Statistical Methods for Integrative Genomics in Cancer

DESCRIPTION（由申请人提供）：我们的目标是开发新的统计方法，以解决“后gwas”时代癌症遗传流行病学家面临的一些主要问题，并说明它们在各种结直肠癌（CRC）研究中发现新生物学的用途。这些方法利用先前的生物学知识为整合基因组学分析提供信息（项目1），利用系统发育信息推断基因功能作为我们流行病学建模项目的输入（项目2），模拟微生物组和暴露体在癌症风险中的作用（项目3），并利用肿瘤内异质性来了解体细胞肿瘤的进化以及这一过程如何被内部环境修改（项目4）。这四个项目将由一个行政核心和三个共享资源核心支持，分别是功能注释、高性能计算和软件开发。整个计划的动机是提供评估影响的工具的总体目标

DESCRIPTION (provided by applicant): We aim to develop novel statistical methods to address some of the major problems facing cancer genetic epidemiologists in the "post-GWAS" era and to illustrate their use for discovery of novel biology in various colorectal cancer (CRC) studies. These methods leverage prior biological knowledge to inform integrative genomics analyses (Project 1), use phylogenetic information to infer gene function as inputs to our epidemiologic modeling projects (Project 2), model the role of the microbiome and the exposome in cancer risk (Project 3), and exploit intra-tumor heterogeneity to learn about somatic tumor evolution and how this process is modified by the internal environment (Project 4). These four projects will be supported by an administrative core and three shared resource cores on functional annotation, high performance computing, and software development. The entire program is motivated by an overall objective of providing tools for evaluating the impact of potential preventive or therapeutic interventions based on modifiable risk factors. Specifically, the aims of the overall program are (1) to develop statistical analysis methods to integrate multiple types of omics data that describe both constitutional and acquired genomic variation as well as measures of the external and internal environment into comprehensive risk prediction models, leveraging external information; (2) to apply these methods to various studies of CRC etiology and prognosis to uncover novel associations and to develop predictive models that would have translational significance for possible primary, secondary, and tertiary interventions; and (3) to establish an infrastructure (administrative, bioinformatic, computational, software) to support the various research projects and facilitate making our methods accessible to the broader scientific community. This will be achieved by a combination of theoretical developments, simulation studies closely keyed to real data projects, applications to several studies of CRC, and distribution of software for use by outside investigators. Beyond applications to colorectal cancer, our methods will be broadly applicable to other cancer types and many other chronic diseases.