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Benstock SE, Weaver K, Hettema JM, Verhulst B. Using Alternative Definitions of Controls to Increase Statistical Power in GWAS. Behav Genet 2024; 54:353-366. [PMID: 38869698 DOI: 10.1007/s10519-024-10187-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: 01/12/2024] [Accepted: 05/29/2024] [Indexed: 06/14/2024]
Abstract
Genome-wide association studies (GWAS) are often underpowered due to small effect sizes of common single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the greatest statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.
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Affiliation(s)
- Sarah E Benstock
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA
| | - Katherine Weaver
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA
| | - John M Hettema
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA
| | - Brad Verhulst
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA.
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2
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Pérez-Gutiérrez AM, Carmona R, Loucera C, Cervilla JA, Gutiérrez B, Molina E, Lopez-Lopez D, Pérez-Florido J, Zarza-Rebollo JA, López-Isac E, Dopazo J, Martínez-González LJ, Rivera M. Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach. Mol Psychiatry 2024:10.1038/s41380-024-02609-2. [PMID: 38806690 DOI: 10.1038/s41380-024-02609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 05/03/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024]
Abstract
Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.
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Affiliation(s)
- Ana M Pérez-Gutiérrez
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
| | - Rosario Carmona
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
| | - Carlos Loucera
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Jorge A Cervilla
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
- Department of Psychiatry, Faculty of Medicine, University of Granada, Granada, Spain
| | - Blanca Gutiérrez
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
- Department of Psychiatry, Faculty of Medicine, University of Granada, Granada, Spain
| | - Esther Molina
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
- Department of Nursing, Faculty of Health Sciences, University of Granada, Granada, Spain
| | - Daniel Lopez-Lopez
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Javier Pérez-Florido
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
| | - Juan Antonio Zarza-Rebollo
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
| | - Elena López-Isac
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
| | - Joaquín Dopazo
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
| | - Luis Javier Martínez-González
- Genomics Unit, Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research (GENYO), Granada, Spain
| | - Margarita Rivera
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain.
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain.
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain.
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3
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Meng X, Navoly G, Giannakopoulou O, Levey DF, Koller D, Pathak GA, Koen N, Lin K, Adams MJ, Rentería ME, Feng Y, Gaziano JM, Stein DJ, Zar HJ, Campbell ML, van Heel DA, Trivedi B, Finer S, McQuillin A, Bass N, Chundru VK, Martin HC, Huang QQ, Valkovskaya M, Chu CY, Kanjira S, Kuo PH, Chen HC, Tsai SJ, Liu YL, Kendler KS, Peterson RE, Cai N, Fang Y, Sen S, Scott LJ, Burmeister M, Loos RJF, Preuss MH, Actkins KV, Davis LK, Uddin M, Wani AH, Wildman DE, Aiello AE, Ursano RJ, Kessler RC, Kanai M, Okada Y, Sakaue S, Rabinowitz JA, Maher BS, Uhl G, Eaton W, Cruz-Fuentes CS, Martinez-Levy GA, Campos AI, Millwood IY, Chen Z, Li L, Wassertheil-Smoller S, Jiang Y, Tian C, Martin NG, Mitchell BL, Byrne EM, Awasthi S, Coleman JRI, Ripke S, Sofer T, Walters RG, McIntosh AM, Polimanti R, Dunn EC, Stein MB, Gelernter J, Lewis CM, Kuchenbaecker K. Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference. Nat Genet 2024; 56:222-233. [PMID: 38177345 PMCID: PMC10864182 DOI: 10.1038/s41588-023-01596-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/26/2023] [Indexed: 01/06/2024]
Abstract
Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.
