Uncovering the Complex Genetics of Human Character

Molecular Psychiatry https://doi.org/10.1038/s41380-018-0263-6 ARTICLE Uncovering the complex genetics of human character Igor Zwir1,2 ●Javier Arnedo2●Coral Del-Val2●Laura Pulkki-Råback3●Bettina Konte 4●Sarah S. Yang5● Rocio Romero-Zaliz2●Mirka Hintsanen6●Kevin M. Cloninger7●Danilo Garcia 8,9 ●Dragan M. Svrakic1● Sandor Rozsa1●Maribel Martinez1●Leo-Pekka Lyytikäinen 10 ●Ina Giegling4,11 ●Mika Kähönen12 ● Helena Hernandez-Cuervo13 ●Ilkka Seppälä 10 ●Emma Raitoharju10 ●Gabriel A. de Erausquin14 ●Olli Raitakari15 ● Dan Rujescu4●Teodor T. Postolache16,17 ●Joohon Sung5●Liisa Keltikangas-Järvinen3●Terho Lehtimäki10 ● C. Robert Cloninger 1,18 Received: 12 February 2018 / Revised: 18 August 2018 / Accepted: 11 September 2018 © The Author(s) 2018. This article is published with open access Abstract Human personality is 30–60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic–phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory. Introduction Strong evidence for substantial heritability of human personality comes from family, twin, and adoption studies [1]. However, the genetic and phenotypic architecture of human personality is complex and has remained uncertain despite recent advances in genomics and phenomics [2–4]. In general, geneticists must expect the likelihood that many genes affect each trait and each gene affects many traits [5]. When the architecture is complex, the same genetic networks may lead to different phenotypic outcomes (a phenomenon called multifinality in development or pleiotropy in genetics) [6–8]. Likewise, different genetic networks in complex systems may lead to the same outcome (equifinality, which is also described as heterogeneity) [8,9]. Human personality is a striking example of the challenges involved in identifying the specific genes and molecular processes that influence complex traits. Twin studies indicate that between 30% and 60% of the phenotypic variance in personality, as assessed by a variety of instruments, is genetic in origin [10–14]. However, adoption studies and studies that include other family members along with twins show that most of the heritability of personality, as assessed by a variety of instruments, is likely to depend on complex interactions among multiple gene loci (i.e., epistasis) or multiple alleles at a locus (i.e., dominance), rather than the average effects of individual *C. Robert Cloninger [email protected] Extended author information available on the last page of the article. Electronic supplementary material The online version of this article (https://doi.org/10.1038/s41380-018-0263-6) contains supplementary material, which is available to authorized users. 1234567890();,: 1234567890();,: genes [11,13–17]. Put another way, many genes are likely to operate in concert, not separately, to influence the heritability and development of personality. Nevertheless, despite extensive past effort, genome-wide association studies (GWAS) of personality have found few significant associations using a variety of personality instruments [18–20]. The frequent failure to account for most of the heritability of complex traits has been called the “missing” [21]or“hidden”[22] heritability problem. The Temperament and Character Inventory (TCI) measures two domains of personality hypothesized to be related to different genetic and neuronal networks [23]. Imaging studies show that TCI character traits are associated with brain networks for intentional and meta-cognitive processes, such as self-reflection, goal-setting, empathy, and episodic learning, whereas temperament traits are related to generating and conditioning automatic behaviors, such as stress reactions [24–28]. In this article, we focus on TCI character traits of self-directedness (i.e., purposeful, responsible vs. aimless, blaming), Cooperativeness (i.e., helpful, empathic vs. hostile, self-centered), and self-transcendence (i.e., altruistic, spiritual vs. individualistic, skeptical). These are the self-regulatory components of personality that determine the degree to which a person's adaptive functioning is healthy or unhealthy [29]. In related articles, we examine temperament traits and their relations with character in the same samples. We have chosen to apply strictly data-driven machine learning methods in a person-centered approach to GWAS to uncover the complex genotypic and phenotypic architecture of personality [6,30,31] (Supplementary Figure S1). We postulate that personality heritability is not missing, but is distributed in multiple networks of interacting genetic and environmental variables that influence different people [6,31–33]. Subjects and methods Description of the samples Our discovery sample was the Young Finns Study, an epidemiological study of 2149 healthy Finnish children followed regularly from 1980 (ages, 3–18 years) to 2012 (ages, 35–50 years) [34]. Childhood environments were directly assessed with the rearing parents in 1980 and 1983 [35–39]. Adult environments and life events were assessed with subjects in 2001 [40,41]. All subjects (56% women) had thorough standardized genotypic and phenotypic assessments, including administration of the TCI in 1997, 2001, 2007, and 2012 [34,42]. We replicated the results in two independent samples of healthy adults from Germany [43,44] and Korea [45,46], in which comparable genotypic and phenotypic features were available (see Supplement). The Korean study involved 1052 unrelated individuals extracted from a national register (aged 28–81, 57% women). The German study involved 902 subjects (aged 20–74, 49% women) randomly selected from Munich city registry and screened to