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Meta-analysis of genome-wide association studies for personality


Personality can be thought of as a set of characteristics that influence people's thoughts, feelings and behavior across a variety of settings. Variation in personality is predictive of many outcomes in life, including mental health. Here we report on a meta-analysis of genome-wide association (GWA) data for personality in 10 discovery samples (17 375 adults) and five in silico replication samples (3294 adults). All participants were of European ancestry. Personality scores for Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness were based on the NEO Five-Factor Inventory. Genotype data of 2.4M single-nucleotide polymorphisms (SNPs; directly typed and imputed using HapMap data) were available. In the discovery samples, classical association analyses were performed under an additive model followed by meta-analysis using the weighted inverse variance method. Results showed genome-wide significance for Openness to Experience near the RASA1 gene on 5q14.3 (rs1477268 and rs2032794, P=2.8 × 10−8 and 3.1 × 10−8) and for Conscientiousness in the brain-expressed KATNAL2 gene on 18q21.1 (rs2576037, P=4.9 × 10−8). We further conducted a gene-based test that confirmed the association of KATNAL2 to Conscientiousness. In silico replication did not, however, show significant associations of the top SNPs with Openness and Conscientiousness, although the direction of effect of the KATNAL2 SNP on Conscientiousness was consistent in all replication samples. Larger scale GWA studies and alternative approaches are required for confirmation of KATNAL2 as a novel gene affecting Conscientiousness.


The structure of human personality has traditionally been accounted for by a relatively small set of traits. Over the last century, scientific consensus has converged on a taxonomic model of personality traits based on five higher-order dimensions of Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness, known as the Five-Factor Model (FFM).1 These five dimensions are largely independent and provide a broad description of personality. Neuroticism is commonly defined as emotional instability; it involves the experience of negative emotions such as anxiety, depression, hostility and the vulnerability to stress. Extraversion is characterized by positive emotions, gregariousness and the tendency to be active, seek out stimulation and enjoy the company of others. Openness to Experience involves active imagination, aesthetic attentiveness, variety preference and intellectual curiosity. Agreeableness can be defined as the tendency to be cooperative and compassionate rather than suspicious and antagonistic towards others. Lastly, the dimension of Conscientiousness reflects self-discipline, carefulness, thoroughness, organization, deliberation and achievement.

Personality traits predict a host of social, behavioral and health outcomes, such as job performance, longevity and many psychiatric disorders, including substance abuse and dependency, mood disorders such as major depressive disorder (MDD), anxiety disorders and personality disorders.2, 3, 4, 5, 6, 7 For example, Neuroticism reflects a liability trait for MDD and other mood and anxiety disorders and also explains part of the comorbidity among these disorders.3, 6, 8, 9 MDD is also predicted by low Conscientiousness.10, 11 With regard to substance (ab)use, tobacco smokers score high on Neuroticism and low on Conscientiousness.12, 13 A similar but more extreme pattern is seen for cocaine and heroin users; in contrast, marijuana users score high on Openness to Experience and low on Agreeableness and Conscientiousness.13 The FFM dimensions further predict tendencies toward different types of personality disorder, with high scores on Neuroticism and low scores on Agreeableness predicting many of the personality disorders and with low or high scores on Extraversion predicting different disorders.2, 14 Personality is also predictive of beneficial outcomes. High Conscientiousness predicts better performance in the workplace10, 13, 15 and high Extraversion larger participation in regular leisure time exercise.16, 17

Twin, adoption and family studies have convincingly shown that each of the FFM personality dimensions is heritable, with heritability estimates ranging between 33 and 65%.18, 19, 20, 21 Lower-order facets that underlie personality dimensions are genetically correlated,22 confirming the notion that the higher-order personality dimensions are to a large extent genetically homogeneous. Importantly, genetic influences on personality partly overlap with the genetic factors that influence psychiatric disorders.3, 6, 10, 20 Thus, gene-finding efforts for the major personality dimensions may yield important insights into the genetic etiology of psychiatric disease.

Gene-finding studies for personality, including genome-wide linkage and association studies, have largely focused on Neuroticism, as measured by the Eysenck Personality Questionnaire or as part of the FFM.23, 24, 25, 26, 27, 28, 29, 30, 31 Few studies have also included other traits such as Extraversion.27, 31 The study by Terracciano et al.31 is to date the only genome-wide association (GWA) study conducted for all five FFM personality dimensions. This study was performed in an isolated sample of 3972 Sardinians, analyzing 362K single-nucleotide polymorphisms (SNPs). Although none of the observed signals reached genome-wide significance (lowest P-value 9.4 × 10−7), several of the top signals were found in genes that are thought to affect behavioral traits and mental disorders through differential brain functioning (for example, SNAP25 for Neuroticism, CDH13 for Extraversion and CLOCK for Agreeableness).

The aim of the current meta-analytic study was to identify novel genetic variants associated with the FFM personality dimensions by combining GWA study results from 10 studies, including 17 375 individuals of European ancestry from Europe, the United States and Australia. In silico replication of the genome-wide significant SNPs was sought in five additional samples consisting of 3294 individuals.

Materials and methods

Discovery samples

The samples included in the discovery stage of this study are described below. Approval by local institutional review boards was obtained in all studies and written informed consent was given.


The SardiNIA study includes 6148 related individuals from four towns in the Ogliastra province of Sardinia, Italy.21 These individuals represent 62% of the population in these towns. Valid Revised NEO Personality Inventory (NEO-PI-R)1 personality data were available for 5669 individuals, of which 3972 were genotyped (56.7% women). The mean age of all participants was 42.8 years (s.d.=17). The mean age of the men was 43.0 years (s.d.=18) and of the women 42.4 years (s.d.=17). The sample has been described in more detail by Terracciano and co-workers.31

NTR/NESDA—the Netherlands

The Netherlands Twin Registry-Netherlands Study of Depression and Anxiety (NTR/NESDA) study consists of unrelated individuals from Dutch twin families registered at the NTR32 and participants from the NESDA.33 Individuals were selected to be genotyped as part of the Genetic Association Information Network initiative,34 of which 1836 served as controls (mainly from NTR) and 1862 as cases (mainly from NESDA) in a GWA study for MDD.35, 36 Controls were selected for the absence of an MDD diagnosis and/or a low genetic liability for MDD. In this study, 3540 individuals (65.6% women) were included with valid NEO personality and GWA data. The mean age of participants was 44.1 years (s.d.=13). Men were slightly older (M=46.6 years; s.d.=13) than women (M=42.8 years; s.d.=13). Personality data from NTR participants were collected in 200437 and from NESDA participants between 2004 and 2007.33

ERF—the Netherlands

The Erasmus Rucphen Family (ERF) study is a family-based study including over 3000 individuals from an isolated population in the South-West region of the Netherlands.38 There were 2400 individuals for whom both NEO personality and GWA data were available. The mean age of all participants was 49.3 years (s.d.=14.9) and women constituted 55.8% of the total sample (M=47.4, s.d.=15, versus in men M=48.2, s.d.=14).

