Contribution of Epidemiology to Our Understanding of Psychiatric Genetics

2018 ◽  
pp. 1-23 ◽  
Author(s):  
Alison K. Merikangas ◽  
Kathleen R. Merikangas
Keyword(s):  
Mental Disorders ◽  
Genetic Risk ◽  
Psychiatric Epidemiology ◽  
Psychiatric Genetics ◽  
Statistical Tools ◽  
Joint Influence ◽  
Service Patterns ◽  
Human Disorders ◽  
Genetic And Environmental Factors ◽  
Study Designs

The field of psychiatric epidemiology has advanced both methodological and substantive knowledge in our understanding of mental disorders through the following contributions: (1) development of standardized tools that operationalize diagnostic criteria in order to obtain reliable estimates; (2) estimation of the magnitude, correlates and service patterns of mental disorders; (3) documentation of patterns of comorbidity; (4) quantification of disability attributable to mental disorders; and (5) identification of risk and protective factors for mental disorders and their core domains. Community surveys using standardized tools for ascertaining psychiatric disorders have shown that mental disorders are highly prevalent in the general population. With the growing success in identifying genetic risk factors for chronic human disorders, the field of epidemiology will play an important role in defining study designs, appropriate samples, population generalizability, and statistical tools that will facilitate our ability to identify the joint influence of genetic and environmental factors on the susceptibility to mental disorders.

Gene–environment interaction

2020 ◽  
pp. 343-358
Author(s):  
Craig Morgan ◽  
Marta Di Forti ◽  
Helen L. Fisher
Keyword(s):  
Mental Disorders ◽  
Environmental Factors ◽  
Statistical Modelling ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Working Definition ◽  
Gene Environment ◽  
Genetic And Environmental Factors ◽  
Definition Of ◽  
Study Designs

For all major mental disorders there are many factors that, in combination and through multiple pathways, increase or decrease the risk of onset. These include, to varying degrees, genetic and environmental factors. This chapter provides an introduction, from an epidemiological perspective, to the study of gene–environment interaction. It begins by providing a working definition of gene–environment interaction, rooted in a sufficient causes framework, and then considers, in turn, the prominent puzzles and challenges, including the statistical modelling of interaction, the main study designs (including strengths and weaknesses), measurement of environmental exposures, and required sample sizes. The chapter concludes with a consideration of the implications of recent advances in genetics for studies of gene–environment interaction.

Cross-sectional surveys

2020 ◽  
pp. 127-144
Author(s):  
Martin Prince ◽  
Jayati Das-Munshi
Keyword(s):  
Mental Disorders ◽  
Population Level ◽  
Cross Sectional Study ◽  
Psychiatric Epidemiology ◽  
Service Planning ◽  
Base Population ◽  
Sectional Study ◽  
Two Phase ◽  
Cross Sectional ◽  
Study Designs

This chapter provides an overview of cross-sectional study designs. The chapter covers the application of cross-sectional surveys, the importance of defining the base population and methods to determine sampling, the issues of non-response, representativeness, and generalizability in cross-sectional surveys, as well as more specific methodological issues such as two-phase surveys. The practicalities of conducting cross-sectional surveys from initial design stages through to analyses and dissemination stages are covered. An overview is provided on the strength and limitations of cross-sectional surveys and their application to wider contexts, where there may be concerns around service planning and the population-level impact of mental disorders. The chapter concludes with an overview of landmark international cross-sectional surveys which have informed psychiatric epidemiology worldwide.

Epidemiology

2020 ◽  
pp. 27-40
Author(s):  
Elizabeth Ryznar ◽  
Harvey Whiteford
Keyword(s):  
Mental Disorders ◽  
Family Environment ◽  
Catchment Area ◽  
Geographic Area ◽  
Psychiatric Epidemiology ◽  
Global Studies ◽  
Prevention And Treatment ◽  
Nationally Representative ◽  
Genetic And Environmental Factors ◽  
Disease Study

Psychiatric epidemiology is the study of the distribution and determinants of mental disorders in populations, and the application of this to the prevention and treatment of disorders. Both genetic and environmental factors are involved in the onset and course of mental disorders. The field has evolved from smaller studies in one geographic area, often of patients in hospitals, to larger national and global studies. This chapter discusses studies that have aided this transition: the Epidemiological Catchment Area Study, which was the first to adopt a validated diagnostic instrument; the National Comorbidity Survey, which was the first to establish prevalence in a nationally representative sample; and the Global Burden of Disease Study, which was the first to assess mortality and disability of mental disorders globally. This chapter also discusses epidemiological contributions to understanding the aetiology of mental illness, focusing on a landmark paper separating family environment and genetics in schizophrenia.

Reductionist thinking and animal models in neuropsychiatric research

2019 ◽  
Vol 42 ◽  
Author(s):  
Nicole M. Baran
Keyword(s):  
Animal Models ◽  
Mental Disorders ◽  
Environmental Factors ◽  
Neuropsychiatric Disorders ◽  
Model Organisms ◽  
Developmental Genetic ◽  
Term Study ◽  
Genetic And Environmental Factors ◽  
Mechanisms Of Plasticity

AbstractReductionist thinking in neuroscience is manifest in the widespread use of animal models of neuropsychiatric disorders. Broader investigations of diverse behaviors in non-model organisms and longer-term study of the mechanisms of plasticity will yield fundamental insights into the neurobiological, developmental, genetic, and environmental factors contributing to the “massively multifactorial system networks” which go awry in mental disorders.

scholarly journals Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

2021 ◽  
pp. 1-12
Author(s):  
Simon Schmitt ◽  
Tina Meller ◽  
Frederike Stein ◽  
Katharina Brosch ◽  
Kai Ringwald ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Mental Disorders ◽  
Cortical Thickness ◽  
Genetic Risk ◽  
Right Hemisphere ◽  
Autism Spectrum ◽  
Risk Scores ◽  
Grey Matter Volume ◽  
Polygenic Risk ◽  
Risk For Depression

Abstract Background MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood. Methods We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness. Results The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing. Conclusions Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.

