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Koch S, Schmidtke J, Krawczak M, Caliebe A. Clinical utility of polygenic risk scores: a critical 2023 appraisal. J Community Genet 2023; 14:471-487. [PMID: 37133683 PMCID: PMC10576695 DOI: 10.1007/s12687-023-00645-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/31/2023] [Indexed: 05/04/2023] Open
Abstract
Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.
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Affiliation(s)
- Sebastian Koch
- Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jörg Schmidtke
- Amedes MVZ Wagnerstibbe, Hannover, Germany
- Institut für Humangenetik, Medizinische Hochschule Hannover, Hannover, Germany
| | - Michael Krawczak
- Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Amke Caliebe
- Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel, Germany.
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52
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Conway CC, Kotov R, Krueger RF, Caspi A. Translating the hierarchical taxonomy of psychopathology (HiTOP) from potential to practice: Ten research questions. AMERICAN PSYCHOLOGIST 2023; 78:873-885. [PMID: 36227328 PMCID: PMC10097839 DOI: 10.1037/amp0001046] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The Hierarchical Taxonomy of Psychopathology (HiTOP) is a novel diagnostic system grounded in empirical research into the architecture of mental illness. Its basic units are continuous dimensions-as opposed to categories-that are organized into a hierarchy according to patterns of symptom co-occurrence observed in quantitative studies. Previous HiTOP discussions have focused on existing evidence regarding the model's structure and ability to account for neurobiological, social, cultural, and clinical variation. The present article looks ahead to the next decade of applied research and clinical practice using the HiTOP rubric. We highlight 10 topics where HiTOP has the potential to make significant breakthroughs. Research areas include genetic influences, environmental contributions, neural mechanisms, real-time dynamics, and lifespan development of psychopathology. We also discuss development of novel assessments, forecasting methods, and treatments. Finally, we consider implications for clinicians and educators. For each of these domains, we propose directions for future research and venture hypotheses as to what HiTOP will reveal about psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
| | - Roman Kotov
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Robert F. Krueger
- Departments of Psychiatry and Psychology, Stony Brook University, Stony Brook, New York, USA
| | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, USA
- Social, Genetic, and Developmental Psychiatry Research Centre, King’s College London, London, United Kingdom
- PROMENTA Center, University of Oslo, Oslo, Norway
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53
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Siegel P. Genome-wide association study and the randomized controlled trial: A false equivalence. Behav Brain Sci 2023; 46:e200. [PMID: 37694937 DOI: 10.1017/s0140525x22002230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Madole & Harden's assertion that the effects derived from within-family genome-wide association studies (GWASs) and from randomized controlled trials (RCTs) are equivalent is misleading. GWASs are substantially more "non-unitary, non-uniform, and non-explanatory" than RCTs. While the within-family GWAS bring us closer to identifying genetic causes, whether it will change behavioral genetics into a causal science is an open question.
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Affiliation(s)
- Paul Siegel
- School of Natural and Social Sciences, Purchase College, State University of New York, Purchase, NY, USA https://www.purchase.edu/live/profiles/671-paul-siegel
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54
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Zhang W, Rutlin J, Eisenstein SA, Wang Y, Barch DM, Hershey T, Bogdan R, Bijsterbosch JD. Neuroinflammation in the Amygdala Is Associated With Recent Depressive Symptoms. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:967-975. [PMID: 37164312 DOI: 10.1016/j.bpsc.2023.04.011] [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: 12/22/2022] [Revised: 04/03/2023] [Accepted: 04/29/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND Converging evidence suggests that elevated inflammation may contribute to depression. Yet, the link between peripheral inflammation and neuroinflammation in depression is unclear. Here, using data from the UK Biobank, we estimated associations among depression, C-reactive protein (CRP) as a measure of peripheral inflammation, and neuroinflammation as indexed by diffusion basis spectral imaging-based restricted fraction (DBSI-RF). METHODS DBSI-RF was derived from diffusion-weighted imaging data (N = 11,512) for whole-brain gray matter (global-RF), and regions of interest in the bilateral amygdala (amygdala-RF) and hippocampus (hippocampus-RF), and CRP was estimated from blood (serum) samples. Self-reported recent depression symptoms were measured using a 4-item assessment. Linear regressions were used to estimate associations between CRP and DBSI-RFs with depression while adjusting for the following covariates: age, sex, body mass index, smoking, drinking, and medical conditions. RESULTS Elevated CRP was associated with higher depression symptoms (β = 0.04, false discovery rate-corrected p < .005) and reduced global-RF (β = -0.03, false discovery rate-corrected p < .001). Higher amygdala-RF was associated with elevated depression-an effect resilient to added covariates and CRP (β = 0.02, false discovery rate-corrected p < .05). Interestingly, this association was stronger in individuals with a lifetime history of depression (β = 0.07, p < .005) than in those without (β = 0.03, p < .05). Associations between global-RF or hippocampus-RF with depression were not significant, and no DBSI-RF indices indirectly linked CRP with depression (i.e., mediation effect). CONCLUSIONS Peripheral inflammation and DBSI-RF neuroinflammation in the amygdala are independently associated with depression, consistent with animal studies suggesting distinct pathways of peripheral inflammation and neuroinflammation in the pathophysiology of depression and with investigations highlighting the role of the amygdala in stress-induced inflammation and depression.
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Affiliation(s)
- Wei Zhang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri.
| | - Jerrel Rutlin
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Sarah A Eisenstein
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Yong Wang
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, Missouri; Department of Electrical and Systems Engineering, Washington University, St. Louis, Missouri
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Department of Psychological & Brain Sciences, Washington University, St. Louis, Missouri
| | - Tamara Hershey
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Department of Psychological & Brain Sciences, Washington University, St. Louis, Missouri; Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University, St. Louis, Missouri.
| | - Janine D Bijsterbosch
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
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55
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Maina JG, Balkhiyarova Z, Nouwen A, Pupko I, Ulrich A, Boissel M, Bonnefond A, Froguel P, Khamis A, Prokopenko I, Kaakinen M. Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes. Diabetes Care 2023; 46:1707-1714. [PMID: 37494602 PMCID: PMC10465984 DOI: 10.2337/dc22-2373] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 06/21/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVE Depression is a common comorbidity of type 2 diabetes. We assessed the causal relationships and shared genetics between them. RESEARCH DESIGN AND METHODS We applied two-sample, bidirectional Mendelian randomization (MR) to assess causality between type 2 diabetes and depression. We investigated potential mediation using two-step MR. To identify shared genetics, we performed 1) genome-wide association studies (GWAS) separately and 2) multiphenotype GWAS (MP-GWAS) of type 2 diabetes (19,344 case subjects, 463,641 control subjects) and depression using major depressive disorder (MDD) (5,262 case subjects, 86,275 control subjects) and self-reported depressive symptoms (n = 153,079) in the UK Biobank. We analyzed expression quantitative trait locus (eQTL) data from public databases to identify target genes in relevant tissues. RESULTS MR demonstrated a significant causal effect of depression on type 2 diabetes (odds ratio 1.26 [95% CI 1.11-1.44], P = 5.46 × 10-4) but not in the reverse direction. Mediation analysis indicated that 36.5% (12.4-57.6%, P = 0.0499) of the effect from depression on type 2 diabetes was mediated by BMI. GWAS of type 2 diabetes and depressive symptoms did not identify shared loci. MP-GWAS identified seven shared loci mapped to TCF7L2, CDKAL1, IGF2BP2, SPRY2, CCND2-AS1, IRS1, CDKN2B-AS1. MDD has not brought any significant association in either GWAS or MP-GWAS. Most MP-GWAS loci had an eQTL, including single nucleotide polymorphisms implicating the cell cycle gene CCND2 in pancreatic islets and brain and the insulin signaling gene IRS1 in adipose tissue, suggesting a multitissue and pleiotropic underlying mechanism. CONCLUSIONS Our results highlight the importance to prevent type 2 diabetes at the onset of depressive symptoms and the need to maintain a healthy weight in the context of its effect on depression and type 2 diabetes comorbidity.
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Affiliation(s)
- Jared G. Maina
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
| | - Zhanna Balkhiyarova
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, U.K
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, U.K
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, U.K
| | - Arie Nouwen
- Department of Psychology, Middlesex University, London, U.K
| | - Igor Pupko
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, U.K
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, U.K
| | - Anna Ulrich
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, U.K
| | - Mathilde Boissel
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
| | - Amélie Bonnefond
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, U.K
| | - Philippe Froguel
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, U.K
| | - Amna Khamis
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, U.K
| | - Inga Prokopenko
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, U.K
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, U.K
| | - Marika Kaakinen
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, U.K
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, U.K
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, U.K
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Hicks EM, Niarchou M, Goleva S, Kabir D, Ciarcia J, Smoller JW, Davis LK, Nievergelt CM, Koenen KC, Huckins LM, Choi KW. Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.25.23294572. [PMID: 37693435 PMCID: PMC10491282 DOI: 10.1101/2023.08.25.23294572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background Prior epidemiological research has linked PTSD with specific physical health problems, but the comprehensive landscape of medical conditions associated with PTSD remains uncharacterized. Electronic health records (EHR) provide an opportunity to overcome prior clinical knowledge gaps and uncover associations with biological relevance that potentially vary by sex. Methods PTSD was defined among biobank participants (total N=123,365) in a major healthcare system using two ICD code-based definitions: broad (1+ PTSD or acute stress codes versus 0; NCase=14,899) and narrow (2+ PTSD codes versus 0; NCase=3,026). Using a phenome-wide association (PheWAS) design, we tested associations between each PTSD definition and all prevalent disease umbrella categories, i.e., phecodes. We also conducted sex-stratified PheWAS analyses including a sex-by-diagnosis interaction term in each logistic regression. Results A substantial number of phecodes were significantly associated with PTSDNarrow (61%) and PTSDBroad (83%). While top associations were shared between the two definitions, PTSDBroad captured 334 additional phecodes not significantly associated with PTSDNarrow and exhibited a wider range of significantly associated phecodes across various categories, including respiratory, genitourinary, and circulatory conditions. Sex differences were observed, in that PTSDBroad was more strongly associated with osteoporosis, respiratory failure, hemorrhage, and pulmonary heart disease among male patients, and with urinary tract infection, acute pharyngitis, respiratory infections, and overweight among female patients. Conclusions This study provides valuable insights into a diverse range of comorbidities associated with PTSD, including both known and novel associations, while highlighting the influence of sex differences and the impact of defining PTSD using EHR.
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Affiliation(s)
- Emily M Hicks
- Pamela Sklar Division of Psychiatric Genetics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maria Niarchou
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA
| | - Slavina Goleva
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA
| | - Dia Kabir
- Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA
| | - Julia Ciarcia
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Jordan W Smoller
- Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA
| | - Lea K Davis
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA
| | - Caroline M Nievergelt
- University of California San Diego, Department of Psychiatry, La Jolla, CA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA
| | - Karestan C Koenen
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA
- Harvard T.H. Chan School of Public Health, Department of Epidemiology, Boston, MA, US
| | - Laura M Huckins
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Karmel W Choi
- Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA
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57
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Woodward DJ, Thorp JG, Akosile W, Ong JS, Gamazon ER, Derks EM, Gerring ZF. Identification of drug repurposing candidates for the treatment of anxiety: A genetic approach. Psychiatry Res 2023; 326:115343. [PMID: 37473490 PMCID: PMC10493169 DOI: 10.1016/j.psychres.2023.115343] [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: 02/28/2023] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
Anxiety disorders are a group of prevalent and heritable neuropsychiatric diseases. We previously conducted a genome-wide association study (GWAS) which identified genomic loci associated with anxiety; however, the biological consequences underlying the genetic associations are largely unknown. Integrating GWAS and functional genomic data may improve our understanding of the genetic effects on intermediate molecular phenotypes such as gene expression. This can provide an opportunity for the discovery of drug targets for anxiety via drug repurposing. We used the GWAS summary statistics to determine putative causal genes for anxiety using MAGMA and colocalization analyses. A transcriptome-wide association study was conducted to identify genes with differential genetically regulated levels of gene expression in human brain tissue. The genes were integrated with a large drug-gene expression database (Connectivity Map), discovering compounds that are predicted to "normalise" anxiety-associated expression changes. The study identified 64 putative causal genes associated with anxiety (35 genes upregulated; 29 genes downregulated). Drug mechanisms adrenergic receptor agonists, sigma receptor agonists, and glutamate receptor agonists gene targets were enriched in anxiety-associated genetic signal and exhibited an opposing effect on the anxiety-associated gene expression signature. The significance of the project demonstrated genetic links for novel drug candidates to potentially advance anxiety therapeutics.
