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Li X, Guo Y, Liang H, Wang J, Qi L. Genome-wide association analysis of hypertension and epigenetic aging reveals shared genetic architecture and identifies novel risk loci. Sci Rep 2024; 14:17792. [PMID: 39090212 PMCID: PMC11294447 DOI: 10.1038/s41598-024-68751-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024] Open
Abstract
Hypertension is a disease associated with epigenetic aging. However, the pathogenic mechanism underlying this relationship remains unclear. We aimed to characterize the shared genetic architecture of hypertension and epigenetic aging, and identify novel risk loci. Leveraging genome-wide association studies (GWAS) summary statistics of hypertension (129,909 cases and 354,689 controls) and four epigenetic clocks (N = 34,710), we investigated genetic architectures and genetic overlap using bivariate casual mixture model and conditional/conjunctional false discovery rate methods. Functional gene-sets pathway analyses were performed by functional mapping and gene annotation (FUMA) protocol. Hypertension was polygenic with 2.8 K trait-influencing genetic variants. We observed cross-trait genetic enrichment and genetic overlap between hypertension and all four measures of epigenetic aging. Further, we identified 32 distinct genomic loci jointly associated with hypertension and epigenetic aging. Notably, rs1849209 was shared between hypertension and three epigenetic clocks (HannumAge, IEAA, and PhenoAge). The shared loci exhibited a combination of concordant and discordant allelic effects. Functional gene-set analyses revealed significant enrichment in biological pathways related to sensory perception of smell and nervous system processes. We observed genetic overlaps with mixed effect directions between hypertension and all four epigenetic aging measures, and identified 32 shared distinct loci with mixed effect directions, 25 of which were novel for hypertension. Shared genes enriched in biological pathways related to olfaction.
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Affiliation(s)
- Xin Li
- The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, 511436, China
| | - Yu Guo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150086, China
| | - Haihai Liang
- The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, 511436, China.
- Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, 150086, China.
| | - Jinghao Wang
- Department of Pharmacy, the First Affiliated Hospital, Jinan University, Guangzhou, 510630, Guangdong, China.
- The Guangzhou Key Laboratory of Basic and Translational Research on Chronic Diseases, Jinan University, Guangzhou, 510630, Guangdong, China.
| | - Lishuang Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
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2
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Ge YJ, Fu Y, Gong W, Cheng W, Yu JT. Genetic architecture of brain morphology and overlap with neuropsychiatric traits. Trends Genet 2024; 40:706-717. [PMID: 38702264 DOI: 10.1016/j.tig.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 05/06/2024]
Abstract
Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.
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Affiliation(s)
- Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, 266071, China
| | - Weikang Gong
- School of Data Science, Fudan University, Shanghai, China; Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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3
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Bouttle K, Ingold N, O’Mara TA. Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer. Genes (Basel) 2024; 15:939. [PMID: 39062718 PMCID: PMC11276418 DOI: 10.3390/genes15070939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/14/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
Genome-wide association studies (GWAS) have accelerated the exploration of genotype-phenotype associations, facilitating the discovery of replicable genetic markers associated with specific traits or complex diseases. This narrative review explores the statistical methodologies developed using GWAS data to investigate relationships between various phenotypes, focusing on endometrial cancer, the most prevalent gynecological malignancy in developed nations. Advancements in analytical techniques such as genetic correlation, colocalization, cross-trait locus identification, and causal inference analyses have enabled deeper exploration of associations between different phenotypes, enhancing statistical power to uncover novel genetic risk regions. These analyses have unveiled shared genetic associations between endometrial cancer and many phenotypes, enabling identification of novel endometrial cancer risk loci and furthering our understanding of risk factors and biological processes underlying this disease. The current status of research in endometrial cancer is robust; however, this review demonstrates that further opportunities exist in statistical genetics that hold promise for advancing the understanding of endometrial cancer and other complex diseases.
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Affiliation(s)
| | | | - Tracy A. O’Mara
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia (N.I.)
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Wootton O, Shadrin AA, Bjella T, Smeland OB, van der Meer D, Frei O, O'Connell KS, Ueland T, Andreassen OA, Stein DJ, Dalvie S. Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia. Sci Rep 2024; 14:15356. [PMID: 38961113 PMCID: PMC11222449 DOI: 10.1038/s41598-024-66085-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 06/26/2024] [Indexed: 07/05/2024] Open
Abstract
Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.
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Affiliation(s)
- Olivia Wootton
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thomas Bjella
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torill Ueland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dan J Stein
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Shareefa Dalvie
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
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Lu ZA, Ploner A, Birgegård A, Bulik CM, Bergen SE. Shared Genetic Architecture Between Schizophrenia and Anorexia Nervosa: A Cross-trait Genome-Wide Analysis. Schizophr Bull 2024:sbae087. [PMID: 38848516 DOI: 10.1093/schbul/sbae087] [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: 06/09/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SCZ) and anorexia nervosa (AN) are 2 severe and highly heterogeneous disorders showing substantial familial co-aggregation. Genetic factors play a significant role in both disorders, but the shared genetic etiology between them is yet to be investigated. STUDY DESIGN Using summary statistics from recent large genome-wide association studies on SCZ (Ncases = 53 386) and AN (Ncases = 16 992), a 2-sample Mendelian randomization analysis was conducted to explore the causal relationship between SCZ and AN. MiXeR was employed to quantify their polygenic overlap. A conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was adopted to identify loci jointly associated with both disorders. Functional annotation and enrichment analyses were performed on the shared loci. STUDY RESULTS We observed a cross-trait genetic enrichment, a suggestive bidirectional causal relationship, and a considerable polygenic overlap (Dice coefficient = 62.2%) between SCZ and AN. The proportion of variants with concordant effect directions among all shared variants was 69.9%. Leveraging overlapping genetic associations, we identified 6 novel loci for AN and 33 novel loci for SCZ at condFDR <0.01. At conjFDR <0.05, we identified 10 loci jointly associated with both disorders, implicating multiple genes highly expressed in the cerebellum and pituitary and involved in synapse organization. Particularly, high expression of the shared genes was observed in the hippocampus in adolescence and orbitofrontal cortex during infancy. CONCLUSIONS This study provides novel insights into the relationship between SCZ and AN by revealing a shared genetic component and offers a window into their complex etiology.
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Affiliation(s)
- Zheng-An Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Alexander Ploner
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Andreas Birgegård
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sarah E Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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6
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Karadag N, Hagen E, Shadrin AA, van der Meer D, O'Connell KS, Rahman Z, Kutrolli G, Parker N, Bahrami S, Fominykh V, Heuser K, Taubøll E, Steen NE, Djurovic S, Dale AM, Frei O, Andreassen OA, Smeland OB. Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology. Neurol Genet 2024; 10:e200143. [PMID: 38817246 PMCID: PMC11139015 DOI: 10.1212/nxg.0000000000200143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/27/2024] [Indexed: 06/01/2024]
Abstract
Background and Objectives Epilepsies are associated with differences in cortical thickness (TH) and surface area (SA). However, the mechanisms underlying these relationships remain elusive. We investigated the extent to which these phenotypes share genetic influences. Methods We analyzed genome-wide association study data on common epilepsies (n = 69,995) and TH and SA (n = 32,877) using Gaussian mixture modeling MiXeR and conjunctional false discovery rate (conjFDR) analysis to quantify their shared genetic architecture and identify overlapping loci. We biologically interrogated the loci using a variety of resources and validated in independent samples. Results The epilepsies (2.4 k-2.9 k variants) were more polygenic than both SA (1.8 k variants) and TH (1.3 k variants). Despite absent genome-wide genetic correlations, there was a substantial genetic overlap between SA and genetic generalized epilepsy (GGE) (1.1 k), all epilepsies (1.1 k), and juvenile myoclonic epilepsy (JME) (0.7 k), as well as between TH and GGE (0.8 k), all epilepsies (0.7 k), and JME (0.8 k), estimated with MiXeR. Furthermore, conjFDR analysis identified 15 GGE loci jointly associated with SA and 15 with TH, 3 loci shared between SA and childhood absence epilepsy, and 6 loci overlapping between SA and JME. 23 loci were novel for epilepsies and 11 for cortical morphology. We observed a high degree of sign concordance in the independent samples. Discussion Our findings show extensive genetic overlap between generalized epilepsies and cortical morphology, indicating a complex genetic relationship with mixed-effect directions. The results suggest that shared genetic influences may contribute to cortical abnormalities in epilepsies.
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Affiliation(s)
- Naz Karadag
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Espen Hagen
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Alexey A Shadrin
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Dennis van der Meer
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Kevin S O'Connell
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Zillur Rahman
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Gleda Kutrolli
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Nadine Parker
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Shahram Bahrami
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Vera Fominykh
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Kjell Heuser
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Erik Taubøll
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Nils Eiel Steen
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Srdjan Djurovic
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Anders M Dale
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Oleksandr Frei
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Ole A Andreassen
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Olav B Smeland
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
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7
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Lai EY, Huang YT. Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits. Brief Bioinform 2024; 25:bbae327. [PMID: 39007593 PMCID: PMC11247409 DOI: 10.1093/bib/bbae327] [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: 04/09/2024] [Revised: 05/29/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang's composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene-trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.
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Affiliation(s)
- En-Yu Lai
- Institute of Statistical Science, Academia Sinica, No.128, Academia Road, Section 2, Nankang, Taipei 11529, Taiwan
| | - Yen-Tsung Huang
- Institute of Statistical Science, Academia Sinica, No.128, Academia Road, Section 2, Nankang, Taipei 11529, Taiwan
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8
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Reppe S, Gundersen S, Sandve GK, Wang Y, Andreassen OA, Medina-Gomez C, Rivadeneira F, Utheim TP, Hovig E, Gautvik KM. Identification of Transcripts with Shared Roles in the Pathogenesis of Postmenopausal Osteoporosis and Cardiovascular Disease. Int J Mol Sci 2024; 25:5554. [PMID: 38791593 PMCID: PMC11121938 DOI: 10.3390/ijms25105554] [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: 04/11/2024] [Revised: 05/11/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Epidemiological evidence suggests existing comorbidity between postmenopausal osteoporosis (OP) and cardiovascular disease (CVD), but identification of possible shared genes is lacking. The skeletal global transcriptomes were analyzed in trans-iliac bone biopsies (n = 84) from clinically well-characterized postmenopausal women (50 to 86 years) without clinical CVD using microchips and RNA sequencing. One thousand transcripts highly correlated with areal bone mineral density (aBMD) were further analyzed using bioinformatics, and common genes overlapping with CVD and associated biological mechanisms, pathways and functions were identified. Fifty genes (45 mRNAs, 5 miRNAs) were discovered with established roles in oxidative stress, inflammatory response, endothelial function, fibrosis, dyslipidemia and osteoblastogenesis/calcification. These pleiotropic genes with possible CVD comorbidity functions were also present in transcriptomes of microvascular endothelial cells and cardiomyocytes and were differentially expressed between healthy and osteoporotic women with fragility fractures. The results were supported by a genetic pleiotropy-informed conditional False Discovery Rate approach identifying any overlap in single nucleotide polymorphisms (SNPs) within several genes encoding aBMD- and CVD-associated transcripts. The study provides transcriptional and genomic evidence for genes of importance for both BMD regulation and CVD risk in a large collection of postmenopausal bone biopsies. Most of the transcripts identified in the CVD risk categories have no previously recognized roles in OP pathogenesis and provide novel avenues for exploring the mechanistic basis for the biological association between CVD and OP.
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Affiliation(s)
- Sjur Reppe
- Department of Medical Biochemistry, Oslo University Hospital, 0450 Oslo, Norway
- Unger-Vetlesen Institute, Lovisenberg Diaconal Hospital, 0440 Oslo, Norway
- Department of Plastic and Reconstructive Surgery, Oslo University Hospital, 0424 Oslo, Norway
| | - Sveinung Gundersen
- Center for Bioinformatics, Department of Informatics, University of Oslo, 0313 Oslo, Norway
| | - Geir K. Sandve
- Department of Informatics, University of Oslo, 0373 Oslo, Norway; (G.K.S.)
| | - Yunpeng Wang
- NORMENT, Institute of Clinical Medicine, University of Oslo, 0450 Oslo, Norway; (Y.W.); (O.A.A.)
- Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Ole A. Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, 0450 Oslo, Norway; (Y.W.); (O.A.A.)
- Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Carolina Medina-Gomez
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (C.M.-G.); (F.R.)
