wang X, Li MJ, Yan H. Genome-wide analysis of genetic pleiotropy and causal genes across three age-related ocular disorders. Hum Genet 2023; 142:507-522. [PMID: 36917350 DOI: 10.1007/s00439-023-02542-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/04/2023] [Indexed: 03/16/2023]
Abstract
Age-related macular degeneration (AMD), cataract, and glaucoma are leading causes of blindness worldwide. Previous genome-wide association studies (GWASs) have revealed a variety of susceptible loci associated with age-related ocular disorders, yet the genetic pleiotropy and causal genes across these diseases remain poorly understood. By leveraging large-scale genetic and observational data from ocular disease GWASs and UK Biobank (UKBB), we found significant pairwise genetic correlations and consistent epidemiological associations among these ocular disorders. Cross-disease meta-analysis uncovered seven pleiotropic loci, three of which were replicated in an additional cohort. Integration of variants in pleiotropic loci and multiple single-cell omics data identified that Müller cells and astrocytes were likely trait-related cell types underlying ocular comorbidity. In addition, we comprehensively integrated eye-specific gene expression quantitative loci (eQTLs), epigenomic profiling, and 3D genome data to prioritize causal pleiotropic genes. We found that pleiotropic genes were essential in nerve development and eye pigmentation, and targetable by aflibercept and pilocarpine for the treatment of AMD and glaucoma. These findings will not only facilitate the mechanistic research of ocular comorbidities but also benefit the therapeutic optimization of age-related ocular diseases.
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Affiliation(s)
- Xueming Yao
- Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Hongxi Yang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Han Han
- Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xuejing Kou
- Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yuhan Jiang
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Menghan Luo
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Yao Zhou
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Jianhua Wang
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Xutong Fan
- Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Xiaohong Wang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
| | - Mulin Jun Li
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China. .,Department of Bioinformatics, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
| | - Hua Yan
- Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, 300052, China. .,Laboratory of Molecular Ophthalmology, Tianjin Medical University, Tianjin, 300070, China. .,School of Medicine, Nankai University, Tianjin, 300071, China.
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Wightman DP, Savage JE, Tissink E, Romero C, Jansen IE, Posthuma D. The genetic overlap between Alzheimer’s disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson’s disease. Neurobiol Aging 2023; 127:99-112. [PMID: 37045620 DOI: 10.1016/j.neurobiolaging.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/23/2023] [Accepted: 03/03/2023] [Indexed: 03/13/2023]
Abstract
Neurodegenerative diseases are a group of disorders characterized by neuronal cell death causing a variety of physical and mental problems. While these disorders can be characterized by their phenotypic presentation within the nervous system, their aetiologies differ to varying degrees. The majority of previous genetic evidence for overlap between neurodegenerative diseases has been pairwise. In this study, we aimed to identify overlap between the 4 investigated neurodegenerative disorders (Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease) at the variant, gene, genomic locus, gene-set, cell, or tissue level, with specific interest in overlap between 3 or more diseases. Using local genetic correlation, we found 2 loci (TMEM175 and HLA) that were shared across 3 disorders. We also highlighted genes, genomic loci, gene sets, cell types, and tissue types which may be important to 2 or more disorders by analyzing the association of variants with a common factor estimated from the 4 disorders. Our study successfully highlighted genetic loci and tissues associated with 2 or more neurodegenerative diseases.
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36
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Ding H, Xie M, Wang J, Ouyang M, Huang Y, Yuan F, Jia Y, Zhang X, Liu N, Zhang N. Shared genetics of psychiatric disorders and type 2 diabetes:a large-scale genome-wide cross-trait analysis. J Psychiatr Res 2023; 159:185-195. [PMID: 36738649 DOI: 10.1016/j.jpsychires.2023.01.037] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 12/31/2022] [Accepted: 01/26/2023] [Indexed: 01/29/2023]
Abstract
BACKGROUND Individuals with psychiatric disorders have elevated rates of type 2 diabetes comorbidity. Although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and causal link between different type 2 diabetes and psychiatric disorders. METHODS We conducted a large-scale genome-wide cross-trait association study(GWAS) to investigate genetic overlap between type 2 diabetes and anorexia nervosa, attention deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. By post-GWAS functional analysis, we identify variants genes expression in various tissues. Enrichment pathways, potential protein interaction and mendelian randomization also provided to research the relationship between type 2 diabetes and psychiatric disorders. RESULTS We discovered that type 2 diabetes and psychiatric disorders had a significant correlation. We identified 138 related loci, 32 were novel loci. Post-GWAS analysis revealed that 86 differentially expressed genes were located in different brain regions and peripheral blood in type 2 diabetes and related psychiatric disorders. MAPK signaling pathway plays an important role in neural development and insulin signaling. In addition, there is a causal relationship between T2D and mental disorders. In PPI analysis, the central genes of the DEG PPI network were FTO and TCF7L2. CONCLUSION This large-scale genome-wide cross-trait analysis identified shared genetics andpotential causal links between type 2 diabetes and related psychiatric disorders, suggesting potential new biological functions in common among them.
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Affiliation(s)
- Hui Ding
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Minyao Xie
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Jinyi Wang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Mengyuan Ouyang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Yanyuan Huang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Fangzheng Yuan
- School of Psychology, Nanjing Normal University, Nanjing, 210023, PR China
| | - Yunhan Jia
- School of Psychology, Nanjing Normal University, Nanjing, 210023, PR China
| | - Xuedi Zhang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Na Liu
- Department of Medical Psychology, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, PR China.
| | - Ning Zhang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China.