exclude anyone with a history of psychiatric illness in themselves or their first-degree relatives. Personality assessment All subjects completed the TCI to assess seven heritable dimensions of personality [23,47]. The TCI measures four dimensions of temperament and three dimensions of character (self-directedness, cooperativeness, and selftranscendence) with strong reliability, as described in Supplementary Section 1 and Supplementary Table S1 [23,47]. The 13 subscales of character from the TCI were used as the primary data about character in all three samples (Supplementary Section 2). Character profiles for each person were based on median splits of each subscale to distinguish high and low scorers [48]. Personality health indices People at risk of unhealthy personality were identified as the bottom decile of the sum of TCI self-directedness and cooperativeness [48]. Prior work shows this criterion indicates ill-being or personality disorder (i.e., poor physical, mental, and social functioning) [49,50]. In contrast, people with healthy personalities were identified as the top decile of the product of all three TCI character traits. Prior work shows that this criterion indicates well-being or flourishing (i.e., superior physical, mental, and social functioning) [29,48,51]. These indices provided consistent measures of the health status of subjects in all three samples. The health value of a set (i.e., group of people) is the average value of its members. We also identified an empirical index of character functioning by clustering the 13 character subscales of the TCI (Supplementary Section 3 and Table S2). The empirical index of character provided a single comprehensive measure of character functioning that could be associated a posteriori with each SNP set based on semi-supervised learning [52] and used in SNP-set Kernel Association Test (SKAT) [32,33] and heritability analyses. It was highly correlated with the other health indicators (p< E-20, RMSE 0.03). Genotyping The Finnish sample was genotyped by using Illumina Human670-Quad Custom, (i.e., Illumina 670k custom) I. Zwir et al. arrays [53]. The Korean sample used Affymetrix GenomeWide Human SNP Array 6.0 and Illumina HumanCore [45]. The German sample used Affymetrix Genome-Wide Human SNP Array 6.0, Illumina OMNI Express and the 300 Array, prephased and imputed with SHAPEIT2 and IMPUTE2. Some German individuals had also been genotyped on Illumina Omni1-Quad. Quality control was performed for all samples as in prior work [6] (Supplementary Section 3). After quality checks, a subset of SNPs were preselected with the PLINK software suite [54] to reduce the large search space using a generously inclusive threshold (p-value <0.01 without Bonferroni correction) for possible association with character, taking gender and ethnicity into account as covariates of the individual SNPs. Preselecting SNPs identified SNPs that have weak associations with character that are not individually significant genome wide after Bonferroni correction, but provided presumptive candidates for epistatic interactions in a SNP set. The preselection also identified SNPs with a strong additive effect individually, thereby providing a manageably sized initial pool of SNPs as candidates for both the additive and nonadditive components of the genetic architecture of character. We accounted for ethnicity in each sample by using the first three principal components for ancestral stratification of SNP genotypes (Supplementary Section 3) [55]. Computational procedures The cluster analyses used the validated Generalized Factorization Method, which utilizes deep non-negative matrix factorization (NMF) to uncover naturally occurring (i.e., unsupervised) associations between patterns across different types of data, including genetics [56–59] and neuroimages [30,60]. The clustering was entirely data driven without restrictive assumptions about the number or content of the clusters [31]. For example, clusters may have different features, and one subject can belong to more than one cluster [6,30,31,56,61]. The recurrent application of the clustering process is summarized and schematically related to unsupervised deep NMF learning in Supplementary Figure S1 [62]. The advantages of this clustering approach over alternative analyses of single or multiple markers are described in Supplementary Section 4. Our web server application for phenotype–genotype many-to-many relations analysis (PGMRA) in GWAS is published [31] and online at http://phop.ugr.es/fenogeno. The PGMRA method and algorithm are also summarized in Supplementary Sections 5 and 6, which includes a semisupervised classifier of phenotypes from genotypes. PGMRA properly accounts for linkage disequilibrium (LD) efficiently (i.e., without loss of information about complex genotypic–phenotypic relations) (Supplementary Section 4). Statistical analysis correcting for multiple comparisons, as well as gender and ethnicity as covariates of the SNP sets, was performed by the SKAT [32,33], also accessible via PGMRA. Heritability was estimated from a trimmed regression of SNPs on the empirical index of character controlling for outliers and environmental variables [63,64] (also see Supplementary Section 7). Replicability of results was evaluated in the three independent samples for SNP sets, phenotypic sets, and genotypic–phenotypic relations using multi-objective optimization techniques [6], as detailed in Supplementary Section 8. We also evaluated how well the individual genotypic sets were able to predict the classification of the phenotypes in each sample using the PGMRA classifier (Supplementary Section 9). Further details are available