scholarly journals Personality Polygenes, Positive Affect, and Life Satisfaction

2016 ◽  
Vol 19 (5) ◽  
pp. 407-417 ◽  
Author(s):  
Alexander Weiss ◽  
Bart M. L. Baselmans ◽  
Edith Hofer ◽  
Jingyun Yang ◽  
Aysu Okbay ◽  
...  
Keyword(s):  
Life Satisfaction ◽  
Positive Affect ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Genetic Influences ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Neo Five Factor Inventory ◽  
General Wellbeing ◽  
Human Primate

Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximalN= 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes <0.05%) were observed between the extraversion polygenic score and wellbeing measures, and a negative association was observed between the polygenic neuroticism score and life satisfaction. Furthermore, using GWA data, genetic correlations of -0.49 and -0.55 were estimated between neuroticism with life satisfaction and positive affect, respectively. The moderate genetic correlation between neuroticism and wellbeing is in line with twin research showing that genetic influences on wellbeing are also shared with other independent personality domains.

scholarly journals Genetic Association Study of Childhood Aggression across raters, instruments and age

2019 ◽  
Author(s):  
Hill F. Ip ◽  
Camiel M. van der Laan ◽  
Eva M. L. Krapohl ◽  
Isabell Brikell ◽  
Cristina Sánchez-Mora ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Genetic Correlations ◽  
Childhood Aggression ◽  
Age At First Birth ◽  
Nucleotide Polymorphisms ◽  
Obsessive Compulsive ◽  
Psychological Traits ◽  
Compulsive Disorder ◽  
Genome Wide ◽  
Polygenic Scores

AbstractBackgroundHuman aggressive behavior (AGG) has a substantial genetic component. Here we present a large genome-wide association meta-analysis (GWAMA) of childhood AGG.MethodsWe analyzed assessments of AGG for a total of 328,935 observations from 87,485 children (aged 1.5 – 18 years), from multiple assessors, instruments, and ages, while accounting for sample overlap. We performed an overall analysis and meta-analyzed subsets of the data within rater, instrument, and age.ResultsHeritability based on the overall meta-analysis (AGGall) that could be attributed to Single Nucleotide Polymorphisms (SNPs) was 3.31% (SE=0.0038). No single SNP reached genome-wide significance, but gene-based analysis returned three significant genes: ST3GAL3 (P=1.6E-06), PCDH7 (P=2.0E-06) and IPO13 (P=2.5E-06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children and in retrospectively assessed childhood aggression. We obtained moderate-to-strong genetic correlations (rg‘s) with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range |rg|: 0.19 –.1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg =∼-0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range |rg|: 0.46 – 0.60). Genetic correlations between AGG and psychiatric disorders were strongest for mother- and self-reported AGG.ConclusionsThe current GWAMA of childhood aggression provides a powerful tool to interrogate the genetic etiology of AGG by creating individual polygenic scores and genetic correlations with psychiatric traits.

scholarly journals Phenome-wide investigation of health outcomes associated with genetic predisposition to loneliness

2019 ◽  
Vol 28 (22) ◽  
pp. 3853-3865 ◽  
Author(s):  
Abdel Abdellaoui ◽  
Sandra Sanchez-Roige ◽  
Julia Sealock ◽  
Jorien L Treur ◽  
Jessica Dennis ◽  
...  
Keyword(s):  
Genetic Predisposition ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
High Density Lipoproteins ◽  
Physical And Mental Health ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Wide Range ◽  
Genetic Overlap

Abstract Humans are social animals that experience intense suffering when they perceive a lack of social connection. Modern societies are experiencing an epidemic of loneliness. Although the experience of loneliness is universally human, some people report experiencing greater loneliness than others. Loneliness is more strongly associated with mortality than obesity, emphasizing the need to understand the nature of the relationship between loneliness and health. Although it is intuitive that circumstantial factors such as marital status and age influence loneliness, there is also compelling evidence of a genetic predisposition toward loneliness. To better understand the genetic architecture of loneliness and its relationship with associated outcomes, we extended the genome-wide association study meta-analysis of loneliness to 511 280 subjects, and detect 19 significant genetic variants from 16 loci, including four novel loci, as well as 58 significantly associated genes. We investigated the genetic overlap with a wide range of physical and mental health traits by computing genetic correlations and by building loneliness polygenic scores in an independent sample of 18 498 individuals with EHR data to conduct a PheWAS with. A genetic predisposition toward loneliness was associated with cardiovascular, psychiatric, and metabolic disorders and triglycerides and high-density lipoproteins. Mendelian randomization analyses showed evidence of a causal, increasing, the effect of both BMI and body fat on loneliness. Our results provide a framework for future studies of the genetic basis of loneliness and its relationship to mental and physical health.

