Intelligence — the ability to learn, reason and solve problems — is at the forefront of behavioural genetic research. Intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait. Recent genome-wide association studies have successfully identified inherited genome sequence differences that account for 20% of the 50% heritability of intelligence. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of genetic variants. In this Review, we highlight the latest innovations and insights from the genetics of intelligence and their applications and implications for science and society.

Cognitive or intellectual investment theories propose that the development of intelligence is partially influenced by personality traits, in particular by so-called investment traits that determine when, where, and how people invest their time and effort in their intellect. This investment, in turn, is thought to contribute to individual differences in cognitive growth and the accumulation of knowledge across the life span. We reviewed the psychological literature and identified 34 trait constructs and corresponding scales that refer to intellectual investment. The dispositional constructs were further classified into 8 related trait categories that span the construct space of intellectual investment. Subsequently, we sought to estimate the association between the identified investment traits and indicators of adult intellect, including measures of crystallized intelligence, academic performance (e.g., grade point average), college entry tests, and acquired knowledge. A meta-analysis of 112 studies with 236 coefficients and an overall sample of 60,097 participants indicated that investment traits were mostly positively associated with adult intellect markers. Meta-analytic coefficients ranged considerably, from 0 to .58, with an average estimate of .30. We concluded that investment traits are overall positively related to adult intellect; the strength of investment-intellect associations differs across trait scales and markers of intellect; and investment traits have a diverse, multifaceted nature. The meta-analysis also identified areas of inquiry that are currently lacking in empirical research. Limitations, implications, and future directions are discussed.

Low socioeconomic status (SES) children perform on average worse on intelligence tests than children from higher SES backgrounds, but the developmental relationship between intelligence and SES has not been adequately investigated. Here, we use latent growth curve (LGC) models to assess associations between SES and individual differences in the intelligence starting point (intercept) and in the rate and direction of change in scores (slope and quadratic term) from infancy through adolescence in 14,853 children from the Twins Early Development Study (TEDS), assessed 9 times on IQ between the ages of 2 and 16 years. SES was significantly associated with intelligence growth factors: higher SES was related both to a higher starting point in infancy and to greater gains in intelligence over time. Specifically, children from low SES families scored on average 6 IQ points lower at age 2 than children from high SES backgrounds; by age 16, this difference had almost tripled. Although these key results did not vary across girls and boys, we observed gender differences in the development of intelligence in early childhood. Overall, SES was shown to be associated with individual differences in intercepts as well as slopes of intelligence. However, this finding does not warrant causal interpretations of the relationship between SES and the development of intelligence.

A genome-wide polygenic score (GPS), derived from a 2013 genome-wide association study (N=127,000), explained 2% of the variance in total years of education (EduYears). In a follow-up study (N=329,000), a new EduYears GPS explains up to 4%. Here, we tested the association between this latest EduYears GPS and educational achievement scores at ages 7, 12 and 16 in an independent sample of 5825 UK individuals. We found that EduYears GPS explained greater amounts of variance in educational achievement over time, up to 9% at age 16, accounting for 15% of the heritable variance. This is the strongest GPS prediction to date for quantitative behavioral traits. Individuals in the highest and lowest GPS septiles differed by a whole school grade at age 16. Furthermore, EduYears GPS was associated with general cognitive ability (~3.5%) and family socioeconomic status (~7%). There was no evidence of an interaction between EduYears GPS and family socioeconomic status on educational achievement or on general cognitive ability. These results are a harbinger of future widespread use of GPS to predict genetic risk and resilience in the social and behavioral sciences.

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWAS), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWAS, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWAS of cognitive, medical, and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body-mass index (BMI), and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK-representative sample of 6,710 unrelated adolescents. The MPS approach predicted 11% variance in educational achievement, 4.8% in general cognitive ability, and 5.4% in BMI in an independent test set, improving prediction by 1.1%, 1.1%, and 1.6% variance explained over the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

Genome-wide polygenic scores (GPS), which aggregate the effects of thousands of DNA variants from genome-wide association studies (GWAS), have the potential to make genetic predictions for individuals. We conducted a systematic investigation of associations between GPS and many behavioral traits, the behavioral phenome. For 3152 unrelated 16-year-old individuals representative of the United Kingdom, we created 13 GPS from the largest GWAS for psychiatric disorders (for example, schizophrenia, depression and dementia) and cognitive traits (for example, intelligence, educational attainment and intracranial volume). The behavioral phenome included 50 traits from the domains of psychopathology, personality, cognitive abilities and educational achievement. We examined phenome-wide profiles of associations for the entire distribution of each GPS and for the extremes of the GPS distributions. The cognitive GPS yielded stronger predictive power than the psychiatric GPS in our UK-representative sample of adolescents. For example, education GPS explained variation in adolescents’ behavior problems (~0.6%) and in educational achievement (~2%) but psychiatric GPS were associated with neither. Despite the modest effect sizes of current GPS, quantile analyses illustrate the ability to stratify individuals by GPS and opportunities for research. For example, the highest and lowest septiles for the education GPS yielded a 0.5 s.d. difference in mean math grade and a 0.25 s.d. difference in mean behavior problems. We discuss the usefulness and limitations of GPS based on adult GWAS to predict genetic propensities earlier in development.

Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment, with highly genetically correlated traits, to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at ages 12 and 16, we show that we can now predict up to 11 percent of the variance in intelligence and 16 percent in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. We found that multi-trait genomic methods were effective in boosting predictive power. Prediction accuracy varied across polygenic score approaches, however results were similar for different multi-trait and polygenic score methods. We discuss general caveats of multi-trait methods and polygenic score prediction, and conclude that polygenic scores for educational attainment and intelligence are currently the most powerful predictors in the behavioural sciences.

Over the past century, academic performance has become the gatekeeper to institutions of higher education, shaping career paths and individual life trajectories. Accordingly, much psychological research has focused on identifying predictors of academic performance, with intelligence and effort emerging as core determinants. In this article, we propose expanding on the traditional set of predictors by adding a third agency: intellectual curiosity. A series of path models based on a meta-analytically derived correlation matrix showed that (a) intelligence is the single most powerful predictor of academic performance; (b) the effects of intelligence on academic performance are not mediated by personality traits; (c) intelligence, Conscientiousness (as marker of effort), and Typical Intellectual Engagement (as marker of intellectual curiosity) are direct, correlated predictors of academic performance; and (d) the additive predictive effect of the personality traits of intellectual curiosity and effort rival that the influence of intelligence. Our results highlight that a "hungry mind" is a core determinant of individual differences in academic achievement.