Statistical Methods for Integrative Genomics in Cancer

癌症综合基因组学的统计方法

• 批准号: 10707446
• 负责人: William JAMES GAUDERMAN
• 金额: $ 204.77万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707446
• 关键词: Address; Area; Automated Annotation; Big Data; Bioinformatics; Biological; Cancer Etiology; Cancer Prognosis; Clinical; Clinical Data; Clinical Research; Clinical Trials; Collaborations; Communities; Complex; Computer software; Constitution; Constitutional; Core Facility; DNA Methylation; Data; Data Analyses; Data Set; Databases; Development; Disease; Environmental Risk Factor; Epidemiologist; Etiology; Foundations; Gene Expression; Gene set enrichment analysis; Generations; Genes; Genetic Transcription; Genome; Genomics; Genotype; Genotype-Tissue Expression Project; Goals; Heredity; Infrastructure; Intervention; Knowledge; Malignant Neoplasms; Measures; Mediation; Methodology; Methods; Modeling; Multiomic Data; Ontology; Pathway interactions; Phylogenetic Analysis; Prognosis; Proteome; Research Personnel; Research Project Grants; Resources; Risk Factors; Sample Size; Scanning; Software Tools; Source; Spectrum Analysis; Statistical Data Interpretation; Statistical Methods; Structure; Therapeutic Intervention; Time; Tissues; Training; Translational Research; Translations; Work; anticancer research; bioinformatics resource; cancer epidemiology; cancer genetics; cancer risk; cancer therapy; data integration; data resource; design; epidemiologic data; epidemiology study; feature selection; gene environment interaction; gene function; genetic association; genetic epidemiology; genome wide association study; genomic variation; high dimensionality; longitudinal analysis; member; metabolome; metabolomics; methylome; microbiome; modifiable risk; multiple data types; novel; novel strategies; predictive modeling; preventive intervention; programs; risk prediction; risk prediction model; simulation; software development; statistics; tool; trait; transcriptome; transcriptomics; user friendly software

OVERALL ABSTRACT The overall goal of this Program Project is to develop novel statistical methods for integrating multi-omic data to address etiology, prognosis, and treatment of cancer through a collaboration of four closely related projects and four shared cores (see inset). The four projects can be broadly described as spanning the spectrum of analysis challenges including feature selection, mediation, interaction, and characterization. The first of these, “High-Dimensional Regression for Data Integration,” develops new strategies for the analysis of longitudinal -omic data incorporating external functional information, maintaining a rigorous inferential foundation. The second project, “Integration of Omic Data to Estimate Mediation or Latent Structures,” develops novel latent factor and mediation models using high-dimensional omic data or GWAS summary statistics to identify and distinguish genotype, exposure and omic effects. The third project, “Integration of Omic Data in the Analysis of Gene x Environment Interaction,” incorporates gene expression and other -omics data into powerful multi-step approaches to scan for interactions leveraging exposure or disease marginal associations. Project 3 will also add novel approaches to identify transcriptional interactions, hierarchical GxE models with heredity constraints (i.e., requiring interactions to include the corresponding main effects), and extensions to longitudinal, survival, and quantitative traits. The fourth project, “Statistical Methods for Genome Characterization,” automates annotation of gene function using phylogenetic inference to identify new cancer- specific regions of conserved DNA methylation. Project 4 also proposes a novel approach for agnostic pathway gene set enrichment analysis. These projects will be supported by four cores: Administrative Core (A), Functional Annotation Core (B), Computation and Software Development Core (C), and Data Analysis and Research Translation Core (D). Core B will maintain up-to-date copies of key bioinformatics resources and will develop a software application that will provide a single unified portal for creating annotation files that integrates data from multiple resources. Core C will assist with high-volume computing needs and will develop user-friendly software packages that implement novel methods. Core D will focus on translation of new methods, both by supporting applications to real cancer datasets and by developing materials for training outside investigators in the use of our methods and software. Our proposed work will have both methodological and substantive importance. On the one hand, we will develop novel statistical methods that will be applicable to a wide range of cancer epidemiology studies and clinical trials. These methods will, for example, allow more powerful discovery of genetic associations and interactions through leveraging biological information from other sources. They will have translational significance in the areas of risk prediction and targeted interventions. Our program is designed to be highly integrative, with the various projects and cores being inter-related, so that together they will be more informative than any of them could be on their own. Program members have access to extraordinary data resources at USC and elsewhere, assuring that the methods we develop will be motivated by, and applicable to, important questions arising in current cancer research.

