Statistical Methods for Integrative Analysis of Large-Scale Multi-Ethnic Whole Genome Sequencing Studies and Biobanks of Common Diseases

大规模多民族全基因组测序研究和常见疾病生物样本库综合分析的统计方法

• 批准号: 10622567
• 负责人: XIHONG LIN
• 金额: $ 49.98万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10622567
• 关键词: Address; Biological Markers; Cells; Cellular Assay; Cloud Computing; Code; Complex; Computer software; Computing Methodologies; Data; Data Set; Disease; Electronic Health Record; Environment; Epidemiology; European; European ancestry; FAIR principles; Face; Genetic; Genetic Research; Genetic study; Genome; Heart Diseases; Individual; Institution; Intervention; Lung diseases; Mendelian randomization; Meta-Analysis; Methods; Modeling; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; Nature; Pathway interactions; Performance; Population; Prevention strategy; Principal Component Analysis; Research; Risk Factors; Sample Size; Sampling; Statistical Methods; Structure; System; Testing; Trans-Omics for Precision Medicine; Underrepresented Populations; United States National Institutes of Health; Untranslated RNA; Variant; Veterans; biobank; catalyst; cell type; cloud platform; cluster computing; data privacy; disorder risk; empowerment; exome; genetic variant; genome sequencing; genome wide association study; health disparity; improved; interest; learning strategy; multi-ethnic; polygenic risk score; power analysis; privacy protection; programs; rare variant; research study; risk prediction; statistical learning; trait; user friendly software; whole genome

This proposal aims to develop advanced and scalable statistical methods for integrative analysis of large-scale Whole Genome Sequencing (WGS) studies and biobanks of common diseases, such as heart and lung diseases. Genome-Wide Association Studies (GWAS) have revealed thousands of genetic variants associated with many common diseases, but are limited to common variants from a majority of individuals of only European ancestry. Large-scale multi-ethnic WGS studies and biobanks have been rapidly arising to overcome these limitations, and to study the genetic underpinnings of complex diseases and traits in both coding and non-coding rare variants across populations. Examples include the NHLBI Trans-Omics Precision Medicine Program (TOPMed) and the NHGRI Genome Sequencing Program (GSP), UK biobank, and All of Us. Various omics data are also available in TOPMed. Full usage of these datasets can fuel genetic discoveries applicable to genetically understudied populations. These studies consist of hundreds of millions of rare variants (RVs), and their analysis faces several challenges. First, although several methods have been developed for RV analysis, they have limited power for analysis of non-coding RVs, as their functions are unknown or cell-type specific. There is a pressing need to empower RV Association Tests (RVATs) for non-coding variants by developing more powerful statistical learning methods using integrative analysis and incorporating cell-type specific variant functional annotations. Second, large sample sizes of WGS studies and data privacy consideration of many national and institutional biobanks with unbalanced case and control ratios call for distributed WGS analyses. Third, it is of substantial interest to develop polygenic risk scores using both common and rare variants in WGS studies, and to investigate causal effects of biomarkers and omics’ markers on diseases using Mendelian Randomization (MR) using both common and rare variants as instrumental variables. This proposal aims at addressing these needs with four aims. First, we will develop statistical learning based ensemble RVATs to boost power. This ensemble RVAT framework will be extended to use cell-type-specific functional annotations calculated from single-cell assays, and to perform meta-analysis. Second, we will develop distributed methods for important tasks in the analysis of large WGS and federated biobank data: estimating population structure via distributed fast principal component analysis, distributed methods for fitting generalized linear mixed models, and distributed RVATs. Third, we will develop methods for polygenic risk score (PRS) using both common and rare variants in WGS studies, and develop Mendelian Randomization methods for studying the causal effects of biomarkers and omics markers on diseases by using WGS-based PRs as instrumental variables. Fourth, we will develop open-access statistical software capable of implementing our proposed methods in both offline and cloud computing environments. We will apply the proposed methods to the analysis of the TOPMed and GSP data and the biobanks.

该提案旨在开发先进且可扩展的统计方法，用于大规模数据的综合分析 心脏和肺部等常见疾病的全基因组测序 (WGS) 研究和生物库 全基因组关联研究（GWAS）揭示了数千种相关的遗传变异。 与许多常见疾病有关，但仅限于大多数个体的常见变异 欧洲血统的大规模多种族全基因组测序研究和生物库已迅速兴起，以克服这一问题。 这些局限性，并研究复杂疾病的遗传基础和编码和特征 跨人群的非编码罕见变异的例子包括 NHLBI Trans-Omics Precision Medicine。 计划 (TOPMed) 和 NHGRI 基因组测序计划 (GSP)、英国生物银行和 All of Us。 TOPMed 中还提供了组学数据，充分利用这些数据集可以促进适用的遗传发现。 这些研究包括数亿个罕见变异（RV）， 首先，尽管已经为 RV 开发了多种方法，但他们的分析面临着一些挑战。 分析时，它们对非编码 RV 的分析能力有限，因为它们的功能或细胞类型未知 迫切需要通过以下方式为非编码变体提供 RV 关联测试 (RVAT)。 使用综合分析和合并细胞类型开发更强大的统计学习方法 第二，WGS研究的大样本量和数据隐私。 考虑到许多国家和机构生物样本库的案例和对照比率不平衡，需要 第三，使用两者来开发多基因风险评分具有重大意义。 WGS 研究中常见和罕见的变异，并研究生物标志物和组学标志物的因果效应 使用孟德尔随机化 (MR) 来研究疾病，使用常见和罕见变异作为工具 该提案旨在满足这些需求，有四个目标：首先，我们将开发统计数据。 基于学习的集成 RVAT 来提高功率 该集成 RVAT 框架将被扩展使用。 根据单细胞测定计算出细胞类型特异性功能注释，并进行荟萃分析。 其次，我们将为大型 WGS 和联合分析中的重要任务开发分布式方法。 生物样本库数据：通过分布式快速主成分分析估计种群结构，分布式 第三，我们将开发拟合广义线性混合模型和分布式 RVAT 的方法。 使用 WGS 研究中常见和罕见变异的多基因风险评分 (PRS)，并开发孟德尔 利用随机化方法研究生物标志物和组学标志物对疾病的因果影响 第四，我们将开发基于WGS的PR作为工具变量。 我们将在离线和云计算环境中实施我们提出的方法。 提出了分析 TOPMed 和 GSP 数据以及生物库的方法。