scholarly journals Interplay between the genetics of personality traits, severe psychiatric disorders and COVID-19 host genetics in the susceptibility to SARS-CoV-2 infection

BJPsych Open ◽  
2021 ◽  
Vol 7 (6) ◽  
Author(s):  
Urs Heilbronner ◽  
Fabian Streit ◽  
Thomas Vogl ◽  
Fanny Senner ◽  
Sabrina K. Schaupp ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Personality Traits ◽  
Psychiatric Disorders ◽  
Genetic Correlation ◽  
Genetic Association ◽  
Genetic Risk ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Level Data

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus. Aims We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection. Method Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data. Results We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10−5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies. Conclusions We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.

scholarly journals Interplay between the Genetics of Personality Traits, severe Psychiatric Disorders, and COVID-19 Host Genetics in the Susceptibility to SARS-CoV-2 Infection

2021 ◽  
Author(s):  
Urs Heilbronner ◽  
Fabian Streit ◽  
Thomas Vogl ◽  
Fanny Senner ◽  
Schaupp Sabrina ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Personality Traits ◽  
Psychiatric Disorders ◽  
Genetic Association ◽  
Genetic Risk ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Genetic Components ◽  
Level Data

Background The SARS-CoV-2 pandemic, with all its impacts on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behavior and, therefore, the risk of contracting the virus. Aims We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits, and susceptibility to SARS-CoV-2 infection. Methods Linkage disequilibrium score regression was used to explore the genetic correlations of COVID-19 susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n=1346) and the HeiDE (n=3266) study), polygenic risk scores were used to analyze if a genetic association between, psychiatric disorders, personality traits, and COVID-19 susceptibility exists in individual-level data. Results We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (p=1.47x10-5; rg=0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies. Conclusions We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.

scholarly journals Cigarette smoking and personality: interrogating causality using Mendelian randomisation

2018 ◽  
Vol 49 (13) ◽  
pp. 2197-2205 ◽  
Author(s):  
Hannah M. Sallis ◽  
George Davey Smith ◽  
Marcus R. Munafò
Keyword(s):  
Personality Traits ◽  
Association Studies ◽  
Smoking Initiation ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Causal Pathways ◽  
Genome Wide ◽  
Level Data ◽  
Causal Nature

AbstractBackgroundDespite the well-documented association between smoking and personality traits such as neuroticism and extraversion, little is known about the potential causal nature of these findings. If it were possible to unpick the association between personality and smoking, it may be possible to develop tailored smoking interventions that could lead to both improved uptake and efficacy.MethodsRecent genome-wide association studies (GWAS) have identified variants robustly associated with both smoking phenotypes and personality traits. Here we use publicly available GWAS summary statistics in addition to individual-level data from UK Biobank to investigate the link between smoking and personality. We first estimate genetic overlap between traits using LD score regression and then use bidirectional Mendelian randomisation methods to unpick the nature of this relationship.ResultsWe found clear evidence of a modest genetic correlation between smoking behaviours and both neuroticism and extraversion. We found some evidence that personality traits are causally linked to certain smoking phenotypes: among current smokers each additional neuroticism risk allele was associated with smoking an additional 0.07 cigarettes per day (95% CI 0.02–0.12, p = 0.009), and each additional extraversion effect allele was associated with an elevated odds of smoking initiation (OR 1.015, 95% CI 1.01–1.02, p = 9.6 × 10−7).ConclusionWe found some evidence for specific causal pathways from personality to smoking phenotypes, and weaker evidence of an association from smoking initiation to personality. These findings could be used to inform future smoking interventions or to tailor existing schemes.

scholarly journals Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ronald de Vlaming ◽  
Eric A. W. Slob ◽  
Philip R. Jansen ◽  
Alain Dagher ◽  
Philipp D. Koellinger ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Gray Matter Volume ◽  
Genetic Correlations ◽  
Brain Morphology ◽  
Computationally Efficient ◽  
Individual Level ◽  
Level Data ◽  
The Uk ◽  
And Behavior ◽  
The Brain