scholarly journals Continuity of Genetic Risk for Aggressive Behavior Across the Life-Course

2021 ◽  
Author(s):  
Camiel M. van der Laan ◽  
José J. Morosoli-García ◽  
Steve G. A. van de Weijer ◽  
Lucía Colodro-Conde ◽  
Hill F. Ip ◽  
...  
Keyword(s):  
Individual Differences ◽  
The Netherlands ◽  
Life Course ◽  
Aggressive Behavior ◽  
Meta Analysis ◽  
Genetic Influences ◽  
Genome Wide ◽  
A Genome ◽  
Polygenic Scores ◽  
The Life Course

AbstractWe test whether genetic influences that explain individual differences in aggression in early life also explain individual differences across the life-course. In two cohorts from The Netherlands (N = 13,471) and Australia (N = 5628), polygenic scores (PGSs) were computed based on a genome-wide meta-analysis of childhood/adolescence aggression. In a novel analytic approach, we ran a mixed effects model for each age (Netherlands: 12–70 years, Australia: 16–73 years), with observations at the focus age weighted as 1, and decaying weights for ages further away. We call this approach a ‘rolling weights’ model. In The Netherlands, the estimated effect of the PGS was relatively similar from age 12 to age 41, and decreased from age 41–70. In Australia, there was a peak in the effect of the PGS around age 40 years. These results are a first indication from a molecular genetics perspective that genetic influences on aggressive behavior that are expressed in childhood continue to play a role later in life.

Social and Temporal Comparisons as Moderators of Psychological Resources Associations With Wellbeing

2016 ◽  
Vol 37 (2) ◽  
pp. 96-104 ◽  
Author(s):  
Hasida Ben-Zur
Keyword(s):  
Life Satisfaction ◽  
Positive Affect ◽  
Subjective Wellbeing ◽  
Social Comparisons ◽  
Self Esteem ◽  
Psychological Resources ◽  
Low Levels ◽  
Age Range ◽  
General Wellbeing

Abstract. The current study investigated the associations of psychological resources, social comparisons, and temporal comparisons with general wellbeing. The sample included 142 community participants (47.9% men; age range 23–83 years), who compared themselves with others, and with their younger selves, on eight dimensions (e.g., physical health, resilience). They also completed questionnaires assessing psychological resources of mastery and self-esteem, and three components of subjective wellbeing: life satisfaction and negative and positive affect. The main results showed that high levels of psychological resources contributed to wellbeing, with self-enhancing social and temporal comparisons moderating the effects of resources on certain wellbeing components. Specifically, under low levels of mastery or self-esteem self-enhancing social or temporal comparisons were related to either higher life satisfaction or positive affect. The results highlight the role of resources and comparisons in promoting people’s wellbeing, and suggest that self-enhancing comparisons function as cognitive coping mechanisms when psychological resources are low.

scholarly journals Genome-wide association meta-analysis of childhood and adolescent internalising symptoms

2020 ◽  
Author(s):  
Eshim S Jami ◽  
Anke R Hammerschlag ◽  
Hill F Ip ◽  
Andrea G Allegrini ◽  
Beben Benyamin ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Childhood Aggression ◽  
Genome Wide Association ◽  
Childhood And Adolescence ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Genome Wide

