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识别遗传变异 导致疾病(精细定位)和多基因风险评分(PR)的发展，以预测个人- 利用遗传信息来控制疾病风险。随着基因数据集的扩大，这些目标变得越来越多 现实主义。然而，大多数遗传数据集过高地代表了欧洲人群，限制了 科学发现，因果变异的发现，以及非欧洲人口中PR的准确性。如果 如果不加以解决，疾病分类准确性的差异将扩大基于血统的健康差距。中的大多数方法 遗传流行病学每次只考虑一种祖先和一种疾病。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。 第一个目标是开发一种使用来自多个祖先群体的数据进行精细测绘的方法。因果关系 变异体识别提供了对疾病病因的洞察，并帮助研究人员确定药物靶标。总和 单效应(Susie)模型是一种在单个种群中进行精细定位的有效方法。合并 由于基于祖先的模式差异，来自多个种群的数据可以极大地改进精细制图 变异之间的相关性，以及在一些但不是所有祖先中存在具有因果影响的变异。 为此，将开发一种由Susie推动的多种群精细作图方法MultiSuSiE，并 已申请。与其他组件相比，Susie在速度、功能和可解释性方面都有很大优势 精细测绘方法。MultiSuSiE将把最先进的精细映射技术带到多个祖先的背景下。 第二个目标是开发和应用SSCTPR，这是一种基于汇总统计的粗糙集方法，它 利用跨疾病的共享信息。医疗信息服务在医疗信息化中的应用前景广阔 决策和疾病筛查干预。一种新的方法，跨性状惩罚回归(CTPR)， 通过利用跨疾病的共享遗传基础来提高预测精度，但需要难以获得 个人级别的数据。在这一目标下，一种基于CTPR的多特征汇总统计方法ssCTPR将 有待开发和应用。SSCTPR在统计方法上是创新的：SSCTPR将联合对变量进行建模 和疾病，使用惩罚回归，并使用拉普拉斯二次曲线在性状之间共享信息 在多种疾病的情况下有效的惩罚，但尚未使用摘要统计进行调查。 第三个目标是开发一种方法，利用目标1和目标2的方法进步来改进 非欧洲人群中的PRS预测。在非欧洲人群中，PRS预测的准确性很高 低于欧洲人口。随着PR进入诊所，健康结果不公平的人群将 未能从最新的精准医疗创新中受益。在这个目标中，MultiPolyPred，一种建模的方法 将开发和应用多血统个体风险精细测绘和多疾病风险预测系统。我们的 方法将是唯一一种利用多血统精细映射的非欧洲PRS方法。