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Shi J, Wen W, Long J, Gamazon ER, Tao R, Cai Q. Genetic correlation and causal associations between psychiatric disorders and lung cancer risk. J Affect Disord 2024; 356:647-656. [PMID: 38657774 DOI: 10.1016/j.jad.2024.04.080] [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: 08/10/2023] [Revised: 04/04/2024] [Accepted: 04/21/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND Patients with certain psychiatric disorders have increased lung cancer incidence. However, establishing a causal relationship through traditional epidemiological methods poses challenges. METHODS Available summary statistics of genome-wide association studies of cigarette smoking, lung cancer, and eight psychiatric disorders, including attention deficit/hyperactivity disorder (ADHD), autism, depression, major depressive disorder, bipolar disorder, insomnia, neuroticism, and schizophrenia (range N: 46,350-1,331,010) were leveraged to estimate genetic correlations using Linkage Disequilibrium Score Regression and assess causal effect of each psychiatric disorder on lung cancer using two-sample Mendelian randomization (MR) models, comprising inverse-variance weighted (IVW), weighted median, MR-Egger, pleiotropy residual sum and outlier testing (MR-PRESSO), and a constrained maximum likelihood approach (cML-MR). RESULTS Significant positive correlations were observed between each psychiatric disorder and both smoking and lung cancer (all FDR < 0.05), except for the correlation between autism and lung cancer. Both univariable and the cML-MA MR analyses demonstrated that liability to schizophrenia, depression, ADHD, or insomnia was associated with an increased risk of overall lung cancer. Genetic liability to insomnia was linked specifically to squamous cell carcinoma (SCC), while genetic liability to ADHD was associated with an elevated risk of both SCC and small cell lung cancer (all P < 0.05). The later was further supported by multivariable MR analyses, which accounted for smoking. LIMITATIONS Participants were constrained to European ancestry populations. Causal estimates from binary psychiatric disorders may be biased. CONCLUSION Our findings suggest appropriate management of several psychiatric disorders, particularly ADHD, may potentially reduce the risk of developing lung cancer.
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Affiliation(s)
- Jiajun Shi
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, USA; Data Science Institute, Vanderbilt University Medical Center, Nashville, TN 37203, USA; Clare Hall, University of Cambridge, Cambridge CB3 9AL, UK; MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.
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2
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Kiltschewskij DJ, Reay WR, Geaghan MP, Atkins JR, Xavier A, Zhang X, Watkeys OJ, Carr VJ, Scott RJ, Green MJ, Cairns MJ. Alteration of DNA Methylation and Epigenetic Scores Associated With Features of Schizophrenia and Common Variant Genetic Risk. Biol Psychiatry 2024; 95:647-661. [PMID: 37480976 DOI: 10.1016/j.biopsych.2023.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND Unpacking molecular perturbations associated with features of schizophrenia is a critical step toward understanding phenotypic heterogeneity in this disorder. Recent epigenome-wide association studies have uncovered pervasive dysregulation of DNA methylation in schizophrenia; however, clinical features of the disorder that account for a large proportion of phenotypic variability are relatively underexplored. METHODS We comprehensively analyzed patterns of DNA methylation in a cohort of 381 individuals with schizophrenia from the deeply phenotyped Australian Schizophrenia Research Bank. Epigenetic changes were investigated in association with cognitive status, age of onset, treatment resistance, Global Assessment of Functioning scores, and common variant polygenic risk scores for schizophrenia. We subsequently explored alterations within genes previously associated with psychiatric illness, phenome-wide epigenetic covariance, and epigenetic scores. RESULTS Epigenome-wide association studies of the 5 primary traits identified 662 suggestively significant (p < 6.72 × 10-5) differentially methylated probes, with a further 432 revealed after controlling for schizophrenia polygenic risk on the remaining 4 traits. Interestingly, we uncovered many probes within genes associated with a variety of psychiatric conditions as well as significant epigenetic covariance with phenotypes and exposures including acute myocardial infarction, C-reactive protein, and lung cancer. Epigenetic scores for treatment-resistant schizophrenia strikingly exhibited association with clozapine administration, while epigenetic proxies of plasma protein expression, such as CCL17, MMP10, and PRG2, were associated with several features of schizophrenia. CONCLUSIONS Our findings collectively provide novel evidence suggesting that several features of schizophrenia are associated with alteration of DNA methylation, which may contribute to interindividual phenotypic variation in affected individuals.
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Affiliation(s)
- Dylan J Kiltschewskij
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Michael P Geaghan
- Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
| | - Joshua R Atkins
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia
| | - Alexandre Xavier
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Xiajie Zhang
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Oliver J Watkeys
- School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Vaughan J Carr
- School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia; Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Melissa J Green
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia.
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3
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Chen X, Wang S, Shen W. The causal relationship between severe mental illness and risk of lung carcinoma. Medicine (Baltimore) 2024; 103:e37355. [PMID: 38489734 PMCID: PMC10939700 DOI: 10.1097/md.0000000000037355] [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: 11/11/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 03/17/2024] Open
Abstract
Observational studies have suggested a link between severe mental illness (SMI) and risk of lung carcinoma (LC); however, causality has not been established. In this study, we conducted a two-sample, two-step Mendelian randomization (MR) investigation to uncover the etiological influence of SMI on LC risk and quantify the mediating effects of known modifiable risk factors. We obtained summary-level datasets for schizophrenia, major depressive disorder (MDD), and bipolar disorder (BD) from the Psychiatric Genomics Consortium (PGC). Data on single nucleotide polymorphisms (SNPs) associated with lung carcinoma (LC) were sourced from a recent large meta-analysis by McKay et al. We employed two-sample MR and two-step MR utilizing the inverse variance weighted method for causal estimation. Sensitivity tests were conducted to validate causal relationships. In two-sample MR, we identified schizophrenia as a risk factor for LC (OR = 1.06, 95% CI 1.02-1.11, P = 3.48E-03), while MDD (OR = 1.18, 95% CI 0.98-1.42, P = .07) and BD (OR = 1.07, 95% CI 0.99-1.15, P = .09) showed no significant association with LC. In the two-step MR, smoking accounted for 24.66% of the schizophrenia-LC risk association, and alcohol consumption explained 7.59% of the effect. Schizophrenia is a risk factor for lung carcinoma, and smoking and alcohol consumption are the mediating factors in this causal relationship. LC screening should be emphasized in individuals with schizophrenia, particularly in those who smoke and consume alcohol regularly.
