A review of methods and software for polygenic risk score analysis
Polygenic risk scores (PRSs) are emerging as powerful tools for predicting individual susceptibility to various diseases and traits based on genetic variants. These scores integrate information from multiple genetic markers associated with the trait or disease of interest, offering personalized risk...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-08-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-3039.pdf |
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| author | Sara Benoumhani Areej Al-Wabil Niddal Imam Bashayer Alfawaz Amaan Zubairi Dalal Aldossary Mariam AlEissa |
| author_facet | Sara Benoumhani Areej Al-Wabil Niddal Imam Bashayer Alfawaz Amaan Zubairi Dalal Aldossary Mariam AlEissa |
| author_sort | Sara Benoumhani |
| collection | DOAJ |
| description | Polygenic risk scores (PRSs) are emerging as powerful tools for predicting individual susceptibility to various diseases and traits based on genetic variants. These scores integrate information from multiple genetic markers associated with the trait or disease of interest, offering personalized risk assessment and enhancing disease management strategies. PRS is an active area of research and is being studied in various fields, such as disease prediction. This review explores the advancement of PRS research, focusing on methodological approaches, software tools, and applications across diverse disciplines. A systematic literature review identified 40 relevant articles classified based on PRS methods and software. Key methods for PRS computation, including penalized regression and threshold-based approaches, Bayesian approaches, and machine learning approaches, are discussed, along with notable software and their features. Applications of PRS in disease prevention are highlighted. Challenges and future directions, such as increasing diversity in genetic data, integrating environmental factors, and evaluating clinical implications, are also discussed to guide future research and implementation efforts. |
| format | Article |
| id | doaj-art-b38fbabf0237450a82f2aac190e72482 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-b38fbabf0237450a82f2aac190e724822025-08-20T03:40:41ZengPeerJ Inc.PeerJ Computer Science2376-59922025-08-0111e303910.7717/peerj-cs.3039A review of methods and software for polygenic risk score analysisSara Benoumhani0Areej Al-Wabil1Niddal Imam2Bashayer Alfawaz3Amaan Zubairi4Dalal Aldossary5Mariam AlEissa6Artificial Intelligence Research Center, Alfaisal University, Riyadh, Saudi ArabiaArtificial Intelligence Research Center, Alfaisal University, Riyadh, Saudi ArabiaCollege of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaArtificial Intelligence Research Center, Alfaisal University, Riyadh, Saudi ArabiaArtificial Intelligence Research Center, Alfaisal University, Riyadh, Saudi ArabiaArtificial Intelligence Research Center, Alfaisal University, Riyadh, Saudi ArabiaArtificial Intelligence Research Center, Alfaisal University, Riyadh, Saudi ArabiaPolygenic risk scores (PRSs) are emerging as powerful tools for predicting individual susceptibility to various diseases and traits based on genetic variants. These scores integrate information from multiple genetic markers associated with the trait or disease of interest, offering personalized risk assessment and enhancing disease management strategies. PRS is an active area of research and is being studied in various fields, such as disease prediction. This review explores the advancement of PRS research, focusing on methodological approaches, software tools, and applications across diverse disciplines. A systematic literature review identified 40 relevant articles classified based on PRS methods and software. Key methods for PRS computation, including penalized regression and threshold-based approaches, Bayesian approaches, and machine learning approaches, are discussed, along with notable software and their features. Applications of PRS in disease prevention are highlighted. Challenges and future directions, such as increasing diversity in genetic data, integrating environmental factors, and evaluating clinical implications, are also discussed to guide future research and implementation efforts.https://peerj.com/articles/cs-3039.pdfPolygenic risk score (PRS)Polygenic risk scores (PRSs)PRS predictionPRS softwarePRS toolsSystematic literature review |
| spellingShingle | Sara Benoumhani Areej Al-Wabil Niddal Imam Bashayer Alfawaz Amaan Zubairi Dalal Aldossary Mariam AlEissa A review of methods and software for polygenic risk score analysis PeerJ Computer Science Polygenic risk score (PRS) Polygenic risk scores (PRSs) PRS prediction PRS software PRS tools Systematic literature review |
| title | A review of methods and software for polygenic risk score analysis |
| title_full | A review of methods and software for polygenic risk score analysis |
| title_fullStr | A review of methods and software for polygenic risk score analysis |
| title_full_unstemmed | A review of methods and software for polygenic risk score analysis |
| title_short | A review of methods and software for polygenic risk score analysis |
| title_sort | review of methods and software for polygenic risk score analysis |
| topic | Polygenic risk score (PRS) Polygenic risk scores (PRSs) PRS prediction PRS software PRS tools Systematic literature review |
| url | https://peerj.com/articles/cs-3039.pdf |
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