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Zhukovsky P, Tio ES, Coughlan G, Bennett DA, Wang Y, Hohman TJ, Pizzagalli DA, Mulsant BH, Voineskos AN, Felsky D. Genetic influences on brain and cognitive health and their interactions with cardiovascular conditions and depression. Nat Commun 2024; 15:5207. [PMID: 38890310 DOI: 10.1038/s41467-024-49430-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/04/2024] [Indexed: 06/20/2024] Open
Abstract
Approximately 40% of dementia cases could be prevented or delayed by modifiable risk factors related to lifestyle and environment. These risk factors, such as depression and vascular disease, do not affect all individuals in the same way, likely due to inter-individual differences in genetics. However, the precise nature of how genetic risk profiles interact with modifiable risk factors to affect brain health is poorly understood. Here we combine multiple data resources, including genotyping and postmortem gene expression, to map the genetic landscape of brain structure and identify 367 loci associated with cortical thickness and 13 loci associated with white matter hyperintensities (P < 5×10-8), with several loci also showing a significant association with cognitive function. We show that among 220 unique genetic loci associated with cortical thickness in our genome-wide association studies (GWAS), 95 also showed evidence of interaction with depression or cardiovascular conditions. Polygenic risk scores based on our GWAS of inferior frontal thickness also interacted with hypertension in predicting executive function in the Canadian Longitudinal Study on Aging. These findings advance our understanding of the genetic underpinning of brain structure and show that genetic risk for brain and cognitive health is in part moderated by treatable mid-life factors.
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Affiliation(s)
- Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Earvin S Tio
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Gillian Coughlan
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02129, USA
| | - David A Bennett
- Department of Neurological Sciences, RUSH Medical College, Chicago, IL, 60612, USA
| | - Yanling Wang
- Department of Neurological Sciences, RUSH Medical College, Chicago, IL, 60612, USA
| | - Timothy J Hohman
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, 02478, USA
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada.
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada.
| | - Daniel Felsky
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada.
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Rotman Research Institute, Baycrest Hospital, Toronto, ON, M6A 2E1, Canada.
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Smith DM, Parekh P, Kennedy J, Loughnan R, Frei O, Nichols TE, Andreassen OA, Jernigan TL, Dale AM. Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components. Cereb Cortex 2024; 34:bhae234. [PMID: 38850213 PMCID: PMC11161865 DOI: 10.1093/cercor/bhae234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 05/09/2024] [Accepted: 05/19/2024] [Indexed: 06/10/2024] Open
Abstract
The relative contributions of genetic variation and experience in shaping the morphology of the adolescent brain are not fully understood. Using longitudinal data from 11,665 subjects in the ABCD Study, we fit vertex-wise variance components including family effects, genetic effects, and subject-level effects using a computationally efficient framework. Variance in cortical thickness and surface area is largely attributable to genetic influence, whereas sulcal depth is primarily explained by subject-level effects. Our results identify areas with heterogeneous distributions of heritability estimates that have not been seen in previous work using data from cortical regions. We discuss the biological importance of subject-specific variance and its implications for environmental influences on cortical development and maturation.
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Affiliation(s)
- Diana M Smith
- Medical Scientist Training Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Neurosciences Graduate Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Problemveien 11, 0313 Oslo, Norway
| | - Joseph Kennedy
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Robert Loughnan
- Population Neuroscience and Genetics Lab, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Problemveien 11, 0313 Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Problemveien 11, 0313 Oslo, Norway
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7FZ, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Problemveien 11, 0313 Oslo, Norway
| | - Terry L Jernigan
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Cognitive Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Cognitive Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Neuroscience, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
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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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4
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Deng Q, Song C, Lin S. An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics. Eur J Hum Genet 2024; 32:681-690. [PMID: 37237036 PMCID: PMC11153499 DOI: 10.1038/s41431-023-01389-7] [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: 05/19/2022] [Revised: 05/01/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.
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Affiliation(s)
- Qiaolan Deng
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA
| | - Chi Song
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Shili Lin
- Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA.
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5
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Yoo L, Mendoza D, Richard AJ, Stephens JM. KAT8 beyond Acetylation: A Survey of Its Epigenetic Regulation, Genetic Variability, and Implications for Human Health. Genes (Basel) 2024; 15:639. [PMID: 38790268 PMCID: PMC11121512 DOI: 10.3390/genes15050639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Lysine acetyltransferase 8, also known as KAT8, is an enzyme involved in epigenetic regulation, primarily recognized for its ability to modulate histone acetylation. This review presents an overview of KAT8, emphasizing its biological functions, which impact many cellular processes and range from chromatin remodeling to genetic and epigenetic regulation. In many model systems, KAT8's acetylation of histone H4 lysine 16 (H4K16) is critical for chromatin structure modification, which influences gene expression, cell proliferation, differentiation, and apoptosis. Furthermore, this review summarizes the observed genetic variability within the KAT8 gene, underscoring the implications of various single nucleotide polymorphisms (SNPs) that affect its functional efficacy and are linked to diverse phenotypic outcomes, ranging from metabolic traits to neurological disorders. Advanced insights into the structural biology of KAT8 reveal its interaction with multiprotein assemblies, such as the male-specific lethal (MSL) and non-specific lethal (NSL) complexes, which regulate a wide range of transcriptional activities and developmental functions. Additionally, this review focuses on KAT8's roles in cellular homeostasis, stem cell identity, DNA damage repair, and immune response, highlighting its potential as a therapeutic target. The implications of KAT8 in health and disease, as evidenced by recent studies, affirm its importance in cellular physiology and human pathology.
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Affiliation(s)
- Lindsey Yoo
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - David Mendoza
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Allison J. Richard
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
| | - Jacqueline M. Stephens
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
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6
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Guo H, Urban AE, Wong WH. Prioritizing disease-related rare variants by integrating gene expression data. RESEARCH SQUARE 2024:rs.3.rs-4355589. [PMID: 38766095 PMCID: PMC11100897 DOI: 10.21203/rs.3.rs-4355589/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in diseased patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.
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7
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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:S0168-9525(24)00079-9. [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] [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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8
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Annevelink CE, Westra J, Sala-Vila A, Harris WS, Tintle NL, Shearer GC. A Genome-Wide Interaction Study of Erythrocyte ω-3 Polyunsaturated Fatty Acid Species and Memory in the Framingham Heart Study Offspring Cohort. J Nutr 2024; 154:1640-1651. [PMID: 38141771 DOI: 10.1016/j.tjnut.2023.12.035] [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: 05/23/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 12/25/2023] Open
Abstract
BACKGROUND Cognitive decline, and more specifically Alzheimer's disease, continues to increase in prevalence globally, with few, if any, adequate preventative approaches. Several tests of cognition are utilized in the diagnosis of cognitive decline that assess executive function, short- and long-term memory, cognitive flexibility, and speech and motor control. Recent studies have separately investigated the genetic component of both cognitive health, using these measures, and circulating fatty acids. OBJECTIVES We aimed to examine the potential moderating effect of main species of ω-3 polyunsaturated fatty acids (PUFAs) on an individual's genetically conferred risk of cognitive decline. METHODS The Offspring cohort from the Framingham Heart Study was cross-sectionally analyzed in this genome-wide interaction study (GWIS). Our sample included all individuals with red blood cell ω-3 PUFA, genetic, cognitive testing (via Trail Making Tests [TMTs]), and covariate data (N = 1620). We used linear mixed effects models to predict each of the 3 cognitive measures (TMT A, TMT B, and TMT D) by each ω-3 PUFA, single nucleotide polymorphism (SNP) (0, 1, or 2 minor alleles), ω-3 PUFA by SNP interaction term, and adjusting for sex, age, education, APOE ε4 genotype status, and kinship (relatedness). RESULTS Our analysis identified 31 unique SNPs from 24 genes reaching an exploratory significance threshold of 1×10-5. Fourteen of the 24 genes have been previously associated with the brain/cognition, and 5 genes have been previously associated with circulating lipids. Importantly, 8 of the genes we identified, DAB1, SORCS2, SERINC5, OSBPL3, CPA6, DLG2, MUC19, and RGMA, have been associated with both cognition and circulating lipids. We identified 22 unique SNPs for which individuals with the minor alleles benefit substantially from increased ω-3 fatty acid concentrations and 9 unique SNPs for which the common homozygote benefits. CONCLUSIONS In this GWIS of ω-3 PUFA species on cognitive outcomes, we identified 8 unique genes with plausible biology suggesting individuals with specific polymorphisms may have greater potential to benefit from increased ω-3 PUFA intake. Additional replication in prospective settings with more diverse samples is needed.
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Affiliation(s)
- Carmen E Annevelink
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States
| | - Jason Westra
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States
| | - Aleix Sala-Vila
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States; Cardiovascular Risk and Nutrition, Hospital del Mar Research Institute, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - William S Harris
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States; Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, United States
| | - Nathan L Tintle
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States; Department of Population Health Nursing Science, College of Nursing, University of Illinois Chicago, Chicago, IL, United States
| | - Gregory C Shearer
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States.
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9
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Cao C, Li Y, Hu F, Gao X. Modeling refined differences of cortical folding patterns via spatial, morphological, and temporal fusion representations. Cereb Cortex 2024; 34:bhae146. [PMID: 38602743 DOI: 10.1093/cercor/bhae146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/12/2024] Open
Abstract
The gyrus, a pivotal cortical folding pattern, is essential for integrating brain structure-function. This study focuses on 2-Hinge and 3-Hinge folds, characterized by the gyral convergence from various directions. Existing voxel-level studies may not adequately capture the precise spatial relationships within cortical folding patterns, especially when relying solely on local cortical characteristics due to their variable shapes and homogeneous frequency-specific features. To overcome these challenges, we introduced a novel model that combines spatial distribution, morphological structure, and functional magnetic resonance imaging data. We utilized spatio-morphological residual representations to enhance and extract subtle variations in cortical spatial distribution and morphological structure during blood oxygenation, integrating these with functional magnetic resonance imaging embeddings using self-attention for spatio-morphological-temporal representations. Testing these representations for identifying cortical folding patterns, including sulci, gyri, 2-Hinge, and 2-Hinge folds, and evaluating the impact of phenotypic data (e.g. stimulus) on recognition, our experimental results demonstrate the model's superior performance, revealing significant differences in cortical folding patterns under various stimulus. These differences are also evident in the characteristics of sulci and gyri folds between genders, with 3-Hinge showing more variations. Our findings indicate that our representations of cortical folding patterns could serve as biomarkers for understanding brain structure-function correlations.
