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Published in final edited form as: Behav Genet. 2015 Sep 11;46(2):170–182. doi: 10.1007/s10519-015-9735-5

Meta-analysis of genome-wide association studies for extraversion: Findings from the Genetics of Personality Consortium

Stéphanie M van den Berg 1,#,†, Marleen HM de Moor 2,3,4,#, Karin JH Verweij 5,6, Robert F Krueger 7, Michelle Luciano 8,9, Alejandro Arias Vasquez 10,11,12,13, Lindsay K Matteson 7, Jaime Derringer 14, Tõnu Esko 15, Najaf Amin 16, Scott D Gordon 5, Narelle K Hansell 5, Amy B Hart 17, Ilkka Seppälä 18, Jennifer E Huffman 19, Bettina Konte 20, Jari Lahti 21,22, Minyoung Lee 23, Mike Miller 7, Teresa Nutile 24, Toshiko Tanaka 25, Alexander Teumer 26, Alexander Viktorin 27, Juho Wedenoja 28, Abdel Abdellaoui 2, Goncalo R Abecasis 29, Daniel E Adkins 30, Arpana Agrawal 31, Jüri Allik 32,33, Katja Appel 34, Timothy B Bigdeli 23, Fabio Busonero 35, Harry Campbell 36, Paul T Costa 37, George Davey Smith 38, Gail Davies 8,9, Harriet de Wit 39, Jun Ding 67, Barbara E Engelhardt 40, Johan G Eriksson 22,41,42,43,44, Iryna O Fedko 2, Luigi Ferrucci 25, Barbara Franke 10,11,12, Ina Giegling 20, Richard Grucza 31, Annette M Hartmann 20, Andrew C Heath 31, Kati Heinonen 21, Anjali K Henders 5, Georg Homuth 45, Jouke-Jan Hottenga 2, William G Iacono 7, Joost Janzing 11, Markus Jokela 21, Robert Karlsson 27, John P Kemp 38,46, Matthew G Kirkpatrick 39, Antti Latvala 41,28, Terho Lehtimäki 18, David C Liewald 8,9, Pamela AF Madden 31, Chiara Magri 47, Patrik KE Magnusson 27, Jonathan Marten 19, Andrea Maschio 35, Hamdi Mbarek 2, Sarah E Medland 5, Evelin Mihailov 15,48, Yuri Milaneschi 49, Grant W Montgomery 5, Matthias Nauck 50, Michel G Nivard 2, Klaasjan G Ouwens 2, Aarno Palotie 51,52, Erik Pettersson 27, Ozren Polasek 53, Yong Qian 67, Laura Pulkki-Råback 21, Olli T Raitakari 54,55, Anu Realo 32, Richard J Rose 56, Daniela Ruggiero 24, David Schlessinger 67, Carsten O Schmidt 26, Wendy S Slutske 57, Rossella Sorice 24, John M Starr 9,58, Beate St Pourcain 38,59,60, Angelina R Sutin 25,61, Nicholas J Timpson 38, Holly Trochet 19, Sita Vermeulen 12,62, Eero Vuoksimaa 28, Elisabeth Widen 52, Jasper Wouda 1,2, Margaret J Wright 5, Lina Zgaga 36,63, Generation Scotland 64, David Porteous 65, Alessandra Minelli 47, Abraham A Palmer 17,39, Dan Rujescu 20, Marina Ciullo 24, Caroline Hayward 19, Igor Rudan 36, Andres Metspalu 15,33, Jaakko Kaprio 41,28,52, Ian J Deary 8,9, Katri Räikkönen 21, James F Wilson 36, Liisa Keltikangas-Järvinen 21, Laura J Bierut 31, John M Hettema 23, Hans J Grabe 34,66, Brenda WJH Penninx 49, Cornelia M van Duijn 16, David M Evans 38,46, David Schlessinger 67, Nancy L Pedersen 27, Antonio Terracciano 22,62, Matt McGue 7,68, Nicholas G Martin 5, Dorret I Boomsma 2
PMCID: PMC4751159  NIHMSID: NIHMS738388  PMID: 26362575

Abstract

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion data came from multiple personality inventories; extraversion scores were harmonized across cohorts and included as the phenotype in the GWA meta-analysis. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with gene action different from other complex behavioral traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion.

Keywords: Personality, Phenotype Harmonization, Common Genetic Variants, Imputation, Polygenic risk

Introduction

Extraversion is a personality trait characterized by the tendency to experience positive emotions, to be active and feel energetic, to be talkative and to enjoy social interactions. Extraversion is associated with numerous psychosocial, lifestyle and health outcomes, such as academic and job performance, well-being, obesity, substance use, physical activity, Alzheimer’s disease, bipolar disorder and longevity (Furnham et al. 2013; Judge et al. 2013; Middeldorp et al. 2011; Rhodes and Smith 2006; Sutin et al. 2011; Terracciano et al. 2008; Terracciano et al. 2014; Weiss et al. 2008).