in Supplementary Information and elsewhere [56–59]. Results Identifying SNP sets as candidates for causal variability We exhaustively identified 902 non-identical but possibly overlapping SNP sets in the Finnish sample using PGMRA without knowledge of the phenotype. The SNP sets were comprised of different numbers of SNPs and/or subjects, regardless of their phenotypic status. The SNPs were mapped to diverse functional classes of genetic variants that may be located on different chromosomes, frequently even within a single SNP set (Figs. 1a, 2a–d). SNP sets are organized as networks of multilocus genotypes (Fig. 1a, b; Supplementary Figure S2, Supplementary Table S3). They were labeled by a genotypic identification ‘G’, followed by two numbers: the first indicates the maximum number of clusters and the second indicates the order of selection by the algorithm. SNP sets were associated with different health risks (Table 1, Supplementary Table S2). Identifying clusters of subjects with distinct character profiles We identified 342 non-identical but possibly overlapping character sets using the 13 character subscales without knowledge of the genotype. Character sets were labeled by a phenotypic identification “C”to distinguish them from the SNP sets. These fine-grained character sets were nested within five character supersets that were identified by recurrently applying PGMRA to minimize the cophenetic correlation coefficient (Table 2)[62]. In other words, five groups of people had highly distinct character profiles. The people in three of the five character profiles had healthy personalities, which we named resourceful, Uncovering the complex genetics of human character organized, and creative to be consistent with traditional labels for TCI profiles (Table 2). For example, people with the "organized" character profile were high in most subscales of self-directedness and cooperativeness, but were low in all subscales of self-transcendence (i.e., they were controlling, individualistic, and skeptical). People with the "creative" profile were high in all aspects of character, whereas the "resourceful" were only self-directed. I. Zwir et al. In addition, there were two profiles of people with unhealthy personalities. The people with a "dependent" character profile were highly forgiving when abused (CO4), conscientiously considerate of others (CO5), selfdeprecating (SD4), and otherwise low in self-directedness and self-transcendence. The people with an "apathetic" character were low in all aspects of character development (Table 2). Association of SNP sets with character We tested the association of SNP sets with character. The empirical index of character, a single quantitative measure of character functioning, was more strongly associated with SNP sets than with the average effects of their constituent SNPs according to SKAT (Table 1). Forty-two SNP sets had significant associations with character (p< 1E-05). For example, the SNP set G_11_4 has a p-value of 1.22 E-19, whereas the best and average SNPs within this set have 9.00 E-05 and 3.29 E-02 p-values, respectively (Table 1). SKAT [32] and PLINK [54] methods estimated similar p-values for the individual SNPs (R2=0.99, F statistics, p< 3.8 E46), showing that SKAT did not inflate results. Forty-two SNP sets significantly associated with character are described in Table 1. We assigned names to the SNP sets based on prominent molecular processes and pathways that distinguished them (Supplementary Table S4). The character-related SNP sets were comprised of networks of SNPs that mapped 727 genes, nearly all of which are known to influence individual differences in brain functions, particularly regulation of neurodevelopment, neuroplasticity, neuroprotection, connectivity, energy metabolism, stress reactivity, resilience, longevity, learning, and memory (Supplementary Tables S5, S6). Complex genotypic–phenotypic relationships in personality profiles We found that 55 of the 342 character sets were significantly associated with particular SNP sets (hypergeometric statistics, 1E-11 < p< 1E-03, Table 3). The genotypic–phenotypic relations were complex, demonstrating pleiotropy and heterogeneity. For example, G_5_1 involved neuroplasticity and was frequently associated with dependent character sets, but sometimes with apathetic or creative profiles (Table 3). The 55 character sets were associated with the 42 SNP sets in 128 relationships that were significant by a permutation test (Table 3, empirical p< 4.7 E-03). SNP sets (Fig. 1b, Supplementary Figure 2A) often had similar character profiles associated with particular molecular processes (Table 3, Supplementary Tables S4, S7). For example, the organized profile was strongly associated with many SNP sets involving the regulation of inositol– calcium signaling for obtaining food and other goals (e.g., G_8_8, G_11_4) and for neuroprotection against injury (G_12_8). SNP sets regulating episodic learning and hippocampal neurogenesis (e.g., G_7_3, G_12_1) were associated with a creative profile. Relations among SNP sets to one another and to molecular processes We found 12 single and disjoint nodes, and at least three subnetworks composed of highly connected nodes, shown Fig. 1 aTwo examples of SNP sets are represented as heatmap submatrices or biclusters. SNP sets were identified by distinct patterns of molecular features of SNPs in subgroups of subjects. Allele values are indicated as BB (dark blue), AB (intermediate blue), AA (light blue), and missing (black). SNP sets were labeled for specificity by a pair of numbers representing the maximum number of clusters from which the bicluster was selected (e.g., 