scholarly journals Genetic overlap and causal associations between smoking behaviours and psychiatric traits and disorders in adolescents and adults

2020 ◽  
Author(s):  
Wikus Barkhuizen ◽  
Frank Dudbridge ◽  
Angelica Ronald
Keyword(s):  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Structural Equation ◽  
Negative Symptoms ◽  
Genetic Correlations ◽  
Genetic Influences ◽  
Psychotic Experiences ◽  
Genetic Associations ◽  
Genetic Overlap ◽  
Smoking Behaviours

AbstractBackgroundEpidemiological research shows that smoking is associated with psychiatric disorders and psychotic experiences, even after controlling for confounds such as cannabis use and sleep problems. We investigated degree of genetic overlap and tested for causal associations between smoking and psychiatric traits and disorders using genetic data. We tested whether genetic associations existed beyond genetic influences shared with confounding variables.MethodsGenetic correlations were estimated with LD-score regression between smoking behaviours (N=262,990-632,802) and psychiatric disorders (schizophrenia, bipolar disorder and depression; N=41,653-173,005), psychotic experiences in adolescents (N=6,297-10,098) and adults (N=116,787-117,794) and adult schizotypy (N=3,967-4,057). Genomic Structural Equation Modelling was performed to explore the associations while accounting for genetic influences of confounders (cannabis and alcohol use, risk-taking and insomnia). Causal associations were tested using Generalized Summary-based Mendelian Randomization (GSMR).ResultsSignificant genetic correlations were found between smoking and psychiatric disorders (rg = .10 - .38) and adult PE (rg = .33 - .40). After accounting for covarying genetic influences, genetic associations between most smoking phenotypes and schizophrenia and depression remained but not between smoking behaviours and bipolar disorder or most psychotic experiences. GSMR results supported a causal role of smoking initiation on psychiatric disorders and adolescent cognitive and negative psychotic experiences.ConclusionsPleiotropy between smoking behaviours and schizophrenia and depression exists beyond the common genetic basis of known confounders. Smoking also appears to be causally associated with psychiatric disorders and with cognitive PEs and negative symptoms during adolescence. Exploration of the biological links underlying smoking and psychiatric illness would be well-justified.

scholarly journals Genome-wide analysis of adolescent psychotic experiences shows genetic overlap with psychiatric disorders

2018 ◽  
Author(s):  
Oliver Pain ◽  
Frank Dudbridge ◽  
Alastair G. Cardno ◽  
Daniel Freeman ◽  
Yi Lu ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depression ◽  
Psychiatric Disorders ◽  
Negative Symptoms ◽  
Genome Wide Association Study ◽  
Genetic Influences ◽  
Psychotic Experiences ◽  
Psychotic Experience ◽  
Genome Wide ◽  
Genetic Overlap

AbstractThis study aimed to test for overlap in genetic influences between psychotic experience traits shown by adolescents in the community, and clinically-recognized psychiatric disorders in adulthood, specifically schizophrenia, bipolar disorder, and major depression. The full spectra of psychotic experience domains, both in terms of their severity and type (positive, cognitive, and negative), were assessed using self- and parent-ratings in three European community samples aged 15-19 years (Final N incl. siblings = 6,297-10,098). A mega-genome-wide association study (mega-GWAS) for each psychotic experience domain was performed. SNP-heritability of each psychotic experience domain was estimated using genomic-relatedness-based restricted maximum-likelihood (GREML) and linkage disequilibrium-(LD-) score regression. Genetic overlap between specific psychotic experience domains and schizophrenia, bipolar disorder, and major depression was assessed using polygenic risk scoring (PRS) and LD-score regression. GREML returned SNP-heritability estimates of 3-9% for psychotic experience trait domains, with higher estimates for less skewed traits (Anhedonia, Cognitive Disorganization) than for more skewed traits (Paranoia and Hallucinations, Parent-rated Negative Symptoms). Mega-GWAS analysis identified one genome-wide significant association for Anhedonia within IDO2 but which did not replicate in an independent sample. PRS analysis revealed that the schizophrenia PRS significantly predicted all adolescent psychotic experience trait domains (Paranoia and Hallucinations only in non-zero scorers). The major depression PRS significantly predicted Anhedonia and Parent-rated Negative Symptoms in adolescence. Psychotic experiences during adolescence in the community show additive genetic effects and partly share genetic influences with clinically-recognized psychiatric disorders, specifically schizophrenia and major depression.