Extraversion can be measured with multiple inventories that have been developed as parts of different personality theories. For example, extraversion is one of the five personality domains as assessed with the Neuroticism-Extraversion-Openness to Experience (NEO) personality inventories (Costa and McCrae 1992). Also, extraversion is included in Eysenck’s three-dimensional theory of personality (Eysenck and Eysenck 1964; Eysenck and Eysenck 1975; Eysenck et al. 1985). In Cloninger’s theory on temperaments and characters (Cloninger 1987; Cloninger et al. 1993), the temperaments of Harm Avoidance, Novelty Seeking and Reward Dependence are related to extraversion (De Fruyt et al. 2000). Tellegen’s personality theory posits the higher order domain of Positive Emotionality (Patrick et al. 2002), which resembles and is highly correlated with extraversion (Church 1994).

We showed recently, by performing an Item Response Theory (IRT) analysis using test linking (Kolen 2004), that item data on Extraversion, Reward dependence and Positive Emotionality can be harmonized to broadly assess the same underlying extraversion construct (van den Berg et al. 2014). Briefly, the harmonization was carried out in each cohort separately by first fitting an IRT model to data from individuals who had completed at least two different personality questionnaires. Next, based on calibrated item parameters we estimated personality using all available data for each individual, irrespective of what personality questionnaire was used. This harmonization was performed in over 160,000 individuals from 23 cohorts participating in the Genetics of Personality Consortium (GPC). It was demonstrated that the harmonized extraversion phenotype was heritable (broad-sense heritability estimate of 49%). This heritability estimate was based on a meta-analysis in six twin cohorts that are included in the GPC (29,501 twin pairs), and is highly similar to heritability estimates obtained for extraversion as assessed with single measurement instruments (Bouchard and Loehlin 2001; Distel et al. 2009; Finkel and McGue 1997; Keller et al. 2005; Yamagata et al. 2006). In addition, it was found that of the 49% heritability, 24% was estimated to be due to additive genetic variance and 25% due to non-additive genetic variance. Some evidence for qualitative sex differences in the genetic influences on extraversion was also found, as suggested by a genetic correlation in opposite-sex twin pairs of 0.38 (van den Berg et al. 2014). Extraversion becomes more genetically stable during adolescence until it is almost perfectly genetically stable in adulthood (Briley and Tucker-Drob 2014; Kandler 2012), that is, the same genes are responsible for extraversion measured at different ages.

A handful of genome-wide association (GWA) studies for extraversion have been published, aimed at detecting specific single nucleotide polymorphisms (SNPs) that explain part of the heritability. The first GWA study for personality, which focused on the five NEO personality traits, was conducted in 3,972 adults (Terracciano et al. 2010). No genome-wide significant SNP associations were found for extraversion, although some interesting associations with P-values<10−5 were found with SNPs in two cadherin genes and the brain-derived neurotrophic factor (BDNF) gene. A subsequent meta-analysis of GWA results for the NEO personality traits, conducted in 17,375 subjects, also did not yield any genome-wide significant associations for extraversion (De Moor et al. 2012). Two other GWA studies reported a similar lack of genome-wide significance for Cloninger’s temperament scales (Service et al. 2012; Verweij et al. 2010). Interestingly, a study that performed a genetic complex trait analysis (GCTA; Yang et al. 2010) for neuroticism and extraversion, conducted in around 12,000 unrelated individuals, reported that 12% (SE = 3%) of the variance in extraversion was explained by common SNPs of additive effect (Vinkhuyzen et al. 2012). Taking together the results from twin and genome-wide studies, it is suggested that common SNPs of additive effect are important for extraversion but that large sample sizes are required to identify specific variants.

In this study, we report the results of the largest meta-analysis of GWA results for extraversion so far, carried out in 29 cohorts that participate in the GPC. A total of 63,030 subjects with harmonized extraversion and genome-wide genotype data were included in the meta-analysis. A 30th cohort was used for replication. In addition, we computed weighted polygenic scores in an independent cohort and associated them with extraversion, and we estimated variance explained by SNPs in two large cohorts.

Materials and methods

Cohorts

The full meta-analysis was performed on 63,030 subjects from 29 discovery cohorts. All samples were of European origin. Twenty-one cohorts were from Europe, six from the Unites States and two from Australia. Sample sizes of the individual cohorts ranged from 177 to 7,210 subjects. Please note that some cohorts were also part of previously published GWA studies. The Generation Scotland cohort was included as a replication sample (9,783 subjects). A brief overview of all cohorts is provided in Table 1. A description of each individual cohort is found in the Supplementary materials and methods.

Table 1.