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Affiliation(s)
- Damian J Woodward
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia; School of Biomedical Science, Queensland University of Technology, Kelvin Grove, QLD, Australia.
| | - Jackson G Thorp
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Wole Akosile
- School of Medicine, University of Queensland, St Lucia, QLD, Australia
| | - Jue-Sheng Ong
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Centre, Nashville, TN, USA; Clare Hall, University of Cambridge, Cambridge, UK
| | - Eske M Derks
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Zachary F Gerring
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.
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58
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Saklatvala JR, Hanscombe KB, Mahil SK, Tsoi LC, Elder JT, Barker JN, Simpson MA, Smith CH, Dand N. Genetic Validation of Psoriasis Phenotyping in UK Biobank Supports the Utility of Self-Reported Data and Composite Definitions for Large Genetic and Epidemiological Studies. J Invest Dermatol 2023; 143:1598-1601.e10. [PMID: 36870556 PMCID: PMC10689249 DOI: 10.1016/j.jid.2023.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 02/02/2023] [Accepted: 02/05/2023] [Indexed: 03/06/2023]
Affiliation(s)
- Jake R Saklatvala
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom
| | - Ken B Hanscombe
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom; Social, Genetic and Developmental Psychiatry Centre, King's College London, London, United Kingdom
| | - Satveer K Mahil
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust, King's College London, London, United Kingdom
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - James T Elder
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA; Ann Arbor Veterans Affairs Hospital, Ann Arbor, Michigan, USA
| | - Jonathan N Barker
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust, King's College London, London, United Kingdom
| | - Michael A Simpson
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom
| | - Catherine H Smith
- St John's Institute of Dermatology, Guy's and St Thomas' NHS Foundation Trust, King's College London, London, United Kingdom
| | - Nick Dand
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom.
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59
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Wu Y, Goleva SB, Breidenbach LB, Kim M, MacGregor S, Gandal MJ, Davis LK, Wray NR. 150 risk variants for diverticular disease of intestine prioritize cell types and enable polygenic prediction of disease susceptibility. CELL GENOMICS 2023; 3:100326. [PMID: 37492107 PMCID: PMC10363821 DOI: 10.1016/j.xgen.2023.100326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/11/2023] [Accepted: 04/20/2023] [Indexed: 07/27/2023]
Abstract
We conducted a genome-wide association study (GWAS) analysis of diverticular disease (DivD) of intestine within 724,372 individuals and identified 150 independent genome-wide significant DNA variants. Integration of the GWAS results with human gut single-cell RNA sequencing data implicated gut myocyte, mesothelial and stromal cells, and enteric neurons and glia in DivD development. Ninety-five genes were prioritized based on multiple lines of evidence, including SLC9A3, a drug target gene of tenapanor used for the treatment of the constipation subtype of irritable bowel syndrome. A DivD polygenic score (PGS) enables effective risk prediction (area under the curve [AUC], 0.688; 95% confidence interval [CI], 0.645-0.732) and the top 20% PGS was associated with ∼3.6-fold increased DivD risk relative to the remaining population. Our statistical and bioinformatic analyses suggest that the mechanism of DivD is through colon structure, gut motility, gastrointestinal mucus, and ionic homeostasis. Our analyses reinforce the link between gastrointestinal disorders and the enteric nervous system through genetics.
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Affiliation(s)
- Yeda Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Slavina B. Goleva
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lindsay B. Breidenbach
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Minsoo Kim
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Michael J. Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Lea K. Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioural Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, 511-A Light Hall, 2215 Garland Avenue, Nashville, TN 37232, USA
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
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Wang X, Walker A, Revez JA, Ni G, Adams MJ, McIntosh AM, Visscher PM, Wray NR. Polygenic risk prediction: why and when out-of-sample prediction R 2 can exceed SNP-based heritability. Am J Hum Genet 2023; 110:1207-1215. [PMID: 37379836 PMCID: PMC10357496 DOI: 10.1016/j.ajhg.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 06/30/2023] Open
Abstract
In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.
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Affiliation(s)
- Xiaotong Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Alicia Walker
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Joana A Revez
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Guiyan Ni
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, 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.
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61
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Radford-Smith DE, Anthony DC, Benz F, Grist JT, Lyman M, Miller JJ, Probert F. A multivariate blood metabolite algorithm stably predicts risk and resilience to major depressive disorder in the general population. EBioMedicine 2023; 93:104643. [PMID: 37327674 DOI: 10.1016/j.ebiom.2023.104643] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/19/2023] [Accepted: 05/22/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Socioeconomic pressures, sex, and physical health status strongly influence the development of major depressive disorder (MDD) and mask other contributing factors in small cohorts. Resilient individuals overcome adversity without the onset of psychological symptoms, but resilience, as for susceptibility, has a complex and multifaceted molecular basis. The scale and depth of the UK Biobank affords an opportunity to identify resilience biomarkers in rigorously matched, at-risk individuals. Here, we evaluated whether blood metabolites could prospectively classify and indicate a biological basis for susceptibility or resilience to MDD. METHODS Using the UK Biobank, we employed random forests, a supervised, interpretable machine learning statistical method to determine the relative importance of sociodemographic, psychosocial, anthropometric, and physiological factors that govern the risk of prospective MDD onset (total n = 15,710). We then used propensity scores to rigorously match individuals with a history of MDD (n = 491) against a resilient subset of individuals without an MDD diagnosis (retrospectively or during follow-up; n = 491) using an array of key social, demographic, and disease-associated drivers of depression risk. 381 blood metabolites and clinical chemistry variables and 4 urine metabolites were integrated to generate a multivariate random forest-based algorithm using 10-fold cross-validation to predict prospective MDD risk and resilience. OUTCOMES In unmatched individuals, a first case of MDD, with a median time-to-diagnosis of 72 years, can be predicted using random forest classification probabilities with an area under the receiver operator characteristic curve (ROC AUC) of 0.89. Prospective resilience/susceptibility to MDD was then predicted with a ROC AUC of 0.72 (x˜ = 3.2 years follow-up) and 0.68 (x˜ = 7.2 years follow-up). Increased pyruvate was identified as a key biomarker of resilience to MDD and was validated retrospectively in the TwinsUK cohort. INTERPRETATION Blood metabolites prospectively associate with substantially reduced MDD risk. Therapeutic targeting of these metabolites may provide a framework for MDD risk stratification and reduction. FUNDING New York Academy of Sciences' Interstellar Programme Award; Novo Fonden; Lincoln Kingsgate award; Clarendon Fund; Newton-Abraham studentship (University of Oxford). The funders had no role in the development of the present study.
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Affiliation(s)
- Daniel E Radford-Smith
- Department of Pharmacology, University of Oxford, Oxford, OX1 3QT, United Kingdom; Department of Chemistry, University of Oxford, Oxford, OX1 3TA, United Kingdom; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, OX3 7JX, United Kingdom.
| | - Daniel C Anthony
- Department of Pharmacology, University of Oxford, Oxford, OX1 3QT, United Kingdom
| | - Fee Benz
- Department of Pharmacology, University of Oxford, Oxford, OX1 3QT, United Kingdom
| | - James T Grist
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom; The Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, United Kingdom; Department of Radiology, Oxford University Hospitals, Oxford, United Kingdom
| | - Monty Lyman
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Jack J Miller
- Department of Physics, Clarendon Laboratory, Oxford, OX1 3PT, United Kingdom; The MR Research Centre, Aarhus University, Aarhus, Denmark
| | - Fay Probert
- Department of Chemistry, University of Oxford, Oxford, OX1 3TA, United Kingdom
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62
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Sada-Fuente E, Aranda S, Papiol S, Heilbronner U, Moltó MD, Aguilar EJ, González-Peñas J, Andreu-Bernabeu Á, Arango C, Crespo-Facorro B, González-Pinto A, Fañanás L, Arias B, Bobes J, Costas J, Martorell L, Schulze TG, Kalman JL, Vilella E, Muntané G. Common genetic variants contribute to heritability of age at onset of schizophrenia. Transl Psychiatry 2023; 13:201. [PMID: 37308478 PMCID: PMC10261125 DOI: 10.1038/s41398-023-02508-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/23/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
Schizophrenia (SCZ) is a complex disorder that typically arises in late adolescence or early adulthood. Age at onset (AAO) of SCZ is associated with long-term outcomes of the disease. We explored the genetic architecture of AAO with a genome-wide association study (GWAS), heritability, polygenic risk score (PRS), and copy number variant (CNV) analyses in 4 740 subjects of European ancestry. Although no genome-wide significant locus was identified, SNP-based heritability of AAO was estimated to be between 17 and 21%, indicating a moderate contribution of common variants. We also performed cross-trait PRS analyses with a set of mental disorders and identified a negative association between AAO and common variants for SCZ, childhood maltreatment and attention-deficit/hyperactivity disorder. We also investigated the role of copy number variants (CNVs) in AAO and found an association with the length and number of deletions (P-value = 0.03), whereas the presence of CNVs previously reported in SCZ was not associated with earlier onset. To our knowledge, this is the largest GWAS of AAO of SCZ to date in individuals from European ancestry, and the first study to determine the involvement of common variants in the heritability of AAO. Finally, we evidenced the role played by higher SCZ load in determining AAO but discarded the role of pathogenic CNVs. Altogether, these results shed light on the genetic architecture of AAO, which needs to be confirmed with larger studies.
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Affiliation(s)
- Ester Sada-Fuente
- Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili (IISPV), Department of Psychiatry, Universitat Rovira i Virgili (URV), Reus, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Selena Aranda
- Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili (IISPV), Department of Psychiatry, Universitat Rovira i Virgili (URV), Reus, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, Ludwig Maximilian University of Munich, 80336, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, 80336, Munich, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, Ludwig Maximilian University of Munich, 80336, Munich, Germany
| | - María Dolores Moltó
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Genetics, Universitat de Valencia, 46100, Valencia, Spain
- Biomedical Research Institute INCLIVA, 46010, Valencia, Spain
| | - Eduardo J Aguilar
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Biomedical Research Institute INCLIVA, 46010, Valencia, Spain
- Department of Psychiatry, Hospital Clínico Universitario de Valencia, 46010, Valencia, Spain
- Faculty of Medicine, Universidad de Valencia, 46010, Valencia, Spain
| | - Javier González-Peñas
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Gregorio Marañón Health Research Institute (IiSGM), Hospital General Universitario Gregorio Marañón, 28007, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Gregorio Marañón Health Research Institute (IiSGM), Hospital General Universitario Gregorio Marañón, 28007, Madrid, Spain
- Faculty of Medicine, Universidad Complutense, 28007, Madrid, Spain
| | - Celso Arango
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Gregorio Marañón Health Research Institute (IiSGM), Hospital General Universitario Gregorio Marañón, 28007, Madrid, Spain
- Faculty of Medicine, Universidad Complutense, 28007, Madrid, Spain
| | - Benedicto Crespo-Facorro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Universidad de Cantabria, 39005, Santander, Cantabria, Spain
- Hospital Universitario Marqués de Valdecilla-IDIVAL, 39008, Santander, Cantabria, Spain
- Department of Psychiatry, University Hospital Virgen del Rocío, Instituto de Biomedicina de Sevilla (IBiS), Sevilla, Spain
| | - Ana González-Pinto
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Hospital Universitario Araba, Instituto de Investigación Sanitaria Bioaraba, Universidad del País Vasco, 01009, Vitoria, Spain
| | - Lourdes Fañanás
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, Universitat de Barcelona, Institut de Biomedicina de la Universitat de Barcelona (IBUB), 08028, Barcelona, Spain
| | - Barbara Arias
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, Universitat de Barcelona, Institut de Biomedicina de la Universitat de Barcelona (IBUB), 08028, Barcelona, Spain
| | - Julio Bobes
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Faculty of Medicine and Health Sciences - Psychiatry, Universidad de Oviedo, 33006, Oviedo, Spain
- Mental Health Services of Principado de Asturias (SESPA), Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 33011, Oviedo, Spain
- Instituto de Neurociencias del Principado de Asturias (INEUROPA), 33003, Oviedo, Spain
| | - Javier Costas
- Psychiatric Genetics Group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Servizo Galego de Saúde (SERGAS), Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), 15706, Santiago de Compostela, Spain
| | - Lourdes Martorell
- Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili (IISPV), Department of Psychiatry, Universitat Rovira i Virgili (URV), Reus, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, Ludwig Maximilian University of Munich, 80336, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, 80336, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, US
| | - Janos L Kalman
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, Ludwig Maximilian University of Munich, 80336, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, 80336, Munich, Germany
| | - Elisabet Vilella
- Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili (IISPV), Department of Psychiatry, Universitat Rovira i Virgili (URV), Reus, Spain.
