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2015
Background Poorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition. Methods We carried out a genome-wide association study (GWAS) of SRH on 111 749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal SNPs for SRH. Linkage Disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia. Results The GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The st...
Health Services Research
To explore the contribution of genes and environmental factors to variation in a common measure (i.e., a five-point--excellent, very good, good, fair, and poor--Likert scale) of self-reported health. Data were analyzed from 4,638 male-male twin pair members of the Vietnam Era Twin (VET) Registry who responded to a 1987 health survey. Varying models for the relationship between genetic and environmental influences on self-reported health were tested in an attempt to explain the relative contributions of additive genetic, shared and nonshared environmental effects, and health conditions reported since 1975 to perceived health status. A mail and telephone survey of health was administered in 1987 to VET Registry twins. Variance component estimates under the best-fitting model included a 39.6 percent genetic contribution to self-reported health. In a model which included the effect of health condition, genes accounted for 32.5 percent and health condition accounted for 15.0 percent of t...
Twin Research and Human Genetics, 2013
The present study aims to estimate the relative importance of genetic and environmental factors for health-related quality of life (HRQL) measured by the 12-item Short-Form Health Survey (SF-12). The study was based on two Danish twin cohorts (46,417 twin individuals) originating from the nationwide, population-based Danish Twin Registry. The twins were approached by a mailed-out questionnaire in 2002. The questionnaire included the SF-12, information on demographic factors, and questions on a variety of specific diseases. Heritability of the SF-12 includes the physical component summary (PCS) and the mental component summary (MCS); and etiologically important variance components were estimated using multivariate biometric models. The respondents were stratified into six groups, based on age and sex. A total of 33,794 (73%) individual twins responded to the survey. The SF-12 was completed by 29,619 individuals, which included 9,120 complete twin pairs. Overall, the best-fitting model explaining the variance of HRQL was the ACE model. The estimated heritability of the SF-12 was between 11% and 35%, whereas between 65% and 89% could be explained by unique environmental or stochastic factors in the different sex and age groups. The highest heritability was seen among older twins. In addition, the genetic correlation between MCS and PCS scores was low (0.07 and 0.23 for males and females, respectively) among younger and high (0.26 and 0.45 for males and females, respectively) in the oldest age group. Both the largest genetic influence on HRQL and the largest genetic overlap between the scores were seen in the oldest age group, which consisted of twins older than 55. The unique environmental correlation between MCS and PCS were generally negative. The heritability of HRQL differs between different age groups. In general, most of the variance in the SF-12 summary components was determined by unique environmental factors.
The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 2001
Objectives. Self-rated health has been shown to be a predictor for future health status and mortality. The purpose of this study was to investigate age-group and sex differences in genetic and environmental sources of variation for selfrated health.
Behavior Genetics, 2007
Background-We analysed genetic and environmental influences on self-esteem and its stability across adolescence. [1983][1984][1985][1986][1987] were assessed by questionnaire at ages 14y (N= 4132 twin individuals) and 17y (N=3841 twin individuals). Self esteem was measured using the Rosenberg global self-esteem scale and analyzed using quantitative genetic methods for twin data in the Mx statistical package.