总体摘要 该计划项目的总体目标是开发新的统计方法， 多组学数据，通过合作解决癌症的病因，预后和治疗 四个密切相关的项目和四个 共享内核（见插图）。四 项目可以大致描述为 跨越分析范围 挑战包括特征选择， 调解、互动和 特征化其中第一个， “数据的高维回归 整合”，制定新战略 用于纵向组学分析 包含外部函数的数据 信息，保持严格的 推理基础第二 项目，“将OMIC数据整合到 估计中介或潜在 结构，”开发新的潜在因素 和中介模型，使用高维组学数据或GWAS汇总统计来识别 并区分基因型、暴露和组学效应。第三个项目，“OMIC一体化”， 基因与环境相互作用分析中的数据”，结合了基因表达和 将其他组学数据转化为强大的多步骤方法，以扫描利用 暴露或疾病边缘关联。项目3还将增加新的方法， 转录相互作用，具有遗传约束的分级GxE模型（即，要求 相互作用，包括相应的主效应），并扩展到纵向，生存， 和数量性状。第四个项目，“基因组表征的统计方法”， 使用系统发育推断自动注释基因功能，以识别新的癌症- 保守的DNA甲基化的特定区域。项目4还提出了一种新的方法， 不可知途径基因集富集分析。这些项目将得到四个核心的支持： 管理核心（A）、功能注释核心（B）、计算和软件 开发核心（C）和数据分析和研究翻译核心（D）。核心B将 维护关键生物信息学资源的最新副本，并将开发一个软件 一个应用程序，将提供一个统一的门户，用于创建注释文件， 来自多个资源的数据。Core C将协助满足大容量计算需求， 开发用户友好的软件包，实现新的方法。核心D将侧重于 新方法的翻译，既支持应用到真实的癌症数据集， 开发培训外部调查人员使用我们的方法和软件的材料。 我们拟议的工作将具有方法上和实质性的重要性。一方面 另一方面，我们将开发新的统计方法，适用于广泛的癌症 流行病学研究和临床试验。例如，这些方法将允许更强大的 通过利用生物信息发现遗传关联和相互作用， 其他来源它们将在风险预测领域具有转化意义， 有针对性的干预措施。我们的计划旨在高度整合，与各种 项目和核心是相互关联的，所以他们在一起将比任何 他们就只能靠自己了计划成员可以访问以下位置的非凡数据资源： 南加州大学和其他地方，确保我们开发的方法将受到激励，并适用于 当前癌症研究中出现的重要问题。

项目成果

Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees.

• DOI: 10.1371/journal.pcbi.1007948
• 发表时间: 2021-03
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Vega Yon GG;Thomas DC;Morrison J;Mi H;Thomas PD;Marjoram P
• 通讯作者: Marjoram P

partition: A fast and flexible framework for data reduction in R.

• DOI: 10.21105/joss.01991
• 发表时间: 2020-01-01
• 期刊: Journal of open source software
• 作者: Barrett, Malcolm;Millstein, Joshua
• 通讯作者: Millstein, Joshua

slurmR: A lightweight wrapper for HPC with Slurm.

slurmR：带有 Slurm 的 HPC 轻量级包装器。

• DOI: 10.21105/joss.01493
• 发表时间: 2019
• 期刊: Journal of open source software
• 作者: VegaYon,GeorgeG;Marjoram,Paul
• 通讯作者: Marjoram,Paul

PEREGRINE: A genome-wide prediction of enhancer to gene relationships supported by experimental evidence.

• DOI: 10.1371/journal.pone.0243791
• 发表时间: 2020
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Mills C;Muruganujan A;Ebert D;Marconett CN;Lewinger JP;Thomas PD;Mi H
• 通讯作者: Mi H

Rare germline variants in DNA repair genes and the angiogenesis pathway predispose prostate cancer patients to develop metastatic disease.

• DOI: 10.1038/s41416-018-0141-7
• 发表时间: 2018-07
• 期刊: British journal of cancer
• 影响因子: 8.8
• 作者: Mijuskovic M;Saunders EJ;Leongamornlert DA;Wakerell S;Whitmore I;Dadaev T;Cieza-Borrella C;Govindasami K;Brook MN;Haiman CA;Conti DV;Eeles RA;Kote-Jarai Z
• 通讯作者: Kote-Jarai Z