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Affiliation(s)
- Xiaohan Chen
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
| | - Shudan Wang
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
| | - Weiyu Shen
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
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4
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Zhou X, Liu Q, Liu S, Wang L, Sun Z, Sun C, Cui X. Genetic prediction of the causal relationship between schizophrenia and tumors: a Mendelian randomized study. Front Oncol 2024; 14:1321445. [PMID: 38434685 PMCID: PMC10905381 DOI: 10.3389/fonc.2024.1321445] [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: 11/02/2023] [Accepted: 01/26/2024] [Indexed: 03/05/2024] Open
Abstract
Background Patients with schizophrenia are at a higher risk of developing cancer. However, the causal relationship between schizophrenia and different tumor types remains unclear. Methods Using a two-sample, two-way Mendelian randomization method, we used publicly available genome-wide association analysis (GWAS) aggregate data to study the causal relationship between schizophrenia and different cancer risk factors. These tumors included lung adenocarcinoma, lung squamous cell carcinoma, small-cell lung cancer, gastric cancer, alcohol-related hepatocellular cancer, tumors involving the lungs, breast, thyroid gland, pancreas, prostate, ovaries and cervix, endometrium, colon and colorectum, and bladder. We used the inverse variance weighting (IVW) method to determine the causal relationship between schizophrenia and different tumor risk factors. In addition, we conducted a sensitivity test to evaluate the effectiveness of the causality. Results After adjusting for heterogeneity, evidence of a causal relationship between schizophrenia and lung cancer risk was observed (odds ratio [OR]=1.001, 95% confidence interval [CI], 1.000-1.001; P=0.0155). In the sensitivity analysis, the causal effect of schizophrenia on the risk of lung cancer was consistent in both direction and degree. However, no evidence of causality or reverse causality between schizophrenia and other tumors was found. Conclusion This study elucidated a causal relationship between the genetic predictors of schizophrenia and the risk of lung cancer, thereby providing a basis for the prevention, pathogenesis, and treatment of schizophrenia in patients with lung cancer.
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Affiliation(s)
- Xintong Zhou
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Qi Liu
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Shihan Liu
- Department of Otorhinolaryngology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liquan Wang
- Department of Thyroid and Breast Surgery, Weifang People’s Hospital, Weifang, China
| | - Zhongli Sun
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Changgang Sun
- College of Traditional Chinese Medicine, Weifang Medical University, Weifang, China
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China
| | - Xiangning Cui
- Department of Cardiovascular, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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5
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González-Peñas J, de Hoyos L, Díaz-Caneja CM, Andreu-Bernabeu Á, Stella C, Gurriarán X, Fañanás L, Bobes J, González-Pinto A, Crespo-Facorro B, Martorell L, Vilella E, Muntané G, Molto MD, Gonzalez-Piqueras JC, Parellada M, Arango C, Costas J. Recent natural selection conferred protection against schizophrenia by non-antagonistic pleiotropy. Sci Rep 2023; 13:15500. [PMID: 37726359 PMCID: PMC10509162 DOI: 10.1038/s41598-023-42578-0] [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: 11/16/2022] [Accepted: 09/12/2023] [Indexed: 09/21/2023] Open
Abstract
Schizophrenia is a debilitating psychiatric disorder associated with a reduced fertility and decreased life expectancy, yet common predisposing variation substantially contributes to the onset of the disorder, which poses an evolutionary paradox. Previous research has suggested balanced selection, a mechanism by which schizophrenia risk alleles could also provide advantages under certain environments, as a reliable explanation. However, recent studies have shown strong evidence against a positive selection of predisposing loci. Furthermore, evolutionary pressures on schizophrenia risk alleles could have changed throughout human history as new environments emerged. Here in this study, we used 1000 Genomes Project data to explore the relationship between schizophrenia predisposing loci and recent natural selection (RNS) signatures after the human diaspora out of Africa around 100,000 years ago on a genome-wide scale. We found evidence for significant enrichment of RNS markers in derived alleles arisen during human evolution conferring protection to schizophrenia. Moreover, both partitioned heritability and gene set enrichment analyses of mapped genes from schizophrenia predisposing loci subject to RNS revealed a lower involvement in brain and neuronal related functions compared to those not subject to RNS. Taken together, our results suggest non-antagonistic pleiotropy as a likely mechanism behind RNS that could explain the persistence of schizophrenia common predisposing variation in human populations due to its association to other non-psychiatric phenotypes.
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Affiliation(s)
- Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain.
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Carol Stella
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Xaquín Gurriarán
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Lourdes Fañanás
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Julio Bobes
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences - Psychiatry, Universidad de Oviedo, ISPA, INEUROPA, Oviedo, Spain
| | - Ana González-Pinto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- BIOARABA Health Research Institute, OSI Araba, University Hospital, University of the Basque Country, Vitoria, Spain
| | - Benedicto Crespo-Facorro
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Psychiatry, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Seville, Spain
| | - Lourdes Martorell
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - Elisabet Vilella
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - Gerard Muntané
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - María Dolores Molto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Genetics, University of Valencia, Campus of Burjassot, Valencia, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
| | - Jose Carlos Gonzalez-Piqueras
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
- Fundación Investigación Hospital Clínico de Valencia, INCLIVA, 46010, Valencia, Spain
| | - Mara Parellada
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier Costas
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
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6
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Hamoud AR, Bach K, Kakrecha O, Henkel N, Wu X, McCullumsmith RE, O’Donovan SM. Adenosine, Schizophrenia and Cancer: Does the Purinergic System Offer a Pathway to Treatment? Int J Mol Sci 2022; 23:ijms231911835. [PMID: 36233136 PMCID: PMC9570456 DOI: 10.3390/ijms231911835] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/23/2022] [Accepted: 09/29/2022] [Indexed: 11/16/2022] Open
Abstract
For over a century, a complex relationship between schizophrenia diagnosis and development of many cancers has been observed. Findings from epidemiological studies are mixed, with reports of increased, reduced, or no difference in cancer incidence in schizophrenia patients. However, as risk factors for cancer, including elevated smoking rates and substance abuse, are commonly associated with this patient population, it is surprising that cancer incidence is not higher. Various factors may account for the proposed reduction in cancer incidence rates including pathophysiological changes associated with disease. Perturbations of the adenosine system are hypothesized to contribute to the neurobiology of schizophrenia. Conversely, hyperfunction of the adenosine system is found in the tumor microenvironment in cancer and targeting the adenosine system therapeutically is a promising area of research in this disease. We outline the current biochemical and pharmacological evidence for hypofunction of the adenosine system in schizophrenia, and the role of increased adenosine metabolism in the tumor microenvironment. In the context of the relatively limited literature on this patient population, we discuss whether hypofunction of this system in schizophrenia, may counteract the immunosuppressive role of adenosine in the tumor microenvironment. We also highlight the importance of studies examining the adenosine system in this subset of patients for the potential insight they may offer into these complex disorders.