Department of medical epidemiology and biostatistiscs, 2006
Even though self-rated health is increasingly accepted as an important measure of health status, there are several uncertainties as to why people differ in their health perception. The extent of age differences across the life span and whether there are different determinants in men than in women is unclear. The overall objective of this thesis was to increase understanding as to why individuals differ in their health perception through quantitative genetic and epidemiological approaches using subsamples from the Swedish Twin Registry. By studying twins, it is possible to estimate the relative importance of genetic and environmental factors for self-rated health. In Paper I we included both like-and opposite sexed twins in adult ages in order to evaluate cross-sectional age group and sex differences in the relative importance of genetic and environmental factors for self-rated health. Individual specific environmental variance is the most important component in adults under 45 years whereas the increase in total variance in the older age groups (45-74) is primarily due to genetic influences. The individual specific environment becomes more important again in the oldest age group (>74). No significant sex differences were found in variance components. Similarly, the same genetic effects were of importance in men and women. Paper II investigated decreases in means and increases in individual differences with age longitudinally. Results indicate that previously reported changes in self-rated health over the life span primarily reflect cohort differences rather than age changes. Stability between time points reflects both environmental and genetic factors. Intact cognitive functioning is an important aspect of health with increasing age, therefore, Paper III focused on the associations between self-rated health and cognitive abilities in normal aging. There was only slight evidence that associations between selfrated health and cognitive test scores were mediated by chronic disease conditions. In the age group younger than 67 years, associations between self-rated health and spatial reasoning and perceptual speed were found, mediated by both genetic and environmental factors. In the older age group (≥ 67 years), associations between selfrated health and verbal ability, spatial reasoning, perceptual speed and visual memory were entirely due to genetic factors. Paper IV investigated the importance of health behavior and risk factors for future self-ratings of health. We found that recurrent headache, back-and neck pain, lack of exercise, smoking, obesity, perceived stress, unemployment and personality were associated with poor self-rated health, some 25+ years later. Genetic and familial factors only slightly influenced the relationships between recurrent headache, exercise, obesity, and poor self-rated health. In conclusion, both genetic and environmental factors are of importance for individual differences in self-rated health and the effect is equal for men and women. Genetic effects for self-rated health can probably be explained by genetic influences on disease status. Childhood socioeconomic status did not explain the finding of cohort differences in self-rated health. Societal changes not tapped by our measure more likely explain these differences. We found weak associations between self-rated health and cognitive abilities, indicating that cognition is not substantially influencing self-rated health in a normally aging population. Finally, health behavior and risk factors are of importance for self-rated health. Lifestyle changes such as reduced weight and more exercise might help prevent people from experiencing their health as poor in the future. This in turn might result in a decrease in morbidity and increase in survival.
Nature Genetics, 2019
We introduce two novel methods for multivariate genome-wide association meta-analysis (GWAMA) of related traits that correct for sample overlap. A broad range of simulation scenarios supports the added value of our multivariate methods relative to univariate GWAMA. We applied the novel methods to life satisfaction, positive affect, neuroticism, and depressive symptoms, collectively referred to as the well-being spectrum (N obs = 2,370,390), and found 304 significant independent signals. Our multivariate approaches resulted in a 26% increase in the number of independent signals relative to the four univariate GWAMA, and in a ~ 57% increase in the predictive power of polygenic risk scores. Supporting transcriptome-and methylome-wide analyses (TWAS/MWAS) uncovered an additional 17 and 75 independent loci, respectively. Bioinformatic analyses, based on gene expression in brain tissues and cells, showed that genes differentially expressed in the subiculum and GABAergic interneurons are enriched in their effect on the wellbeing spectrum. In the past decade, genome-wide association studies (GWAS) have provided insights into the genetic basis of quantitative variation in complex traits 1. With summary statistics of these GWASs becoming public and the development of linkage disequilibrium score regression (LDSC) 2,3 , genetic correlations between traits can be systematically estimated (e.g. Brainstorm consortium 4). Levering this widely observed genetic overlap between traits, we introduce two novel methods for multivariate genome-wide association metaanalysis, where we define a multivariate model as a model where the effect of a single SNP is considered for multiple traits: 1) N-weighted multivariate GWAMA (N-GWAMA), with a unitary effect of the SNP on all traits, and 2) model averaging GWAMA (MA-GWAMA), where we relaxed the assumption of a unitary effect of the SNP on all traits. Both methods are well equipped to deal with (unknown) sample overlap. The dependence between effect sizes (error correlation) induced by possible sample overlap is estimated from the univariate GWAMA using LDSC 2,3. Furthermore, the univariate LDSC intercept is used to correct for population stratification and cryptic relatedness. Both methods have advantages over existing methods. In contrast to MultiPhen 5 , CCA (mv-PLINK) 6 , Combined-PC 7 , and mv-BIMBAM 8 , both our methods can be applied without the need of individual-level genotypic data as only GWAS/GWAMA summary-statistics are required. Additionally, in contrast to S Hom 9 , N-and MA-GWAMA take a more precise estimate of the error correlation into account. In contrast to MTAG 10 , MA-GWAMA , similar to S Het 9 , generates trait specific estimates for each SNP allowing for a certain degree of heterogeneity (see online methods). Finally, in contrast to TATES 11 , both N-GWAMA and MA-GWAMA generate effect sizes for the multivariate effect where TATES only generates a P-value. The absence of a signed statistic in TATES complicates or even prohibits polygenic prediction.