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Affiliation(s)
- Chunhong Cao
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Yongquan Li
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Fang Hu
- The Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, 423043 Chenzhou, China
| | - Xieping Gao
- The Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, 410081 Changsha, China
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10
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Peng Q, Gilder DA, Bernert RA, Karriker-Jaffe KJ, Ehlers CL. Genetic factors associated with suicidal behaviors and alcohol use disorders in an American Indian population. Mol Psychiatry 2024; 29:902-913. [PMID: 38177348 PMCID: PMC11176067 DOI: 10.1038/s41380-023-02379-3] [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: 05/18/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 01/06/2024]
Abstract
American Indians (AI) demonstrate the highest rates of both suicidal behaviors (SB) and alcohol use disorders (AUD) among all ethnic groups in the US. Rates of suicide and AUD vary substantially between tribal groups and across different geographical regions, underscoring a need to delineate more specific risk and resilience factors. Using data from over 740 AI living within eight contiguous reservations, we assessed genetic risk factors for SB by investigating: (1) possible genetic overlap with AUD, and (2) impacts of rare and low-frequency genomic variants. Suicidal behaviors included lifetime history of suicidal thoughts and acts, including verified suicide deaths, scored using a ranking variable for the SB phenotype (range 0-4). We identified five loci significantly associated with SB and AUD, two of which are intergenic and three intronic on genes AACSP1, ANK1, and FBXO11. Nonsynonymous rare and low-frequency mutations in four genes including SERPINF1 (PEDF), ZNF30, CD34, and SLC5A9, and non-intronic rare and low-frequency mutations in genes OPRD1, HSD17B3 and one lincRNA were significantly associated with SB. One identified pathway related to hypoxia-inducible factor (HIF) regulation, whose 83 nonsynonymous rare and low-frequency variants on 10 genes were significantly linked to SB as well. Four additional genes, and two pathways related to vasopressin-regulated water metabolism and cellular hexose transport, also were strongly associated with SB. This study represents the first investigation of genetic factors for SB in an American Indian population that has high risk for suicide. Our study suggests that bivariate association analysis between comorbid disorders can increase statistical power; and rare and low-frequency variant analysis in a high-risk population enabled by whole-genome sequencing has the potential to identify novel genetic factors. Although such findings may be population specific, rare functional mutations relating to PEDF and HIF regulation align with past reports and suggest a biological mechanism for suicide risk and a potential therapeutic target for intervention.
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Affiliation(s)
- Qian Peng
- Department of Neuroscience, The Scripps Research Institute, La Jolla, CA, USA.
| | - David A Gilder
- Department of Neuroscience, The Scripps Research Institute, La Jolla, CA, USA
| | - Rebecca A Bernert
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | | | - Cindy L Ehlers
- Department of Neuroscience, The Scripps Research Institute, La Jolla, CA, USA
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11
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Tissink EP, Shadrin AA, van der Meer D, Parker N, Hindley G, Roelfs D, Frei O, Fan CC, Nagel M, Nærland T, Budisteanu M, Djurovic S, Westlye LT, van den Heuvel MP, Posthuma D, Kaufmann T, Dale AM, Andreassen OA. Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study. Nat Commun 2024; 15:2655. [PMID: 38531894 DOI: 10.1038/s41467-024-46817-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 03/12/2024] [Indexed: 03/28/2024] Open
Abstract
Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.
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Affiliation(s)
- E P Tissink
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands.
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands.
| | - A A Shadrin
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - D van der Meer
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - N Parker
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - G Hindley
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, 16 De Crespigny Park, London, SE5 8AB, United Kingdom
| | - D Roelfs
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - O Frei
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - C C Fan
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, 92037, USA
| | - M Nagel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - T Nærland
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway
| | - M Budisteanu
- Prof. Dr. Alex Obregia Clinical Hospital of Psychiatry, Bucharest, Romania
- "Victor Babes" National Institute of Pathology, Bucharest, Romania
| | - S Djurovic
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - L T Westlye
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - M P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
| | - D Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
| | - T Kaufmann
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - A M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, 92037, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92037, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, 92037, USA
| | - O A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway.
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway.
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12
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Guo H, Urban AE, Wong WH. Prioritizing disease-related rare variants by integrating gene expression data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585836. [PMID: 38562756 PMCID: PMC10983955 DOI: 10.1101/2024.03.19.585836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.
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Affiliation(s)
- Hanmin Guo
- Department of Statistics, Stanford University, Stanford, California 94305, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Alexander Eckehart Urban
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Wing Hung Wong
- Department of Statistics, Stanford University, Stanford, California 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California 94305, USA
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13
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Shukla R. Two Distinct Biotypes in Major Depression Unveiled. Biol Psychiatry 2024; 95:382-384. [PMID: 38325914 DOI: 10.1016/j.biopsych.2023.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 02/09/2024]
Affiliation(s)
- Rammohan Shukla
- Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming.
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14
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Li J, Long Z, Sheng W, Du L, Qiu J, Chen H, Liao W. Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes. Biol Psychiatry 2024; 95:414-425. [PMID: 37573006 DOI: 10.1016/j.biopsych.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied. METHODS Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes. RESULTS We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies. CONCLUSIONS Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.
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Affiliation(s)
- Jiao Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Zhiliang Long
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Wei Sheng
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Lian Du
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Wei Liao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
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15
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Wang S, Li T, Zhao B, Dai W, Yao Y, Li C, Li T, Zhu H, Zhang H. Identification and validation of supervariants reveal novel loci associated with human white matter microstructure. Genome Res 2024; 34:20-33. [PMID: 38190638 PMCID: PMC10904010 DOI: 10.1101/gr.277905.123] [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: 03/18/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects. In this study, we adopt the concept of supervariants, a combination of alleles in multiple loci, to account for potential multi-SNP effects. We perform supervariant identification and validation to identify loci associated with 22 white matter fractional anisotropy phenotypes derived from diffusion tensor imaging. To increase reproducibility, we use United Kingdom (UK) Biobank White British (n = 30,842) data for discovery and internal validation, and UK Biobank White but non-British (n = 1927) data, Europeans from the Adolescent Brain Cognitive Development study (n = 4399) data, and Europeans from the Human Connectome Project (n = 319) data for external validation. We identify 23 novel loci on the discovery set that have not been reported in the previous GWASs on white matter microstructure. Among them, three supervariants on genomic regions 5q35.1, 8p21.2, and 19q13.32 have P-values lower than 0.05 in the meta-analysis of the three independent validation data sets. These supervariants contain genetic variants located in genes that have been related to brain structures, cognitive functions, and neuropsychiatric diseases. Our findings provide a better understanding of the genetic architecture underlying white matter microstructure.
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Affiliation(s)
- Shiying Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Ting Li
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104-1686, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Yisha Yao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Cai Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Heping Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA;
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16
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Miller AP, Gizer IR. Neurogenetic and multi-omic sources of overlap among sensation seeking, alcohol consumption, and alcohol use disorder. Addict Biol 2024; 29:e13365. [PMID: 38380706 PMCID: PMC10882188 DOI: 10.1111/adb.13365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/26/2023] [Accepted: 11/26/2023] [Indexed: 02/22/2024]
Abstract
Sensation seeking is bidirectionally associated with levels of alcohol consumption in both adult and adolescent samples, and shared neurobiological and genetic influences may in part explain these associations. Links between sensation seeking and alcohol use disorder (AUD) may primarily manifest via increased alcohol consumption rather than through direct effects on increasing problems and consequences. Here the overlap among sensation seeking, alcohol consumption, and AUD was examined using multivariate modelling approaches for genome-wide association study (GWAS) summary statistics in conjunction with neurobiologically informed analyses at multiple levels of investigation. Meta-analytic and genomic structural equation modelling (GenomicSEM) approaches were used to conduct GWAS of sensation seeking, alcohol consumption, and AUD. Resulting summary statistics were used in downstream analyses to examine shared brain tissue enrichment of heritability and genome-wide evidence of overlap (e.g., stratified GenomicSEM, RRHO, genetic correlations with neuroimaging phenotypes), and to identify genomic regions likely contributing to observed genetic overlap across traits (e.g., H-MAGMA and LAVA). Across approaches, results supported shared neurogenetic architecture between sensation seeking and alcohol consumption characterised by overlapping enrichment of genes expressed in midbrain and striatal tissues and variants associated with increased cortical surface area. Alcohol consumption and AUD evidenced overlap in relation to variants associated with decreased frontocortical thickness. Finally, genetic mediation models provided evidence of alcohol consumption mediating associations between sensation seeking and AUD. This study extends previous research by examining critical sources of neurogenetic and multi-omic overlap among sensation seeking, alcohol consumption, and AUD which may underlie observed phenotypic associations.
