Novel Statistical Methods for Development of Polygenic Scores in Multi-Ancestry Cohorts

多祖先队列中多基因评分开发的新统计方法

• 批准号: 10464189
• 负责人: Sophia Gunn
• 金额: $ 4.68万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10464189
• 关键词: Accounting; Affect; Area; Arrhythmia; Atrial Fibrillation; Cardiovascular Diseases; Cardiovascular system; Clinical; Clinical Research; Communities; Computer software; Data; Derivation procedure; Development; Disease; Educational workshop; Ensure; Environment; European; Fellowship; Gene Frequency; Generations; Genes; Genetic; Genetic Research; Genetic Risk; Genetic study; Goals; Grant; Heart failure; Individual; Lead; Linkage Disequilibrium; Measures; Mentors; Methodology; Methods; Molecular; Morbidity - disease rate; Participant; Performance; Persons; Population; Prevention; Publishing; Racial Equity; Research; Research Personnel; Risk; Risk Factors; Sampling; Sampling Studies; Scoring Method; Statistical Methods; Stroke; Stroke prevention; Sum; Target Populations; Time; Training; Underrepresented Populations; United States; Validation; Variant; Veterans; Weight; Work; Writing; cardiovascular disorder epidemiology; cohort; design; disorder risk; epidemiology study; experience; genetic risk assessment; genome wide association study; high risk; improved; interest; method development; mortality; novel; precision medicine; programs; racial and ethnic disparities; responsible research conduct; risk prediction; risk variant; scientific computing; simulation; skills; statistics; study population; tool; trait

Project Abstract Polygenic scores (PGSs) are measures of an individual’s genetic risk of disease, derived from the results of genome-wide association study (GWAS) summary statistics1. They are a promising tool to identify individuals at high genetic risk and can also be used to assess causal effects of risk factors and examine gene-environment interactions2. However, PGSs are highly ancestry-dependent, and current PGSs do not perform well in underrepresented populations in statistical genetic research3. There is a need to develop new PGS methods that can improve PGS performance in these underrepresented populations. In particular, improved PGS for atrial fibrillation (AF) is vital for ensuring that advances in genetic research of AF are available to all. AF is often asymptomatic, and without treatment can lead to other cardiovascular diseases, including heart failure, stroke, and cardiovascular mortality4. With improved PGS for AF, high risk individuals can be identified and treated5. Previous work has demonstrated that combining GWAS results across ancestries can improve PGS performance, however this work has been limited to two ancestries6, 7. We propose to develop methods for constructing PGSs with multiple ancestry data to improve the performance of PGSs, in particular in underrepresented populations. We will take two distinct approaches combining the results of multiple ancestry- specific GWAS data. Our first aim will develop a method to create improved ancestry-specific scores. Our second aim will develop a method to create one trans-ancestry PGS. We will assess the performance of our new statistical methods using simulation studies, and will validate our methods using AF data from the Million Veterans Program (MVP)8. Additionally, we will make our novel methods available to the greater research community by publishing our methods on GitHub. We will focus our applications of the methods on AF, but our methods can be used for a wide range of diseases. Advancing PGS methods so that they perform well for individuals who are under-represented in genetic studies is imperative for ensuring that advances in genetic research are beneficial to all. My mentoring team has outstanding experience in genetic research of AF, and is committed to supporting me in my training and professional development. We have designed a training plan which includes training in mechanisms and epidemiology of cardiovascular disease, advanced statistical methodologies, and professional development such as scientific writing and responsible conduct of research. Through this fellowship, I will develop the skills to achieve my long-term goal of becoming an independent researcher in statistical genetics with expertise in cardiovascular disease.

项目摘要 多基因评分（PGSs）是对个体疾病遗传风险的测量，来自以下结果： 全基因组关联研究（GWAS）摘要他们是一个有前途的工具，以确定个人在 高遗传风险，也可用于评估风险因素的因果影响和检查基因-环境 相互作用2.然而，PGSs是高度依赖于祖先的，目前的PGSs在以下方面表现不佳： 统计遗传学研究中代表性不足的人群3。需要开发新的PGS方法， 可以改善PGS在这些代表性不足的人群中的表现。特别是，改善了心房肌的PGS， 因此，预防房颤（AF）对于确保所有人都能获得AF遗传研究的进展至关重要。AF通常 无症状，不治疗可导致其他心血管疾病，包括心力衰竭，中风， 和心血管死亡率4.通过改善房颤的PGS，可以识别和治疗高风险个体5。 以前的工作已经证明，将GWAS结果跨祖先组合可以改善PGS 性能，但是这项工作已被限制到两个祖先6，7。我们建议开发方法， 构建具有多个祖先数据的PGS，以提高PGS的性能，特别是 代表性不足的人群。我们将采取两种不同的方法，结合多个祖先的结果- 具体的GWAS数据我们的第一个目标是开发一种方法来创建改进的祖先特定分数。我们的第二 aim将开发一种方法来创建一个跨祖先的PGS。我们将评估我们新的 统计方法使用模拟研究，并将验证我们的方法使用AF数据从百万 8.第八章：此外，我们将使我们的新方法可用于更大的研究 通过在GitHub上发布我们的方法。我们将把我们的应用程序的方法对AF，但我们的 这些方法可用于广泛的疾病。 推进PGS方法，使其在遗传学代表性不足的个体中表现良好 为了确保遗传研究的进展造福于所有人，研究是必不可少的。我的指导团队 在房颤遗传研究方面的杰出经验，并致力于支持我的培训和 专业发展。我们设计了一个培训计划，其中包括机制培训， 心血管疾病的流行病学，先进的统计方法和专业发展 例如科学写作和负责任的研究行为。通过这个奖学金，我将发展技能， 实现我的长期目标，成为一个独立的研究人员在统计遗传学与专业知识， 心血管疾病