Internalising symptoms in childhood and adolescence are as heritable as adult depression and anxiety, yet little is known of their molecular basis. This genome-wide association meta-analysis of internalising symptoms included repeated observations from 64,641 individuals, aged between 3 and 18. The N-weighted meta-analysis of overall internalising symptoms (INToverall) detected no genome-wide significant hits and showed low SNP heritability (1.66%, 95% confidence intervals 0.84-2.48%, Neffective=132,260). Stratified analyses showed rater-based heterogeneity in genetic effects, with self-reported internalising symptoms showing the highest heritability (5.63%, 95% confidence intervals 3.08-8.18%). Additive genetic effects on internalising symptoms appeared stable over age, with overlapping estimates of SNP heritability from early-childhood to adolescence. Gene-based analyses showed significant associations with three genes: WNT3 (p=1.13×10-06), CCL26 (p=1.88×10-06), and CENPO (p=2.54×10-06). Of these, WNT3 was previously associated with neuroticism, with which INToverall also shared a strong genetic correlation (rg=0.76). Genetic correlations were also observed with adult anxiety, depression, and the wellbeing spectrum (|rg|> 0.70), as well as with insomnia, loneliness, attention-deficit hyperactivity disorder, autism, and childhood aggression (range |rg|=0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. Overall, childhood and adolescent internalising symptoms share substantial genetic vulnerabilities with adult internalising disorders and other childhood psychiatric traits, which could explain both the persistence of internalising symptoms over time, and the high comorbidity amongst childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

scholarly journals Meta-analysis of 208370 East Asians identifies 113 susceptibility loci for systemic lupus erythematosus

2020 ◽  
pp. annrheumdis-2020-219209
Author(s):  
Xianyong Yin ◽  
Kwangwoo Kim ◽  
Hiroyuki Suetsugu ◽  
So-Young Bang ◽  
Leilei Wen ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Large Scale ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Susceptibility Loci ◽  
Systemic Lupus ◽  
East Asians ◽  
Genome Wide ◽  
Causal Variants

ObjectiveSystemic lupus erythematosus (SLE), an autoimmune disorder, has been associated with nearly 100 susceptibility loci. Nevertheless, these loci only partially explain SLE heritability and their putative causal variants are rarely prioritised, which make challenging to elucidate disease biology. To detect new SLE loci and causal variants, we performed the largest genome-wide meta-analysis for SLE in East Asian populations.MethodsWe newly genotyped 10 029 SLE cases and 180 167 controls and subsequently meta-analysed them jointly with 3348 SLE cases and 14 826 controls from published studies in East Asians. We further applied a Bayesian statistical approach to localise the putative causal variants for SLE associations.ResultsWe identified 113 genetic regions including 46 novel loci at genome-wide significance (p<5×10−8). Conditional analysis detected 233 association signals within these loci, which suggest widespread allelic heterogeneity. We detected genome-wide associations at six new missense variants. Bayesian statistical fine-mapping analysis prioritised the putative causal variants to a small set of variants (95% credible set size ≤10) for 28 association signals. We identified 110 putative causal variants with posterior probabilities ≥0.1 for 57 SLE loci, among which we prioritised 10 most likely putative causal variants (posterior probability ≥0.8). Linkage disequilibrium score regression detected genetic correlations for SLE with albumin/globulin ratio (rg=−0.242) and non-albumin protein (rg=0.238).ConclusionThis study reiterates the power of large-scale genome-wide meta-analysis for novel genetic discovery. These findings shed light on genetic and biological understandings of SLE.

scholarly journals Genome-wide association meta-analysis of corneal curvature identifies novel loci and shared genetic influences across axial length and refractive error

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Qiao Fan ◽  
◽  
Alfred Pozarickij ◽  
Nicholas Y. Q. Tan ◽  
Xiaobo Guo ◽  
...  
Keyword(s):  
Refractive Error ◽  
Axial Length ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Genetic Influences ◽  
Corneal Curvature ◽  
Genome Wide ◽  
Shared Genetic

scholarly journals Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

2018 ◽  
Vol 21 (2) ◽  
pp. 84-88 ◽  
Author(s):  
W. David Hill
Keyword(s):  
Genetic Information ◽  
Drug Targets ◽  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Nootropic Drug ◽  
Genome Wide