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Affiliation(s)
- Alex P. Miller
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Ian R. Gizer
- Department of Psychological SciencesUniversity of MissouriColumbiaMissouriUSA
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17
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Zagkos L, Dib MJ, Cronjé HT, Elliott P, Dehghan A, Tzoulaki I, Gill D, Daghlas I. Cerebrospinal Fluid C1-Esterase Inhibitor and Tie-1 Levels Affect Cognitive Performance: Evidence from Proteome-Wide Mendelian Randomization. Genes (Basel) 2024; 15:71. [PMID: 38254961 PMCID: PMC10815381 DOI: 10.3390/genes15010071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/29/2023] [Accepted: 01/01/2024] [Indexed: 01/24/2024] Open
Abstract
OBJECTIVE The association of cerebrospinal fluid (CSF) protein levels with cognitive function in the general population remains largely unexplored. We performed Mendelian randomization (MR) analyses to query which CSF proteins may have potential causal effects on cognitive performance. METHODS AND ANALYSIS Genetic associations with CSF proteins were obtained from a genome-wide association study conducted in up to 835 European-ancestry individuals and for cognitive performance from a meta-analysis of GWAS including 257,841 European-ancestry individuals. We performed Mendelian randomization (MR) analyses to test the effect of randomly allocated variation in 154 genetically predicted CSF protein levels on cognitive performance. Findings were validated by performing colocalization analyses and considering cognition-related phenotypes. RESULTS Genetically predicted C1-esterase inhibitor levels in the CSF were associated with a better cognitive performance (SD units of cognitive performance per 1 log-relative fluorescence unit (RFU): 0.23, 95% confidence interval: 0.12 to 0.35, p = 7.91 × 10-5), while tyrosine-protein kinase receptor Tie-1 (sTie-1) levels were associated with a worse cognitive performance (-0.43, -0.62 to -0.23, p = 2.08 × 10-5). These findings were supported by colocalization analyses and by concordant effects on distinct cognition-related and brain-volume measures. CONCLUSIONS Human genetics supports a role for the C1-esterase inhibitor and sTie-1 in cognitive performance.
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Affiliation(s)
- Loukas Zagkos
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
| | - Marie-Joe Dib
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Héléne T. Cronjé
- Department of Public Health, Section of Epidemiology, University of Copenhagen, 1165 Copenhagen, Denmark;
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
- UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, London W1T 7NF, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
- UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, London W1T 7NF, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
- UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, London W1T 7NF, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK
- Centre for Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
| | - Iyas Daghlas
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA;
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18
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Scharf C, Koschutnig K, Zussner T, Fink A, Tilp M. Twelve weeks of physical exercise breaks with coordinative exercises at the workplace increase the sulcal depth and decrease gray matter volume in brain structures related to visuomotor processes. Brain Struct Funct 2024; 229:63-74. [PMID: 38070007 PMCID: PMC10827861 DOI: 10.1007/s00429-023-02732-w] [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: 03/17/2023] [Accepted: 11/03/2023] [Indexed: 01/31/2024]
Abstract
Physical exercise can evoke changes in the brain structure. Consequently, these can lead to positive impacts on brain health. However, physical exercise studies including coordinative exercises are rare. Therefore, in this study, we investigated how 12 weeks of physical exercise breaks (PEBs) with coordinative exercises, focusing mainly on juggling tasks, affected the brain structure. The participants were randomly allocated to an intervention group (IG, n = 16; 42.8 ± 10.2 years) and a control group (CG, n = 9; 44.2 ± 12.3 years). The IG performed the PEBs with coordinative exercises twice per week for 15-20 min per session. Before the intervention, after 6 weeks of the intervention, and after 12 weeks of the intervention, participants underwent a high-resolution 3T T1-weighted magnetic resonance imagining scan. Juggling performance was assessed by measuring the time taken to perform a three-ball cascade. A surface-based analysis revealed an increase in vertex-wise cortical depth in a cluster including the inferior parietal lobe after 6 and 12 weeks of training in the IG. After 12 weeks, the IG showed a decrease in gray matter (GM) volume in a cluster primarily involving the right insula and the right operculum. The changes in the GM volume were related to improvements in juggling performance. No significant changes were found for the CG. To conclude, the present study showed that regular engagement in PEBs with coordinative exercises led to changes in brain structures strongly implicated in visuomotor processes involving hand and arm movements.
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Affiliation(s)
- Carina Scharf
- Institute of Human Movement Science, Sport and Health, University of Graz, Mozartgasse 14, 8010, Graz, Austria.
| | - Karl Koschutnig
- Institute of Psychology, University of Graz, Universitätsplatz 2, 8010, Graz, Austria
| | - Thomas Zussner
- Institute of Psychology, University of Graz, Universitätsplatz 2, 8010, Graz, Austria
| | - Andreas Fink
- Institute of Psychology, University of Graz, Universitätsplatz 2, 8010, Graz, Austria
| | - Markus Tilp
- Institute of Human Movement Science, Sport and Health, University of Graz, Mozartgasse 14, 8010, Graz, Austria
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19
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Ge YJ, Wu BS, Zhang Y, Chen SD, Zhang YR, Kang JJ, Deng YT, Ou YN, He XY, Zhao YL, Kuo K, Ma Q, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Lemaitre H, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Feng JF, Tan L, Dong Q, Schumann G, Cheng W, Yu JT. Genetic architectures of cerebral ventricles and their overlap with neuropsychiatric traits. Nat Hum Behav 2024; 8:164-180. [PMID: 37857874 DOI: 10.1038/s41562-023-01722-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 09/12/2023] [Indexed: 10/21/2023]
Abstract
The cerebral ventricles are recognized as windows into brain development and disease, yet their genetic architectures, underlying neural mechanisms and utility in maintaining brain health remain elusive. Here we aggregated genetic and neuroimaging data from 61,974 participants (age range, 9 to 98 years) in five cohorts to elucidate the genetic basis of ventricular morphology and examined their overlap with neuropsychiatric traits. Genome-wide association analysis in a discovery sample of 31,880 individuals identified 62 unique loci and 785 candidate genes associated with ventricular morphology. We replicated over 80% of loci in a well-matched cohort of lateral ventricular volume. Gene set analysis revealed enrichment of ventricular-trait-associated genes in biological processes and disease pathogenesis during both early brain development and degeneration. We explored the age-dependent genetic associations in cohorts of different age groups to investigate the possible roles of ventricular-trait-associated loci in neurodevelopmental and neurodegenerative processes. We describe the genetic overlap between ventricular and neuropsychiatric traits through comprehensive integrative approaches under correlative and causal assumptions. We propose the volume of the inferior lateral ventricles as a heritable endophenotype to predict the risk of Alzheimer's disease, which might be a consequence of prodromal Alzheimer's disease. Our study provides an advance in understanding the genetics of the cerebral ventricles and demonstrates the potential utility of ventricular measurements in tracking brain disorders and maintaining brain health across the lifespan.
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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
| | - 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 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
| | - 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
| | - 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
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- 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
| | - Xiao-Yu He
- 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
| | - Yong-Li Zhao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Kevin Kuo
- 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
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 'Trajectoires développementales & psychiatrie', University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 'Trajectoires développementales & psychiatrie', University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France
- AP-HP, Sorbonne University, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 'Trajectoires développementales & psychiatrie', University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | | | - Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
- Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Tomáš Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine, Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Jian-Feng Feng
- 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, Beijing, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Qiang Dong
- 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
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine, Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - 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, Beijing, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer 79 Center, 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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20
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Akula SK, Exposito-Alonso D, Walsh CA. Shaping the brain: The emergence of cortical structure and folding. Dev Cell 2023; 58:2836-2849. [PMID: 38113850 PMCID: PMC10793202 DOI: 10.1016/j.devcel.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 04/08/2023] [Accepted: 11/10/2023] [Indexed: 12/21/2023]
Abstract
The cerebral cortex-the brain's covering and largest region-has increased in size and complexity in humans and supports higher cognitive functions such as language and abstract thinking. There is a growing understanding of the human cerebral cortex, including the diversity and number of cell types that it contains, as well as of the developmental mechanisms that shape cortical structure and organization. In this review, we discuss recent progress in our understanding of molecular and cellular processes, as well as mechanical forces, that regulate the folding of the cerebral cortex. Advances in human genetics, coupled with experimental modeling in gyrencephalic species, have provided insights into the central role of cortical progenitors in the gyrification and evolutionary expansion of the cerebral cortex. These studies are essential for understanding the emergence of structural and functional organization during cortical development and the pathogenesis of neurodevelopmental disorders associated with cortical malformations.
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Affiliation(s)
- Shyam K Akula
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA, USA; Allen Discovery Center for Human Brain Evolution, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
| | - David Exposito-Alonso
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA, USA; Allen Discovery Center for Human Brain Evolution, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
| | - Christopher A Walsh
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA, USA; Allen Discovery Center for Human Brain Evolution, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.
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21
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Kang Y, Kang W, Kim A, Tae WS, Ham BJ, Han KM. Decreased cortical gyrification in major depressive disorder. Psychol Med 2023; 53:7512-7524. [PMID: 37154200 DOI: 10.1017/s0033291723001216] [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] [Indexed: 05/10/2023]
Abstract
BACKGROUND Early neurodevelopmental deviations, such as abnormal cortical folding patterns, are candidate biomarkers of major depressive disorder (MDD). We aimed to investigate the association of MDD with the local gyrification index (LGI) in each cortical region at the whole-brain level, and the association of the LGI with clinical characteristics of MDD. METHODS We obtained T1-weighted images from 234 patients with MDD and 215 healthy controls (HCs). The LGI values from 66 cortical regions in the bilateral hemispheres were automatically calculated according to the Desikan-Killiany atlas. We compared the LGI values between the MDD and HC groups using analysis of covariance, including age, sex, and years of education as covariates. The association between the clinical characteristics and LGI values was investigated in the MDD group. RESULTS Compared with HCs, patients with MDD showed significantly decreased LGI values in the cortical regions, including the bilateral ventrolateral and dorsolateral prefrontal cortices, medial and lateral orbitofrontal cortices, insula, right rostral anterior cingulate cortex, and several temporal and parietal regions, with the largest effect size in the left pars triangularis (Cohen's f2 = 0.361; p = 1.78 × 10-13). Regarding the association of clinical characteristics with LGIs within the MDD group, recurrence and longer illness duration were associated with increased gyrification in several occipital and temporal regions, which showed no significant difference in LGIs between the MDD and HC groups. CONCLUSIONS These findings suggest that the LGI may be a relatively stable neuroimaging marker associated with MDD predisposition.