33 clusters may produce more specific than 21) and the order in which they were selected by the method (e.g., 4th bicluster or factor selected by FNMF when the maximum number of clusters was 21) and usually have a prefix G for genotype or P for phenotype. Only a subset of optimal and cohesive sets are selected across all number of clusters (See Supplementary Methods). The SNPs within each SNP set can map to different chromosomes (e.g., 6 and 8) and exhibit distinct molecular consequences (see Supplementary Table S3). The pie chart shows the percentage of SNPs within a SNP set that belong to each type of consequence. bDissection of a GWAS in a Finnish population to identify the genotypic and phenotypic architecture of personality measured by the TCI. The genotypic network is depicted as nodes (SNP sets) linked by shared SNPs (blue lines) and/or subjects (red lines) (see also Supplementary Figure S3A for additional subnetworks). Each SNP set maps to one or more genes (see Supplementary Table S6 for full list of genes associated with each SNP set). SNP sets associated with each of the five general character profiles are distinguished by color-coding as shown in the legend (see Table 3). c,dComparison of level of ill-being (cwhere high values indicate ill-being) and for level of well-being (dwhere high values indicate well-being) in groups of subjects with each of the five character profiles specified by both phenotypic and genotypic information (evaluated by ANOVA). (Compare with either genetic or phenotypic assessment alone in Supplementary Figure S6). eVariation in health status of SNP sets: well (blue, see d), ill (orange, see c), intermediate (gray). f12 genotypic-phenotypic pipelines connect different sets of genes to the same character dimension (see also Supplementary Tables S9–S12). Red lines indicate direct connections, whereas blue lines and “&”indicate composite connections. gSurface showing the pattern of health status of the subjects in this study based on SNP set information only (i.e., interpolation from Table 1). The probability of well-being in the z-axis varies from high (red for high well-being) to low (green). The order of the SNP sets is based on shared subjects (xaxis) and on shared SNPs (y-axis) measured by hypergeometric statistics, so SNP sets sharing more SNPs and/or subjects are nearby (see ill health surface in Supplementary Figure S4). hSurface showing the pattern of health status of subjects based on both genotypic information (SNP sets) and phenotypic information (character sets) (as in Table 3). The probability of well-being in the z-axis varies from high (red, high well-being) to low (green). The sharing of subjects is shown for both SNP sets (x-axis) and character sets (y-axis) (see ill health surface in Supplementary Figure S5) Uncovering the complex genetics of human character in Fig. 1b and Supplementary Figure S3A. These networks were relatively disjoint (i.e., sharing few SNPs and subjects; see Supplementary Information 9. Identification of Subnetworks), suggesting that these are distinct antecedents of personality. These nearly disjoint networks vary in size and complexity: one subnetwork connected eight SNP sets I. Zwir et al. (Supplementary Figure S3A), whereas others had only a single SNP set. One network contained SNP sets primarily connected by shared SNPs, but not subjects (e.g., G_10_1 learning/memory and G_7_7 olfaction, Fig. 1b), as expected when the same SNPs had different allele values. This network was associated with dependent and organized personality profiles (Fig. 1b). Both shared subjects and SNPs connected the other two networks (Fig. 1b), as occurs when one network is a subset of another. The first network was primarily composed of organized (e.g., components of inositol signaling by G_11_4, G_8_8, G_3_1) and apathetic (e.g., G_21_3 cellular senescence, G_7_2 GPCR dysregulation) profiles. The second network displayed creative (e.g., G_3_2, G_7_3, G_9_8) and dependent (e.g., G_38_8, G_5_1) profiles. Finally, some SNP sets within a network do not share SNPs, but independently specify almost the same individuals (e.g., G_8_8 inositol/chemokine signaling, G_7_2 GPCR dysregulation, Fig. 1b), as expected when distinct subsets of genotypic features influence a common pathway or consequence. Heterogenic pathways influence the same character trait The genes associated with each of the five character profiles are largely different. In all, 68% of the 727 genes associated with character were unique to a single character profile: 208 with organized, 89 with creative, 70 with dependent, and 130 with apathetic (Supplementary Table S8). Consequently, there were multiple groups of genes that lead to each individual character trait, as depicted in Fig. 1f. For example, high self-directedness occurs in individuals with the resourceful, organized, and creative profiles, even though these profiles have different genetic backgrounds. Put another way, individual character traits were genetically more heterogeneous than the multidimensional character profiles. We refer to the multiple genotypic–phenotypic networks that contribute to individual traits as a pipeline, as outlined in Fig. 1f. Detailed descriptions of the specific genes and molecular processes we found in the pipelines for each of the three character traits are presented in Supplementary Tables S9–S12. Complex genotypic–phenotypic relationships influence health status The combination of genotypic and phenotypic information provided more information than either alone for both wellbeing (Fig. 1g vs. Fig. 1h) and ill-being (Supplementary Figures S4 vs. S5). When health status was based on the joint relationship