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Affiliation(s)
- Abdul-Rizaq Hamoud
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA
| | - Karen Bach
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA
| | - Ojal Kakrecha
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA
| | - Nicholas Henkel
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA
| | - Xiaojun Wu
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA
| | - Robert E. McCullumsmith
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA
- Neurosciences Institute, ProMedica, Toledo, OH 43606, USA
| | - Sinead M. O’Donovan
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA
- Correspondence:
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7
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Li C, Yang T, Ou R, Shang H. Overlapping Genetic Architecture Between Schizophrenia and Neurodegenerative Disorders. Front Cell Dev Biol 2022; 9:797072. [PMID: 35004692 PMCID: PMC8740133 DOI: 10.3389/fcell.2021.797072] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/06/2021] [Indexed: 02/05/2023] Open
Abstract
Epidemiological and clinical studies have suggested comorbidity between schizophrenia and several neurodegenerative disorders. However, little is known whether there exists shared genetic architecture. To explore their relationship from a genetic and transcriptomic perspective, we applied polygenic and linkage disequilibrium-informed methods to examine the genetic correlation between schizophrenia and amyotrophic lateral sclerosis (ALS), Parkinson’s disease, Alzheimer’s disease and frontotemporal dementia. We further combined genome-wide association summary statistics with large-scale transcriptomic datasets, to identify putative shared genes and explore related pathological tissues. We identified positive and significant correlation between schizophrenia and ALS at genetic (correlation 0.22; 95% CI: 0.16–0.28; p = 4.00E-04) and transcriptomic (correlation 0.08; 95% CI: 0.04–0.11; p = 0.034) levels. We further demonstrated that schizophrenia- and ALS-inferred gene expression overlap significantly in four tissues including skin, small intestine, brain cortex and lung, and highlighted three genes, namely GLB1L3, ZNHIT3 and TMEM194A as potential mediators of the correlation between schizophrenia and ALS. Our findings revealed overlapped gene expression profiles in specific tissues between schizophrenia and ALS, and identified novel potential shared genes. These results provided a better understanding for the pleiotropy of schizophrenia, and paved way for future studies to further elucidate the molecular drivers of schizophrenia.
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Affiliation(s)
- Chunyu Li
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Tianmi Yang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Ruwei Ou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Huifang Shang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
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8
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Baklouti E, Karoui M, Kammoun R, Ellouze F. Case report: Schizophrenia and hypertrophic osteoarthropathy, a rare syndrome hiding a life-threatening condition. Clin Case Rep 2022; 10:e05247. [PMID: 35059198 PMCID: PMC8755596 DOI: 10.1002/ccr3.5247] [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: 07/29/2021] [Revised: 11/26/2021] [Accepted: 12/05/2021] [Indexed: 11/10/2022] Open
Abstract
Schizophrenia is associated to somatic disorders especially cardio-vascular and auto-immune. Through this case report, we describe an association with hypertrophic osteoarthropathy (HPO). For this patient, it was a paraneoplastic syndrome secondary to lung cancer. This syndrome is rare but important to recognize since it could hide a life-threatening condition.
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Affiliation(s)
- Emna Baklouti
- Psychiatry DepartmentRazi Psychiatric HospitalManoubaTunisia
| | - Mehdi Karoui
- Psychiatry DepartmentRazi Psychiatric HospitalManoubaTunisia
| | - Rania Kammoun
- Psychiatry DepartmentRazi Psychiatric HospitalManoubaTunisia
| | - Faten Ellouze
- Psychiatry DepartmentRazi Psychiatric HospitalManoubaTunisia
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9
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Okamoto L, Watanabe S, Deno S, Nie X, Maruyama J, Tomita M, Hatano A, Yugi K. Meta-analysis of transcriptional regulatory networks for lipid metabolism in neural cells from schizophrenia patients based on an open-source intelligence approach. Neurosci Res 2021; 175:82-97. [PMID: 34979163 DOI: 10.1016/j.neures.2021.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 12/23/2021] [Accepted: 12/23/2021] [Indexed: 01/13/2023]
Abstract
There have been a number of reports about the transcriptional regulatory networks in schizophrenia. However, most of these studies were based on a specific transcription factor or a single dataset, an approach that is inadequate to understand the diverse etiology and underlying common characteristics of schizophrenia. Here we reconstructed and compared the transcriptional regulatory network for lipid metabolism enzymes using 15 public transcriptome datasets of neural cells from schizophrenia patients. Since many of the well-known schizophrenia-related SNPs are in enhancers, we reconstructed a network including enhancer-dependent regulation and found that 53.3 % of the total number of edges (7,577 pairs) involved regulation via enhancers. By examining multiple datasets, we found common and unique transcriptional modes of regulation. Furthermore, enrichment analysis of SNPs that were connected with genes in the transcriptional regulatory networks by eQTL suggested an association with hematological cell counts and some other traits/diseases, whose relationship to schizophrenia was either not or insufficiently reported in previous studies. Based on these results, we suggest that in future studies on schizophrenia, information on genotype, comorbidities and hematological cell counts should be included, along with the transcriptome, for a more detailed genetic stratification and mechanistic exploration of schizophrenia.
