Mapping Multiple Complex and Omics Trait-Associations Using Summary Statistics

使用汇总统计映射多个复杂和组学特征关联

• 批准号: 9911704
• 负责人: Kevin James Gleason
• 金额: $ 3.67万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-06 至 2020-08-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9911704
• 关键词: Affect; Biological; Breast; Clinical; Colorectal; Colorectal Cancer; Communities; Complex; Computing Methodologies; Coupled; DNA Methylation; Data; Data Set; Detection; Disease; Disease susceptibility; Epithelial ovarian cancer; Gene Expression; Genetic; Genetic Variation; Genetic study; Genomic Segment; Joints; Knowledge; Lead; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of ovary; Measures; Methods; Molecular; Molecular Analysis; Multiomic Data; Outcome; Ovarian; Phenotype; Predisposition; Process; Proteins; Proteome; Proteomics; Quantitative Trait Loci; Sampling; Signal Transduction; Single Nucleotide Polymorphism; Software Tools; Statistical Methods; Structure; System; The Cancer Genome Atlas; Tissues; Tumor Tissue; Untranslated RNA; Variant; Work; biobank; cancer risk; cancer type; cell type; computerized tools; disorder risk; flexibility; genetic risk factor; genetic variant; genome wide association study; improved; large scale data; malignant breast neoplasm; mammary epithelium; method development; methylome; molecular phenotype; multiple omics; novel; pleiotropism; risk sharing; risk variant; statistics; success; tool; trait; transcriptome; tumor

Project Summary For many disease-associated genetic variants, the functional mechanism through which the variant affects disease susceptibility is unknown. Because genetic variants also affect molecular phenotypes such as the transcriptome, methylome and proteome, studying “omics” outcomes may lead to an improved understanding of disease processes. In particular, joint analysis of multi-omics data may enhance our knowledge of how genetic effects on these outcomes are coordinated in a multi-level molecular system to contribute to disease susceptibility. Since genetic effects on molecular phenotypes may further depend on tissue, cell type, or other conditions, the scientific community would benefit from continued development of methods to integrate multi- omics data across conditions or contexts. However, the large scale of the data coupled with unknown correlation structures across features or conditions makes such analyses challenging. In this project, we propose efficient methods to integrate summary statistics from multiple studies of genetic effects on complex and omics phenotypes. To improve upon existing multi-omics integrative approaches that take summary statistics as input, we expand joint analyses to more than three data types or conditions, and allow the sets of statistics to come from overlapping samples. Preliminary results presented in the application demonstrate that the proposed methods are computationally feasible and produce results that are consistent with current biological knowledge. Proposed applications of the methods have the potential to identify novel associations or provide new evidence for known associations between omics features and cancer risk. The success of this work will provide flexible methods and computational tools that can be applied to other diseases and settings.

项目摘要 对于许多疾病相关的遗传变异，变异影响的功能机制 疾病易感性未知。因为遗传变异也会影响分子表型， 转录组，甲基化组和蛋白质组，研究“组学”的结果可能会导致更好的理解 疾病的过程。特别是，对多组学数据的联合分析可能会增强我们对如何 遗传对这些结果的影响在一个多层次的分子系统中相互协调，从而导致疾病 易感性由于对分子表型的遗传效应可能进一步取决于组织、细胞类型或其他因素， 在这种情况下，科学界将受益于不断发展的方法， 跨条件或上下文的组学数据。然而，大规模的数据加上未知的 跨特征或条件的相关性结构使得这种分析具有挑战性。本课题 提出有效的方法来整合综合统计的遗传效应的复杂的多项研究 和组学表型。为了改进现有的多组学综合方法， 统计作为输入，我们将联合分析扩展到三种以上的数据类型或条件，并允许 统计数据来自重叠样本。申请中提出的初步结果表明， 所提出的方法在计算上是可行的，并且产生与当前的方法一致的结果。 生物学知识。这些方法的拟议应用有可能识别新的关联或 为组学特征与癌症风险之间的已知关联提供了新的证据。的成功 这项工作将提供灵活的方法和计算工具，可应用于其他疾病和环境。