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Hatoum AS, Colbert SM, Johnson EC, Huggett SB, Deak JD, Pathak G, Jennings MV, Paul SE, Karcher NR, Hansen I, Baranger DA, Edwards A, Grotzinger A, Tucker-Drob EM, Kranzler HR, Davis LK, Sanchez-Roige S, Polimanti R, Gelernter J, Edenberg HJ, Bogdan R, Agrawal A. Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders. NATURE. MENTAL HEALTH 2023; 1:210-223. [PMID: 37250466 PMCID: PMC10217792 DOI: 10.1038/s44220-023-00034-y] [Citation(s) in RCA: 56] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/10/2023] [Indexed: 05/31/2023]
Abstract
Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of 1,025,550 individuals of European descent and 92,630 individuals of African descent. Nineteen independent SNPs were genome-wide significant (P < 5e-8) for the general addiction risk factor (addiction-rf), which showed high polygenicity. Across ancestries, PDE4B was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An addiction-rf polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targets.
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Affiliation(s)
- Alexander S. Hatoum
- Washington University School of Medicine, Department of
Psychiatry, Saint Louis, USA
| | - Sarah M.C. Colbert
- Washington University School of Medicine, Department of
Psychiatry, Saint Louis, USA
| | - Emma C. Johnson
- Washington University School of Medicine, Department of
Psychiatry, Saint Louis, USA
| | | | - Joseph D. Deak
- Department of Psychiatry, Division of Human Genetics, Yale
School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven,
CT, USA
| | - Gita Pathak
- Department of Psychiatry, Division of Human Genetics, Yale
School of Medicine, New Haven, CT, USA
| | - Mariela V. Jennings
- UC San Diego School of Medicine, Department of Psychiatry,
San Diego, CA, USA
| | - Sarah E. Paul
- Department of Psychological & Brain Sciences,
Washington University in St. Louis
| | - Nicole R. Karcher
- Washington University School of Medicine, Department of
Psychiatry, Saint Louis, USA
| | - Isabella Hansen
- Department of Psychological & Brain Sciences,
Washington University in St. Louis
| | - David A.A. Baranger
- Washington University School of Medicine, Department of
Psychiatry, Saint Louis, USA
| | - Alexis Edwards
- Virginia Institute of Psychiatric and Behavioral Genetics,
Virginia Commonwealth University, Richmond, VA, USA
| | - Andrew Grotzinger
- University of Colorado-Boulder, Institute for Behavioral
Genetics, Boulder, CO, USA
| | | | - Elliot M. Tucker-Drob
- University of Texas at Austin, Department of Psychology and
Population Research Center, Austin, TX, USA
| | - Henry R. Kranzler
- Center for Studies of Addiction, Department of
Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia,
PA, USA
- VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Lea K. Davis
- Department of Medicine, Division of Genetic Medicine,
Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences,
Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt
University Medical Center, Nashville, TN, USA
| | - Sandra Sanchez-Roige
- UC San Diego School of Medicine, Department of Psychiatry,
San Diego, CA, USA
- Department of Medicine, Division of Genetic Medicine,
Vanderbilt University, Nashville, TN, USA
| | - Renato Polimanti
- Department of Psychiatry, Division of Human Genetics, Yale
School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven,
CT, USA
| | - Joel Gelernter
- Department of Psychiatry, Division of Human Genetics, Yale
School of Medicine, New Haven, CT, USA
- University of Texas at Austin, Department of Psychology and
Population Research Center, Austin, TX, USA
- Department of Genetics, Yale School of Medicine, New
Haven, CT, USA
- Department of Neuroscience, Yale School of Medicine, New
Haven, CT, USA
| | - Howard J. Edenberg
- Department of Medical and Molecular Genetics, Indiana
University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana
University School of Medicine, Indianapolis, IN, USA
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences,
Washington University in St. Louis
| | - Arpana Agrawal
- Washington University School of Medicine, Department of
Psychiatry, Saint Louis, USA
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Johnson EC, Kapoor M, Hatoum AS, Zhou H, Polimanti R, Wendt FR, Walters RK, Lai D, Kember RL, Hartz S, Meyers JL, Peterson RE, Ripke S, Bigdeli TB, Fanous AH, Pato CN, Pato MT, Goate AM, Kranzler HR, O'Donovan MC, Walters JTR, Gelernter J, Edenberg HJ, Agrawal A. Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder. Psychol Med 2023; 53:1196-1204. [PMID: 34231451 PMCID: PMC8738774 DOI: 10.1017/s003329172100266x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders. METHODS We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific. RESULTS We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). CONCLUSIONS Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Manav Kapoor
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Hang Zhou
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Frank R Wendt
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Sarah Hartz
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jacquelyn L Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
- Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Roseann E Peterson
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Tim B Bigdeli
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Ayman H Fanous
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Carlos N Pato
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Michele T Pato
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Michael C O'Donovan
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - James T R Walters
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - Joel Gelernter
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
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Ellison JE, Kumar S, Steingrimsson JA, Adhikari D, Charlesworth CJ, McConnell KJ, Trivedi AN, Trikalinos TA, Forbes SP, Panagiotou OA. Comparison of Low-Value Care Among Commercial and Medicaid Enrollees. J Gen Intern Med 2023; 38:954-960. [PMID: 36175761 PMCID: PMC10039208 DOI: 10.1007/s11606-022-07823-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 09/16/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Low-value healthcare is costly and inefficient and may adversely affect patient outcomes. Despite increases in low-value service use, little is known about how the receipt of low-value care differs across payers. OBJECTIVE To evaluate differences in the use of low-value care between patients with commercial versus Medicaid coverage. DESIGN Retrospective observational analysis of the 2017 Rhode Island All-payer Claims Database, estimating the probability of receiving each of 14 low-value services between commercial and Medicaid enrollees, adjusting for patient sociodemographic and clinical characteristics. Ensemble machine learning minimized the possibility of model misspecification. PARTICIPANTS Medicaid and commercial enrollees aged 18-64 with continuous coverage and an encounter at which they were at risk of receiving a low-value service. INTERVENTION Enrollment in Medicaid or Commercial insurance. MAIN MEASURES Use of one of 14 validated measures of low-value care. KEY RESULTS Among 110,609 patients, Medicaid enrollees were younger, had more comorbidities, and were more likely to be female than commercial enrollees. Medicaid enrollees had higher rates of use for 7 low-value care measures, and those with commercial coverage had higher rates for 5 measures. Across all measures of low-value care, commercial enrollees received more (risk difference [RD] 6.8 percentage points; CI: 6.6 to 7.0) low-value services than their counterparts with Medicaid. Commercial enrollees were also more likely to receive low-value services typically performed in the emergency room (RD 11.4 percentage points; CI: 10.7 to 12.2) and services that were less expensive (RD 15.3 percentage points; CI 14.6 to 16.0). CONCLUSION Differences in the provision of low-value care varied across measures, though average use was slightly higher among commercial than Medicaid enrollees. This difference was more pronounced for less expensive services indicating that financial incentives may not be the sole driver of low-value care.
