Genome-wide interaction study with major depression identifies novel variants associated with cognitive function.

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Abstract

Major Depressive Disorder (MDD) often is associated with significant cognitive dysfunction. We conducted a meta-analysis of genome-wide interaction of MDD and cognitive function using data from four large European cohorts in a total of 3510 MDD cases and 6057 controls. In addition, we conducted analyses using polygenic risk scores (PRS) based on data from the Psychiatric Genomics Consortium (PGC) on the traits of MDD, Bipolar disorder (BD), Schizophrenia (SCZ), and mood instability (MIN). Functional exploration contained gene expression analyses and Ingenuity Pathway Analysis (IPA®). We identified a set of significantly interacting single nucleotide polymorphisms (SNPs) between MDD and the genome-wide association study (GWAS) of cognitive domains of executive function, processing speed, and global cognition. Several of these SNPs are located in genes expressed in brain, with important roles such as neuronal development (REST), oligodendrocyte maturation (TNFRSF21), and myelination (ARFGEF1). IPA® identified a set of core genes from our dataset that mapped to a wide range of canonical pathways and biological functions (MPO, FOXO1, PDE3A, TSLP, NLRP9, ADAMTS5, ROBO1, REST). Furthermore, IPA® identified upstream regulator molecules and causal networks impacting on the expression of dataset genes, providing a genetic basis for further clinical exploration (vitamin D receptor, beta-estradiol, tadalafil). PRS of MIN and meta-PRS of MDD, MIN and SCZ were significantly associated with all cognitive domains. Our results suggest several genes involved in physiological processes for the development and maintenance of cognition in MDD, as well as potential novel therapeutic agents that could be explored in patients with MDD associated cognitive dysfunction.

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The CES-D Scale: A Self-Report Depression Scale for Research in the General Population

L Radloff (1977)

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Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Spencer L James, Degu Abate, Kalkidan Hassen Abate … (2020)

Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1–4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0–8·4) while the total sum of global YLDs increased from 562 million (421–723) to 853 million (642–1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6–9·2) for males and 6·5% (5·4–7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782–3252] per 100 000 in males vs s1400 [1279–1524] per 100 000 in females), transport injuries (3322 [3082–3583] vs 2336 [2154–2535]), and self-harm and interpersonal violence (3265 [2943–3630] vs 5643 [5057–6302]). Interpretation Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. Funding Bill & Melinda Gates Foundation.

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METAL: fast and efficient meta-analysis of genomewide association scans

Cristen Willer, Yun Li, Gonçalo R Abecasis (2010)

Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu

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Journal

Journal ID (iso-abbrev): Mol Psychiatry

Title: Molecular psychiatry

Publisher: Springer Science and Business Media LLC

ISSN (Electronic): 1476-5578

ISSN (Print): 1359-4184

Publication date (Electronic): February 2022

Volume: 27

Issue: 2

Affiliations

[1 ] Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.

[2 ] Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia.

[3 ] Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.

[4 ] Institute for Translational Psychiatry, University of Münster, Münster, Germany.

[5 ] Institute for Translational Neuroscience, University of Münster, Münster, Germany.

[6 ] Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg - UKGM Marburg, Marburg, Germany.

[7 ] Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany.

[8 ] Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany.

[9 ] Centre for Human Genetics, University of Marburg, Marburg, Germany.

[10 ] Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany.

[11 ] Division of Psychiatry, University of Edinburgh, Edinburgh, UK.

[12 ] Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK.

[13 ] Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.

[14 ] MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK.

[15 ] Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.

[16 ] German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany.

[17 ] Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.

[18 ] Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.

[19 ] Northern Adelaide Mental Health Service, Salisbury, SA, Australia.

[20 ] Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia. Bernhard.Baune@ukmuenster.de.

[21 ] Department of Psychiatry and Psychotherapy, University Hospital Münster, University of Münster, Münster, Germany. Bernhard.Baune@ukmuenster.de.

[22 ] Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia. Bernhard.Baune@ukmuenster.de.

[23 ] The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia. Bernhard.Baune@ukmuenster.de.

Article

Publisher Item ID: 10.1038/s41380-021-01379-5 Mid ID: EMS137572

DOI: 10.1038/s41380-021-01379-5

PMC ID: 7612684

PubMed ID: 34782712

SO-VID: ab19f364-7612-4117-9c8b-fb8e1457933f

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Genome-wide interaction study with major depression identifies novel variants associated with cognitive function.

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Most cited references 72

The CES-D Scale: A Self-Report Depression Scale for Research in the General Population

Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

METAL: fast and efficient meta-analysis of genomewide association scans

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Cited by 11

Most referenced authors 11,887