Scientific Reports, 2021
Self-rated health (SRH) is one of the most frequently used indicators in health and social research. Its robust association with mortality in very different populations implies that it is a comprehensive measure of health status and may even reflect the condition of the human organism beyond clinical diagnoses. Yet the biological basis of SRH is poorly understood. We used data from three independent European population samples (N approx. 15,000) to investigate the associations of SRH with 150 biomolecules in blood or urine (biomarkers). Altogether 57 biomarkers representing different organ systems were associated with SRH. In almost half of the cases the association was independent of disease and physical functioning. Biomarkers weakened but did not remove the association between SRH and mortality. We propose three potential pathways through which biomarkers may be incorporated into an individual’s subjective health assessment, including (1) their role in clinical diseases; (2) thei...
European journal of human genetics : EJHG, 2017
Despite the recent explosive rise in number of genetic markers for complex disease traits identified in genome-wide association studies, there is still a large gap between the known heritability of these traits and the part explained by these markers. To gauge whether this 'heritability gap' is closing, we first identified genome-wide significant SNPs from the literature and performed replication analyses for 32 highly relevant traits from five broad disease areas in 13 436 subjects of the Lifelines Cohort. Next, we calculated the variance explained by multi-SNP genetic risk scores (GRSs) for each trait, and compared it to their broad- and narrow-sense heritabilities captured by all common SNPs. The majority of all previously-associated SNPs (median=75%) were significantly associated with their respective traits. All GRSs were significant, with unweighted GRSs generally explaining less phenotypic variance than weighted GRSs, for which the explained variance was highest for h...
Twin Research and Human Genetics, 2009
We reanalyze previously published data on 309 MZ and 333 DZ twin pairs aged 25 to 74 years from the MIDUS survey, a nationally representative archived sample, to examine how much of the genetic covariance between a general factor of personality (GFP), a lower-order life history factor, and a general physical and mental health factor, is of the nonadditive variety. We found nonadditive genetic effects (D) could not be ruled out as a contributor to the shared variance of these three latent factors to a Super-KLife History factor. We suggest these genetic correlations support the view that a slow (K-selected) life history strategy, good health, and the GFP coevolved and are mutually coadapted through directional selection.
American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2012
Measures of personality and psychological distress are correlated and exhibit genetic covariance. We conducted univariate genome-wide SNP ($2.5 million) and gene-based association analyses of these traits and examined the overlap in results across traits, including a prediction analysis of mood states using genetic polygenic scores for personality. Measures of neuroticism, extraversion, and symptoms of anxiety, depression, and general psychological distress were collected in eight European cohorts (n ranged 546-1,338; maximum total n ¼ 6,268) whose mean age ranged from 55 to 79 years. Meta-analysis of the cohort results was performed, with follow-up associations of the top SNPs and genes investigated in independent cohorts (n ¼ 527-6,032). Suggestive association (P ¼ 8 Â 10 À8 ) of rs1079196 in the FHIT gene was observed with symptoms of anxiety. Other notable associations (P < 6.09 Â 10 À6 ) included SNPs in five genes for neuroticism (LCE3C, POLR3A, LMAN1L, ULK3, SCAMP2), KIAA0802 for extraversion, and NOS1 for general psychological distress. An association between symptoms of depression and rs7582472 (near to MGAT5 and NCKAP5) was replicated in two independent samples, but other replication findings were less consistent. Gene-based tests identified a significant locus on chromosome 15 (spanning five genes) associated with neuroticism which replicated (P < 0.05) in an independent cohort. Support for common genetic effects among personality and mood (particularly neuroticism and depressive symptoms) was found in terms of SNP association overlap and polygenic score prediction. The variance explained by individual SNPs was very small (up to 1%) confirming that there are no moderate/large effects of common SNPs on personality and related traits.