SAGE—United States of America

The Study of Addiction: Genetics and Environment (SAGE) is part of the Gene Environment Association Studies initiative funded by the National Human Genome Research Institute. The sample consists of Diagnostic and Statistical Manual of Mental Disorders, 4th Edition alcohol-dependent cases and -non-dependent controls.39 The original SAGE sample included 4121 unrelated individuals. Of these, 2223 subjects had data available from the NEO Five-Factor Inventory (NEO-FFI). We removed 476 subjects owing to non-European ancestry, eight individuals were removed owing to missing genotypes and 139 were removed because their genotyping consent did not include the use of their personality data. This resulted in a final sample size of 1600. Of these 1600, 60.1% were women. The mean age of all participants was 39.6 (s.d.=9), of the men 40.4 (s.d.=10) and of the women 39.0 (s.d.=9).


The Helsinki Birth Cohort Study (HBCS) is composed of 8760 individuals born between the years 1934 and 1944 in one of the two main maternity hospitals in Helsinki, Finland. Between 2001 and 2003, a randomly selected sample of 928 men and 1075 women participated in a clinical follow-up study with a focus on cardiovascular, metabolic and reproductive health, cognitive function and depressive symptoms. In 2004, various psychological phenotypes were assessed, including the NEO personality dimensions. There were 1443 subjects with both valid phenotype and genotype data (59.8% women). The mean age of the subjects was 63.4 years (s.d.=3). The mean age of the men was 63.3 years (s.d.=3) and of the women was 63.5 years (s.d.=3). Detailed information on the selection of the HBCS participants and on the study design can be found elsewhere.40, 41, 42

NAG/IRPG study—Australia

Phenotype data from this study was collected as part of the Nicotine Addiction Genetics study (NAG), for which families were targeted based on heavy smoking index cases identified in previous interviews and questionnaires.43, 44 Personality items, from the NEO-FFI, were included in a questionnaire mailed to all participants. Genotype data came from the Interactive Research Project Grants (IRPG). Valid personality and genotype data were available for 1349 individuals aged 21–85 years (M=45.4, s.d.=13.1). Of these, 56% were women (M=45.4, s.d.=13) and 44% were men (M=45.3, s.d.=13).

QIMR study—Australia

Data from Australian adolescents were collected in twin family studies conducted at the Queensland Institute of Medical Research (QIMR). Participants were mainly recruited through primary and secondary schools in Queensland for studies of melanocytic naevi (moles).45 NEO personality data (NEO-PI-R or NEO-FFI) were collected as part of the cognition study (in-person testing, 1996-ongoing),46 as well as a health and well-being study (a mail/phone study 2002–2003),46 and a study of borderline personality disorder (online/paper survey 2004–2006).47 For this study, personality and genotypic data were available for 1090 individuals (616 women), of whom 254 were monozygotic twin pairs (for whom average phenotypic data were analyzed). Participants ranged in age from 16 to 27 years (M=19.4, s.d.=3). The mean ages in men and women were very similar (M=19.2, s.d.=3 in men versus M=19.4, s.d.=3 in women).

LBC1936—United Kingdom

The Lothian Birth Cohort (LBC) study consists of a cohort of 1091 individuals born in 1936 (LBC1936). Most subjects lived independently in the Lothian region (Edinburgh city and surrounding area) of Scotland. The majority of subjects took part in the Scottish Mental Surveys of 1947, and were assessed again on cognition and medical traits at roughly 70 years of age.48 A fuller description about participant recruitment and testing can be found elsewhere.48, 49 There were 888 subjects (447 women) who successfully filled in the NEO-FFI and survived the DNA and genotyping quality control procedures. The mean age of these 888 subjects was 69.6 (s.d.=1). The mean ages of the men and women were the same.

Baltimore Longitudinal Study of Aging—United States of America

The Baltimore Longitudinal Study of Aging is an ongoing multidisciplinary study of community-dwelling volunteers. Personality traits were assessed from 1989 to 2008, and multiple assessments were available for most participants. Although personality traits are generally stable over time,50, 51 to provide more robust estimates, we used the average across all available assessments. For this study, we examined data from 848 subjects of European descent that were successfully genotyped and completed the NEO-PI-R questionnaire at least once. In this sample, mean age was 68.5 years (s.d.=17) with 46% of women. The mean age of the men was 60.8 years (s.d.=16) and of the women 55.9 years (s.d.=17).


The Estonian cohort comes from the population-based biobank of the Estonian Genome Project of University of Tartu (EGPUT). The project is conducted according to the Estonian Gene Research Act and all participants have signed the broad informed consent ( In total, 38 000 individuals aged 18 years or older participated in this cohort (33% men, 67% women). The population distributions of the cohort reflect those of the Estonian population (83% Estonians, 14% Russians and 3% other). Subjects were randomly recruited by the general practitioners (GP) and physicians in the hospitals.53 A Computer-Assisted Personal interview was conducted during 1–2 h at doctors’ offices. Data on demographics, genealogy, educational and occupational history, lifestyle and anthropometric and physiological data were assessed. For this study, GWA was performed on 600 randomly selected subjects with both Illumina HumanCNV370 genotype (array according to Illumina protocol ( in Estonian Biocenter Genotyping Core Facility) and the NEO-PI-3 questionnaire data available.54 In this sample, the age range was 18–87 years (mean 45.7 years (s.d. 16)). The sample consisted of 250 men (mean age 45.5 years (s.d.=16)) and 350 women (mean age 45.7 years (s.d.=16)).

Replication samples

The samples for in silico replication are described below. In total, the sample size was 3294 subjects.

NTR+—the Netherlands

Within the Netherlands Twin Register (NTR), several genotyping projects (additional to the first genome-wide genotyping study that was part of the Genetic Association Information Network-MDD study) have been undertaken whose data were combined in this study to form the replication set. All individuals came from the NTR-Biobank study.55 In total, 1920 individuals with valid NEO-FFI and GWA data were available for replication. The mean age of participants was 46.9 years (s.d.=15) and 67% were women. This sample included 127 MZ twin pairs (254 twins) with phenotype data in both twins. Those twin pairs were treated as one case in the analysis by averaging their phenotypic scores, resulting in a sample with 1793 subjects for analysis. For 1475 subjects, GWA data were available on one SNP chip; for 318 subjects, GWA data were assessed on two chips. For the majority of the 1475 subjects genotyped on one chip (N=1286; 87%), genotyping was part of the NTR2 genotyping study using the Illumina Human660W-Quad chip. These subjects were unrelated and unselected for any phenotype. The remaining subjects were genotyped as part of the GenomEUtwin study (N=137 subjects; Illumina 370K chip), an Attention Deficit Hyperactivity Disorder study (N=34 subjects; Affymetrix 6.0) and the MDD2000 study (N=18 MDD cases; Illumina 907K chip). Quality control of genotype data and subsequent imputation using IMPUTE software was conducted on separate sets, and on the full set of all genotyped individuals within the NTR. For the purposes of this replication study, after imputation we selected the SNPs from the discovery set that showed genome-wide significance, checked their quality and subsequently analyzed the SNPs.