scholarly journals Genetic overlap and causal associations between smoking behaviours and mental health

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wikus Barkhuizen ◽  
Frank Dudbridge ◽  
Angelica Ronald
Keyword(s):  
Mental Health ◽  
Psychiatric Disorders ◽  
Risk Taking ◽  
Smoking Initiation ◽  
Causal Role ◽  
Genetic Influences ◽  
Psychotic Experiences ◽  
Genetic Associations ◽  
Genetic Overlap ◽  
Smoking Behaviours

AbstractCigarette smoking is a modifiable behaviour associated with mental health. We investigated the degree of genetic overlap between smoking behaviours and psychiatric traits and disorders, and whether genetic associations exist beyond genetic influences shared with confounding variables (cannabis and alcohol use, risk-taking and insomnia). Second, we investigated the presence of causal associations between smoking initiation and psychiatric traits and disorders. We found significant genetic correlations between smoking and psychiatric disorders and adult psychotic experiences. When genetic influences on known covariates were controlled for, genetic associations between most smoking behaviours and schizophrenia and depression endured (but not with bipolar disorder or most psychotic experiences). Mendelian randomization results supported a causal role of smoking initiation on psychiatric disorders and adolescent cognitive and negative psychotic experiences, although not consistently across all sensitivity analyses. In conclusion, smoking and psychiatric disorders share genetic influences that cannot be attributed to covariates such as risk-taking, insomnia or other substance use. As such, there may be some common genetic pathways underlying smoking and psychiatric disorders. In addition, smoking may play a causal role in vulnerability for mental illness.

scholarly journals Recent advances in the understanding and management of bipolar disorder in adults

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2033 ◽  
Author(s):  
Janusz K. Rybakowski
Keyword(s):  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Recent Progress ◽  
Molecular Genetic ◽  
Mood Stabilizer ◽  
New Drugs ◽  
Specific Drug ◽  
Genetic Approach ◽  
The Past ◽  
Genetic Overlap

This article focuses on some aspects of recent progress in the neurobiology and treatment of bipolar disorder (BD) in adults. A molecular-genetic approach to the etiopathogenesis of the illness resulted in the findings of a genetic overlap between BD and other major psychiatric disorders. Furthermore, a poly-gene-environmental interaction in the development of the illness has been demonstrated. For the management of BD, new drugs with putative mood-stabilizing properties have been introduced in the past two decades. However, none of these can surpass lithium, the prototype mood-stabilizer, still considered the most specific drug for BD. Recent research on lithium, besides providing new data on the neurobiology of BD, has confirmed anti-suicidal, immunomodulatory, and neuroprotective properties of this drug.

scholarly journals Testing structural models of psychopathology at the genomic level

2018 ◽  
Author(s):  
Irwin D. Waldman ◽  
Holly E. Poore ◽  
Justin M. Luningham ◽  
Jingjing Yang
Keyword(s):  
Bipolar Disorder ◽  
Eating Disorders ◽  
Psychiatric Disorders ◽  
Genetic Correlations ◽  
Structural Models ◽  
Higher Order ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Best Fitting ◽  
Fitting Model