Intelligence and educational attainment are strongly genetically correlated. This relationship can be exploited by Multi-Trait Analysis of GWAS (MTAG) to add power to Genome-wide Association Studies (GWAS) of intelligence. MTAG allows the user to meta-analyze GWASs of different phenotypes, based on their genetic correlations, to identify association's specific to the trait of choice. An MTAG analysis using GWAS data sets on intelligence and education was conducted by Lam et al. (2017). Lam et al. (2017) reported 70 loci that they described as ‘trait specific’ to intelligence. This article examines whether the analysis conducted by Lam et al. (2017) has resulted in genetic information about a phenotype that is more similar to education than intelligence.

scholarly journals A combined analysis of genetically correlated traits identifies 107 loci associated with intelligence

2017 ◽  
Author(s):  
W. D. Hill ◽  
G. Davies ◽  
A. M. McIntosh ◽  
C. R. Gale ◽  
I. J. Deary
Keyword(s):  
Household Income ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Correlated Traits ◽  
Single Measure ◽  
Combined Analysis ◽  
Genome Wide

AbstractIntelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including many physical and mental health variables. Both education and household income are strongly genetically correlated with intelligence, at rg =0.73 and rg =0.70 respectively. This allowed us to utilize a novel approach, Multi-Trait Analysis of Genome-wide association studies (MTAG; Turley et al. 2017), to combine two large genome-wide association studies (GWASs) of education and household income to increase power in the largest GWAS on intelligence so far (Sniekers et al. 2017). This study had four goals: firstly, to facilitate the discovery of new genetic loci associated with intelligence; secondly, to add to our understanding of the biology of intelligence differences; thirdly, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predict phenotypic intelligence variance in an independent sample. We apply MTAG to three large GWAS: Sniekers et al (2017) on intelligence, Okbay et al. (2016) on Educational attainment, and Hill et al. (2016) on household income. By combining these three samples our functional sample size increased from 78 308 participants to 147 194. We found 107 independent loci associated with intelligence, implicating 233 genes, using both SNP-based and gene-based GWAS. We find evidence that neurogenesis may explain some of the biological differences in intelligence as well as genes expressed in the synapse and those involved in the regulation of the nervous system. We show that the results of our combined analysis demonstrate the same pattern of genetic correlations as a single measure/the simple measure of intelligence, providing support for the meta-analysis of these genetically-related phenotypes. We find that our MTAG meta-analysis of intelligence shows similar genetic correlations to 26 other phenotypes when compared with a GWAS consisting solely of cognitive tests. Finally, using an independent sample of 6 844 individuals we were able to predict 7% of intelligence using SNP data alone.

scholarly journals Genome-wide association study meta-analysis of the Alcohol Use Disorder Identification Test (AUDIT) in two population-based cohorts (N=141,932)

2018 ◽  
Author(s):  
Sandra Sanchez-Roige ◽  
Abraham A. Palmer ◽  
Pierre Fontanillas ◽  
Sarah L. Elson ◽  
Mark J. Adams ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Alcohol Dependence ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Population Based ◽  
Major Depressive ◽  
Genome Wide ◽  
Disorder Identification ◽  
Dsm Iv

AbstractAlcohol use disorders (AUD) are common conditions that have enormous social and economic consequences. We obtained quantitative measures using the Alcohol Use Disorder Identification Test (AUDIT) from two population-based cohorts of European ancestry: UK Biobank (UKB; N=121,604) and 23andMe (N=20,328) and performed a genome-wide association study (GWAS) meta-analysis. We also performed GWAS for AUDIT items 1-3, which focus on consumption (AUDIT-C), and for items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; we also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P was positively genetically correlated with schizophrenia (rg=0.22, p=3.0×10−10), major depressive disorder (rg=0.26, p=5.6×10−3), and attention-deficit/hyperactivity disorder (ADHD; rg=0.23, p=1.1×10−5), whereas AUDIT-C was negatively genetically correlated with major depressive disorder (rg=−0.24, p=3.7×10−3) and ADHD (rg=−0.10, p=1.8×10−2). We also used the AUDIT data in the UKB to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total score of ≤4 as controls and ≥12 as cases produced a high genetic correlation with DSM-IV alcohol dependence (rg=0.82, p=3.2×10−6) while retaining most subjects. We conclude that AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and AUD.

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