Methods for multi-ancestry and multi-trait fine-mapping and genetic risk prediction

多祖先、多性状精细定位和遗传风险预测方法

• 批准号: 10678066
• 负责人: Jordan Rossen
• 金额: $ 3.99万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2026-06-24
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678066
• 关键词: Acceleration; Biology; Clinic; Collection; Data; Data Collection; Data Set; Development; Disease; Drug Targeting; Electronic Health Record; Equity; Etiology; European; European ancestry; Genetic; Genetic Risk; Goals; Individual; Intervention; Laplacian; Linkage Disequilibrium; Maps; Medical; Methodology; Methods; Modeling; Pattern; Performance; Phenotype; Population; Population Heterogeneity; Publishing; Research; Research Personnel; Risk; Scoring Method; Speed; Statistical Models; Sum; Time; Variant; base; biobank; causal variant; data privacy; disease-causing mutation; disorder risk; electronic health information; falls; genetic architecture; genetic epidemiology; genetic information; genetic variant; health disparity; health inequalities; health outcome disparity; high standard; improved; innovation; insight; interest; polygenic risk score; precision medicine; risk prediction; screening; statistics; trait; trend

Project Summary: Two fundamental goals in genetic epidemiology are the identification of genetic variants that cause disease (fine-mapping) and the development of polygenic risk scores (PRS) that predict individual- level disease risk using genetic information. As genetic datasets expand, these goals become increasingly realistic. However, most genetic datasets overrepresent European populations, limiting the generalizability of scientific findings, the discovery of causal variants, and the accuracy of PRS in non-European populations. If unaddressed, differences in PRS accuracy will widen ancestry-based health disparities. Most methods in genetic epidemiology consider one ancestry and disease at a time. This research proposes methods for causal variant identification and genetic risk prediction that share information across ancestries and diseases. The first aim is to develop a method for fine-mapping using data from multiple ancestry groups. Causal variant identification provides insight into disease etiology and helps researchers identify drug targets. The sum of single effects (SuSiE) model is a powerful approach for fine-mapping in a single population. Incorporating data from multiple populations can greatly improve fine-mapping due to ancestry-based differences in patterns of correlation between variants and the presence of variants with causal effects in some, but not all ancestries. In this aim, MultiSuSiE, a multi-population fine-mapping method motivated by SuSiE will be developed and applied. SuSiE provides substantial benefits in terms of speed, power, and interpretability compared to other fine-mapping methods. MultiSuSiE will bring the state-of-the-art in fine-mapping to the multi-ancestry context. The second aim is to develop and apply ssCTPR, a summary statistic based PRS method that leverages shared information across diseases. PRS show great promise for informing medical treatment decisions and disease screening interventions. A recent method, cross-trait penalized regression (CTPR), boosts prediction accuracy by leveraging shared genetic bases across diseases but requires difficult-to-obtain individual-level data. In this aim, ssCTPR, a multi-trait summary statistic-based method motivated by CTPR will be developed and applied. ssCTPR is innovative in its statistical approach: ssCTPR will jointly model variants and diseases, use penalized regression, and share information across traits using a Laplacian quadratic penalty that is effective in the multi-disease setting, but has not been investigated using summary statistics. The third aim is to develop a method that uses the methodological advances of aims 1 and 2 to improve PRS prediction in non-European populations. PRS prediction accuracy in non-European populations is much lower than in European populations. As PRS enter the clinic, populations with inequitable health outcomes will fail to benefit from the latest in precision medicine innovation. In this aim, MultiPolyPred, a method that models individual risk using multi-ancestry fine-mapping and a multi-disease PRS will be developed and applied. Our method will be the only non-European PRS method to leverage multi-ancestry fine-mapping.

项目摘要：遗传流行病学的两个基本目标是遗传变异的识别 这会导致疾病（微图）和多基因风险评分（PR）的发展，以预测个体 使用遗传信息的水平疾病风险。随着遗传数据集的扩展，这些目标变得越来越多 实际的。但是，大多数遗传数据集过多代表欧洲人群，从而限制了 科学发现，因果变异的发现以及非欧洲人群中PR的准确性。如果 未解决的PRS准确性差异将扩大基于血统的健康差异。大多数方法 遗传流行病学一次考虑一个祖先和疾病。这项研究提出了因果关系的方法 跨祖先和疾病共享信息的变异识别和遗传风险预测。 第一个目的是使用来自多个祖先组的数据来开发用于仔细映射的方法。因果 变体鉴定提供了对疾病病因的见解，并帮助研究人员识别药物靶标。总和 单个效应（Susie）模型是单个人群中精细映射的强大方法。合并 由于基于祖先的模式差异，来自多个人群的数据可以大大改善精细映射 在某些但并非所有祖先的变体与存在因果关系的变体之间的相关性。 在此目标中，将开发由Susie促进的多人构图的多人绘制方法，并且 应用。与其他 精细映射方法。 MultiSusie将把最新映射的最新映射带入多学院上下文。 第二个目的是开发和应用SSCTPR，这是一种基于统计统计的PRS方法， 利用跨疾病共享信息。 PRS显示出通知医疗的巨大希望 决策和疾病筛查干预措施。最近的一种方法，跨特征惩罚回归（CTPR）， 通过利用跨疾病的共享遗传碱来提高预测准确性，但需要难以侵蚀 个人级别数据。在此目标中，SSCTPR是由CTPR促进的基于统计统计的多特征摘要的方法 被开发和应用。 SSCTPR在其统计方法中具有创新性：SSCTPR将共同模拟变体 和疾病，使用惩罚回归并使用laplacian Quadratic在特征上共享信息 在多疾病环境中有效的罚款，但尚未使用摘要统计数据进行调查。 第三个目的是开发一种使用目标1和2的方法学进步来改进的方法 非欧洲人群中的PR预测。非欧洲人口中的PR预测准确性很大 低于欧洲人口。当公关进入诊所时，患有不平等健康成果的人群将 无法从最新的精密医学创新中受益。在此目标中，多重培养，一种模拟的方法 将开发和应用多项式精细映射和多用途PRS的个人风险。我们的 方法将是唯一利用多功能精细映射的非欧洲PRS方法。