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (C.M.-G.); (F.R.)
| | - Tor P. Utheim
- Department of Medical Biochemistry, Oslo University Hospital, 0450 Oslo, Norway
- Department of Plastic and Reconstructive Surgery, Oslo University Hospital, 0424 Oslo, Norway
| | - Eivind Hovig
- Department of Informatics, University of Oslo, 0373 Oslo, Norway; (G.K.S.)
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, 0424 Oslo, Norway
| | - Kaare M. Gautvik
- Unger-Vetlesen Institute, Lovisenberg Diaconal Hospital, 0440 Oslo, Norway
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9
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Malone SG, Davis CN, Piserchia Z, Setzer MR, Toikumo S, Zhou H, Winterlind EL, Gelernter J, Justice A, Leggio L, Rentsch CT, Kranzler HR, Gray JC. Alcohol use disorder and body mass index show genetic pleiotropy and shared neural associations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.03.24306773. [PMID: 38746260 PMCID: PMC11092735 DOI: 10.1101/2024.05.03.24306773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Background The prevalence of co-occurring heavy alcohol consumption and obesity is increasing in the United States. Despite neurobiological overlap in the regulation of alcohol consumption and eating behavior, alcohol- and body mass index (BMI)-related phenotypes show no or minimal genetic correlation. We hypothesized that the lack of genetic correlation is due to mixed effect directions of variants shared by AUD and BMI. Methods We applied MiXeR, to investigate shared genetic architecture between AUD and BMI in individuals of European ancestry. We used conjunctional false discovery rate (conjFDR) analysis to detect loci associated with both phenotypes and their directional effect, Functional Mapping and Annotation (FUMA) to identify lead single nucleotide polymorphisms (SNPs), Genotype-Tissue Expression (GTEx) samples to examine gene expression enrichment across tissue types, and BrainXcan to evaluate the shared associations of AUD and BMI with brain image-derived phenotypes. Results MiXeR analysis indicated polygenic overlap of 80.9% between AUD and BMI, despite a genetic correlation (r g ) of -.03. ConjFDR analysis yielded 56 lead SNPs with the same effect direction and 76 with the opposite direction. Of the 132 shared lead SNPs, 53 were novel for both AUD and BMI. GTEx analyses identified significant overexpression in the frontal cortex (BA9), hypothalamus, cortex, anterior cingulate cortex (BA24), hippocampus, and amygdala. Amygdala and caudate nucleus gray matter volumes were significantly associated with both AUD and BMI in BrainXcan analyses. Conclusions More than half of variants significantly associated with AUD and BMI had opposite directions of effect for the traits, supporting our hypothesis that this is the basis for their lack of genetic correlation. Follow-up analyses identified brain regions implicated in executive functioning, reward, homeostasis, and food intake regulation. Together, these findings clarify the extensive polygenic overlap between AUD and BMI and elucidate several overlapping neurobiological mechanisms.
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10
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Pisanu C, Congiu D, Meloni A, Paribello P, Patrinos GP, Severino G, Ardau R, Chillotti C, Manchia M, Squassina A. Dissecting the genetic overlap between severe mental disorders and markers of cellular aging: Identification of pleiotropic genes and druggable targets. Neuropsychopharmacology 2024; 49:1033-1041. [PMID: 38402365 PMCID: PMC11039620 DOI: 10.1038/s41386-024-01822-5] [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: 11/22/2023] [Revised: 01/17/2024] [Accepted: 02/04/2024] [Indexed: 02/26/2024]
Abstract
Patients with severe mental disorders such as bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) show a substantial reduction in life expectancy, increased incidence of comorbid medical conditions commonly observed with advanced age and alterations of aging hallmarks. While severe mental disorders are heritable, the extent to which genetic predisposition might contribute to accelerated cellular aging is not known. We used bivariate causal mixture models to quantify the trait-specific and shared architecture of mental disorders and 2 aging hallmarks (leukocyte telomere length [LTL] and mitochondrial DNA copy number), and the conjunctional false discovery rate method to detect shared genetic loci. We integrated gene expression data from brain regions from GTEx and used different tools to functionally annotate identified loci and investigate their druggability. Aging hallmarks showed low polygenicity compared with severe mental disorders. We observed a significant negative global genetic correlation between MDD and LTL (rg = -0.14, p = 6.5E-10), and no significant results for other severe mental disorders or for mtDNA-cn. However, conditional QQ plots and bivariate causal mixture models pointed to significant pleiotropy among all severe mental disorders and aging hallmarks. We identified genetic variants significantly shared between LTL and BD (n = 17), SCZ (n = 55) or MDD (n = 19), or mtDNA-cn and BD (n = 4), SCZ (n = 12) or MDD (n = 1), with mixed direction of effects. The exonic rs7909129 variant in the SORCS3 gene, encoding a member of the retromer complex involved in protein trafficking and intracellular/intercellular signaling, was associated with shorter LTL and increased predisposition to all severe mental disorders. Genetic variants underlying risk of SCZ or MDD and shorter LTL modulate expression of several druggable genes in different brain regions. Genistein, a phytoestrogen with anti-inflammatory and antioxidant effects, was an upstream regulator of 2 genes modulated by variants associated with risk of MDD and shorter LTL. While our results suggest that shared heritability might play a limited role in contributing to accelerated cellular aging in severe mental disorders, we identified shared genetic determinants and prioritized different druggable targets and compounds.
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Affiliation(s)
- Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy.
| | - Donatella Congiu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - Anna Meloni
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - Pasquale Paribello
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
| | - George P Patrinos
- Laboratory of Pharmacogenomics and Individualized Therapy, School of Health Sciences, Department of Pharmacy, University of Patras, Patras, Greece
- College of Medicine and Health Sciences, Department of Genetics and Genomics, United Arab Emirates University, Al‑Ain, Abu Dhabi, UAE
- Zayed Center for Health Sciences, United Arab Emirates University, Al‑Ain, Abu Dhabi, UAE
| | - Giovanni Severino
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - Raffaella Ardau
- Unit of Clinical Pharmacology, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Caterina Chillotti
- Unit of Clinical Pharmacology, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy.
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11
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Carrión-Castillo A, Boeckx C. Insights into the genetic architecture of cerebellar lobules derived from the UK Biobank. Sci Rep 2024; 14:9488. [PMID: 38664414 DOI: 10.1038/s41598-024-59699-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/15/2024] [Indexed: 06/19/2024] Open
Abstract
In this work we endeavor to further understand the genetic architecture of the cerebellum by examining the genetic underpinnings of the different cerebellar lob(ul)es, identifying their genetic relation to cortical and subcortical regions, as well as to psychiatric disorders, as well as traces of their evolutionary trajectories. We confirm the moderate heritability of cerebellar volumes, and reveal genetic clustering and variability across their different substructures, which warranted a detailed analysis using this higher structural resolution. We replicated known genetic correlations with several subcortical volumes, and report new cortico-cerebellar genetic correlations, including negative genetic correlations between anterior cerebellar lobules and cingulate, and positive ones between lateral Crus I and lobule VI with cortical measures in the fusiform region. Heritability partitioning for evolutionary annotations highlighted that the vermis of Crus II has depleted heritability in genomic regions of "archaic introgression deserts", but no enrichment/depletion of heritability in any other cerebellar regions. Taken together, these findings reveal novel insights into the genetic underpinnings of the different cerebellar lobules.
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Affiliation(s)
- Amaia Carrión-Castillo
- Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain.
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
| | - Cedric Boeckx
- Universitat de Barcelona, Barcelona, Spain.
- Universitat de Barcelona Institute of Complex Systems, Barcelona, Spain.
- Universitat de Barcelona Institute of Neurosciences, Barcelona, Spain.
- Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain.
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12
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Zhang X, Zhu Z, Huang Y, Shang X, O'Brien TJ, Kwan P, Ha J, Wang W, Liu S, Zhang X, Kiburg K, Bao Y, Wang J, Yu H, He M, Zhang L. Shared genetic aetiology of Alzheimer's disease and age-related macular degeneration by APOC1 and APOE genes. BMJ Neurol Open 2024; 6:e000570. [PMID: 38646507 PMCID: PMC11029327 DOI: 10.1136/bmjno-2023-000570] [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: 11/01/2023] [Accepted: 04/04/2024] [Indexed: 04/23/2024] Open
Abstract
Background Alzheimer's disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights into the underlying pathogenesis and inform integrated prevention and treatment for both diseases. Methods A stratified quantile-quantile (QQ) plot was constructed to detect the pleiotropy among AD and AMD based on genome-wide association studies data from 17 008 patients with AD and 30 178 patients with AMD. A Bayesian conditional false discovery rate-based (cFDR) method was used to identify pleiotropic genes. UK Biobank was used to verify the pleiotropy analysis. Biological network and enrichment analysis were conducted to explain the biological reason for pleiotropy phenomena. A diagnostic test based on gene expression data was used to predict biomarkers for AD and AMD based on pleiotropic genes and their regulators. Results Significant pleiotropy was found between AD and AMD (significant leftward shift on QQ plots). APOC1 and APOE were identified as pleiotropic genes for AD-AMD (cFDR <0.01). Network analysis revealed that APOC1 and APOE occupied borderline positions on the gene co-expression networks. Both APOC1 and APOE genes were enriched on the herpes simplex virus 1 infection pathway. Further, machine learning-based diagnostic tests identified that APOC1, APOE (areas under the curve (AUCs) >0.65) and their upstream regulators, especially ZNF131, ADNP2 and HINFP, could be potential biomarkers for both AD and AMD (AUCs >0.8). Conclusion In this study, we confirmed the genetic pleiotropy between AD and AMD and identified APOC1 and APOE as pleiotropic genes. Further, the integration of multiomics data identified ZNF131, ADNP2 and HINFP as novel diagnostic biomarkers for AD and AMD.
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Affiliation(s)
- Xueli Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Zhuoting Zhu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yu Huang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xianwen Shang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Terence J O'Brien
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Jason Ha
- Alfred Health, Melbourne, Victoria, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Shunming Liu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Katerina Kiburg
- Centre for Eye Research, University of Melbourne, East Melbourne, Victoria, Australia
| | - Yining Bao
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Jing Wang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Mingguang He
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Lei Zhang
- Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
- School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
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13
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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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14
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Tesfaye M, Jaholkowski P, Shadrin AA, van der Meer D, Hindley GF, Holen B, Parker N, Parekh P, Birkenæs V, Rahman Z, Bahrami S, Kutrolli G, Frei O, Djurovic S, Dale AM, Smeland OB, O’Connell KS, Andreassen OA. Identification of Novel Genomic Loci for Anxiety and Extensive Genetic Overlap with Psychiatric Disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.01.23294920. [PMID: 37693403 PMCID: PMC10491354 DOI: 10.1101/2023.09.01.23294920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background Anxiety disorders are prevalent and anxiety symptoms co-occur with many psychiatric disorders. We aimed to identify genomic risk loci associated with anxiety, characterize its genetic architecture, and genetic overlap with psychiatric disorders. Methods We used the GWAS of anxiety symptoms, schizophrenia, bipolar disorder, major depression, and attention deficit hyperactivity disorder (ADHD). We employed MiXeR and LAVA to characterize the genetic architecture and genetic overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of genomic loci associated with anxiety and those shared with psychiatric disorders. Gene annotation and gene set analyses were conducted using OpenTargets and FUMA, respectively. Results Anxiety was polygenic with 12.9k estimated genetic risk variants and overlapped extensively with psychiatric disorders (4.1-11.4k variants). MiXeR and LAVA revealed predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 114 novel loci for anxiety by conditioning on the psychiatric disorders. We also identified loci shared between anxiety and major depression (n = 47), bipolar disorder (n = 33), schizophrenia (n = 71), and ADHD (n = 20). Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways and differential tissue expression in more diverse tissues than those annotated to the shared loci. Conclusions Anxiety is a highly polygenic phenotype with extensive genetic overlap with psychiatric disorders. These genetic overlaps enabled the identification of novel loci for anxiety. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified genetic loci implicate molecular pathways that may lead to potential drug targets.
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Affiliation(s)
- Markos Tesfaye
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Piotr Jaholkowski
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A. Shadrin
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Dennis van der Meer
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F.L. Hindley
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Børge Holen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nadine Parker
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pravesh Parekh
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Viktoria Birkenæs
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Zillur Rahman
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gleda Kutrolli
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Clinical Science, University of Bergen, Bergen, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Olav B. Smeland
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kevin S. O’Connell
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
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15
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Enduru N, Fernandes BS, Bahrami S, Dai Y, Andreassen OA, Zhao Z. Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence. Mol Psychiatry 2024:10.1038/s41380-024-02510-y. [PMID: 38499654 DOI: 10.1038/s41380-024-02510-y] [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: 09/11/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/20/2024]
Abstract
The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9, and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.
