| Title: | Overcoming attenuation bias in regressions using polygenic indices |
| Journal: | Nature Communications |
| Published: | 25 Jul 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37491308/ |
| DOI: | https://doi.org/10.1038/s41467-023-40069-4 |
| URL: | https://www.nature.com/articles/s41467-023-40069-4.pdf |
Publication 11645
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Abstract
Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.</p>
10 Authors
- Hans van Kippersluis
- Pietro Biroli
- Rita Dias Pereira
- Titus J. Galama
- Stephanie von Hinke
- S. Fleur W. Meddens
- Dilnoza Muslimova
- Eric A. W. Slob
- Ronald de Vlaming
- Cornelius A. Rietveld
1 Application
| Application ID | Title |
|---|---|
| 41382 | Gene-Environment Interplay in the Generation of Health and Education Inequalities (GEIGHEI) |
Enabling scientific discoveries that improve human health