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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/122368


    Title: Genome-Wide Polygenic Risk Score for Predicting High Risk Glaucoma Individuals of Han Chinese Ancestry
    Authors: Yu-Jer Hsiao;Hao-Kai Chuang;Sheng-Chu Chi;Yung-Yu Wang;Pin-Hsuan Chiang;Pai-Chi Teng;Tung-Mei Kuang;Aliaksandr A. Yarmishyn;Tai-Chi Lin;De-Kuang Hwang;Shih-Jen Chen;Shih-Hwa Chiou;Mei-Ju Chen;Ai-Ru Hsieh(謝璦如);Chih-Chien Hsu
    Keywords: glaucoma;genome-wide association studies;polygenic risk score;Asian population;biobank;retrospective study
    Date: 2021-11-09
    Issue Date: 2022-03-04 12:14:18 (UTC+8)
    Abstract: Glaucoma is a progressive and irreversible blindness-causing disease. However, the underlying genetic factors and molecular mechanisms remain poorly understood. Previous genome-wide association studies (GWAS) have made tremendous progress on the SNP-based disease association and characterization. However, most of them were conducted for Europeans. Since differential genetic characteristics among ethnic groups were evident in glaucoma, it is worthwhile to complete its genetic landscape from the larger cohorts of Asian individuals. Here, we present a GWAS based on the Taiwan Biobank. Among 1013 glaucoma patients and 36,562 controls, we identified a total of 138 independent glaucoma-associated SNPs at the significance level of p < 1 × 10−5. After clumping genetically linked SNPs (LD clumping), 134 independent SNPs with p < 10−4 were recruited to construct a Polygenic Risk Score (PRS). The model achieved an area under the receiver operating characteristic curve (AUC) of 0.8387 (95% CI = [0.8269–0.8506]), and those within the top PRS quantile had a 45.48-fold increased risk of glaucoma compared with those within the lowest quantile. The PRS model was validated with an independent cohort that achieved an AUC of 0.7283, thereby showing the effectiveness of our polygenic risk score in predicting individuals in the Han Chinese population with higher glaucoma risks.
    Relation: Journal of Personalized Medicine 11(11), 1169
    DOI: 10.3390/jpm11111169
    Appears in Collections:[統計學系暨研究所] 期刊論文

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