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Coleman JRI, Gaspar HA, Bryois J, Breen G. The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls. Biol Psychiatry 2020; 88:169-184. [PMID: 31926635 PMCID: PMC8136147 DOI: 10.1016/j.biopsych.2019.10.015] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 09/27/2019] [Accepted: 10/15/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND Mood disorders (including major depressive disorder and bipolar disorder) affect 10% to 20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Multiple approaches have shown considerable sharing of risk factors across mood disorders despite their diagnostic distinction. METHODS To clarify the shared molecular genetic basis of major depressive disorder and bipolar disorder and to highlight disorder-specific associations, we meta-analyzed data from the latest Psychiatric Genomics Consortium genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; nonoverlapping N = 609,424). RESULTS Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More loci from the Psychiatric Genomics Consortium analysis of major depression than from that for bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single-episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment-the relationship is positive in bipolar disorder but negative in major depressive disorder. CONCLUSIONS The mood disorders share several genetic associations, and genetic studies of major depressive disorder and bipolar disorder can be combined effectively to enable the discovery of variants not identified by studying either disorder alone. However, we demonstrate several differences between these disorders. Analyzing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.
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Affiliation(s)
- Jonathan R I Coleman
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; National Institute for Health Research Maudsley Biomedical Research Centre, King's College London, London, United Kingdom
| | - Héléna A Gaspar
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; National Institute for Health Research Maudsley Biomedical Research Centre, King's College London, London, United Kingdom
| | - Julien Bryois
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gerome Breen
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; National Institute for Health Research Maudsley Biomedical Research Centre, King's College London, London, United Kingdom.
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Hill WD, Davies NM, Ritchie SJ, Skene NG, Bryois J, Bell S, Di Angelantonio E, Roberts DJ, Xueyi S, Davies G, Liewald DCM, Porteous DJ, Hayward C, Butterworth AS, McIntosh AM, Gale CR, Deary IJ. Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income. Nat Commun 2019; 10:5741. [PMID: 31844048 PMCID: PMC6915786 DOI: 10.1038/s41467-019-13585-5] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 11/11/2019] [Indexed: 01/01/2023] Open
Abstract
Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABAergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities.
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Affiliation(s)
- W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Nathan G Skene
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- UCL Institute of Neurology, Queen Square, London, UK
- Department of Medicine, Division of Brain Sciences, Imperial College, London, UK
| | - Julien Bryois
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Steven Bell
- The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- UK Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Emanuele Di Angelantonio
- The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- UK Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
- NHS Blood and Transplant, Cambridge, UK
| | - David J Roberts
- Cambridge Substantive Site, Health Data Research UK, Wellcome Genome Campus, Hinxton, UK
- BRC Haematology Theme and Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHS Blood and Transplant - Oxford Centre, Oxford, UK
| | - Shen Xueyi
- Division of Psychiatry, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - David C M Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - David J Porteous
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Adam S Butterworth
- The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- UK Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Andrew M McIntosh
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Catharine R Gale
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, SO16 6YD, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
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Hill WD, Davies NM, Ritchie SJ, Skene NG, Bryois J, Bell S, Di Angelantonio E, Roberts DJ, Xueyi S, Davies G, Liewald DCM, Porteous DJ, Hayward C, Butterworth AS, McIntosh AM, Gale CR, Deary IJ. Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income. Nat Commun 2019; 10:5741. [PMID: 31844048 DOI: 10.1101/573691] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 11/11/2019] [Indexed: 05/25/2023] Open
Abstract
Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABAergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities.
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Affiliation(s)
- W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Nathan G Skene
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- UCL Institute of Neurology, Queen Square, London, UK
- Department of Medicine, Division of Brain Sciences, Imperial College, London, UK
| | - Julien Bryois
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Steven Bell
- The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- UK Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Emanuele Di Angelantonio
- The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- UK Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
- NHS Blood and Transplant, Cambridge, UK
| | - David J Roberts
- Cambridge Substantive Site, Health Data Research UK, Wellcome Genome Campus, Hinxton, UK
- BRC Haematology Theme and Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHS Blood and Transplant - Oxford Centre, Oxford, UK
| | - Shen Xueyi
- Division of Psychiatry, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - David C M Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - David J Porteous
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Adam S Butterworth
- The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- UK Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Andrew M McIntosh
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Catharine R Gale
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, SO16 6YD, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
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A Further Comment on 'Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets' by Lam et al. Twin Res Hum Genet 2018; 21:538-545. [PMID: 30293537 PMCID: PMC6390408 DOI: 10.1017/thg.2018.55] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Lam et al. (2018) respond to a commentary of their paper entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ Lam et al. (2017). While Lam et al. (2018) have now provided the recommended quality control metrics for their paper, problems remain. Specifically, Lam et al. (2018) do not dispute that the results of their multi-trait analysis of genome-wide association study (MTAG) analysis has produced a phenotype with a genetic correlation of one with three measures of education, but do claim the associations found are specific to the trait of cognitive ability. In this brief paper, it is empirically demonstrated that the phenotype derived by Lam et al. (2017) is more genetically similar to education than cognitive ability. In addition, it is shown that of the genome-wide significant loci identified by Lam et al. (2017) are loci that are associated with education rather than with cognitive ability.
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Multi-Trait Analysis of GWAS and Biological Insights Into Cognition: A Response to Hill (2018). Twin Res Hum Genet 2018; 21:394-397. [PMID: 30001766 DOI: 10.1017/thg.2018.46] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84-88) presented a critique of our recently published paper in Cell Reports entitled 'Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets' (Lam et al., Cell Reports, Vol. 21, 2017, 2597-2613). Specifically, Hill offered several interrelated comments suggesting potential problems with our use of a new analytic method called Multi-Trait Analysis of GWAS (MTAG) (Turley et al., Nature Genetics, Vol. 50, 2018, 229-237). In this brief article, we respond to each of these concerns. Using empirical data, we conclude that our MTAG results do not suffer from 'inflation in the FDR [false discovery rate]', as suggested by Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84-88), and are not 'more relevant to the genetic contributions to education than they are to the genetic contributions to intelligence'.
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