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Affiliation(s)
- Youbin Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Wooyoung Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Aram Kim
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Woo-Suk Tae
- Brain Convergence Research Center, Korea University, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Brain Convergence Research Center, Korea University, Seoul, Republic of Korea
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Kyu-Man Han
- Brain Convergence Research Center, Korea University, Seoul, Republic of Korea
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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22
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Xue H, Xu X, Yan Z, Cheng J, Zhang L, Zhu W, Cui G, Zhang Q, Qiu S, Yao Z, Qin W, Liu F, Liang M, Fu J, Xu Q, Xu J, Xie Y, Zhang P, Li W, Wang C, Shen W, Zhang X, Xu K, Zuo XN, Ye Z, Yu Y, Xian J, Yu C. Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population. iScience 2023; 26:108005. [PMID: 37822511 PMCID: PMC10562876 DOI: 10.1016/j.isci.2023.108005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/29/2023] [Accepted: 09/18/2023] [Indexed: 10/13/2023] Open
Abstract
Correlation between blood-oxygen-level-dependent (BOLD) and cerebral blood flow (CBF) has been used as an index of neurovascular coupling. Hippocampal BOLD-CBF correlation is associated with neurocognition, and the reduced correlation is associated with neuropsychiatric disorders. We conducted the first genome-wide association study of the hippocampal BOLD-CBF correlation in 4,832 Chinese Han subjects. The hippocampal BOLD-CBF correlation had an estimated heritability of 16.2-23.9% and showed reliable genome-wide significant association with a locus at 3q28, in which many variants have been linked to neuroimaging and cerebrospinal fluid markers of Alzheimer's disease. Gene-based association analyses showed four significant genes (GMNC, CRTC2, DENND4B, and GATAD2B) and revealed enrichment for mast cell calcium mobilization, microglial cell proliferation, and ubiquitin-related proteolysis pathways that regulate different cellular components of the neurovascular unit. This is the first unbiased identification of the association of hippocampal BOLD-CBF correlation, providing fresh insights into the genetic architecture of hippocampal neurovascular coupling.
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Affiliation(s)
- Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou 325027, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province & Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an 710038, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People’s Armed Police Force, Tianjin 300162, China
| | - Shijun Qiu
- Department of Medical Imaging, the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou 510405, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin 300203, China
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Peng Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin 300192, China
| | - Xiaochu Zhang
- Division of Life Science and Medicine, University of Science & Technology of China, Hefei 230027, China
| | - Kai Xu
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center at IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
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23
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Zhao S, Chi L, Chen H. CEGA: a method for inferring natural selection by comparative population genomic analysis across species. Genome Biol 2023; 24:219. [PMID: 37789379 PMCID: PMC10548728 DOI: 10.1186/s13059-023-03068-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/20/2023] [Indexed: 10/05/2023] Open
Abstract
We developed maximum likelihood method for detecting positive selection or balancing selection using multilocus or genomic polymorphism and divergence data from two species. The method is especially useful for investigating natural selection in noncoding regions. Simulations demonstrate that the method outperforms existing methods in detecting both positive and balancing selection. We apply the method to population genomic data from human and chimpanzee. The list of genes identified under selection in the noncoding regions is prominently enriched in pathways related to the brain and nervous system. Therefore, our method will serve as a useful tool for comparative population genomic analysis.
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Affiliation(s)
- Shilei Zhao
- CAS Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
- School of Future Technology, College of Life Sciences and Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lianjiang Chi
- CAS Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
| | - Hua Chen
- CAS Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- China National Center for Bioinformation, Beijing, 100101, China.
- School of Future Technology, College of Life Sciences and Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China.
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
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24
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Smeland OB, Kutrolli G, Bahrami S, Fominykh V, Parker N, Hindley GFL, Rødevand L, Jaholkowski P, Tesfaye M, Parekh P, Elvsåshagen T, Grotzinger AD, Steen NE, van der Meer D, O’Connell KS, Djurovic S, Dale AM, Shadrin AA, Frei O, Andreassen OA. The shared genetic risk architecture of neurological and psychiatric disorders: a genome-wide analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.21.23292993. [PMID: 37503175 PMCID: PMC10371109 DOI: 10.1101/2023.07.21.23292993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
While neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathobiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here, we performed a comprehensive analysis of GWAS data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic risk and biological underpinnings. Using complementary statistical tools, we demonstrate widespread genetic overlap across the disorders, even in the absence of genetic correlations. This indicates that a large set of common variants impact risk of multiple neurological and psychiatric disorders, but with divergent effect sizes. Furthermore, biological interrogation revealed a range of biological processes associated with neurological diseases, while psychiatric disorders consistently implicated neuronal biology. Altogether, the study indicates that neurological and psychiatric disorders share key etiological aspects, which has important implications for disease classification, precision medicine, and clinical practice.
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Affiliation(s)
- Olav B. Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gleda Kutrolli
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Vera Fominykh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nadine Parker
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F. L. Hindley
- 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
| | - Linn Rødevand
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Piotr Jaholkowski
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Markos Tesfaye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry, St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torbjørn Elvsåshagen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Andrew D. Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | | | | | - Nils Eiel Steen
- 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, The Netherlands
| | - Kevin S. O’Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M. Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, USA
- Department of Neurosciences, University of California San Diego, La Jolla, USA
- Department of Radiology, University of California, San Diego, La Jolla, USA
| | - Alexey A. Shadrin
- 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
| | - 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, Oslo, Norway
| | - Ole A. Andreassen
- 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
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25
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Shi R, Xiang S, Jia T, Robbins TW, Kang J, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Lin X, Sahakian BJ, Feng J. Structural neurodevelopment at the individual level - a life-course investigation using ABCD, IMAGEN and UK Biobank data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.20.23295841. [PMID: 37790416 PMCID: PMC10543061 DOI: 10.1101/2023.09.20.23295841] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages and the neurobiological basis underlying individual heterogeneity remains poorly understood. Using structural magnetic resonance imaging from the IMAGEN cohort (n=1,543), we show that adolescents can be clustered into three groups defined by distinct developmental patterns of whole-brain gray matter volume (GMV). Genetic and epigenetic determinants of group clustering and long-term impacts of neurodevelopment in mid-to-late adulthood were investigated using data from the ABCD, IMAGEN and UK Biobank cohorts. Group 1, characterized by continuously decreasing GMV, showed generally the best neurocognitive performances during adolescence. Compared to Group 1, Group 2 exhibited a slower rate of GMV decrease and worsened neurocognitive development, which was associated with epigenetic changes and greater environmental burden. Further, Group 3 showed increasing GMV and delayed neurocognitive development during adolescence due to a genetic variation, while these disadvantages were attenuated in mid-to-late adulthood. In summary, our study revealed novel clusters of adolescent structural neurodevelopment and suggested that genetically-predicted delayed neurodevelopment has limited long-term effects on mental well-being and socio-economic outcomes later in life. Our results could inform future research on policy interventions aimed at reducing the financial and emotional burden of mental illness.
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26
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Brown AL, Sok P, Raghubar KP, Lupo PJ, Richard MA, Morrison AC, Yang JJ, Stewart CF, Okcu MF, Chintagumpala MM, Gajjar A, Kahalley LS, Conklin H, Scheurer ME. Genetic susceptibility to cognitive decline following craniospinal irradiation for pediatric central nervous system tumors. Neuro Oncol 2023; 25:1698-1708. [PMID: 37038335 PMCID: PMC10479777 DOI: 10.1093/neuonc/noad072] [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: 10/25/2022] [Indexed: 04/12/2023] Open
Abstract
BACKGROUND Survivors of pediatric central nervous system (CNS) tumors treated with craniospinal irradiation (CSI) exhibit long-term cognitive difficulties. Goals of this study were to evaluate longitudinal effects of candidate and novel genetic variants on cognitive decline following CSI. METHODS Intelligence quotient (IQ), working memory (WM), and processing speed (PS) were longitudinally collected from patients treated with CSI (n = 241). Genotype-by-time interactions were evaluated using mixed-effects linear regression to identify common variants (minor allele frequency > 1%) associated with cognitive performance change. Novel variants associated with cognitive decline (P < 5 × 10-5) in individuals of European ancestry (n = 163) were considered replicated if they demonstrated consistent genotype-by-time interactions (P < .05) in individuals of non-European ancestries (n = 78) and achieved genome-wide statistical significance (P < 5 × 10-8) in a meta-analysis across ancestry groups. RESULTS Participants were mostly males (65%) diagnosed with embryonal tumors (98%) at a median age of 8.3 years. Overall, 1150 neurocognitive evaluations were obtained (median = 5, range: 2-10 per participant). One of the five loci previously associated with cognitive outcomes in pediatric CNS tumors survivors demonstrated significant time-dependent IQ declines (PPARA rs6008197, P = .004). Two variants associated with IQ in the general population were associated with declines in IQ after Bonferroni correction (rs9348721, P = 1.7 × 10-5; rs31771, P = 7.8 × 10-4). In genome-wide analyses, we identified novel loci associated with accelerated declines in IQ (rs116595313, meta-P = 9.4 × 10-9), WM (rs17774009, meta-P = 4.2 × 10-9), and PS (rs77467524, meta-P = 1.5 × 10-8; rs17630683, meta-P = 2.0 × 10-8; rs73249323, meta-P = 3.1 × 10-8). CONCLUSIONS Inherited genetic variants involved in baseline cognitive functioning and novel susceptibility loci jointly influence the degree of treatment-associated cognitive decline in pediatric CNS tumor survivors.