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Affiliation(s)
- Jacqueline E Ellison
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, RI, USA
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, RI, USA
- Department of Health Policy and Management, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA
| | - Soryan Kumar
- Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Jon A Steingrimsson
- Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA
- Department of Biostatistics, Brown University School of Public Health, Providence, RI, USA
| | | | | | - K John McConnell
- Center for Health Systems Effectiveness, Oregon Health & Science University, Portland, OR, USA
- Department of Emergency Medicine, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Amal N Trivedi
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, RI, USA
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, RI, USA
| | - Thomas A Trikalinos
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, RI, USA
- Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI, USA
| | - Shaun P Forbes
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, RI, USA
- Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI, USA
| | - Orestis A Panagiotou
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, RI, USA.
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, RI, USA.
- Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI, USA.
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40
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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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Inflammatory and infectious upper respiratory diseases associate with 41 genomic loci and type 2 inflammation. Nat Commun 2023; 14:83. [PMID: 36653354 PMCID: PMC9849224 DOI: 10.1038/s41467-022-33626-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/26/2022] [Indexed: 01/19/2023] Open
Abstract
Inflammatory and infectious upper respiratory diseases (ICD-10: J30-J39), such as diseases of the sinonasal tract, pharynx and larynx, are growing health problems yet their genomic similarity is not known. We analyze genome-wide association to eight upper respiratory diseases (61,195 cases) among 260,405 FinnGen participants, meta-analyzing diseases in four groups based on an underlying genetic correlation structure. Aiming to understand which genetic loci contribute to susceptibility to upper respiratory diseases in general and its subtypes, we detect 41 independent genome-wide significant loci, distinguishing impact on sinonasal or pharyngeal diseases, or both. Fine-mapping implicated non-synonymous variants in nine genes, including three linked to immune-related diseases. Phenome-wide analysis implicated asthma and atopic dermatitis at sinonasal disease loci, and inflammatory bowel diseases and other immune-mediated disorders at pharyngeal disease loci. Upper respiratory diseases also genetically correlated with autoimmune diseases such as rheumatoid arthritis, autoimmune hypothyroidism, and psoriasis. Finally, we associated separate gene pathways in sinonasal and pharyngeal diseases that both contribute to type 2 immunological reaction. We show shared heritability among upper respiratory diseases that extends to several immune-mediated diseases with diverse mechanisms, such as type 2 high inflammation.
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42
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Rajaprakash M, Dean LT, Palmore M, Johnson SB, Kaufman J, Fallin DM, Ladd-Acosta C. DNA methylation signatures as biomarkers of socioeconomic position. ENVIRONMENTAL EPIGENETICS 2022; 9:dvac027. [PMID: 36694711 PMCID: PMC9869656 DOI: 10.1093/eep/dvac027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 11/22/2022] [Accepted: 12/13/2022] [Indexed: 06/12/2023]
Abstract
This review article provides a framework for the use of deoxyribonucleic acid (DNA) methylation (DNAm) biomarkers to study the biological embedding of socioeconomic position (SEP) and summarizes the latest developments in the area. It presents the emerging literature showing associations between individual- and neighborhood-level SEP exposures and DNAm across the life course. In contrast to questionnaire-based methods of assessing SEP, we suggest that DNAm biomarkers may offer an accessible metric to study questions about SEP and health outcomes, acting as a personal dosimeter of exposure. However, further work remains in standardizing SEP measures across studies and evaluating consistency across domains, tissue types, and time periods. Meta-analyses of epigenetic associations with SEP are offered as one approach to confirm the replication of DNAm loci across studies. The development of DNAm biomarkers of SEP would provide a method for examining its impact on health outcomes in a more robust way, increasing the rigor of epidemiological studies.