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Affiliation(s)
- Nitesh Enduru
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Brisa S Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Shahram Bahrami
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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16
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Zhang J, Qiu H, Zhao Q, Liao C, Guoli Y, Luo Q, Zhao G, Zhang N, Wang S, Zhang Z, Lei M, Liu F, Peng Y. Genetic overlap between schizophrenia and cognitive performance. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:31. [PMID: 38443399 PMCID: PMC10914834 DOI: 10.1038/s41537-024-00453-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 02/16/2024] [Indexed: 03/07/2024]
Abstract
Schizophrenia (SCZ), a highly heritable mental disorder, is characterized by cognitive impairment, yet the extent of the shared genetic basis between schizophrenia and cognitive performance (CP) remains poorly understood. Therefore, we aimed to explore the polygenic overlap between SCZ and CP. Specifically, the bivariate causal mixture model (MiXeR) was employed to estimate the extent of genetic overlap between SCZ (n = 130,644) and CP (n = 257,841), and conjunctional false discovery rate (conjFDR) approach was used to identify shared genetic loci. Subsequently, functional annotation and enrichment analysis were carried out on the identified genomic loci. The MiXeR analyses revealed that 9.6 K genetic variants are associated with SCZ and 10.9 K genetic variants for CP, of which 9.5 K variants are shared between these two traits (Dice coefficient = 92.8%). By employing conjFDR, 236 loci were identified jointly associated with SCZ and CP, of which 139 were novel for the two traits. Within these shared loci, 60 exhibited consistent effect directions, while 176 had opposite effect directions. Functional annotation analysis indicated that the shared genetic loci were mainly located in intronic and intergenic regions, and were found to be involved in relevant biological processes such as nervous system development, multicellular organism development, and generation of neurons. Together, our findings provide insights into the shared genetic architecture between SCZ and CP, suggesting common pathways and mechanisms contributing to both traits.
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Affiliation(s)
- Jianfei Zhang
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, China
| | - Hao Qiu
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, China
| | - Qiyu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chongjian Liao
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Yuxuan Guoli
- The Second Hospital of Tianjin Medial University, Tianjin, China
| | - Qi Luo
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Guoshu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Nannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghuan Lei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
| | - Yanmin Peng
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China.
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17
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Nordengen K, Cappelletti C, Bahrami S, Frei O, Pihlstrøm L, Henriksen SP, Geut H, Rozemuller AJM, van de Berg WDJ, Andreassen OA, Toft M. Pleiotropy with sex-specific traits reveals genetic aspects of sex differences in Parkinson's disease. Brain 2024; 147:858-870. [PMID: 37671566 PMCID: PMC10907091 DOI: 10.1093/brain/awad297] [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: 03/01/2023] [Revised: 08/01/2023] [Accepted: 08/22/2023] [Indexed: 09/07/2023] Open
Abstract
Parkinson's disease is an age-related neurodegenerative disorder with a higher incidence in males than females. The causes for this sex difference are unknown. Genome-wide association studies (GWAS) have identified 90 Parkinson's disease risk loci, but the genetic studies have not found sex-specific differences in allele frequency on autosomal chromosomes or sex chromosomes. Genetic variants, however, could exert sex-specific effects on gene function and regulation of gene expression. To identify genetic loci that might have sex-specific effects, we studied pleiotropy between Parkinson's disease and sex-specific traits. Summary statistics from GWASs were acquired from large-scale consortia for Parkinson's disease (n cases = 13 708; n controls = 95 282), age at menarche (n = 368 888 females) and age at menopause (n = 69 360 females). We applied the conditional/conjunctional false discovery rate (FDR) method to identify shared loci between Parkinson's disease and these sex-specific traits. Next, we investigated sex-specific gene expression differences in the superior frontal cortex of both neuropathologically healthy individuals and Parkinson's disease patients (n cases = 61; n controls = 23). To provide biological insights to the genetic pleiotropy, we performed sex-specific expression quantitative trait locus (eQTL) analysis and sex-specific age-related differential expression analysis for genes mapped to Parkinson's disease risk loci. Through conditional/conjunctional FDR analysis we found 11 loci shared between Parkinson's disease and the sex-specific traits age at menarche and age at menopause. Gene-set and pathway analysis of the genes mapped to these loci highlighted the importance of the immune response in determining an increased disease incidence in the male population. Moreover, we highlighted a total of nine genes whose expression or age-related expression in the human brain is influenced by genetic variants in a sex-specific manner. With these analyses we demonstrated that the lack of clear sex-specific differences in allele frequencies for Parkinson's disease loci does not exclude a genetic contribution to differences in disease incidence. Moreover, further studies are needed to elucidate the role that the candidate genes identified here could have in determining a higher incidence of Parkinson's disease in the male population.
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Affiliation(s)
- Kaja Nordengen
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
| | - Chiara Cappelletti
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
- Department of Mechanical, Electronics and Chemical Engineering, Faculty of Technology, Art and Design, OsloMet—Oslo Metropolitan University, 0130 Oslo, Norway
- Department of Research, Innovation and Education, Oslo University Hospital, 0424 Oslo, Norway
| | - Shahram Bahrami
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0450 Oslo, Norway
| | - Oleksandr Frei
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0450 Oslo, Norway
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | | | - Hanneke Geut
- Section of Clinical Neuroanatomy and Biobanking, Department of Anatomy and Neurosciences, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 Amsterdam, The Netherlands
| | - Annemieke J M Rozemuller
- Department of Pathology, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 Amsterdam, The Netherlands
| | - Wilma D J van de Berg
- Section of Clinical Neuroanatomy and Biobanking, Department of Anatomy and Neurosciences, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 Amsterdam, The Netherlands
| | - Ole A Andreassen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0450 Oslo, Norway
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
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18
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Holen B, Kutrolli G, Shadrin AA, Icick R, Hindley G, Rødevand L, O'Connell KS, Frei O, Parker N, Tesfaye M, Deak JD, Jahołkowski P, Dale AM, Djurovic S, Andreassen OA, Smeland OB. Genome-wide analyses reveal shared genetic architecture and novel risk loci between opioid use disorder and general cognitive ability. Drug Alcohol Depend 2024; 256:111058. [PMID: 38244365 DOI: 10.1016/j.drugalcdep.2023.111058] [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: 06/21/2023] [Revised: 11/09/2023] [Accepted: 12/03/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND Opioid use disorder (OUD), a serious health burden worldwide, is associated with lower cognitive function. Recent studies have demonstrated a negative genetic correlation between OUD and general cognitive ability (COG), indicating a shared genetic basis. However, the specific genetic variants involved, and the underlying molecular mechanisms remain poorly understood. Here, we aimed to quantify and identify the genetic basis underlying OUD and COG. METHODS We quantified the extent of genetic overlap between OUD and COG using a bivariate causal mixture model (MiXeR) and identified specific genetic loci applying conditional/conjunctional FDR. Finally, we investigated biological function and expression of implicated genes using available resources. RESULTS We estimated that ~94% of OUD variants (4.8k out of 5.1k variants) also influence COG. We identified three novel OUD risk loci and one locus shared between OUD and COG. Loci identified implicated biological substrates in the basal ganglia. CONCLUSION We provide new insights into the complex genetic risk architecture of OUD and its genetic relationship with COG.
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Affiliation(s)
- Børge Holen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway.
| | - Gleda Kutrolli
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Alexey A Shadrin
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Romain Icick
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway; INSERM UMR-S1144, Université Paris Cité, F-75006, France
| | - Guy Hindley
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Linn Rødevand
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Kevin S O'Connell
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Nadine Parker
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Markos Tesfaye
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway; NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Joseph D Deak
- Yale School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Piotr Jahołkowski
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA 92093, USA; Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway
| | - Olav B Smeland
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0407, Norway.
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19
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Qiu S, Sun Y, Guo J, Zhang Y, Hu Y. Genome-wide analysis reveals extensive genetic overlap between childhood phenotypes and later-life type 2 diabetes. Comput Biol Med 2024; 171:108065. [PMID: 38387379 DOI: 10.1016/j.compbiomed.2024.108065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/26/2023] [Accepted: 01/27/2024] [Indexed: 02/24/2024]
Abstract
Observational studies have indicated a potential influence of childhood phenotypes on the later development of type 2 diabetes (T2D). However, the underlying biological mechanisms remain unclear. In this study, we conducted a comprehensive genome-wide analysis to investigate the shared genetic architecture and genetic loci between nine childhood phenotypes (N = 4202-620,26) and later-life T2D (N = 80,154) using genetic correlation, mendelian randomization (MR), and conjunctional false discovery rate (conjFDR) statistical frameworks. Our findings demonstrated substantial genetic correlations and pleiotropic enrichment between childhood obesity, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and later-life T2D. Childhood obesity exhibited a significant association with increased later-life T2D risk through 10 mediators, 6 of which were adulthood obesity-related phenotypes. Additionally, we identified 69, 83, 3, 5, 10, 5, 3, and 7 loci shared between childhood obesity, BMI, SBP, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoprotein A-I (ApoA-I), apolipoprotein B (ApoB), and T2D at conjFDR <0.05, with the majority of these loci being novel discoveries. Overall, our study reveals extensive genetic overlap between childhood obesity-related phenotypes and T2D with concordant effect directions, shedding new light on variants and phenotypes with lifelong effects.
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Affiliation(s)
- Shizheng Qiu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
| | - Yige Sun
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Jiahe Guo
- School of Future Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Yu Zhang
- Beidahuang Industry Group General Hospital, Harbin, 150088, China.
| | - Yang Hu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
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20
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Fu M, Tran T, Eskin E, Lajonchere C, Pasaniuc B, Geschwind DH, Vossel K, Chang TS. Multi-class Modeling Identifies Shared Genetic Risk for Late-onset Epilepsy and Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.05.24302353. [PMID: 38370677 PMCID: PMC10871371 DOI: 10.1101/2024.02.05.24302353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Background Previous studies have established a strong link between late-onset epilepsy (LOE) and Alzheimer's disease (AD). However, their shared genetic risk beyond the APOE gene remains unclear. Our study sought to examine the shared genetic factors of AD and LOE, interpret the biological pathways involved, and evaluate how AD onset may be mediated by LOE and shared genetic risks. Methods We defined phenotypes using phecodes mapped from diagnosis codes, with patients' records aged 60-90. A two-step Least Absolute Shrinkage and Selection Operator (LASSO) workflow was used to identify shared genetic variants based on prior AD GWAS integrated with functional genomic data. We calculated an AD-LOE shared risk score and used it as a proxy in a causal mediation analysis. We used electronic health records from an academic health center (UCLA Health) for discovery analyses and validated our findings in a multi-institutional EHR database (All of Us). Results The two-step LASSO method identified 34 shared genetic loci between AD and LOE, including the APOE region. These loci were mapped to 65 genes, which showed enrichment in molecular functions and pathways such as tau protein binding and lipoprotein metabolism. Individuals with high predicted shared risk scores have a higher risk of developing AD, LOE, or both in their later life compared to those with low-risk scores. LOE partially mediates the effect of AD-LOE shared genetic risk on AD (15% proportion mediated on average). Validation results from All of Us were consistent with findings from the UCLA sample. Conclusions We employed a machine learning approach to identify shared genetic risks of AD and LOE. In addition to providing substantial evidence for the significant contribution of the APOE-TOMM40-APOC1 gene cluster to shared risk, we uncovered novel genes that may contribute. Our study is one of the first to utilize All of Us genetic data to investigate AD, and provides valuable insights into the potential common and disease-specific mechanisms underlying AD and LOE, which could have profound implications for the future of disease prevention and the development of targeted treatment strategies to combat the co-occurrence of these two diseases.
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Affiliation(s)
- Mingzhou Fu
- Mary S. Easton Center for Alzheimer’s Research and Care, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Medical Informatics Home Area, Department of Bioinformatics, University of California, Los Angeles, CA 90095, USA
| | - Thai Tran
- Medical Informatics Home Area, Department of Bioinformatics, University of California, Los Angeles, CA 90095, USA
| | - Eleazar Eskin
- Department of Computational Medicine, University of California, Los Angeles, CA 90095, USA
| | - Clara Lajonchere
- Institute of Precision Health, University of California, Los Angeles, CA 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, University of California, Los Angeles, CA 90095, USA
| | - Daniel H. Geschwind
- Institute of Precision Health, University of California, Los Angeles, CA 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Keith Vossel
- Mary S. Easton Center for Alzheimer’s Research and Care, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Timothy S Chang
- Mary S. Easton Center for Alzheimer’s Research and Care, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
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21
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Koch E, Pardiñas AF, O'Connell KS, Selvaggi P, Camacho Collados J, Babic A, Marshall SE, Van der Eycken E, Angulo C, Lu Y, Sullivan PF, Dale AM, Molden E, Posthuma D, White N, Schubert A, Djurovic S, Heimer H, Stefánsson H, Stefánsson K, Werge T, Sønderby I, O'Donovan MC, Walters JTR, Milani L, Andreassen OA. How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry. Biol Psychiatry 2024:S0006-3223(24)00003-9. [PMID: 38185234 DOI: 10.1016/j.biopsych.2024.01.001] [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: 08/08/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.
