| Title: | Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction |
| Journal: | Communications Biology |
| Published: | 13 Feb 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38351177/ |
| DOI: | https://doi.org/10.1038/s42003-024-05874-7 |
| URL: | https://www.nature.com/articles/s42003-024-05874-7.pdf |
Publication 8743
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Abstract
Polygenic risk score (PRS) is useful for capturing an individual's genetic susceptibility. However, previous studies have not fully exploited the potential of the risk factor PRS (RFPRS) for disease prediction. We explored the potential of integrating disease-related RFPRSs with disease PRS to enhance disease prediction performance. We constructed 112 RFPRSs and analyzed the association of RFPRSs with diseases to identify disease-related RFPRSs in 700 diseases, using the UK Biobank dataset. We uncovered 6157 statistically significant associations between 247 diseases and 109 RFPRSs. We estimated the disease PRSs of 70 diseases that exhibited statistically significant heritability, to generate RFDiseasemetaPRS - a combined PRS integrating RFPRSs and disease PRS - and compare the prediction performance metrics between RFDiseasemetaPRS and disease PRS. RFDiseasemetaPRS showed better performance for Nagelkerke's pseudo-R2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R2 considered by variance of R2 in 31 out of 70 diseases. Additionally, we assessed risk classification between two models by examining OR between the top 10% and remaining 90% individuals for the 31 diseases; RFDiseasemetaPRS exhibited better R2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.</p>
7 Authors
- Hyein Jung
- Hae-Un Jung
- Eun Ju Baek
- Shin Young Kwon
- Ji-One Kang
- Ji Eun Lim
- Bermseok Oh
1 Application
| Application ID | Title |
|---|---|
| 83990 | Genetic and environmental analysis for disease prediction models |
Enabling scientific discoveries that improve human health