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Affiliation(s)
- Meghna Rajaprakash
- Department of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, MD 21205, USA
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Lorraine T Dean
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Meredith Palmore
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Sara B Johnson
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Joan Kaufman
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Daniele M Fallin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
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43
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Berndt SI, Vijai J, Benavente Y, Camp NJ, Nieters A, Wang Z, Smedby KE, Kleinstern G, Hjalgrim H, Besson C, Skibola CF, Morton LM, Brooks-Wilson AR, Teras LR, Breeze C, Arias J, Adami HO, Albanes D, Anderson KC, Ansell SM, Bassig B, Becker N, Bhatti P, Birmann BM, Boffetta P, Bracci PM, Brennan P, Brown EE, Burdett L, Cannon-Albright LA, Chang ET, Chiu BCH, Chung CC, Clavel J, Cocco P, Colditz G, Conde L, Conti DV, Cox DG, Curtin K, Casabonne D, De Vivo I, Diepstra A, Diver WR, Dogan A, Edlund CK, Foretova L, Fraumeni JF, Gabbas A, Ghesquières H, Giles GG, Glaser S, Glenn M, Glimelius B, Gu J, Habermann TM, Haiman CA, Haioun C, Hofmann JN, Holford TR, Holly EA, Hutchinson A, Izhar A, Jackson RD, Jarrett RF, Kaaks R, Kane E, Kolonel LN, Kong Y, Kraft P, Kricker A, Lake A, Lan Q, Lawrence C, Li D, Liebow M, Link BK, Magnani C, Maynadie M, McKay J, Melbye M, Miligi L, Milne RL, Molina TJ, Monnereau A, Montalvan R, North KE, Novak AJ, Onel K, Purdue MP, Rand KA, Riboli E, Riby J, Roman E, Salles G, Sborov DW, Severson RK, Shanafelt TD, Smith MT, Smith A, Song KW, Song L, Southey MC, Spinelli JJ, Staines A, Stephens D, Sutherland HJ, Tkachuk K, Thompson CA, Tilly H, Tinker LF, Travis RC, Turner J, Vachon CM, Vajdic CM, Van Den Berg A, Van Den Berg DJ, Vermeulen RCH, Vineis P, Wang SS, Weiderpass E, Weiner GJ, Weinstein S, Doo NW, Ye Y, Yeager M, Yu K, Zeleniuch-Jacquotte A, Zhang Y, Zheng T, Ziv E, Sampson J, Chatterjee N, Offit K, Cozen W, Wu X, Cerhan JR, Chanock SJ, Slager SL, Rothman N. Distinct germline genetic susceptibility profiles identified for common non-Hodgkin lymphoma subtypes. Leukemia 2022; 36:2835-2844. [PMID: 36273105 PMCID: PMC10337695 DOI: 10.1038/s41375-022-01711-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/22/2022] [Accepted: 09/15/2022] [Indexed: 11/08/2022]
Abstract
Lymphoma risk is elevated for relatives with common non-Hodgkin lymphoma (NHL) subtypes, suggesting shared genetic susceptibility across subtypes. To evaluate the extent of mutual heritability among NHL subtypes and discover novel loci shared among subtypes, we analyzed data from eight genome-wide association studies within the InterLymph Consortium, including 10,629 cases and 9505 controls. We utilized Association analysis based on SubSETs (ASSET) to discover loci for subsets of NHL subtypes and evaluated shared heritability across the genome using Genome-wide Complex Trait Analysis (GCTA) and polygenic risk scores. We discovered 17 genome-wide significant loci (P < 5 × 10-8) for subsets of NHL subtypes, including a novel locus at 10q23.33 (HHEX) (P = 3.27 × 10-9). Most subset associations were driven primarily by only one subtype. Genome-wide genetic correlations between pairs of subtypes varied broadly from 0.20 to 0.86, suggesting substantial heterogeneity in the extent of shared heritability among subtypes. Polygenic risk score analyses of established loci for different lymphoid malignancies identified strong associations with some NHL subtypes (P < 5 × 10-8), but weak or null associations with others. Although our analyses suggest partially shared heritability and biological pathways, they reveal substantial heterogeneity among NHL subtypes with each having its own distinct germline genetic architecture.
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Affiliation(s)
- Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA.