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Affiliation(s)
| | | | | | - Daniel F Levey
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Dora Koller
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
| | - Gita A Pathak
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Nastassja Koen
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - J Michael Gaziano
- Department of Medicine, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Dan J Stein
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Heather J Zar
- SAMRC Unit on Child and Adolescent Health, Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa
| | - Megan L Campbell
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | | | - Bhavi Trivedi
- Blizard Institute, Queen Mary University of London, London, UK
| | - Sarah Finer
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Nick Bass
- Division of Psychiatry, UCL, London, UK
| | | | | | | | | | | | - Susan Kanjira
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Po-Hsiu Kuo
- Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsi-Chung Chen
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
- Center of Sleep Disorders, National Taiwan University Hospital, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science and Division of Psychiatry, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli County, Taiwan
| | | | - Roseann E Peterson
- Department of Psychiatry, VCU, Richmond, VA, USA
- Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany
- Computational Health Centre, Helmholtz Munich, Neuherberg, Germany
- Department of Medicine, Technical University of Munich, Munich, Germany
| | - Yu Fang
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
| | - Srijan Sen
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Laura J Scott
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Margit Burmeister
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ky'Era V Actkins
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Monica Uddin
- College of Public Health, University of South Florida, Tampa, FL, USA
| | - Agaz H Wani
- College of Public Health, University of South Florida, Tampa, FL, USA
| | - Derek E Wildman
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Allison E Aiello
- Robert N. Butler Columbia Aging Center, Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Robert J Ursano
- Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Masahiro Kanai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jill A Rabinowitz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - George Uhl
- Neurology and Pharmacology, University of Maryland, Maryland VA Healthcare System, Baltimore, MD, USA
| | - William Eaton
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Carlos S Cruz-Fuentes
- Departamento de Genética, Instituto Nacional de Psiquiatría 'Ramón de la Fuente Muñíz', Mexico City, Mexico
| | - Gabriela A Martinez-Levy
- Departamento de Genética, Instituto Nacional de Psiquiatría 'Ramón de la Fuente Muñíz', Mexico City, Mexico
| | - Adrian I Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | | | - Yunxuan Jiang
- Department of Biostatistics, Emory University, Atlanta, GA, USA
- 23andMe, Inc., Mountain View, CA, USA
| | - Chao Tian
- 23andMe, Inc., Mountain View, CA, USA
| | - Nicholas G Martin
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Brittany L Mitchell
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Enda M Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Swapnil Awasthi
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
| | - Jonathan R I Coleman
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stephan Ripke
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, MA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Institute for Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Renato Polimanti
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Erin C Dunn
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Murray B Stein
- Department of Psychiatry, UC San Diego School of Medicine, La Jolla, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity, University of California San Diego, La Jolla, CA, USA
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Joel Gelernter
- Department of Psychiatry, VA CT Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Cathryn M Lewis
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Medical and Molecular Genetics, King's College London, London, UK
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4
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Benstock SE, Weaver K, Hettema J, Verhulst B. Using Alternative Definitions of Controls to Increase Statistical Power in GWAS. RESEARCH SQUARE 2024:rs.3.rs-3858178. [PMID: 38352402 PMCID: PMC10862954 DOI: 10.21203/rs.3.rs-3858178/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Genome-wide association studies (GWAS) are underpowered due to small effect sizes of single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the most statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.