In this German cohort, 2420 healthy control participants were randomly selected from the general population of Munich, Germany, and contacted by mail. We included 476 individuals (56% women) with GWA data (Illumina HumanHap300 chip) in this study. Several screenings were conducted before the volunteers were enrolled in the study. First, subjects who responded were initially screened by phone for the absence of neuropsychiatric disorders. Second, detailed medical and psychiatric histories were assessed for the participants and their first-degree relatives by using a semistructured interview.56 Third, if no exclusion criteria were fulfilled, they were invited to a comprehensive interview including the SCID to validate the absence of any lifetime psychotic disorder.57 In addition, the Family History Assessment Module was conducted to exclude psychotic disorders among their first-degree relatives. A neurological examination was also conducted to exclude subjects with current central nervous system impairment. If participants were older than 60 years, the Mini-Mental Status Test was performed to exclude subjects with possible cognitive impairment.58 Only participants with German descent (all four grandparents German) were included. Furthermore, a large battery of personality questionnaires, for example, on aggression, impulsivity or neuroticism (NEO-PI-R) was obtained as well as data on life events and traumatic events. The mean age of the sample was 46 years (s.d.=15).


In the Estonian cohort, additional data of 380 individuals with valid NEO-FFI and GWA data have become available for replication. For a more detailed description of this cohort, see the description above for EGPUT. The mean age of participants was 38.9 years (s.d.=15). Almost half of the sample (49.5%) was female.


The Cilento study is a population-based study that includes 2137 individuals from three isolated populations of South Italy. Of these individuals, 859 were genotyped on the 370K SNP map from Illumina. Imputation of 2.5M HapMap SNPs was obtained using MACH software. Genome-wide significant SNPs were selected, checked and analyzed. Data available from the NEO-PI-R questionnaire were available for 343 genotyped subjects representing the final sample. Of this sample, 65.6% were women. The mean age of all participants was 58.9 years (s.d.=19), of the men 59.5 years (s.d.=18.8) and of the women 58.7 years (s.d.=19).

ERF2—The Netherlands

The ERF2 sample consisted of 302 additionally genotyped individuals with NEO personality data within the family-based ERF study (see the description above for more information on this study). The mean age of these individuals was 50.1 years (s.d.=14). Women constituted 50.3% of the sample.

Personality assessment

Personality scores for the five factors Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness were based on the 60 items of the NEO-FFI (12 items per factor).1 Items were answered on a 5-point Likert-type scale ranging from strongly disagree (0) to strongly agree (4). In the SardiNIA, BSLA, Germany and Cilento studies, these items were taken from the 240-item NEO-PI-R.1 In the QIMR study, the 60 items were taken from the 240-item NEO-PI-R for part of the sample; the remaining subjects filled in the 60-item NEO-FFI.1 In the NTR, NESDA, ERF, SAGE, HBCS, NAG/IRPG and LBC1936 studies, personality was assessed using the 60-item NEO-FFI. In the Estonian study samples, the 60 NEO-FFI items59 were taken from the NEO-PI-3.54, 60

In each study, summed scores were computed for all five personality dimensions (after reversing negatively keyed items). If more than three items were missing per dimension, the summed score for that dimension was not computed. If three or less items were missing, missing data were imputed by taking the individual's average score for the valid items of that dimension. The mean scores of the five personality dimensions in each study are provided in Table 1.

Table 1 Mean scores of the five personality dimensions in the 10 studies participating in the GWASNEO Consortium, stratified across sex

Genotyping and imputation

DNA was extracted from blood samples in all participating studies. A detailed overview of SNP genotyping, including the platforms used and subsequent quality control, is given in Table 2. The studies used Illumina platforms, except for SardiNIA and NTR/NESDA, which used Affymetrix and Perlegen platforms, respectively. Genotype data were checked in each study independently, using slightly different inclusion criteria. Among the basic checks that were performed were checks for European ancestry, Mendelian errors, gender inconsistencies and high genome-wide homozygosity. Checks for relatedness were carried out in those samples that aimed to use unrelated individuals. Genotype data were further checked based on Hardy–Weinberg equilibrium, minor allele frequencies (MAF), SNP call rate (% of subjects with missing genotypes per SNP) and sample call rate (% of missing SNPs per subject).

Table 2 Genotyping information in the 10 studies participating in the GWASNEO Consortium

To compare results at the SNP level, we imputed 2.5M common SNPs included in HapMap, using the HapMap phase II CEU data as the reference sample. Most studies used NCBI build 36 (UCSC hg18), although in the NTR/NESDA study build 35 (UCSC hg17) was used. Imputation was carried out using IMPUTE for the NTR/NESDA, SAGE and EGPUT samples (consisting of unrelated individuals).61 For all other samples, genotype data were imputed using MACH software. For those studies that contained related individuals, a maximum likelihood approach was used that takes advantage of the relatedness among individuals.62 Throughout this paper, the location of SNPs reported is taken from the build 36 (release 22) HapMap data.

Statistical analyses

GWA analysis in each discovery sample

GWA analyses were conducted in each study independently using linear regression (under an additive model) and including sex and age as covariates. For those studies that used IMPUTE software to impute missing genotype data, association analyses were conducted in SNPTEST, taking the uncertainty of the imputed genotypes into account.61 For the studies that used MACH to impute their data, either MACH QTL or Merlin was used for association analyses. For the three studies with related individuals (SardiNIA, ERF and QIMR), association analyses were performed in Merlin using a variance components approach, which takes into account the relatedness among individuals in these samples.62

Meta-analysis of GWA results across discovery samples

A meta-analysis of the results was conducted using the weighted inverse variance method in METAL (,63 which computes a pooled effect estimate (ln(beta)), its standard error and its P-value by weighing the effect estimates of the individual samples by the inverse of its variance and by taking into account the direction of effect. Poorly imputed SNPs (r2 or proper_info <0.30) and SNPs with low MAF (<0.01) were excluded, resulting in a final data set of 2.4M SNPs. We corrected for any population stratification effects by applying genomic control in each sample before meta-analysis. The genomic control inflation factors (λ) for the five personality dimensions for all participating studies ranged between 0.99 and 1.12 (Supplementary Table 1). After applying a genomic control correction to the results from the individual studies, the λ's for the meta-analyzed results were 1.02, 1.01, 1.03, 1.00 and 1.02, respectively, for Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness. The corresponding Quantile–Quantile plots are provided in Supplementary Figure 1. To consider an SNP result genome-wide significant, we used the threshold of P<5 × 10−8 per trait as proposed for populations of European descent.64

Gene-based tests

In addition to the meta-analytic association testing per SNP, we also evaluated the significance of all genes across the genome. We followed the procedure proposed by Liu et al.65 and incorporated in the program VEGAS, which is suitable for meta-analysis results because it does not require raw genotype data, but instead uses the P-values of SNPs as input. Gene-based P-values were obtained by using a maximum of 107 simulations to correctly account for the linkage disequilibrium structure among SNPs within a gene. We included SNPs located up to 20 kb down- or upstream of a gene. A gene was considered genome-wide significant if a P<2.5 × 10−6 (0.05/20 000) was obtained.

Replication analyses

Replication of the SNPs that turned out genome-wide significant was performed in five independent samples. In each sample, an additive test was conducted, with sex and age as covariates. SNPs in each sample were checked for MAF, Hardy–Weinberg equilibrium and if imputed, for imputation quality. The evidence for replication was summarized across samples by conducting a weighted inverse variance meta-analysis. A P<0.05 was taken as significant evidence of replication.


