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Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic-phenotypic networks and environmental interactions related to human temperament. 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 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37-53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.

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.

Identifying distinctive subtypes of schizophrenia could ultimately enhance diagnostic and prognostic accuracy. We aimed to uncover neuroanatomical subtypes of chronic schizophrenia patients to test whether stratification can enhance computer-aided discrimination of patients from control subjects. Unsupervised, data-driven clustering of structural MRI (sMRI) data was used to identify 2 subtypes of schizophrenia patients drawn from a US-based open science repository (n = 71) and we quantified classification improvements compared to controls (n = 74) using supervised machine learning. We externally validated the unsupervised and supervised learning models in a heterogeneous German validation sample (n = 316), and characterized symptom, cognition, and longitudinal symptom change signatures. Stratification improved classification accuracies from 68.5% to 73% (subgroup 1) and 78.8% (subgroup 2), respectively. Increased accuracy was also found when models were externally validated, and an average gain of 9% was found in supplementary analyses. The first subgroup was associated with cortical and subcortical volume reductions coupled with substantially longer illness duration, whereas the second subgroup was mainly characterized by cortical reductions, reduced illness duration, and comparatively less negative symptoms. Individuals within each subgroup could be identified using just 10 clinical questions at an accuracy of 81.2%, and differential cognitive and symptom course signatures were suggested in multivariate analyses. Our findings suggest that sMRI-based subtyping enhances the neuroanatomical discrimination of schizophrenia by identifying generalizable brain patterns that align with a clinical staging model of the disorder. These findings could be used to improve illness stratification for biomarker-based computer-aided diagnoses. [ABSTRACT FROM AUTHOR]

Background.Dysfunctional behavioural and neural processing of reward has been found in currently depressed individuals. However, little is known about altered reward processing in remitted depressed individuals.Method.A total of 23 medication-free individuals with remitted major depressive disorder (rMDD) and 23 matched healthy controls (HCs) performed a reward task during functional magnetic resonance imaging. We also investigated reward dependence, novelty seeking and harm avoidance using the Tridimensional Personality Questionnaire and their association with neural responses of reward processing.Results.Compared to HCs, individuals with rMDD exhibited enhanced responses to reward-predicting cues in the hippocampus, amygdala and superior frontal gyrus. When reward was delivered, rMDD subjects did not significantly differ from HCs. In both groups neural activity during reward anticipation was negatively correlated with harm avoidance.Conclusions.Our results show that rMDD is characterized by hyperactivation in fronto-limbic regions during reward anticipation. Alterations in neural activation during reward processing might reflect an increased effort in remitted depressed individuals to allocate neural activity for executive and evaluative processes during anticipatory reward processing. [ABSTRACT FROM PUBLISHER]

Our study tested whether an association of the 861C allele of the serotonin 5-HT1B gene (HTR1B) with antisocial alcoholism exists in the German population. The HTR1B G861C polymorphism was genotyped in 588 subjects of German descent, comprising 250 non-alcoholic controls and 338 alcohol-dependent subjects, of whom 56 exhibited a dissocial personality disorder (DSPD). The Tridimensional Personality Questionnaire was assessed in 109 alcohol-dependent males to explore an effect of the 861C allele to risk-taking behaviour. Our results revealed no evidence for an association of the 861C allele with antisocial alcoholism (p > 0.63). There were no significant differences in the personality traits, novelty-seeking, harm avoidance and reward dependence between 46 male alcoholics carrying the 861C allele compared to those 63 alcoholics lacking it (p > 0.52). Our results do not provide evidence that the 861C allele contributes a substantial vulnerability effect to antisocial behavior in German alcohol-dependent subjects. [ABSTRACT FROM AUTHOR]