Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals1. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample1,2 of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Singlenucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are referentially 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 behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment 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 diseases.
Aysu Okbay, Jonathan P. Beauchamp, Mark Alan Fontana, James J. Lee, Tune H. Pers, Cornelius A. Rietveld & Patrick Turley, et al. Genome-wide association study identifies 74 loci associated with educational attainment, doi:10.1038/nature17671.
The Social Science Genetic Association Consortium
We, the Social Science Genetic Association Consortium (SSGAC), aim to bring together the expertise of medical geneticists and social scientists to study how a range of health-relevant outcomes are influenced by specific genetic variants, the environment (including lifestyle), and their interaction. In accessing the U.K. Biobank data, we are specifically interested in the following health-relevant outcomes: cognitive function, dementia, depression, smoking, and alcohol drinking. Our research will contribute to quantifying how several risk factors (e.g. lifestyle, environment, genes), both separately and in combination, influence public health and well-being. Incorporating insights from the social sciences and investigating social scientific outcomes helps to achieve this objective. For example, a GWAS on subjective well-being in a very large sample could identify genetic factors associated with (absence of) depression that would not be possible to identify by studying depression directly in a much smaller sample. Furthermore, accurate polygenic risk scores can be used to study how lifestyle and environmental factors mediate genetic effects on health. We will use several methods, e.g.:
? Genomewide association studies (GWAS) that aim to identify individual genetic variants associated with a particular outcome.
? GWAS of a ?proxy phenotype??a biologically-distal phenotype available in larger samples?to identify candidate genetic variants for association with a health-relevant outcome available in smaller samples.
? Estimation of economic and statistical models of health-relevant outcomes as a function of genetic variants, environmental factors, and their interaction. We will typically use all available observations in the UKB that (i) are of European decent, (ii) have been successfully genotyped, and (iii) have measures of the phenotype(s) under investigation.
|Lead investigator:||Professor Daniel Benjamin|
|Lead institution:||National Bureau of Economic Research|
6 related Returns
|Return ID||App ID||Description||Archive Date|
|3065||11425||Are Bigger Brains Smarter? Evidence From a Large-Scale Preregistered Study||14 Dec 2020|
|718||11425||Genetic variants associated with subjective well-being, depressive symptoms,and neuroticism identified through genome-wide analysis||17 Oct 2017|
|2159||11425||Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences||7 Apr 2020|
|2887||11425||Multi-trait analysis of genome-wide association summary statistics using MTAG||27 Nov 2020|
|1782||11425||Pleiotropy-robust Mendelian Randomization||30 Sep 2019|
|3586||11425||Resource Profile and User Guide of the Polygenic Index Repository||24 Jun 2021|
|718||Gemome-wide association study identifies 74 loci associated with educational attainment||Okbay et al||2016||Nature 2016|