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Affiliation(s)
- Austin L Brown
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas
| | - Pagna Sok
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas
| | | | - Philip J Lupo
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas
| | - Melissa A Richard
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Jun J Yang
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee
| | - Clinton F Stewart
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee
| | - Mehmet Fatih Okcu
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas
| | | | - Amar Gajjar
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, Tennessee
| | - Lisa S Kahalley
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas
| | - Heather Conklin
- Psychology Department, St. Jude Children’s Research Hospital, Memphis, Tennessee
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27
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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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Zhao Y, Zhong Y, Chen W, Chang S, Cao Q, Wang Y, Yang L. Ocular and neural genes jointly regulate the visuospatial working memory in ADHD children. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2023; 19:14. [PMID: 37658396 PMCID: PMC10472596 DOI: 10.1186/s12993-023-00216-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 08/16/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVE Working memory (WM) deficits have frequently been linked to attention deficit hyperactivity disorder (ADHD). Despite previous studies suggested its high heritability, its genetic basis, especially in ADHD, remains unclear. The current study aimed to comprehensively explore the genetic basis of visual-spatial working memory (VSWM) in ADHD using wide-ranging genetic analyses. METHODS The current study recruited a cohort consisted of 802 ADHD individuals, all met DSM-IV ADHD diagnostic criteria. VSWM was assessed by Rey-Osterrieth complex figure test (RCFT), which is a widely used psychological test include four memory indexes: detail delayed (DD), structure delayed (SD), structure immediate (SI), detail immediate (DI). Genetic analyses were conducted at the single nucleotide polymorphism (SNP), gene, pathway, polygenic and protein network levels. Polygenic Risk Scores (PRS) were based on summary statistics of various psychiatric disorders, including ADHD, autism spectrum disorder (ASD), major depressive disorder (MDD), schizophrenia (SCZ), obsessive compulsive disorders (OCD), and substance use disorder (SUD). RESULTS Analyses at the single-marker level did not yield significant results (5E-08). However, the potential signals with P values less than E-05 and their mapped genes suggested the regulation of VSWM involved both ocular and neural system related genes, moreover, ADHD-related genes were also involved. The gene-based analysis found RAB11FIP1, whose encoded protein modulates several neurodevelopment processes and visual system, as significantly associated with DD scores (P = 1.96E-06, Padj = 0.036). Candidate pathway enrichment analyses (N = 53) found that forebrain neuron fate commitment significantly enriched in DD (P = 4.78E-04, Padj = 0.025), and dopamine transport enriched in SD (P = 5.90E-04, Padj = 0.031). We also observed a significant negative relationship between DD scores and ADHD PRS scores (P = 0.0025, Empirical P = 0.048). CONCLUSIONS Our results emphasized the joint contribution of ocular and neural genes in regulating VSWM. The study reveals a shared genetic basis between ADHD and VSWM, with GWAS indicating the involvement of ADHD-related genes in VSWM. Additionally, the PRS analysis identifies a significant relationship between ADHD-PRS and DD scores. Overall, our findings shed light on the genetic basis of VSWM deficits in ADHD, and may have important implications for future research and clinical practice.
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Affiliation(s)
- Yilu Zhao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital), NHC Key Laboratory of Mental Health (Peking University), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Yuanxin Zhong
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital), NHC Key Laboratory of Mental Health (Peking University), 51 Huayuan Bei Road, Beijing, 100191, China
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Wei Chen
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital), NHC Key Laboratory of Mental Health (Peking University), 51 Huayuan Bei Road, Beijing, 100191, China
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital), NHC Key Laboratory of Mental Health (Peking University), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Qingjiu Cao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital), NHC Key Laboratory of Mental Health (Peking University), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Yufeng Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital), NHC Key Laboratory of Mental Health (Peking University), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Li Yang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital), NHC Key Laboratory of Mental Health (Peking University), 51 Huayuan Bei Road, Beijing, 100191, China.
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Pretzsch CM, Ecker C. Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review. Front Neurosci 2023; 17:1172779. [PMID: 37457001 PMCID: PMC10347684 DOI: 10.3389/fnins.2023.1172779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/09/2023] [Indexed: 07/18/2023] Open
Abstract
Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly in vivo and across age. In this review, we aim to summarize the existing literature in autism (and neurotypicals) to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying (neuropathological) biological processes, and (iii) associated genomic variation.
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Affiliation(s)
- Charlotte M. Pretzsch
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
| | - Christine Ecker
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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Cruchaga C, Western D, Timsina J, Wang L, Wang C, Yang C, Ali M, Beric A, Gorijala P, Kohlfeld P, Budde J, Levey A, Morris J, Perrin R, Ruiz A, Marquié M, Boada M, de Rojas I, Rutledge J, Oh H, Wilson E, Guen YL, Alvarez I, Aguilar M, Greicius M, Pastor P, Pulford D, Ibanez L, Wyss-Coray T, Sung YJ, Phillips B. Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer's disease. RESEARCH SQUARE 2023:rs.3.rs-2814616. [PMID: 37333337 PMCID: PMC10275048 DOI: 10.21203/rs.3.rs-2814616/v1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The integration of quantitative trait loci (QTL) with disease genome-wide association studies (GWAS) has proven successful at prioritizing candidate genes at disease-associated loci. QTL mapping has mainly been focused on multi-tissue expression QTL or plasma protein QTL (pQTL). Here we generated the largest-to-date cerebrospinal fluid (CSF) pQTL atlas by analyzing 7,028 proteins in 3,107 samples. We identified 3,373 independent study-wide associations for 1,961 proteins, including 2,448 novel pQTLs of which 1,585 are unique to CSF, demonstrating unique genetic regulation of the CSF proteome. In addition to the established chr6p22.2-21.32 HLA region, we identified pleiotropic regions on chr3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron-specificity and neurological development. We also integrated this pQTL atlas with the latest Alzheimer's disease (AD) GWAS through PWAS, colocalization and Mendelian Randomization and identified 42 putative causal proteins for AD, 15 of which have drugs available. Finally, we developed a proteomics-based risk score for AD that outperforms genetics-based polygenic risk scores. These findings will be instrumental to further understand the biology and identify causal and druggable proteins for brain and neurological traits.
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Affiliation(s)
| | - Dan Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Washington University School of Medicine
| | | | | | | | | | | | - Patsy Kohlfeld
- Washington University School of Medicine, St Louis, MO, USA
| | | | | | | | | | | | | | - Mercè Boada
- Memory Clinic of Fundaciò ACE, Catalan Institute of Applied Neurosciences
| | | | | | | | | | | | - Ignacio Alvarez
- Fundació Docència i Recerca Mútua Terrassa, Terrassa, Barcelona, Spain
| | | | | | - Pau Pastor
- University Hospital Germans Trias i Pujol
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Miller AP, Gizer IR. Neurogenetic and multi-omic sources of overlap among sensation seeking, alcohol consumption, and alcohol use disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.30.23290733. [PMID: 37333128 PMCID: PMC10274973 DOI: 10.1101/2023.05.30.23290733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Sensation seeking is bidirectionally associated with levels of alcohol consumption in both adult and adolescent samples and shared neurobiological and genetic influences may in part explain this association. Links between sensation seeking and alcohol use disorder (AUD) may primarily manifest via increased alcohol consumption rather than through direct effects on increasing problems and consequences. Here the overlap between sensation seeking, alcohol consumption, and AUD was examined using multivariate modeling approaches for genome-wide association study (GWAS) summary statistics in conjunction with neurobiologically-informed analyses at multiple levels of investigation. Meta-analytic and genomic structural equation modeling (GenomicSEM) approaches were used to conduct GWAS of sensation seeking, alcohol consumption, and AUD. Resulting summary statistics were used in downstream analyses to examine shared brain tissue enrichment of heritability and genome-wide evidence of overlap (e.g., stratified GenomicSEM, RRHO, genetic correlations with neuroimaging phenotypes) and to identify genomic regions likely contributing to observed genetic overlap across traits (e.g., HMAGMA, LAVA). Across approaches, results supported shared neurogenetic architecture between sensation seeking and alcohol consumption characterized by overlapping enrichment of genes expressed in midbrain and striatal tissues and variants associated with increased cortical surface area. Alcohol consumption and AUD evidenced overlap in relation to variants associated with decreased frontocortical thickness. Finally, genetic mediation models provided evidence of alcohol consumption mediating associations between sensation seeking and AUD. This study extends previous research by examining critical sources of neurogenetic and multi-omic overlap among sensation seeking, alcohol consumption, and AUD which may underlie observed phenotypic associations.
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Affiliation(s)
- Alex P. Miller
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Ian R. Gizer
- Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
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Fürtjes AE, Arathimos R, Coleman JRI, Cole JH, Cox SR, Deary IJ, de la Fuente J, Madole JW, Tucker‐Drob EM, Ritchie SJ. General dimensions of human brain morphometry inferred from genome-wide association data. Hum Brain Mapp 2023; 44:3311-3323. [PMID: 36987996 PMCID: PMC10171533 DOI: 10.1002/hbm.26283] [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] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 02/01/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2 = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.