AbstractHuman variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.

scholarly journals Estimating Heritability and Genetic Correlation in Case Control Studies Directly and with Summary Statistics

2018 ◽  
Author(s):  
Omer Weissbrod ◽  
Jonathan Flint ◽  
Saharon Rosset
Keyword(s):  
Genetic Correlation ◽  
Association Studies ◽  
Genetic Correlations ◽  
Large Data ◽  
Case Control ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Case Control Studies ◽  
Individual Level

AbstractMethods that estimate heritability and genetic correlations from genome-wide association studies have proven to be powerful tools for investigating the genetic architecture of common diseases and exposing unexpected relationships between disorders. Many relevant studies employ a case-control design, yet most methods are primarily geared towards analyzing quantitative traits. Here we investigate the validity of three common methods for estimating genetic heritability and genetic correlation. We find that the Phenotype-Correlation-Genotype-Correlation (PCGC) approach is the only method that can estimate both quantities accurately in the presence of important non-genetic risk factors, such as age and sex. We extend PCGC to work with summary statistics that take the case-control sampling into account, and demonstrate that our new method, PCGC-s, accurately estimates both heritability and genetic correlations and can be applied to large data sets without requiring individual-level genotypic or phenotypic information. Finally, we use PCGC-S to estimate the genetic correlation between schizophrenia and bipolar disorder, and demonstrate that previous estimates are biased due to incorrect handling of sex as a strong risk factor. PCGC-s is available at https://github.com/omerwe/PCGCs.

scholarly journals Estimating genetic correlation jointly using individual-level and summary-level GWAS data

2021 ◽  
Author(s):  
Yiliang Zhang ◽  
Youshu Cheng ◽  
Yixuan Ye ◽  
Wei Jiang ◽  
Qiongshi Lu ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Association Studies ◽  
Real Data ◽  
Efficient Estimation ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Correlation Estimation ◽  
Level Data ◽  
Summary Data

AbstractWith the increasing accessibility of individual-level data from genome wide association studies, it is now common for researchers to have individual-level data of some traits in one specific population. For some traits, we can only access public released summary-level data due to privacy and safety concerns. The current methods to estimate genetic correlation can only be applied when the input data type of the two traits of interest is either both individual-level or both summary-level. When researchers have access to individual-level data for one trait and summary-level data for the other, they have to transform the individual-level data to summary-level data first and then apply summary data-based methods to estimate the genetic correlation. This procedure is computationally and statistically inefficient and introduces information loss. We introduce GENJI (Genetic correlation EstimatioN Jointly using Individual-level and summary data), a method that can estimate within-population or transethnic genetic correlation based on individual-level data for one trait and summary-level data for another trait. Through extensive simulations and analyses of real data on within-population and transethnic genetic correlation estimation, we show that GENJI produces more reliable and efficient estimation than summary data-based methods. Besides, when individual-level data are available for both traits, GENJI can achieve comparable performance than individual-level data-based methods. Downstream applications of genetic correlation can benefit from more accurate estimates. In particular, we show that more accurate genetic correlation estimation facilitates the predictability of cross-population polygenic risk scores.

The genetic and environmental relationship between major depression and the five-factor model of personality

2009 ◽  
Vol 40 (5) ◽  
pp. 801-806 ◽  
Author(s):  
K. S. Kendler ◽  
J. Myers
Keyword(s):  
Major Depression ◽  
Personality Traits ◽  
Genetic Correlation ◽  
Big Five ◽  
Genetic Risk ◽  
Factor Model ◽  
Five Factor Model ◽  
Genetic Correlations ◽  
Big Five Personality Traits ◽  
Big Five Personality