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5
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Nguyen TD, Kowalec K, Pasman J, Larsson H, Lichtenstein P, Dalman C, Sullivan PF, Kuja-Halkola R, Lu Y. Genetic Contribution to the Heterogeneity of Major Depressive Disorder: Evidence From a Sibling-Based Design Using Swedish National Registers. Am J Psychiatry 2023; 180:714-722. [PMID: 37644812 PMCID: PMC10632940 DOI: 10.1176/appi.ajp.20220906] [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] [Indexed: 08/31/2023]
Abstract
OBJECTIVE Major depressive disorder (MDD) is highly heterogeneous. Standard typology partly captures the disorder's symptomatic heterogeneity, although whether it adequately captures etiological heterogeneity remains elusive. The aim of this study was to investigate the genetic characterization of MDD heterogeneity. METHODS Using Swedish patient register data on 1.5 million individuals, the authors identified 46,255 individuals with specialist-diagnosed MDD. Eighteen subgroups were identified based on nine comparison groups defined by clinical and psychosocial features, including severity, recurrence, comorbidities, suicidality, impairment, disability, care unit, and age at diagnosis. A sibling-based design and classic quantitative genetic models were applied to estimate heritability of MDD subgroups and genetic correlations between subgroups. RESULTS Estimates of heritability ranged from 30.5% to 58.3% across subgroups. The disabled and youth-onset subgroups showed significantly higher heritability (55.1%-58.3%) than the overall MDD sample (45.3%, 95% CI=43.0-47.5), and the subgroups with single-episode MDD and without psychiatric comorbidity showed significantly lower estimates (30.5%-34.4%). Estimates of genetic correlations between the subgroups within comparison groups ranged from 0.33 to 0.90. Seven of nine genetic correlations were significantly smaller than 1, suggesting differences in underlying genetic architecture. These results were largely consistent with previous work using genomic data. CONCLUSIONS The findings of differential heritability and partially distinct genetic components in subgroups provide important insights into the genetic heterogeneity of MDD and a deeper etiological understanding of MDD clinical subgroups.
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Affiliation(s)
- Thuy-Dung Nguyen
- Department of Medical Epidemiology and Biostatistics (Nguyen, Kowalec, Pasman, Larsson, Lichtenstein, Sullivan, Kuja-Halkola, Lu) and Department of Global Public Health (Nguyen, Dalman, Lu), Karolinska Institutet, Stockholm; College of Pharmacy, University of Manitoba, Winnipeg, Canada (Kowalec); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson); Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill (Sullivan)
| | - Kaarina Kowalec
- Department of Medical Epidemiology and Biostatistics (Nguyen, Kowalec, Pasman, Larsson, Lichtenstein, Sullivan, Kuja-Halkola, Lu) and Department of Global Public Health (Nguyen, Dalman, Lu), Karolinska Institutet, Stockholm; College of Pharmacy, University of Manitoba, Winnipeg, Canada (Kowalec); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson); Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill (Sullivan)
| | - Joëlle Pasman
- Department of Medical Epidemiology and Biostatistics (Nguyen, Kowalec, Pasman, Larsson, Lichtenstein, Sullivan, Kuja-Halkola, Lu) and Department of Global Public Health (Nguyen, Dalman, Lu), Karolinska Institutet, Stockholm; College of Pharmacy, University of Manitoba, Winnipeg, Canada (Kowalec); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson); Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill (Sullivan)
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics (Nguyen, Kowalec, Pasman, Larsson, Lichtenstein, Sullivan, Kuja-Halkola, Lu) and Department of Global Public Health (Nguyen, Dalman, Lu), Karolinska Institutet, Stockholm; College of Pharmacy, University of Manitoba, Winnipeg, Canada (Kowalec); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson); Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill (Sullivan)
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics (Nguyen, Kowalec, Pasman, Larsson, Lichtenstein, Sullivan, Kuja-Halkola, Lu) and Department of Global Public Health (Nguyen, Dalman, Lu), Karolinska Institutet, Stockholm; College of Pharmacy, University of Manitoba, Winnipeg, Canada (Kowalec); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson); Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill (Sullivan)
| | - Christina Dalman
- Department of Medical Epidemiology and Biostatistics (Nguyen, Kowalec, Pasman, Larsson, Lichtenstein, Sullivan, Kuja-Halkola, Lu) and Department of Global Public Health (Nguyen, Dalman, Lu), Karolinska Institutet, Stockholm; College of Pharmacy, University of Manitoba, Winnipeg, Canada (Kowalec); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson); Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill (Sullivan)
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics (Nguyen, Kowalec, Pasman, Larsson, Lichtenstein, Sullivan, Kuja-Halkola, Lu) and Department of Global Public Health (Nguyen, Dalman, Lu), Karolinska Institutet, Stockholm; College of Pharmacy, University of Manitoba, Winnipeg, Canada (Kowalec); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson); Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill (Sullivan)
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics (Nguyen, Kowalec, Pasman, Larsson, Lichtenstein, Sullivan, Kuja-Halkola, Lu) and Department of Global Public Health (Nguyen, Dalman, Lu), Karolinska Institutet, Stockholm; College of Pharmacy, University of Manitoba, Winnipeg, Canada (Kowalec); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson); Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill (Sullivan)