| | - Joseph Vijai
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yolanda Benavente
- Cancer Epidemiology Research Programme, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- CIBER de Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Nicola J Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Alexandra Nieters
- Institute for Immunodeficiency, University Medical Center Freiburg, Freiburg, Germany
| | - Zhaoming Wang
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, USA
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Karin E Smedby
- Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Hematology Center, Karolinska University Hospital, Stockholm, Sweden
| | | | - Henrik Hjalgrim
- Department of Epidemiology Research, Division of Health Surveillance and Research, Statens Serum Institut, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Haematology, Rigshospitalet, Copenhagen, Denmark
- Danish Cancer Society Research Center, Danish Cancer Society, Copenhagen, Denmark
| | - Caroline Besson
- Centre Hospitalier de Versailles, Le Chesnay, France
- Université Paris-Saclay, UVSQ, Inserm, Équipe "Exposome et Hérédité", CESP, Villejuif, France
| | - Christine F Skibola
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA, USA
| | - Lindsay M Morton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Angela R Brooks-Wilson
- Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Lauren R Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Charles Breeze
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Joshua Arias
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Hans-Olov Adami
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Institute of Health and Society, Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Kenneth C Anderson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Stephen M Ansell
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Bryan Bassig
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Nikolaus Becker
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Baden-Württemberg, Germany
| | - Parveen Bhatti
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Brenda M Birmann
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Paolo Boffetta
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, New York, 11794, NY, USA
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, 41026, Italy
| | - Paige M Bracci
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Paul Brennan
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Elizabeth E Brown
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Laurie Burdett
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, MA, USA
| | - Lisa A Cannon-Albright
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Ellen T Chang
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Center for Health Sciences, Exponent, Inc., Menlo Park, CA, USA
| | - Brian C H Chiu
- Department of Public Health Sciences University of Chicago, Chicago, IL, USA
| | - Charles C Chung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Jacqueline Clavel
- CRESS, UMR1153, INSERM, Villejuif, France
- Université de Paris-Cité, Villejuif, France
| | - Pierluigi Cocco
- Centre for Occupational and Environmental Health, Division of Population Science, Health Services Research & Primary Care, University of Manchester, Manchester, United Kingdom
| | - Graham Colditz
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Lucia Conde
- Bill Lyons Informatics Centre, UCL Cancer Institute, University College London, London, United Kingdom
| | - David V Conti
- Department of Population and Public Health Sciences, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David G Cox
- INSERM U1052, Cancer Research Center of Lyon, Centre Léon Bérard, Lyon, France
| | - Karen Curtin
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Delphine Casabonne
- Cancer Epidemiology Research Programme, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- CIBER de Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Immaculata De Vivo
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Arjan Diepstra
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - W Ryan Diver
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Ahmet Dogan
- Departments of Laboratory Medicine and Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christopher K Edlund
- Department of Population and Public Health Sciences, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Joseph F Fraumeni
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Attilio Gabbas
- Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, Monserrato, Cagliari, Italy
| | - Hervé Ghesquières
- Department of Hematology, Hospices Civils de Lyon, Lyon Sud Hospital, Pierre Benite, France
- CIRI, Centre International de Recherche en Infectiologie, Team Lymphoma Immuno-Biology, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VC, Australia
| | - Sally Glaser
- Cancer Prevention Institute of California, Fremont, CA, USA
- Stanford Cancer Institute, Stanford, CA, USA
| | - Martha Glenn
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Bengt Glimelius
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jian Gu
- Department of Epidemiology, MD Anderson Cancer Center, Houston, TX, USA
| | | | - Christopher A Haiman
- Department of Population and Public Health Sciences, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Corinne Haioun
- Lymphoid Malignancies Unit, Henri Mondor Hospital and University Paris Est, Créteil, France
| | - Jonathan N Hofmann
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Theodore R Holford
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Elizabeth A Holly
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Amy Hutchinson
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, MA, USA
| | - Aalin Izhar
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rebecca D Jackson
- Division of Endocrinology, Diabetes and Metabolism, The Ohio State University, Columbus, OH, USA
| | - Ruth F Jarrett
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Rudolph Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Baden-Württemberg, Germany
| | - Eleanor Kane
- Department of Health Sciences, University of York, York, United Kingdom
| | - Laurence N Kolonel
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Yinfei Kong
- Information Systems and Decision Sciences, California State University, Fullerton, Fullerton, CA, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Anne Kricker
- Sydney School of Public Health, The University of Sydney, Sydney, NSW, Australia
| | - Annette Lake
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | | | - Dalin Li
- F. Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mark Liebow
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Brian K Link
- Department of Internal Medicine, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Corrado Magnani
- CPO-Piemonte and Unit of Medical Statistics and Epidemiology, Department Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Marc Maynadie
- INSERM U1231, EA 4184, Registre des Hémopathies Malignes de Côte d'Or, University of Burgundy and Dijon University Hospital, Dijon, France
| | - James McKay
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Mads Melbye
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Jebsen Center for Genetic epidemiology, NTNU, Trondheim, Norway
- Danish Cancer Society Research Center, Copenhagen, Denmark
- Department of Genetics, Stanford University Medical School, Stanford, CA, USA
| | - Lucia Miligi
- Environmental and Occupational Epidemiology Unit, Cancer Prevention and Research Institute (ISPO), Florence, Italy
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VC, Australia
| | - Thierry J Molina
- Department of Pathology, APHP, Necker and Robert Debré, Université Paris Cité, Institut Imagine, INSERM U1163, Paris, France
| | - Alain Monnereau
- CRESS, UMR1153, INSERM, Villejuif, France
- Registre des hémopathies malignes de la Gironde, Institut Bergonié, Bordeaux, Cedex, France
| | | | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Anne J Novak
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kenan Onel
- Donald and Barbara Zucker School of Medicine, Hofstra/Northwell, Hempstead, New York, NY, USA
| | - Mark P Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Kristin A Rand
- Department of Population and Public Health Sciences, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Elio Riboli