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Affiliation(s)
- Anna E. Fürtjes
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
| | - Ryan Arathimos
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - Jonathan R. I. Coleman
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - James H. Cole
- Department of NeuroimageingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonWC1V 6LJUK
- Dementia Research CentreInstitute of Neurology, University College LondonLondonWC1N 3BGUK
| | - Simon R. Cox
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Ian J. Deary
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Javier de la Fuente
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - James W. Madole
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Elliot M. Tucker‐Drob
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Stuart J. Ritchie
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
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Gilder D, Bernert R, Karriker-Jaffe K, Ehlers C, Peng Q. Genetic Factors Associated with Suicidal Behaviors and Alcohol Use Disorders in an American Indian Population. RESEARCH SQUARE 2023:rs.3.rs-2950284. [PMID: 37398076 PMCID: PMC10312956 DOI: 10.21203/rs.3.rs-2950284/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
American Indians (AI) demonstrate the highest rates of both suicidal behaviors (SB) and alcohol use disorders (AUD) among all ethnic groups in the US. Rates of suicide and AUD vary substantially between tribal groups and across different geographical regions, underscoring a need to delineate more specific risk and resilience factors. Using data from over 740 AI living within eight contiguous reservations, we assessed genetic risk factors for SB by investigating: (1) possible genetic overlap with AUD, and (2) impacts of rare and low frequency genomic variants. Suicidal behaviors included lifetime history of suicidal thoughts and acts, including verified suicide deaths, scored using a ranking variable for the SB phenotype (range 0-4). We identified five loci significantly associated with SB and AUD, two of which are intergenic and three intronic on genes AACSP1, ANK1, and FBXO11. Nonsynonymous rare mutations in four genes including SERPINF1 (PEDF), ZNF30, CD34, and SLC5A9, and non-intronic rare mutations in genes OPRD1, HSD17B3 and one lincRNA were significantly associated with SB. One identified pathway related to hypoxia-inducible factor (HIF) regulation, whose 83 nonsynonymous rare variants on 10 genes were significantly linked to SB as well. Four additional genes, and two pathways related to vasopressin-regulated water metabolism and cellular hexose transport, also were strongly associated with SB. This study represents the first investigation of genetic factors for SB in an American Indian population that has high risk for suicide. Our study suggests that bivariate association analysis between comorbid disorders can increase statistical power; and rare variant analysis in a high-risk population enabled by whole-genome sequencing has the potential to identify novel genetic factors. Although such findings may be population specific, rare functional mutations relating to PEDF and HIF regulation align with past reports and suggest a biological mechanism for suicide risk and a potential therapeutic target for intervention.
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Wang T, Chen X, Zhang J, Feng Q, Huang M. Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases. Med Image Anal 2023; 88:102842. [PMID: 37247468 DOI: 10.1016/j.media.2023.102842] [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: 11/02/2022] [Revised: 03/01/2023] [Accepted: 05/15/2023] [Indexed: 05/31/2023]
Abstract
Imaging genetics is a crucial tool that is applied to explore potentially disease-related biomarkers, particularly for neurodegenerative diseases (NDs). With the development of imaging technology, the association analysis between multimodal imaging data and genetic data is gradually being concerned by a wide range of imaging genetics studies. However, multimodal data are fused first and then correlated with genetic data in traditional methods, which leads to an incomplete exploration of their common and complementary information. In addition, the inaccurate formulation in the complex relationships between imaging and genetic data and information loss caused by missing multimodal data are still open problems in imaging genetics studies. Therefore, in this study, a deep multimodality-disentangled association analysis network (DMAAN) is proposed to solve the aforementioned issues and detect the disease-related biomarkers of NDs simultaneously. First, the imaging data are nonlinearly projected into a latent space and imaging representations can be achieved. The imaging representations are further disentangled into common and specific parts by using a multimodal-disentangled module. Second, the genetic data are encoded to achieve genetic representations, and then, the achieved genetic representations are nonlinearly mapped to the common and specific imaging representations to build nonlinear associations between imaging and genetic data through an association analysis module. Moreover, modality mask vectors are synchronously synthesized to integrate the genetic and imaging data, which helps the following disease diagnosis. Finally, the proposed method achieves reasonable diagnosis performance via a disease diagnosis module and utilizes the label information to detect the disease-related modality-shared and modality-specific biomarkers. Furthermore, the genetic representation can be used to impute the missing multimodal data with our learning strategy. Two publicly available datasets with different NDs are used to demonstrate the effectiveness of the proposed DMAAN. The experimental results show that the proposed DMAAN can identify the disease-related biomarkers, which suggests the proposed DMAAN may provide new insights into the pathological mechanism and early diagnosis of NDs. The codes are publicly available at https://github.com/Meiyan88/DMAAN.
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Affiliation(s)
- Tao Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Xiumei Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Jiawei Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
| | - Meiyan Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
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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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Makowski C, Wang H, Srinivasan A, Qi A, Qiu Y, van der Meer D, Frei O, Zou J, Visscher P, Yang J, Chen CH. Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness. Proc Natl Acad Sci U S A 2023; 120:e2214834120. [PMID: 36893272 PMCID: PMC10089183 DOI: 10.1073/pnas.2214834120] [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: 08/30/2022] [Accepted: 01/18/2023] [Indexed: 03/11/2023] Open
Abstract
Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.
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Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Hao Wang
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Anjali Srinivasan
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Anna Qi
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Yuqi Qiu
- School of Statistics, East China Normal University, Shanghai20050, China
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, University of Oslo, Oslo0450, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, University of Oslo, Oslo0450, Norway
| | - Jingjing Zou
- Division of Biostatistics and Bioinformatics, University of California San Diego, La Jolla, CA92093
| | - Peter M. Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD4072, Australia
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang310024, China
| | - Chi-Hua Chen
- Department of Radiology, University of California San Diego, La Jolla, CA92093
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Akula SK, Marciano JH, Lim Y, Exposito-Alonso D, Hylton NK, Hwang GH, Neil JE, Dominado N, Bunton-Stasyshyn RK, Song JHT, Talukdar M, Schmid A, Teboul L, Mo A, Shin T, Finander B, Beck SG, Yeh RC, Otani A, Qian X, DeGennaro EM, Alkuraya FS, Maddirevula S, Cascino GD, Giannini C, Burrage LC, Rosenfield JA, Ketkar S, Clark GD, Bacino C, Lewis RA, Segal RA, Bazan JF, Smith KA, Golden JA, Cho G, Walsh CA. TMEM161B regulates cerebral cortical gyration, Sonic Hedgehog signaling, and ciliary structure in the developing central nervous system. Proc Natl Acad Sci U S A 2023; 120:e2209964120. [PMID: 36669111 PMCID: PMC9942790 DOI: 10.1073/pnas.2209964120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 12/14/2022] [Indexed: 01/22/2023] Open
Abstract
Sonic hedgehog signaling regulates processes of embryonic development across multiple tissues, yet factors regulating context-specific Shh signaling remain poorly understood. Exome sequencing of families with polymicrogyria (disordered cortical folding) revealed multiple individuals with biallelic deleterious variants in TMEM161B, which encodes a multi-pass transmembrane protein of unknown function. Tmem161b null mice demonstrated holoprosencephaly, craniofacial midline defects, eye defects, and spinal cord patterning changes consistent with impaired Shh signaling, but were without limb defects, suggesting a CNS-specific role of Tmem161b. Tmem161b depletion impaired the response to Smoothened activation in vitro and disrupted cortical histogenesis in vivo in both mouse and ferret models, including leading to abnormal gyration in the ferret model. Tmem161b localizes non-exclusively to the primary cilium, and scanning electron microscopy revealed shortened, dysmorphic, and ballooned ventricular zone cilia in the Tmem161b null mouse, suggesting that the Shh-related phenotypes may reflect ciliary dysfunction. Our data identify TMEM161B as a regulator of cerebral cortical gyration, as involved in primary ciliary structure, as a regulator of Shh signaling, and further implicate Shh signaling in human gyral development.
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Affiliation(s)
- Shyam K. Akula
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Harvard-Massachusetts Institute of Technology MD/PhD Program, Program in Neuroscience, Harvard Medical School, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
| | - Jack H. Marciano
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
| | - Youngshin Lim
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA90048
| | - David Exposito-Alonso
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
| | - Norma K. Hylton
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Harvard-Massachusetts Institute of Technology MD/PhD Program, Program in Neuroscience, Harvard Medical School, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
| | - Grace H. Hwang
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA02115
- Department of Neurobiology, Harvard Medical School, Boston, MA02115
| | - Jennifer E. Neil
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
| | - Nicole Dominado
- Department of Anatomy & Physiology, The University of Melbourne, Melbourne, VIC3010, Australia
| | | | - Janet H. T. Song
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
| | - Maya Talukdar
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Harvard-Massachusetts Institute of Technology MD/PhD Program, Program in Neuroscience, Harvard Medical School, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
| | - Aloisia Schmid
- Department of Physics/Electron Microscopy Core, Northeastern University, Boston, MA02115
| | - Lydia Teboul
- Mary Lyon Centre, United Kingdom Medical Research Council Harwell, Didcot, Oxfordshire,OX11 0RD, UK
| | - Alisa Mo
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
| | - Taehwan Shin
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
| | - Benjamin Finander
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
| | - Samantha G. Beck
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
| | - Rebecca C. Yeh
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
| | - Aoi Otani
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
| | - Xuyu Qian
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
| | - Ellen M. DeGennaro
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
| | - Fowzan S. Alkuraya
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, 11564 Riyadh, Saudi Arabia
| | - Sateesh Maddirevula
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, 11564 Riyadh, Saudi Arabia
| | | | - Caterina Giannini
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN55905
| | | | - Lindsay C. Burrage
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX77030
- Departments of Pediatrics, Baylor College of Medicine, Houston, TX77030
- Neurology, Baylor College of Medicine, Houston, TX77030
- Neuroscience, Baylor College of Medicine, Houston, TX77030
| | - Jill A. Rosenfield
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX77030
| | - Shamika Ketkar
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX77030
| | - Gary D. Clark
- Departments of Pediatrics, Baylor College of Medicine, Houston, TX77030
- Neurology, Baylor College of Medicine, Houston, TX77030
- Neuroscience, Baylor College of Medicine, Houston, TX77030
| | - Carlos Bacino
- Departments of Pediatrics, Baylor College of Medicine, Houston, TX77030
- Neurology, Baylor College of Medicine, Houston, TX77030
- Neuroscience, Baylor College of Medicine, Houston, TX77030
| | - Richard A. Lewis
- Departments of Pediatrics, Baylor College of Medicine, Houston, TX77030
- Neurology, Baylor College of Medicine, Houston, TX77030
- Neuroscience, Baylor College of Medicine, Houston, TX77030
| | - Rosalind A. Segal
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA02115
- Department of Neurobiology, Harvard Medical School, Boston, MA02115
| | - J. Fernando Bazan
- Unit for Structural Biology, Vlaams Instituut voor Biotechnologie-UGent Center for Inflammation Research, 9052Ghent, Belgium
| | - Kelly A. Smith
- Department of Anatomy & Physiology, The University of Melbourne, Melbourne, VIC3010, Australia
| | - Jeffrey A. Golden
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA90048
| | - Ginam Cho
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA90048
| | - Christopher A. Walsh
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA02115
- Harvard-Massachusetts Institute of Technology MD/PhD Program, Program in Neuroscience, Harvard Medical School, Boston, MA02115
- Howard Hughes Medical Institute, Boston Children’s Hospital Boston, Boston, MA02115
- Department of Pediatrics, Harvard Medical School, Boston, MA02115
- Department of Neurology, Harvard Medical School, Boston, MA02115
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TMEM161B modulates radial glial scaffolding in neocortical development. Proc Natl Acad Sci U S A 2023; 120:e2209983120. [PMID: 36669109 PMCID: PMC9942823 DOI: 10.1073/pnas.2209983120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
TMEM161B encodes an evolutionarily conserved widely expressed novel 8-pass transmembrane protein of unknown function in human. Here we identify TMEM161B homozygous hypomorphic missense variants in our recessive polymicrogyria (PMG) cohort. Patients carrying TMEM161B mutations exhibit striking neocortical PMG and intellectual disability. Tmem161b knockout mice fail to develop midline hemispheric cleavage, whereas knock-in of patient mutations and patient-derived brain organoids show defects in apical cell polarity and radial glial scaffolding. We found that TMEM161B modulates actin filopodia, functioning upstream of the Rho-GTPase CDC42. Our data link TMEM161B with human PMG, likely regulating radial glia apical polarity during neocortical development.