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Affiliation(s)
- Elise Koch
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pierluigi Selvaggi
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - José Camacho Collados
- CardiffNLP, School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | | | | | - Erik Van der Eycken
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Cecilia Angulo
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden; Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, California; Departments of Radiology, Psychiatry, and Neurosciences, University of California, San Diego, La Jolla, California
| | - Espen Molden
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nathan White
- CorTechs Laboratories, Inc., San Diego, California
| | | | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; The Norwegian Centre for Mental Disorders Research Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Hakon Heimer
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Nordic Society of Human Genetics and Precision Medicine, Copenhagen, Denmark
| | | | | | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark; Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark; Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Ida Sønderby
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - James T R Walters
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia; Genetics and Personalized Medicine Clinic, Tartu University Hospital, Tartu, Estonia
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway.
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22
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Parker N, Cheng W, Hindley GFL, O'Connell KS, Karthikeyan S, Holen B, Shadrin AA, Rahman Z, Karadag N, Bahrami S, Lin A, Steen NE, Ueland T, Aukrust P, Djurovic S, Dale AM, Smeland OB, Frei O, Andreassen OA. Genetic Overlap Between Global Cortical Brain Structure, C-Reactive Protein, and White Blood Cell Counts. Biol Psychiatry 2024; 95:62-71. [PMID: 37348803 DOI: 10.1016/j.biopsych.2023.06.008] [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: 01/19/2023] [Revised: 06/02/2023] [Accepted: 06/11/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND For many brain disorders, a subset of patients jointly exhibit alterations in cortical brain structure and elevated levels of circulating immune markers. This may be driven in part by shared genetic architecture. Therefore, we investigated the phenotypic and genetic associations linking global cortical surface area and thickness with blood immune markers (i.e., white blood cell counts and plasma C-reactive protein levels). METHODS Linear regression was used to assess phenotypic associations in 30,823 UK Biobank participants. Genome-wide and local genetic correlations were assessed using linkage disequilibrium score regression and local analysis of covariance annotation. The number of shared trait-influencing genetic variants was estimated using MiXeR. Shared genetic architecture was assessed using a conjunctional false discovery rate framework, and mapped genes were included in gene-set enrichment analyses. RESULTS Cortical structure and blood immune markers exhibited predominantly inverse phenotypic associations. There were modest genome-wide genetic correlations, the strongest of which were for C-reactive protein levels (rg_surface_area = -0.13, false discovery rate-corrected p = 4.17 × 10-3; rg_thickness = -0.13, false discovery rate-corrected p = 4.00 × 10-2). Meanwhile, local genetic correlations showed a mosaic of positive and negative associations. White blood cells shared on average 46.24% and 38.64% of trait-influencing genetic variants with surface area and thickness, respectively. Additionally, surface area shared 55 unique loci with the blood immune markers while thickness shared 15. Overall, monocyte count exhibited the largest genetic overlap with cortical brain structure. A series of gene enrichment analyses implicated neuronal-, astrocytic-, and schizophrenia-associated genes. CONCLUSIONS The findings indicate shared genetic underpinnings for cortical brain structure and blood immune markers, with implications for neurodevelopment and understanding the etiology of brain-related disorders.
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Affiliation(s)
- Nadine Parker
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Weiqiu Cheng
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sandeep Karthikeyan
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Børge Holen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Zillur Rahman
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Naz Karadag
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aihua Lin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nils Eiel Steen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thor Ueland
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway; KG Jebsen Thrombosis Research and Expertise Centre, University of Tromsø, Tromsø, Norway
| | - Pål Aukrust
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway; Section of Clinical Immunology and Infectious Disease, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, California; Department of Psychiatry, University of California, San Diego, La Jolla, California; Department of Neurosciences, University of California San Diego, La Jolla, California; Department of Radiology, University of California San Diego, La Jolla, California
| | - Olav B Smeland
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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23
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Jaholkowski P, Hindley GFL, Shadrin AA, Tesfaye M, Bahrami S, Nerhus M, Rahman Z, O’Connell KS, Holen B, Parker N, Cheng W, Lin A, Rødevand L, Karadag N, Frei O, Djurovic S, Dale AM, Smeland OB, Andreassen OA. Genome-wide Association Analysis of Schizophrenia and Vitamin D Levels Shows Shared Genetic Architecture and Identifies Novel Risk Loci. Schizophr Bull 2023; 49:1654-1664. [PMID: 37163672 PMCID: PMC10686370 DOI: 10.1093/schbul/sbad063] [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] [Indexed: 05/12/2023]
Abstract
Low vitamin D (vitD) levels have been consistently reported in schizophrenia (SCZ) suggesting a role in the etiopathology. However, little is known about the role of underlying shared genetic mechanisms. We applied a conditional/conjunctional false discovery rate approach (FDR) on large, nonoverlapping genome-wide association studies for SCZ (N cases = 53 386, N controls = 77 258) and vitD serum concentration (N = 417 580) to evaluate shared common genetic variants. The identified genomic loci were characterized using functional analyses and biological repositories. We observed cross-trait SNP enrichment in SCZ conditioned on vitD and vice versa, demonstrating shared genetic architecture. Applying the conjunctional FDR approach, we identified 72 loci jointly associated with SCZ and vitD at conjunctional FDR < 0.05. Among the 72 shared loci, 40 loci have not previously been reported for vitD, and 9 were novel for SCZ. Further, 64% had discordant effects on SCZ-risk and vitD levels. A mixture of shared variants with concordant and discordant effects with a predominance of discordant effects was in line with weak negative genetic correlation (rg = -0.085). Our results displayed shared genetic architecture between SCZ and vitD with mixed effect directions, suggesting overlapping biological pathways. Shared genetic variants with complex overlapping mechanisms may contribute to the coexistence of SCZ and vitD deficiency and influence the clinical picture.
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Affiliation(s)
- Piotr Jaholkowski
- 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
| | - Guy F L Hindley
- 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
- Institute of Psychiatry, Psychology and Neuroscience, King’s College
London, London, UK
| | - Alexey A Shadrin
- 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
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and
Oslo University Hospital, Oslo, Norway
| | - Markos Tesfaye
- 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
- Department of Psychiatry, St. Paul’s Hospital Millennium Medical
College, Addis Ababa, Ethiopia
| | - Shahram Bahrami
- 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
| | - Mari Nerhus
- 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
- Department of Special Psychiatry, Akershus University
Hospital, Lørenskog, Norway
- Division of Health Services Research and Psychiatry,
Institute of Clinical Medicine, Campus Ahus, University of Oslo,
Oslo, Norway
| | - Zillur Rahman
- 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
| | - Kevin S O’Connell
- 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
| | - Børge Holen
- 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
| | - Nadine Parker
- 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
| | - Weiqiu Cheng
- 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
| | - Aihua Lin
- 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
| | - 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
| | - Naz Karadag
- 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
| | - Oleksandr Frei
- 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
- Center for Bioinformatics, Department of Informatics, University of
Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital,
Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of
Bergen, Bergen, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego,
La Jolla, CA
- Multimodal Imaging Laboratory, University of California San
Diego, La Jolla, CA
- Department of Psychiatry, University of California, San
Diego, La Jolla, CA
- Department of Neurosciences, University of California San
Diego, La Jolla, CA
| | - Olav B Smeland
- 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
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and
Oslo University Hospital, Oslo, Norway
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24
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Wootton O, Shadrin AA, Bjella T, Smeland OB, van der Meer D, Frei O, O’Connell KS, Ueland T, Andreassen OA, Stein DJ, Dalvie S. Genomic Insights into the Shared and Distinct Genetic Architecture of Cognitive Function and Schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.13.23298348. [PMID: 38014326 PMCID: PMC10680895 DOI: 10.1101/2023.11.13.23298348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Cognitive impairment is a major determinant of functional outcomes in schizophrenia, and efforts to understand the biological basis of cognitive dysfunction in the disorder are ongoing. Previous studies have suggested genetic overlap between global cognitive ability and schizophrenia, but further work is needed to delineate the shared genetic architecture. Here, we apply genomic structural equation modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank (UKB). We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). We identified three broad factors (visuo-spatial, verbal analytic and decision/reaction time) that underly the genetic correlations between the UKB cognitive tests. Global genetic correlations showed a significant but moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found evidence of substantial polygenic overlap between each cognitive factor and schizophrenia but show that most loci shared between the latent cognitive factors and schizophrenia have unique patterns of association with the cognitive factors. Biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we find that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptom dimensions. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.
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Affiliation(s)
- Olivia Wootton
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Alexey A. Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thomas Bjella
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B. Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway
| | - Kevin S O’Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torill Ueland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dan J. Stein
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk & Resilience in Mental Disorders, South Africa
| | - Shareefa Dalvie
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
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25
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Rødevand L, Rahman Z, Hindley GFL, Smeland OB, Frei O, Tekin TF, Kutrolli G, Bahrami S, Hoseth EZ, Shadrin A, Lin A, Djurovic S, Dale AM, Steen NE, Andreassen OA. Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors. Am J Psychiatry 2023; 180:815-826. [PMID: 37752828 DOI: 10.1176/appi.ajp.20220660] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
OBJECTIVE Schizophrenia is associated with increased risk of cardiovascular disease (CVD), although there is variation in risk among individuals. There are indications of shared genetic etiology between schizophrenia and CVD, but the nature of the overlap remains unclear. The aim of this study was to fill this gap in knowledge. METHODS Overlapping genetic architectures between schizophrenia and CVD risk factors were assessed by analyzing recent genome-wide association study (GWAS) results. The bivariate causal mixture model (MiXeR) was applied to estimate the number of shared variants and the conjunctional false discovery rate (conjFDR) approach was used to pinpoint specific shared loci. RESULTS Extensive genetic overlap was found between schizophrenia and CVD risk factors, particularly smoking initiation (N=8.6K variants) and body mass index (BMI) (N=8.1K variants). Several specific shared loci were detected between schizophrenia and BMI (N=304), waist-to-hip ratio (N=193), smoking initiation (N=293), systolic (N=294) and diastolic (N=259) blood pressure, type 2 diabetes (N=147), lipids (N=471), and coronary artery disease (N=35). The schizophrenia risk loci shared with smoking initiation had mainly concordant effect directions, and the risk loci shared with BMI had mainly opposite effect directions. The overlapping loci with lipids, blood pressure, waist-to-hip ratio, type 2 diabetes, and coronary artery disease had mixed effect directions. Functional analyses implicated mapped genes that are expressed in brain tissue and immune cells. CONCLUSIONS These findings indicate a genetic propensity to smoking and a reduced genetic risk of obesity among individuals with schizophrenia. The bidirectional effects of the shared loci with the other CVD risk factors may imply differences in genetic liability to CVD across schizophrenia subgroups, possibly underlying the variation in CVD comorbidity.
