1. |
- Smith, Jennifer A, et al.
(author)
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Genome-wide association study identifies 74 loci associated with educational attainment
- 2016
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In: Nature (London). - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 533:7604, s. 539-542
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Journal article (peer-reviewed)abstract
- 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.
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2. |
- Frazier-Wood, Alexis C., et al.
(author)
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Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses
- 2016
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In: Nature Genetics. - : Nature Research (part of Springer Nature). - 1061-4036 .- 1546-1718. ; 48, s. 624-
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Journal article (peer-reviewed)abstract
- Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (vertical bar(p) over cap vertical bar approximate to 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.
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3. |
- Becker, Joel, et al.
(author)
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Resource profile and user guide of the Polygenic Index Repository
- 2021
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In: Nature Human Behaviour. - : Nature Research (part of Springer Nature). - 2397-3374. ; 51:6, s. 694-695
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Journal article (peer-reviewed)abstract
- Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the ‘additive SNP factor’. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
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4. |
- Lee, James J, et al.
(author)
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Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.
- 2018
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In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 50:8, s. 1112-1121
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Journal article (peer-reviewed)abstract
- Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1million individuals and identify 1,271independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
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5. |
- Okbay, Aysu, et al.
(author)
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Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals.
- 2022
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In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 54:4, s. 437-449
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Journal article (peer-reviewed)abstract
- 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.
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6. |
- Benjamin, Daniel J., et al.
(author)
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Redefine statistical significance
- 2018
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In: Nature Human Behaviour. - : Nature Research (part of Springer Nature). - 2397-3374. ; 2:1, s. 6-10
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Journal article (other academic/artistic)
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