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

项目总结/摘要 青光眼是世界上不可逆失明的主要原因，预计影响超过1.1亿人 到2040年为了识别青光眼高风险个体，风险分层是重要的，因为早期 检查和治疗有助于降低不可逆转的视力丧失的风险。因为青光眼有很高的 多基因风险评分（PRS）已被开发用于检查基于以下因素的风险分层： 基因变异然而，由于全基因组关联研究最常在 欧洲血统的人口，PRS可能无法很好地推广到非欧洲血统的人口。这是一 青光眼的主要问题，因为非欧洲血统的人口承担着不成比例的负担， 青光眼，包括更早的发病年龄，更快的疾病进展和更高的视力丧失风险。 All of Us研究计划提供了进一步改进青光眼PRS模型的机会， 为这种复杂的疾病加强精确的医学方法。首先，最近发布的全基因组 All of Us Researcher上的测序数据提供了来自不同基因组的额外基因组数据。 这一研究将增加青光眼基因组研究的文献。我们计划开发一个 广泛的PRS模型，这些模型将根据来自All of Us的数据进行训练和测试（目标1）。此外，我们将 利用一种创新工具，根据变异体在疾病特异性细胞中的调节活性对其进行注释 类型（在这种情况下，眼睛组织）以提高跨祖先PRS模型的预测准确性（即，使 在一个群体上训练的模型更广泛地推广到其他群体）（目标2）。最后，因为所有 包括有关环境因素的详细数据，如临床数据，医疗保健服务， 利用率和健康的社会决定因素，它代表了一个独特的机会，以更好地了解基因- 青光眼中的环境相互作用，并通过同时分析 多种数据类型（目标3）。 本研究旨在通过上述策略提高青光眼患者的PRS。 此外，这些目标可能会发现新的风险变体，并产生关于其风险的新见解。 功能重要性。这些将代表青光眼精准医学的重要进展， 特别是对于传统上在生物医学研究中代表性不足的各种人群， 受这种致盲性眼病的影响最为严重。

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.