Overview of 29 discovery cohorts and 1 replication cohort of the Genetics of Personality Consortium

Cohort # Subjects * # SNPs ** Cohort # Subjects * # SNPs **
1 ALSPAC 4705 6454153 16 MCTFR 7099 6569999
2 BLSA 820 4989411 17 MGS 2101 5900898
3 BRESCIA 177 3549919 18 NBS 1832 5603447
4 CHICAGO 311 3755416 19 NESDA 2227 4707569
5 CILENTO 627 1123089 20 NTR 6416 5339798
6 COGA 647 5127101 21 ORCADES 1650 4265590
7 COGEND 1279 5932838 22 PAGES 476 4547293
8 EGCUT 1184 5574695 23 QIMR
adolescents		 2842 5957064
9 ERF 2300 5142865 24 QIMR adults 7210 6343920
10 FTC EPI 567 4870096 25 SardiNIA 4332 6291135
11 FTC NEO 813 5092018 26 SHIP 2213 5913428
12 HBCS 1456 5612790 27 STR 4903 6519094
13 KORCULA 808 5094034 28 VIS 909 5327671
14 LBC1921 437 4363611 29 YFS 1737 5914679
15 LBC1936 952 5168754 Total 63030 7460147
30 Generation
Scotland		 9 783 74
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*

Number of subjects with valid latent score for Extraversion and SNP data (after imputation and cleaning)

**

Number of SNPs (after imputation and cleaning) with valid association results that entered the meta-analysis

NA=Not Applicable for replication cohort because only top hits were sought to replicate.

Phenotyping

A harmonized latent extraversion score was estimated for all participants in all 29 cohorts that were included in the GWA meta-analysis. This score was based on all available extraversion item data for each individual (for a detailed description see van den Berg et al. 2014). Extraversion item data came from the extraversion scales of the NEO Personality Inventory, the NEO Five Factor Inventory, the 50-item Big-Five version of the International Personality Item Pool inventory, the Eysenck Personality Questionnaire and the Eysenck Personality Inventory, from the Reward Dependence scale of the Cloninger’s Tridimensional Personality Questionnaire, and from the Positive Emotionality scale of the Multidimensional Personality Questionnaire (see van den Berg et al. 2014 and Supplementary materials and methods). In the Generation Scotland cohort that was included for replication of top signals, extraversion was based on the summed score of the extraversion scale of the EPQ Revised Short Form.

Genotyping and imputation

Genotyping in all cohorts was carried out on Illumina or Affymetrix platforms, after which quality control (QC) was performed, followed by imputation of genotypes. QC of genotype data was performed in each cohort separately, with cohort specific criteria. Standard QC checks included tests of European ancestry, sex inconsistencies, Mendelian errors, and high genome-wide homozygosity. Checks for relatedness were conducted in those cohorts that aimed to include unrelated individuals only. Other checks of genotype data were based on minor allele frequencies (MAF), SNP call rate (% of subjects with missing genotypes per SNP), sample call rate (% of missing SNPs per subject) and Hardy–Weinberg Equilibrium (HWE). Genotype datawere imputed using the 1000Genomes phase 1 version 3 (build37, hg19) reference panel with standard software packages such as IMPUTE, MACH, or Minimac, see Supplementary Table 1.

Statistical analyses

GWA analysis per cohort

GWA analyses were conducted independently in each cohort. Since the cohorts were based on different research designs (case-control, population twin studies, extended pedigrees, etc.), GWA methods were optimized for each cohort separately. Extraversion scores were regressed on each SNP under an additive model. Sex and age were included as covariates. Covariates such as ancestry Principal Components (PCs) were added if deemed necessary for a particular cohort. In all analyses, the uncertainty of the imputed genotypes was taken into account, either using dosage scores or mixtures of distributions. In those cohorts that included related individuals, the dependency among participants was accounted for using cohort-specific methods. Standard software packages for GWA analyses were used (see Supplementary Table 1).

Meta-analysis of GWA results across cohorts

A meta-analysis of the GWA results was conducted with the weighted inverse variance method in METAL (http://www.sph.umich.edu/csg/abecasis/metal/index.html). Excluded from meta-analysis were poorly imputed SNPs (r2<0.30 or proper_info<0.40) and SNPs with low MAF (MAF < √(5/N, which corresponds to less than 5 estimated individuals in the least frequent genotype group, under the assumption of HWE). This resulted in a total number of 7,460,147 unique SNPs in the final meta-analysis (with 1.1M to 6.6M SNPs across cohorts). For 2,182 SNPs, SNP locations could not be matched with rs names. For an additional 516,362 SNPS, results were based on one cohort only and therefore left out of the analysis, so that the results are based on 6,941,603 SNPs. Genomic control inflation factors (lambda), Manhattan plots and quantile–quantile plots per cohort are provided in Supplementary Table 2 and Supplementary Figures 1 and 2. A P-value of 5 × 10−8 was used as the threshold for genome-wide significance.

The meta-analysis results (P-values per SNP) were used as the input to compute P-values at the gene level. We performed these analyses in KGG (Li et al. 2012). A P-value of 2.87 10−6 was used as the threshold for genome-wide significance in these gene-wide analyses, based on a False-Discovery Rate computed using (Benjamini and Hochberg 1995).

All GWAS SNP top hits with a P-value smaller than 1 × 10−5 were selected for replication in the Generation Scotland cohort. In addition, a full meta-analysis was performed on the top hits including the Generation Scotland cohort.

