| Application: | 11425, The Social Science Genetic Association Consortium |
| Title: | Resource Profile and User Guide of the Polygenic Index Repository |
| Size: | 138.2 MB |
| Archived: | 24 Jun 2021 |
| Stability: | Complete |
| Personal: | Contains individual-level data |
Return 3586
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Notes
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.Application 11425
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 |
| 717 | 11425 | Gemome-wide association study identifies 74 loci associated with educational attainment | 17 Oct 2017 |
| 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 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3587 | Resource profile and user guide of the Polygenic Index Repository | Joel Becker (+41) | 2021 | Nature Human Behaviour |
Enabling scientific discoveries that improve human health