Genome-wide association studies (GWAS) have revealed hundreds of genetic loci associated with the vulnerability to major psychiatric disorders, and post-GWAS analyses have shown substantial genetic correlations among these disorders. This evidence supports the existence of a higher-order structure of psychopathology at both the genetic and phenotypic levels. Despite recent efforts by collaborative consortia such as the Hierarchical Taxonomy of Psychopathology (HiTOP), this structure remains unclear. In this study, we tested multiple alternative structural models of psychopathology at the genomic level, using the genetic correlations among fourteen psychiatric disorders and related psychological traits estimated from GWAS summary statistics. The best-fitting model included four correlated higher-order factors – externalizing, internalizing, thought problems, and neurodevelopmental disorders – which showed distinct patterns of genetic correlations with external validity variables and accounted for substantial genetic variance in their constituent disorders. A bifactor model including a general factor of psychopathology as well as the four specific factors fit worse than the above model. Several model modifications were tested to explore the placement of some disorders – such as bipolar disorder, obsessive-compulsive disorder, and eating disorders – within the broader psychopathology structure. The best-fitting model indicated that eating disorders and obsessive-compulsive disorder, on the one hand, and bipolar disorder and schizophrenia, on the other, load together on the same thought problems factor. These findings provide support for several of the HiTOP higher-order dimensions and suggest a similar structure of psychopathology at the genomic and phenotypic levels.

scholarly journals Dissecting the genetic overlap of education, socioeconomic status, and mental health

2020 ◽  
Author(s):  
F. R. Wendt ◽  
G. A. Pathak ◽  
T. Lencz ◽  
J. H. Krystal ◽  
J. Gelernter ◽  
...  
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Socioeconomic Status ◽  
Psychiatric Disorders ◽  
Genetic Risk ◽  
Neuronal Cell ◽  
Cell Types ◽  
Brain Morphology ◽  
Genetic Overlap ◽  
Genome Wide Data

AbstractSocioeconomic status (SES) and education (EDU) are phenotypically associated with psychiatric disorders and behavior. It remains unclear how these associations influence the genetic risk for mental health traits and EDU/SES individually. Using information from >1 million individuals, we conditioned the genetic risk for psychiatric disorders, personality traits, brain imaging phenotypes, and externalizing behaviors with genome-wide data for EDU/SES. Accounting for EDU/SES significantly affected the observed heritability of psychiatric traits ranging from 2.44% h2 decrease for bipolar disorder to 29.0% h2 decrease for Tourette syndrome. Neuroticism h2 significantly increased by 20.23% after conditioning with SES. After EDU/SES conditioning, novel neuronal cell-types were identified for risky behavior (excitatory), major depression (inhibitory), schizophrenia (excitatory and GABAergic), and bipolar disorder (excitatory). Conditioning with EDU/SES also revealed unidirectional causality between brain morphology and mental health phenotypes. Our results indicate genetic discoveries of mental health outcomes may be limited by genetic overlap with EDU/SES.

scholarly journals Determining the stability of genome-wide factors in BMI between ages 40 to 69 years.

2021 ◽  
Author(s):  
Nathan A Gillespie ◽  
Amanda E Gentry ◽  
Robert M Kirkpatrick ◽  
Hermine H Maes ◽  
Chandra A Reynolds ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genetic Influences ◽  
Equation Modeling ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
The Stability ◽  
Early Old Age

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of genetic variation influencing BMI from midlife and beyond is unknown. By analyzing BMI data collected from 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 73 years. We then applied genomic structural equation modeling (gSEM) to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin variation in BMI from middle to early old age in men and women alike.

scholarly journals Genetic Architecture of 11 Major Psychiatric Disorders at Biobehavioral, Functional Genomic, and Molecular Genetic Levels of Analysis

2020 ◽  
Author(s):  
Andrew D. Grotzinger ◽  
Travis T. Mallard ◽  
Wonuola A. Akingbuwa ◽  
Hill F. Ip ◽  
Mark J. Adams ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Structural Equation ◽  
Genetic Architecture ◽  
Molecular Genetic ◽  
Genetic Correlations ◽  
Levels Of Analysis ◽  
Functional Genomic ◽  
Association Analyses ◽  
Psychotic Features ◽  
Genomic Regions

