Integrated Analysis for Genetic Association and Prediction

遗传关联与预测的综合分析

• 批准号: 9768384
• 负责人: Juan Pablo Lewinger
• 金额: $ 38.98万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9768384
• 关键词: Address; Algorithms; Biological; Cancer Prognosis; Clinical; Clinical Research; Colorectal Cancer; Complex; Copy Number Polymorphism; DNA Sequence Alteration; Data; Data Analyses; Data Set; Databases; Development; Dimensions; Disease; Etiology; Gene Expression; Genes; Genome; Genomics; Genotype; Goals; Grouping; Hand; Health; Individual; Light; Malignant Neoplasms; Measurement; Measures; Methods; Methylation; MicroRNAs; Modeling; Molecular; Multiomic Data; Oncogenes; Ontology; Operative Surgical Procedures; Outcome; Patients; Pattern; Predisposition; Probability; Property; Recurrence; Regulation; Series; Statistical Data Interpretation; Statistical Methods; Structure; Supervision; Technology; The Cancer Genome Atlas; Uncertainty; Variant; Work; analytical tool; anticancer research; base; cancer genetics; cancer genomics; cancer recurrence; cohort; colon cancer patients; design; disease phenotype; epidemiology study; epigenome; flexibility; genetic analysis; genetic association; genome wide association study; genome-wide; genomic data; genomic platform; genomic profiles; high dimensionality; improved; insight; novel; novel marker; oncology; outcome forecast; personalized medicine; platform-independent; predictive modeling; risk variant; statistics; tool; translational study; treatment response; tumor; tumor progression

ABSTRACT The overall goal of this project is to develop novel statistical methods for integrative analysis of genomic data in cancer research. We propose to develop analytical tools that can integrate data from multiple genomic platforms and incorporate external omic information from publically available databases. These tools will be applicable to both etiological studies geared toward causal discovery and to clinical and translational studies geared toward predictive modeling. Advances in high-throughput molecular technologies have enabled large-scale omic projects (e.g. Encode, The Cancer Genome Atlas, Epigenome Roadmap) to generate vast amounts of information on the structure, function and regulation of the genome. In addition to this publically available data, individual studies are increasingly generating multiplatform genomic profiles (e.g. genotypes, gene expression, methylation copy number variation, miRNA) to elucidate the complex mechanisms of cancer development and progression, and investigate determinants and predictors of health and clinical outcomes. Integration across these multiple genomic “dimensions” and incorporation of the available external information can increase the ability to discovery causal relationships (e.g. Cancer-SNP associations), enhance prediction and prognosis modeling (e.g. cancer aggressiveness), and provide insights into biological mechanisms. We propose two analytic approaches aimed at addressing the challenges to effective integration across multiplatform genomic data and incorporation of external information from omic projects. The first approach (Aim 1) is a Bayesian regression and feature selection method that can integrate prior omic information in a very flexible manner allowing the data to `speak for itself' to determine which pieces of external information are relevant for the problem at hand. The method works with individual-level data and also with meta-analytic summaries, making it well suited for analyzing data from large multi-study consortia. The second approach (Aim 2) is a regularized regression and feature selection method for integrating multiplatform genomic features measured on the same set of individuals. The method is designed to scale to the very large numbers of features typical of genomewide platforms, to account for the different properties of each genomic data type, and to incorporate relevant external information to increase efficiency. Both approaches can be applied for causal discovery and for developing predictive and prognostic models. We will apply our methods to search for novel risk variants in the CORECT consortium of genome association studies, and to construct a prognostic model of CRC recurrence based on genomewide expression methylation data in the ColoCare consortium cohort of CRC patients. This work will provide new tools for analyzing high-dimensional multi-platform genomic that can take advantage of available external information.

抽象的 该项目的总体目标是开发新的统计方法，用于基因组数据的综合分析 我们建议开发可以整合多个基因组数据的分析工具。 这些工具将整合来自公开数据库的外部组学信息。 适用于旨在发现因果关系的病因学研究以及临床和转化研究 面向预测建模。 高通量分子技术的进步使得大规模组学项目成为可能（例如 Encode、The 癌症基因组图谱、表观基因组路线图）生成大量有关结构的信息， 除了这些公开数据外，还进行了个别研究。 越来越多地生成多平台基因组图谱（例如基因型、基因表达、甲基化拷贝 数量变异，miRNA）来阐明癌症发生和进展的复杂机制，以及 研究健康和临床结果的决定因素和预测因素。 基因组“维度”和可用外部信息的结合可以提高 发现因果关系（例如癌症-SNP 关联），增强预测和预后建模 （例如癌症侵袭性），并提供了对生物学机制的见解。 旨在解决跨多平台基因组数据有效集成的挑战的方法 第一种方法（目标 1）是贝叶斯回归。 和特征选择方法，可以以非常灵活的方式整合先验组学信息，从而允许 数据“不言而喻”，以确定哪些外部信息与当前问题相关。 该方法适用于个人级别的数据以及元分析摘要，使其非常适合 分析来自大型多研究联盟的数据。第二种方法（目标 2）是正则化回归和 用于集成在同一组上测量的多平台基因组特征的特征选择方法 该方法旨在扩展到全基因组的大量典型特征。 平台，以考虑每种基因组数据类型的不同属性，并纳入相关的 外部信息以提高效率。这两种方法都可以应用于因果发现和 我们将应用我们的方法来寻找新的风险变异。 CORECT 基因组关联研究联盟，构建 CRC 复发的预后模型 基于 ColoCare 联盟 CRC 患者队列的全基因组表达甲基化数据。 这项工作将为分析高维多平台基因组提供新工具 利用可用的外部信息。