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Affiliation(s)
- Linn Rødevand
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Zillur Rahman
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Guy F L Hindley
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Olav B Smeland
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Oleksandr Frei
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Tahir Filiz Tekin
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Gleda Kutrolli
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Shahram Bahrami
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Eva Z Hoseth
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Alexey Shadrin
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Aihua Lin
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Srdjan Djurovic
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Anders M Dale
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Nils Eiel Steen
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Ole A Andreassen
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
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26
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Chen J, Li H, Wu Y, Li Y, Liao S. Shared genetic links between bladder cancer and obesity-related traits: A conjunctional false discovery rate study. Medicine (Baltimore) 2023; 102:e35145. [PMID: 37800791 PMCID: PMC10552987 DOI: 10.1097/md.0000000000035145] [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: 05/12/2023] [Accepted: 08/18/2023] [Indexed: 10/07/2023] Open
Abstract
Bladder cancer (BCa) is a common cancer worldwide and is often linked with obesity-related comorbidities, but little is known about the underlying genetic mechanisms. To investigate these mechanisms, we used various quantitative tools, including conditional quantile-quantile (Q-Q) plots, conditional false discovery rate (cFDR), and conjunctional conditional false discovery rate (ccFDR), to explore the pleiotropic enrichment of risk loci between BCa and obesity-related traits. We also performed an expression quantitative trait locus (eQTL) analysis to assess the relationship between shared risk loci and gene expression. Finally, we conducted functional annotation using Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analysis. Our findings indicated that there was successive enrichment for a range of obesity-related traits, including body fat percentage, body mass index, fasting insulin, type 2 diabetes mellitus, fasting glucose, high-density lipoprotein cholesterol, total triglycerides, and waist-to-hip ratio. Using the tools mentioned above, we identified 18 significant SNPs and 18 closely related genes (cFDR<0.01) under the condition of 8 obesity-related traits. The SNPs included rs143004880, rs73301337, rs10798572, rs11594929, rs17019138, rs2877, rs149795948, rs142509736, rs12727575, rs1571277, rs12131828, rs635634, rs76895963, rs118081211, rs7044247, rs138895564, rs4135275, and rs148023060. Additionally, we identified 15 novel loci using ccFDR, including rs143004880, rs73301337, rs10798572, rs11594929, rs17019138, rs2877, rs142509736, rs1571277, rs635634, rs76895963, rs12131828, rs118081211, rs7044247, rs138895564, and rs4135275. Of the 2 significant loci that modify gene expression, rs12131828 and rs635634 were identified. The functional annotation indicated that the conditional risk genes mainly participated in the regulation of gene silencing. Our study provided evidence of pleiotropic enrichment between BCa and 8 obesity-related traits, and we identified potential genetic mechanisms underlying this relationship. These findings may help in developing targeted clinical treatments for BCa.
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Affiliation(s)
- Jiaqi Chen
- Department of Urology, Affiliated Sanming First Hospital, Fujian Medical University, Sanming, Fujian, China
| | - Hu Li
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Yongyang Wu
- Department of Urology, Affiliated Sanming First Hospital, Fujian Medical University, Sanming, Fujian, China
| | - Yahui Li
- Department of Traditional Chinese Medicine, Affiliated Sanming First Hospital, Fujian Medical University, Sanming, Fujian, China
| | - Shangfan Liao
- Department of Urology, Affiliated Sanming First Hospital, Fujian Medical University, Sanming, Fujian, China
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27
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Sha Z, Warrier V, Bethlehem RA, Schultz LM, Merikangas A, Sun KY, Gur RC, Gur RE, Shinohara RT, Seidlitz J, Almasy L, Andreassen OA, Alexander-Bloch AF. The overlapping genetic architecture of psychiatric disorders and cortical brain structure. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.05.561040. [PMID: 37873315 PMCID: PMC10592957 DOI: 10.1101/2023.10.05.561040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Both psychiatric vulnerability and cortical structure are shaped by the cumulative effect of common genetic variants across the genome. However, the shared genetic underpinnings between psychiatric disorders and brain structural phenotypes, such as thickness and surface area of the cerebral cortex, remains elusive. In this study, we employed pleiotropy-informed conjunctional false discovery rate analysis to investigate shared loci across genome-wide association scans of regional cortical thickness, surface area, and seven psychiatric disorders in approximately 700,000 individuals of European ancestry. Aggregating regional measures, we identified 50 genetic loci shared between psychiatric disorders and surface area, as well as 26 genetic loci shared with cortical thickness. Risk alleles exhibited bidirectional effects on both cortical thickness and surface area, such that some risk alleles for each disorder increased regional brain size while other risk alleles decreased regional brain size. Due to bidirectional effects, in many cases we observed extensive pleiotropy between an imaging phenotype and a psychiatric disorder even in the absence of a significant genetic correlation between them. The impact of genetic risk for psychiatric disorders on regional brain structure did exhibit a consistent pattern across highly comorbid psychiatric disorders, with 80% of the genetic loci shared across multiple disorders displaying consistent directions of effect. Cortical patterning of genetic overlap revealed a hierarchical genetic architecture, with the association cortex and sensorimotor cortex representing two extremes of shared genetic influence on psychiatric disorders and brain structural variation. Integrating multi-scale functional annotations and transcriptomic profiles, we observed that shared genetic loci were enriched in active genomic regions, converged on neurobiological and metabolic pathways, and showed differential expression in postmortem brain tissue from individuals with psychiatric disorders. Cumulatively, these findings provide a significant advance in our understanding of the overlapping polygenic architecture between psychopathology and cortical brain structure.
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Affiliation(s)
- Zhiqiang Sha
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | | | - Laura M. Schultz
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Alison Merikangas
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin Y. Sun
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
- Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, Perelman School of Medicine, United States
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
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28
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Hope S, Shadrin AA, Lin A, Bahrami S, Rødevand L, Frei O, Hübenette SJ, Cheng W, Hindley G, Nag H, Ulstein L, Efrim-Budisteanu M, O'Connell K, Dale AM, Djurovic S, Nærland T, Andreassen OA. Bidirectional genetic overlap between autism spectrum disorder and cognitive traits. Transl Psychiatry 2023; 13:295. [PMID: 37709755 PMCID: PMC10502136 DOI: 10.1038/s41398-023-02563-7] [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] [Received: 03/23/2022] [Revised: 06/27/2023] [Accepted: 07/17/2023] [Indexed: 09/16/2023] Open
Abstract
Autism spectrum disorder (ASD) is a highly heritable condition with a large variation in cognitive function. Here we investigated the shared genetic architecture between cognitive traits (intelligence (INT) and educational attainment (EDU)), and risk loci jointly associated with ASD and the cognitive traits. We analyzed data from genome-wide association studies (GWAS) of INT (n = 269,867), EDU (n = 766,345) and ASD (cases n = 18,381, controls n = 27,969). We used the bivariate causal mixture model (MiXeR) to estimate the total number of shared genetic variants, local analysis of co-variant annotation (LAVA) to estimate local genetic correlations, conditional false discovery rate (cond/conjFDR) to identify specific overlapping loci. The MiXeR analyses showed that 12.7k genetic variants are associated with ASD, of which 12.0k variants are shared with EDU, and 11.1k are shared with INT with both positive and negative relationships within overlapping variants. The majority (59-68%) of estimated shared loci have concordant effect directions, with a positive, albeit modest, genetic correlation between ASD and EDU (rg = 0.21, p = 2e-13) and INT (rg = 0.22, p = 4e-12). We discovered 43 loci jointly associated with ASD and cognitive traits (conjFDR<0.05), of which 27 were novel for ASD. Functional analysis revealed significant differential expression of candidate genes in the cerebellum and frontal cortex. To conclude, we quantified the genetic architecture shared between ASD and cognitive traits, demonstrated mixed effect directions, and identified the associated genetic loci and molecular pathways. The findings suggest that common genetic risk factors for ASD can underlie both better and worse cognitive functioning across the ASD spectrum, with different underlying biology.
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Affiliation(s)
- Sigrun Hope
- K.G. Jebsen Centre for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Department of Neurohabilitation, Oslo University Hospital, Oslo, Norway.
- NevSom, Department of Rare Disorders and Disabilities, Oslo University Hospital, Oslo, Norway.
| | - Alexey A Shadrin
- K.G. Jebsen Centre for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aihua Lin
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Linn Rødevand
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Saira J Hübenette
- K.G. Jebsen Centre for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy Hindley
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Heidi Nag
- Frambu Resource Centre for Rare Disorders, Siggerud, Norway
| | | | - Magdalena Efrim-Budisteanu
- Prof. Dr. Alex Obregia Clinical Hospital of Psychiatry, Bucharest, Romania
- "Victor Babes", Național Institute of Pathology, Bucharest, Romania
| | - Kevin O'Connell
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Sciences, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Srdjan Djurovic
- K.G. Jebsen Centre for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Terje Nærland
- K.G. Jebsen Centre for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NevSom, Department of Rare Disorders and Disabilities, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- K.G. Jebsen Centre for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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29
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Steen NE, Rahman Z, Szabo A, Hindley GFL, Parker N, Cheng W, Lin A, O’Connell KS, Sheikh MA, Shadrin A, Bahrami S, Karthikeyan S, Hoseth EZ, Dale AM, Aukrust P, Smeland OB, Ueland T, Frei O, Djurovic S, Andreassen OA. Shared Genetic Loci Between Schizophrenia and White Blood Cell Counts Suggest Genetically Determined Systemic Immune Abnormalities. Schizophr Bull 2023; 49:1345-1354. [PMID: 37319439 PMCID: PMC10483470 DOI: 10.1093/schbul/sbad082] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
BACKGROUND Immune mechanisms are indicated in schizophrenia (SCZ). Recent genome-wide association studies (GWAS) have identified genetic variants associated with SCZ and immune-related phenotypes. Here, we use cutting edge statistical tools to identify shared genetic variants between SCZ and white blood cell (WBC) counts and further understand the role of the immune system in SCZ. STUDY DESIGN GWAS results from SCZ (patients, n = 53 386; controls, n = 77 258) and WBC counts (n = 56 3085) were analyzed. We applied linkage disequilibrium score regression, the conditional false discovery rate method and the bivariate causal mixture model for analyses of genetic associations and overlap, and 2 sample Mendelian randomization to estimate causal effects. STUDY RESULTS The polygenicity for SCZ was 7.5 times higher than for WBC count and constituted 32%-59% of WBC count genetic loci. While there was a significant but weak positive genetic correlation between SCZ and lymphocytes (rg = 0.05), the conditional false discovery rate method identified 383 shared genetic loci (53% concordant effect directions), with shared variants encompassing all investigated WBC subtypes: lymphocytes, n = 215 (56% concordant); neutrophils, n = 158 (49% concordant); monocytes, n = 146 (47% concordant); eosinophils, n = 135 (56% concordant); and basophils, n = 64 (53% concordant). A few causal effects were suggested, but consensus was lacking across different Mendelian randomization methods. Functional analyses indicated cellular functioning and regulation of translation as overlapping mechanisms. CONCLUSIONS Our results suggest that genetic factors involved in WBC counts are associated with the risk of SCZ, indicating a role of immune mechanisms in subgroups of SCZ with potential for stratification of patients for immune targeted treatment.
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Affiliation(s)
- Nils Eiel Steen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Zillur Rahman
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Attila Szabo
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Nadine Parker
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aihua Lin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kevin S O’Connell
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Mashhood A Sheikh
- Research Institute of Internal Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Sandeep Karthikeyan
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Eva Z Hoseth
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Sciences, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Pål Aukrust
- Research Institute of Internal Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
- Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Thor Ueland
- Research Institute of Internal Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
- K.G. Jebsen—Thrombosis Research and Expertise Center (TREC), University of Tromsø, Tromsø, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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30
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Hindley G, Shadrin AA, van der Meer D, Parker N, Cheng W, O'Connell KS, Bahrami S, Lin A, Karadag N, Holen B, Bjella T, Deary IJ, Davies G, Hill WD, Bressler J, Seshadri S, Fan CC, Ueland T, Djurovic S, Smeland OB, Frei O, Dale AM, Andreassen OA. Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy. Nat Hum Behav 2023; 7:1584-1600. [PMID: 37365406 PMCID: PMC10824266 DOI: 10.1038/s41562-023-01630-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 05/16/2023] [Indexed: 06/28/2023]
Abstract
Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the 'pleiotropy-informed' multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association studies of the Big 5 personality traits and cognitive function on our multivariate findings, boosting genetic discovery in other personality traits and improving polygenic prediction. These findings advance our understanding of the polygenic architecture of these complex mental traits, indicating a prominence of pleiotropic genetic effects across higher order domains of mental function such as personality and cognitive function.
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Affiliation(s)
- Guy Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK.
| | - Alexey A Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway.
| | - Dennis van der Meer
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Nadine Parker
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aihua Lin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Naz Karadag
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Børge Holen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Thomas Bjella
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Gail Davies
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - W David Hill
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Chun Chieh Fan
- Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Torill Ueland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway.