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics (Nguyen, Kowalec, Pasman, Larsson, Lichtenstein, Sullivan, Kuja-Halkola, Lu) and Department of Global Public Health (Nguyen, Dalman, Lu), Karolinska Institutet, Stockholm; College of Pharmacy, University of Manitoba, Winnipeg, Canada (Kowalec); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson); Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill (Sullivan)
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6
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van Loo HM, de Vries YA, Taylor J, Todorovic L, Dollinger C, Kendler KS. Clinical characteristics indexing genetic differences in bipolar disorder - a systematic review. Mol Psychiatry 2023; 28:3661-3670. [PMID: 37968345 DOI: 10.1038/s41380-023-02297-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 09/29/2023] [Accepted: 10/06/2023] [Indexed: 11/17/2023]
Abstract
Bipolar disorder is a heterogenous condition with a varied clinical presentation. While progress has been made in identifying genetic variants associated with bipolar disorder, most common genetic variants have not yet been identified. More detailed phenotyping (beyond diagnosis) may increase the chance of finding genetic variants. Our aim therefore was to identify clinical characteristics that index genetic differences in bipolar disorder.We performed a systematic review of all genome-wide molecular genetic, family, and twin studies investigating familial/genetic influences on the clinical characteristics of bipolar disorder. We performed an electronic database search of PubMed and PsycInfo until October 2022. We reviewed title/abstracts of 2693 unique records and full texts of 391 reports, identifying 445 relevant analyses from 142 different reports. These reports described 199 analyses from family studies, 183 analyses from molecular genetic studies and 63 analyses from other types of studies. We summarized the overall evidence per phenotype considering study quality, power, and number of studies.We found moderate to strong evidence for a positive association of age at onset, subtype (bipolar I versus bipolar II), psychotic symptoms and manic symptoms with familial/genetic risk of bipolar disorder. Sex was not associated with overall genetic risk but could indicate qualitative genetic differences. Assessment of genetically relevant clinical characteristics of patients with bipolar disorder can be used to increase the phenotypic and genetic homogeneity of the sample in future genetic studies, which may yield more power, increase specificity, and improve understanding of the genetic architecture of bipolar disorder.
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Affiliation(s)
- Hanna M van Loo
- Department of Psychiatry and Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
| | - Ymkje Anna de Vries
- Department of Child and Adolescent Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jacob Taylor
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luka Todorovic
- Department of Psychiatry and Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Child and Adolescent Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Camille Dollinger
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
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7
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Bouzid A, Almidani A, Zubrikhina M, Kamzanova A, Ilce BY, Zholdassova M, Yusuf AM, Bhamidimarri PM, AlHaj HA, Kustubayeva A, Bernstein A, Burnaev E, Sharaev M, Hamoudi R. Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders including NRG1. Neurobiol Stress 2023; 26:100555. [PMID: 37583471 PMCID: PMC10423927 DOI: 10.1016/j.ynstr.2023.100555] [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: 03/14/2023] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 08/17/2023] Open
Abstract
Major depressive disorder (MDD) is a common mental disorder and is amongst the most prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset due to its heterogeneous phenotype and complex etiology. Hence, early detection using diagnostic biomarkers is critical for rapid intervention. In this study, a mixture of AI and bioinformatics were used to mine transcriptomic data from publicly available datasets including 170 MDD patients and 121 healthy controls. Bioinformatics analysis using gene set enrichment analysis (GSEA) and machine learning (ML) algorithms were applied. The GSEA revealed that differentially expressed genes in MDD patients are mainly enriched in pathways related to immune response, inflammatory response, neurodegeneration pathways and cerebellar atrophy pathways. Feature selection methods and ML provided predicted models based on MDD-altered genes with ≥75% of accuracy. The integrative analysis between the bioinformatics and ML approaches identified ten key MDD-related biomarkers including NRG1, CEACAM8, CLEC12B, DEFA4, HP, LCN2, OLFM4, SERPING1, TCN1 and THBS1. Among them, NRG1, active in synaptic plasticity and neurotransmission, was the most robust and reliable to distinguish between MDD patients and healthy controls amongst independent external datasets consisting of a mixture of populations. Further evaluation using saliva samples from an independent cohort of MDD and healthy individuals confirmed the upregulation of NRG1 in patients with MDD compared to healthy controls. Functional mapping to the human brain regions showed NRG1 to have high expression in the main subcortical limbic brain regions implicated in depression. In conclusion, integrative bioinformatics and ML approaches identified putative non-invasive diagnostic MDD-related biomarkers panel for the onset of depression.
