| 1. |
- 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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| 2. |
- 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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| 3. |
- Johannesson, Magnus, et al.
(author)
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The Psychometric and Empirical Properties of Measures of Risk Preferences
- 2024
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In: SSRN Electronic Journal. - : Elsevier BV. - 1556-5068.
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Other publication (peer-reviewed)abstract
- We conduct a detailed examination of the psychometric and empirical properties of some commonly used survey-based measures of risk preferences in a population-based sample of 11,000 twins. Using a model that provides a general framework for making inferences about the component of measured risk attitudes that is not due to measurement error, we show the measurement-error adjustment leads to substantially larger estimates of the predictive power of risk attitudes, of the size of the gender gap, and of the magnitude of the sibling correlation. Risk attitudes are predictive of investment decisions, entrepreneurship, and health behaviors such as smoking and drinking, are robustly associated with cognitive ability and personality, and our estimates are often larger than those in the literature. One implication of our results is that the small amounts of variation that the risk measures have previously been reported to explain are in part artifacts of imperfect measurement.
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| 4. |
- Khalili, Bita, et al.
(author)
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Associations between common genetic variants and income provide insights about the socioeconomic health gradient
- 2024
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Other publication (other academic/artistic)abstract
- We conducted a genome-wide association study (GWAS) on income among individuals of European descent and leveraged the results to investigate the socio-economic health gradient (N=668,288). We found 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes. Our GWAS-derived polygenic index captures 1 - 4% of income variance, with only one-fourth attributed to direct genetic effects. A phenome-wide association study using this polygenic index showed reduced risks for a broad spectrum of diseases, including hypertension, obesity, type 2 diabetes, coronary atherosclerosis, depression, asthma, and back pain. The income factor showed a substantial genetic correlation (0.92, s.e. = .006) with educational attainment (EA). Accounting for EA's genetic overlap with income revealed that the remaining genetic signal for higher income related to better mental health but reduced physical health benefits and increased participation in risky behaviours such as drinking and smoking.
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