Polygenic risk score analysis

Additional analyses were conducted to test whether extraversion could be predicted in an independent target cohort based on the GWA meta-analysis results. The target cohort was the Netherlands Twin Register (NTR) cohort (8,648 subjects). Polygenic risk scores for this cohort were estimated using LDpred {Vilhjalmsson, 2015 #1238} that takes into account linkage disequilibrium among the SNPs. The estimation was based on a GWA meta-analysis in which the NTR and NESDA cohorts were excluded (further referred to as the discovery set). With the LD-corrected polygenic risk scores, generalized estimating equation (GEE) modeling was applied to test whether the polygenic risk scores predicted extraversion in the target cohort. The covariates age, sex and ten PCs were included as fixed effects in the model. The model also included a random intercept with family number as the cluster variable, to account for dependency among family members. Clear ethnic and other outliers on the PCs were excluded from the analysis.

Variance explained by SNPs

In the NTR cohort and the QIMR Berghofer Medical Research Institute (QIMR) adult cohort (see also Supplementary materials and methods), GCTA software (Visscher et al. 2010; Yang et al. 2010) was used to estimate the proportion of variance in extraversion that can be explained by common SNPs of additive effect. In the NTR, this analysis was carried out in a set of 3,597 unrelated individuals and in the QIMR adult cohort this was done in 3,369 unrelated individuals (in each cohort one member per family was selected with harmonized extraversion and genome-wide SNP data). GCTA analysis was based on best guess genotypes obtained in PLINK using a threshold of a maximum genotype probability >0.70, and additionally filtering on r-squared>0.80. Next, in estimating the GRM matrix in the GCTA software, SNPs with MAF <0.05 were excluded. The additive genetic relationship matrices (GRM) estimated based on SNPs for all individuals formed the basis to estimate the proportion of phenotypic variance explained by SNPs in the NTR and QIMR cohorts. In other words, it was determined to what extent phenotypic similarity between individuals corresponds to genetic similarity (at the SNP level). For both NTR and QIMR, sex, age and a set of population-specific PCs were included as covariates.

Results

Meta-analysis of GWA results

Meta-analysis of GWA results across the 29 discovery cohorts did not yield genome-wide significant SNPs associated with extraversion. The lowest P-value observed was 2.9 × 10−7 for a SNP located on chromosome 2. There were 74 SNPs with P-values < 1 × 10−5. The Manhattan and quantile-quantile plots are provided in Figures 1 and 2. A list with the top five SNPs is given in Table 2. A list with all SNPs that reached the level of suggestive genome-wide significance (P < 1 × 10−5) is found in Supplementary Table 3. A gene-based test showed one significant hit for LOC101928162, a long non-coding RNA site, P = 2.87 × 10−6. A list with the top five genes from the gene-based analysis is provided in Table 3. Supplementary Table 4 provides the top 30 genes. Among the top 30 genes was Brain-Derived Neurotrophic Factor (BDNF, P = 0.0003), a gene also implicated, though not genome-wide significant, in Terraciano et al. (2010), as was the BDNF anti-sense RNA gene (P = 0.0001).

Table 2.

Top SNPs from the meta-analysis of GWA results in 29 discovery cohorts for extraversion, and their replication in the Generation Scotland cohort, in the Genetics of Personality Consortium

Discovery results Replication results
SNP Chr_BP Alleles Closest gene Effect SE P-value Effect SE P-value
rs2024488 2_217662968 A/G LOC101928250 −.0303 .0059 2.939 10−7 0.0285 0.0164 0.08244
rs2712162 2_217661788 T/C LOC101928250 −.0300 .0059 3.872 10−7 0.0278 0.0164 0.08947
rs797182 12_10900487 A/G <NA> −.0277 .0056 6.673 10−7 −0.0135 0.0153 0.37721
rs8010306 14_37150160 A/G SLC25A21 .0629 .0128 8.730 10−7 0.0180 0.0314 0.56650
rs117292860 19_2227621 A/C DOT1L .0553 .0113 9.191 10−7 −0.0350 0.0239 0.14368
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Table 3.

Top genes from the meta-analysis of GWA results in 29 discovery cohorts for Extraversion in the Genetics of Personality Consortium

Gene Full gene name Pathways p-value
LOC101928162 [long non-coding RNA] unknown 0.00000287
LOC729506 [long non-coding RNA] unknown 0.00000893
PLEKHJ1 Pleckstrin Homology Domain Containing,
Family J Member 1		 phospholipid binding, circadian clock functioning 0.0000132
POU2F3 POU Class 2 Homeobox 3 Influenza A 0.0000179
CRTAC1 Cartilage Acidic Protein 1 unknown 0.0000297
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Results of the follow-up analysis of the top five SNPs in the Generation Scotland cohort can be found in Table 2. Of the top five SNPs, none showed a significant effect. For an overview of the replication results of all top SNPs with P-value < 1 × 10−5 see Supplementary Table 3. Of the 74 SNPs tested in the replication cohort, three SNPs showed nominal evidence of association (P<0.05), which is less than the number expected based on chance alone (0.05*74=3.7).