Two SNPs for Openness to Experience on chromosome 5q14.3 and one SNP for Conscientiousness on chromosome 18q21.1 passed the genome-wide significance level of P<5 × 10−8 in the discovery stage (Table 3). The genome-wide meta-analyzed association results for the five personality dimensions are given in Supplementary Figure 2. Top SNPs for Neuroticism, Extraversion and Agreeableness (Supplementary Tables 2–6) did not reach genome-wide significance (lowest P value>10−8).

Table 3 GWAS with openness to experience and conscientiousness in the discovery samples of the GWASNEO Consortium for the Five-Factor Model of personality, and associations in the replication samples

The two genome-wide significant SNPs for Openness to Experience (rs1477268, rs2032794, r2 among SNPs ranges between 0.92 and 1 across studies) are located on chromosome 5q14.3 in an intergenic region 135 kb downstream from the RASA1 gene (lowest P=2.8 × 10−8, with an explained variance of 0.22%) (Figure 1a). The gene-based P-value for RASA1 was 0.02. RASA1 codes for a GTPase-activating protein involved in intracellular signaling and cellular proliferation and differentiation. The gene is highly expressed in the bone marrow and bone, and modestly in the brain.66 Further, Figure 2a shows that the effect for rs1477268 is in the same direction for nine of the 10 studies. Heterogeneity in results across studies was not significant (χ2=9.15, df=9, P=0.42). The SNP was genotyped in seven of the studies and imputed with high quality in the SardiNIA, NTR/NESDA and ERF studies (r2 or proper_info>0.97). The MAFs were very similar across studies and ranged between 0.15 and 0.24. Furthermore, genotype and allele proportions of rs1477268 are in Hardy–Weinberg equilibrium in all studies (P>0.01). The association of these two SNPs with Openness to Experience could not, however, be replicated (combined P across the replication samples 0.53 and 0.55, respectively, for rs1477268 and rs2032794, combined P across discovery and replication samples 1.84 × 10−6 and 1.70 × 10−6).

Figure 1

Regional association plots of the two single-nucleotide polymorphisms (SNPs) genome-wide significant in the discovery set for Openness to Experience and Conscientiousness. (a) Chromosome 5 locus for Openness to Experience. (b) Chromosome 18 locus for Conscientiousness. Physical positions of SNPs and genes are based on build 36 (hg18). The top SNP is shown in blue. SNPs that have an r2 between 0.8 and 1 with the top SNP are shown in violet, SNPs with an r2 between 0.5 and 0.8 in red, SNPs with an r2 between 0.2 and 0.5 in orange and an r2<0.2 in yellow.

PowerPoint slide

Figure 2

Association of the two single-nucleotide polymorphisms (SNPs) genome-wide significant in the discovery set with Openness to Experience and Conscientiousness. (a) Association of rs1477268 with Openness to Experience. (b) Association of rs2576037 with Conscientiousness. Effects are reported for the minor allele (see Supplementary Tables 4 and 6).

PowerPoint slide

The genome-wide significant SNP for Conscientiousness (rs2576037) is located in an intron of the KATNAL2 gene on chromosome 18q21.1 (P=4.9 × 10−8, explained variance 0.21%). The second-most significant SNP for Conscientiousness (rs7233515) is a non-synonymous SNP in the same gene (lowest P=7.9 × 10−8, the r2 with rs2576037 ranges between 0.92 and 1 across studies; Figure 1b). Variation in this SNP leads to a N88S amino-acid change, suggesting a biologically relevant variation. In eight studies, the direction of the effect for rs2576037 was the same (Figure 2b). In spite of this, there was nominal significant heterogeneity in the regression coefficients across studies (χ2=17.98, df=9, P=0.04). To test which study caused the observed heterogeneity, we reran the meta-analysis multiple times, by each time excluding one of the individual studies. Two studies seemed to account for the heterogeneity. Excluding the Finnish HBCS study (with the largest effect), heterogeneity was no longer significant (χ2=11.30, df=8, P=0.19) and the pooled P-value became 4.3 × 10−6. When excluding the NAG/IRPG study (a small opposite effect), heterogeneity was also no longer observed (χ2=11.20, df=8, P=0.19) and the pooled P-value became 2.2 × 10−9. The SNP was genotyped in seven of the studies and imputed with high quality in the SardiNIA, NTR/NESDA and ERF studies (r2 or proper_info>0.98). The MAFs were very similar across studies and ranged between 0.37 and 0.46 and distributions were in Hardy–Weinberg equilibrium in all studies (P>0.01).

KATNAL2 encodes a protein similar to the A subunit of the p60 katanin protein and is widely expressed in the central nervous system.66 Katanin p60 acts to sever microtubules in the axons of neurons and is thought to play a role in neuronal migration, axonal growth and dendritic pruning.67, 68, 69 Thus, the KATNAL2 gene may play a role in neurodevelopment. Several other SNPs located in the nearby PIAS2, HDHD2 and IER3IP1 genes that are in relatively high linkage disequilibrium (r2>0.5) with the top SNP showed suggestive evidence for association (P<1 × 10−5) (Figure 1b and Supplementary Table 6). The PIAS2 gene is involved in the regulation of transcription factors involved in the mitogen-activated protein kinase signaling pathway. Less is known about the biological function of the HDHD2 and IER3IP1 genes, but all three genes are moderately expressed in the brain.66

KATNAL2 was significant in gene-based tests as well (Table 3 and Supplementary Table 7). The cluster of small TCEB3 genes, located within the KATNAL2 gene (Figure 1b), was also significant, but the other genes in the region were not genome-wide significant. This suggests that the causal variant may be located in or very near the KATNAL2 gene rather than in any of the surrounding genes. The association of rs2576037 was again not significant in the replication stage (combined P across replication samples=0.36), although the direction of effect was consistent with the effect found in the discovery stage (Supplementary Table 8). The combined P-value across all discovery and replication samples for rs2576037 was 1.02 × 10−7.

We also investigated the significance of SNPs that have previously been reported in the first GWA study for the FFM personality traits (Supplementary Table 9) and in the two GWA studies for Neuroticism (Supplementary Table 10).23, 24, 30 None of the SNPs reported in these studies were significant (P>0.001).


This study suggests evidence for two new loci associated with two dimensions of personality: an intergenic region 135 kb downstream from RASA1 on 5q14.3 for Openness to Experience and the KATNAL2 gene region on 18q21.1 for Conscientiousness. However, these loci were not unequivocally replicated. The KATNAL2 gene was also significant in the gene-based test. However, in the replication samples the effect did not reach a level of significance, although there was a consistency of direction of effects. Thus, KATNAL2 might present a novel gene for personality. It should be noted, however, that even if the signal represents a true finding, the effect size is small. No genome-wide significant results were found for Neuroticism, Extraversion and Agreeableness.

Power analyses showed that the genome-wide significant variants could not have been detected in any of the individual studies at the genome-wide significant level (power to detect these effects at α=5 × 10−8 in sample sizes smaller than 4000 is less than 1%), but the power to detect these effects in the current meta-analytic study with a sample size of 17 375 is 77% for the top SNP for Openness to Experience and 72% for the top SNP of Conscientiousness. Additional power analyses showed that with a power of 80%, the meta-analysis could detect much smaller effect sizes than any of the individual studies (0.23% explained variance versus 1–6.5% explained variance for sample size of individual studies between 600 and 3972; explained variances correspond to standardized betas of 0.05 versus 0.1–0.25).