of SNP sets and character sets, all five character profiles were well distinguished in terms of the probabilities of ill-being (p< 3.89E-26, ANOVA statistics, Fig. 1c) and well-being (p< 3.68E-65, ANOVA, Fig. 1d). In contrast, when health status was based on character scores only, the probability of ill-being was greater in only two profiles and that of well-being was greater in only one profile (Supplementary Figure S6). We identified candidate regulatory genes that we called switch genes because of their relationship to changes in health status among people with the same character profile (Fig. 1e). For example, all apathetic SNP sets were associated with ill-being except G_9_3, which was associated with well-being. In contrast, the creative SNP sets were associated with well-being except for G_7_7, which was associated with ill-being. The 150 switch genes included 50% protein coding genes, 18% RNA genes, 15% pseudogenes, 3% transcription factors, and 4% others (Supplementary Table S13). Overall about 67% of the 727 genes associated with character sets may be involved in regulatory processes: these included transcriptional regulators (10%), lncRNAs (24%), other RNA genes (6%), and targets of microRNAs (27%), as identified in the TRANSFAC®release 2017.1 database (Supplementary Table S14). We identified two microRNAs (MIR431, MIR1762) in association with character, and they target 74 and 119 of the 727 genes we found associated with character in TRANSFAC, respectively. In particular, lnc RNAs were more commonly associated with character only then with temperament and character, whereas protein-coding genes were more commonly associated with both temperament and character, as shown in Fig. 2a, b. Replication of results in two independent samples We tested the replicability of our findings in the Finnish study by carrying out the same analyses in the German and Korean samples. In all, 95% of the 42 SNP sets associated Fig. 2 a,bTypes of genetic variants mapped by SNP sets associated with character: aSpecific molecular consequences (Supplementary Table S5) and btheir subtypes. Genes related only to character sets (red) were less often protein coding and more often RNA genes than those also associated with temperament sets (blue color). cCell displaying the molecular pathways containing genes associated only with the organized profile. The uncovered genes influence the phosphatidyl inositol/calcium second-messenger signaling system that regulates the seeking of food and other goals in response to external environmental signals (see also Supplementary Tables S4, S7). dMultiple SNPs within a SNP set can affect a single or multiple genes in many ways (Supplementary Table S3). Within the MTA3 gene, SNPs in the SNP set G_12_1 may affect both coding and regulatory regions (thereby inhibiting transcription), whereas SNPs from SNP set 40_26 are mostly located in intronic regions (thereby blocking or decreasing protein production). The SNP sets are associated with profiles exhibiting distinct character features (creative vs. apathetic) Uncovering the complex genetics of human character with character sets in the Finnish sample were identified in one or both of the replication samples: 36 were identified in both the Korean and German samples, three in the Korean sample only, and one in the German sample only (Supplementary Table S15). In addition, 96% of the 55 character sets associated with SNP sets in the Finnish sample were replicated in one or both of the replication samples: 46 in both, six in Korean sample only, and one in the German sample only (Table S16). The genotypic–phenotypic relations between SNP and character sets identified in the Finnish sample closely matched those observed in the Korean study (94%) and in the German (84%) study (Table S17). The replication of the 25 character sets associated with ill-being in the Finnish sample was reduced in the German sample (72%) compared with the Korean sample (84%)(ANOVA, p=0.01), as expected because the Table 1 Description of 42 SNP sets associated with character sets (p< 1E-05) Finnish sample Probability of health aGenes SNP sets SNP-set name % Coding SKAT pvalue Best SNP Average SNPs aSubjects aSNPs Well-being Ill-being G_3_1 Inositol-calcium signaling* 62 2.88E-102 3.01E-05 2.64E-01 311 2163 0.06 0.1 >300 G_8_8 Inositol/chemokine pathways 67 2.21E-55 8.55E-05 1.99E-01 224 611 0.08 0.07 291 G_7_2 GPCR dysregulation 62 2.07E-31 9.00E-05 2.44E-01 211 303 0.09 0.23 142 G_7_3 Neurogenesis 63 1.67E-20 1.07E-04 1.85E-01 133 364 0.17 0.36 136 G_11_4 Inositol signaling 55 1.22E-19 9.00E-05 3.29E-02 141 172 0.07 0.04 51 G_12_8 Neuroprotection 62 1.49E-16 2.53E-04 3.39E-01 173 285 0.09 0.03 111 G_7_7 Olfaction 56 1.32E-11 3.27E-05 2.25E-01 145 193 0.03 0.1 55 G_36_29 Electron transport 50 5.37E-09 4.27E-04 3.37E-01 25 185 0.08 0.48 76 G_31_8 Neurotrophin 55 1.15E-08 2.90E-05 3.13E-01 54 183 0.09 0.54 64 G_28_15 Histone methylation 44 2.77E-08 3.76E-05 2.23E-01 101 123 0.08 0.38 34 G_9_8 Neuroregulation 57 3.82E-08 1.11E-04 3.48E-01 209 230 0.17 0.12 77 G_24_6 GFI1-neurite outgrowth 36 5.22E-08 2.65E-04 7.47E-02 72 63 0.1 0.08 14 G_19_5 DARPP320neuroplasticity 30 8.31E-08 1.23E-04 2.13E-01 86 59 0.16 0.22 10 G_33_15 ERK-neurodevelopment 53 1.60E-07 4.47E-04 8.47E-02 26 67 0.27 0.23 19 G_23_2 Biogenic amine synthesis 50 2.24E-07 1.39E-04 9.46E-02 42 56 0.05 0.29 8 G_3_2 PAK-neuroprotection 63 3.08E-07 1.70E-05 1.80E-01 133 197 0.18 0.24 35 G_22_6 Blood–brain barrier 59 3.37E-07 2.53E-04 2.35E-01 37 93 0.08 0.16 32 G_34_13 CREB-episodic learning 54 3.88E-07 2.38E-04 1.28E-01 41 49 0.05 0.29 13 G_40_26 Dopamine-feedback 65 6.08E-07 3.88E-04 2.61E-01 39 98 