Polygenic risk score analysis

After leaving out the outliers, 8201 persons remained in the GEE analysis. The LDpred-based risk scores significantly predicted extraversion in the target cohort, B = 0.059, X2(1) = 27.30, P < 0.001.

Variance explained by SNPs

In the NTR cohort, an estimated 5.0% (SE= 7.2) of the variance in extraversion was explained by all SNPs, but this estimate was not significantly different from zero (P = 0.24). In the QIMR cohort, 0.0001% (SE=15) of the variance was explained by SNPs (P = 0.46).

Discussion

This study aimed to determine the influence of common genetic variants on extraversion in 63,030 individuals from 29 cohorts in the GPC. First, a meta-analysis of GWA analyses across 29 discovery cohorts showed no genome-wide significant SNPs. Top SNPs detected in the meta-analysis of GWA results in the discovery phase were not replicated in the Generation Scotland cohort. The SNPs with lowest P-values have no previously reported relationship with personality, psychopathology or brain functioning.

Polygenic risk scores based on the meta-analysis results predicted extraversion in an independent data set. SNP-based heritabilities for extraversion were not significantly different from zero in two large cohorts of the GPC.

With the current meta-analysis we more than tripled the sample size as compared to the largest previously published meta-analysis for extraversion (De Moor et al. 2012). Yet, no genome-wide significant SNPs were found. This suggests that a sample size of 63,030 subjects is still not large enough to detect any specific variants. Possibly the additive genetic variance estimated based on the classical twin design is overestimated, or common SNPs do not tag rare causal variants. Some have argued that an even larger sample size will not lead to clearer signals for single variants for personality traits (Turkheimer et al. 2014). They argue that the heritability of personality traits represents nonspecific genetic background, which is composed of so many genetic variants with extremely small effect sizes that individually these have no causal biological interpretation. It should be noted however that some successes, although modest, have been achieved in detecting single variants in very large samples for other complex and related behavioral traits. For example, genome-wide meta- and mega-analyses in tens or hundreds of thousands of subjects for schizophrenia and educational attainment have shown genome-wide significant results (Rietveld et al. 2013; Ripke et al. 2014). An earlier metanalysis on neuroticism in our consortium did yield a genome-wide significant hit (De Moor 2015). It is interesting to note that despite the fact that for extraversion no genome-wide significant findings emerged for SNPs, we were able to predict extraversion in an independent data set. This indicates that some true signal is entailed in the meta-analysis results.

The results of the polygenic risk score analysis are in contrast with the results from the GCTA analysis, in which no significant proportion of variance explained by SNPs was detected in two large cohorts of the GPC. Our study on neuroticism reported a proportion of 15% (De Moor 2015). The current extraversion GCTA findings are also somewhat at odds with two previous GCTA studies for personality traits. One study focused on neuroticism and extraversion as measured with different instruments in four cohorts, and found on average 12% explained variance for extraversion, although across cohorts these estimates varied widely (0-27%) (Vinkhuyzen et al. 2012). Estimates for neuroticism also varied, but were generally lower than for extraversion in this study, with an average of 6% explained variance. In another study, between 4.2 and 9.9% of explained variances were found for the four Cloninger temperaments in a combined sample of four cohorts (Verweij et al. 2012). The proportions of variances for Harm Avoidance, Novelty Seeking and Persistence were significant at P<0.05, whereas interestingly the proportion of variance for Reward Dependence was not. It should be noted that both these studies included the QIMR cohort in their analyses, so there is some overlap in subjects analyzed in these studies compared the QIMR data set that we conducted GCTA analysis in. The difference is that in the earlier studies extraversion and reward dependence were based on single personality inventories, while for the results presented in this study extraversion scores harmonized among different personality inventories were analyzed. The non-significant variance estimate in the QIMR study for harmonized extraversion is replicated in the NTR cohort. What our results and the results in the previous studies have in common though, is that the estimates are considerably smaller than the heritability estimates based on twin studies, whether significant or not. Given that about half of the heritability of extraversion consists of non-additive genetic variance (van den Berg et al. 2014), it is not unlikely that this discrepancy is caused by the influence of common variants that interact within loci (dominance) or across loci (epistasis). In addition, the influence of rare variants may be implicated. The relatively limited influence of common additive genetic variation, as well as a previously reported finding that higher levels of inbreeding are associated with less socially desirable personality trait levels, has led to the idea that the genetic variation in personality traits may have been maintained by mutation–selection balance (Verweij et al., 2012), and our results our consistent with this idea.

Although there were no genome-wide significant results for individual SNPs, in the gene-based analysis, there was a significant hit for one locus, LOC101928162. However, the function of this long noncoding RNA site remains elusive. Interestingly, the BDNF gene and the BDNF anti-sense RNA gene were among the top genes; this BDNF gene was also implicated, though not genome-wide significant, in Terraciano et al. (2010). Liu et al (2005) reported a trend towards association of BDNF variants with substance abuse, Jiao et al. (2011) reported an association with obesity, and Furman et al. (2010) reported an association with smoking initiation. As extraversion is known to be associated with life style, obesity and substance abuse, we deem this to be an interesting candidate gene for extraversion in future studies.

