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PAGE-G: Precision Approach combining Genes and Environment in Glaucoma

PAGE-G: Precision Approach combining Genes and Environment in Glaucoma
PAGE-G:青光眼基因与环境相结合的精准方法
批准号:
10797646
负责人:
Sally Liu Baxter
金额:
$31.6万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-30 至 2025-08-31

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中文摘要
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英文摘要
PROJECT SUMMARY/ABSTRACT Glaucoma is the world’s leading cause of irreversible blindness and is projected to affect >110 million people by 2040. Risk stratification is important in order to identify individuals at high risk of glaucoma, since early detection and treatment can help decrease the risk of irreversible vision loss. Because glaucoma has a high level of heritability, polygenic risk scores (PRS) have been developed to examine risk stratification based on genetic variants. However, because genome-wide association studies have most commonly been performed in European descent populations, PRS may not generalize well to non-European descent populations. This is a major issue for glaucoma given that non-European descent populations carry a disproportionate burden of glaucoma, including earlier age of onset, faster disease progression, and higher risk of vision loss. The All of Us Research Program offers an opportunity to further improve PRS models for glaucoma and enhance a precision medicine approach for this complex condition. First, the recent release of whole genome sequencing data on the All of Us Researcher Workbench provides additional genomic data from a diverse cohort of participants to add to the growing literature of genomic studies in glaucoma. We plan to develop a wide array of PRS models that will be trained and tested on data from All of Us (Aim 1). Further, we will leverage an innovative tool that annotates variants according to their regulatory activity in disease-specific cell types (in this case, eye tissues) to improve the predictive accuracy of trans-ancestry PRS models (i.e., make models trained on one population more widely generalizable to other populations) (Aim 2). Finally, because All of Us includes detailed data regarding environmental factors such as clinical data, healthcare access and utilization, and social determinants of health, it represents a unique opportunity to better understand gene- environment interactions in glaucoma and further refine risk stratification by simultaneously analyzing a multitude of data types (Aim 3). This study aims to improve PRS for glaucoma by enhancing performance via the strategies outlined above. Additionally, these aims may potentially uncover new risk variants and generate new insights regarding their functional importance. These will represent important advancements in precision medicine in glaucoma, particularly for diverse populations who have been traditionally underrepresented in biomedical research yet are most severely affected by this blinding eye disease.
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Bridge2AI: Salutogenesis Data Generation Project
  • 批准号:
    10858583
  • 项目类别:
  • 资助金额:
    $84.18万
  • 财政年份:
    2022
  • 负责人:
    Sally Liu Baxter
  • 依托单位:
Bridge2AI: Salutogenesis Data Generation Project
  • 批准号:
    10471118
  • 项目类别:
  • 资助金额:
    $783.8万
  • 财政年份:
    2022
  • 负责人:
    Sally Liu Baxter
  • 依托单位:
Short-Term Research training In Vision and Eye health (STRIVE)
Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention
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