- School of Public Health, Imperial College London, London, United Kingdom
| | - Jacques Riby
- Department of Epidemiology, School of Public Health and Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
- Division of Environmental Health Sciences, University of California Berkeley School of Public Health, Berkeley, CA, USA
| | - Eve Roman
- Department of Health Sciences, University of York, York, United Kingdom
| | - Gilles Salles
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Douglas W Sborov
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Richard K Severson
- Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI, USA
| | - Tait D Shanafelt
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Martyn T Smith
- Division of Environmental Health Sciences, University of California Berkeley School of Public Health, Berkeley, CA, USA
| | - Alexandra Smith
- Department of Health Sciences, University of York, York, United Kingdom
| | - Kevin W Song
- Leukemia/Bone Marrow Transplantation Program, BC Cancer Agency, Vancouver, BC, Canada
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Lei Song
- Center for Cancer Research, National Cancer Institute, Frederick, MA, USA
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VC, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, VC, 3010, Australia
| | - John J Spinelli
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Anthony Staines
- School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland
| | - Deborah Stephens
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Heather J Sutherland
- Leukemia/Bone Marrow Transplantation Program, BC Cancer Agency, Vancouver, BC, Canada
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Kaitlyn Tkachuk
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Hervé Tilly
- Centre Henri Becquerel, Université de Rouen, Rouen, France
| | - Lesley F Tinker
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ruth C Travis
- Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom
| | - Jenny Turner
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, Australia
- Department of Histopathology, Douglass Hanly Moir Pathology, Sydney, NSW, Australia
| | - Celine M Vachon
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Claire M Vajdic
- The Kirby Institute, University of New South Wales, Sydney, NSW, Australia
| | - Anke Van Den Berg
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - David J Van Den Berg
- Department of Population and Public Health Sciences, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Roel C H Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Paolo Vineis
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Human Genetics Foundation, Turin, Italy
| | - Sophia S Wang
- Division of Health Analytics, City of Hope Beckman Research Institute, Duarte, CA, USA
| | | | - George J Weiner
- Department of Internal Medicine, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Stephanie Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Nicole Wong Doo
- Concord Clinical School, University of Sydney, Concord, NSW, Australia
| | - Yuanqing Ye
- Department of Epidemiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, MA, USA
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Anne Zeleniuch-Jacquotte
- Department of Population Health, New York University School of Medicine, New York, NY, USA
- Department of Environmental Medicine, New York University School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, NYU Langone Medical Center, New York, NY, USA
| | - Yawei Zhang
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Tongzhang Zheng
- Department of Epidemiology, Brown University, Providence, RI, USA
| | - Elad Ziv
- Division of General Internal Medicine, Department of Medicine, Institute of Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Joshua Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Nilanjan Chatterjee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MA, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MA, USA
| | - Kenneth Offit
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wendy Cozen
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA
| | - Xifeng Wu
- Department of Epidemiology, MD Anderson Cancer Center, Houston, TX, USA
| | - James R Cerhan
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
| | - Susan L Slager
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Md, USA
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Woodward AA, Urbanowicz RJ, Naj AC, Moore JH. Genetic heterogeneity: Challenges, impacts, and methods through an associative lens. Genet Epidemiol 2022; 46:555-571. [PMID: 35924480 PMCID: PMC9669229 DOI: 10.1002/gepi.22497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/06/2022] [Accepted: 07/19/2022] [Indexed: 01/07/2023]
Abstract
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.
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Affiliation(s)
- Alexa A. Woodward
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ryan J. Urbanowicz
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Adam C. Naj
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jason H. Moore
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
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Evidence that the pituitary gland connects type 2 diabetes mellitus and schizophrenia based on large-scale trans-ethnic genetic analyses. J Transl Med 2022; 20:501. [PMID: 36329495 PMCID: PMC9632150 DOI: 10.1186/s12967-022-03704-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/05/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Previous studies on European (EUR) samples have obtained inconsistent results regarding the genetic correlation between type 2 diabetes mellitus (T2DM) and Schizophrenia (SCZ). A large-scale trans-ethnic genetic analysis may provide additional evidence with enhanced power. OBJECTIVE We aimed to explore the genetic basis for both T2DM and SCZ based on large-scale genetic analyses of genome-wide association study (GWAS) data from both East Asian (EAS) and EUR subjects. METHODS A range of complementary approaches were employed to cross-validate the genetic correlation between T2DM and SCZ at the whole genome, autosomes (linkage disequilibrium score regression, LDSC), loci (Heritability Estimation from Summary Statistics, HESS), and causal variants (MiXeR and Mendelian randomization, MR) levels. Then, genome-wide and transcriptome-wide cross-trait/ethnic meta-analyses were performed separately to explore the effective shared organs, cells and molecular pathways. RESULTS A weak genome-wide negative genetic correlation between SCZ and T2DM was found for the EUR (rg = - 0.098, P = 0.009) and EAS (rg =- 0.053 and P = 0.032) populations, which showed no significant difference between the EUR and EAS populations (P = 0.22). After Bonferroni correction, the rg remained significant only in the EUR population. Similar results were obtained from analyses at the levels of autosomes, loci and causal variants. 25 independent variants were firstly identified as being responsible for both SCZ and T2DM. The variants associated with the two disorders were significantly correlated to the gene expression profiles in the brain (P = 1.1E-9) and pituitary gland (P = 1.9E-6). Then, 61 protein-coding and non-coding genes were identified as effective genes in the pituitary gland (P < 9.23E-6) and were enriched in metabolic pathways related to glutathione mediated arsenate detoxification and to D-myo-inositol-trisphosphate. CONCLUSION Here, we show that a negative genetic correlation exists between SCZ and T2DM at the whole genome, autosome, locus and causal variant levels. We identify pituitary gland as a common effective organ for both diseases, in which non-protein-coding effective genes, such as lncRNAs, may be responsible for the negative genetic correlation. This highlights the importance of molecular metabolism and neuroendocrine modulation in the pituitary gland, which may be responsible for the initiation of T2DM in SCZ patients.
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Taraszka K, Zaitlen N, Eskin E. Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations. PLoS Genet 2022; 18:e1010447. [PMID: 36342933 PMCID: PMC9671458 DOI: 10.1371/journal.pgen.1010447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 11/17/2022] [Accepted: 09/27/2022] [Indexed: 11/09/2022] Open
Abstract
We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.