BackgroundCertain personality traits have long been suspected to reflect an enduring vulnerability to major depression (MD) in part because of shared genetic risk factors. Although many have agreed that normative personality is well captured by the ‘Big-Five’ personality traits of Openness (O), Conscientiousness (C), Extraversion (E), Agreeableness (A) and Neuroticism (N), to date genetically informative studies have only examined the relationship between MD and N and E.MethodQuestionnaires were completed on a website, yielding a sample of 44 112 subjects including both members of 542 same-sex twin pairs. Personality was measured by the Big Five Inventory. Structural modeling was performed by Mx.ResultsThree of the big-five personality traits – O, E and A – had small phenotypic associations with risk for MD and small genetic correlations. Two traits – N and C – had stronger phenotypic associations (positive for N and negative for C) with the following estimates of the genetic correlation with MD: +0.43 for N and −0.36 for C. N and C were moderately negatively correlated. Controlling for N reduced the genetic correlation between C and MD more than controlling for C reduced the genetic correlation between N and MD.ConclusionsA large proportion of the genetic risk for MD that is expressed via personality is captured by N, with a modest amount due to C, and small amounts from O, E and A.

scholarly journals Estimating genetic correlation jointly using individual-level and summary-level GWAS data

2021 ◽  
Author(s):  
Hongyu Zhao ◽  
Yiliang Zhang ◽  
Youshu Cheng ◽  
Yixuan Ye ◽  
Wei Jiang ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Association Studies ◽  
Real Data ◽  
Efficient Estimation ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Correlation Estimation ◽  
Level Data ◽  
Summary Data

Abstract With the increasing accessibility of individual-level data from genome wide association studies, it is now common for researchers to have individual-level data of some traits in one specific population. For some traits, we can only access public released summary-level data due to privacy and safety concerns. The current methods to estimate genetic correlation can only be applied when the input data type of the two traits of interest is either both individual-level or both summary-level. When researchers have access to individual-level data for one trait and summary-level data for the other, they have to transform the individual-level data to summary-level data first and then apply summary data-based methods to estimate the genetic correlation. This procedure is computationally and statistically inefficient and introduces information loss. We introduce GENJI (Genetic correlation EstimatioN Jointly using Individual-level and summary data), a method that can estimate within-population or transethnic genetic correlation based on individual-level data for one trait and summary-level data for another trait. Through extensive simulations and analyses of real data on within-population and transethnic genetic correlation estimation, we show that GENJI produces more reliable and efficient estimation than summary data-based methods. Besides, when individual-level data are available for both traits, GENJI can achieve comparable performance than individual-level data-based methods. Downstream applications of genetic correlation can benefit from more accurate estimates. In particular, we show that more accurate genetic correlation estimation facilitates the predictability of cross-population polygenic risk scores.

Shared genetic architecture across psychiatric disorders

2021 ◽  
pp. 1-7
Author(s):  
Andrew D. Grotzinger
Keyword(s):  
Risk Factors ◽  
Psychiatric Disorders ◽  
Genetic Correlations ◽  
Fine Grained ◽  
Level Data ◽  
Symptom Level ◽  
Genetic Level ◽  
Genetic Signal ◽  
Family Based ◽  
Shared Genetic

Abstract Psychiatric disorders overlap substantially at the genetic level, with family-based methods long pointing toward transdiagnostic risk pathways. Psychiatric genomics has progressed rapidly in the last decade, shedding light on the biological makeup of cross-disorder risk at multiple levels of analysis. Over a hundred genetic variants have been identified that affect multiple disorders, with many more to be uncovered as sample sizes continue to grow. Cross-disorder mechanistic studies build on these findings to cluster transdiagnostic variants into meaningful categories, including in what tissues or when in development these variants are expressed. At the upper-most level, methods have been developed to estimate the overall shared genetic signal across pairs of traits (i.e. single-nucleotide polymorphism-based genetic correlations) and subsequently model these relationships to identify overarching, genomic risk factors. These factors can subsequently be associated with external traits (e.g. functional imaging phenotypes) to begin to understand the makeup of these transdiagnostic risk factors. As psychiatric genomic efforts continue to expand, we can begin to gain even greater insight by including more fine-grained phenotypes (i.e. symptom-level data) and explicitly considering the environment. The culmination of these efforts will help to inform bottom-up revisions of our current nosology.