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Affiliation(s)
- Lisa Okamoto
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan; Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan; Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-0882, Japan
| | - Soyoka Watanabe
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan; Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan
| | - Senka Deno
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan; Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan; Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-0882, Japan
| | - Xiang Nie
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Junichi Maruyama
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan; Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-0882, Japan
| | - Atsushi Hatano
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan; Department of Omics and Systems Biology, Niigata University Graduate School of Medical and Dental Sciences, 757 Ichibancho, Asahimachi-dori, Chuo Ward, Niigata City, 951-8510, Japan
| | - Katsuyuki Yugi
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan; Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan; Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; PRESTO, Japan Science and Technology Agency, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.
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10
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Kopylov AT, Petrovsky DV, Stepanov AA, Rudnev VR, Malsagova KA, Butkova TV, Zakharova NV, Kostyuk GP, Kulikova LI, Enikeev DV, Potoldykova NV, Kulikov DA, Zulkarnaev AB, Kaysheva AL. Convolutional neural network in proteomics and metabolomics for determination of comorbidity between cancer and schizophrenia. J Biomed Inform 2021; 122:103890. [PMID: 34438071 DOI: 10.1016/j.jbi.2021.103890] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 08/18/2021] [Accepted: 08/19/2021] [Indexed: 12/18/2022]
Abstract
The association between cancer risk and schizophrenia is widely debated. Despite many epidemiological studies, there is still no strong evidence regarding the molecular basis for the comorbidity between these two pathological conditions. The vast majority of assays have been performed using clinical records of schizophrenic patients or those undergoing cancer treatment and monitored for sufficient time to find shared features between the considered conditions. We performed mass spectrometry-based proteomic and metabolomic investigations of patients with different cancer phenotypes (breast, ovarian, renal, and prostate) and patients with schizophrenia. The resulting vast quantity of proteomic and metabolomic data were then processed using systems biology and one-dimensional (1D) convolutional neural network (1DCNN) machine learning approaches. Traditional systematic approaches permit the segregation of schizophrenia and cancer phenotypes on the level of biological processes, while 1DCNN recognized "signatures" that could segregate distinct cancer phenotypes and schizophrenia at the comorbidity level. The designed network efficiently discriminated unrelated pathologies with a model accuracy of 0.90 and different subtypes of oncophenotypes with an accuracy of 0.94. The proposed strategy integrates systematic analysis of identified compounds and application of 1DCNN model for unidentified ones to reveal the similarity between distinct phenotypes.
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Affiliation(s)
- Arthur T Kopylov
- Biobanking Group, Branch of Institute of Biomedical Chemistry "Scientific and Education Center," 10 Pogodinskaya str., 119121 Moscow, Russian Federation.
| | - Denis V Petrovsky
- Biobanking Group, Branch of Institute of Biomedical Chemistry "Scientific and Education Center," 10 Pogodinskaya str., 119121 Moscow, Russian Federation
| | - Alexander A Stepanov
- Biobanking Group, Branch of Institute of Biomedical Chemistry "Scientific and Education Center," 10 Pogodinskaya str., 119121 Moscow, Russian Federation
| | - Vladimir R Rudnev
- Biobanking Group, Branch of Institute of Biomedical Chemistry "Scientific and Education Center," 10 Pogodinskaya str., 119121 Moscow, Russian Federation
| | - Kristina A Malsagova
- Biobanking Group, Branch of Institute of Biomedical Chemistry "Scientific and Education Center," 10 Pogodinskaya str., 119121 Moscow, Russian Federation
| | - Tatyana V Butkova
- Biobanking Group, Branch of Institute of Biomedical Chemistry "Scientific and Education Center," 10 Pogodinskaya str., 119121 Moscow, Russian Federation
| | - Natalya V Zakharova
- N.A.Alekseev 1(st) Clinical Hospital of Psychiatry, Moscow Healthcare Department, 2 Zagorodnoe road, 115119, Russian Federation
| | - Georgy P Kostyuk
- N.A.Alekseev 1(st) Clinical Hospital of Psychiatry, Moscow Healthcare Department, 2 Zagorodnoe road, 115119, Russian Federation
| | - Liudmila I Kulikova
- Institute of Mathematical Problems of Biology RAS-the Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, 3 Institutskaya str., 142290 Pushchino, Moscow Region, Russian Federation
| | - Dmitry V Enikeev
- Institute of Urology and Reproductive Health, Sechenov University, 2/1 Bolshaya Pirogovskaya str., 119435 Moscow, Russian Federation
| | - Natalia V Potoldykova
- Institute of Urology and Reproductive Health, Sechenov University, 2/1 Bolshaya Pirogovskaya str., 119435 Moscow, Russian Federation
| | - Dmitry A Kulikov
- M.F. Vladimirsky Moscow Regional Research and Clinical Institute, 61/2 Schepkina str., 129110 Moscow, Russian Federation
| | - Alexey B Zulkarnaev
- M.F. Vladimirsky Moscow Regional Research and Clinical Institute, 61/2 Schepkina str., 129110 Moscow, Russian Federation
| | - Anna L Kaysheva
- Biobanking Group, Branch of Institute of Biomedical Chemistry "Scientific and Education Center," 10 Pogodinskaya str., 119121 Moscow, Russian Federation
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11
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De R, Sutradhar R, Kurdyak P, Aktar S, Pole JD, Baxter N, Nathan PC, Gupta S. Incidence and Predictors of Mental Health Outcomes Among Survivors of Adolescent and Young Adult Cancer: A Population-Based Study Using the IMPACT Cohort. J Clin Oncol 2021; 39:1010-1019. [DOI: 10.1200/jco.20.02019] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Risk and predictors of long-term mental health outcomes in survivors of adolescent and young adult (AYA) cancers are poorly characterized. Mental health is consequently neglected in long-term follow-up. METHODS We identified all AYA in Ontario, Canada age 15-21 years when diagnosed with one of six common cancers between 1992-2012 using a population-based database, and compared them with matched controls. Linkage to provincial healthcare data allowed analysis of rates of outpatient (family physician and psychiatrist) visits for psychiatric indications and time to severe psychiatric events (emergency room visit, hospitalization, and suicide). Demographic-, disease-, and treatment-related predictors of adverse outcomes, including treatment setting (adult v pediatric), were examined. RESULTS Among 2,208 survivors and 10,457 matched controls, 5-year survivors experienced higher rates of outpatient mental health visits than controls (671 visits per 1,000 person-years v 506; adjusted rate ratio [RR] 1.3; 95% CI, 1.1 to 1.5; P = .006). Risk of a severe psychiatric episode was also increased among survivors (adjusted hazard ratio [HR], 1.2; 95% CI, 1.1 to 1.4, P = .008). Risk of a psychotic disorder–associated severe event was doubled in survivors (HR, 2.0, 95% CI, 1.3 to 2.4; P = .007) although absolute risk remained low (15-year cumulative incidence 1.7%; 95% CI, 1.0 to 2.7). In multivariable analysis, survivors treated in adult centers experienced substantially higher outpatient visit rates compared with those treated in pediatric settings (RR 1.8; 95% CI, 1.0 to 3.1; P = .04). CONCLUSION Survivors of AYA cancer are at substantially increased risk of adverse mental health outcomes, with those treated in adult centers at particular risk. Although absolute incidence was low, survivors were at increased risk of psychotic disorder–associated severe events. Long-term mental health surveillance is warranted, as is research into effective interventions during or after cancer treatment.
