SummaryEducational attainment (EA) is strongly influenced by social and other environmental factors, but genetic factors are also estimated to account for at least 20% of the variation across individuals1. We report the results of a genome-wide association study (GWAS) for EA that extends our earlier discovery sample1,2 of 101,069 individuals to 293,723 individuals, and a replication in an independent sample of 111,349 individuals from the UK Biobank. We now identify 74 genome-wide significant loci associated with number of years of schooling completed. Single-nucleotide polymorphisms (SNPs) associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioral phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because EA is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric disease.

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

BackgroundRegular physical activity (PA) confers many positive effects on health and well-being. Sedentary behavior (SB), in turn, is a risk factor for health, regardless of the level of moderate to vigorous PA. The present study describes the levels of objectively measured SB, breaks in SB, standing still and PA among Finnish adults.MethodsThis cross-sectional analysis is based on the sub-sample of the population-based Health 2011 Study of Finnish adults. The study population consisted of 18-to-85-year old men and women who wore a waist-worn triaxial accelerometer (Hookie AM 20) for at least 4 days, for at least 10 h per day (n = 1587) during a week. PA and SB were objectively assessed from the raw accelerometric data using novel processing and analysis algorithms with mean amplitude deviation as the processing method. The data was statistically analyzed using cross-tabulations, analysis of variance and analysis of covariance.ResultsThe participants were on average 52 years old, 57 % being women. Participants were sedentary 59 % of their waking wear time, mainly sitting. They spent 17 % of the time standing still, 15 % in light intensity PA, 9 % in moderate PA and less than 1 % in vigorous PA. Participants aged 30–39 years had the highest number of breaks in SB per day. Younger participants (<30 years of age) had more moderate and vigorous PA than older ones (≥60 years of age), and 30–60-year-olds had the greatest amount of light PA.ConclusionsParticipants spent nearly 60 % of their waking time sedentary, and the majority of their daily PA was light. From a public health perspective it is important to find effective ways to decrease SB as well as to increase the level of PA. Our analysis method of raw accelerometer data may allow more precise assessment of dose-response relationships between objectively measured PA and SB and various indicators of health and well-being.

Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members' polygenic profile score for education to predict their parents' longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total n deaths = 79,702) and ∼2.4% lower risk for fathers (total n deaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity.genetics | education | longevity | prediction | polygenic score

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