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: 10794931
• 负责人: Sophia Gunn
• 金额: $ 4.77万
• 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/10794931
• 关键词: Accounting; Affect; Area; Arrhythmia; Atrial Fibrillation; Cardiovascular Diseases; Cardiovascular system; Clinical; Clinical Research; Communities; Computer software; Data; Dedications; Derivation procedure; Development; Disease; Educational workshop; Ensure; European; European ancestry; Fellowship; Gene Frequency; Generations; Genetic; Genetic Research; Genetic Risk; Genetic study; Goals; Grant; Heart failure; Individual; 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; Training; Underrepresented Populations; United States; Validation; Variant; Veterans; Weight; Work; Writing; cardiovascular disorder epidemiology; cohort; design; disorder risk; epidemiology study; ethnic disparity; experience; gene environment interaction; genetic risk assessment; genome wide association study; high risk population; improved; interest; method development; mortality; novel; precision medicine; programs; racial disparity; 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 (PGS) 是衡量个体疾病遗传风险的指标，源自以下结果： 全基因组关联研究（GWAS）摘要统计1。它们是识别个人身份的有前途的工具 高遗传风险，还可用于评估风险因素的因果影响并检查基因环境 互动2.然而，PGS 具有高度的血统依赖性，目前的 PGS 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 可以提高这些代表性不足的人群中的 PGS 表现。特别是，改进的心房PGS 房颤（AF）对于确保所有人都能获得房颤基因研究的进展至关重要。自动对焦经常发生 无症状且未经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。通过改进 AF 的 PGS，可以识别和治疗高风险个体5。 先前的工作表明，结合不同血统的 GWAS 结果可以改善 PGS 性能，但是这项工作仅限于两个祖先6、7。我们建议开发方法 使用多个祖先数据构建 PGS 以提高 PGS 的性能，特别是在 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。我们的首要目标是开发一种方法来创建改进的特定血统分数。我们的第二个 Target 将开发一种方法来创建跨祖先 PGS。我们将评估新产品的性能 使用模拟研究的统计方法，并将使用来自百万的 AF 数据验证我们的方法 退伍军人计划 (MVP)8。此外，我们将把我们的新方法用于更广泛的研究 通过在 GitHub 上发布我们的方法来社区。我们将把这些方法的应用重点放在 AF 上，但是我们的 方法可用于多种疾病。 推进 PGS 方法，使其对遗传代表性不足的个体表现良好 为了确保基因研究的进步造福所有人，研究至关重要。我的导师团队有 在 AF 基因研究方面拥有出色的经验，并致力于支持我的培训和 专业发展。我们制定了培训计划，包括机制培训和培训 心血管疾病流行病学、先进统计方法和专业发展 例如科学写作和负责任的研究行为。通过这次奖学金，我将培养以下技能： 实现我的长期目标，成为一名具有统计遗传学专业知识的独立研究员 心血管疾病。