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.
| | - Gerard Muntané
- Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili (IISPV), Department of Psychiatry, Universitat Rovira i Virgili (URV), Reus, Spain.
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.
- Institut de Biologia Evolutiva (UPF-CSIC), Departament de Medicina i Ciències de la Vida, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.
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63
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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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64
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Wu X, Zhang W, Zhao X, Zhang L, Xu M, Hao Y, Xiao J, Zhang B, Li J, Kraft P, Smoller JW, Jiang X. Investigating the relationship between depression and breast cancer: observational and genetic analyses. BMC Med 2023; 21:170. [PMID: 37143087 PMCID: PMC10161423 DOI: 10.1186/s12916-023-02876-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 04/20/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Both depression and breast cancer (BC) contribute to a substantial global burden of morbidity and mortality among women, and previous studies have observed a potential depression-BC link. We aimed to comprehensively characterize the phenotypic and genetic relationships between depression and BC. METHODS We first evaluated phenotypic association using longitudinal follow-up data from the UK Biobank (N = 250,294). We then investigated genetic relationships leveraging summary statistics from the hitherto largest genome-wide association study of European individuals conducted for depression (N = 500,199), BC (N = 247,173), and its subtypes based on the status of estrogen receptor (ER + : N = 175,475; ER - : N = 127,442). RESULTS Observational analysis suggested an increased hazard of BC in depression patients (HR = 1.10, 95%CIs = 0.95-1.26). A positive genetic correlation between depression and overall BC was observed ([Formula: see text] = 0.08, P = 3.00 × 10-4), consistent across ER + ([Formula: see text] = 0.06, P = 6.30 × 10-3) and ER - subtypes ([Formula: see text] = 0.08, P = 7.20 × 10-3). Several specific genomic regions showed evidence of local genetic correlation, including one locus at 9q31.2, and four loci at, or close, to 6p22.1. Cross-trait meta-analysis identified 17 pleiotropic loci shared between depression and BC. TWAS analysis revealed five shared genes. Bi-directional Mendelian randomization suggested risk of depression was causally associated with risk of overall BC (OR = 1.12, 95%Cis = 1.04-1.19), but risk of BC was not causally associated with risk of depression. CONCLUSIONS Our work demonstrates a shared genetic basis, pleiotropic loci, and a putative causal relationship between depression and BC, highlighting a biological link underlying the observed phenotypic relationship; these findings may provide important implications for future studies aimed reducing BC risk.
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Affiliation(s)
- Xueyao Wu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China
| | - Wenqiang Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China
| | - Xunying Zhao
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China
| | - Li Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China
| | - Minghan Xu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China
| | - Yu Hao
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China
| | - Jinyu Xiao
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China
| | - Ben Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China
| | - Jiayuan Li
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China
| | - Peter Kraft
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, and Department of Psychiatry, Massachusetts General Hospital, MA, Boston, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, MA, Cambridge, USA
| | - Xia Jiang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min Nan Lu, Chengdu, Sichuan, 610041, People's Republic of China.
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Solna, Sweden.
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
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65
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Lichter K, Klüpfel C, Stonawski S, Hommers L, Blickle M, Burschka C, Das F, Heißler M, Hellmuth A, Helmel J, Kranemann L, Lechner K, Lehrieder D, Sauter A, Schiele MA, Vijayakumar V, von Broen M, Weiß C, Morbach C, Störk S, Gelbrich G, Heuschmann PU, Higuchi T, Buck A, Homola GA, Pham M, Menke A, Domschke K, Kittel-Schneider S, Deckert J. Deep phenotyping as a contribution to personalized depression therapy: the GEParD and DaCFail protocols. J Neural Transm (Vienna) 2023; 130:707-722. [PMID: 36959471 PMCID: PMC10121520 DOI: 10.1007/s00702-023-02615-8] [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: 12/22/2022] [Accepted: 02/27/2023] [Indexed: 03/25/2023]
Abstract
Depressive patients suffer from a complex of symptoms of varying intensity compromising their mood, emotions, self-concept, neurocognition, and somatic function. Due to a mosaic of aetiologies involved in developing depression, such as somatic, neurobiological, (epi-)genetic factors, or adverse life events, patients often experience recurrent depressive episodes. About 20-30% of these patients develop difficult-to-treat depression. Here, we describe the design of the GEParD (Genetics and Epigenetics of Pharmaco- and Psychotherapy in acute and recurrent Depression) cohort and the DaCFail (Depression-associated Cardiac Failure) case-control protocol. Both protocols intended to investigate the incremental utility of multimodal biomarkers including cardiovascular and (epi-)genetic markers, functional brain and heart imaging when evaluating the response to antidepressive therapy using comprehensive psychometry. From 2012 to 2020, 346 depressed patients (mean age 45 years) were recruited to the prospective, observational GEParD cohort protocol. Between 2016 and 2020, the DaCFail case-control protocol was initiated integrating four study subgroups to focus on heart-brain interactions and stress systems in patients > 50 years with depression and heart failure, respectively. For DaCFail, 120 depressed patients (mean age 60 years, group 1 + 2), of which 115 also completed GEParD, and 95 non-depressed controls (mean age 66 years) were recruited. The latter comprised 47 patients with heart failure (group 3) and 48 healthy subjects (group 4) of a population-based control group derived from the Characteristics and Course of Heart Failure Stages A-B and Determinants of Progression (STAAB) cohort study. Our hypothesis-driven, exploratory study design may serve as an exemplary roadmap for a standardized, reproducible investigation of personalized antidepressant therapy in an inpatient setting with focus on heart comorbidities in future multicentre studies.
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Affiliation(s)
- Katharina Lichter
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Catherina Klüpfel
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
- Interdisciplinary Center for Clinical Research, University Hospital of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
- Department of Clinical Research and Epidemiology, Comprehensive Heart Failure Center (CHFC), University Hospital of Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
| | - Saskia Stonawski
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
- Interdisciplinary Center for Clinical Research, University Hospital of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
- Department of Clinical Research and Epidemiology, Comprehensive Heart Failure Center (CHFC), University Hospital of Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
| | - Leif Hommers
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
- Interdisciplinary Center for Clinical Research, University Hospital of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
- Department of Clinical Research and Epidemiology, Comprehensive Heart Failure Center (CHFC), University Hospital of Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
| | - Manuel Blickle
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
- Department of Clinical Research and Epidemiology, Comprehensive Heart Failure Center (CHFC), University Hospital of Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
| | - Carolin Burschka
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Felix Das
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Marlene Heißler
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Anna Hellmuth
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Jaqueline Helmel
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Leonie Kranemann
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Karin Lechner
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Dominik Lehrieder
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Amelie Sauter
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Miriam A Schiele
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Hauptstr. 5, 79104, Freiburg, Germany
| | - Vithusha Vijayakumar
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Michael von Broen
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Carolin Weiß
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Caroline Morbach
- Department of Clinical Research and Epidemiology, Comprehensive Heart Failure Center (CHFC), University Hospital of Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
- Department of Medicine I, University Hospital of Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Stefan Störk
- Department of Clinical Research and Epidemiology, Comprehensive Heart Failure Center (CHFC), University Hospital of Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
- Department of Medicine I, University Hospital of Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Götz Gelbrich
- Department of Clinical Research and Epidemiology, Comprehensive Heart Failure Center (CHFC), University Hospital of Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
- Clinical Trial Center, University Hospital of Würzburg, Würzburg, Germany
| | - Peter U Heuschmann
- Department of Clinical Research and Epidemiology, Comprehensive Heart Failure Center (CHFC), University Hospital of Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
- Clinical Trial Center, University Hospital of Würzburg, Würzburg, Germany
| | - Takahiro Higuchi
- Department of Clinical Research and Epidemiology, Comprehensive Heart Failure Center (CHFC), University Hospital of Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
- Department of Nuclear Medicine, University Hospital of Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
- Dentistry and Pharmaceutical Sciences, Okayama University Graduate School of Medicine, Okayama, Japan
| | - Andreas Buck
- Department of Nuclear Medicine, University Hospital of Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - György A Homola
- Department of Neuroradiology, University Hospital of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
| | - Mirko Pham
- Department of Neuroradiology, University Hospital of Würzburg, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
| | - Andreas Menke
- Department of Psychosomatic Medicine and Psychotherapy, Medical Park Chiemseeblick, Rathausstr. 25, 83233, Bernau am Chiemsee, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Nußbaumstr. 7, 80336, Munich, Germany
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Hauptstr. 5, 79104, Freiburg, Germany
- Center for Basics in NeuroModulation, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Jürgen Deckert
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany.
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Fabbri C, Mutz J, Lewis CM, Serretti A. Depressive symptoms and neuroticism-related traits are the main factors associated with wellbeing independent of the history of lifetime depression in the UK Biobank. Psychol Med 2023; 53:3000-3008. [PMID: 35695039 PMCID: PMC10235644 DOI: 10.1017/s003329172100502x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 11/08/2021] [Accepted: 11/16/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Wellbeing has a fundamental role in determining life expectancy and major depressive disorder (MDD) is one of the main modulating factors of wellbeing. This study evaluated the modulators of wellbeing in individuals with lifetime recurrent MDD (RMDD), single-episode MDD (SMDD) and no MDD in the UK Biobank. METHODS Scores of happiness, meaningful life and satisfaction about functioning were condensed in a functioning-wellbeing score (FWS). We evaluated depression and anxiety characteristics, neuroticism-related traits, physical diseases, lifestyle and polygenic risk scores (PRSs) of psychiatric disorders. Other than individual predictors, we estimated the cumulative contribution to FWS of each group of predictors. We tested the indirect role of neuroticism on FWS through the modulation of depression manifestations using a mediation analysis. RESULTS We identified 47 966, 21 117 and 207 423 individuals with lifetime RMDD, SMDD and no MDD, respectively. Depression symptoms and personality showed the largest impact on FWS (variance explained ~20%), particularly self-harm, worthlessness feelings during the worst depression, chronic depression, loneliness and neuroticism. Personality played a stronger role in SMDD. Anxiety characteristics showed a higher effect in SMDD and no MDD groups. Neuroticism played indirect effects through specific depressive symptoms that modulated FWS. Physical diseases and lifestyle explained only 4-5% of FWS variance. The PRS of MDD showed the largest effect on FWS compared to other PRSs. CONCLUSIONS This was the first study to comprehensively evaluate the predictors of wellbeing in relation to the history of MDD. The identified variables are important to identify individuals at risk and promote wellbeing.
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Affiliation(s)
- Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Julian Mutz
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Cathryn M. Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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McGeary JE, Benca-Bachman CE, Risner VA, Beevers CG, Gibb BE, Palmer RHC. Associating broad and clinically defined polygenic scores for depression with depression-related phenotypes. Sci Rep 2023; 13:6534. [PMID: 37085695 PMCID: PMC10121555 DOI: 10.1038/s41598-023-33645-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 04/16/2023] [Indexed: 04/23/2023] Open
Abstract
Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank in an independent sample of adults (N = 210; 100% European Ancestry) who were extensively phenotyped for depression and related neurocognitive traits (e.g., rumination, emotion regulation, anhedonia, and resting frontal alpha asymmetry). The UK Biobank-derived PGSBD had small associations with MDD, depression severity, anhedonia, cognitive reappraisal, brooding, and suicidal ideation but only the association with suicidal ideation remained statistically significant after correcting for multiple comparisons. Similarly small associations were observed for the PGSMDD but none remained significant after correcting for multiple comparisons. These findings provide important initial guidance about the expected effect sizes between current UKB PGSs for depression and depression-related neurocognitive phenotypes.