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31
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Bang L, Bahrami S, Hindley G, Smeland OB, Rødevand L, Jaholkowski PP, Shadrin A, Connell KSO, Frei O, Lin A, Rahman Z, Cheng W, Parker N, Fan CC, Dale AM, Djurovic S, Bulik CM, Andreassen OA. Genome-wide analysis of anorexia nervosa and major psychiatric disorders and related traits reveals genetic overlap and identifies novel risk loci for anorexia nervosa. Transl Psychiatry 2023; 13:291. [PMID: 37658054 PMCID: PMC10474135 DOI: 10.1038/s41398-023-02585-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 07/04/2023] [Accepted: 08/10/2023] [Indexed: 09/03/2023] Open
Abstract
Anorexia nervosa (AN) is a heritable eating disorder (50-60%) with an array of commonly comorbid psychiatric disorders and related traits. Although significant genetic correlations between AN and psychiatric disorders and related traits have been reported, their shared genetic architecture is largely understudied. We investigated the shared genetic architecture of AN and schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), mood instability (Mood), neuroticism (NEUR), and intelligence (INT). We applied the conditional false discovery rate (FDR) method to identify novel risk loci for AN, and conjunctional FDR to identify loci shared between AN and related phenotypes, to summarize statistics from relevant genome-wide association studies (GWAS). Individual GWAS samples varied from 72,517 to 420,879 participants. Using conditional FDR we identified 58 novel AN loci. Furthermore, we identified 38 unique loci shared between AN and major psychiatric disorders (SCZ, BIP, and MD) and 45 between AN and psychological traits (Mood, NEUR, and INT). In line with genetic correlations, the majority of shared loci showed concordant effect directions. Functional analyses revealed that the shared loci are involved in 65 unique pathways, several of which overlapped across analyses, including the "signal by MST1" pathway involved in Hippo signaling. In conclusion, we demonstrated genetic overlap between AN and major psychiatric disorders and related traits, and identified novel risk loci for AN by leveraging this overlap. Our results indicate that some shared characteristics between AN and related disorders and traits may have genetic underpinnings.
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Affiliation(s)
- Lasse Bang
- Department of Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
- Regional Department for Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Guy Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Linn Rødevand
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Piotr P Jaholkowski
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kevin S O' Connell
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aihua Lin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Zillur Rahman
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Nadine Parker
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Chun C Fan
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
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32
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Tesfaye M, Jaholkowski P, Hindley GFL, Shadrin AA, Rahman Z, Bahrami S, Lin A, Holen B, Parker N, Cheng W, Rødevand L, Frei O, Djurovic S, Dale AM, Smeland OB, O'Connell KS, Andreassen OA. Shared genetic architecture between irritable bowel syndrome and psychiatric disorders reveals molecular pathways of the gut-brain axis. Genome Med 2023; 15:60. [PMID: 37528461 PMCID: PMC10391890 DOI: 10.1186/s13073-023-01212-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/12/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Irritable bowel syndrome (IBS) often co-occurs with psychiatric and gastrointestinal disorders. A recent genome-wide association study (GWAS) identified several genetic risk variants for IBS. However, most of the heritability remains unidentified, and the genetic overlap with psychiatric and somatic disorders is not quantified beyond genome-wide genetic correlations. Here, we characterize the genetic architecture of IBS, further, investigate its genetic overlap with psychiatric and gastrointestinal phenotypes, and identify novel genomic risk loci. METHODS Using GWAS summary statistics of IBS (53,400 cases and 433,201 controls), and psychiatric and gastrointestinal phenotypes, we performed bivariate casual mixture model analysis to characterize the genetic architecture and genetic overlap between these phenotypes. We leveraged identified genetic overlap to boost the discovery of genomic loci associated with IBS, and to identify specific shared loci associated with both IBS and psychiatric and gastrointestinal phenotypes, using the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework. We used functional mapping and gene annotation (FUMA) for functional analyses. RESULTS IBS was highly polygenic with 12k trait-influencing variants. We found extensive polygenic overlap between IBS and psychiatric disorders and to a lesser extent with gastrointestinal diseases. We identified 132 independent IBS-associated loci (condFDR < 0.05) by conditioning on psychiatric disorders (n = 127) and gastrointestinal diseases (n = 24). Using conjFDR, 70 unique loci were shared between IBS and psychiatric disorders. Functional analyses of shared loci revealed enrichment for biological pathways of the nervous and immune systems. Genetic correlations and shared loci between psychiatric disorders and IBS subtypes were different. CONCLUSIONS We found extensive polygenic overlap of IBS and psychiatric and gastrointestinal phenotypes beyond what was revealed with genetic correlations. Leveraging the overlap, we discovered genetic loci associated with IBS which implicate a wide range of biological pathways beyond the gut-brain axis. Genetic differences may underlie the clinical subtype of IBS. These results increase our understanding of the pathophysiology of IBS which may form the basis for the development of individualized interventions.
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Affiliation(s)
- Markos Tesfaye
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- NORMENT, Department of Clinical Sciences, University of Bergen, Bergen, Norway.
| | - Piotr Jaholkowski
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alexey A Shadrin
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Zillur Rahman
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aihua Lin
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Børge Holen
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nadine Parker
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Linn Rødevand
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- NORMENT, Department of Clinical Sciences, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Olav B Smeland
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, 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, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway.
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33
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O'Connell KS, Koch E, Lenk HÇ, Akkouh IA, Hindley G, Jaholkowski P, Smith RL, Holen B, Shadrin AA, Frei O, Smeland OB, Steen NE, Dale AM, Molden E, Djurovic S, Andreassen OA. Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia. Psychiatry Res 2023; 325:115217. [PMID: 37146461 PMCID: PMC10788293 DOI: 10.1016/j.psychres.2023.115217] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/03/2023] [Accepted: 04/21/2023] [Indexed: 05/07/2023]
Abstract
Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR <0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.
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Affiliation(s)
- Kevin S O'Connell
- 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.
| | - Elise Koch
- 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
| | - Hasan Çağın Lenk
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway; Section for Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Ibrahim A Akkouh
- 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; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Guy Hindley
- 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; Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, United Kingdom
| | - Piotr Jaholkowski
- 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
| | - Robert Løvsletten Smith
- 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; Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Børge Holen
- 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
| | - Alexey A Shadrin
- 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; KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- 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; Center for Bioinformatics, Department of Informatics, University of Oslo, 0316 Oslo, Norway
| | - Olav B Smeland
- 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
| | - Nils Eiel Steen
- 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
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA 92093, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Espen Molden
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway; Section for Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, 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; KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway.
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Roelfs D, Frei O, van der Meer D, Tissink E, Shadrin A, Alnaes D, Andreassen OA, Westlye LT, Kaufmann T. Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank. BMC Psychiatry 2023; 23:461. [PMID: 37353766 PMCID: PMC10290393 DOI: 10.1186/s12888-023-04905-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 05/26/2023] [Indexed: 06/25/2023] Open
Abstract
Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45-82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.
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Affiliation(s)
- Daniel Roelfs
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Oleksandr Frei
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Elleke Tissink
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, 1081 HV, The Netherlands
| | - Alexey Shadrin
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnaes
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Bjørknes College, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany.
- German Center for Mental Health (DZPG), Tübingen, Germany.
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35
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Qiao J, Wu Y, Zhang S, Xu Y, Zhang J, Zeng P, Wang T. Evaluating significance of European-associated index SNPs in the East Asian population for 31 complex phenotypes. BMC Genomics 2023; 24:324. [PMID: 37312035 DOI: 10.1186/s12864-023-09425-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWASs) have identified many single-nucleotide polymorphisms (SNPs) associated with complex phenotypes in the European (EUR) population; however, the extent to which EUR-associated SNPs can be generalized to other populations such as East Asian (EAS) is not clear. RESULTS By leveraging summary statistics of 31 phenotypes in the EUR and EAS populations, we first evaluated the difference in heritability between the two populations and calculated the trans-ethnic genetic correlation. We observed the heritability estimates of some phenotypes varied substantially across populations and 53.3% of trans-ethnic genetic correlations were significantly smaller than one. Next, we examined whether EUR-associated SNPs of these phenotypes could be identified in EAS using the trans-ethnic false discovery rate method while accounting for winner's curse for SNP effect in EUR and difference of sample sizes in EAS. We found on average 54.5% of EUR-associated SNPs were also significant in EAS. Furthermore, we discovered non-significant SNPs had higher effect heterogeneity, and significant SNPs showed more consistent linkage disequilibrium and allele frequency patterns between the two populations. We also demonstrated non-significant SNPs were more likely to undergo natural selection. CONCLUSIONS Our study revealed the extent to which EUR-associated SNPs could be significant in the EAS population and offered deep insights into the similarity and diversity of genetic architectures underlying phenotypes in distinct ancestral groups.
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Affiliation(s)
- Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yuxuan Wu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yue Xu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jinhui Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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Zhang J, Zhang S, Qiao J, Wang T, Zeng P. Similarity and diversity of genetic architecture for complex traits between East Asian and European populations. BMC Genomics 2023; 24:314. [PMID: 37308816 DOI: 10.1186/s12864-023-09434-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/07/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently. RESULTS By leveraging summary statistics of 37 traits from East Asian (Nmax=254,373) or European (Nmax=693,529) populations, we first evaluated the trans-ethnic genetic correlation (ρg) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with [Formula: see text] ranging from 0.53 (se = 0.11) for adult-onset asthma to 0.98 (se = 0.17) for hemoglobin A1c. However, 88.9% of the genetic correlation estimates were significantly less than one, indicating potential heterogeneity in genetic effect across populations. We next identified common associated SNPs using the conjunction conditional false discovery rate method and observed 21.7% of trait-associated SNPs can be identified simultaneously in both populations. Among these shared associated SNPs, 20.8% showed heterogeneous influence on traits between the two ancestral populations. Moreover, we demonstrated that population-common associated SNPs often exhibited more consistent linkage disequilibrium and allele frequency pattern across ancestral groups compared to population-specific or null ones. We also revealed population-specific associated SNPs were much likely to undergo natural selection compared to population-common associated SNPs. CONCLUSIONS Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping.
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Affiliation(s)
- Jinhui Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Jiahao Qiao
- 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.
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
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Yang Z, Li D, He Y, Chen X, Li Z. Unrevealing the shared genetic mechanisms underlying C-reactive protein and schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2023; 126:110785. [PMID: 37150315 DOI: 10.1016/j.pnpbp.2023.110785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 04/18/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
Longitudinal observational studies and Mendelian randomization research have obtained contradictory conclusions regarding the association between C-reactive protein (CRP) level and schizophrenia risk. However, the shared genetic mechanisms underlying CRP and schizophrenia remain poorly understood. Here, we examined the global and local genetic correlations using summary statistics from large-scale genome-wide association studies (GWAS) on CRP level and schizophrenia. Furthermore, we identified their shared genetic variants by applying the conditional false discovery rate approach and performed functional analyses of shared variants to explore the shared genetic mechanisms underlying CRP level and schizophrenia. We found a significant negative genetic correlation at the whole genome level and five significant local genetic correlations between CRP level and schizophrenia. Eight-three shared genetic loci were identified, from which single-nucleotide polymorphism (SNP) presents mixed effects on the increased CRP level and schizophrenia risk. Additionally, we identified 64 and 73 candidate genes that were mapped from SNPs with"concordant effect"(ceSNPs) and"discordant effect"(deSNPs) on the CRP level and schizophrenia risk respectively. Functional analyses revealed that genes mapped from ceSNPs and deSNPs exhibited similar patterns of human brain developmental expression trajectories and biological processes, but differed in expression levels and cell-type-specific enrichment in brain tissues. Our findings demonstrated mixed effects of shared genetic architecture between CRP level and schizophrenia, proving a deeper insight into the shared genetic aetiology underlying the CRP level and schizophrenia.
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Affiliation(s)
- Zihao Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China
| | - David Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Ying He
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China; China National Technology Institute on Mental Disorders & Hunan Key Laboratory of Psychiatry and Mental Health, Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Xiaogang Chen
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China; China National Technology Institute on Mental Disorders & Hunan Key Laboratory of Psychiatry and Mental Health, Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Zongchang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, PR China; China National Technology Institute on Mental Disorders & Hunan Key Laboratory of Psychiatry and Mental Health, Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, PR China.
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38
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Wu BS, Ge YJ, Zhang W, Chen SD, Xiang ST, Zhang YR, Ou YN, Jiang YC, Tan L, Cheng W, Suckling J, Feng JF, Yu JT, Mao Y. Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders. Neuroimage 2023; 269:119928. [PMID: 36740028 DOI: 10.1016/j.neuroimage.2023.119928] [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: 11/23/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown. METHODS We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders. RESULTS We identified 11 genetic loci (P < 8.3 × 10-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories. CONCLUSIONS Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.
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Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - John Suckling
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Ying Mao
- Department of Neurosurgery and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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Genetic correlation and gene-based pleiotropy analysis for four major neurodegenerative diseases with summary statistics. Neurobiol Aging 2023; 124:117-128. [PMID: 36740554 DOI: 10.1016/j.neurobiolaging.2022.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/25/2022] [Accepted: 12/27/2022] [Indexed: 01/02/2023]
Abstract
Recent genome-wide association studies suggested shared genetic components between neurodegenerative diseases. However, pleiotropic association patterns among them remain poorly understood. We here analyzed 4 major neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), and found suggestively positive genetic correlation. We next implemented a gene-centric pleiotropy analysis with a powerful method called PLACO and detected 280 pleiotropic associations (226 unique genes) with these diseases. Functional analyses demonstrated that these genes were enriched in the pancreas, liver, heart, blood, brain, and muscle tissues; and that 42 pleiotropic genes exhibited drug-gene interactions with 341 drugs. Using Mendelian randomization, we discovered that AD and PD can increase the risk of developing ALS, and that AD and ALS can also increase the risk of developing FTD, respectively. Overall, this study provides in-depth insights into shared genetic components and causal relationship among the 4 major neurodegenerative diseases, indicating genetic overlap and causality commonly drive their co-occurrence. It also has important implications on the etiology understanding, drug development and therapeutic targets for neurodegenerative diseases.
