Methods for Integrative Genomic Data Analysis

摘要 该项目的广泛、长期目标涉及发展新的统计方法、理论和 用于大规模多个高维基因组数据的统计建模的计算工具 重要的生物学问题和实验。新的高通量技术和下一代测序 生成各种类型的超高维遗传学、基因组学、表观基因组学、代谢组学数据，以便 对各种复杂的表型有一个综合的了解。来自不同物种的基因组数据的综合分析 ENT群体和组织可以潜在地增加检测与疾病相关的遗传变异和 基因，并提供了在基因组研究中进行因果推断的可能性，最终导致理解 疾病致病途径和基于基因组学的风险预测。当前项目的特定fic目标是 开发新的统计模型和方法，用于多基因风险分数(PR)预测和综合分析 可能的致病基因和途径的fi阳离子的eQTL和全基因组遗传关联数据 复杂的疾病。为了有效地利用不同种族群体和不同组织的数据，这 项目将开发几种新的迁移学习方法，以实现对多基因风险得分的更好估计 并提高在少数群体中检测性状相关变异的能力。该项目也将发展 元学习预测种族和组织特异性fic基因表达的方法，以增加 全转录组关联分析(TWAS)。最后，全基因组共定位分析的统计方法 将开发能够有效地将GTEx数据与GWAS关联摘要统计相结合的方法，以便确定 可能的致病基因和途径。这些方法依赖于多种再开发方法的新集成。 高维回归、高维高斯序列模型和子空间估计。新的 这些方法可以应用于不同类型的基因组数据，理想地将有助于促进基因的fi阳离子。 以及各种复杂人类疾病的生物学途径和基于基因组学的疾病风险预测- 提顿。本研究将为迁移学习和元学习提供统计方法和理论支持 在高维基因组数据中研究复杂的表型并提供对每个生物领域的见解 由各种数据集表示，包括阿尔茨海默病、心脏代谢综合征和慢性肾脏 疾病。所有算法、软件工具和由此产生的多基因风险评分模型和组织特异性fic基因表达- Sion预测模型以及详细的文档将在GitHub上提供。

Abstract The broad, long-term objective of this project concerns the development of novel statistical methods, theory and computational tools for statistical modeling of large-scale multiple high-dimensional genomic data motivated by im- portant biological questions and experiments. New high-throughput technologies and next generation sequencing are generating various types of very high-dimensional genetics, genomic, epigenomics, metabolomics data in order to obtain an integrative understanding of various complex phenotypes. Integrative analysis of genomic data from differ- ent populations and tissues can potentially increase the power of detecting disease associated genetic variants and genes, and provide the possibility of making causal inference in genomic studies, eventually leading to understanding of the disease causal pathways and genomics-based risk prediction. The specific aims of the current project are to develop new statistical models and methods for polygenic risk score (PRS) prediction and for integrative analysis of eQTL and genome wide genetic association (GWAS) data for identification of possible causal genes and pathways of complex diseases. In order to effectively utilize data across different ethnicity groups and different tissues, this project will develop several novel transfer learning methods in order to achieve better estimate of polygenic risk scores and to increase the power of detecting trait associated variants in minority populations. The project will also develop method of meta-learning to predict ethnicity- and tissue-specific gene expressions in order to increase the power of transcriptome-wide association analysis (TWAS). Finally, statistical methods for genome-wide co-localization analysis that can effectively integrate GTEx data with GWAS association summary statistics will be developed in order to identify possible causal disease genes and pathways. These methods hinge on novel integration of methods for multiple re- lated high-dimensional regressions, high-dimensional Gaussian sequence models and subspace estimation. The new methods can be applied to different types of genomic data and will ideally help facilitate the identification of genes as well as the biological pathways underlying various complex human diseases and genomics-based disease risk predic- tion. The work proposed here will contribute statistical methodology and theory for transfer learning and meta-learning in high-dimensional genomic data to study complex phenotypes and to offer insights into each of the biological areas represented by the various data sets, including Alzheimer's disease, cardiometabolic syndrome, and chronic kidney disease. All algorithms, software tools and the resulting polygenic risk score models and tissue-specific gene expres- sion prediction models together with detailed documentation will be made available on the GitHub.