Overdiagnosis and Overtreatment

2020 ◽  
pp. 41-52
Author(s):  
Joel Paris
Keyword(s):  
Pharmaceutical Industry ◽  
Mental Disorders ◽  
Antidepressant Drugs ◽  
Psychiatric Epidemiology ◽  
Opinion Leaders ◽  
Main Form

Psychiatry has embraced overdiagnosis both because it does not understand the causes of mental disorders and because clinicians are keen to use the tools they already have for treatment. These trends have most notable effects on the overprescription of antidepressant drugs. Overdiagnosis has also infiltrated psychiatric epidemiology, since most studies are DSM-based. All these factors have supported diagnostic epidemics, in which identification of certain categories increases dramatically over relatively brief periods of time. When drugs are believed to be the main form of treatment, these trends have been further encouraged by the pharmaceutical industry, and by opinion leaders who are sponsored by Big Pharma.

Making the Case

2020 ◽  
pp. 29-56
Author(s):  
Richard McCarty
Keyword(s):  
Mental Disorders ◽  
Stressful Life Events ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Psychiatric Genetics ◽  
Nucleotide Polymorphisms ◽  
Individual Genome ◽  
Diagnostic Categories ◽  
Genome Wide ◽  
Research Findings

Genome-wide association studies (GWAS) have revolutionized the field of psychiatric genetics by examining genetic variation at millions of single-nucleotide polymorphisms (SNPs) in many thousands of individual genome using microarrays. The sample sizes for these studies range from tens of thousands on up. Results to date from GWAS have called into question the validity of current diagnostic categories in psychiatry. For example, there may be some level of genetic risk that is shared across many psychiatric disorders, with the final symptom clusters of a given disorder being shaped by other genetic, epigenetic, and environmental variables. Research findings on three mental disorders are evaluated to make the case that stressful life events play a crucial role in the etiology of mental disorders. The mental disorders discussed include schizophrenia, bipolar disorder, and depression. These findings set the stage for the remainder of the book.

Animal Models

2018 ◽  
pp. 195-202
Author(s):  
Tadafumi Kato
Keyword(s):  
Animal Models ◽  
Mental Disorders ◽  
Neural Circuit ◽  
Chromosomal Abnormalities ◽  
Psychiatric Genetics ◽  
Genetic Animal Model ◽  
Animal Models Of Depression ◽  
Validity Criteria ◽  
New Treatment ◽  
Choice Of Species

Animal models of mental disorders are important for the development of new treatment and biomarkers. Animal models should satisfy three validity criteria. The choice of species used for animal models depends on the purposes of the study. There are several established models of chromosomal abnormalities for autism and schizophrenia. Stress-induced animal models of depression are controversial. There have been no established models for bipolar disorder, but the authors recently proposed a genetic animal model showing recurrent spontaneous depression-like episodes. Optogenetic manipulation of neural circuit is also used for modeling of mental disorders. Progress in psychiatric genetics will lead to the generation of valid animal models of psychiatric disorders.

Statistical Genetics

2018 ◽  
pp. 57-69 ◽  
Author(s):  
Till F. M. Andlauer ◽  
Bertram Müller-Myhsok ◽  
Stephan Ripke
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Meta Analysis ◽  
Power Calculation ◽  
Genome Wide Association Studies ◽  
Psychiatric Genetics ◽  
Factors Influencing ◽  
Genome Wide ◽  
Basic Concepts ◽  
Study Designs

Over more than the last decade, hypothesis-free genome-wide association studies (GWAS) have been widely used to detect genetic factors influencing phenotypes of interest. The basic principle of GWAS has been unchanged since the beginning: a series of univariate tests is conducted on all genetic variants available across the genome. We present study designs and commonly used methods for genome-wide studies, with a focus on the analysis of common variants. The basic concepts required for an application of GWAS in psychiatric genetics are introduced, from power calculation to meta-analysis. This chapter will help the reader in gaining the knowledge required for participation in and realization of GWAS of both qualitative and quantitative traits.

Genetic epidemiology: Overview

2020 ◽  
pp. 327-342
Author(s):  
Frühling Rijsdijk ◽  
Paul F. O’Reilly
Keyword(s):  
Gene Mapping ◽  
Genetic Epidemiology ◽  
Genetic Variants ◽  
Theoretical Basis ◽  
Random Mating ◽  
Behavioural Genetics ◽  
Human Disorders ◽  
Behavioural Phenotypes ◽  
Genomic Regions ◽  
Study Designs

This chapter demonstrates the principles behind some of the major genetic study designs used in psychiatry research. The first part focuses on behavioural genetic designs, while the second part describes designs for ‘gene mapping’. Behavioural genetics examines the genetic basis of behavioural phenotypes, including both disorders and ‘normal’ dimensional traits. The theoretical basis is derived from population genetics, including properties such as segregation ratios, random mating, genetic variance, and genetic correlation between relatives. The second part of the chapter deals with gene mapping designs, in which specific genetic variants or genomic regions associated with a disorder or trait are identified. A brief outline of the most popular current approaches to the analysis of the genetics of complex human disorders is also provided.

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