This study comes with a number of limitations that should be acknowledged. Genotyping, QC, and imputation were carried out separately in each cohort. Although procedures were standardized as much as possible, perfect standardization was not possible in practice. Any difference in procedures may have caused some loss of statistical power to detect SNPs in the meta-analysis. Similarly, extraversion item data were harmonized as much as possible, as described in detail by Van den Berg et al. (2014). In general harmonization was very successful, but it should be noted that the Reward Dependence item data from the TCI were least successfully linked to the extraversion data from the other inventories. This may additionally have caused some loss in power. Importantly however, it should be noted that by combining genotype and phenotype data across cohorts as performed in this study, a substantial increase in sample size was obtained. It is nontrivial that the gain in power associated with this increase in sample size largely outweighs any potential loss in power due to any remaining genotyping or phenotyping differences across cohorts.

Taken together, extraversion is a heritable, highly polygenic personality trait with a genetic background that may be qualitatively different from that of other complex behavioral traits. Future studies are required to further increase our knowledge of which types of genetic variants, by which modes of gene action, constitute the heritable nature of extraversion. Ultimately, this knowledge can be used to increase our understanding of how extraversion is related to various important psychosocial and health outcomes.

Supplementary Material

S_01

Figure 1.

Figure 1

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Manhattan plot for meta-analysis results of 29 discovery cohorts for Extraversion in the Genetics of Personality Consortium

Figure 2.

Figure 2

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Quantile-Quantile plots for meta-analysis results of 29 discovery cohorts for extraversion in the Genetics of Personality Consortium

Acknowledgements

We would like to thank all participating subjects. Analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org), which is financially supported by the Netherlands Organization for Scientific Research (NWO 480-05-003).

ALSPAC We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The UK Medical Research Council (grant 74882), the Wellcome Trust (grant 076467) and the University of Bristol provide core support for ALSPAC. We thank 23andMe for funding the genotyping of the ALSPAC children’s sample. This publication is the work of the authors, and they will serve as guarantors for the contents of this paper.

BLSA We acknowledge support from the Intramural Research Program of the NIH, National Institute on Aging. We thank Robert McCrae.

BRESCIA We acknowledge support from the Italian Ministry of Health (RC and RF2007 Conv. 42) and Regione Lombardia (ID: 17387 SAL-13). We thank Ilaria Gandin for imputation analysis support.

CHICAGO This work was supported by NIH grants, DA007255 (ABH), HG006265 (to BEE), DA02812 (to HdW), and DA021336 and DA024845 (to AAP). BEE was also funded through the Bioinformatics Research Development Fund, supported by Kathryn and George Gould. We wish to thank Andrew D. Skol for providing advice about genotype calling.

CILENTO We acknowledge Dr Maria Enza Amendola for the test administration and thank the personnel working in the organization of the study in the villages. MC received funding support from the Italian Ministry of Universities (FIRB - RBNE08NKH7, INTEROMICS Flaghip Project), the Assessorato Ricerca Regione Campania, the Fondazione con il SUD (2011-PDR-13), and the Fondazione Banco di Napoli.

SAGE – COGA/CONGEND 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 (GENEVA) under GEI. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA Coordinating Center (U01 HG004446). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Support for collection of datasets and samples was provided by the Collaborative Study on the Genetics of Alcoholism (COGA; U10 AA008401) and the Collaborative Genetic Study of Nicotine Dependence (COGEND; P01 CA089392). 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 (COGA), Principal Investigators B. Porjesz, V. Hesselbrock, H. Edenberg, L. Bierut, includes ten different centers: University of Connecticut (V. Hesselbrock); Indiana University (H.J. Edenberg, J. Nurnberger Jr., T. Foroud); University of Iowa (S. Kuperman, J. Kramer); SUNY Downstate (B. Porjesz); Washington University in St. Louis (L. Bierut, A. Goate, J. Rice, K. Bucholz); University of California at San Diego (M. Schuckit); Rutgers University (J. Tischfield); Texas Biomedical Research Institute (L. Almasy), Howard University (R. Taylor) and Virginia Commonwealth University (D. Dick). Other COGA collaborators include: L. Bauer (University of Connecticut); D. Koller, S. O’Connor, L. Wetherill, X. Xuei (Indiana University); Grace Chan (University of Iowa); S. Kang, N. Manz, M. Rangaswamy (SUNY Downstate); J. Rohrbaugh, J-C Wang (Washington University in St. Louis); A. Brooks (Rutgers University); and F. Aliev (Virginia Commonwealth University). A. Parsian and M. Reilly are the NIAAA Staff Collaborators. 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 (COGEND) project is a collaborative research group and part of the NIDA Genetics Consortium. Subject collection was supported by NIH grant P01 CA089392 (L.J. Bierut) from the National Cancer Institute. Phenotypic and genotypic data are stored in the NIDA Center for Genetic Studies (NCGS) at http://zork.wustl.edu/ under NIDA Contract HHSN271200477451C (J. Tischfield and J. Rice). Jaime Derringer was supported by NIH T32 MH016880.