The findings of this study show that large-scale collaborative studies with combined sample sizes in the order of thousands or ten thousands still have difficulties in identifying common genetic variants that influence complex phenotypes such as personality traits. It could be that the effects of many SNPs are even smaller than the 0.2% that we were able to detect in this study at a genome-wide significance level. Larger GWA studies may reveal these variants, as has been already successfully shown for human height in a large meta-analytic GWA study of over 180 000 individuals, in which at least 180 loci were identified together explaining about 10% of the variation in height.70 In addition, a recent paper using a novel technique to estimate the genetic variance explained by all SNPs, without focusing on genome-wide significance of individual SNPs, showed that common SNP variation explained about half of the heritability of human height.71 These papers are consistent with the notion that common SNP variation is important in explaining complex highly polygenic traits. It also suggests that the meta-analytic GWA study that we present here was only able to detect the top few SNPs with the largest effect sizes related to personality.

Many other explanations to explain the heritability of complex traits have been put forward.72 One of these is that other variants that are currently not captured with the genome-wide SNP platforms (including copy number and rare variants) play a role in explaining variation in personality. Next-generation sequencing may reveal more genetic variants that account for the heritability of complex traits including personality. Nevertheless, identification of genetic variants, even if effect sizes are small, remains an important goal, because these variants can be critical entry points to increased understanding of the biological processes underlying personality as well as psychiatric disorders and other personality-related health, social and behavioral outcomes.


  1. 1

    Costa PT, McCrae RR . Professional Manual: Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor-Inventory (NEO-FFI). Psychological Assessment Resources: Odessa, FL, 1992.

    Google Scholar 

  2. 2

    Samuel DB, Widiger TA . A meta-analytic review of the relationships between the five-factor model and DSM-IV-TR personality disorders: a facet level analysis. Clin Psychol Rev 2008; 28: 1326–1342.

    Article  Google Scholar 

  3. 3

    Hettema JM, Neale MC, Myers JM, Prescott CA, Kendler KS . A population-based twin study of the relationship between neuroticism and internalizing disorders. Am J Psychiatry 2006; 163: 857–864.

    Article  Google Scholar 

  4. 4

    Terracciano A, Lockenhoff CE, Zonderman AB, Ferrucci L, Costa PT . Personality predictors of longevity: activity, emotional stability, and conscientiousness. Psychosom Med 2008; 70: 621–627.

    Article  Google Scholar 

  5. 5

    Dick DM, Aliev F, Wang JC, Grucza RA, Schuckit M, Kuperman S et al. Using dimensional models of externalizing psychopathology to aid in gene identification. Arch Gen Psychiatry 2008; 65: 310–318.

    Article  Google Scholar 

  6. 6

    Kendler KS, Gatz M, Gardner CO, Pedersen NL . Personality and major depression—a Swedish longitudinal, population-based twin study. Arch Gen Psychiatry 2006; 63: 1113–1120.

    Article  Google Scholar 

  7. 7

    Lahey BB . Public health significance of neuroticism. Am Psychol 2009; 64: 241–256.

    Article  Google Scholar 

  8. 8

    Bienvenu OJ, Samuels JF, Costa PT, Reti IM, Eaton WW, Nestadt G . Anxiety and depressive disorders and the five-factor model of personality: a higher- and lower-order personality trait investigation in a community sample. Depress Anxiety 2004; 20: 92–97.

    Article  Google Scholar 

  9. 9

    Fanous A, Gardner CO, Prescott CA, Cancro R, Kendler KS . Neuroticism, major depression and gender: a Population-Based Twin Study. Psychol Med 2002; 32: 719–728.

    CAS  Article  Google Scholar 

  10. 10

    Kendler KS, Myers J . The genetic and environmental relationship between major depression and the five-factor model of personality. Psychol Med 2009; 40: 1–6.

    Google Scholar 

  11. 11

    Weiss A, Sutin AR, Duberstein PR, Friedman B, Bagby RM, Costa PT . The personality domains and styles of the five-factor model are related to incident depression in Medicare recipients aged 65 to 100. Am J Geriatr Psychiatry 2009; 17: 591–601.

    Article  Google Scholar 

  12. 12

    Terracciano A, Costa PT . Smoking and the Five-Factor Model of personality. Addiction 2004; 99: 472–481.

    Article  Google Scholar 

  13. 13

    Terracciano A, Lockenhoff CE, Crum RM, Bienvenu OJ, Costa PT . Five-factor model personality profiles of drug users. BMC Psychiatry 2008; 8: 22.

    Article  Google Scholar 

  14. 14

    Saulsman LM, Page AC . The five-factor model and personality disorder empirical literature: a meta-analytic review. Clin Psychol Rev 2004; 23: 1055–1085.

    Article  Google Scholar 

  15. 15

    Thoresen CJ, Bradley JC, Bliese PD, Thoresen JD . The big five personality traits and individual job performance growth trajectories in maintenance and transitional job stages. J Appl Psychol 2004; 89: 835–853.

    Article  Google Scholar 

  16. 16

    De Moor MHM, Beem AL, Stubbe JH, Boomsma DI, de Geus EJC . Regular exercise, anxiety, depression and personality: a population-based study. Prev Med 2006; 42: 273–279.

    CAS  Article  Google Scholar 

  17. 17

    Rhodes RE, Smith NEI . Personality correlates of physical activity: a review and meta-analysis. Br J Sports Med 2006; 40: 958–965.

    CAS  Article  Google Scholar 

  18. 18

    Bouchard TJ, Loehlin JC . Genes, evolution, and personality. Behav Genet 2001; 31: 243–273.

    Article  Google Scholar 

  19. 19

    Vernon PA, Martin RA, Schermer JA, Mackie A . A behavioral genetic investigation of humor styles and their correlations with the Big-5 personality dimensions. Pers Ind Diff 2008; 44: 1116–1125.

    Article  Google Scholar 

  20. 20

    Distel MA, Trull TJ, Willemsen G, Vink JM, Derom CA, Lynskey MT et al. The Five Factor Model of personality and borderline personality disorder: a genetic analysis of comorbidity. Biol Psychiatry 2009; 66: 1131–1138.

    Article  Google Scholar 

  21. 21

    Pilia G, Chen WM, Scuteri A, Orru M, Albai G, Dei M et al. Heritability of cardiovascular and personality traits in 6,148 sardinians. Plos Genet 2006; 2: 1207–1223.

    CAS  Article  Google Scholar 

  22. 22

    Jang KL, Livesley WJ, Angleitner A, Riemann R, Vernon PA . Genetic and environmental influences on the covariance of facets defining the domains of the five-factor model of personality. Pers Ind Diff 2002; 33: 83–101.

    Article  Google Scholar 

  23. 23

    Shifman S, Bhomra A, Smiley S, Wray NR, James MR, Martin NG et al. A whole genome association study of neuroticism using DNA pooling. Mol Psychiatry 2008; 13: 302–312.

    CAS  Article  Google Scholar 

  24. 24

    van den Oord EJ, Kuo PH, Hartmann AM, Webb BT, Moller HJ, Hettema JM et al. Genomewide association analysis followed by a replication study implicates a novel candidate gene for neuroticism. Arch Gen Psychiatry 2008; 65: 1062–1071.