scholarly journals Genetic factors influencing a neurobiological substrate for psychiatric disorders

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Till F. M. Andlauer ◽  
Thomas W. Mühleisen ◽  
Felix Hoffstaedter ◽  
Alexander Teumer ◽  
Katharina Wittfeld ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Risk ◽  
Meta Analysis ◽  
Anterior Cingulate ◽  
Psychiatric Disease ◽  
Salience Network ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Common Substrate ◽  
The Common

AbstractA retrospective meta-analysis of magnetic resonance imaging voxel-based morphometry studies proposed that reduced gray matter volumes in the dorsal anterior cingulate and the left and right anterior insular cortex—areas that constitute hub nodes of the salience network—represent a common substrate for major psychiatric disorders. Here, we investigated the hypothesis that the common substrate serves as an intermediate phenotype to detect genetic risk variants relevant for psychiatric disease. To this end, after a data reduction step, we conducted genome-wide association studies of a combined common substrate measure in four population-based cohorts (n = 2271), followed by meta-analysis and replication in a fifth cohort (n = 865). After correction for covariates, the heritability of the common substrate was estimated at 0.50 (standard error 0.18). The top single-nucleotide polymorphism (SNP) rs17076061 was associated with the common substrate at genome-wide significance and replicated, explaining 1.2% of the common substrate variance. This SNP mapped to a locus on chromosome 5q35.2 harboring genes involved in neuronal development and regeneration. In follow-up analyses, rs17076061 was not robustly associated with psychiatric disease, and no overlap was found between the broader genetic architecture of the common substrate and genetic risk for major depressive disorder, bipolar disorder, or schizophrenia. In conclusion, our study identified that common genetic variation indeed influences the common substrate, but that these variants do not directly translate to increased disease risk. Future studies should investigate gene-by-environment interactions and employ functional imaging to understand how salience network structure translates to psychiatric disorder risk.

scholarly journals Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours

2017 ◽  
Author(s):  
Jorien L. Treur ◽  
Mark Gibson ◽  
Amy E Taylor ◽  
Peter J Rogers ◽  
Marcus R Munafò
Keyword(s):  
Genetic Correlation ◽  
Sleep Duration ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genetic Correlations ◽  
Causal Effects ◽  
Genome Wide Association Studies ◽  
Caffeine Consumption ◽  
Insomnia Complaints

AbstractStudy Objectives:Higher caffeine consumption has been linked to poorer sleep and insomnia complaints. We investigated whether these observational associations are the result of genetic risk factors influencing both caffeine consumption and poorer sleep, and/or whether they reflect (possibly bidirectional) causal effects.Methods:Summary-level data were available from genome-wide association studies (GWAS) on caffeine consumption (n=91,462), sleep duration, and chronotype (i.e., being a ‘morning’ versus an ‘evening’ person) (both n=128,266), and insomnia complaints (n=113,006). Linkage disequilibrium (LD) score regression was used to calculate genetic correlations, reflecting the extent to which genetic variants influencing caffeine consumption and sleep behaviours overlap. Causal effects were tested with bidirectional, two-sample Mendelian randomization (MR), an instrumental variable approach that utilizes genetic variants robustly associated with an exposure variable as an instrument to test causal effects. Estimates from individual genetic variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods.Results:There was no clear evidence for genetic correlation between caffeine consumption and sleep duration (rg=0.000,p=0.998), chronotype (rg=0.086,p=0.192) or insomnia (rg=-0.034,p=0.700). Two-sample Mendelian randomization analyses did not support causal effects from caffeine consumption to sleep behaviours, or the other way around.Conclusions:We found no evidence in support of genetic correlation or causal effects between caffeine consumption and sleep. While caffeine may have acute effects on sleep when taken shortly before habitual bedtime, our findings suggest that a more sustained pattern of high caffeine consumption is likely associated with poorer sleep through shared environmental factors.

Sign in / Sign up

Export Citation Format

Share Document