| Title: | Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives |
| Journal: | Nature Communications |
| Published: | 17 Jun 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32555176/ |
| DOI: | https://doi.org/10.1038/s41467-020-16829-x |
| URL: | https://www.nature.com/articles/s41467-020-16829-x.pdf |
| Citations: | 31 (6 in last 2 years) as of 8 Aug 2024 |
Publication 4768
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 risk scores are emerging as a potentially powerful tool to predict future phenotypes of target individuals, typically using unrelated individuals, thereby devaluing information from relatives. Here, for 50 traits from the UK Biobank data, we show that a design of 5,000 individuals with first-degree relatives of target individuals can achieve a prediction accuracy similar to that of around 220,000 unrelated individuals (mean prediction accuracy = 0.26 vs. 0.24, mean fold-change = 1.06 (95% CI: 0.99-1.13), P-value = 0.08), despite a 44-fold difference in sample size. For lifestyle traits, the prediction accuracy with 5,000 individuals including first-degree relatives of target individuals is significantly higher than that with 220,000 unrelated individuals (mean prediction accuracy = 0.22 vs. 0.16, mean fold-change = 1.40 (1.17-1.62), P-value = 0.025). Our findings suggest that polygenic prediction integrating family information may help to accelerate precision health and clinical intervention.7 Authors
- Buu Truong
- Xuan Zhou
- Jisu Shin
- Jiuyong Li
- Julius H. J. van der Werf
- Thuc D. Le
- S. Hong Lee
Enabling scientific discoveries that improve human health