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Affiliation(s)
- John E McGeary
- Providence Veterans Affairs Medical Center, Providence, RI, USA
| | - Chelsie E Benca-Bachman
- Providence Veterans Affairs Medical Center, Providence, RI, USA.
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, 36 Eagle Row, Atlanta, GA, 30322, USA.
| | - Victoria A Risner
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, 36 Eagle Row, Atlanta, GA, 30322, USA
| | | | - Brandon E Gibb
- Department of Psychology State, University of New York at Binghamton, Binghamton, NY, USA
| | - Rohan H C Palmer
- Providence Veterans Affairs Medical Center, Providence, RI, USA
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, 36 Eagle Row, Atlanta, GA, 30322, USA
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68
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Hicks EM, Seah C, Cote A, Marchese S, Brennand KJ, Nestler EJ, Girgenti MJ, Huckins LM. Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. Transl Psychiatry 2023; 13:129. [PMID: 37076454 PMCID: PMC10115809 DOI: 10.1038/s41398-023-02412-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 04/21/2023] Open
Abstract
Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.
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Affiliation(s)
- Emily M Hicks
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Alanna Cote
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Shelby Marchese
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Kristen J Brennand
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Eric J Nestler
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
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Valkovskaya M, Hassan A, Zartaloudi E, Hussain F, Umar M, Khizar B, Khattak I, Gill SA, Khan SUDA, Dogar IA, Mustafa AB, Ansari MA, Qalb I Hyder S, Ali M, Ilyas N, Channar P, Mughal N, Channa S, Mufti K, Mufti AA, Hussain MI, Shafiq S, Tariq M, Khan MK, Chaudhry ST, Choudhary AR, Ali MN, Ali G, Hussain A, Rehman M, Ahmad N, Farooq S, Naeem F, Nasr T, Lewis G, Knowles JA, Ayub M, Kuchenbaecker K. Study protocol of DIVERGE, the first genetic epidemiological study of major depressive disorder in Pakistan. Psychiatr Genet 2023; 33:69-78. [PMID: 36538573 PMCID: PMC9997631 DOI: 10.1097/ypg.0000000000000333] [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: 08/31/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Globally, 80% of the burdenof major depressive disorder (MDD) pertains to low- and middle-income countries. Research into genetic and environmental risk factors has the potential to uncover disease mechanisms that may contribute to better diagnosis and treatment of mental illness, yet has so far been largely limited to participants with European ancestry from high-income countries. The DIVERGE study was established to help overcome this gap and investigate genetic and environmental risk factors for MDD in Pakistan. METHODS DIVERGE aims to enrol 9000 cases and 4000 controls in hospitals across the country. Here, we provide the rationale for DIVERGE, describe the study protocol and characterise the sample using data from the first 500 cases. Exploratory data analysis is performed to describe demographics, socioeconomic status, environmental risk factors, family history of mental illness and psychopathology. RESULTS AND DISCUSSION Many participants had severe depression with 74% of patients who experienced multiple depressive episodes. It was a common practice to seek help for mental health struggles from faith healers and religious leaders. Socioeconomic variables reflected the local context with a large proportion of women not having access to any education and the majority of participants reporting no savings. CONCLUSION DIVERGE is a carefully designed case-control study of MDD in Pakistan that captures diverse risk factors. As the largest genetic study in Pakistan, DIVERGE helps address the severe underrepresentation of people from South Asian countries in genetic as well as psychiatric research.
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Affiliation(s)
| | - Arsalan Hassan
- Department of Pharmacy, University of Peshawar, Peshawar, Pakistan
| | - Eirini Zartaloudi
- Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Fahad Hussain
- Lahore Institute of Research and Development, Lahore
| | - Muhammad Umar
- Lahore Institute of Research and Development, Lahore
| | - Bakht Khizar
- Lahore Institute of Research and Development, Lahore
| | | | | | | | | | - Ali Burhan Mustafa
- Department of Psychiatry and Behavioural Sciences, Sheikh Zayed Medical College/Hospital, Rahim Yar Khan
| | - Moin Ahmed Ansari
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Syed Qalb I Hyder
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Muhammad Ali
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Nilofar Ilyas
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Parveen Channar
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Nazish Mughal
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Sumera Channa
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | | | | | | | | | | | | | | | | | | | - Gohar Ali
- Department of Psychiatry, Saidu Teaching Hospital
| | | | | | - Noman Ahmad
- Punjab Institute of Mental Health (PIMH), Lahore, Pakistan
| | - Saeed Farooq
- School of Medicine, Keele University, Keele
- Innovation Department, Midlands Partnership NHS Foundation Trust, Staffotdshire, UK
| | - Farooq Naeem
- Department of Psychiatry, University of Toronto
- Center for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Tanveer Nasr
- Lahore Institute of Research and Development, Lahore
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, UK
| | - James A. Knowles
- Human Genetics Institute of New Jersey (HGINJ), Rutgers University, New Brunswick, New Jersey, USA
| | - Muhammad Ayub
- Division of Psychiatry, University College London, London, UK
| | - Karoline Kuchenbaecker
- Division of Psychiatry, University College London, London, UK
- UCL Genetics Institute, University College London, London, UK
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Waszczuk MA, Miao J, Docherty AR, Shabalin AA, Jonas KG, Michelini G, Kotov R. General v. specific vulnerabilities: polygenic risk scores and higher-order psychopathology dimensions in the Adolescent Brain Cognitive Development (ABCD) Study. Psychol Med 2023; 53:1937-1946. [PMID: 37310323 PMCID: PMC10958676 DOI: 10.1017/s0033291721003639] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Polygenic risk scores (PRSs) capture genetic vulnerability to psychiatric conditions. However, PRSs are often associated with multiple mental health problems in children, complicating their use in research and clinical practice. The current study is the first to systematically test which PRSs associate broadly with all forms of childhood psychopathology, and which PRSs are more specific to one or a handful of forms of psychopathology. METHODS The sample consisted of 4717 unrelated children (mean age = 9.92, s.d. = 0.62; 47.1% female; all European ancestry). Psychopathology was conceptualized hierarchically as empirically derived general factor (p-factor) and five specific factors: externalizing, internalizing, neurodevelopmental, somatoform, and detachment. Partial correlations explored associations between psychopathology factors and 22 psychopathology-related PRSs. Regressions tested which level of the psychopathology hierarchy was most strongly associated with each PRS. RESULTS Thirteen PRSs were significantly associated with the general factor, most prominently Chronic Multisite Pain-PRS (r = 0.098), ADHD-PRS (r = 0.079), and Depression-PRS (r = 0.078). After adjusting for the general factor, Depression-PRS, Neuroticism-PRS, PTSD-PRS, Insomnia-PRS, Chronic Back Pain-PRS, and Autism-PRS were not associated with lower order factors. Conversely, several externalizing PRSs, including Adventurousness-PRS and Disinhibition-PRS, remained associated with the externalizing factor (|r| = 0.040-0.058). The ADHD-PRS remained uniquely associated with the neurodevelopmental factor (r = 062). CONCLUSIONS PRSs developed to predict vulnerability to emotional difficulties and chronic pain generally captured genetic risk for all forms of childhood psychopathology. PRSs developed to predict vulnerability to externalizing difficulties, e.g. disinhibition, tended to be more specific in predicting behavioral problems. The results may inform translation of existing PRSs to pediatric research and future clinical practice.
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Affiliation(s)
- Monika A. Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Jiaju Miao
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Anna R. Docherty
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Andrey A. Shabalin
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | | | - Giorgia Michelini
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
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71
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Hou K, Ding Y, Xu Z, Wu Y, Bhattacharya A, Mester R, Belbin GM, Buyske S, Conti DV, Darst BF, Fornage M, Gignoux C, Guo X, Haiman C, Kenny EE, Kim M, Kooperberg C, Lange L, Manichaikul A, North KE, Peters U, Rasmussen-Torvik LJ, Rich SS, Rotter JI, Wheeler HE, Wojcik GL, Zhou Y, Sankararaman S, Pasaniuc B. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat Genet 2023; 55:549-558. [PMID: 36941441 PMCID: PMC11120833 DOI: 10.1038/s41588-023-01338-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023]
Abstract
Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93-0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
| | - Yi Ding
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Ziqi Xu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Yue Wu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Rachel Mester
- Graduate Program in Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Gillian M Belbin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - David V Conti
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Burcu F Darst
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Christopher Haiman
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michelle Kim
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Leslie Lange
- Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Kari E North
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ulrike Peters
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Heather E Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, USA
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Ying Zhou
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Sriram Sankararaman
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
- Department of Computer Science, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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Han K, Ji L, Xie Q, Liu L, Wu X, He L, Shi Y, Zhang R, He G, Dong Z, Yu T. Different roles of microbiota and genetics in the prediction of treatment response in major depressive disorder. J Psychiatr Res 2023; 161:402-411. [PMID: 37023596 DOI: 10.1016/j.jpsychires.2023.03.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 04/08/2023]
Abstract
The roles of gut microbiota and susceptibility genes in patients with major depression disorder (MDD) are not well understood. Examining the microbiome and host genetics might be helpful for clinical decision-making. Patients with MDD were recruited in this study and subsequently treated for eight weeks. We identified the differences between the population with a response after two weeks and those with a response after eight weeks. The factors that were significantly correlated with efficacy were used to predict the treatment response. The differences in the importance of microbiota and genetics in prediction were analyzed. Our study identified rs58010457 as a potentially key locus affecting the treatment effect. Different microbiota and enriched pathways might play different roles in the response after two and eight weeks. We found that the area under the curve (AUC) value was greater than 0.8 for both random forest models. The contribution of different components to the AUC was evaluated by removing genetic information, microbiota abundance, and pathway data. The gut microbiome was an important predictor of the response after eight weeks, while genetics was an important predictor of the response after two weeks. These results suggested a dynamic effect of interaction among genetics and gut microbes on treatment. Furthermore, these results provide new guidance for clinical decisions: in cases of inadequate treatment effects after two weeks, the composition of the intestinal flora can be improved by diet therapy, which could ultimately affect the efficacy.
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Affiliation(s)
- Ke Han
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Lei Ji
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Qinglian Xie
- Out-patient Department of West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Liangjie Liu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Xi Wu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Lin He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Yi Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Rong Zhang
- Shanghai Center for Women and Children's Health, 339 Luding Road, Shanghai, 200062, China
| | - Guang He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.
| | - Zaiquan Dong
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Tao Yu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Center for Women and Children's Health, 339 Luding Road, Shanghai, 200062, China.
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Nudel R, Christensen RV, Kalnak N, Schwinn M, Banasik K, Dinh KM, Erikstrup C, Pedersen OB, Burgdorf KS, Ullum H, Ostrowski SR, Hansen TF, Werge T. Developmental language disorder - a comprehensive study of more than 46,000 individuals. Psychiatry Res 2023; 323:115171. [PMID: 36963307 DOI: 10.1016/j.psychres.2023.115171] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/16/2023] [Accepted: 03/14/2023] [Indexed: 03/26/2023]
Abstract
Developmental language disorder (DLD) is characterized by enduring low language abilities with a significant functional impact, in the absence of biomedical conditions in which language impairment is part of a complex of impairments. There is a lack of awareness of DLD even among healthcare professionals. Here we estimated the prevalence of DLD and its links to reading and learning difficulties and physical and mental health in the Danish Blood Donor Study (N = 46,547), where DLD-related information is based on questionnaires (self-report). We compared the questionnaire-derived DLD status with the relevant language-related diagnoses from hospital registers. We also investigated the genetic architecture of DLD in a subset of the cohort (N = 18,380). DLD was significantly associated with reading and learning difficulties and poorer mental and physical health. DLD prevalence was 3.36%-3.70% based on questionnaires, compared with 0.04% in hospital registers. Our genetic analyses identified one genome-wide significant locus, but not a significant heritability estimate. Our study shows that DLD has health-related implications that may last into adulthood, and that DLD may be undiagnosed in general healthcare. Furthermore, DLD is likely more genetically heterogeneous than narrower developmental language phenotypes. Our results emphasize the need to raise awareness of DLD and consider criteria for molecular studies of DLD to reduce case heterogeneity.