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Affiliation(s)
- Amal Bouzid
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Abdulrahman Almidani
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Maria Zubrikhina
- Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Altyngul Kamzanova
- The Center for Cognitive Neuroscience, Al Farabi Kazakh National University, Kazakhstan
| | - Burcu Yener Ilce
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Manzura Zholdassova
- The Center for Cognitive Neuroscience, Al Farabi Kazakh National University, Kazakhstan
| | - Ayesha M. Yusuf
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Poorna Manasa Bhamidimarri
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Hamid A. AlHaj
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- Faculty of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Almira Kustubayeva
- The Center for Cognitive Neuroscience, Al Farabi Kazakh National University, Kazakhstan
| | - Alexander Bernstein
- Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Evgeny Burnaev
- Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Maxim Sharaev
- Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Rifat Hamoudi
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- Faculty of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, London, United Kingdom
- ASPIRE Precision Medicine Research Institute Abu Dhabi, University of Sharjah, Sharjah, United Arab Emirates
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8
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Gomez L, Díaz-Torres S, Colodro-Conde L, Garcia-Marin LM, Yap CX, Byrne EM, Yengo L, Lind PA, Wray NR, Medland SE, Hickie IB, Lupton MK, Rentería ME, Martin NG, Campos AI. Phenotypic and genetic factors associated with donation of DNA and consent to record linkage for prescription history in the Australian Genetics of Depression Study. Eur Arch Psychiatry Clin Neurosci 2023; 273:1359-1368. [PMID: 36422680 DOI: 10.1007/s00406-022-01527-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 11/15/2022] [Indexed: 11/27/2022]
Abstract
Samples can be prone to ascertainment and attrition biases. The Australian Genetics of Depression Study is a large publicly recruited cohort (n = 20,689) established to increase the understanding of depression and antidepressant treatment response. This study investigates differences between participants who donated a saliva sample or agreed to linkage of their records compared to those who did not. We observed that older, male participants with higher education were more likely to donate a saliva sample. Self-reported bipolar disorder, ADHD, panic disorder, PTSD, substance use disorder, and social anxiety disorder were associated with lower odds of donating a saliva sample, whereas anorexia was associated with higher odds of donation. Male and younger participants showed higher odds of agreeing to record linkage. Participants with higher neuroticism scores and those with a history of bipolar disorder were also more likely to agree to record linkage whereas participants with a diagnosis of anorexia were less likely to agree. Increased likelihood of consent was associated with increased genetic susceptibility to anorexia and reduced genetic risk for depression, and schizophrenia. Overall, our results show moderate differences among these subsamples. Most current epidemiological studies do not search for attrition biases at the genetic level. The possibility to do so is a strength of samples such as the AGDS. Our results suggest that analyses can be made more robust by identifying attrition biases both on the phenotypic and genetic level, and either contextualising them as a potential limitation or performing sensitivity analyses adjusting for them.