European Journal of Human Genetics, 2013
Personality traits are complex phenotypes related to psychosomatic health. Individually, various gene finding methods have not achieved much success in finding genetic variants associated with personality traits. We performed a meta-analysis of four genome-wide linkage scans (N ¼ 6149 subjects) of five basic personality traits assessed with the NEO Five-Factor Inventory. We compared the significant regions from the meta-analysis of linkage scans with the results of a meta-analysis of genome-wide association studies (GWAS) (NB17 000). We found significant evidence of linkage of neuroticism to chromosome 3p14 (rs1490265, LOD ¼ 4.67) and to chromosome 19q13 (rs628604, LOD ¼ 3.55); of extraversion to 14q32 (ATGG002, LOD ¼ 3.3); and of agreeableness to 3p25 (rs709160, LOD ¼ 3.67) and to two adjacent regions on chromosome 15, including 15q13 (rs970408, LOD ¼ 4.07) and 15q14 (rs1055356, LOD ¼ 3.52) in the individual scans. In the meta-analysis, we found strong evidence of linkage of extraversion to 4q34, 9q34, 10q24 and 11q22, openness to 2p25, 3q26, 9p21, 11q24, 15q26 and 19q13 and agreeableness to 4q34 and 19p13. Significant evidence of association in the GWAS was detected between openness and rs677035 at 11q24 (P-value ¼ 2.6 Â 10 À06 , KCNJ1). The findings of our linkage meta-analysis and those of the GWAS suggest that 11q24 is a susceptible locus for openness, with KCNJ1 as the possible candidate gene.
Nature Genetics, 2016
In designing our study, we faced a tradeoff between analyzing a smaller sample with a homogeneous phenotype measure versus attaining a larger sample by jointly analyzing data from multiple cohorts with heterogeneous measures. For example, in our analysis of subjective well-being, we included measures of both life satisfaction and positive affect, even though these constructs are conceptually distinct 7,8. In the Supplementary Note and Supplementary Figure 1, we present a theoretical framework for evaluating the costs and benefits of pooling heterogeneous measures. In our context, given the high genetic correlation across measures, the framework predicts that pooling increases statistical power to detect variants. This prediction is supported by our results. RESULTS GWAS of subjective well-being Following a prespecified analysis plan, we conducted a samplesize-weighted meta-analysis using data from 59 cohorts (n = 298,420 individuals) of cohort-level GWAS summary statistics. The phenotype measure was life satisfaction, positive affect, or (in some cohorts) a measure combining life satisfaction and positive affect. We confirmed previous findings 9 of high pairwise genetic correlation between life satisfaction and positive affect using bivariate LD Score regression 10 ( = 0.981 (s.e.m. = 0.065); Supplementary Table 1). Details on the 59 participating cohorts, their phenotype measures, genotyping, quality control filters, and association models are provided in the Online Methods, Supplementary Note, and Supplementary Tables 2-6. As expected under polygenicity 11 , we observed inflation of the median test statistic (λ GC = 1.206). The estimated intercept from LD Score regression (1.012) suggests that nearly all of the inflation is due to polygenic signal rather than bias. We also performed family-based analyses that similarly suggested minimal confounding due to Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 6,460), and neuroticism (n = 70,9). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (| |≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.