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Affiliation(s)
- Ron Nudel
- CORE - Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark; iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark.
| | - Rikke Vang Christensen
- Department of Nordic Studies and Linguistics, University of Copenhagen, Emil Holms Kanal 2, Copenhagen 2300, Denmark.
| | - Nelli Kalnak
- Department of Women's and Children's Health, Karolinska Institute, Stockholm, Sweden; Department of Speech-Language Pathology, Helsingborg Hospital, Helsingborg, Sweden.
| | - Michael Schwinn
- Department of Clinical Immunology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Karina Banasik
- Novo Nordisk foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Khoa Manh Dinh
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Ole Birger Pedersen
- Department of Immunology, Næstved Hospital, Næstved, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kristoffer Sølvsten Burgdorf
- Department of Immunology, Rigshospitalet, Copenhagen, Denmark; Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Folkmann Hansen
- Novo Nordisk foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Danish Headache Center, Rigshospitalet Glostrup, Glostrup, Denmark
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Institute of Biological Psychiatry, Roskilde, Denmark
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74
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Al-Soufi L, Costas J. Genetic susceptibility for schizophrenia after adjustment by genetic susceptibility for smoking: implications in identification of risk genes and genetic correlation with related traits. Psychol Med 2023; 53:1-11. [PMID: 36876478 DOI: 10.1017/s0033291723000326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
BACKGROUND Prevalence of smoking in schizophrenia (SCZ) is larger than in general population. Genetic studies provided some evidence of a causal effect of smoking on SCZ. We aim to characterize the genetic susceptibility to SCZ affected by genetic susceptibility to smoking. METHODS Multi-trait-based conditional and joint analysis was applied to the largest European SCZ genome-wide association studies (GWAS) to remove genetic effects on SCZ driven by smoking, estimated by generalized summary data-based Mendelian randomization. Enrichment analysis was performed to compare original v. conditional GWAS. Change in genetic correlation between SCZ and relevant traits after conditioning was assessed. Colocalization analysis was performed to identify specific loci confirming general findings. RESULTS Conditional analysis identified 19 new risk loci for SCZ and 42 lost loci whose association with SCZ may be partially driven by smoking. These results were strengthened by colocalization analysis. Enrichment analysis indicated a higher association of differentially expressed genes at prenatal brain stages after conditioning. Genetic correlation of SCZ with substance use and dependence, attention deficit-hyperactivity disorder, and several externalizing traits significantly changed after conditioning. Colocalization of association signal between SCZ and these traits was identified for some of the lost loci, such as CHRNA2, CUL3, and PCDH7. CONCLUSIONS Our approach led to identification of potential new SCZ loci, loci partially associated to SCZ through smoking, and a shared genetic susceptibility between SCZ and smoking behavior related to externalizing phenotypes. Application of this approach to other psychiatric disorders and substances may lead to a better understanding of the role of substances on mental health.
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Affiliation(s)
- Laila Al-Soufi
- Psychiatric Genetics group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain
- Red de Investigación en Atención Primaria de Adicciones (RIAPAd), Spain
- Department of Zoology, Genetics and Physical Anthropology, Universidade de Santiago de Compostela (USC), Santiago de Compostela, Galicia, Spain
| | - Javier Costas
- Psychiatric Genetics group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain
- Red de Investigación en Atención Primaria de Adicciones (RIAPAd), Spain
- Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
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75
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Huang L, Tang S, Rietkerk J, Appadurai V, Krebs MD, Schork AJ, Werge T, Zuber V, Kendler K, Cai N. Polygenic analyses show important differences between MDD symptoms collected using PHQ9 and CIDI-SF. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.27.23286527. [PMID: 36909638 PMCID: PMC10002792 DOI: 10.1101/2023.02.27.23286527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Symptoms of Major Depressive Disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire 9 (PHQ9, for current symptoms), and the Composite International Diagnostic Interview Short-Form (CIDI-SF, for lifetime worst-episode symptoms). Using data from the UKBiobank, we show that corresponding symptoms endorsed through PHQ9 and CIDI-SF have low to moderate genetic correlations (rG=0.43-0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip-structure in CIDI-SF. Through a combination of Mendelian Randomization (MR) and polygenic prediction analyses, we find that PHQ9 symptoms are more associated with traits which reflect general dysphoria, while the skip-structure in CIDI-SF allows for the identification of heterogeneity among likely MDD cases. This has important implications on factor analyses performed on their respective genetic covariance matrices for the purpose of identification of genetic factors behind MDD symptom dimensions and heterogeneity.
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Affiliation(s)
- Lianyun Huang
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Sonja Tang
- School of Public Health, Imperial College London, UK
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Jolien Rietkerk
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Vivek Appadurai
- Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital – Mental Health Services CPH, Copenhagen, Denmark
| | - Morten Dybdahl Krebs
- Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital – Mental Health Services CPH, Copenhagen, Denmark
| | - Andrew J. Schork
- Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital – Mental Health Services CPH, Copenhagen, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
- Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Sciences, Copenhagen University
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital – Mental Health Services CPH, Copenhagen, Denmark
- Lundbeck Foundation GeoGenetics Centre, Natural History Museum of Denmark, University of Copenhagen, Copenhagen 1350, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Verena Zuber
- School of Public Health, Imperial College London, UK
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Kenneth Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
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76
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.16.23286035. [PMID: 36824893 PMCID: PMC9949187 DOI: 10.1101/2023.02.16.23286035] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yilin Yang
- Department of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M. O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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77
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Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
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Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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78
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Han K, Ji L, Wang C, Shao Y, Chen C, Liu L, Feng M, Yang F, Wu X, Li X, Xie Q, He L, Shi Y, He G, Dong Z, Yu T. The host genetics affects gut microbiome diversity in Chinese depressed patients. Front Genet 2023; 13:976814. [PMID: 36699448 PMCID: PMC9868868 DOI: 10.3389/fgene.2022.976814] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
The gut microbiome and host genetics are both associated with major depressive disorder (MDD); however, the molecular mechanisms among the associations are poorly understood, especially in the Asian, Chinese group. Our study applied linear discriminant analysis (LDA) effect size (LEfSe) and genome-wide association analysis in the cohort with both gut sequencing data and genomics data. We reported the different gut microbiota characteristics between MDD and control groups in the Chinese group and further constructed the association between host genetics and the gut microbiome. Actinobacteria and Pseudomonades were found more in the MDD group. We found significant differences in the ACE and Chao indexes of alpha diversity while no discrepancy in beta diversity. We found three associations between host genetics with microbiome features: beta diversity and rs6108 (p = 8.65 × 10-9), Actinobacteria and rs77379751 (p = 8.56 × 10-9), and PWY-5913 and rs1775633082 (p = 4.54 × 10-8). A species of the Romboutsia genus was co-associated with the species of Ruminococcus gnavus in an internetwork through four genes: METTL8, ITGB2, OTULIN, and PROSER3, with a strict threshold (p < 5 × 10-4). Furthermore, our findings suggested that the gut microbiome diversity might affect microRNA expression in the brain and influenced SERPINA5 and other spatially close genes afterward. These findings suggest new linkages between depression and gut microbiome in Asian, Chinese people, which might be mediated by genes and microRNA regulation in space distance.
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Affiliation(s)
- Ke Han
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Lei Ji
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Chenliu Wang
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Shao
- Asbios (Tianjin) Biotechnology Co., Ltd., Tianjin, China
| | - Changfeng Chen
- School of Mental Health, Jining Medical University, Jining, China
| | - Liangjie Liu
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Mofan Feng
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Fengping Yang
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Xi Wu
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Xingwang Li
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Qinglian Xie
- Out-patient Department of West China Hospital, Sichuan University, Chengdu, China
| | - Lin He
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Yi Shi
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Guang He
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China,*Correspondence: Guang He, ; Zaiquan Dong, ; Tao Yu,
| | - Zaiquan Dong
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, China,*Correspondence: Guang He, ; Zaiquan Dong, ; Tao Yu,
| | - Tao Yu
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China,Shanghai Center for Women and Children’s Health, Shanghai, China,*Correspondence: Guang He, ; Zaiquan Dong, ; Tao Yu,
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79
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Pinakhina D, Yermakovich D, Vergasova E, Kasyanov E, Rukavishnikov G, Rezapova V, Kolosov N, Sergushichev A, Popov I, Kovalenko E, Ilinskaya A, Kim A, Plotnikov N, Ilinsky V, Neznanov N, Mazo G, Kibitov A, Rakitko A, Artomov M. GWAS of depression in 4,520 individuals from the Russian population highlights the role of MAGI2 ( S-SCAM) in the gut-brain axis. Front Genet 2023; 13:972196. [PMID: 36685848 PMCID: PMC9845291 DOI: 10.3389/fgene.2022.972196] [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: 06/17/2022] [Accepted: 12/01/2022] [Indexed: 01/05/2023] Open
Abstract
We present the results of the depression Genome-wide association studies study performed on a cohort of Russian-descent individuals, which identified a novel association at chromosome 7q21 locus. Gene prioritization analysis based on already known depression risk genes indicated MAGI2 (S-SCAM) as the most probable gene from the locus and potential susceptibility gene for the disease. Brain and gut expression patterns were the main features highlighting functional relatedness of MAGI2 to the previously known depression risk genes. Local genetic covariance analysis, analysis of gene expression, provided initial suggestive evidence of hospital anxiety and depression scale and diagnostic and statistical manual of mental disorders scales having a different relationship with gut-brain axis disturbance. It should be noted, that while several independent methods successfully in silico validate the role of MAGI2, we were unable to replicate genetic association for the leading variant in the MAGI2 locus, therefore the role of rs521851 in depression should be interpreted with caution.
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Affiliation(s)
| | | | | | - Evgeny Kasyanov
- V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint-Petersburg, Russia
| | - Grigory Rukavishnikov
- V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint-Petersburg, Russia
| | - Valeriia Rezapova
- ITMO University, Saint-Petersburg, Russia,Almazov National Medical Research Center, Saint-Petersburg, Russia,Broad Institute, Cambridge, MA, United States
| | - Nikita Kolosov
- ITMO University, Saint-Petersburg, Russia,Almazov National Medical Research Center, Saint-Petersburg, Russia,Broad Institute, Cambridge, MA, United States
| | | | | | | | | | | | | | - Valery Ilinsky
- Genotek Ltd., Moscow, Russia,V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint-Petersburg, Russia
| | - Nikholay Neznanov
- V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint-Petersburg, Russia,First Pavlov State Medical University of St. Petersburg, Saint-Petersburg, Russia
| | - Galina Mazo
- V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint-Petersburg, Russia
| | - Alexander Kibitov
- V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint-Petersburg, Russia
| | - Alexander Rakitko
- Genotek Ltd., Moscow, Russia,V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint-Petersburg, Russia
| | - Mykyta Artomov
- Broad Institute, Cambridge, MA, United States,Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, United States,The Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, United States,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States,*Correspondence: Mykyta Artomov,
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80
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Akingbuwa WA, Hammerschlag AR, Allegrini AG, Sallis H, Kuja-Halkola R, Rimfeld K, Lichtenstein P, Lundstrom S, Munafò MR, Plomin R, Nivard MG, Bartels M, Middeldorp CM. Multivariate analyses of molecular genetic associations between childhood psychopathology and adult mood disorders and related traits. Am J Med Genet B Neuropsychiatr Genet 2023; 192:3-12. [PMID: 36380638 PMCID: PMC7615008 DOI: 10.1002/ajmg.b.32922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/07/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022]
Abstract
Ubiquitous associations have been detected between different types of childhood psychopathology and polygenic risk scores based on adult psychiatric disorders and related adult outcomes, indicating that genetic factors partly explain the association between childhood psychopathology and adult outcomes. However, these analyses in general do not take into account the correlations between the adult trait and disorder polygenic risk scores. This study aimed to further clarify the influence of genetic factors on associations between childhood psychopathology and adult outcomes by accounting for these correlations. Using a multivariate multivariable regression, we analyzed associations of childhood attention-deficit/hyperactivity disorder (ADHD), internalizing, and social problems, with polygenic scores (PGS) of adult disorders and traits including major depression, bipolar disorder, subjective well-being, neuroticism, insomnia, educational attainment, and body mass index (BMI), derived for 20,539 children aged 8.5-10.5 years. After correcting for correlations between the adult phenotypes, major depression PGS were associated with all three childhood traits, that is, ADHD, internalizing, and social problems. In addition, BMI PGS were associated with ADHD symptoms and social problems, while neuroticism PGS were only associated with internalizing problems and educational attainment PGS were only associated with ADHD symptoms. PGS of bipolar disorder, subjective well-being, and insomnia were not associated with any childhood traits. Our findings suggest that associations between childhood psychopathology and adult traits like insomnia and subjective well-being may be primarily driven by genetic factors that influence adult major depression. Additionally, specific childhood phenotypes are genetically associated with educational attainment, BMI and neuroticism.