EGCUT 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).

ERF The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme "Quality of Life and Management of the Living Resources" of 5th Framework Programme (no. QLG2-CT-2002-01254). The ERF study was further supported by ENGAGE consortium and CMSB. High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organisation for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). ERF was further supported by the ZonMw grant (project 91111025). We are grateful to all study participants and their relatives, general practitioners and neurologists for their contributions and to P. Veraart for her help in genealogy, J. Vergeer for the supervision of the laboratory work and P. Snijders for his help in data collection.

Finnish Twin Cohort (FTC) We acknowledge support from the Academy of Finland Center of Excellence in Complex Disease Genetics (grant numbers: 213506, 129680), the Academy of Finland (grants 100499, 205585, 118555 and 141054 to JK, grant 257075 to EV), Global Research Awards for Nicotine Dependence (GRAND), ENGAGE (European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number 201413), DA12854 to P A F Madden, and AA-12502, AA-00145, and AA-09203 to RJRose, AA15416 and K02AA018755 to DM Dick.

HBCS We thank all study participants as well as everybody involved in the Helsinki Birth Cohort Study. Helsinki Birth Cohort Study has been supported by grants from the Academy of Finland, the Finnish Diabetes Research Society, Folkhälsan Research Foundation, Novo Nordisk Foundation, Finska Läkaresällskapet, Signe and Ane Gyllenberg Foundation, University of Helsinki, Ministry of Education, Ahokas Foundation, Emil Aaltonen Foundation.

KORCULA The CROATIA-Korcula study was funded by grants from the Medical Research Council (UK), European Commission Framework 6 project EUROSPAN (Contract No. LSHG-CT-2006-018947) and Republic of Croatia Ministry of Science, Education and Sports research grants to I.R. (108-1080315-0302). We would like to acknowledge the invaluable contributions of the recruitment team in Korcula, the administrative teams in Croatia and Edinburgh and the people of Korcula. The SNP genotyping for the CROATIA-Korcula cohort was performed in Helmholtz Zentrum München, Neuherberg, Germany.

LBC1921 & LBC1936 For the Lothian Birth Cohorts, we thank Paul Redmond for database management; 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 blood extraction 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 Age UK (The Disconnected Mind). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1). Funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged. IJD, DJP and colleagues receive support from Wellcome Trust Strategic Award 104036/Z/14/Z.

MCTFR We would like to thank Rob Kirkpatrick for his help running analyses.

Research reported in this publication was supported by the National Institutes of Health under award numbers R37DA005147, R01AA009367, R01AA011886, R01DA013240, R01MH066140, and U01DA024417.

MGS Samples were collected under the following grants: NIMH Schizophrenia Genetics Initiative U01s: MH46276, MH46289, and MH46318; and Molecular Genetics of Schizophrenia Part 1 (MGS1) and Part 2 (MGS2) R01s: MH67257, MH59588, MH59571, MH59565, MH59587, MH60870, MH60879, MH59566, MH59586, and MH61675. Genotyping and analyses were funded under the MGS U01s: MH79469 and MH79470.

NBS Principal investigators of the Nijmegen Biomedical Study are L.A.L.M. Kiemeney, M. den Heijer, A.L.M. Verbeek, D.W. Swinkels and B. Franke.

NESDA The Netherlands Study of Depression and Anxiety (NESDA) were funded by the Netherlands Organization for Scientific Research (Geestkracht program grant 10-000-1002); the Center for Medical Systems Biology (CMSB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University’s EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam. Genotyping was funded by the US National Institute of Mental Health (RC2MH089951) as part of the American Recovery and Reinvestment Act of 2009. BP is financially supported by NWO-VIDI grant no. 91811602.

NTR 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 NOW VENI grant No. 016-115-035. 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 (http://www.ncbi.nlm.nih.gov/dbgap, accession number phs000020.v1.p1).

ORCADES was supported by the Chief Scientist Office of the Scottish Government, the Royal Society, the MRC Human Genetics Unit, Arthritis Research UK and the European Union framework program 6 EUROSPAN project (contract no. LSHG-CT-2006-018947). DNA extractions were performed at the Wellcome Trust Clinical Research Facility in Edinburgh. We would like to acknowledge the research nurses in Orkney, the administrative team in Edinburgh and the people of Orkney.

