| Title: | Resource profile and user guide of the Polygenic Index Repository |
| Journal: | Nat Hum Behav |
| Published: | 1 Dec 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34140656/ |
| DOI: | https://doi.org/10.1038/s41562-021-01119-3 |
Publication 3587
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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.1 Author
- Becker J et al.
1 Application
| Application ID | Title |
|---|---|
| 11425 | The Social Science Genetic Association Consortium |
Enabling scientific discoveries that improve human health