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Affiliation(s)
- Wonuola A Akingbuwa
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Anke R Hammerschlag
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Child Health Research Centre, the University of Queensland, Brisbane, Queensland, Australia
| | - Andrea G Allegrini
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Division of Psychology and Language Sciences, Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Hannah Sallis
- School of Psychological Science, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kaili Rimfeld
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sebastian Lundstrom
- Centre for Ethics Law and Mental Health, Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden
| | - Marcus R Munafò
- School of Psychological Science, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust, University of Bristol, Bristol, UK
| | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Christel M Middeldorp
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Child Health Research Centre, the University of Queensland, Brisbane, Queensland, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Services, Brisbane, Queensland, Australia
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81
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Maes HHM, Lapato DM, Schmitt JE, Luciana M, Banich MT, Bjork JM, Hewitt JK, Madden PA, Heath AC, Barch DM, Thompson WK, Iacono WG, Neale MC. Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®. Behav Genet 2023; 53:1-24. [PMID: 36357558 PMCID: PMC9823057 DOI: 10.1007/s10519-022-10123-w] [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: 09/19/2021] [Accepted: 10/11/2022] [Indexed: 11/12/2022]
Abstract
Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9-10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.
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Affiliation(s)
- Hermine H. M. Maes
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA ,grid.224260.00000 0004 0458 8737Massey Cancer Center, Virginia Commonwealth University, Richmond, VA USA
| | - Dana M. Lapato
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA
| | - J. Eric Schmitt
- grid.25879.310000 0004 1936 8972Departments of Radiology and Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Monica Luciana
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Marie T. Banich
- grid.266190.a0000000096214564Department of Psychology and Neuroscience, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA
| | - James M. Bjork
- grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
| | - John K. Hewitt
- grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute for Behavioral Genetics, University of Colorado, Boulder, USA
| | - Pamela A. Madden
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Andrew C. Heath
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Deanna M. Barch
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Wes K. Thompson
- grid.266100.30000 0001 2107 4242Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Lab, University of California at San Diego, La Jolla, CA USA
| | - William G. Iacono
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Michael C. Neale
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
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82
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Genetic substrates of bipolar disorder risk in Latino families. Mol Psychiatry 2023; 28:154-167. [PMID: 35948660 DOI: 10.1038/s41380-022-01705-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/22/2022] [Accepted: 07/07/2022] [Indexed: 01/07/2023]
Abstract
Genetic studies of bipolar disorder (BP) have been conducted in the Latin American population, to date, in several countries, including Mexico, the United States, Costa Rica, Colombia, and, to a lesser extent, Brazil. These studies focused primarily on linkage-based designs utilizing families with multiplex cases of BP. Significant BP loci were identified on Chromosomes 18, 5 and 8, and fine mapping suggested several genes of interest underlying these linkage peaks. More recently, studies in these same pedigrees yielded significant linkage loci for BP endophenotypes, including measures of activity, sleep cycles, and personality traits. Building from findings in other populations, candidate gene association analyses in Latinos from Mexican and Central American ancestry confirmed the role of several genes (including CACNA1C and ANK3) in conferring BP risk. Although GWAS, methylation, and deep sequencing studies have only begun in these populations, there is evidence that CNVs and rare SNPs both play a role in BP risk of these populations. Large segments of the Latino populations in the Americas remain largely unstudied regarding BP genetics, but evidence to date has shown that this type of research can be successfully conducted in these populations and that the genetic underpinnings of BP in these cohorts share at least some characteristics with risk genes identified in European and other populations.
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83
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van Loo HM, Beijers L, Wieling M, de Jong TR, Schoevers RA, Kendler KS. Prevalence of internalizing disorders, symptoms, and traits across age using advanced nonlinear models. Psychol Med 2023; 53:78-87. [PMID: 33849670 PMCID: PMC9874996 DOI: 10.1017/s0033291721001148] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 02/02/2021] [Accepted: 03/12/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND Most epidemiological studies show a decrease of internalizing disorders at older ages, but it is unclear how the prevalence exactly changes with age, and whether there are different patterns for internalizing symptoms and traits, and for men and women. This study investigates the impact of age and sex on the point prevalence across different mood and anxiety disorders, internalizing symptoms, and neuroticism. METHODS We used cross-sectional data on 146 315 subjects, aged 18-80 years, from the Lifelines Cohort Study, a Dutch general population sample. Between 2012 and 2016, five current internalizing disorders - major depression, dysthymia, generalized anxiety disorder, social phobia, and panic disorder - were assessed according to DSM-IV criteria. Depressive symptoms, anxiety symptoms, neuroticism, and negative affect (NA) were also measured. Generalized additive models were used to identify nonlinear patterns across age, and to investigate sex differences. RESULTS The point prevalence of internalizing disorders generally increased between the ages of 18 and 30 years, stabilized between 30 and 50, and decreased after age 50. The patterns of internalizing symptoms and traits were different. NA and neuroticism gradually decreased after age 18. Women reported more internalizing disorders than men, but the relative difference remained stable across age (relative risk ~1.7). CONCLUSIONS The point prevalence of internalizing disorders was typically highest between age 30 and 50, but there were differences between the disorders, which could indicate differences in etiology. The relative gap between the sexes remained similar across age, suggesting that changes in sex hormones around the menopause do not significantly influence women's risk of internalizing disorders.
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Affiliation(s)
- Hanna M. van Loo
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Lian Beijers
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Martijn Wieling
- Department of Information Science, University of Groningen, Groningen, The Netherlands
| | | | - Robert A. Schoevers
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Research School of Behavioural and Cognitive Neurosciences (BCN), Groningen, The Netherlands
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
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84
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Odintsova VV, Hagenbeek FA, van der Laan CM, van de Weijer S, Boomsma DI. Genetics and epigenetics of human aggression. HANDBOOK OF CLINICAL NEUROLOGY 2023; 197:13-44. [PMID: 37633706 DOI: 10.1016/b978-0-12-821375-9.00005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2023]
Abstract
There is substantial variation between humans in aggressive behavior, with its biological etiology and molecular genetic basis mostly unknown. This review chapter offers an overview of genomic and omics studies revealing the genetic contribution to aggression and first insights into associations with epigenetic and other omics (e.g., metabolomics) profiles. We allowed for a broad phenotype definition including studies on "aggression," "aggressive behavior," or "aggression-related traits," "antisocial behavior," "conduct disorder," and "oppositional defiant disorder." Heritability estimates based on family and twin studies in children and adults of this broadly defined phenotype of aggression are around 50%, with relatively small fluctuations around this estimate. Next, we review the genome-wide association studies (GWAS) which search for associations with alleles and also allow for gene-based tests and epigenome-wide association studies (EWAS) which seek to identify associations with differently methylated regions across the genome. Both GWAS and EWAS allow for construction of Polygenic and DNA methylation scores at an individual level. Currently, these predict a small percentage of variance in aggression. We expect that increases in sample size will lead to additional discoveries in GWAS and EWAS, and that multiomics approaches will lead to a more comprehensive understanding of the molecular underpinnings of aggression.
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Affiliation(s)
- Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands; Mental Health Division, Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Mental Health Division, Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Camiel M van der Laan
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, The Netherlands
| | - Steve van de Weijer
- Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands.
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85
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Zhu Y, Jha SC, Shutta KH, Huang T, Balasubramanian R, Clish CB, Hankinson SE, Kubzansky LD. Psychological distress and metabolomic markers: A systematic review of posttraumatic stress disorder, anxiety, and subclinical distress. Neurosci Biobehav Rev 2022; 143:104954. [PMID: 36368524 PMCID: PMC9729460 DOI: 10.1016/j.neubiorev.2022.104954] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 08/30/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022]
Abstract
Psychological distress can be conceptualized as an umbrella term encompassing symptoms of depression, anxiety, posttraumatic stress disorder (PTSD), or stress more generally. A systematic review of metabolomic markers associated with distress has the potential to reveal underlying molecular mechanisms linking distress to adverse health outcomes. The current systematic review extends prior reviews of clinical depressive disorders by synthesizing 39 existing studies that examined metabolomic markers for PTSD, anxiety disorders, and subclinical psychological distress in biological specimens. Most studies were based on small sets of pre-selected candidate metabolites, with few metabolites overlapping between studies. Vast heterogeneity was observed in study design and inconsistent patterns of association emerged between distress and metabolites. To gain a more robust understanding of distress and its metabolomic signatures, future research should include 1) large, population-based samples and longitudinal assessments, 2) replication and validation in diverse populations, 3) and agnostic metabolomic strategies profiling hundreds of targeted and nontargeted metabolites. Addressing these research priorities will improve the scope and reproducibility of future metabolomic studies of psychological distress.
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Affiliation(s)
- Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Shaili C Jha
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Tianyi Huang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Susan E Hankinson
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Laura D Kubzansky
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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86
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Rayner C, Coleman JRI, Skelton M, Armour C, Bradley J, Buckman JEJ, Davies MR, Hirsch CR, Hotopf M, Hübel C, Jones IR, Kalsi G, Kingston N, Krebs G, Lin Y, Monssen D, McIntosh AM, Mundy JR, Peel AJ, Rimes KA, Rogers HC, Smith DJ, Ter Kuile AR, Thompson KN, Veale D, Wingrove J, Walters JTR, Breen G, Eley TC. Patient characteristics associated with retrospectively self-reported treatment outcomes following psychological therapy for anxiety or depressive disorders - a cohort of GLAD study participants. BMC Psychiatry 2022; 22:719. [PMID: 36401199 PMCID: PMC9675224 DOI: 10.1186/s12888-022-04275-6] [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: 05/21/2021] [Accepted: 09/20/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Progress towards stratified care for anxiety and depression will require the identification of new predictors. We collected data on retrospectively self-reported therapeutic outcomes in adults who received psychological therapy in the UK in the past ten years. We aimed to replicate factors associated with traditional treatment outcome measures from the literature. METHODS Participants were from the Genetic Links to Anxiety and Depression (GLAD) Study, a UK-based volunteer cohort study. We investigated associations between retrospectively self-reported outcomes following therapy, on a five-point scale (global rating of change; GRC) and a range of sociodemographic, clinical and therapy-related factors, using ordinal logistic regression models (n = 2890). RESULTS Four factors were associated with therapy outcomes (adjusted odds ratios, OR). One sociodemographic factor, having university-level education, was associated with favourable outcomes (OR = 1.37, 95%CI: 1.18, 1.59). Two clinical factors, greater number of reported episodes of illness (OR = 0.95, 95%CI: 0.92, 0.97) and higher levels of personality disorder symptoms (OR = 0.89, 95%CI: 0.87, 0.91), were associated with less favourable outcomes. Finally, reported regular use of additional therapeutic activities was associated with favourable outcomes (OR = 1.39, 95%CI: 1.19, 1.63). There were no statistically significant differences between fully adjusted multivariable and unadjusted univariable odds ratios. CONCLUSION Therapy outcome data can be collected quickly and inexpensively using retrospectively self-reported measures in large observational cohorts. Retrospectively self-reported therapy outcomes were associated with four factors previously reported in the literature. Similar data collected in larger observational cohorts may enable detection of novel associations with therapy outcomes, to generate new hypotheses, which can be followed up in prospective studies.
