Methods to Identify, Validate & Interpret GWAS Loci in Multi-ethnic Meta-analysis

识别、验证方法

• 批准号: 10291183
• 负责人: Dajiang Liu
• 金额: $ 57.57万
• 依托单位: PENNSYLVANIA STATE UNIV HERSHEY MED CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-08-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10291183
• 关键词: Address; Age; Architecture; Behavior; Biology; Blood Pressure; Cardiovascular Diseases; Collaborations; Complex; Complex Genetic Trait; Computer software; Data; Data Analyses; Data Set; Detection; Development; Environmental Exposure; European; Frequencies; Genetic; Genetic Models; Genetic Research; Genetic study; Genomics; Genotype; Goals; Heritability; Heterogeneity; Human Genetics; Individual; Joints; Linkage Disequilibrium; Lipids; Malignant Neoplasms; Measurement; Meta-Analysis; Methodology; Methods; Modeling; Modernization; Non-Insulin-Dependent Diabetes Mellitus; Participant; Pattern; Performance; Phenotype; Population; Prevention strategy; Privacy; Probability; Reproducibility; Research; Resolution; Respiration Disorders; Risk Factors; Sampling; Series; Signal Transduction; Smoking; Software Tools; Speed; Statistical Methods; Training; Variant; addiction; base; causal variant; clinical translation; cohort; cost; drinking; flexibility; genetic architecture; genetic variant; genome wide association study; human disease; improved; indexing; innovation; modifiable risk; multi-ethnic; novel strategies; novel therapeutics; public health relevance; rare variant; risk prediction; software development; software infrastructure; statistics; tool; trait

ABSTRACT Large scale genetic datasets have revolutionized human genetic research. In the past decade, genome-wide association studies have identified numerous genetic variants associated with various complex traits. These discoveries have informed new biology and led to novel therapeutics. Yet, most studies focused on European samples. As the next step, consortia efforts have begun to aggregate datasets from diverse non-European populations. Most of these studies seek to aggregate summary association statistics and perform meta-analysis instead of aggregating individual level data, which are easier to implement, equally powerful and more protective for participants’ privacy. There are many new analytical challenges for trans-ethnic meta-analysis, which demands new methodology development. In this application, we propose to develop a series of novel approaches to understand the genetic architecture of complex traits in trans-ethnic meta-analysis. Specifically, we will develop methods to assess reproducibility of identified GWAS signals (Aim 1). We will improve models of genetic effect heterogeneity in trans-ethnic meta-analysis, in order to improve the power for association analysis (Aim 2). We will also adapt the model to enhance the identification of causal variants (Aim 3) and improve risk predictions (Aim 4). Finally, we will develop innovative software architectures to implement these methods and make them scalable for meta-analysis of sequencing age (Aim 5). To accomplish these research goals, we assembled a synergistic research team with leading expertise in complex trait genetics, statistical genetics and large scale computation. In the past few years, our research team developed software tools that are being used in hundreds of genetic studies. The team also got extensively involved in applied data analysis. We will continue our existing collaborations, and team up with leaders in the GSCAN, GIANT, GLGC, T2D and ICBP consortia to help advance the trans-ethnic analyses for smoking and drinking addiction, anthropometric traits, lipids levels, type II diabetes and blood pressures. Together, these datasets consist of >20 million phenotypic measurements on >5 million individuals. These collaborations will greatly advance our understanding on the genetic architecture, facilitate clinical translation and also maximize the impact of our developed methodologies.

抽象的 大规模的遗传数据集彻底改变了人类遗传研究。在过去的十年中，全基因组 关联研究已经确定了许多与各种复杂性状相关的遗传变异。这些 发现已经了解了新的生物学，并导致了新的疗法。然而，大多数研究都集中在欧洲 样品。下一步，财团的努力已经开始汇总来自潜水员非欧洲的数据集 人群。这些研究中的大多数旨在汇总关联统计并进行荟萃分析 而不是汇总更易于实施的个人级别数据，同样强大且受到保护 对于参与者的隐私。跨种族荟萃分析有许多新的分析挑战，这是 需要新的方法论开发。在此应用中，我们建议开发一系列新颖 了解跨种族荟萃分析中复杂性状的遗传结构的方法。具体来说， 我们将开发评估已识别GWAS信号的可重复性的方法（AIM 1）。我们将改善模型 跨种族荟萃分析中遗传效应异质性，以提高关联的能力 分析（目标2）。我们还将调整模型以增强因果变体的识别（AIM 3）和 改善风险预测（目标4）。最后，我们将开发创新的软件体系结构来实施这些架构 方法并使它们可用于测序年龄的荟萃分析（AIM 5）。完成这些研究 目标，我们组建了一个具有复杂特征遗传学的领先专业知识的协同研究团队，统计 遗传学和大规模计算。在过去的几年中，我们的研究团队开发了软件工具 用于数百个遗传研究。该团队还广泛参与了应用数据分析。 我们将继续我们的现有合作，并与GSCAN，GIANT，GLGC，T2D和 ICBP财团有助于推进吸烟和饮酒成瘾的跨种族分析，人体测量学 特征，脂质水平，II型糖尿病和血压。这些数据集共同包含> 2000万 > 500万个人的表型测量。这些合作将使我们的理解很大 关于遗传结构，促进临床翻译，并最大程度地发挥我们发达的影响 方法论。