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40
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Zhong Y, Wu Y, Yang Y, Chen Y, Hui R, Zhang M, Zhang W. Association of MPPED2 gene variant rs10767873 with kidney function and risk of cardiovascular disease in patients with hypertension. J Hum Genet 2023; 68:393-398. [PMID: 36797372 DOI: 10.1038/s10038-022-01118-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 02/18/2023]
Abstract
Changes in kidney function and the progression of chronic kidney disease (CKD) are associated with the risk of cardiovascular disease (CVD) and influenced by genetic factors. However, the association between genetic variants and kidney function in patients treated with antihypertensive drugs remains uncertain. This study aimed to examine the association between 30 variants locating at the 22 genes and the risk of kidney function evaluated by the estimated glomerular filtration rate (eGFR) in 1911 patients with hypertension from a Chinese community-based longitudinal cohort (including 1220 participants with CKD and 691 without CKD at baseline). By using multivariate linear regression analysis after adjustment for age, sex, traditional cardiovascular risk factors, and the use of antihypertensive drugs, as well as after correction for multiple comparison, patients with rs10767873T allele of the metallophosphoesterase domain containing 2 (MPPED2) gene were associated with higher level of eGFR (β = 0.041, p = 0.01) and lower levels of serum creatinine (β = -0.068, p = 0.001) and serum uric acid (β = -0.047, p = 0.02). But variant rs10767873 was not found to be associated with the risk of CKD, regardless of the types of antihypertensive drugs used. During a median 2.25-year follow-up, 152 CVD events were documented. Interestingly, patients with the rs10767873TT genotype had an increased risk of CVD events (hazard ratio, 1.74, 95% confidence interval, 1.11 to 2.73; p = 0.02) compared with patients carrying the wild-type genotype of rs10767873CC. In conclusion, our findings suggest variant rs10767873 of the MPPED2 gene is associated with kidney function and risk of CVD in Chinese hypertensive patients.
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Affiliation(s)
- Yixuan Zhong
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Yiyi Wu
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China.,The First Affiliated Hospital of Anhui University of Science and Technology (The First People's Hospital of Huainan City), Huainan, 232000, Anhui, China
| | - Yunyun Yang
- The First Affiliated Hospital of Xiamen University; Clinical laboratory; Xiamen Key Laboratory of Genetic Testing, Xiamen, 361000, Fujian, China
| | - Yu Chen
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Rutai Hui
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Mei Zhang
- The First Affiliated Hospital of Anhui University of Science and Technology (The First People's Hospital of Huainan City), Huainan, 232000, Anhui, China.
| | - Weili Zhang
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China. .,Central-China Branch of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Hospital, Zhengzhou, 450046, China.
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41
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Rong SS, Yu X. Phenotypic and Genetic Links between Body Fat Measurements and Primary Open-Angle Glaucoma. Int J Mol Sci 2023; 24:ijms24043925. [PMID: 36835334 PMCID: PMC9958617 DOI: 10.3390/ijms24043925] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/09/2023] [Accepted: 02/11/2023] [Indexed: 02/17/2023] Open
Abstract
The phenotypic and genetic links between body fat phenotypes and primary open-angle glaucoma (POAG) are unclear. We conducted a meta-analysis of relevant longitudinal epidemiological studies to evaluate the phenotypic link. To identify genetic links, we performed genetic correlation analysis and pleiotropy analysis of genome-wide association study summary statistics datasets of POAG, intraocular pressure (IOP), vertical cup-to-disc ratio, obesity, body mass index (BMI), and waist-to-hip ratio. In the meta-analysis, we first established that obese and underweight populations have a significantly higher risk of POAG using longitudinal data. We also discovered positive genetic correlations between POAG and BMI and obesity phenotypes. Finally, we identified over 20 genomic loci jointly associated with POAG/IOP and BMI. Among them, the genes loci CADM2, RP3-335N17.2, RP11-793K1.1, RPS17P5, and CASC20 showed the lowest false discovery rate. These findings support the connection between body fat phenotypes and POAG. The newly identified genomic loci and genes render further functional investigation.
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Affiliation(s)
- Shi Song Rong
- Department of Ophthalmology, Massachusetts Eye and Ear, Mass General Brigham, Harvard Medical School, Boston, MA 02114, USA
- Correspondence:
| | - Xinting Yu
- Department of Medicine, Brigham and Women’s Hospital, Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
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42
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Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
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Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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43
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Kang X, Ploner A, Wang Y, Ludvigsson JF, Williams DM, Pedersen NL, Wirdefeldt K. Genetic overlap between Parkinson's disease and inflammatory bowel disease. Brain Commun 2023; 5:fcad002. [PMID: 36687396 PMCID: PMC9847552 DOI: 10.1093/braincomms/fcad002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/01/2022] [Accepted: 01/02/2023] [Indexed: 01/05/2023] Open
Abstract
Parkinson's disease and inflammatory bowel disease have been increasingly associated, implying shared pathophysiology. To explore biological explanations for the reported connection, we leveraged summary statistics of updated genome-wide association studies and characterized the genetic overlap between the two diseases. Aggregated genetic association data were available for 37 688 cases versus 981 372 controls for Parkinson's disease and 25 042 cases versus 34 915 controls for inflammatory bowel disease. Genetic correlation was estimated with the high-definition likelihood method. Genetic variants with joint association to both diseases were identified by conditional false discovery rate framework and further annotated to reveal shared loci, genes, and enriched pathways. For both Crohn's disease and ulcerative colitis, the two main subtypes of inflammatory bowel disease, we detected weak but statistically significant genetic correlations with Parkinson's disease (Crohn's disease: rg = 0.06, P = 0.01; ulcerative colitis: rg = 0.06, P = 0.03). A total of 1290 variants in 27 independent genomic loci were detected to associate with Parkinson's disease and Crohn's disease at conjunctional false discovery rate under 0.01 and 1359 variants in 15 loci were pleiotropic to Parkinson's disease and ulcerative colitis. Among the identified pleiotropic loci, 23 are novel and have never been associated with both phenotypes. A mixture of loci conferring either same or opposing genetic effects on two phenotypes was also observed. Positional and expression quantitative trait loci mapping prioritized 296 and 253 genes for Parkinson's disease with Crohn's disease and ulcerative colitis, respectively, among which only <10% are differentially expressed in both colon and substantia nigra. These genes were identified to overrepresent in pathways regulating gene expression and post-translational modification beyond several immune-related pathways enriched by major histocompatibility complex genes. In conclusion, we found robust evidence for a genetic link between Parkinson's disease and inflammatory bowel disease. The identified genetic overlap is complex at the locus and gene levels, indicating the presence of both synergistic and antagonistic pleiotropy. At the functional level, our findings implied a role of immune-centered mechanisms in the reported gut-brain connection.
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Affiliation(s)
- Xiaoying Kang
- Correspondence to: Xiaoying Kang Department of Medical Epidemiology and Biostatistics Karolinska Institutet, Nobels väg 12A 171 65 Solna, Sweden E-mail:
| | - Alexander Ploner
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17165 Stockholm, Sweden
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17165 Stockholm, Sweden
| | - Jonas F Ludvigsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17165 Stockholm, Sweden,Department of Pediatrics, Örebro University Hospital, SE-70116 Örebro, Sweden
| | - Dylan M Williams
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17165 Stockholm, Sweden,MRC Unit for Lifelong Health and Ageing at UCL, University College London, London WC1E 7HE, UK
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17165 Stockholm, Sweden
| | - Karin Wirdefeldt
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17165 Stockholm, Sweden,Department of Clinical Neuroscience, Karolinska Institutet, SE-17177 Stockholm, Sweden
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44
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Chen Y, Han Y, Wu Y, Hui R, Yang Y, Zhong Y, Zhang S, Zhang W. Pharmacogenetic association of the NR1H3 promoter variant with antihypertensive response among patients with hypertension: A longitudinal study. Front Pharmacol 2023; 14:1083134. [PMID: 36950018 PMCID: PMC10025344 DOI: 10.3389/fphar.2023.1083134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/20/2023] [Indexed: 03/08/2023] Open
Abstract
Background: The genetic factors in assessing therapeutic efficacy and predicting antihypertensive drug response are unclear. Therefore, this study aims to identify the associations between variants and antihypertensive drug response. Methods: A longitudinal study including 1837 hypertensive patients was conducted in Northern China and followed up for a median 2.24 years. The associations of 11 candidate variants with blood pressure changes in response to antihypertensive drugs and with the risk of cardiovascular events during the follow-up were examined. The dual-luciferase assay was carried out to assess the effect of genetic variants on gene transcriptional activity. Results: The variant rs11039149A>G in the promoter of nuclear receptor subfamily 1 group H member 3 (NR1H3) was associated with the change in systolic blood pressure (ΔSBP) in response to calcium channel blockers (CCBs) monotherapy. Patients carrying rs11039149AG genotype showed a significant increase of systolic blood pressure (SBP) at follow-up compared with AA carriers, and the difference of ΔSBP between AG and AA carriers was 5.94 mm Hg (95%CI: 2.09-9.78, p = 0.002). In 1,184 patients with CCBs therapy, SBP levels decreased in AA carriers, but increased in AG carriers, the difference of ΔSBP between AG and AA carriers was 8.04 mm Hg (95%CI: 3.28-12.81, p = 0.001). Further analysis in 359 patients with CCBs monotherapy, the difference of ΔSBP between AG and AA carriers was 15.25 mm Hg (95%CI: 6.48-24.02, p = 0.001). However, there was no significant difference in ΔSBP between AG and AA carriers with CCBs multitherapy. The rs11039149A>G was not associated with the cardiovascular events incidence during the follow-up. Additionally, transcriptional factor forkhead box C1 (FOXC1) bound to the NR1H3 promoter containing rs11039149A and significantly increased the transcriptional activity, while rs11039149 A to G change led to a loss-of-function and disabled FOXC1 binding. For the other 10 variants, associations with blood pressure changes or risk of cardiovascular events were not observed. Conclusion: Hypertensive patients with rs11039149AG genotype in the NR1H3 gene have a significant worse SBP control in response to CCBs monotherapy compared with AA carriers. Our findings suggest that the NR1H3 gene might act as a promising genetic factor to affect individual sensitivity to antihypertensive drugs.
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Affiliation(s)
- Yu Chen
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China
| | - Yuqing Han
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China
| | - Yiyi Wu
- The First Affiliated Hospital of Anhui University of Science and Technology, The First People’s Hospital of Huainan City, Huainan, Anhui, China
| | - Rutai Hui
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China
| | - Yunyun Yang
- Clinical Laboratory, Xiamen Key Laboratory of Genetic Testing, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China
| | - Yixuan Zhong
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China
| | - Shuyuan Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China
| | - Weili Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China
- Central-China Branch of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Hospital, Zhengzhou, China
- *Correspondence: Weili Zhang,
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Lv H, Li J, Gao K, Zeng L, Xue R, Liu X, Zhou C, Yue W, Yu H. Identification of genetic loci that overlap between schizophrenia and metabolic syndrome. Psychiatry Res 2022; 318:114947. [PMID: 36399892 DOI: 10.1016/j.psychres.2022.114947] [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: 08/21/2022] [Revised: 10/25/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022]
Abstract
Patients with schizophrenia (SCZ) frequently exhibit an elevated risk of metabolic syndrome (MetS), which may lead to a worse clinical outcome. Even though these two phenotypes are genetically linked, little is known about their shared genetic determinants. Here, we investigated whether SCZ and MetS share genetic risk factors. To examine the genetic overlap between the two disorders, we applied a comprehensive genetic overlap analysis by integrating GWAS data for SCZ (n = 320,404) and MetS (n = 291,107) at the genome, genetic variants, and gene levels. At the genome level, we observed polygenic overlap between SCZ and MetS by utilizing LDSC (rg=-0.09, P = 1 × 10-4) and GNOVA (rho=-0.04, P = 1.39 × 10-8) analysis. At the SNP level, we performed conjunctional FDR (conjFDR) analysis to identify genetic variants simultaneously associated with two phenotypes. Based on conjFDR < 0.05, we identified 22 loci shared between SCZ and MetS. At the gene level, we further demonstrated that SCZ- and MetS-inferred gene expression overlapped across 49 GTEx tissues and highlighted the PCCB and KCTD13 genes as putative mediators of the genetic association. Overall, these findings shed novel light on the association between SCZ and MetS, and potentially enhance our knowledge of the high comorbidity and genetic processes that overlap between the two disorders.