0.08 0.36 17 G_21_3 Cellular senescence 62 1.12E-06 1.85E-04 3.55E-01 60 117 0.1 0.23 34 G_20_2 Enhanced memory 79 1.59E-06 2.78E-04 2.34E-01 25 80 0.24 0.12 19 G_28_11 Sensory transduction 44 2.07E-06 8.29E-04 2.71E-01 32 81 0.22 0.06 9 G_12_1 Episodic learning 61 5.06E-06 9.00E-05 3.41E-01 146 189 0.2 0.06 66 G_41_33 GPCR neuroplasticity 40 5.31E-06 4.91E-04 2.71E-01 56 76 0.11 0.21 15 G_9_3 Pyrimidine metabolism 50 6.03E-06 4.54E-05 2.55E-02 164 35 0.12 0.04 6 G_26_14 Glucose transport 59 6.98E-06 1.08E-04 2.20E-01 46 75 0.09 0.24 27 G_20_3 Fatty acid oxidation 48 8.03E-06 2.07E-04 3.04E-01 36 82 0.03 0.33 21 G_23_19 Org2-RNA 0 1.17E-05 3.76E-05 9.37E-04 87 32 0.08 0.03 4 G_17_14 Dep-RNA 0 1.17E-05 3.76E-05 9.37E-04 47 32 0.09 0.15 4 G_27_25 Org3-RNA 0 1.17E-05 3.76E-05 9.37E-04 54 32 0.11 0.07 4 G_41_40 Apath-RNA 0 1.17E-05 3.76E-05 9.37E-04 34 32 0.09 0.03 4 G_38_10 Org5-RNA 0 1.17E-05 3.76E-05 9.37E-04 27 32 0.15 0.19 4 G_33_19 Res-RNA 0 1.17E-05 3.76E-05 9.37E-04 43 32 0.09 0.12 4 G_21_4 Org1-RNA 0 1.28E-05 3.76E-05 9.41E-02 68 43 0.07 0.07 4 G_10_1 Learning/memory 47 1.29E-05 3.27E-05 7.44E-02 131 48 0.07 0.06 15 G_35_12 Org4-RNA 0 1.33E-05 3.76E-05 9.32E-04 45 31 0.16 0.09 3 G_35_4 O-linked glycosylation 57 1.39E-05 3.74E-04 2.83E-01 42 37 0.05 0.24 7 G_5_1 CDK neuroplasticity 100 1.78E-05 1.70E-05 5.60E-02 100 91 0.17 0.4 1 G_19_9 Aurora-B 38 1.91E-05 2.65E-04 1.25E-01 20 46 0.1 0.4 8 G_36_27 Aging regulation 25 1.96E-05 8.29E-04 1.60E-01 27 57 0.33 0.22 4 G_16_9 Olfactory signaling 58 2.00E-05 2.31E-04 1.79E-01 64 36 0.08 0.17 12 G_33_11 Self-control 50 3.67E-05 2.65E-04 9.39E-02 43 32 0.26 0.07 15 The SNP sets are named based on molecular pathways and neuronal functions of the genes that distinguish the sets from one another (see Supplementary Table S4). Percentage coding indicates the percentage of protein coding genes. Strengths of association are compared for the SNP set, the best SNP, and average SNP based on SKAT p-values. The number of subjects and SNPs comprising each SNP set is specified. The probabilities of the well-being and ill-being are given for subjects in each SNP set (see also Supplementary Table S2) aGenes indicates the genes mapped by the SNP set (Figure S6), where genes can be mapped by more than one SNP set I. Zwir et al. Table 2 Description of the five character profiles (supersets) and composite character sets identified by PGMRA from profiles of TCI subscales (Y =yes) Char sets Supersets Name sd1 sd2 sd3 sd4 sd5 co1 co2 co3 co4 co5 st1 st2 st3 Lsd1 Lsd2 Lsd3 Lsd4 Lsd5 Lco1 Lco2 Lco3 Lco4 Lco5 Lst1 Lst2 Lst3 #S Well Being Ill Being C_14_8 1 Resourceful Y Y Y 79 0.01 0.01 C_10_7 1 Y Y Y Y 102 0.41 0 C_10_6 1 Y Y Y Y 33 0.03 0.38 C_14_13 2 Organized Y Y Y Y Y Y Y 92 0.09 0 C_14_9 2 Y Y Y Y Y Y Y Y 42 0.02 0.29 C_9_8 2 Y YYYYYYY Y Y Y Y 72 0.12 0 C_12_9 2 Y Y Y Y Y Y Y Y 41 0.54 0 C_6_5 2 Y Y YYYYYYYY Y Y 56 0.07 0 C_8_7 2 Y Y YYYYYYYY Y Y Y 161 0.04 0 C_5_1 2 Y Y YYYYYYYY Y Y Y 169 0.05 0 C_3_1 2 Y Y YYYYYYYY YYY2930 0 C_4_4 2 Y Y YYYYYYYY Y Y Y 190 0.01 0 C_7_7 2 Y Y Y Y Y Y Y Y Y Y Y Y 101 0.07 0 C_9_6 2 Y Y Y Y Y Y Y Y Y Y YYY610.11 0.02 C_7_5 2 Y Y Y Y Y 34 0.5 0.06 C_9_1 2 Y Y Y Y Y Y Y Y Y 46 0 0.02 C_15_5 3 Creative Y Y 100 0.72 0.03 C_12_7 3 YY 52 0.9 0.04 C_11_3 3 YYY 34 0.97 0.03 C_13_1 3 Y Y Y 42 0.9 0.02 C_14_1 3 Y Y Y Y Y Y Y Y 28 0.25 0.11 C_7_2 3 Y Y Y Y Y Y Y Y Y 66 0.98 0 C_8_8 3 Y Y Y Y Y Y Y Y Y Y Y 39 1 0 C_4_3 3 Y Y Y Y Y Y Y Y Y Y Y 72 0.97 0 C_5_5 3 Y Y Y Y Y Y Y Y Y Y Y 73 1 0 C_6_1 3 Y Y Y Y Y Y Y Y Y Y Y 32 1 0 C_3_3 3 Y Y YYYYYYYYYYY 135 0.92 0 C_9_2 3 YYYY Y Y 4 0.05 0.2 C_15_7 4 Dependent Y Y 121 0.03 0.02 C_14_5 4 Y Y Y 55 0 0.29 C_15_13 4 YYY 29 0 0.79 C_12_6 4 Y Y Y Y Y Y 44 0.09 0.41 C_4_2 4 Y Y Y Y Y Y Y 40 0 0.45 C_7_4 4 Y Y Y Y Y Y Y 23 0 0.74 C_5_3 4 Y Y Y Y Y Y Y Y Y 48 0.02 0.29 C_6_3 4 Y Y Y Y Y Y YYY370 0.32 C_9_5 4 Y Y Y Y Y Y Y Y Y Y Y 31 0 0.26 C_10_2 5 Apathetic Y Y Y 116 0.01 0 C_11_4 5 YYY500 0.28 C_8_3 5 Y YYY390 0.64 C_14_11 5 YYYY YY 620 0.47 C_10_8 5 Y Y Y Y Y Y YYY330 0.79 C_3_2 5 YYYYYYYYYY 530 1 C_12_5 5 Y Y Y Y YYY220 0.09 C_14_7 5 YYYYY 40 1 Uncovering the complex genetics of human character 6. Arnedo J, Svrakic DM, Del Val C, Romero-Zaliz R, HernandezCuervo H, Fanous AH, et al. Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies. Am J Psychiatry. 2015;172: 139–53. 7. Cervone D. Personality architecture: within-person structures and processes. Annu Rev Psychol. 2005;56:423–52. 8. Cicchetti D, Rogosch FA. Equifinality and multifinality in developmental psychopatology. Dev Psychopathol. 1996;8:597– 600. 9. de Vries PJ. Genetics and neuropsychiatric disorders: genomewide, yet narrow. Nat Med. 2009;15:850–1. 10. Gillespie NA, Cloninger CR, Heath AC, Martin NG. The genetic and environmental relationship between Cloninger's dimensions of temperament and character. Pers Individ Dif. 2003;35:1931–46. 11. Tellegen A, Lykken TD, Bouchard TJ, Wilcox KJ, Segal NL, Rich S. Personality similarity in twins reared apart and together. J Personal Social Psychol. 1988;54:1031–9. 12. Yamagata S, Suzuki A, Ando J, Ono Y, Kijima N, Yoshimura K, et al. Is the genetic structure of human personality universal? A cross-cultural twin study from North America, Europe, and Asia. J Pers Soc Psychol. 2006;90:987–98. 13. Finkel D, McGue M. Sex differences and nonadditivity in heritability of the Multidimensional Personality Questionnaire Scales. J Pers Soc Psychol. 1997;72:929–38. 14. Keller MC, Coventry WL, Heath AC, Martin NG. Widespread evidence for non-additive genetic variation in Cloninger's and Eysenck's Personality Dimensions using a Twin Plus Sibling Design. Behav Genet. 2005;35:707–21. 