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Affiliation(s)
- Lina Gomez
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Santiago Díaz-Torres
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Statistical Genetics Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Lucía Colodro-Conde
- Psychiatric Genetics Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Luis M Garcia-Marin
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Chloe X Yap
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Penelope A Lind
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Psychiatric Genetics Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Queensland Institute of Technology, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Sarah E Medland
- Psychiatric Genetics Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - Michelle K Lupton
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Miguel E Rentería
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Adrian I Campos
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
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9
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Adams MJ, Thorp JG, Jermy BS, Kwong ASF, Kõiv K, Grotzinger AD, Nivard MG, Marshall S, Milaneschi Y, Baune BT, Müller-Myhsok B, Penninx BW, Boomsma DI, Levinson DF, Breen G, Pistis G, Grabe HJ, Tiemeier H, Berger K, Rietschel M, Magnusson PK, Uher R, Hamilton SP, Lucae S, Lehto K, Li QS, Byrne EM, Hickie IB, Martin NG, Medland SE, Wray NR, Tucker-Drob EM, Lewis CM, McIntosh AM, Derks EM. Genetic structure of major depression symptoms across clinical and community cohorts. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.05.23292214. [PMID: 37461564 PMCID: PMC10350129 DOI: 10.1101/2023.07.05.23292214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.
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Affiliation(s)
- Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Jackson G Thorp
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU
| | - Bradley S Jermy
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, FI
| | - Alex S F Kwong
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Kadri Kõiv
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, EE
| | - Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, US
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, US
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Sally Marshall
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Bernhard T Baune
- Department of Psychiatry, University of Melbourne, Melbourne, VIC, AU
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, AU
- Department of Psychiatry, University of Münster, Münster, NRW, DE
| | - Bertram Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, BY, DE
- Munich Cluster for Systems Neurology (SyNergy), Munich, BY, DE
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Brenda Wjh Penninx
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Dorret I Boomsma
- Department of Biological Psychology & Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Douglas F Levinson
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, US
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, King's College London, London, UK
| | - Giorgio Pistis
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, VD, CH
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald MV, DE
| | - Henning Tiemeier
- Child and Adolescent Psychiatry, Erasmus University Medical Center Rotterdam, Rotterdam, NL
- Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, MA, US
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, NRW, DE
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, DE
| | - Patrik K Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE
| | - Rudolf Uher
- Psychiatry, Dalhousie University, Halifax, NS, CA
| | - Steven P Hamilton
- Psychiatry, Kaiser Permanente Northern California, San Francisco, CA, US
| | | | - Kelli Lehto
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, EE
| | - Qingqin S Li
- Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ, US
| | - Enda M Byrne
- Child Health Research Centre, University of Queensland, Brisbane, QLD, AU
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Sydney, NSW, AU
| | - Nicholas G Martin
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU
| | - Sarah E Medland
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, AU
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, AU
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, US
- Population Research Center, University of Texas at Austin, Austin, TX, US
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Institute for Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Eske M Derks
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU
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10
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Flint J. The genetic basis of major depressive disorder. Mol Psychiatry 2023; 28:2254-2265. [PMID: 36702864 PMCID: PMC10611584 DOI: 10.1038/s41380-023-01957-9] [Citation(s) in RCA: 28] [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: 06/18/2022] [Revised: 12/30/2022] [Accepted: 01/11/2023] [Indexed: 01/27/2023]
Abstract
The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.
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Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, Billy and Audrey Wilder Endowed Chair in Psychiatry and Neuroscience, Center for Neurobehavioral Genetics, 695 Charles E. Young Drive South, 3357B Gonda, Box 951761, Los Angeles, CA, 90095-1761, USA.