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Affiliation(s)
- Kodi Taraszka
- Department of Computer Science, University of California, Los Angeles, California, United States of America
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, California, United States of America
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, California, United States of America
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, University of California, Los Angeles, California, United States of America
- * E-mail:
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47
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Ding H, Ouyang M, Wang J, Xie M, Huang Y, Yuan F, Jia Y, Zhang X, Liu N, Zhang N. Shared genetics between classes of obesity and psychiatric disorders: A large-scale genome-wide cross-trait analysis. J Psychosom Res 2022; 162:111032. [PMID: 36137488 DOI: 10.1016/j.jpsychores.2022.111032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/16/2022] [Accepted: 08/31/2022] [Indexed: 10/31/2022]
Abstract
AIMS Epidemiological studies demonstrate an association between classes of obesity and psychiatric disorders, although little is known about shared genetics and causality of association. Thus, we aimed to investigate shared genetics and causal link between different classes of obesity and psychiatric disorders. METHODS We used genome-wide association study (GWAS) summary data range from 9725 to 500,199 sample sizes of European descent, conducted a large-scale genome-wide cross-trait association study to investigate genetic overlap between the classes of obesity and anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. We conducted transcriptome-wide association study analysis (TWAS) to identified variants regulated gene expression in those related disorders. Finally, pathway enrichment analysis to identified major pathways. RESULTS In the combined analysis, we replicated 211 previously reported loci and discovered 58 novel independent loci that were associated with all three classes of obesity and related psychiatric disorders. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to exocrine/endocrine, digestive, circulatory, adipose, digestive, respiratory, and nervous systems, such as DCC, NEGR1, INO80E. Mendelian randomization analyses suggested that there may be a two-way or one-way causal relationship between obesity and psychiatric disorders. CONCLUSION This large-scale genome-wide cross-trait analysis identified shared genetics and potential causal links between classes of obesity and psychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, anorexia nervosa, major depressive disorder, schizophrenia, and obsessive-compulsive disorder). Such shared genetics suggests potential new biological functions in common among them.
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Affiliation(s)
- Hui Ding
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Mengyuan Ouyang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Jinyi Wang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Minyao Xie
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Yanyuan Huang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Fangzheng Yuan
- School of Psychology, Nanjing Normal University, Nanjing 210023, China
| | - Yunhan Jia
- School of Psychology, Nanjing Normal University, Nanjing 210023, China
| | - Xuedi Zhang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Na Liu
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu 210029, China.
| | - Ning Zhang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu 210029, China.
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Polimanti R, Wendt FR, Pathak GA, Tylee DS, Tcheandjieu C, Hilliard AT, Levey DF, Adhikari K, Gaziano JM, O'Donnell CJ, Assimes TL, Stein MB, Gelernter J. Understanding the comorbidity between posttraumatic stress severity and coronary artery disease using genome-wide information and electronic health records. Mol Psychiatry 2022; 27:3961-3969. [PMID: 35986173 PMCID: PMC10986859 DOI: 10.1038/s41380-022-01735-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 02/07/2023]
Abstract
The association between coronary artery disease (CAD) and posttraumatic stress disorder (PTSD) contributes to the high morbidity and mortality observed for these conditions. To understand the dynamics underlying PTSD-CAD comorbidity, we investigated large-scale genome-wide association (GWA) statistics from the Million Veteran Program (MVP), the UK Biobank (UKB), the Psychiatric Genomics Consortium, and the CARDIoGRAMplusC4D Consortium. We observed a genetic correlation of CAD with PTSD case-control and quantitative outcomes, ranging from 0.18 to 0.32. To investigate possible cause-effect relationships underlying these genetic correlations, we performed a two-sample Mendelian randomization (MR) analysis, observing a significant bidirectional relationship between CAD and PTSD symptom severity. Genetically-determined PCL-17 (PTSD 17-item Checklist) total score was associated with increased CAD risk (odds ratio = 1.04; 95% confidence interval, 95% CI = 1.01-1.06). Conversely, CAD genetic liability was associated with reduced PCL-17 total score (beta = -0.42; 95% CI = -0.04 to -0.81). Because of these opposite-direction associations, we conducted a pleiotropic meta-analysis to investigate loci with concordant vs. discordant effects on PCL-17 and CAD, observing that concordant-effect loci were enriched for molecular pathways related to platelet amyloid precursor protein (beta = 1.53, p = 2.97 × 10-7) and astrocyte activation regulation (beta = 1.51, p = 2.48 × 10-6) while discordant-effect loci were enriched for biological processes related to lipid metabolism (e.g., triglyceride-rich lipoprotein particle clearance, beta = 2.32, p = 1.61 × 10-10). To follow up these results, we leveraged MVP and UKB electronic health records (EHR) to assess longitudinal changes in the association between CAD and posttraumatic stress severity. This EHR-based analysis highlighted that earlier CAD diagnosis is associated with increased PCL-total score later in life, while lower PCL total score was associated with increased risk of a later CAD diagnosis (Mann-Kendall trend test: MVP tau = 0.932, p < 2 × 10-16; UKB tau = 0.376, p = 0.005). In conclusion, both our genetically-informed analyses and our EHR-based follow-up investigation highlighted a bidirectional relationship between PTSD and CAD where multiple pleiotropic mechanisms are likely to be involved.
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Affiliation(s)
- Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA.
