We review the genetically informed literature on the genetics of personality. Over the past century, quantitative genetic studies, using identical and fraternal twins, have demonstrated that differences in human personality are substantially heritable. We focus on more contemporary questions to which that basic observation has led. We examine whether differences in the heritability of personality are replicable across different traits, samples, and studies; how the heritability of personality relates to its reliability; and how behavior genetics can be employed in studies of validity, and we discuss the stability of personality in genetic and environmental variance. The appropriate null hypothesis in behavior genetics is not that genetic or environmental influence on personality is zero. Instead, we offer a phenotypic null hypothesis, which states that genetic variance is not an independent mechanism of individual differences in personality but rather a reflection of processes that are best conceptualized at the phenotypic level.

According to the proposal of the general factor of personality (GFP), socially desirable personality traits have been selected for throughout evolution because they increase fitness. However, it remains unknown whether people high on this factor actually behave in socially desirable ways or whether they simply endorse traits of positive valence. We separated these two sources of variance by having 619 participants respond to 120 personality adjectives organised into 30 quadruples balanced for content and valence (e.g. unambitious, easy-going, driven and workaholic tapped the trait achievement-striving). An exploratory six-factor solution fit well, and the factors resembled the Big Five. We subsequently extracted a higher-order factor from this solution, which appeared similar to the GFP. A Schmid-Leiman transformation of the higher-order factor, however, revealed that it clustered items of similar valence but opposite content (e.g. at the negative pole, unambitious and workaholic), rendering it an implausible description of evolved adaptive behaviour. Isolating this evaluative factor using exploratory structural equation modelling generated factors consisting of items of similar descriptive content but different valence (e.g. driven and workaholic), and the correlations among these factors were of small magnitude, indicating that the putative GFP capitalises primarily on evaluative rather than descriptive variance. Implications are discussed.

Married adults show better psychological adjustment and physical health than their separated/divorced or never-married counterparts. However, this apparent “marriage benefit” may be due to social selection, social causation, or both processes. Genetically informed research designs offer critical advantages for helping to disentangle selection from causation by controlling for measured and unmeasured genetic and shared environmental selection. Using young-adult twin and sibling pairs from the National Longitudinal Study of Adolescent Health (Harris, 2009), we conducted genetically informed analyses of the association between entry into marriage, cohabitation, or singlehood and multiple indices of psychological and physical health. The relation between physical health and marriage was completely explained by nonrandom selection. For internalizing behaviors, selection did not fully explain the benefits of marriage or cohabitation relative to being single, whereas for externalizing symptoms, marriage predicted benefits over cohabitation. The genetically informed approach provides perhaps the strongest nonexperimental evidence that these observed effects are causal.

In the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; American Psychiatric Association, 2013) personality disorder trait model, maladaptive behavior is located at one end of continuous scales. Widiger and colleagues, however, have argued that maladaptive behavior exists at both ends of trait continua. We propose that the role of evaluative variance differentiates these two perspectives and that once evaluation is isolated, maladaptive behaviors emerge at both ends of nonevaluative trait dimensions. In Study 1, we argue that evaluative variance is worthwhile to measure separately from descriptive content because it clusters items by valence regardless of content (e.g., lazy and workaholic; apathetic and anxious; gullible and paranoid; timid and hostile, etc.), which is unlikely to describe a consistent behavioral style. We isolate evaluation statistically (Study 2) and at the time of measurement (Study 3) to show that factors unrelated to valence evidence maladaptive behavior at both ends. We argue that nonevaluative factors, which display maladaptive behavior at both ends of continua, may better approximate ways in which individuals actually behave.

Objective Individual measures of socioeconomic status (SES) suppress genetic variance in Body Mass Index (BMI). Our objective was to examine the influence of both individual-level (i.e., educational attainment, household income) and macro-level (i.e., neighborhood socioeconomic advantage) SES indicators on genetic contributions to BMI. Method The study used education level data from 4,162 monozygotic (MZ) and 1,900 dizygotic (DZ) same-sex twin pairs (64% female); income level data from 3,498 MZ and 1,534 DZ (65% female) pairs; and neighborhood-level socioeconomic deprivation data from 2,327 MZ and 948 DZ pairs (65% female). Covariates included age (M = 40.4 ± 17.5 years), sex, and ethnicity. The co-twin control model was used to evaluate the mechanisms through which SES influences BMI (e.g., through genetic vs. environmental pathways), and a gene-by-environment interaction model was used to test whether residual variance in BMI, after controlling for the main effects of SES, was moderated by socioeconomic measures. Results SES significantly predicted BMI. The association was non-causal, however, and instead was driven primarily through a common underlying genetic background that tended to grow less influential as SES increased. After controlling for the main effect of SES, both genetic and non-shared environmental variance decreased with increasing SES. Conclusions The impact of individual and macro-level SES on BMI extends beyond its main effects. The influence of genes on BMI is moderated by individual and macro-level measures of SES, such that when SES is higher, genetic factors become less influential.