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Affiliation(s)
- Honggang Lv
- Department of Psychiatry, Jining Medical University, Jining, Shandong 272067, China
| | - Juan Li
- Department of Psychiatry, Jining Medical University, Jining, Shandong 272067, China
| | - Kai Gao
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Peking University Sixth Hospital (Institute of Mental Health), Beijing 100191, China
| | - Lingsi Zeng
- Department of Psychiatry, Jining Medical University, Jining, Shandong 272067, China
| | - Ranran Xue
- Department of Psychiatry, Shandong Daizhuang Hospital, Jining, Shandong 272051, China
| | - Xia Liu
- Department of Psychiatry, Shandong Daizhuang Hospital, Jining, Shandong 272051, China
| | - Cong Zhou
- Department of Psychiatry, Jining Medical University, Jining, Shandong 272067, China
| | - Weihua Yue
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Peking University Sixth Hospital (Institute of Mental Health), Beijing 100191, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Hao Yu
- Department of Psychiatry, Jining Medical University, Jining, Shandong 272067, China.
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Zheng H, Sun J, Pang T, Liu J, Lu L, Chang S. Identify novel, shared and disorder-specific genetic architecture of major depressive disorder, insomnia and chronic pain. J Psychiatr Res 2022; 155:511-517. [PMID: 36191519 DOI: 10.1016/j.jpsychires.2022.09.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 08/01/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022]
Abstract
Major depressive disorder (MDD), insomnia (INS) and chronic pain (CP) often have high comorbidity and show high genetic correlation. Here we aimed to better characterize their novel, shared and disorder-specific genetic architecture. Based on genome-wide association study (GWAS) summary data, we applied the conditional false discovery rate (condFDR) and conjunctional FDR (conjFDR) approach to investigate the novel and overlapped genetic loci for MDD, INS and CP. In addition, putative disorder-specific SNP associations were analyzed by conditioning the other two traits. The functions of the identified genomic loci were explored by performing gene set enrichment analysis (GSEA) for the loci mapped genes. We identified 22, 43 and 91 novel risk loci for MDD, INS and CP. GSEA for the loci mapped genes highlighted olfactory signaling pathway for MDD novel loci, breast cancer related gene set for both INS and CP novel loci, and nervous system related development, structure and activity for CP. Furthermore, we identified three loci jointly associated with the three disorders, including 13q14.3 locus with nearby gene OLFM4, 14q21.1 locus with nearby gene LRFN5 and 5q21.2 locus located in intergenic region. In addition, we identified one specific loci for MDD, 7 for INS and 11 for CP respectively by conditioning the other two traits, which were mapped to 68 genes for MDD, 85 for INS and 100 for CP. The MDD specific genes are enriched in immune system related pathways. This study increases understanding of the genetic architectures underlying MDD, INS and CP. The shared underlying genetic risk may help to explain the high comorbidity rates of the disorders.
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Affiliation(s)
- Haohao Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Jie Sun
- Center for Pain Medicine, Peking University Third Hospital, Beijing, 100191, China
| | - Tao Pang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Jiajia Liu
- School of Nursing, Peking University, Beijing, 100191, China
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China; Chinese Academy of Medical Sciences Research Unit (No.2018RU006), Peking University, Beijing, 100191, China; National Institute on Drug Dependence, Peking University, Beijing, 100191, China
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China; Chinese Academy of Medical Sciences Research Unit (No.2018RU006), Peking University, Beijing, 100191, China.
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47
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Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives. Transl Psychiatry 2022; 12:447. [PMID: 36241627 PMCID: PMC9568576 DOI: 10.1038/s41398-022-02193-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 09/06/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022] Open
Abstract
Cortical morphology is a key determinant of cognitive ability and mental health. Its development is a highly intricate process spanning decades, involving the coordinated, localized expression of thousands of genes. We are now beginning to unravel the genetic architecture of cortical morphology, thanks to the recent availability of large-scale neuroimaging and genomic data and the development of powerful biostatistical tools. Here, we review the progress made in this field, providing an overview of the lessons learned from genetic studies of cortical volume, thickness, surface area, and folding as captured by neuroimaging. It is now clear that morphology is shaped by thousands of genetic variants, with effects that are region- and time-dependent, thereby challenging conventional study approaches. The most recent genome-wide association studies have started discovering common genetic variants influencing cortical thickness and surface area, yet together these explain only a fraction of the high heritability of these measures. Further, the impact of rare variants and non-additive effects remains elusive. There are indications that the quickly increasing availability of data from whole-genome sequencing and large, deeply phenotyped population cohorts across the lifespan will enable us to uncover much of the missing heritability in the upcoming years. Novel approaches leveraging shared information across measures will accelerate this process by providing substantial increases in statistical power, together with more accurate mapping of genetic relationships. Important challenges remain, including better representation of understudied demographic groups, integration of other 'omics data, and mapping of effects from gene to brain to behavior across the lifespan.
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48
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Icick R, Shadrin A, Holen B, Karadag N, Lin A, Hindley G, O'Connell K, Frei O, Bahrami S, Høegh MC, Cheng W, Fan CC, Djurovic S, Dale AM, Lagerberg TV, Smeland OB, Andreassen OA. Genetic overlap between mood instability and alcohol-related phenotypes suggests shared biological underpinnings. Neuropsychopharmacology 2022; 47:1883-1891. [PMID: 35953530 PMCID: PMC9485134 DOI: 10.1038/s41386-022-01401-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/20/2022] [Accepted: 07/16/2022] [Indexed: 11/09/2022]
Abstract
Alcohol use disorder (AUD) is a pervasive and devastating mental illness with high comorbidity rates with other mental disorders. Understanding the genetic architecture of this comorbidity could be improved by focusing on intermediate traits that show positive genetic correlation with the disorders. Thus, we aimed to characterize the shared vs. unique polygenicity of AUD, alcohol consumption (AC) and mood instability (MOOD) -beyond genetic correlation, and boost discovery for jointly-associated loci. Summary statistics for MOOD (a binary measure of the tendency to report frequent mood swings), AC (number of standard drinks over a typical consumption week) and AUD GWASs (Ns > 200,000) were analyzed to characterize the cross-phenotype associations between MOOD and AC, MOOD and AUD and AC and AUD. To do so, we used a newly established pipeline that combines (i) the bivariate causal mixture model (MiXeR) to quantify polygenic overlap and (ii) the conjunctional false discovery rate (conjFDR) to discover specific jointly associated genomic loci, which were mapped to genes and biological functions. MOOD was highly polygenic (10.4k single nucleotide polymorphisms, SNPs, SD = 2k) compared to AC (4.9k SNPs, SD = 0.6k) and AUD (4.3k SNPs, SD = 2k). The polygenic overlap of MOOD and AC was twice that of MOOD and AUD (98% vs. 49%), with opposite genetic correlation (-0.2 vs. 0.23), as confirmed in independent samples. MOOD&AUD associated SNPs were significantly enriched for brain genes, conversely to MOOD&AC. Among 38 jointly associated loci, fifteen were novel for MOOD, AC and AUD. MOOD, AC and AUD were also strongly associated at the phenotypic level. Overall, using multilevel polygenic quantification, joint loci discovery and functional annotation methods, we evidenced that the polygenic overlap between MOOD and AC/AUD implicated partly shared biological underpinnings, yet, clearly distinct functional patterns between MOOD&AC and MOOD&AUD, suggesting new mechanisms for the comorbidity of AUD with mood disorders.
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Affiliation(s)
- Romain Icick
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway.
- Université de Paris Cité, INSERM UMR-S1144, F-75006, Paris, France.
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Børge Holen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Naz Karadag
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Aihua Lin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Guy Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Kevin O'Connell
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, PO box 1080, Blindern, 0316, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Margrethe Collier Høegh
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Chun C Fan
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Trine Vik Lagerberg
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
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Cognitive performance protects against Alzheimer's disease independently of educational attainment and intelligence. Mol Psychiatry 2022; 27:4297-4306. [PMID: 35840796 DOI: 10.1038/s41380-022-01695-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 06/21/2022] [Accepted: 06/30/2022] [Indexed: 02/07/2023]
Abstract
Mendelian-randomization (MR) studies using large-scale genome-wide association studies (GWAS) have identified causal association between educational attainment and Alzheimer's disease (AD). However, the underlying mechanisms are still required to be explored. Here, we conduct univariable and multivariable MR analyses using large-scale educational attainment, cognitive performance, intelligence and AD GWAS datasets. In stage 1, we found significant causal effects of educational attainment on cognitive performance (beta = 0.907, 95% confidence interval (CI): 0.884-0.930, P < 1.145E-299), and vice versa (beta = 0.571, 95% CI: 0.557-0.585, P < 1.145E-299). In stage 2, we found that both increase in educational attainment (odds ratio (OR) = 0.72, 95% CI: 0.66-0.78, P = 1.39E-14) and cognitive performance (OR = 0.69, 95% CI: 0.64-0.75, P = 1.78E-20) could reduce the risk of AD. In stage 3, we found that educational attainment may protect against AD dependently of cognitive performance (OR = 1.07, 95% CI: 0.90-1.28, P = 4.48E-01), and cognitive performance may protect against AD independently of educational attainment (OR = 0.69, 95% CI: 0.53-0.89, P = 5.00E-03). In stage 4, we found significant causal effects of cognitive performance on intelligence (beta = 0.907, 95% CI: 0.877-0.938, P < 1.145E-299), and vice versa (beta = 0.957, 95% CI: 0.937-0.978, P < 1.145E-299). In stage 5, we identified that cognitive performance may protect against AD independently of intelligence (OR = 0.74, 95% CI: 0.61-0.90, P = 2.00E-03), and intelligence may protect against AD dependently of cognitive performance (OR = 1.17, 95% CI: 0.40-3.43, P = 4.48E-01). Collectively, our univariable and multivariable MR analyses highlight the protective role of cognitive performance in AD independently of educational attainment and intelligence. In addition to the intelligence, we extend the mechanisms underlying the associations of educational attainment with AD.
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Wu Y, Li Y, Zhu J, Long J. Shared genetics and causality underlying epilepsy and attention-deficit hyperactivity disorder. Psychiatry Res 2022; 316:114794. [PMID: 35994864 DOI: 10.1016/j.psychres.2022.114794] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/06/2022] [Accepted: 08/12/2022] [Indexed: 11/18/2022]
Abstract
The prevalence of attention deficit hyperactivity disorder (ADHD) in patients with epilepsy was much higher than prevalence in general population, and vice versa. The mechanisms underlying comorbid ADHD and epilepsy remained largely unknown. Here, we systematically analyzed the genetic correlation, causality, shared genetics and specific trait related tissues by using linkage disequilibrium score regression (LDSC), two sample Mendelian randomization (TwoSampleMR), bivariate causal mixture model (MiXeR), conjunctional false discovery rate (conjFDR) and LDSC applied to specifically expressed genes based on genome wide association studies (GWASs) data of ADHD and epilepsy. We found that ADHD had significant positive genetic association with epilepsy. Two-sample Mendelian randomization analysis with genome wide significant single nucleotide polymorphisms (SNPs) as instrument variables suggested a positively causal effect of ADHD on epilepsy. Using MiXeR, which estimates the total amount of shared variants, we observed 1 K causal variants overlapped between ADHD and epilepsy. At conjFDR <0.05, ADHD shared 2 distinct genomic loci with Epilepsy. Further disease-relevant tissues analysis showed that cortex, substantia nigra, amygdala and hippocampus were both associated with ADHD and epilepsy. Our results suggested that ADHD was genetically correlated with epilepsy, which might be due to the fact that they shared common pathogenic sites and tissues origin.
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Affiliation(s)
- Yong Wu
- Research Center for Mental Health and Neuroscience, Wuhan Mental Health Center, Wuhan, Hubei 430012, China
| | - Yichen Li
- Radiology Department, Wuhan Mental Health Center, Wuhan, Hubei 430012, China
| | - Junhong Zhu
- Department of Mental Rehabilitation, Wuhan Mental Health Center, Wuhan, Hubei 430012, China.
| | - Jingyi Long
- Department of Child & Adolescent Psychiatry, Wuhan Mental Health Center, Wuhan, Hubei 430012, China.
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