15. Verweij KJ, Yang J, Lahti J, Veijola J, Hintsanen M, PulkkiRaback L, et al. Maintenance of genetic variation in human personality: testing evolutionary models by estimating heritability due to common causal variants and investigating the effect of distant inbreeding. Evolution. 2012;66:3238–51. 16. Eaves LJ, Heath AC, Neale MC, Hewitt JK, Martin NG. Sex differences and non-additivity in the effects of genes on personality. Twin Res. 1998;1:131–7. 17. Eaves LJ, Heath AC, Martin NG, Maes HH, Neale MC, Kendler KS, et al. Comparing the biological and cultural inheritance of personality and social attitudes in the Virginia 30,000 study of twins and their relatives. Twin Res. 1999;2:62–80. 18. Service SK, Verweij KJ, Lahti J, Congdon E, Ekelund J, Hintsanen M, et al. A genome-wide meta-analysis of association studies of Cloninger's Temperament Scales. Transl Psychiatry. 2012;2:e116. 19. Verweij KJH, Zietsch BP, Medland SE, Gordon SD, Benyamin B, Nyholt DR, et al. A genome-wide association study of Cloninger's temperament scales: Implications for the evolutionary genetics of personality. Biol Psychol. 2010;85:306–17. 20. de Moor MH, Costa PT, Terracciano A, Krueger RF, de Geus EJ, Toshiko T, et al. Meta-analysis of genome-wide association studies for personality. Mol Psychiatry. 2012;17:337–49. 21. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010;11: 446–50. 22. Williams SM, Haines JL. Correcting away the hidden heritability. Ann Hum Genet. 2011;75:348–50. 23. Cloninger CR, Svrakic DM, Przybeck TR. A psychobiological model of temperament and character. Arch Gen Psychiatry. 1993;50:975–90. 24. Van Schuerbeek P, Baeken C, De Raedt R, De Mey J, Luypaert R. Individual differences in local gray and white matter volumes reflect differences in temperament and character: a voxel-based morphometry study in healthy young females. Brain Res. 2011; 1371:32–42. 25. Quoidbach J, Hansenne M, Mottet C. Personality and mental time travel: a differential approach to autonoetic consciousness. Conscious Cogn. 2008;17:1082–92. 26. Gardini S, Cloninger CR, Venneri A. Individual differences in personality traits reflect structural variance in specific brain regions. Brain Res Bull. 2009;79:265–70. 27. Lei X, Chen C, Xue F, He Q, Liu Q, Moyzis RK, et al. Fiber connectivity between the striatum and cortical and subcortical regions is associated with temperaments in Chinese males. Neuroimage. 2014;89:226–34. 28. Van Schuerbeek P, Baeken C, Luypaert R, De Raedt R, De Mey J. Does the amygdala response correlate with the personality trait 'harm avoidance' while evaluating emotional stimuli explicitly? Behav Brain Funct. 2014;10:18. 29. Cloninger CR. What makes people healthy, happy, and fulfilled in the face of current world challenges? Mens Sana Monogr. 2013;11:16–24. 30. Arnedo J, Mamah D, Baranger DA, Harms MP, Barch DM, Svrakic DM, et al. Decomposition of brain diffusion imaging data uncovers latent schizophrenias with distinct patterns of white matter anisotropy. Neuroimage. 2015;120:43–54. 31. Arnedo J, del Val C, de Erausquin GA, Romero-Zaliz R, Svrakic D, Cloninger CR, et al. PGMRA: a web server for (phenotype x genotype) many-to-many relation analysis in GWAS. Nucleic Acids Res. 2013;41(Web Server issue):W142–9. 32. Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, et al. Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet. 2010;86:929–42. 33. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89:82–93. 34. Raitakari OT, Juonala M, Ronnemaa T, Keltikangas-Jarvinen L, Rasanen L, Pietikainen M, et al. Cohort profile: the cardiovascular risk in Young Finns Study. Int J Epidemiol. 2008; 37:1220–6. 35. Keltikangas-Jarvinen L, Raikkonen K, Ekelund J, Peltonen L. Nature and nurture in novelty seeking. Mol Psychiatry. 2004; 9:308–11. 36. Jokela M, Keltikangas-Jarvinen L, Kivimaki M, Puttonen S, Elovainio M, Rontu R, et al. Serotonin receptor 2A gene and the influence of childhood maternal nurturance on adulthood depressive symptoms. Arch Gen Psychiatry. 2007;64:356–60. 37. Jokela M, Lehtimaki T, Keltikangas-Jarvinen L. The serotonin receptor 2A gene moderates the influence of parental socioeconomic status on adulthood harm avoidance. Behav Genet. 2007;37:567–74. 38. Keltikangas-Jarvinen L, Jokela M, Hintsanen M, Salo J, Hintsa T, Alatupa S, et al. Does genetic background moderate the association between parental education and school achievement? Genes Brain Behav. 2010;9:318–24. 39. Lahti J, Raikkonen K, Ekelund J, Peltonen L, Raitakari O, Keltikangas-Jarvinen K. Socio-demographic characteristics moderate the association between DRD4 and novelty seeking. Pers Individ Dif. 2006;40:533–43. 40. Jokela M, Lehtimaki T, Keltikangas-Jarvinen L. The influence of urban/rural residency on depressive symptoms is moderated by the serotonin receptor 2A gene. Am J Med Genet B Neuropsychiatr Genet. 2007;144B:918–22. 41. Elovainio M, Jokela M, Kivimaki M, Pulkki-Raback L, Lehtimaki T, Airla N, et al. Genetic variants in the DRD2 gene moderate the relationship between stressful life events and depressive symptoms in adults: cardiovascular risk in young Finns study. Psychosom Med. 2007;69:391–5. 42. Josefsson K, Jokela M, Cloninger CR, Hintsanen M, Salo J, Hintsa T, et al. Maturity and change in personality: developmental I. Zwir et al. trends of temperament and character in adulthood. Dev Psychopathol. 2013;25:713–27. 43. Cook TB, Brenner LA, Cloninger CR, Langenberg P, Igbide A, Giegling I, et al. Latent Infection with toxoplasma gondii: association with trait aggression and impulsivity in healthy adults. J Psychiatr Res. 2015;60:87–94. 44. Reeves GM, Mazaheri S, Snitker S, Langenberg P, Giegling I, Hartmann AM, et al. A positive association between T. gondii seropositivity and obesity. Front Public Health. 