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11
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Hou Z, Jiang W, Li F, Liu X, Hou Z, Yin Y, Zhang H, Zhang H, Xie C, Zhang Z, Kong Y, Yuan Y. Linking individual variability in functional brain connectivity to polygenic risk in major depressive disorder. J Affect Disord 2023; 329:55-63. [PMID: 36842648 DOI: 10.1016/j.jad.2023.02.104] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/19/2023] [Accepted: 02/20/2023] [Indexed: 02/28/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly heterogeneous disease, which brings great difficulties to clinical diagnosis and therapy. Its mechanism is still unknown. Prior neuroimaging studies mainly focused on mean differences between patients and healthy controls (HC), largely ignoring individual differences between patients. METHODS This study included 112 MDD patients and 93 HC subjects. Resting-state functional MRI data were obtained to examine the patterns of individual variability of brain functional connectivity (IVFC). The genetic risk of pathways including dopamine, 5-hydroxytryptamine (5-HT), norepinephrine (NE), hypothalamic-pituitary-adrenal (HPA) axis, and synaptic plasticity was assessed by multilocus genetic profile scores (MGPS), respectively. RESULTS The IVFC pattern of the MDD group was similar but higher than that in HCs. The inter-network functional connectivity in the default mode network contributed to altered IVFC in MDD. 5-HT, NE, and HPA pathway genes affected IVFC in MDD patients. The age of onset, duration, severity, and treatment response, were correlated with IVFC. IVFC in the left ventromedial prefrontal cortex had a mediating effect between MGPS of the 5-HT pathway and baseline depression severity. LIMITATIONS Environmental factors and differences in locations of functional areas across individuals were not taken into account. CONCLUSIONS This study found MDD patients had significantly different inter-individual functional connectivity variations than healthy people, and genetic risk might affect clinical manifestations through brain function heterogeneity.
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Affiliation(s)
- Zhuoliang Hou
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Fan Li
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Xiaoyun Liu
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Zhenghua Hou
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Yingying Yin
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Haisan Zhang
- Departments of Clinical Magnetic Resonance Imaging, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
| | - Hongxing Zhang
- Departments of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; School of Psychology, Xinxiang Medical University, Xinxiang 453003, China
| | - Chunming Xie
- Department of Neurology, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Zhijun Zhang
- Department of Neurology, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Youyong Kong
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China.
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China.
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12
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Han W, Wang N, Han M, Ban M, Sun T, Xu J. Reviewing the role of gut microbiota in the pathogenesis of depression and exploring new therapeutic options. Front Neurosci 2022; 16:1029495. [PMID: 36570854 PMCID: PMC9772619 DOI: 10.3389/fnins.2022.1029495] [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: 08/29/2022] [Accepted: 11/24/2022] [Indexed: 12/14/2022] Open
Abstract
The relationship between gut microbiota (GM) and mental health is one of the focuses of psychobiology research. In recent years, the microbial-gut-brain axis (MGBA) concept has gradually formed about this bidirectional communication between gut and brain. But how the GM is involved in regulating brain function and how they affect emotional disorders these mechanisms are tenuous and limited to animal research, and often controversial. Therefore, in this review, we attempt to summarize and categorize the latest advances in current research on the mechanisms of GM and depression to provide valid information for future diagnoses and therapy of mental disorders. Finally, we introduced some antidepressant regimens that can help restore gut dysbiosis, including classic antidepressants, Chinese materia medica (CMM), diet, and exogenous strains. These studies provide further insight into GM's role and potential pathways in emotion-related diseases, which holds essential possible clinical outcomes for people with depression or related psychiatric disorders. Future research should focus on clarifying the causal role of GM in disease and developing microbial targets, applying these findings to the prevention and treatment of depression.