Translational psychiatry, 2018
Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use, and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome-wide association study in 116,255 UK Biobank participants who responded yes/no to the question "Would you consider yourself a risk taker?" Risk takers (compared with controls) were more likely to be men, smokers, and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 l...
Molecular …, 2008
Personality traits are summarized by five broad dimensions with pervasive influences on major life outcomes, strong links to psychiatric disorders, and clear heritable components. To identify genetic variants associated with each of the five dimensions of personality we performed a genome wide association (GWA) scan of 3,972 individuals from a genetically isolated population within Sardinia, Italy. Based on analyses of 362,129 single nucleotide polymorphisms (SNPs) we found several strong signals within or near genes previously implicated in psychiatric disorders. They include the association of Neuroticism with SNAP25 (rs362584, P = 5 × 10−5), Extraversion with BDNF and two cadherin genes (CDH13 and CDH23; Ps < 5 × 10−5), Openness with CNTNAP2 (rs10251794, P = 3 × 10−5), Agreeableness with CLOCK (rs6832769, P = 9 × 10−6), and Conscientiousness with DYRK1A (rs2835731, P = 3 × 10−5). Effect sizes were small (less than 1% of variance), and most failed to replicate in the follow-up independent samples (N up to 3,903), though the association between Agreeableness and CLOCK was supported in two of three replication samples (overall P = 2 × 10−5). We infer that a large number of loci may influence personality traits and disorders, requiring larger sample sizes for the GWA approach to identify significant genetic variants.
Journal of Personality, 2016
Our study aims to estimate the proportion of the phenotypic variance of Neuroticism and its facet scales that can be attributed to common SNPs in two adult populations from Estonia (EGCUT; N = 3,292) and the Netherlands (Lifelines; N = 13,383). Method: Genomic-Relatedness-Matrix Restricted Maximum Likelihood (GREML) using Genome-wide Complex Trait Analysis (GCTA) software was employed. To build upon previous research, we used self- and informant-reports of the 30-facet NEO personality inventories and analyzed both the usual sum scores and the residual facet scores of Neuroticism. Results: In the EGCUT cohort, the proportion of phenotypic variance explained by the additive effects of common genetic variants in self- and informant-reported Neuroticism domain scores was 15.2% (p = .070, SE = .11) and 6.2% (p = .293, SE = .12), respectively. The SNP-based heritability estimates at the level of Neuroticism facet scales differed greatly across cohorts and modes of measurement but were generally higher (a) for self- than for informant-reports, and (b) for sum than for residual scores. Conclusions: Our findings indicate that a large proportion of the heritability of Neuroticism is not captured by additive genetic effects of common SNPs with some evidence for gene-environment interaction across cohorts.
Molecular Psychiatry, 2012
Background-C-reactive protein (CRP) is a heritable marker of chronic inflammation that is strongly associated with cardiovascular disease. We aimed to identify genetic variants that are associated with CRP levels.
eLife, 2022
Background: Mitochondrial DNA copy number (mtDNAcn) in tissues and blood can be altered in conditions like diabetes and major depression and may play a role in aging and longevity. However, little is known about the association between mtDNAcn and personality traits linked to emotional states, metabolic health, and longevity. This study tests the hypothesis that blood mtDNAcn is related to personality traits and mediates the association between personality and mortality. Methods: We assessed the big five personality domains and facets using the Revised NEO Personality Inventory (NEO-PI-R), assessed depressive symptoms with the Center for Epidemiologic Studies Depression Scale (CES-D), estimated mtDNAcn levels from whole-genome sequencing, and tracked mortality in participants from the Baltimore Longitudinal Study of Aging. Results were replicated in the SardiNIA Project. Results: We found that mtDNAcn was negatively associated with the Neuroticism domain and its facets and positively associated with facets from the other four domains. The direction and size of the effects were replicated in the SardiNIA cohort and were robust to adjustment for potential confounders in both samples. Consistent with the Neuroticism finding, higher depressive symptoms were associated with lower mtDNAcn. Finally, mtDNAcn mediated the association between personality and mortality risk.
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