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Affiliation(s)
- Riddhita De
- Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Rinku Sutradhar
- Cancer Research Program, ICES, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Paul Kurdyak
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Suriya Aktar
- Cancer Research Program, ICES, Toronto, Ontario, Canada
| | - Jason D. Pole
- Cancer Research Program, ICES, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Pediatric Oncology Group of Ontario, Toronto, Ontario, Canada
- Centre for Health Services Research, The University of Queensland, Brisbane, Australia
| | - Nancy Baxter
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Australia
| | - Paul C. Nathan
- Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Cancer Research Program, ICES, Toronto, Ontario, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sumit Gupta
- Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Cancer Research Program, ICES, Toronto, Ontario, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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12
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Liley J, Wallace C. Accurate error control in high-dimensional association testing using conditional false discovery rates. Biom J 2021; 63:1096-1130. [PMID: 33682201 PMCID: PMC7612315 DOI: 10.1002/bimj.201900254] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 12/05/2020] [Accepted: 12/30/2020] [Indexed: 01/13/2023]
Abstract
High-dimensional hypothesis testing is ubiquitous in the biomedical sciences, and informative covariates may be employed to improve power. The conditional false discovery rate (cFDR) is a widely used approach suited to the setting where the covariate is a set of p-values for the equivalent hypotheses for a second trait. Although related to the Benjamini–Hochberg procedure, it does not permit any easy control of type-1 error rate and existing methods are over-conservative. We propose a newmethod for type-1 error rate control based on identifyingmappings from the unit square to the unit interval defined by the estimated cFDR and splitting observations so that each map is independent of the observations it is used to test. We also propose an adjustment to the existing cFDR estimator which further improves power. We show by simulation that the new method more than doubles potential improvement in power over unconditional analyses compared to existing methods. We demonstrate our method on transcriptome-wide association studies and show that the method can be used in an iterative way, enabling the use of multiple covariates successively. Our methods substantially improve the power and applicability of cFDR analysis.
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Affiliation(s)
- James Liley
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Department of Medicine, Addenbrookes Hospital, University of Cambridge, Cambridge, UK
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Department of Medicine, Addenbrookes Hospital, University of Cambridge, Cambridge, UK.,Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge, UK
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13
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A shared genetic contribution to breast cancer and schizophrenia. Nat Commun 2020; 11:4637. [PMID: 32934226 PMCID: PMC7492262 DOI: 10.1038/s41467-020-18492-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 08/21/2020] [Indexed: 01/12/2023] Open
Abstract
An association between schizophrenia and subsequent breast cancer has been suggested; however the risk of schizophrenia following a breast cancer is unknown. Moreover, the driving forces of the link are largely unclear. Here, we report the phenotypic and genetic positive associations of schizophrenia with breast cancer and vice versa, based on a Swedish population-based cohort and GWAS data from international consortia. We observe a genetic correlation of 0.14 (95% CI 0.09–0.19) and identify a shared locus at 19p13 (GATAD2A) associated with risks of breast cancer and schizophrenia. The epidemiological bidirectional association between breast cancer and schizophrenia may partly be explained by the genetic overlap between the two phenotypes and, hence, shared biological mechanisms. Schizophrenia has been associated with increased risk of breast cancer, yet the risk of schizophrenia following breast cancer is unclear. Here, the authors show a bidirectional association between breast cancer and schizophrenia in Sweden and a shared genetic contribution to both diseases.
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14
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Muntané G, Farré X, Bosch E, Martorell L, Navarro A, Vilella E. The shared genetic architecture of schizophrenia, bipolar disorder and lifespan. Hum Genet 2020; 140:441-455. [PMID: 32772156 DOI: 10.1007/s00439-020-02213-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 07/27/2020] [Indexed: 12/11/2022]
Abstract
Psychiatric disorders such as Schizophrenia (SCZ) and Bipolar Disorder (BD) represent an evolutionary paradox, as they exhibit strong negative effects on fitness, such as decreased fecundity and early mortality, yet they persist at a worldwide prevalence of approximately 1%. Molecular mechanisms affecting lifespan, which may be widely common among complex diseases with fitness effects, can be studied by the integrated analysis of data from genome-wide association studies (GWAS) of human longevity together with any disease of interest. Here, we report the first of such studies, focusing on the genetic overlap-pleiotropy-between two psychiatric disorders with shortened lifespan, SCZ and BD, and human parental lifespan (PLS) as a surrogate of life expectancy. Our results are twofold: first, we demonstrate extensive polygenic overlap between SCZ and PLS and to a lesser extent between BD and PLS. Second, we identified novel loci shared between PLS and SCZ (n = 39), and BD (n = 8). Whereas most of the identified SCZ (66%) and BD (62%) pleiotropic risk alleles were associated with reduced lifespan, we also detected some antagonistic protective alleles associated to shorter lifespans. In fact, top-associated SNPs with SCZ seems to explain longevity variance explained (LVE) better than many other life-threatening diseases, including Type 2 diabetes and most cancers, probably due to a high overlap with smoking-related pathways. Overall, our study provides evidence of a genetic burden driven through premature mortality among people with SCZ, which can have profound implications for understanding, and potentially treating, the mortality gap associated with this psychiatric disorder.