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Affiliation(s)
- Christopher Rayner
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jonathan R I Coleman
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Megan Skelton
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Cherie Armour
- Research Centre for Stress Trauma & Related Conditions (STARC), School of Psychology, Queen's University Belfast (QUB), Belfast, Northern Ireland, UK
| | - John Bradley
- NIHR BioResource, Cambridge University Hospitals NHS Foundation, Cambridge Biomedical Campus, Cambridge, UK
| | - Joshua E J Buckman
- Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London, 1-19 Torrington Place, WC1E 7HB, London, UK
- iCope - Camden & Islington Psychological Therapies Services - Camden & Islington NHS Foundation Trust, St Pancras Hospital, NW1 0PE, London, UK
| | - Molly R Davies
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Colette R Hirsch
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
- South London and Maudsley NHS Foundation Trust, Denmark Hill, SE5 8AZ, London, UK
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Christopher Hübel
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
- Aarhus Business and Social Sciences, National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Ian R Jones
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Gursharan Kalsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Nathalie Kingston
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Georgina Krebs
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, Denmark Hill, SE5 8AZ, London, UK
| | - Yuhao Lin
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Dina Monssen
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Jessica R Mundy
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alicia J Peel
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Katharine A Rimes
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Henry C Rogers
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Daniel J Smith
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Abigail R Ter Kuile
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Katherine N Thompson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - David Veale
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
- South London and Maudsley NHS Foundation Trust, Denmark Hill, SE5 8AZ, London, UK
| | - Janet Wingrove
- South London and Maudsley NHS Foundation Trust, Denmark Hill, SE5 8AZ, London, UK
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Gerome Breen
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Thalia C Eley
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK.
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Lancaster EE, Lapato DM, Peterson RE. Understanding the genetics of peripartum depression: Research challenges, strategies, and opportunities. Front Genet 2022; 13:1022188. [PMID: 36468033 PMCID: PMC9714263 DOI: 10.3389/fgene.2022.1022188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/18/2022] [Indexed: 09/12/2023] Open
Abstract
Peripartum depression (PD) is a common mood disorder associated with negative outcomes for mother and child. PD is an understudied disorder in psychiatric genetics, and progress characterizing its genetic architecture has been limited by a lack of disorder-specific research, heterogeneous and evolving phenotypic definitions, inadequate representation of global populations, low-powered studies, and insufficient data amenable to large meta-analyses. The increasing availability of large-scale, population-level efforts, like biobanks, have the potential to accelerate scientific discovery and translational research by leveraging clinical, molecular, and self-report data from hundreds of thousands of individuals. Although these efforts will not fully equip researchers to confront every challenge posed by systemic issues in data collection, such as the reliance on minimal phenotyping strategies, the field is in a position to learn from other successful psychiatric genetic investigations. This review summarizes the current state of PD genetics research and highlights research challenges, including the impact of phenotype depth, measurement, and definition on the replicability and interpretability of genomic research. Recommendations for advancing health equity and improving the collection, analysis, discussion, and reporting of measures for PD research are provided.
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Affiliation(s)
- Eva E. Lancaster
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Dana M. Lapato
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Roseann E. Peterson
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry and Behavioral Health, Institute for Genomics in Health, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
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88
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Wu Y, Wang L, Zhang CY, Li M, Li Y. Genetic similarities and differences among distinct definitions of depression. Psychiatry Res 2022; 317:114843. [PMID: 36115168 DOI: 10.1016/j.psychres.2022.114843] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/22/2022] [Accepted: 09/09/2022] [Indexed: 01/04/2023]
Abstract
Depression is a common and complex psychiatric illness with considerable heritability. Genome-wide association studies (GWAS) have been conducted among different definitions of depression based on different diagnostic criteria. However, the heritability explained by different depression GWAS and the identified loci varied widely. To understand the genetic architectures of different definitions of depression, we conducted a series of genetic analyses including linkage disequilibrium score regression (LDSC), Mendelian randomization, and polygenic overlap quantification and identification. Different definitions of depression and other common psychiatric traits were included in this analysis. We found that although genetic correlations between different definitions of depression were relatively high, they showed substantially different genetic correlation and causality with other psychiatric traits. Using bivariate causal mixture mode (MiXeR) and conjunctional false discovery rate (conjFDR) approach, we observed both shared and unique risk loci across different definitions of depression. Further functional mapping with expression quantitative trait loci (eQTL) information from multiple brain tissues and single cell types indicated distinct genes underlying different definitions of depression, and pathways associated with synapses were significantly enriched in the illness. Our study showed that the genetic architectures of different definitions of depression were distinct and genetic studies of depression should be conducted more cautious.
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Affiliation(s)
- Yong Wu
- Research Center for Mental Health and Neuroscience, Wuhan Mental Health Center, Wuhan, 430012, Hubei, China.
| | - Lu Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, Yunnan, China
| | - Chu-Yi Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, Yunnan, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, Yunnan, China
| | - Yi Li
- Research Center for Mental Health and Neuroscience, Wuhan Mental Health Center, Wuhan, 430012, Hubei, China; Department of Psychiatry, Wuhan Mental Health Center, Wuhan, 430012, Hubei, China; Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, 430012, Hubei, China.
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89
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Weissman MM. Pursuing the epidemiology and familial risks of depression and developing an evidence based psychotherapy. Psychiatry Res 2022; 317:114754. [PMID: 36070660 DOI: 10.1016/j.psychres.2022.114754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/26/2022] [Accepted: 07/28/2022] [Indexed: 11/15/2022]
Abstract
This commentary, as requested, presents the highlights of my research career. The epidemiology of psychiatric disorders study, challenged in a small study, the notion that diagnosis for psychiatric disorders could be made in a community survey. This pilot study was the basis for the Epidemiology Catchment Area Study (ECA) with 18,000 participants and the many more updated surveys, which followed. The families at High and Low Risk for Depression study in its 40th year challenged the notion that children didn't get depressed and showed that parental depression was the major risk for depression, which began in youth and reoccurred over the lifespan. Interpersonal psychotherapy (IPT), now has been tested in over 150 clinical trials, recommended by the World Health Organization (WHO), globally in China, Germany, Ukraine, and many more countries.
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Affiliation(s)
- Myrna M Weissman
- Diane Goldman Kemper Family Professor of Epidemiology and Psychiatry, Chief, Division of Translational Epidemiology, Columbia University Vagelos College of Physicians and Surgeons, New York State Psychiatric Institute, 1051 Riverside Drive -Unit 24, New York, NY 10032, United States.
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90
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Casanova F, O'Loughlin J, Lewis C, Frayling TM, Wood AR, Tyrrell J. Simulated distributions from negative experiments highlight the importance of the body mass index distribution in explaining depression-body mass index genetic risk score interactions. Int J Epidemiol 2022; 51:1581-1592. [PMID: 35388897 PMCID: PMC9557895 DOI: 10.1093/ije/dyac052] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 03/21/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Depression and obesity are complex global health problems. Recent studies suggest that a genetic predisposition to obesity might be accentuated in people with depression, but these analyses are prone to bias. Here, we tested the hypothesis that depression accentuates genetic susceptibility to obesity and applied negative control experiments to test whether any observed interactions were real or driven by confounding and statistical biases. METHODS We used data from up to 378 000 Europeans in UK Biobank, a 73 variant body mass index (BMI) genetic risk score, two depression measures [depression symptoms (DS), major depression (MD)] and an antidepressant usage variable available. We tested whether (i) depression and (ii) antidepressant treatment accentuated genetic susceptibility to obesity. Finally, we performed negative control experiments by sampling individuals at random so that they had BMI distributions identical to depression cases and controls. RESULTS Depression was associated with an accentuation of an individual's genetic risk of obesity with evidence of interactions for both DS and MD (Pinteraction = 7 × 10-4 and 7 × 10-5 respectively). Antidepressant usage within DS cases accentuated genetic obesity risk (Pinteraction = 9 × 10-4), but not for MD (Pinteraction = 0.13). Negative control experiments suggested that the observed interactions for MD (empirical-P = 0.067) may be driven by statistical biases or confounding factors but were not possible with the larger DS groups. Antidepressant usage interaction also appears to be driven by statistical artefacts (empirical-P = 0.510 using MD and 0.162 using DS). CONCLUSION We have highlighted the importance of running negative experiments to confirm putative interactions in gene-environment studies. We provide some tentative evidence that depression accentuates an individual's genetic susceptibility to higher BMI but demonstrated that the BMI distributions within cases and controls might drive these interactions.
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Affiliation(s)
- Francesco Casanova
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Jessica O'Loughlin
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Cathryn Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Medical & Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Andrew R Wood
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Jessica Tyrrell
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
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91
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Jermy BS, Hagenaars S, Coleman JRI, Vassos E, Lewis CM. Risk factor profiles for depression following childbirth or a chronic disease diagnosis: case-control study. BJPsych Open 2022; 8:e182. [PMID: 36205003 PMCID: PMC9634597 DOI: 10.1192/bjo.2022.586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Progress towards understanding the aetiology of major depression is compromised by its clinical heterogeneity. The variety of contexts underlying the development of a major depressive episode may contribute to such heterogeneity. AIMS To compare risk factor profiles for three subgroups of major depression according to episode context. METHOD Using self-report questionnaires and administrative records from the UK Biobank, we characterised three contextual subgroups of major depression: postpartum depression (3581 cases), depression following diagnosis of a chronic disease (409 cases) and a more typical (named heterogeneous) major depression phenotype excluding the two other contexts (34 699 cases). Controls with the same exposure were also defined. We tested each subgroup for association with the polygenic risk scores (PRS) for major depression and with other risk factors previously associated with major depression (bipolar disorder PRS, neuroticism, reported trauma in childhood and adulthood, socioeconomic status, family history of depression, education). RESULTS Major depression PRS was associated with all subgroups, but postpartum depression cases had higher PRS than heterogeneous major depression cases (OR = 1.06, 95% CI 1.02-1.10). Relative to heterogeneous depression, postpartum depression was more weakly associated with adulthood trauma and neuroticism. Depression following diagnosis of a chronic disease had weaker association with neuroticism and reported trauma in adulthood and childhood relative to heterogeneous depression. CONCLUSIONS The observed differences in risk factor profiles according to the context of a major depressive episode help provide insight into the heterogeneity of depression. Future studies dissecting such heterogeneity could help reveal more refined aetiological insights.
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Affiliation(s)
- Bradley S. Jermy
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
- Correspondence: Bradley Jermy.
| | - Saskia Hagenaars
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Jonathan R. I. Coleman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Evangelos Vassos
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Cathryn M. Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK; and Department of Medical & Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, UK
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92
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Fabbri C, Mutz J, Lewis CM, Serretti A. Stratification of individuals with lifetime depression and low wellbeing in the UK Biobank. J Affect Disord 2022; 314:281-292. [PMID: 35878836 DOI: 10.1016/j.jad.2022.07.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/30/2022] [Accepted: 07/17/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Previous studies stratified patients with major depressive disorder (MDD) based on their clinical characteristics. This study used this approach in individuals with lifetime MDD who reported low wellbeing, a group of high clinical relevance. METHODS We selected participants in the UK Biobank (UKB) with lifetime MDD and a wellbeing score in the lowest 25 %. A wellbeing score was previously created considering happiness, belief that own life is meaningful, health satisfaction and functioning in relevant areas. In the selected group, we applied latent class analysis using mood-spectrum symptoms and personality traits as input variables, then we compared the clinical-demographic and genetic (polygenic risk scores, PRSs) characteristics of the identified classes. RESULTS A total of 13,896 individuals were included and a model with five classes showed the best performance. The most common class (31.25 %) was characterised by periods of irritable mood and trait irritability with high neuroticism. A rarer class (16.49 %) showed depressive-manic mood fluctuations and risk-taking personality, higher percentage of males, atypical depressive symptoms, lower socio-economic status, higher PRS for attention-deficit hyperactivity disorder and lower PRS for education. The second most common class (29.79 %) showed worry as main personality trait with low risk of manic/irritable manifestations. The remaining classes showed an anxious-irritable personality profile and a purely depressive profile (4.92 % and 17.55 %, respectively). LIMITATIONS Our results may reflect the characteristics of UKB participants. CONCLUSIONS Subthreshold manic/irritable mood fluctuations and personality traits irritability and neuroticism may distinguish the most common groups with poor wellbeing in lifetime MDD.