- VA CT Healthcare Center, West Haven, CT, USA.
| | - Frank R Wendt
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Daniel S Tylee
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | | | - Austin T Hilliard
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Keyrun Adhikari
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - J Michael Gaziano
- Department of Medicine, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Christopher J O'Donnell
- Department of Medicine, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Themistocles L Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
- Departments of Genetics and of Neuroscience, Yale School of Medicine, New Haven, CT, USA
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49
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Sanchez-Roige S, Kember RL, Agrawal A. Substance use and common contributors to morbidity: A genetics perspective. EBioMedicine 2022; 83:104212. [PMID: 35970022 PMCID: PMC9399262 DOI: 10.1016/j.ebiom.2022.104212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/27/2022] [Accepted: 07/28/2022] [Indexed: 11/23/2022] Open
Abstract
Excessive substance use and substance use disorders (SUDs) are common, serious and relapsing medical conditions. They frequently co-occur with other diseases that are leading contributors to disability worldwide. While heavy substance use may potentiate the course of some of these illnesses, there is accumulating evidence suggesting common genetic architectures. In this narrative review, we focus on four heritable medical conditions - cardiometabolic disease, chronic pain, depression and COVID-19, which are commonly overlapping with, but not necessarily a direct consequence of, SUDs. We find persuasive evidence of underlying genetic liability that predisposes to both SUDs and chronic pain, depression, and COVID-19. For cardiometabolic disease, there is greater support for a potential causal influence of problematic substance use. Our review encourages de-stigmatization of SUDs and the assessment of substance use in clinical settings. We assert that identifying shared pathways of risk has high translational potential, allowing tailoring of treatments for multiple medical conditions. FUNDING: SSR acknowledges T29KT0526, T32IR5226 and DP1DA054394; RLK acknowledges AA028292; AA acknowledges DA054869 & K02DA032573. The funders had no role in the conceptualization or writing of the paper.
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Affiliation(s)
- Sandra Sanchez-Roige
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA.
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50
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Li J, Li YR, Glessner JT, Yang J, March ME, Kao C, Vaccaro CN, Bradfield JP, Li J, Mentch FD, Qu H, Qi X, Chang X, Hou C, Abrams DJ, Qiu H, Wei Z, Connolly JJ, Wang F, Snyder J, Flatø B, Thompson SD, Langefeld CD, Lie BA, Munro JE, Wise C, Sleiman PMA, Hakonarson H. Identification of Novel Loci Shared by Juvenile Idiopathic Arthritis Subtypes Through Integrative Genetic Analysis. Arthritis Rheumatol 2022; 74:1420-1429. [PMID: 35347896 PMCID: PMC9542075 DOI: 10.1002/art.42129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 03/11/2022] [Accepted: 03/25/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Juvenile idiopathic arthritis (JIA) is the most common chronic immune-mediated joint disease among children and encompasses a heterogeneous group of immune-mediated joint disorders classified into 7 subtypes according to clinical presentation. However, phenotype overlap and biologic evidence suggest a shared mechanistic basis between subtypes. This study was undertaken to systematically investigate shared genetic underpinnings of JIA subtypes. METHODS We performed a heterogeneity-sensitive genome-wide association study encompassing a total of 1,245 JIA cases (classified into 7 subtypes) and 9,250 controls, followed by fine-mapping of candidate causal variants at each genome-wide significant locus, functional annotation, and pathway and network analysis. We further identified candidate drug targets and drug repurposing opportunities by in silico analyses. RESULTS In addition to the major histocompatibility complex locus, we identified 15 genome-wide significant loci shared between at least 2 JIA subtypes, including 10 novel loci. Functional annotation indicated that candidate genes at these loci were expressed in diverse immune cell types. CONCLUSION This study identified novel genetic loci shared by JIA subtypes. Our findings identified candidate mechanisms underlying JIA subtypes and candidate targets with drug repurposing opportunities for JIA treatment.
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Affiliation(s)
- Jin Li
- Department of Cell Biology, the Province and Ministry Co‐sponsored Collaborative Innovation Center for Medical EpigeneticsSchool of Basic Medical Sciences, Tianjin Medical UniversityTianjinChina
| | - Yun R. Li
- The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, City of Hope Comprehensive Cancer Center, Duarte, California, and Translational Genomics Research InstitutePhoenixArizona
| | | | - Jie Yang
- Department of Cell Biology, the Province and Ministry Co‐sponsored Collaborative Innovation Center for Medical EpigeneticsSchool of Basic Medical Sciences, Tianjin Medical UniversityTianjinChina
| | | | - Charlly Kao
- The Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | | | | | - Junyi Li
- Department of Cell Biology, the Province and Ministry Co‐sponsored Collaborative Innovation Center for Medical EpigeneticsSchool of Basic Medical Sciences, Tianjin Medical UniversityTianjinChina
| | - Frank D. Mentch
- The Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Hui‐Qi Qu
- The Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Xiaohui Qi
- Department of Cell Biology, the Province and Ministry Co‐sponsored Collaborative Innovation Center for Medical EpigeneticsSchool of Basic Medical Sciences, Tianjin Medical UniversityTianjinChina
| | - Xiao Chang
- The Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Cuiping Hou
- The Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Debra J. Abrams
- The Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Haijun Qiu
- The Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Zhi Wei
- New Jersey Institute of TechnologyNewark
| | | | - Fengxiang Wang
- The Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - James Snyder
- The Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Berit Flatø
- Oslo University HospitalRikshospitaletOsloNorway
| | - Susan D. Thompson
- University of Cincinnati College of Medicine and Cincinnati Children's Hospital Medical CenterCincinnatiOhio
| | - Carl D. Langefeld
- Wake Forest University School of MedicineWinston‐SalemNorth Carolina
| | | | | | | | | | - Hakon Hakonarson
- The Children's Hospital of Philadelphia and University of PennsylvaniaPhiladelphia
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