We systematically interrogate the joint genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic, and molecular genetic levels of analysis. We identify four broad factors (Neurodevelopmental, Compulsive, Psychotic, and Internalizing) that underlie genetic correlations among the disorders, and test whether these factors adequately explain their genetic correlations with biobehavioral traits. We introduce Stratified Genomic Structural Equation Modelling, which we use to identify gene sets and genomic regions that disproportionately contribute to pleiotropy, including protein-truncating variant intolerant genes expressed in excitatory and GABAergic brain cells that are enriched for pleiotropy between disorders with psychotic features. Multivariate association analyses detect a total of 152 (20 novel) independent loci which act on the four factors, and identify nine loci that act heterogeneously across disorders within a factor. Despite moderate to high genetic correlations across all 11 disorders, we find very little utility of, or evidence for, a single dimension of genetic risk across psychiatric disorders.

scholarly journals Characterising the shared genetic determinants of bipolar disorder, schizophrenia and risk-taking

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guy Hindley ◽  
Shahram Bahrami ◽  
Nils Eiel Steen ◽  
Kevin S. O’Connell ◽  
Oleksandr Frei ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Risk Taking ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Risky Behaviours ◽  
Shared Risk ◽  
Shared Genetic

AbstractIncreased risk-taking is a central component of bipolar disorder (BIP) and is implicated in schizophrenia (SCZ). Risky behaviours, including smoking and alcohol use, are overrepresented in both disorders and associated with poor health outcomes. Positive genetic correlations are reported but an improved understanding of the shared genetic architecture between risk phenotypes and psychiatric disorders may provide insights into underlying neurobiological mechanisms. We aimed to characterise the genetic overlap between risk phenotypes and SCZ, and BIP by estimating the total number of shared variants using the bivariate causal mixture model and identifying shared genomic loci using the conjunctional false discovery rate method. Summary statistics from genome wide association studies of SCZ, BIP, risk-taking and risky behaviours were acquired (n = 82,315–466,751). Genomic loci were functionally annotated using FUMA. Of 8.6–8.7 K variants predicted to influence BIP, 6.6 K and 7.4 K were predicted to influence risk-taking and risky behaviours, respectively. Similarly, of 10.2–10.3 K variants influencing SCZ, 9.6 and 8.8 K were predicted to influence risk-taking and risky behaviours, respectively. We identified 192 loci jointly associated with SCZ and risk phenotypes and 206 associated with BIP and risk phenotypes, of which 68 were common to both risk-taking and risky behaviours and 124 were novel to SCZ or BIP. Functional annotation implicated differential expression in multiple cortical and sub-cortical regions. In conclusion, we report extensive polygenic overlap between risk phenotypes and BIP and SCZ, identify specific loci contributing to this shared risk and highlight biologically plausible mechanisms that may underlie risk-taking in severe psychiatric disorders.

scholarly journals Multivariate GWAS elucidates the genetic architecture of alcohol consumption and misuse, corrects biases, and reveals novel associations with disease

2020 ◽  
Author(s):  
Travis T Mallard ◽  
Jeanne E Savage ◽  
Emma C Johnson ◽  
Yuye Huang ◽  
Alexis C Edwards ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Alcohol Use ◽  
Psychiatric Disorders ◽  
Alcohol Use Disorder ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Socioeconomic Outcomes ◽  
Alcohol Involvement

ABSTRACTGenome-wide association studies (GWASs) of the Alcohol Use Disorder Identification Test (AUDIT), a ten-item screener for alcohol use disorder (AUD), have elucidated novel loci for alcohol consumption and misuse. However, these studies also revealed that GWASs can be influenced by numerous biases (e.g., measurement error, selection bias), which have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders/medical conditions. To explore these unexpected differences in genetic correlations, we conducted the first item-level and largest GWAS of AUDIT items (N=160,824), and applied a multivariate framework to mitigate previous biases. In doing so, we identified novel patterns of similarity (and dissimilarity) among the AUDIT items, and found evidence of a correlated two-factor structure at the genetic level (Consumption and Problems, rg=.80). Moreover, by applying empirically-derived weights to each of the AUDIT items, we constructed an aggregate measure of alcohol consumption that is strongly associated with alcohol dependence (rg=.67) and several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by performing polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, we identified novel genetic associations between alcohol consumption, alcohol misuse, and human health. Our work further emphasizes the value of AUDIT for both clinical and genetic studies of AUD, and the importance of using multivariate methods to study genetic associations that are more closely related to AUD.

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