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

摘要 大规模的基因数据集已经彻底改变了人类基因研究。在过去的十年里， 关联研究已经确定了与各种复杂性状相关的许多遗传变异。这些 这些发现为新的生物学提供了信息，并导致了新的治疗方法。然而，大多数研究都集中在欧洲。 样品作为下一步，联盟的努力已经开始汇总来自不同的非欧洲国家的数据集。 人口。这些研究中的大多数寻求汇总汇总关联统计数据并进行荟萃分析 而不是聚合单个级别的数据，后者更容易实现，同样强大，更具保护性 参与者的隐私。跨种族荟萃分析面临许多新的分析挑战， 需要新的方法论发展。在本申请中，我们提出开发一系列新颖的 在跨种族荟萃分析中理解复杂性状遗传结构的方法。具体地说， 我们将开发方法来评估已识别的GWAS信号的再现性（目标1）。我们将改进模型 跨种族荟萃分析中遗传效应异质性的研究，以提高关联能力 分析（目标2）。我们还将调整模型，以加强因果变异的识别（目标3）， 改进风险预测（目标4）。最后，我们将开发创新的软件架构来实现这些功能。 方法，并使其可扩展的荟萃分析测序年龄（目标5）。为了完成这些研究 目标，我们组建了一个协同研究团队，在复杂的性状遗传学，统计学， 遗传学和大规模计算。在过去的几年里，我们的研究团队开发了软件工具， 被用于数百项基因研究。该团队还广泛参与了应用数据分析。 我们将继续我们现有的合作，并与GSCAN，GIANT，GLGC，T2D和 ICBP财团帮助推进跨种族分析吸烟和饮酒成瘾，人体测量 特征、血脂水平、II型糖尿病和血压。总的来说，这些数据集包括> 2000万个 对> 500万个体的表型测量。这些合作将极大地推进我们对 基因结构，促进临床翻译，并最大限度地发挥我们的发达国家的影响， 方法论。

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.