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Affiliation(s)
- Gerard Muntané
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Hospital Universitari Institut Pere Mata, IISPV Universitat Rovira i Virgili, Reus, Spain. .,Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.
| | - Xavier Farré
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Elena Bosch
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Lourdes Martorell
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Hospital Universitari Institut Pere Mata, IISPV Universitat Rovira i Virgili, Reus, Spain
| | - Arcadi Navarro
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.,Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats, ICREA, Barcelona, Spain.,Barcelonaβeta Brain Research Center, Fundació Pasqual Maragall, Barcelona, Spain
| | - Elisabet Vilella
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Hospital Universitari Institut Pere Mata, IISPV Universitat Rovira i Virgili, Reus, Spain
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15
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Ullah A, Long X, Mat WK, Hu T, Khan MI, Hui L, Zhang X, Sun P, Gao M, Wang J, Wang H, Li X, Sun W, Qiao M, Xue H. Highly Recurrent Copy Number Variations in GABRB2 Associated With Schizophrenia and Premenstrual Dysphoric Disorder. Front Psychiatry 2020; 11:572. [PMID: 32695026 PMCID: PMC7338560 DOI: 10.3389/fpsyt.2020.00572] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 06/03/2020] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE Although single-nucleotide polymorphisms in GABRB2, the gene encoding for GABAA receptors β2 subunit, have been associated with schizophrenia (SCZ), it is unknown whether there is any association of copy number variations (CNVs) in this gene with either SCZ or premenstrual dysphoric disorder (PMDD). METHODS In this study, the occurrences of the recurrent CNVs esv2730987 in Intron 6 and nsv1177513 in Exon 11 of GABRB2 in Chinese and German SCZ, and Chinese PMDD patients were compared to controls of same ethnicity and gender by quantitative PCR (qPCR). RESULTS The results demonstrated that copy-number-gains were enriched in both SCZ and PMDD patients with significant odds ratios (OR). For combined-gender SCZ patients versus controls, about two-fold increases were observed in both ethnic groups at both esv2730987 (OR = 2.15, p = 5.32E-4 in Chinese group; OR = 2.79, p = 8.84E-3 in German group) and nsv1177513 (OR = 3.29, p = 1.28E-11 in Chinese group; OR = 2.44, p = 6.17E-5 in German group). The most significant copy-number-gains were observed in Chinese females at nsv1177513 (OR = 3.41), and German females at esv2730987 (OR=3.96). Copy-number-gains were also enriched in Chinese PMDD patients versus controls at esv2730987 (OR = 10.53, p = 4.34E-26) and nsv1177513 (OR = 2.39, p = 3.19E-5). CONCLUSION These findings established for the first time the association of highly recurrent CNVs with SCZ and PMDD, suggesting the presence of an overlapping genetic basis with shared biomarkers for these two common psychiatric disorders.
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Affiliation(s)
- Ata Ullah
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Xi Long
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Wai-Kin Mat
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Taobo Hu
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Muhammad Ismail Khan
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Li Hui
- Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Xiangyang Zhang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Peng Sun
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Mingzhou Gao
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jieqiong Wang
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Haijun Wang
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xia Li
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Wenjun Sun
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Mingqi Qiao
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Hong Xue
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
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16
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Farré X, Spataro N, Haziza F, Rambla J, Navarro A. Genome-phenome explorer (GePhEx): a tool for the visualization and interpretation of phenotypic relationships supported by genetic evidence. Bioinformatics 2020; 36:890-896. [PMID: 31393550 DOI: 10.1093/bioinformatics/btz622] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 07/05/2019] [Accepted: 08/07/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Association studies based on SNP arrays and Next Generation Sequencing technologies have enabled the discovery of thousands of genetic loci related to human diseases. Nevertheless, their biological interpretation is still elusive, and their medical applications limited. Recently, various tools have been developed to help bridging the gap between genomes and phenomes. To our knowledge, however none of these tools allows users to retrieve the phenotype-wide list of genetic variants that may be linked to a given disease or to visually explore the joint genetic architecture of different pathologies. RESULTS We present the Genome-Phenome Explorer (GePhEx), a web-tool easing the visual exploration of phenotypic relationships supported by genetic evidences. GePhEx is primarily based on the thorough analysis of linkage disequilibrium between disease-associated variants and also considers relationships based on genes, pathways or drug-targets, leveraging on publicly available variant-disease associations to detect potential relationships between diseases. We demonstrate that GePhEx does retrieve well-known relationships as well as novel ones, and that, thus, it might help shedding light on the patho-physiological mechanisms underlying complex diseases. To this end, we investigate the potential relationship between schizophrenia and lung cancer, first detected using GePhEx and provide further evidence supporting a functional link between them. AVAILABILITY AND IMPLEMENTATION GePhEx is available at: https://gephex.ega-archive.org/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xavier Farré
- Department of Experimental and Health Sciences, Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Catalonia 08003, Spain
| | - Nino Spataro
- Department of Experimental and Health Sciences, Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Catalonia 08003, Spain.,Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Spain
| | - Frederic Haziza
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Spain
| | - Jordi Rambla
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Spain
| | - Arcadi Navarro
- Department of Experimental and Health Sciences, Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Catalonia 08003, Spain.,Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), PRBB, Barcelona, Catalonia 08003, Spain
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17
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Smeland OB, Frei O, Fan CC, Shadrin A, Dale AM, Andreassen OA. The emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges. Psychiatr Genet 2019; 29:152-159. [PMID: 31464996 PMCID: PMC10752571 DOI: 10.1097/ypg.0000000000000234] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies have transformed psychiatric genetics and provided novel insights into the genetic etiology of psychiatric disorders. Two major discoveries have emerged; the disorders are polygenic, with a large number of common variants each with a small effect and many genetic variants influence more than one phenotype, suggesting shared genetic etiology. These concepts have the potential to revolutionize the current classification system with diagnostic categories and facilitate development of better treatments. However, to reach clinical impact, we need larger samples and better analytical tools, as most polygenic factors remain undetected. We here present statistical approaches designed to improve the yield of existing genome-wide association studies for polygenic phenotypes. We review how these tools have informed the current knowledge on the genetic architecture of psychiatric disorders, focusing on schizophrenia, bipolar disorder and major depression, and overlap with psychological and cognitive traits. We discuss application of statistical tools for stratification and prediction.
