medRxiv : the preprint server for health sciences · 2025
Multimodal Prediction of Primary Open-Angle Glaucoma Using Polygenic Risk Scores and Clinical Features in a High-Risk African Ancestry Cohort.
СтатьяПрепринтНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhu Yan, Ikuzwe Sindikubwabo Aude Benigne, Bradford Yuki, Salowe Rebecca, Caruth Lannawill, Pham Kenneth, Laxmi Moksha, Vrathasha Vrathasha, Aibo Marine-Ayan Ibrahim, Lee Roy, Halimitabrizi Mina, Rosa Isabel Di, Ghaffari Leila, He Jie, Ritchie Marylyn D, O'Brien Joan M, Setia-Verma Shefali
Аннотация
Primary open-angle glaucoma (POAG) disproportionately affects individuals of African ancestry, yet early detection tools remain limited. Using the largest African ancestry POAG cohort (N = 7,352), we developed machine learning models that integrate clinical features with four ancestry-matched polygenic risk scores (PRS): two genome-wide (PRS-CS POAAGG, PRS-CS MEGA) and two curated (MTAG PRS, MEGA PRS). Models trained on a clean cohort (N = 196) and tested in 1,013 suspects showed improved prediction with PRS, especially curated scores. MEGA PRS-based predictions correlated with IOP, CDR, and RNFL, validating biological relevance. Combining PRS with inter-eye asymmetry features further enhanced performance. These results support curated PRS such as MEGA PRS and MTAG PRS as interpretable, equitable tools for early glaucoma risk stratification.