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Affiliation(s)
- Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Julian Mutz
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
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93
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Lencz T, Sabatello M, Docherty A, Peterson RE, Soda T, Austin J, Bierut L, Crepaz-Keay D, Curtis D, Degenhardt F, Huckins L, Lazaro-Munoz G, Mattheisen M, Meiser B, Peay H, Rietschel M, Walss-Bass C, Davis LK. Concerns about the use of polygenic embryo screening for psychiatric and cognitive traits. Lancet Psychiatry 2022; 9:838-844. [PMID: 35931093 PMCID: PMC9930635 DOI: 10.1016/s2215-0366(22)00157-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/01/2022] [Accepted: 04/23/2022] [Indexed: 12/19/2022]
Abstract
Private companies have begun offering services to allow parents undergoing in-vitro fertilisation to screen embryos for genetic risk of complex diseases, including psychiatric disorders. This procedure, called polygenic embryo screening, raises several difficult scientific and ethical issues, as discussed in this Personal View. Polygenic embryo screening depends on the statistical properties of polygenic risk scores, which are complex and not well studied in the context of this proposed clinical application. The clinical, social, and ethical implications of polygenic embryo screening have barely been discussed among relevant stakeholders. To our knowledge, the International Society of Psychiatric Genetics is the first professional biomedical organisation to issue a statement regarding polygenic embryo screening. For the reasons discussed in this Personal View, the Society urges caution and calls for additional research and oversight on the use of polygenic embryo screening.
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Affiliation(s)
- Todd Lencz
- Divison of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA; Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA; Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
| | - Maya Sabatello
- Division of Ethics, Department of Medical Humanities and Ethics, Columbia University, New York, NY, USA
| | - Anna Docherty
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Roseann E Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Takahiro Soda
- Department of Psychiatry, University of Florida College of Medicine, Gainesville, FL, USA
| | - Jehannine Austin
- Departments of Psychiatry and Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Laura Bierut
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | | | - David Curtis
- UCL Genetics Institute, University College London, London, United Kingdom
| | - Franziska Degenhardt
- Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Duisburg, Germany
| | - Laura Huckins
- Departments of Psychiatry and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Manuel Mattheisen
- Department of Psychiatry, Dalhousie Medical School, Halifax, NS, Canada
| | - Bettina Meiser
- Prince of Wales Clinical School, University of New South Wales, NSW, Australia
| | - Holly Peay
- Genomics, Bioinformatics, and Translational Research Center, RTI International, Raleigh, NC, USA
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Consuelo Walss-Bass
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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94
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Derks EM, Thorp JG, Gerring ZF. Ten challenges for clinical translation in psychiatric genetics. Nat Genet 2022; 54:1457-1465. [PMID: 36138228 DOI: 10.1038/s41588-022-01174-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/27/2022] [Indexed: 11/09/2022]
Abstract
Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.
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Affiliation(s)
- Eske M Derks
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
| | - Jackson G Thorp
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Zachary F Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
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95
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Using a polygenic score in a family design to understand genetic influences on musicality. Sci Rep 2022; 12:14658. [PMID: 36038631 PMCID: PMC9424203 DOI: 10.1038/s41598-022-18703-w] [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: 05/08/2022] [Accepted: 08/17/2022] [Indexed: 11/30/2022] Open
Abstract
To further our understanding of the genetics of musicality, we explored associations between a polygenic score for self-reported beat synchronization ability (PGSrhythm) and objectively measured rhythm discrimination, as well as other validated music skills and music-related traits. Using family data, we were able to further explore potential pathways of direct genetic, indirect genetic (through passive gene–environment correlation) and confounding effects (such as population structure and assortative mating). In 5648 Swedish twins, we found PGSrhythm to predict not only rhythm discrimination, but also melody and pitch discrimination (betas between 0.11 and 0.16, p < 0.001), as well as other music-related outcomes (p < 0.05). In contrast, PGSrhythm was not associated with control phenotypes not directly related to music. Associations did not deteriorate within families (N = 243), implying that indirect genetic or confounding effects did not inflate PGSrhythm effects. A correlation (r = 0.05, p < 0.001) between musical enrichment of the family childhood environment and individuals' PGSrhythm, suggests gene–environment correlation. We conclude that the PGSrhythm captures individuals' general genetic musical propensity, affecting musical behavior more likely direct than through indirect or confounding effects.
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96
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Epigenetic profiling of social communication trajectories and co-occurring mental health problems: a prospective, methylome-wide association study. Dev Psychopathol 2022; 34:854-863. [PMID: 33494854 PMCID: PMC8622455 DOI: 10.1017/s0954579420001662] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
While previous studies suggest that both genetic and environmental factors play an important role in the development of autism-related traits, little is known about potential biological mechanisms underlying these associations. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we examined prospective associations between DNA methylation (DNAm: nbirth = 804, nage 7 = 877) and trajectories of social communication deficits at age 8-17 years. Methylomic variation at three loci across the genome (false discovery rate = 0.048) differentiated children following high (n = 80) versus low (n = 724) trajectories of social communication deficits. This differential DNAm was specific to the neonatal period and not observed at 7 years of age. Associations between DNAm and trajectory membership remained robust after controlling for co-occurring mental health problems (i.e., hyperactivity/inattention, conduct problems). The three loci identified at birth were not replicated in the Generation R Study. However, to the best of our knowledge, ALSPAC is the only study to date that is prospective enough to examine DNAm in relation to longitudinal trajectories of social communication deficits from childhood to adolescence. Although the present findings might point to potentially novel sites that differentiate between a high versus low trajectory of social communication deficits, the results should be considered tentative until further replicated.
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97
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The Australian Genetics of Depression Study: New Risk Loci and Dissecting Heterogeneity Between Subtypes. Biol Psychiatry 2022; 92:227-235. [PMID: 34924174 DOI: 10.1016/j.biopsych.2021.10.021] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/21/2021] [Accepted: 10/24/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a common and highly heterogeneous psychiatric disorder, but little is known about the genetic characterization of this heterogeneity. Understanding the genetic etiology of MDD can be challenging because large sample sizes are needed for gene discovery-often achieved with a trade-off in the depth of phenotyping. METHODS The Australian Genetics of Depression Study is the largest stand-alone depression cohort with both genetic data and in-depth phenotyping and comprises a total of 15,792 participants of European ancestry, 92% of whom met diagnostic criteria for MDD. We leveraged the unique nature of this cohort to conduct a meta-analysis with the largest publicly available depression genome-wide association study to date and subsequently used polygenic scores to investigate genetic heterogeneity across various clinical subtypes of MDD. RESULTS We increased the number of known genome-wide significant variants associated with depression from 103 to 126 and found evidence of association of novel genes implicated in neuronal development. We found that a polygenic score for depression explained 5.7% of variance in MDD liability in our sample. Finally, we found strong support for genetic heterogeneity in depression with differential associations of multiple psychiatric and comorbid traits with age of onset, longitudinal course, and various subtypes of MDD. CONCLUSIONS Until now, this degree of detailed phenotyping in such a large sample of MDD cases has not been possible. Along with the discovery of novel loci, we provide support for differential pathways to illness models that recognize the overlap with other common psychiatric disorders as well as pathophysiological differences.
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98
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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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99
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Pedroso I, Kumbhare SV, Joshi B, Saravanan SK, Mongad DS, Singh-Rambiritch S, Uday T, Muthukumar KM, Irudayanathan C, Reddy-Sinha C, Dulai PS, Sinha R, Almonacid DE. Mental Health Symptom Reduction Using Digital Therapeutics Care Informed by Genomic SNPs and Gut Microbiome Signatures. J Pers Med 2022; 12:jpm12081237. [PMID: 36013186 PMCID: PMC9409755 DOI: 10.3390/jpm12081237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
Neuropsychiatric diseases and obesity are major components of morbidity and health care costs, with genetic, lifestyle, and gut microbiome factors linked to their etiology. Dietary and weight-loss interventions can help improve mental health, but there is conflicting evidence regarding their efficacy; and moreover, there is substantial interindividual heterogeneity that needs to be understood. We aimed to identify genetic and gut microbiome factors that explain interindividual differences in mental health improvement after a dietary and lifestyle intervention for weight loss. We recruited 369 individuals participating in Digbi Health’s personalized digital therapeutics care program and evaluated the association of 23 genetic scores, the abundance of 178 gut microbial genera, and 42 bacterial pathways with mental health. We studied the presence/absence of anxiety or depression, or sleep problems at baseline and improvement on anxiety, depression, and insomnia after losing at least 2% body weight. Participants lost on average 5.4% body weight and >95% reported improving mental health symptom intensity. There were statistically significant correlations between: (a) genetic scores with anxiety or depression at baseline, gut microbial functions with sleep problems at baseline, and (b) genetic scores and gut microbial taxa and functions with anxiety, depression, and insomnia improvement. Our results are concordant with previous findings, including the association between anxiety or depression at baseline with genetic scores for alcohol use disorder and major depressive disorder. As well, our results uncovered new associations in line with previous epidemiological literature. As evident from previous literature, we also observed associations of gut microbial signatures with mental health including short-chain fatty acids and bacterial neurotoxic metabolites specifically with depression. Our results also show that microbiome and genetic factors explain self-reported mental health status and improvement better than demographic variables independently. The genetic and microbiome factors identified in this study provide the basis for designing and personalizing dietary interventions to improve mental health.
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Affiliation(s)
- Inti Pedroso
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | - Shreyas Vivek Kumbhare
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | - Bharat Joshi
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | - Santosh K. Saravanan
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | | | - Simitha Singh-Rambiritch
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | - Tejaswini Uday
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | - Karthik Marimuthu Muthukumar
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | - Carmel Irudayanathan
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | - Chandana Reddy-Sinha
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | - Parambir S. Dulai
- Division of Gastroenterology, Northwestern University, Chicago, IL 60208, USA;
| | - Ranjan Sinha
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
| | - Daniel Eduardo Almonacid
- Digbi Health, Mountain View, CA 94040, USA; (I.P.); (S.V.K.); (B.J.); (S.K.S.); (S.S.-R.); (T.U.); (K.M.M.); (C.I.); (C.R.-S.); (R.S.)
- Correspondence:
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The interaction of early life factors and depression-associated loci affecting the age at onset of the depression. Transl Psychiatry 2022; 12:294. [PMID: 35879288 PMCID: PMC9314326 DOI: 10.1038/s41398-022-02042-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 11/25/2022] Open
Abstract
Multiple previous studies explored the associations between early life factors and the age at onset of the depression. However, they only focused on the influence of environmental or genetic factors, without considering the interactions between them. Based on previous genome-wide association study (GWAS) data, we first calculated polygenic risk score (PRS) for depression. Regression analyses were conducted to assess the interacting effects of depression PRS and 5 early life factors, including felt hated by family member (N = 40,112), physically abused by family (N = 40,464), felt loved (N = 35633), and sexually molested (N = 41,595) in childhood and maternal smoking during pregnancy (N = 38,309), on the age at onset of the depression. Genome-wide environment interaction studies (GWEIS) were then performed to identify the genes interacting with early life factors for the age at onset of the depression. In regression analyses, we observed significant interacting effects of felt loved as a child and depression PRS on the age at onset of depression in total sample (β = 0.708, P = 5.03 × 10-3) and males (β = 1.421, P = 7.64 × 10-4). GWEIS identified a novel candidate loci interacting with felt loved as a child at GSAP (rs2068031, P = 4.24 × 10-8) and detected several genes with suggestive significance association, such as CMYA5 (rs7343, P = 2.03 × 10-6) and KIRREL3 (rs535603, P = 4.84 × 10-6) in males. Our results indicate emotional care in childhood may affect the age at onset of depression, especially in males, and GSAP plays an important role in their interaction.
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