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颖的统计方法，以整合基因组数据的综合分析 癌症研究。我们建议开发可以从多个基因组中整合数据的分析工具 平台并包含来自公开可用数据库的外部OMIC信息。这些工具将是 适用于两种旨在因果发现以及临床和转化研究的病因研究 面向预测建模。 高通量分子技术的进步已经实现了大规模的OMIC项目（例如编码， 癌症基因组图集，表观基因组路线图），以产生有关该结构的大量信息， 基因组的功能和调节。除了这些公开可用的数据外，个别研究是 越来越多地产生乘数基因组谱（例如基因型，基因表达，甲基化拷贝 数量变化，miRNA），以阐明癌症发展和进展的复杂机制，以及 调查确定和预测健康和临床结果。这些倍数的集成 基因组“维度”和可用外部信息的结合可以增加 发现因果关系（例如癌症-SNP关联），增强预测和预后建模 （例如，癌症的侵略性），并提供了对生物学机制的见解。我们提出了两个分析 旨在应对跨乘数基因组数据和有效整合的挑战的方法 从OMIC项目中纳入外部信息。第一种方法（AIM 1）是贝叶斯回归 和特征选择方法，可以以非常灵活的方式集成先前的OMIC信息，从而允许 数据以“自言自语”，以确定哪些外部信息与当前的问题有关。 该方法可与单个级别的数据以及荟萃分析摘要一起使用，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（AIM 2）是正规化回归， 用于整合在同一集中测量的乘数基因组特征的特征选择方法 个人。该方法旨在扩展到基因组的典型特征 平台，以说明每种基因组数据类型的不同属性，并结合相关 外部信息以提高效率。两种方法都可以用于因果发现和 开发预测和预后模型。我们将应用我们的方法来搜索新颖的风险变体 基因组关联研究的Corect联盟，并构建了CRC复发的预后模型 基于CRC患者的Colocare联盟队列中的全基因组表达甲基化数据。这 工作将为分析的高维多平台基因组提供新的工具，可以采用 可用外部信息的优势。