New framework boosts the accuracy of disease risk prediction from genetic data
Researchers at Cedars-Sinai have developed a new computational framework called AB-PRS to improve the accuracy of polygenic risk scores for various diseases. By identifying genetic signals missed by traditional methods, the tool aims to provide more personalized medical care.
Why it matters
Enhanced genetic risk prediction could lead to earlier interventions and more effective preventative medicine for common conditions like diabetes and cancer.
edited by Sadie Harley , reviewed by Robert Egan
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Add as preferred source Credit: Pixabay/CC0 Public Domain Investigators at Cedars-Sinai Health Sciences University have developed a computational framework to improve how genetic data is used to estimate an individual's inherited risk of developing conditions like Alzheimer's disease, type 2 diabetes, high blood pressure and breast cancer.
The framework, called Adaptive Boosting of Pre-trained Polygenic Risk Scores (AB-PRS) and described in Nature Communications , builds on existing polygenic risk scores —tools that estimate disease risk based on many genetic variants across the genome—and identifies additional genetic signals that may not be fully captured by current scoring methods.
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