2013;1:73. 45. Gombojav B, Song YM, Lee K, Yang S, Kho M, Hwang YC, et al. The healthy twin study, Korea updates: resources for omics and genome epidemiology studies. Twin Res Hum Genet. 2013;16:241–5. 46. Sung J, Cho SI, Lee K, Ha M, Choi EY, Choi JS, et al. Healthy twin: a twin-family study of Korea--protocols and current status. Twin Res Hum Genet. 2006;9:844–8. 47. Cloninger CR, Przybeck TR, Svrakic DM, Wetzel RD. The temperament and character inventory (tci): a guide to its development and use. Washington University Center for Psychobiology of Personality: St. Louis, MO; 1994. 48. Cloninger CR, Zohar AH. Personality and the perception of health and happiness. J Affect Disord. 2011;128:24–32. 49. Svrakic DM, Whitehead C, Przybeck TR, Cloninger CR. Differential diagnosis of personality disorders by the seven-factor model of temperament and character. Arch Gen Psychiatry. 1993;50:991–9. 50. Cloninger CR. A practical way to diagnosis personality disorder: a proposal. J Pers Disord. 2000;14:99–108. 51. Zohar AH, Cloninger CR, McCraty R. Personality and heart rate variability: exploring pathways from personality to cardiac coherence and health. J Social Sci. 2013;1:32–9. 52. Cichocki A, Zdunek R, Phan AH, Amari S-i. Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. John Wiley & Sons, Inc; 2009. 53. Smith EN, Chen W, Kahonen M, Kettunen J, Lehtimaki T, Peltonen L, et al. Longitudinal genome-wide association of cardiovascular disease risk factors in the Bogalusa heart study. PLoS Genet. 2010;6:e1001094. 54. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81:559–75. 55. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9. 56. Zwir I, Shin D, Kato A, Nishino K, LatifiT, Solomon F, et al. Dissecting the PhoP regulatory network of Escherichia coli and Salmonella enterica. Proc Natl Acad Sci USA. 2005;102: 2862–7. 57. Zwir I, Huang H, Groisman EA. Analysis of differentiallyregulated genes within a regulatory network by GPS genome navigation. Bioinformatics. 2005;21:4073–83. 58. Romero-Zaliz R, Del Val C, Cobb JP, Zwir I. Onto-CC: a web server for identifying gene ontology conceptual clusters. Nucleic Acids Res. 2008;36(Web Server issue):W352–7. 59. Harari O, Park SY, Huang H, Groisman EA, Zwir I. Defining the plasticity of transcription factor binding sites by deconstructing DNA consensus sequences: the PhoP-binding sites among gamma/enterobacteria. PLoS Comput Biol. 2010;6: e1000862. 60. Gupta CN, Castro E, Rachkonda S, van Erp TGM, Potkin S, Ford JM, et al. Biclustered independent component analysis for complex biomarker and subtype identification from structural magnetic resonance images in schizophrenia. Front Psychiatry. 2017;8:179. 61. Marquand AF, Wolfers T, Mennes M, Buitelaar J, Beckmann CF. Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1:433–47. 62. Le Roux J, Hershey JR, Weininger F. Deep NMF for speech separation. IEEE Internatiional Conference on Acoustics, Speech, and Signal Processing (ICASSP). Mitsubishi Electric Research Laboratories, Inc: Cambridge, MA; 2015. 63. International Schizophrenia C, Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748–52. 64. 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; 467:832–8. 65. Eley DS, Cloninger CR, Walter L, Laurence C, Synnott R, Wilkinson D. The relationship between resilience and personality traits in doctors: implications for enhancing well-being. PeerJ. 2013;1:e216. Affiliations Igor Zwir1,2 ●Javier Arnedo2●Coral Del-Val2●Laura Pulkki-Råback3●Bettina Konte 4●Sarah S. Yang5● Rocio Romero-Zaliz2●Mirka Hintsanen6●Kevin M. Cloninger7●Danilo Garcia 8,9 ●Dragan M. Svrakic1● Sandor Rozsa1●Maribel Martinez1●Leo-Pekka Lyytikäinen 10 ●Ina Giegling4,11 ●Mika Kähönen12 ● Helena Hernandez-Cuervo13 ●Ilkka Seppälä 10 ●Emma Raitoharju10 ●Gabriel A. de Erausquin14 ●Olli Raitakari15 ● Dan Rujescu4●Teodor T. Postolache16,17 ●Joohon Sung5●Liisa Keltikangas-Järvinen3●Terho Lehtimäki10 ● C. Robert Cloninger 1,18 1Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA 2Department of Computer Science, University of Granada, Granada, Spain 3Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland 4Department of Psychiatry, Martin-Luther-University HalleWittenberg, Halle, Germany 5Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea 6University of Oulu, Unit of Psychology, Faculty of Education, Oulu, Finland 7Anthropedia Foundation, St. Louis, MO, USA Uncovering the complex genetics of human character 8Department of Psychology, University of Gothenburg, Gothenburg, Sweden 9Blekinge Centre of Competence, Blekinge County Council, Karlskrona, Sweden 10 Department of Clinical Chemistry, Fimlab Laboratories, Faculty of Medicine and Life Sciences, Finnish Cardiovascular Research Center-Tampere, University of Tampere, Tampere, Finland 11 Ludwig-Maximilian University, University Clinic, Munich, Germany 12 Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland 13 Departments of Psychiatry and Neurosurgery, University of South Florida, Tampa, FL, USA 14 Institute of Neurosciences, Department of Psychiatry and Neurology, School of Medicine, University of Texas Rio-Grande Valley, Harlingen, TX, USA 15 Research Centre of Applied and Preventive Cardiovascular Medicine, Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, University of Turku, Turku, Finland 16 Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA 17 Rocky Mountain Mental Illness, Research, Education, Clinical Center for Veteran Suicide Prevention, Denver, CO, USA 18 Department of Genetics, Department of Psychological and Brain Sciences, and School of Medicine, School of Arts and Sciences, Washington University, St. Louis, MO, USA I. Zwir et al.