    Article  Google Scholar 

  25. 25

    Kuo PH, Neale MC, Riley BP, Patterson DG, Walsh D, Prescott CA et al. A genome-wide linkage analysis for the personality trait neuroticism in the Irish affected sib-pair study of alcohol dependence. Am J Med Genet B 2007; 144B: 463–468.

    CAS  Article  Google Scholar 

  26. 26

    Neale BM, Sullivan PF, Kendler KS . A genome scan of neuroticism in nicotine dependent smokers. Am J Med Genet B 2005; 132B: 65–69.

    Article  Google Scholar 

  27. 27

    Gillespie NA, Zhu G, Evans DM, Medland SE, Wright MJ, Martin NG . A genome-wide scan for Eysenckian personality dimensions in adolescent twin sibships: psychoticism, extraversion, neuroticism, and lie. J Pers 2008; 76: 1415–1446.

    PubMed  Google Scholar 

  28. 28

    Fullerton J, Cubin M, Tiwari H, Wang C, Bomhra A, Davidson S et al. Linkage analysis of extremely discordant and concordant sibling pairs identifies quantitative-trait loci that influence variation in the human personality trait neuroticism. Am J Hum Genet 2003; 72: 879–890.

    CAS  Article  Google Scholar 

  29. 29

    Nash MW, Huezo-Diaz P, Sterne A, Purcell S, Hoda F, Cherny SS et al. Genome-wide linkage analysis of a composite index of neuroticism and mood-related scales in extreme selected sibships. Hum Mol Genet 2004; 13: 2173–2182.

    CAS  Article  Google Scholar 

  30. 30

    Hettema JM, Van den Oord EJCG, An SS, Kendler KS, Chen XN . Follow-up association study of novel neuroticism gene MAMDC1. Psychiatric Genet 2009; 19: 213–214.

    Article  Google Scholar 

  31. 31

    Terracciano A, Sanna S, Uda M, Deiana B, Usala G, Busonero F et al. Genome-wide association scan for five major dimensions of personality. Mol Psychiatry 2010; 15: 647–656.

    CAS  Article  Google Scholar 

  32. 32

    Boomsma DI, de Geus EJC, Vink JM, Stubbe JH, Distel MA, Hottenga JJ et al. Netherlands Twin Register: from twins to twin families. Twin Res Hum Genet 2006; 9: 849–857.

    Article  Google Scholar 

  33. 33

    Penninx BWJH, Beekman ATF, Smit JH, Zitman FG, Nolen WA, Spinhoven P et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatric Res 2008; 17: 121–140.

    Article  Google Scholar 

  34. 34

    Manolio TA, Rodriguez LL, Brooks L, Abecasis G, Ballinger D, Daly M et al. New models of collaboration in genome-wide association studies: the Genetic Association Information Network. Nat Genet 2007; 39: 1045–1051.

    CAS  Article  Google Scholar 

  35. 35

    Boomsma DI, Willemsen G, Sullivan PF, Heutink P, Meijer P, Sondervan D et al. Genome-wide association of major depression: description of samples for the GAIN Major Depressive Disorder Study: NTR and NESDA biobank projects. Eur J Hum Genet 2008; 16: 335–342.

    CAS  Article  Google Scholar 

  36. 36

    Sullivan PF, de Geus EJC, Willemsen G, James MR, Smit JH, Zandbelt T et al. Genomewide association for major depressive disorder: a possible role for the presynaptic protein piccolo. Mol Psychiatry 2009; 14: 359–375.

    CAS  Article  Google Scholar 

  37. 37

    Distel MA, Ligthart L, Willemsen G, Nyholt DR, Trull TJ, Boomsma DI . Personality, health and lifestyle in a questionnaire family study: a comparison between highly cooperative and less cooperative families. Twin Res Hum Genet 2007; 10: 348–353.

    Article  Google Scholar 

  38. 38

    Pardo LM, MacKay I, Oostra B, van Duijn CM, Aulchenko YS . The effect of genetic drift in a young genetically isolated population. Ann Hum Genet 2005; 69: 288–295.

    CAS  Article  Google Scholar 

  39. 39

    Bierut LJ, Agrawal A, Bucholz KK, Doheny KF, Laurie C, Pugh E et al. A genome-wide association study of alcohol dependence. Proc Natl Acad Sci USA 2010; 107: 5082–5087.

    CAS  Article  Google Scholar 

  40. 40

    Barker DJP, Osmond C, Forsen TJ, Kajantie E, Eriksson JG . Trajectories of growth among children who have coronary events as adults. N Engl J Med 2005; 353: 1802–1809.

    CAS  Article  Google Scholar 

  41. 41

    Eriksson JG, Osmond C, Kajantie E, Forsen TJ, Barker DJP . Patterns of growth among children who later develop type 2 diabetes or its risk factors. Diabetologia 2006; 49: 2853–2858.

    CAS  Article  Google Scholar 

  42. 42

    Raikkonen K, Pesonen AK, Heinonen K, Lahti J, Kajantie E, Forsen T et al. Infant growth and hostility in adult life. Psychosom Med 2008; 70: 306–313.

    Article  Google Scholar 

  43. 43

    Pergadia ML, Agrawal A, Loukola A, Montgomery GW, Broms U, Saccone SF . Genetic linkage findings for DSM-IV nicotine withdrawal in two populations. Am J Med Genet B 2009; 150B: 950–959.

    CAS  Article  Google Scholar 

  44. 44

    Saccone SF, Pergadia ML, Loukola A, Broms U, Montgomery GW, Wang JC et al. Genetic linkage to chromosome 22q12 for a heavy-smoking quantitative trait in two independent samples. Am J Hum Genet 2007; 80: 856–866.

    CAS  Article  Google Scholar 

  45. 45

    Aitken JF, Green A, Eldridge A, Green L, Pfitzner J, Battistutta D et al. Comparability of nevus counts between and within examiners, and comparison with computer image-analysis. Br J Cancer 1994; 69: 487–491.

    CAS  Article  Google Scholar 

  46. 46

    Wright MJ, Martin NG . Brisbane adolescent twin study: outline of study methods and research projects. Aus J Psychol 2004; 56: 65–78.

    Article  Google Scholar 

  47. 47

    Distel MA, Trull TJ, Derom CA, Thiery EW, Grimmer MA, Martin NG et al. Heritability of borderline personality disorder features is similar across three countries. Psychol Med 2008; 38: 1219–1229.

    CAS  Article  Google Scholar 

  48. 48

    Deary IJ, Gow AJ, Taylor MD, Corley J, Brett C, Wilson V et al. The Lothian Birth Cohort 1936: a study to examine influences on cognitive ageing from age 11 to age 70 and beyond. BMC Geriatr 2007; 7: 28.

    Article  Google Scholar 

  49. 49

    Deary IJ, Whiteman MC, Starr JM, Whalley LJ, Fox HC . The impact of childhood intelligence on later life: following up the Scottish Mental Surveys of 1932 and 1947. J Pers Soc Psychol 2004; 86: 130–147.

    Article  Google Scholar 

  50. 50

    Terracciano A, McCrae RR, Brant LJ, Costa PT . Hierarchical linear modeling analyses of the NEO-PI-R scales in the Baltimore longitudinal study of aging. Psychol Aging 2005; 20: 493–506.