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Affiliation(s)
- Olav B. Smeland
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Chun-Chieh Fan
- Center for Human Development, University of California, San Diego, USA
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, University of California, USA
- Department of Neuroscience, University of California, San Diego, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, California, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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18
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Smeland OB, Frei O, Shadrin A, O'Connell K, Fan CC, Bahrami S, Holland D, Djurovic S, Thompson WK, Dale AM, Andreassen OA. Discovery of shared genomic loci using the conditional false discovery rate approach. Hum Genet 2019; 139:85-94. [PMID: 31520123 DOI: 10.1007/s00439-019-02060-2] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 08/08/2019] [Indexed: 02/07/2023]
Abstract
In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remains undetected. We here review the conditional false discovery rate (condFDR) method, a model-free strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e., cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.
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Affiliation(s)
- Olav B Smeland
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0424, Oslo, Norway.
| | - Oleksandr Frei
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0424, Oslo, Norway
| | - Alexey Shadrin
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0424, Oslo, Norway
| | - Kevin O'Connell
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0424, Oslo, Norway
| | - Chun-Chieh Fan
- Department of Cognitive Science, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.,Department of Radiology, University of California of San Diego, La Jolla, San Diego, CA, 92093, USA
| | - Shahram Bahrami
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0424, Oslo, Norway
| | - Dominic Holland
- Department of Radiology, University of California of San Diego, La Jolla, San Diego, CA, 92093, USA.,Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, San Diego, CA, 92037, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.,NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Wesley K Thompson
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, San Diego, CA, 92093, USA
| | - Anders M Dale
- Department of Cognitive Science, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.,Department of Radiology, University of California of San Diego, La Jolla, San Diego, CA, 92093, USA.,Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, San Diego, CA, 92037, USA
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0424, Oslo, Norway.
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19
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Abstract
Until recently, advances in understanding the genetic architecture of psychiatric disorders have been impeded by a historic, and often mandated, commitment to the use of traditional, and unvalidated, categorical diagnoses in isolation as the relevant phenotype. Such studies typically required lengthy structured interviews to delineate differences in the character and duration of behavioral symptomatology amongst disorders that were thought to be etiologic, and they were often underpowered as a result. Increasing acceptance of the fact that co-morbidity in psychiatric disorders is the rule rather than the exception has led to alternative designs in which shared dimensional symptomatology is analyzed as a quantitative trait and to association analyses in which combined polygenic risk scores are computationally compared across multiple traditional categorical diagnoses to identify both distinct and unique genetic and environmental elements. Increasing evidence that most mental disorders share many common genetic risk variants and environmental risk modifiers suggests that the broad spectrum of psychiatric pathology represents the pleiotropic display of a more limited series of pathologic events in neuronal development than was originally believed, regulated by many common risk variants and a smaller number of rare ones.
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Affiliation(s)
- Tova Fuller
- Deptartment of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco School of Medicine, San Francisco, CA, USA
| | - Victor Reus
- Deptartment of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco School of Medicine, San Francisco, CA, USA
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20
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Abstract
BACKGROUND Lung cancer risk factors, like tobacco smoking, are highly prevalent in patients with schizophrenia. Whether these patients have a higher risk of lung cancer remains unknown. AIMS We aimed to investigate whether patients with schizophrenia have a higher incidence of lung cancer compared with general population, in a meta-analysis. METHOD Eligible studies were searched from PubMed and EMBASE databases to identify cases of lung cancer in patients with schizophrenia and the general population. This meta-analysis utilised the random-effects model and prediction interval was used to calculate the heterogeneity of these eligible studies. We assessed the quality of evidence with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. RESULTS There were 12 studies, totalling 496 265 patients, included in this meta-analysis. The data showed that the baseline schizophrenia diagnosis was not associated with any changes in lung cancer incidence in the overall population, with a standardised incidence ratio of 1.11 (95% CI 0.90-1.37; P = 0.31), although there was a significant heterogeneity among these studies (I2 = 94%). Moreover, there was also a substantial between-study variance with wide prediction interval values (0.47-2.64). The data were consistent for both males and females. CONCLUSIONS Up-to-date evidence from epidemiological studies indicates the lack of certainty about the association between schizophrenia diagnosis and lung cancer incidence.
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Affiliation(s)
- Chuanjun Zhuo
- Professor, Department of Psychiatric-Neuroimaging-Genetics Laboratory, Tianjin Anding Hospital; Department of Psychiatry and Comorbidity, Mental Health Teaching Hospital, Tianjin Medical University; Department of Psychiatry, Jinning Medical University; and Department of Psychiatry, Wenzhou Seventh People's Hospital, China,Correspondence: Chuanjun Zhuo, Department of Psychiatry and Comorbidity, Mental Health Teaching Hospital, Tianjin Medical University, 13 Liulin Road, Hexi District, Tianjin 300300, China.
