Integrated Analysis for Genetic Association and Prediction

摘要 这个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 癌症研究。我们建议开发能够整合来自多个基因组的数据的分析工具 平台，并纳入来自公共数据库的外部基因组信息。这些工具将是 适用于旨在发现因果关系的病因学研究以及临床和转化性研究 面向预测性建模。 高通量分子技术的进步使大规模的基因组计划成为可能(例如编码、 癌症基因组图谱，表观基因组路线图)，以产生关于结构的大量信息， 基因组的功能和调节。除了这些公开的数据，个别研究还包括 越来越多地生成多平台基因组图谱(例如，基因类型、基因表达、甲基化拷贝 数字变异，miRNA)，以阐明癌症发展和进展的复杂机制，以及 调查健康和临床结果的决定因素和预测因素。跨多个领域的集成 基因组的“维度”和可获得的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)，并提供对生物机制的见解。我们提出了两个分析 旨在应对跨多平台基因组数据有效集成的挑战的方法 纳入来自基因组项目的外部信息。第一种方法(目标1)是贝叶斯回归 和特征选择方法，该方法可以非常灵活地集成先验信息，从而允许 数据“不言而喻”，以确定哪些外部信息与手头问题相关。 该方法适用于个人级别的数据，也适用于元分析摘要，使其非常适合于 分析来自大型多项研究联盟的数据。第二种方法(目标2)是正则化回归和 一种整合在同一集合上测量的多平台基因组特征的特征选择方法 个人。该方法是为了适应全基因组的大量典型特征而设计的 平台，以说明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 开发预测性和预测性模型。我们将应用我们的方法来搜索新的风险变量 联合基因组关联研究，并构建结直肠癌复发的预后模型 基于全基因组表达的甲基化数据在ColoCare联盟的CRC患者队列中。这 这项工作将为分析高维多平台基因组提供新的工具 利用可用的外部信息。

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.