    Article  Google Scholar 

  51. 51

    Terracciano A, Costa PT, McCrae RR . Personality plasticity after age 30. Pers Soc Psychol Bull 2006; 32: 999–1009.

    Article  Google Scholar 

  52. 52

    Metspalu A . The Estonian Genome Project. Drug Develop Res 2004; 62: 97–101.

    CAS  Article  Google Scholar 

  53. 53

    Nelis M, Esko T, Magi R, Zimprich F, Zimprich A, Toncheva D et al. Genetic structure of Europeans: a view from the North-East. PLoS ONE 2009; 4: e5472.

    Article  Google Scholar 

  54. 54

    McCrae RR, Costa PT, Martin TA . The NEO-PI-3: a more readable revised NEO Personality Inventory. J Pers Assess 2005; 84: 261–270.

    Article  Google Scholar 

  55. 55

    Willemsen G, de Geus EJC, Bartels M, van Beijsterveldt CEM, Brooks AI, Estourgie-van Burk GF et al. The Netherlands Twin Register Biobank: a resource for genetic epidemiological studies. Twin Res Hum Genet 2010; 13: 231–245.

    Article  Google Scholar 

  56. 56

    Rice JP, Reich T, Bucholz KK, Neuman RJ, Fishman R, Rochberg N et al. Comparison of direct interview and family history diagnoses of alcohol dependence. Alcohol Clin Exp Res 1995; 19: 1018–1023.

    CAS  Article  Google Scholar 

  57. 57

    First MB, Spitzer RL, Gibbon M, Williams JB . Structured Clinical Interview for DSM-IV Axis I Disorders—Patient Edition. Biometrics Research Department, New York State Psychiatric Institute: NY, 1995.

    Google Scholar 

  58. 58

    Folstein MF, Folstein SE, McHugh PR . Mini-Mental-Status-Test, German Edition. Beltz: Weinheim, 1993.

    Google Scholar 

  59. 59

    Allik J, Laidra K, Realo A, Pullmann H . Personality development from 12 to 18 years of age: changes in mean levels and structure of traits. Eur J Pers 2004; 18: 445–462.

    Article  Google Scholar 

  60. 60

    Kallasmaa T, Allik J, Realo A, McCrae RR . The Estonian version of the NEO-PI-R: an examination of universal and culture-specific aspects of the five-factor model. Eur J Pers 2000; 14: 265–278.

    Article  Google Scholar 

  61. 61

    Marchini J, Howie B, Myers S, McVean G, Donnelly P . A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 2007; 39: 906–913.

    CAS  Article  Google Scholar 

  62. 62

    Chen WM, Abecasis GR . Family-based association tests for genomewide association scans. Am J Hum Genet 2007; 81: 913–926.

    CAS  Article  Google Scholar 

  63. 63

    Abecasis G . METAL. 2009 URL:

  64. 64

    Dudbridge F, Gusnanto A . Estimation of significance thresholds for genomewide association scans. Genet Epidemiol 2008; 32: 227–234.

    Article  Google Scholar 

  65. 65

    Liu JZ, McRae AF, Nyholt DR, Medland SE, Wray NR, Brown KM et al. A versatile gene-based test for genome-wide association studies. Am J Hum Genet 2010; 87: 139–145.

    CAS  Article  Google Scholar 

  66. 66

    Liu X, Yu XP, Zack DJ, Zhu H, Qian J . TiGER: a database for tissue-specific gene expression and regulation. BMC Bioinform 2008; 9: 271.

    Article  Google Scholar 

  67. 67

    Karabay A, Yu WQ, Solowska JM, Baird DH, Baas PW . Axonal growth is sensitive to the levels of katanin, a protein that severs microtubules. J Neurosci 2004; 24: 5778–5788.

    CAS  Article  Google Scholar 

  68. 68

    Lee HH, Jan LY, Jan YN . Drosophila IKK-related kinase Ik2 and Katanin p60-like 1 regulate dendrite pruning of sensory neuron during metamorphosis. Proc Natl Acad Sci USA 2009; 106: 6363–6368.

    CAS  Article  Google Scholar 

  69. 69

    Toyo-Oka K, Sasaki S, Yano Y, Mori D, Kobayashi T, Toyoshima YY et al. Recruitment of katanin p60 by phosphorylated NDEL1, an LIS1 interacting protein, is essential for mitotic cell division and neuronal migration. Hum Mol Genet 2005; 14: 3113–3128.

    CAS  Article  Google Scholar 

  70. 70

    Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 2010; 467: 832–838.

    CAS  Article  Google Scholar 

  71. 71

    Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 2010; 42: 565–569.

    CAS  Article  Google Scholar 

  72. 72

    Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ et al. Finding the missing heritability of complex diseases. Nature 2009; 461: 747–753.

