Intelligence is highly heritable and a major determinant of human health and well-being. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

In the largest twin study to date, we confirmed that heritability for AD is high and that the same genetic factors are influential for both men and women. However, nongenetic risk factors also play an important role and might be the focus for interventions to reduce disease risk or delay disease onset.

Positive and negative affect, measured by the Bradburn Affect Balance Scale, were studied in a longitudinal sample spanning from 1971 to 1994. The sample (N = 2,804) represented 4 generations of families. Linear trend analyses compared generations over time for positive and negative affect and also examined the possible influences of neuroticism and extraversion on initial levels of affect and patterns of change in affect. Negative affect decreased with age for all generations, although the rate was attenuated among the oldest adults. Higher neuroticism scores also attenuated the decrease in negative affect across time. For positive affect, the younger and middle-aged adults showed marked stability, but the older group evidenced a small decrease over time. Higher levels of extraversion were related to more stability in positive affect.

While two factors are currently thought to underlie individual differences in schizotypal personality, three factors may best explain schizotypal traits. This study used confirmatory factor analysis to assess five competing models of schizotypal personality in the general population: null model, one-factor model, simple two-factor model, Kendler two-factor model, and three-factor model. The computer program LISREL was used to analyze Schizotypal Personality Questionnaire subscale scores that reflect the nine traits of schizotypal personality. The scores were obtained from (1) a sample of 822 undergraduates and (2) a replication sample of 102 subjects drawn from the community. Results indicate replicable support for a three-factor model reflecting cognitive-perceptual, interpersonal, and disorganized latent factors. Low intercorrelations between the first two factors and the lack of fit by a one-factor model are partially inconsistent with recent notions that a single vulnerability dimension underlies schizotypal personality. It is argued that future investigations should assess the correlates of all three schizotypal factors in clinical and nonclinical samples in addition to the two more traditional factors. It is hypothesized that three factors of schizophrenic symptomatology observed in recent studies may reflect an exaggeration of three analogous factors found in the general population.

General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16–102) and find 148 genome-wide significant independent loci (P < 5 × 10−8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.

Little is known about the contribution of genetic and environmental factors to risk for juvenile psychopathology. The Virginia Twin Study of Adolescent Behavioral Development allows these contributions to be estimated. A population-based, unselected sample of 1412 Caucasian twin pairs aged 8-16 years was ascertained through Virginia schools. Assessment of the children involved semi-structured face-to-face interviews with both twins and both parents using the Child and Adolescent Psychiatric Assessment (CAPA). Self-report questionnaires were also completed by parents, children, and teachers. Measures assessed DSM-III-R symptoms of Attention Deficit Hyperactivity Disorder (ADHD). Conduct Disorder, Oppositional Defiant Disorder, Overanxious Disorder, Separation Anxiety, and Depressive Disorder. Factorially derived questionnaire scales were also extracted. Scores were normalized and standardized by age and sex. Maximum likelihood methods were used to estimate contributions of additive and nonadditive genetic effects, the shared and unique environment, and sibling imitation or contrast effects. Estimates were tested for heterogeneity over sexes. Generally, monozygotic (MZ) twins correlated more highly than dizygotic (DZ) twins, parental ratings more than child ratings, and questionnaire scales more highly than interviews. DZ correlations were very low for measures of ADHD and DZ variances were greater than MZ variances for these variables. Correlations sometimes differed between sexes but those for boy-girl pairs were usually similar to those for like-sex pairs. Most of the measures showed small to moderate additive genetic effects and moderate to large effects of the unique individual environment. Measures of ADHD and related constructs showed marked sibling contrast effects. Some measures of oppositional behavior and conduct disorder showed shared environmental effects. There were marked sex differences in the genetic contribution to separation anxiety, otherwise similar genetic effects appear to be expressed in boys and girls. Effects of rater biases on the genetic analysis are considered. The study supports a widespread influence of genetic factors on risk to adolescent psychopathology and suggests that the contribution of different types of social influence may vary consistently across domains of measurement.

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Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating, and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within-to between-family GPS predictions of eight outcomes (anthropometric, cognitive, personality, and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modeling, simultaneously estimating within-and between-family effects for target-and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement, and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) much of this within-and between-family difference for cognitive traits disappeared after controlling for family socioeconomic status (SES), suggesting that SES is a major source of between-family prediction through rGE mechanisms. These results provide insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.