Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages
Abstract Autoimmune diseases often exhibit a preclinical stage before diagnosis. Electronic health record (EHR) based-biobanks contain genetic data and diagnostic information, which can identify preclinical individuals at risk for progression. Biobanks typically have small numbers of cases, which ar...
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55636-6 |
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| author | Chen Wang Havell Markus Avantika R. Diwadkar Chachrit Khunsriraksakul Laura Carrel Bingshan Li Xue Zhong Xingyan Wang Xiaowei Zhan Galen T. Foulke Nancy J. Olsen Dajiang J. Liu Bibo Jiang |
| author_facet | Chen Wang Havell Markus Avantika R. Diwadkar Chachrit Khunsriraksakul Laura Carrel Bingshan Li Xue Zhong Xingyan Wang Xiaowei Zhan Galen T. Foulke Nancy J. Olsen Dajiang J. Liu Bibo Jiang |
| author_sort | Chen Wang |
| collection | DOAJ |
| description | Abstract Autoimmune diseases often exhibit a preclinical stage before diagnosis. Electronic health record (EHR) based-biobanks contain genetic data and diagnostic information, which can identify preclinical individuals at risk for progression. Biobanks typically have small numbers of cases, which are not sufficient to construct accurate polygenic risk scores (PRS). Importantly, progression and case-control phenotypes may have shared genetic basis, which we can exploit to improve prediction accuracy. We propose a novel method Genetic Progression Score (GPS) that integrates biobank and case-control study to predict the disease progression risk. Via penalized regression, GPS incorporates PRS weights for case-control studies as prior and forces model parameters to be similar to the prior if the prior improves prediction accuracy. In simulations, GPS consistently yields better prediction accuracy than alternative strategies relying on biobank or case-control samples only and those combining biobank and case-control samples. The improvement is particularly evident when biobank sample is smaller or the genetic correlation is lower. We derive PRS for the progression from preclinical rheumatoid arthritis and systemic lupus erythematosus in the BioVU biobank and validate them in All of Us. For both diseases, GPS achieves the highest prediction $${R}^{2}$$ R 2 and the resulting PRS yields the strongest correlation with progression prevalence. |
| format | Article |
| id | doaj-art-dcdb9d33eb044eb0897989b8468b8fa4 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-dcdb9d33eb044eb0897989b8468b8fa42025-01-05T12:37:58ZengNature PortfolioNature Communications2041-17232025-01-0116111710.1038/s41467-024-55636-6Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stagesChen Wang0Havell Markus1Avantika R. Diwadkar2Chachrit Khunsriraksakul3Laura Carrel4Bingshan Li5Xue Zhong6Xingyan Wang7Xiaowei Zhan8Galen T. Foulke9Nancy J. Olsen10Dajiang J. Liu11Bibo Jiang12Bioinformatics and Genomics Graduate Program, College of Medicine, Penn State UniversityBioinformatics and Genomics Graduate Program, College of Medicine, Penn State UniversityBioinformatics and Genomics Graduate Program, College of Medicine, Penn State UniversityBioinformatics and Genomics Graduate Program, College of Medicine, Penn State UniversityDepartment of Biochemistry and Molecular Biology, College of Medicine, Penn State UniversityDepartment of Molecular Physiology & Biophysics, Vanderbilt UniversityDepartment of Medicine, Division of Genetic Medicine, Vanderbilt University Medical CenterDepartment of Public Health Sciences, College of Medicine, Penn State UniversityDepartment of Statistical Science, Southern Methodist UniversityDepartment of Public Health Sciences, College of Medicine, Penn State UniversityDepartment of Medicine, College of Medicine, Penn State UniversityBioinformatics and Genomics Graduate Program, College of Medicine, Penn State UniversityDepartment of Public Health Sciences, College of Medicine, Penn State UniversityAbstract Autoimmune diseases often exhibit a preclinical stage before diagnosis. Electronic health record (EHR) based-biobanks contain genetic data and diagnostic information, which can identify preclinical individuals at risk for progression. Biobanks typically have small numbers of cases, which are not sufficient to construct accurate polygenic risk scores (PRS). Importantly, progression and case-control phenotypes may have shared genetic basis, which we can exploit to improve prediction accuracy. We propose a novel method Genetic Progression Score (GPS) that integrates biobank and case-control study to predict the disease progression risk. Via penalized regression, GPS incorporates PRS weights for case-control studies as prior and forces model parameters to be similar to the prior if the prior improves prediction accuracy. In simulations, GPS consistently yields better prediction accuracy than alternative strategies relying on biobank or case-control samples only and those combining biobank and case-control samples. The improvement is particularly evident when biobank sample is smaller or the genetic correlation is lower. We derive PRS for the progression from preclinical rheumatoid arthritis and systemic lupus erythematosus in the BioVU biobank and validate them in All of Us. For both diseases, GPS achieves the highest prediction $${R}^{2}$$ R 2 and the resulting PRS yields the strongest correlation with progression prevalence.https://doi.org/10.1038/s41467-024-55636-6 |
| spellingShingle | Chen Wang Havell Markus Avantika R. Diwadkar Chachrit Khunsriraksakul Laura Carrel Bingshan Li Xue Zhong Xingyan Wang Xiaowei Zhan Galen T. Foulke Nancy J. Olsen Dajiang J. Liu Bibo Jiang Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages Nature Communications |
| title | Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages |
| title_full | Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages |
| title_fullStr | Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages |
| title_full_unstemmed | Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages |
| title_short | Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages |
| title_sort | integrating electronic health records and gwas summary statistics to predict the progression of autoimmune diseases from preclinical stages |
| url | https://doi.org/10.1038/s41467-024-55636-6 |
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