| | - Hongqing Zhuang
- Professor, Department of Radiation Oncology, Peking University Third Hospital, China
| | - Xiangyang Gao
- Professor, Health Management Institute, Big Data Analysis Center, Chinese PLA General Hospital, China
| | - Patrick Todd Triplett
- Assistant Professor, Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, USA
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21
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Kondratyev N, Golov A, Alfimova M, Lezheiko T, Golimbet V. Prediction of smoking by multiplex bisulfite PCR with long amplicons considering allele-specific effects on DNA methylation. Clin Epigenetics 2018; 10:130. [PMID: 30352621 PMCID: PMC6199807 DOI: 10.1186/s13148-018-0565-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 10/10/2018] [Indexed: 12/13/2022] Open
Abstract
Background Methylation of DNA is associated with a variety of biological processes. With whole-genome studies of DNA methylation, it became possible to determine a set of genomic sites where DNA methylation is associated with a specific phenotype. A method is needed that allows detailed follow-up studies of the sites, including taking into account genetic information. Bisulfite PCR is a natural choice for this kind of task, but multiplexing is one of the most important problems impeding its implementation. To address this task, we took advantage of a recently published method based on Pacbio sequencing of long bisulfite PCR products (single-molecule real-time bisulfite sequencing, SMRT-BS) and tested the validity of the improved methodology with a smoking phenotype. Results Herein, we describe the “panhandle” modification of the method, which permits a more robust PCR with multiple targets. We applied this technique to determine smoking by DNA methylation in 71 healthy people and 83 schizophrenia patients (n = 50 smokers and n = 104 non-smokers, Russians of the Moscow region). We used five targets known to be influenced by smoking (regions of genes AHRR, ALPPL2, IER3, GNG12, and GFI1). We discovered significant allele-specific methylation effects in the AHRR and IER3 regions and assessed how this information could be exploited to improve the prediction of smoking based on the collected DNA methylation data. We found no significant difference in the methylation profiles of selected targets in relation to schizophrenia suggesting that smoking affects methylation at the studied genomic sites in healthy people and schizophrenia patients in a similar way. Conclusions We determined that SMRT-BS with “panhandle” modification performs well in the described setting. Additional information regarding methylation and allele-specific effects could improve the predictive accuracy of DNA methylation-based models, which could be valuable for both basic research and clinical applications. Electronic supplementary material The online version of this article (10.1186/s13148-018-0565-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nikolay Kondratyev
- Clinical Genetics Laboratory, Mental Health Research Center, Moscow, Russia.
| | - Arkady Golov
- Clinical Genetics Laboratory, Mental Health Research Center, Moscow, Russia
| | - Margarita Alfimova
- Clinical Genetics Laboratory, Mental Health Research Center, Moscow, Russia
| | - Tatiana Lezheiko
- Clinical Genetics Laboratory, Mental Health Research Center, Moscow, Russia
| | - Vera Golimbet
- Clinical Genetics Laboratory, Mental Health Research Center, Moscow, Russia
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22
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Amlie-Wolf A, Tang M, Mlynarski EE, Kuksa PP, Valladares O, Katanic Z, Tsuang D, Brown CD, Schellenberg GD, Wang LS. INFERNO: inferring the molecular mechanisms of noncoding genetic variants. Nucleic Acids Res 2018; 46:8740-8753. [PMID: 30113658 PMCID: PMC6158604 DOI: 10.1093/nar/gky686] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/11/2018] [Accepted: 07/18/2018] [Indexed: 01/02/2023] Open
Abstract
The majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, affecting regulatory elements including transcriptional enhancers. However, characterizing their effects requires the integration of GWAS results with context-specific regulatory activity and linkage disequilibrium annotations to identify causal variants underlying noncoding association signals and the regulatory elements, tissue contexts, and target genes they affect. We propose INFERNO, a novel method which integrates hundreds of functional genomics datasets spanning enhancer activity, transcription factor binding sites, and expression quantitative trait loci with GWAS summary statistics. INFERNO includes novel statistical methods to quantify empirical enrichments of tissue-specific enhancer overlap and to identify co-regulatory networks of dysregulated long noncoding RNAs (lncRNAs). We applied INFERNO to two large GWAS studies. For schizophrenia (36,989 cases, 113,075 controls), INFERNO identified putatively causal variants affecting brain enhancers for known schizophrenia-related genes. For inflammatory bowel disease (IBD) (12,882 cases, 21,770 controls), INFERNO found enrichments of immune and digestive enhancers and lncRNAs involved in regulation of the adaptive immune response. In summary, INFERNO comprehensively infers the molecular mechanisms of causal noncoding variants, providing a sensitive hypothesis generation method for post-GWAS analysis. The software is available as an open source pipeline and a web server.
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Affiliation(s)
- Alexandre Amlie-Wolf
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mitchell Tang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Elisabeth E Mlynarski
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Pavel P Kuksa
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Otto Valladares
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zivadin Katanic
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Debby Tsuang
- VA Puget Sound Health Care System, Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Christopher D Brown
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Gerard D Schellenberg
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li-San Wang
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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23
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Srinivasan S, Bettella F, Frei O, Hill WD, Wang Y, Witoelar A, Schork AJ, Thompson WK, Davies G, Desikan RS, Deary IJ, Melle I, Ueland T, Dale AM, Djurovic S, Smeland OB, Andreassen OA. Enrichment of genetic markers of recent human evolution in educational and cognitive traits. Sci Rep 2018; 8:12585. [PMID: 30135563 PMCID: PMC6105609 DOI: 10.1038/s41598-018-30387-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 07/30/2018] [Indexed: 12/13/2022] Open
Abstract
Higher cognitive functions are regarded as one of the main distinctive traits of humans. Evidence for the cognitive evolution of human beings is mainly based on fossil records of an expanding cranium and an increasing complexity of material culture artefacts. However, the molecular genetic factors involved in the evolution are still relatively unexplored. Here, we investigated whether genomic regions that underwent positive selection in humans after divergence from Neanderthals are enriched for genetic association with phenotypes related to cognitive functions. We used genome wide association data from a study of college completion (N = 111,114), one of educational attainment (N = 293,623) and two different studies of general cognitive ability (N = 269,867 and 53,949). We found nominally significant polygenic enrichment of associations with college completion (p = 0.025), educational attainment (p = 0.043) and general cognitive ability (p = 0.015 and 0.025, respectively), suggesting that variants influencing these phenotypes are more prevalent in evolutionarily salient regions. The enrichment remained significant after controlling for other known genetic enrichment factors, and for affiliation to genes highly expressed in the brain. These findings support the notion that phenotypes related to higher order cognitive skills typical of humans have a recent genetic component that originated after the separation of the human and Neanderthal lineages.
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Affiliation(s)
- Saurabh Srinivasan
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Francesco Bettella
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Yunpeng Wang
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aree Witoelar
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Andrew J Schork
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Wesley K Thompson
- Institute of Biological Psychiatry, Mental Health Center St. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Rahul S Desikan
- Neuroradiology Section, Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA, USA
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ingrid Melle
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Torill Ueland
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California at San Diego, La Jolla, CA, USA
- Center for Human Development, University of California at San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Olav B Smeland
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
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