Genome-wide association study identifies 74 loci associated with educational attainment
By A. Okbay, J. Beauchamp, M. Fontana, James J. Lee, T. Pers, C. A. Rietveld, P. Turley, Guo-Bo Chen, V. Emilsson, S. Meddens and 246 more. Nature, 2016
1,288 citations recorded by the source corpus.
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 individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample 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. Single-nucleotide polymorphisms 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 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.
Subfields: Genetics, Pediatrics, Perinatology and Child Health
View Paper: https://www.semanticscholar.org/paper/acac8b6183d26c8d0133a08544513fbd0a2f201b
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In Genetics
- George Davey Smith
- Gibran Hemani
- E. Sanderson
- J. Bowden
- F. Hartwig
- N. Davies
- Naomi R. Wray
- Jian Yang
In Pediatrics, Perinatology and Child Health
- J. Bowden
- Gibran Hemani
- E. Sanderson
- F. Hartwig
- N. Davies
- George Davey Smith
- Stephen Burgess
- S. Thompson
Citations in the corpus
- Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique (2016, methodology): “The genetic instrument rs3 was not available in the outcome GWAS.”
- A resource-efficient tool for mixed model association analysis of large-scale data (2019, methodology): “To further demonstrate the issue above, we selected 24 real phenotypes from the UKB to estimate the genetic variance using different methods (the same 24 traits as used in the UKB real data analyses except for education attainment which was reconstructed following the method in Ref.”
- An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings (2018, methodology): “The MVMR Egger analysis was repeated using the effect of each SNP on education, cognitive ability and BMI taken from GWAS estimates.(44,45,47) The magnitude of the estimated effects differ in this analysis, as the outcome variable is BMI rather than the natural log of BMI, although these results als”