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Ganesh S, Vemula A, Bhattacharjee S, Mathew K, Ithal D, Navin K, Nadella RK, Viswanath B, Sullivan PF, Jain S, Purushottam M. Whole exome sequencing in dense families suggests genetic pleiotropy amongst Mendelian and complex neuropsychiatric syndromes. Sci Rep 2022; 12:21128. [PMID: 36476812 PMCID: PMC9729597 DOI: 10.1038/s41598-022-25664-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/02/2022] [Indexed: 12/13/2022] Open
Abstract
Whole Exome Sequencing (WES) studies provide important insights into the genetic architecture of serious mental illness (SMI). Genes that are central to the shared biology of SMIs may be identified by WES in families with multiple affected individuals with diverse SMI (F-SMI). We performed WES in 220 individuals from 75 F-SMI families and 60 unrelated controls. Within pedigree prioritization employed criteria of rarity, functional consequence, and sharing by ≥ 3 affected members. Across the sample, gene and gene-set-wide case-control association analysis was performed with Sequence Kernel Association Test (SKAT). In 14/16 families with ≥ 3 sequenced affected individuals, we identified a total of 78 rare predicted deleterious variants in 78 unique genes shared by ≥ 3 members with SMI. Twenty (25%) genes were implicated in monogenic CNS syndromes in OMIM (OMIM-CNS), a fraction that is a significant overrepresentation (Fisher's Exact test OR = 2.47, p = 0.001). In gene-set SKAT, statistically significant association was noted for OMIM-CNS gene-set (SKAT-p = 0.005) but not the synaptic gene-set (SKAT-p = 0.17). In this WES study in F-SMI, we identify private, rare, protein altering variants in genes previously implicated in Mendelian neuropsychiatric syndromes; suggesting pleiotropic influences in neurodevelopment between complex and Mendelian syndromes.
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Affiliation(s)
- Suhas Ganesh
- grid.417719.d0000 0004 1767 5549Central Institute of Psychiatry, Kanke, Ranchi, India ,grid.47100.320000000419368710Schizophrenia Neuropharmacology Research Group, Department of Psychiatry, Yale University School of Medicine, New Haven, USA
| | - Alekhya Vemula
- grid.416861.c0000 0001 1516 2246Molecular Genetics Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | | | - Kezia Mathew
- grid.416861.c0000 0001 1516 2246Molecular Genetics Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Dhruva Ithal
- grid.416861.c0000 0001 1516 2246Molecular Genetics Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Karthick Navin
- grid.416861.c0000 0001 1516 2246Molecular Genetics Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Ravi Kumar Nadella
- grid.416861.c0000 0001 1516 2246Molecular Genetics Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India ,Department of Psychiatry, Varma Hospital, Bhimavaram, India
| | - Biju Viswanath
- grid.416861.c0000 0001 1516 2246Molecular Genetics Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Patrick F. Sullivan
- grid.10698.360000000122483208University of North Carolina at Chapel Hill, Chapel Hill, NC USA ,grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, Stockholm, Sweden
| | | | - Sanjeev Jain
- grid.416861.c0000 0001 1516 2246Molecular Genetics Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Meera Purushottam
- grid.416861.c0000 0001 1516 2246Molecular Genetics Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
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40
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Li J, Zhang Y, Huang Z, Jiang Y, Ren Z, Liu D, Zhang J, La Piana R, Chen Y. Cortical and subcortical morphological alterations in motor subtypes of Parkinson's disease. NPJ Parkinsons Dis 2022; 8:167. [PMID: 36470900 PMCID: PMC9723125 DOI: 10.1038/s41531-022-00435-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Parkinson's disease (PD) can be classified into an akinetic-rigid (AR) and a tremor-dominant (TD) subtype based on predominant motor symptoms. Patients with different motor subtypes often show divergent clinical manifestations; however, the underlying neural mechanisms remain unclear. This study aimed to characterize the cortical and subcortical morphological alterations in motor subtypes of PD. T1-weighted MRI images were obtained for 90 patients with PD (64 with the AR subtype and 26 with the TD subtype) and 56 healthy controls (HCs). Cortical surface area, sulcal depth (measured by Freesurfer's Sulc index), and subcortical volume were computed to identify the cortical and subcortical morphological alterations in the two motor subtypes. Compared with HCs, we found widespread surface area reductions in the AR subtype yet sparse surface area reductions in the TD subtype. We found no significant Sulc change in the AR subtype yet increased Sulc in the right supramarginal gyrus in the TD subtype. The hippocampal volumes in both subtypes were lower than those of HCs. In PD patients, the surface area of left posterior cingulate cortex was positively correlated with Mini-Mental State Examination (MMSE) score, while the Sulc value of right middle frontal gyrus was positively correlated with severity of motor impairments. Additionally, the hippocampal volumes were positively correlated with MMSE and Montreal Cognitive Assessment scores and negatively correlated with severity of motor impairments and Hoehn & Yahr scores. Taken together, these findings may contribute to a better understanding of the neural substrates underlying the distinct symptom profiles in the two PD subtypes.
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Affiliation(s)
- Jianyu Li
- grid.54549.390000 0004 0369 4060Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 P. R. China
| | - Yuanchao Zhang
- grid.54549.390000 0004 0369 4060Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 P. R. China
| | - Zitong Huang
- grid.54549.390000 0004 0369 4060Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 P. R. China
| | - Yihan Jiang
- grid.54549.390000 0004 0369 4060Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 P. R. China
| | - Zhanbing Ren
- grid.263488.30000 0001 0472 9649Department of Physical Education, Shenzhen University, Shenzhen, 518060 China
| | - Daihong Liu
- grid.452285.cDepartment of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030 P. R. China
| | - Jiuquan Zhang
- grid.452285.cDepartment of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030 P. R. China
| | - Roberta La Piana
- grid.14709.3b0000 0004 1936 8649Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 0G4 Canada
| | - Yifan Chen
- grid.54549.390000 0004 0369 4060Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 P. R. China
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41
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Wren GH, Davies W. X-linked ichthyosis: New insights into a multi-system disorder. SKIN HEALTH AND DISEASE 2022; 2:e179. [PMID: 36479267 PMCID: PMC9720199 DOI: 10.1002/ski2.179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/09/2022] [Indexed: 11/07/2022]
Abstract
Background X-linked ichthyosis (XLI) is a rare genetic condition almostexclusively affecting males; it is characterised by abnormal desquamation and retentionhyperkeratosis, and presents with polygonal brown scales. Most cases resultfrom genetic deletions within Xp22.31 spanning the STS (steroid sulfatase)gene, with the remaining cases resulting from STS-specific mutations. For manyyears it has been recognised that individuals with XLI are at increased risk ofcryptorchidism and corneal opacities. Methods We discuss emerging evidence that such individuals are alsomore likely to be affected by a range of neurodevelopmental and psychiatrictraits, by cardiac arrhythmias, and by rare fibrotic and bleeding-relatedconditions. We consider candidate mechanisms that may confer elevatedlikelihood of these individual conditions, and propose a novel commonbiological risk pathway. Results Understanding the prevalence, nature and co-occurrence ofcomorbidities associated with XLI is critical for ensuring early identificationof symptoms and for providing the most effective genetic counselling andmultidisciplinary care for affected individuals. Conclusion Future work in males with XLI, and in new preclinical andcellular model systems, should further clarify underlying pathophysiologicalmechanisms amenable to therapeutic intervention.
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Affiliation(s)
| | - William Davies
- School of Psychology Cardiff University Cardiff UK.,School of Medicine Cardiff University Cardiff UK.,Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK.,Neuroscience and Mental Health Innovation Institute Cardiff University Cardiff UK
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42
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Cheng W, van der Meer D, Parker N, Hindley G, O'Connell KS, Wang Y, Shadrin AA, Alnæs D, Bahrami S, Lin A, Karadag N, Holen B, Fernandez-Cabello S, Fan CC, Dale AM, Djurovic S, Westlye LT, Frei O, Smeland OB, Andreassen OA. Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood. Mol Psychiatry 2022; 27:5167-5176. [PMID: 36100668 DOI: 10.1038/s41380-022-01751-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 01/14/2023]
Abstract
Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74-96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.