    CAS  Article  Google Scholar 

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We would like to thank the individuals who participated in the studies. Meta-analysis and statistical analyses for the NAG/IPRG, QIMR and NTR/NESDA studies were carried out on the Genetic Cluster Computer (, which is financially supported by the Netherlands Organization for Scientific Research (NWO 480-05-003). SardiNIA: We acknowledge support from the Intramural Research Program of the NIH, National Institute on Aging. Funding was provided by the National Institute on Aging, NIH Contract No. NO1-AG-1-2109 to the SardiNIA (‘ProgeNIA’) team. NTR/NESDA: We acknowledge financial support from the Netherlands Organization for Scientific Research (NWO): Grants 575-25-006, 480-04-004, 904-61-090; 904-61-193, 400-05-717 and Spinozapremie SPI 56-464-14192. MHMdeM is financially supported by ZonMW (Addiction) Grant No. 311-60008. We further acknowledge financial support from the Center for Medical Systems Biology (NWO Genomics), the Centre for Neurogenomics and Cognitive Research (CNCR-VU); EU/QLRT-2001-01254; NIMH R01 MH059160; Geestkracht program of ZonMW (10-000-1002); matching funds from universities and mental healthcare institutes involved in NESDA. Genotyping was funded by the Genetic Association Information Network (GAIN) of the Foundation for the US National Institutes of Health, and analysis was supported by grants from Genetic Association Information Network and the NIMH (MH081802). Genotype data were obtained from dbGaP (, accession number phs000020.v1.p1). ERF: The genotyping for the ERF study was supported by EUROSPAN (European Special Populations Research Network) and the European Commission FP6 STRP Grant (018947; LSHG-CT-2006-01947). The ERF study was further supported by grants from the Netherlands Organization for Scientific Research, Erasmus MC, the Centre for Medical Systems Biology (CMSB) and the Netherlands Brain Foundation (HersenStichting Nederland). We are grateful to all patients and their relatives, general practitioners and neurologists for their contributions and to P Veraart for her help in genealogy, Jeannette Vergeer for the supervision of the laboratory work and P Snijders for his help in data collection. SAGE: Funding support for the Study of Addiction: Genetics and Environment (SAGE) was provided through the NIH Genes, Environment and Health Initiative (GEI) (U01 HG004422). SAGE is one of the genome-wide association studies funded as part of the Gene Environment Association Studies under GEI. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the Gene Environment Association Studies initiative Coordinating Center (U01 HG004446). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Support for collection of data sets and samples was provided by the Collaborative Study on the Genetics of Alcoholism (U10 AA008401), the Collaborative Genetic Study of Nicotine Dependence (P01 CA089392) and the Family Study of Cocaine Dependence (R01 DA013423). Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the NIH GEI (U01HG004438), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse and the NIH contract ‘High-throughput genotyping for studying the genetic contributions to human disease’ (HHSN268200782096C). The Collaborative Study on the Genetics of Alcoholism, principal investigators: B Porjesz, V Hesselbrock, H Edenberg, LJ Bierut, includes 10 different centers: University of Connecticut (V Hesselbrock); Indiana University (HJ Edenberg, J Nurnberger Jr, T Foroud); University of Iowa (S Kuperman, J Kramer); SUNY Downstate (B Porjesz); Washington University in St Louis (LJ Bierut, A Goate, J Rice, K Bucholz); University of California at San Diego (M Schuckit); Rutgers University (J Tischfield); Southwest Foundation (L Almasy), Howard University (R Taylor) and Virginia Commonwealth University (D Dick). A Parsian and M Reilly are the NIAAA Staff Collaborators. We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting-Kai Li, P Michael Conneally, Raymond Crowe and Wendy Reich, for their critical contributions. This national collaborative study is supported by NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). The Collaborative Genetic Study of Nicotine Dependence project is a collaborative research group and part of the NIDA Genetics Consortium. Subject collection was supported by NIH Grant CA89392 (PI—LJ Bierut) from the National Cancer Institute. Genotyping work at Perlegen Sciences was performed under NIDA Contract HHSN271200477471C. Phenotypic and genotypic data are stored in the NIDA Center for Genetic Studies (NCGS) at under NIDA Contract HHSN271200477451C (PIs—J Tischfield and J Rice). Genotyping services were also provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, Contract No. HHSN268200782096. In memory of Theodore Reich, founding principal investigator of COGEND, we are indebted to his leadership in the establishment and nurturing of COGEND and acknowledge with great admiration his seminal scientific contributions to the field. Lead investigators directing data collection are LJ Bierut, Naomi Breslau, Dorothy Hatsukami and Eric Johnson. We thank Heidi Kromrei and Tracey Richmond for their assistance in data collection. HBCS: We acknowledge financial support from the Academy of Finland (Grant No. 120315 and 129287 to EW, 1129457 and 1216965 to KR, 120386 and 125876 to JGE), the European Science Foundation (EuroSTRESS), the Wellcome Trust (Grant No. 89061/Z/09/Z and 089062/Z/09/Z) and the Signe and Ane Gyllenberg foundation. NAG/IRPG: This study is supported by NIH Grants DA12854 (to PAFM), AA07728, AA07580, AA11998, AA13320 and AA13321 (to ACH); and grants from the Australian National Health and Medical Research Council; MLP is supported by DA019951. QIMR: We thank Marlene Grace and Ann Eldridge for sample collection; Anjali Henders, Megan Campbell, Lisa Bowdler, Steven Crooks and staff of the Molecular Epidemiology Laboratory for sample processing and preparation; Harry Beeby, David Smyth and Daniel Park for IT support. We acknowledge support from the Australian Research Council (A7960034, A79906588, A79801419, DP0212016, DP0343921), Beyond Blue and the Borderline Personality Disorder Research Foundation. Genotyping was funded by the National Health and Medical Research Council (Medical Bioinformatics Genomics Proteomics Program, 389891). Further, we gratefully acknowledge Drs Dale R Nyholt and especially Scott Gordon for their substantial efforts involving the QC and preparation of the QIMR and NAG/IRPG GWA data sets. Dr Nyholt also contributed 8% of the NAG/IRPG GWA cohort (NHMRC IDs 339462, 442981, 389938, 496739). LBC1936: We thank David Liewald and Paul Redmond for technical assistance; the study Secretary Paula Davies; Alan Gow, Michelle Taylor, Janie Corley, Caroline Brett and Caroline Cameron for data collection and data entry; nurses and staff at the Wellcome Trust Clinical Research Facility, where subjects were tested and genotyping was performed; staff at the Lothian Health Board, and the staff at the SCRE Centre, University of Glasgow. The research was supported by a program grant from Research Into Ageing. The research continues with program grants from Help the Aged/Age Concern (The Disconnected Mind). GWA funding awarded by the Biotechnology and Biological Sciences Research Council (BBSRC) to IJD and AT. ML is a Royal Society of Edinburgh/Lloyds TSB Foundation for Scotland Personal Research Fellow. The study was conducted within the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, supported by the (BBSRC), Engineering and Physical Sciences Research Council (EPSRC), Economic and Social Research Council (ESRC) and Medical Research Council (MRC), as part of the cross-council Lifelong Health and Wellbeing Initiative. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) ( The ECDF is partially supported by the eDIKT initiative ( Baltimore Longitudinal Study of Aging: We acknowledge support from the Intramural Research Program of the NIH, National Institute on Aging. We thank Robert McCrae. EGPUT: AM and TE received support from FP7 Grants (201413 ENGAGE, 212111 BBMRI, ECOGENE (No. 205419, EBC)) and OpenGENE. AM and TE also received targeted financing from Estonian Government SF0180142s08 and by EU via the European Regional Development Fund, in the frame of Centre of Excellence in Genomics. The genotyping of the Estonian Genome Project samples were performed in Estonian Biocentre Genotyping Core Facility, AM and TE thank Mari Nelis and Viljo Soo for their contributions. AR and JA were supported by a grant from the Estonian Ministry of Science and Education (SF0180029s08).

Author contributions

Writing group: MHMdeM, PTC, ATer., RFK, CMvanD, DIB. Analytic group: MHMdeM, J-JH, TE, ML, TT, SS, ATen, LML, NKH, SEM, NRW, EW, DLC, KR, GRA, NA. Study design and project management: DIB, EJCdeG, PSu, BWJHP, PAFM, MLP, AM, IJD, MJW, NGM, NRW, GWM, JGE, AP, LP, KR, MU, LF, DS, CMvanD, BAO, PTC, ATer. Sample and phenotype data collection: MAD, GW, EJCdeG, BWJHP, PSp, AM, AR, JA, PAFM, ACH, NGM, MLP, MJW, NGM, NRW, LJB, KR, JGE, MU, LF, DS, ACJW, PTC, ATer. Data preparation: MHMdeM, MAD, J-JH, GW, EJCdeG, CAH, TE, AR, MLP, GD, ML, ATen, LML, SEM, NKH, PL, RG, AA, JD, EW, DLC, YSA.

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Correspondence to M H M de Moor.

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Competing interests

PTC Jr received royalties from the NEO Five-Factor Inventory. LJB is an inventor on the patent ‘Markers for Addiction’ (US 20070258898) covering the use of certain SNPs in determining the diagnosis, prognosis and treatment of addiction. Dr LJB served as a consultant for Pfizer Inc. in 2008. The other authors declare that they have no potential conflict of interest.

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de Moor, M., Costa, P., Terracciano, A. et al. Meta-analysis of genome-wide association studies for personality. Mol Psychiatry 17, 337–349 (2012).

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  • personality
  • Five-Factor Model
  • genome-wide association
  • meta-analysis
  • genetic variants

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