A genome-wide association study of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent SNPs are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈ 2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.

The American Psychiatric Association estimates that 3 to 7 per cent of all school aged children are diagnosed with attention deficit hyperactivity disorder (ADHD). Even after correcting for general cognitive ability, numerous studies report a negative association between ADHD and educational achievement. With polygenic scores we examined whether genetic variants that have a positive influence on educational attainment have a protective effect against ADHD. The effect sizes from a large GWA metaanalysis of educational attainment in adults were used to calculate polygenic scores in an independent sample of 12-year-old children from the Netherlands Twin Register. Linear mixed models showed that the polygenic scores significantly predicted educational achievement, school performance, ADHD symptoms and attention problems in children. These results confirm the genetic overlap between ADHD and educational achievement, indicating that one way to gain insight into genetic variants responsible for variation in ADHD is to include data on educational achievement, which are available at a larger scale.

No abstract

The trend of socioeconomic differences in physical activity is largely unknown in Finland. In this study, we examined socioeconomic trends in leisure-time and commuting physical activity among Finns in 1978-2002. Nationwide data were derived from an annually repeated cross-sectional Finnish Adult Health Behavior Survey. People under the age of 25, students, the unemployed, and retirees were excluded from the analysis. The final data set included 25 513 women and 25 302 men. Socioeconomic variables included education, occupation, and household income. Odds ratios for being physically active and 95% confidence intervals were calculated. People with the lowest income were less leisure-time and commuting physically active. Among women, low occupational status was associated with high commuting physical activity whereas among men such an association was not found. No educational differences among men in leisure-time and commuting physical activity over time were found. Some indications were found that educational differences in leisure-time physical activity among women might have been reversed. Our data suggest that socioeconomic differences in leisure-time and commuting physical activity are quite small and have remained similar between 1978 and 2002.

An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9,662 Major Depressive Disorder (MDD) cases and 14,949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15,138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 [0.75–0.82] per standard deviation increase in EA. With data of 884,105 autosomal common SNPs, three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on 120,000 subjects) and MDD (using a ten-fold leave-one-out procedure in the current sample) (ii) bivariate Genomic-Relationship-Matrix Restricted Maximum Likelihood (GREML), and (iii) SNP effect concordance analysis (SECA). With these methods we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not due to measurable pleiotropic genetic effects, which suggests that environmental factors could be involved such as, for example, socioeconomic status.