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Affiliation(s)
- Weiqiu Cheng
- 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
| | - Nadine Parker
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy Hindley
- 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, UK
| | - Kevin S O'Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Centre for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aihua Lin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Naz Karadag
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Børge Holen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sara Fernandez-Cabello
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Chun-Chieh Fan
- Center for Multimodal Imaging and Genetics, 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
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, 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.,Department of Radiology, 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
| | - Lars T Westlye
- 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
| | - Oleksandr Frei
- 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
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition. Commun Biol 2022; 5:1271. [PMID: 36402844 PMCID: PMC9675774 DOI: 10.1038/s42003-022-04237-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 11/08/2022] [Indexed: 11/21/2022] Open
Abstract
Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h2 = .25 vs. .13, p = 1.8x10-7). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (rg = .49, p = 2.7x10-22). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.
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44
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Sun BB, Loomis SJ, Pizzagalli F, Shatokhina N, Painter JN, Foley CN, Jensen ME, McLaren DG, Chintapalli SS, Zhu AH, Dixon D, Islam T, Ba Gari I, Runz H, Medland SE, Thompson PM, Jahanshad N, Whelan CD. Genetic map of regional sulcal morphology in the human brain from UK biobank data. Nat Commun 2022; 13:6071. [PMID: 36241887 PMCID: PMC9568560 DOI: 10.1038/s41467-022-33829-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 10/05/2022] [Indexed: 12/24/2022] Open
Abstract
Genetic associations with macroscopic brain structure can provide insights into brain function and disease. However, specific associations with measures of local brain folding are largely under-explored. Here, we conducted large-scale genome- and exome-wide associations of regional cortical sulcal measures derived from magnetic resonance imaging scans of 40,169 individuals in UK Biobank. We discovered 388 regional brain folding associations across 77 genetic loci, with genes in associated loci enriched for expression in the cerebral cortex, neuronal development processes, and differential regulation during early brain development. We integrated brain eQTLs to refine genes for various loci, implicated several genes involved in neurodevelopmental disorders, and highlighted global genetic correlations with neuropsychiatric phenotypes. We provide an interactive 3D visualisation of our summary associations, emphasising added resolution of regional analyses. Our results offer new insights into the genetic architecture of brain folding and provide a resource for future studies of sulcal morphology in health and disease.
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Affiliation(s)
- Benjamin B Sun
- Translational Biology, Research & Development, Biogen Inc., Cambridge, MA, US.
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Stephanie J Loomis
- Translational Biology, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Fabrizio Pizzagalli
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Natalia Shatokhina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Jodie N Painter
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Christopher N Foley
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Optima Partners, Edinburgh, UK
| | - Megan E Jensen
- Clinical Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Donald G McLaren
- Clinical Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | | | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Daniel Dixon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Tasfiya Islam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Heiko Runz
- Translational Biology, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US.
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US.
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45
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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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46
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The psychiatric risk gene BRD1 modulates mitochondrial bioenergetics by transcriptional regulation. Transl Psychiatry 2022; 12:319. [PMID: 35941107 PMCID: PMC9359996 DOI: 10.1038/s41398-022-02053-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 06/24/2022] [Accepted: 07/01/2022] [Indexed: 12/03/2022] Open
Abstract
Bromodomain containing 1 (BRD1) encodes an epigenetic regulator that controls the expression of genetic networks linked to mental illness. BRD1 is essential for normal brain development and its role in psychopathology has been demonstrated in genetic and preclinical studies. However, the neurobiology that bridges its molecular and neuropathological effects remains poorly explored. Here, using publicly available datasets, we find that BRD1 targets nuclear genes encoding mitochondrial proteins in cell lines and that modulation of BRD1 expression, irrespective of whether it is downregulation or upregulation of one or the other existing BRD1 isoforms (BRD1-L and BRD1-S), leads to distinct shifts in the expression profile of these genes. We further show that the expression of nuclear genes encoding mitochondrial proteins is negatively correlated with the expression of BRD1 mRNA during human brain development. In accordance, we identify the key gate-keeper of mitochondrial metabolism, Peroxisome proliferator-activated receptor (PPAR) among BRD1's co-transcription factors and provide evidence that BRD1 acts as a co-repressor of PPAR-mediated transcription. Lastly, when using quantitative PCR, mitochondria-targeted fluorescent probes, and the Seahorse XFe96 Analyzer, we demonstrate that modulation of BRD1 expression in cell lines alters mitochondrial physiology (mtDNA content and mitochondrial mass), metabolism (reducing power), and bioenergetics (among others, basal, maximal, and spare respiration) in an expression level- and isoform-dependent manner. Collectively, our data suggest that BRD1 is a transcriptional regulator of nuclear-encoded mitochondrial proteins and that disruption of BRD1's genomic actions alters mitochondrial functions. This may be the mechanism underlying the cellular and atrophic changes of neurons previously associated with BRD1 deficiency and suggests that mitochondrial dysfunction may be a possible link between genetic variation in BRD1 and psychopathology in humans.
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47
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Bahrami S, Nordengen K, Shadrin AA, Frei O, van der Meer D, Dale AM, Westlye LT, Andreassen OA, Kaufmann T. Distributed genetic architecture across the hippocampal formation implies common neuropathology across brain disorders. Nat Commun 2022; 13:3436. [PMID: 35705537 PMCID: PMC9200849 DOI: 10.1038/s41467-022-31086-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 06/01/2022] [Indexed: 12/13/2022] Open
Abstract
Despite its major role in complex human functions across the lifespan, most notably navigation, learning and memory, much of the genetic architecture of the hippocampal formation is currently unexplored. Here, through multivariate genome-wide association analysis in volumetric data from 35,411 white British individuals, we reveal 177 unique genetic loci with distributed associations across the hippocampal formation. We identify genetic overlap with eight brain disorders with typical onset at different stages of life, where common genes suggest partly age- and disorder-independent mechanisms underlying hippocampal pathology.
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Affiliation(s)
- Shahram Bahrami
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Kaja Nordengen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Alexey A Shadrin
- Norwegian 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, Oslo, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research, 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
| | - Anders M Dale
- Department of Radiology, School of Medicine, University of California, San Diego, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, CA, USA
| | - Lars T Westlye
- Norwegian 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, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Norwegian 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, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research, 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.
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48
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Nawaz MS, Einarsson G, Bustamante M, Gisladottir RS, Walters GB, Jonsdottir GA, Skuladottir AT, Bjornsdottir G, Magnusson SH, Asbjornsdottir B, Unnsteinsdottir U, Sigurdsson E, Jonsson PV, Palmadottir VK, Gudjonsson SA, Halldorsson GH, Ferkingstad E, Jonsdottir I, Thorleifsson G, Holm H, Thorsteinsdottir U, Sulem P, Gudbjartsson DF, Stefansson H, Thorgeirsson TE, Ulfarsson MO, Stefansson K. Thirty novel sequence variants impacting human intracranial volume. Brain Commun 2022; 4:fcac271. [PMID: 36415660 PMCID: PMC9677475 DOI: 10.1093/braincomms/fcac271] [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: 04/13/2022] [Revised: 06/16/2022] [Accepted: 10/20/2022] [Indexed: 11/14/2022] Open
Abstract
Intracranial volume, measured through magnetic resonance imaging and/or estimated from head circumference, is heritable and correlates with cognitive traits and several neurological disorders. We performed a genome-wide association study meta-analysis of intracranial volume (n = 79 174) and found 64 associating sequence variants explaining 5.0% of its variance. We used coding variation, transcript and protein levels, to uncover 12 genes likely mediating the effect of these variants, including GLI3 and CDK6 that affect cranial synostosis and microcephaly, respectively. Intracranial volume correlates genetically with volumes of cortical and sub-cortical regions, cognition, learning, neonatal and neurological traits. Parkinson's disease cases have greater and attention deficit hyperactivity disorder cases smaller intracranial volume than controls. Our Mendelian randomization studies indicate that intracranial volume associated variants either increase the risk of Parkinson's disease and decrease the risk of attention deficit hyperactivity disorder and neuroticism or correlate closely with a confounder.
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Affiliation(s)
- Muhammad Sulaman Nawaz
- deCODE genetics/Amgen Inc., Sturlugata 8, 102 Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland
| | | | | | - Rosa S Gisladottir
- deCODE genetics/Amgen Inc., Sturlugata 8, 102 Reykjavik, Iceland.,School of Humanities, University of Iceland, Saemundargata 2, 102 Reykjavik, Iceland
| | - G Bragi Walters
- deCODE genetics/Amgen Inc., Sturlugata 8, 102 Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland
| | | | | | | | | | | | | | - Engilbert Sigurdsson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland.,Department of Psychiatry, Landspitali-National University Hospital, Hringbraut 101, 101 Reykjavik, Iceland
| | - Palmi V Jonsson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland.,Department of Geriatric Medicine, Landspitali University Hospital, Hringbraut 101, 101 Reykjavik, Iceland
| | - Vala Kolbrun Palmadottir
- Department of Internal Medicine, Landspitali University Hospital, Hringbraut 101, 101 Reykjavik, Iceland
| | | | - Gisli H Halldorsson
- deCODE genetics/Amgen Inc., Sturlugata 8, 102 Reykjavik, Iceland.,School of Engineering and Natural Sciences, University of Iceland, Taeknigardur, Dunhagi 5, 107 Reykjavik, Iceland
| | - Egil Ferkingstad
- deCODE genetics/Amgen Inc., Sturlugata 8, 102 Reykjavik, Iceland
| | | | | | - Hilma Holm
- deCODE genetics/Amgen Inc., Sturlugata 8, 102 Reykjavik, Iceland
| | | | - Patrick Sulem
- deCODE genetics/Amgen Inc., Sturlugata 8, 102 Reykjavik, Iceland
| | | | | | | | - Magnus O Ulfarsson
- deCODE genetics/Amgen Inc., Sturlugata 8, 102 Reykjavik, Iceland.,Faculty of Electrical and Computer Engineering, University of Iceland, Taeknigardur, Dunhagi 5, 107 Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Sturlugata 8, 102 Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland
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