PAGES none

QIMR Berghofer adolescents/adults We thank Marlene Grace and Ann Eldridge for sample collection; 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 grants A79600334, A79906588, A79801419, DP0212016, DP0343921, DP0664638, and DP1093900 (to NGM and MJW), Beyond Blue and the Borderline Personality Disorder Research Foundation (to NGM), NIH Grants DA12854 (to PAFM), AA07728, AA07580, AA11998, AA13320, AA13321 (to ACH) and MH66206 (to WSS); and grants from the Australian National Health and Medical Research Council; MLP is supported by DA019951. Genotyping was partly funded by the National Health and Medical Research Council (Medical Bioinformatics Genomics Proteomics Program, 389891) and the 5th Framework Programme (FP-5) GenomEUtwin Project (QLG2-CT-2002-01254). Further genotyping at the Center for Inherited Disease Research was supported by a grant to the late Richard Todd, M.D., Ph.D., former Principal Investigator of Grant AA13320. SEM and GWM are supported by the National Health and Medical Research Council Fellowship Scheme. Further, we gratefully acknowledge Dr Dale R Nyholt for his substantial involvement in the QC and preparation of the QIMR GWA data sets. Dr Nyholt also contributed 8% of the GWAS for the QIMR adult cohort (NHMRC IDs 339462, 442981, 389938, 496739).

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.

SHIP SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthcare, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. The University of Greifswald is a member of the ‘Center of Knowledge Interchange’ program of the Siemens AG. This work was also funded by the German Research Foundation (DFG: GR 1912/5-1).

STR The STR was supported by grants from the Ministry for Higher Education, the Swedish Research Council (M-2005-1112 and 2009-2298), GenomEUtwin (EU/QLRT-2001-01254; QLG2-CT-2002-01254), NIH grant DK U01-066134, The Swedish Foundation for Strategic Research (SSF; ICA08-0047), the Swedish Heart-Lung Foundation, the Royal Swedish Academy of Science, and ENGAGE (within the European Union Seventh Framework Programme, HEALTH-F4-2007-201413).

VIS The CROATIA-Vis study was funded by grants from the Medical Research Council (UK) and Republic of Croatia Ministry of Science, Education and Sports research grants to I.R. (108-1080315-0302). We would like to acknowledge the staff of several institutions in Croatia that supported the field work, including but not limited to The University of Split and Zagreb Medical Schools, the Institute for Anthropological Research in Zagreb and Croatian Institute for Public Health. The SNP genotyping for the CROATIA-Vis cohort was performed in the core genotyping laboratory of the Wellcome Trust Clinical Research Facility at the Western General Hospital, Edinburgh, Scotland.

YFS The Young Finns Study has been financially supported by the Academy of Finland (grants 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), 41071 (Skidi), and 265869 (Mind)), the Social Insurance Institution of Finland, Kuopio, Tampere and Turku University Hospital Medical Funds (grant 9N035 for Dr. Lehtimäki), Juho Vainio Foundation, Paavo Nurmi Foundation, Finnish Foundation of Cardiovascular Research and Finnish Cultural Foundation, Tampere Tuberculosis Foundation and Emil Aaltonen Foundation (for Dr. Lehtimäki). The expert technical assistance in statistical analysis by Irina Lisinen, Mika Helminen, and Ville Aalto is gratefully acknowledged.

Generation Scotland SFHS is funded by the Scottish Executive Health Department, Chief Scientist Office, grant number CZD/16/6. Exome array genotyping for GS:SFHS was funded by the Medical Research Council UK and performed at the Wellcome Trust Clinical Research Facility Genetics Core at Western General Hospital, Edinburgh, UK. We would like to acknowledge the invaluable contributions of the families who took part in the Generation Scotland: Scottish Family Health Study, the general practitioners and Scottish School of Primary Care for their help in recruiting them, and the whole Generation Scotland team, which includes academic researchers, IT staff, laboratory technicians, statisticians and research managers.

Footnotes

Authors’ contributions

Writing group: SMvdB, MHMdeM, KJHV, RFK, ML, AAV, LKM, JD, TE, DIB

Analytic group: MHMdeM, SMvdB, KJHV, ML, AAV, LKM, JDe, TE, NA, SG, NKH, ABH, JH, BK, JL, ML, MM, TT, ATeu, AV, JW, IOF, NT, DME, TL, IS, EP, GRA, JM, HM

Study design and project management: LF, LPR, JGE, AAP, GWM, MJW, PAFM, DP, AMin, AP, DR, MC, IG, CH, IR, AMet, JK, IJD, KR, JFW, LKJ, JMH, HJG, BWJHP, CMvD, DME, NLP, PTC, ATer, MMG, NGM, DIB, RFK, AAV, GDS, TL, OTR, PKEM, KH, JMS, DS, GRA, HC, WGI, JDi

Sample and phenotype data collection: BWJHP, AMet, AR, JA, PAFM, ACH, NGM, MJW, KA, MN, LJB, JGE, LF, PTC, IG, AMH, ATer, GDS, MGK, HdW, AAP, AKH, WSS, RS, DR, Amin, MJ, LPR, LKJ, OTR, PKEM, EV, KH, AM, FB, OP, LZ

Data preparation: SMvdB, MHMdeM, JW, KGO, JJH, SEM, NKH, YM, TE, AR, GD, ML, RG, AA, JD, EW, GD, BEE, COS, GH, KJHV, SDG, DEA, TBB, JPK, NJT, BP, BK, CM, MJ, AL, ARS, ATer, DCL, HT

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