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Affiliation(s)
- Wenjie Han
- Department of Breast Medicine, Liaoning Cancer Hospital, Cancer Hospital of China Medical University, Shenyang, China,Department of Pharmacology, Liaoning Cancer Hospital, Cancer Hospital of China Medical University, Shenyang, China
| | - Na Wang
- Department of Breast Medicine, Liaoning Cancer Hospital, Cancer Hospital of China Medical University, Shenyang, China,Department of Pharmacology, Liaoning Cancer Hospital, Cancer Hospital of China Medical University, Shenyang, China
| | - Mengzhen Han
- Department of Breast Medicine, Liaoning Cancer Hospital, Cancer Hospital of China Medical University, Shenyang, China,Department of Pharmacology, Liaoning Cancer Hospital, Cancer Hospital of China Medical University, Shenyang, China
| | - Meng Ban
- Liaoning Microhealth Biotechnology Co., Ltd., Shenyang, China
| | - Tao Sun
- Department of Breast Medicine, Liaoning Cancer Hospital, Cancer Hospital of China Medical University, Shenyang, China,Department of Breast Medicine, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital, Shenyang, China
| | - Junnan Xu
- Department of Breast Medicine, Liaoning Cancer Hospital, Cancer Hospital of China Medical University, Shenyang, China,Department of Pharmacology, Liaoning Cancer Hospital, Cancer Hospital of China Medical University, Shenyang, China,Department of Breast Medicine, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital, Shenyang, China,*Correspondence: Junnan Xu,
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13
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Affiliation(s)
- Cathryn M Lewis
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Lewis, Vassos); Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London (Lewis)
| | - Evangelos Vassos
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Lewis, Vassos); Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London (Lewis)
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14
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Polimanti R. Not Only Gene Discovery: Genome-wide Association Studies and Polygenic Risk Scores as Tools to Dissect the Heterogeneity of Major Depressive Disorder. Biol Psychiatry 2022; 92:177-178. [PMID: 35835505 PMCID: PMC9514509 DOI: 10.1016/j.biopsych.2022.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 05/02/2022] [Indexed: 11/13/2022]
Affiliation(s)
- Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, West Haven, Connecticut; VA Connecticut Health Care Center, West Haven, Connecticut.
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Kennedy HL, Dinkler L, Kennedy MA, Bulik CM, Jordan J. How genetic analysis may contribute to the understanding of avoidant/restrictive food intake disorder (ARFID). J Eat Disord 2022; 10:53. [PMID: 35428338 PMCID: PMC9013144 DOI: 10.1186/s40337-022-00578-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 04/08/2022] [Indexed: 12/29/2022] Open
Abstract
Avoidant/restrictive food intake disorder (ARFID) was introduced in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Unlike anorexia nervosa, ARFID is characterised by avoidant or restricted food intake that is not driven by weight or body shape-related concerns. As with other eating disorders, it is expected that ARFID will have a significant genetic risk component; however, sufficiently large-scale genetic investigations are yet to be performed in this group of patients. This narrative review considers the current literature on the diagnosis, presentation, and course of ARFID, including evidence for different presentations, and identifies fundamental questions about how ARFID might fit into the fluid landscape of other eating and mental disorders. In the absence of large ARFID GWAS, we consider genetic research on related conditions to point to possible features or mechanisms relevant to future ARFID investigations, and discuss the theoretical and clinical implications an ARFID GWAS. An argument for a collaborative approach to recruit ARFID participants for genome-wide association study is presented, as understanding the underlying genomic architecture of ARFID will be a key step in clarifying the biological mechanisms involved, and the development of interventions and treatments for this serious, and often debilitating disorder.
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Affiliation(s)
- Hannah L Kennedy
- Department of Psychological Medicine, University of Otago, Christchurch, PO Box 4345, Christchurch, 8140, New Zealand
| | - Lisa Dinkler
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, 171 77, Stockholm, Sweden.,Gillberg Neuropsychiatry Centre, Sahlgrenska Academy, University of Gothenburg, 411 19, Gothenburg, Sweden
| | - Martin A Kennedy
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Cynthia M Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, 171 77, Stockholm, Sweden.,Department of Psychiatry, University of North Carolina at Chapel Hill, CB #7160, 101 Manning Drive, Chapel Hill, NC, 27599-7160, USA.,Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jennifer Jordan
- Department of Psychological Medicine, University of Otago, Christchurch, PO Box 4345, Christchurch, 8140, New Zealand.
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