ArticlePeerJ. Computer science2025
A review of methods and software for polygenic risk score analysis.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- PGS-GS: a framework integrating polygenic scores and genomic selection in animal breeding.Briefings in bioinformatics · 2026Article
- Scoping Review of Polygenic Risk Scores in Kidney-Related Traits.Kidney international reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.