Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-024-05874-7 |
| _version_ | 1827326501321703424 |
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| author | Hyein Jung Hae-Un Jung Eun Ju Baek Shin Young Kwon Ji-One Kang Ji Eun Lim Bermseok Oh |
| author_facet | Hyein Jung Hae-Un Jung Eun Ju Baek Shin Young Kwon Ji-One Kang Ji Eun Lim Bermseok Oh |
| author_sort | Hyein Jung |
| collection | DOAJ |
| description | 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-R 2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R 2 considered by variance of R 2 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 R 2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies. |
| first_indexed | 2024-03-07T14:46:30Z |
| format | Article |
| id | doaj.art-3e2addf2f1f84989a4f24031e9e7ccfa |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-03-07T14:46:30Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-3e2addf2f1f84989a4f24031e9e7ccfa2024-03-05T19:58:48ZengNature PortfolioCommunications Biology2399-36422024-02-017111310.1038/s42003-024-05874-7Integration of risk factor polygenic risk score with disease polygenic risk score for disease predictionHyein Jung0Hae-Un Jung1Eun Ju Baek2Shin Young Kwon3Ji-One Kang4Ji Eun Lim5Bermseok Oh6Department of Biomedical Science, Graduate School, Kyung Hee UniversityDepartment of Biomedical Science, Graduate School, Kyung Hee UniversityMendel IncDepartment of Biomedical Science, Graduate School, Kyung Hee UniversityDepartment of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee UniversityDepartment of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee UniversityDepartment of Biomedical Science, Graduate School, Kyung Hee UniversityAbstract 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-R 2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R 2 considered by variance of R 2 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 R 2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.https://doi.org/10.1038/s42003-024-05874-7 |
| spellingShingle | Hyein Jung Hae-Un Jung Eun Ju Baek Shin Young Kwon Ji-One Kang Ji Eun Lim Bermseok Oh Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction Communications Biology |
| title | Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction |
| title_full | Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction |
| title_fullStr | Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction |
| title_full_unstemmed | Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction |
| title_short | Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction |
| title_sort | integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction |
| url | https://doi.org/10.1038/s42003-024-05874-7 |
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