Methods for Analysis of Genomic Data with Auxiliary Information

具有辅助信息的基因组数据分析方法

• 批准号: 10188885
• 负责人: Jun Chen
• 金额: $ 20.48万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10188885
• 关键词: Accounting; Biological; Cells; Charge; Clinic; Clinical; Collaborations; Communities; Computer software; Data; Data Analyses; Data Analytics; Data Set; Detection; Development; Diagnosis; Elements; Environment; Epigenetic Process; Face; Family; Gene Expression; Genetic Annotation; Genomics; Goals; Grant; Information Distribution; Lasso; Location; Manuscripts; Medical; Medicine; Metagenomics; Methodology; Methods; Modeling; Outcome; Phylogenetic Analysis; Procedures; Prognosis; Property; Research Personnel; Scientific Advances and Accomplishments; Smoking; Specific qualifier value; Statistical Methods; Structure; Testing; Translating; Trees; Variant; Work; base; bioinformatics tool; biomarker discovery; data integration; design; disorder prevention; flexibility; functional genomics; genome wide association study; genome-wide; genomic biomarker; genomic data; high dimensionality; improved; individualized medicine; innovation; interest; novel; open source; predictive modeling; sex; simulation; statistics; theories; tool; user friendly software; user-friendly

Project Summary The broad and long-term objective of this project concerns the development of novel statistical methods and bioinformatics tools for genomic data analytics, with application to individualized medicine. Two goals of genomic data analysis in medicine are to identify genomic biomarkers of clinical outcomes and to build genomic biomarker-based predictive models for disease prevention, diagnosis and prognosis. Both of these tasks face the challenge of insufficient statistical power. Functional genomics studies have produced an enormous amount of data about the structure and function of the genomic elements. Integrating such auxiliary data in the analysis of genomic data could potentially increase the power and interpretability of the analysis. However, methods for integration of auxiliary data in genomic data analysis remain under-developed. This proposal aims to develop novel statistical methods for auxiliary data integration for three fundamental statistical problems. Aim 1 focuses on developing a covariate-adaptive family-wise error rate control procedure integrating auxiliary data. The procedure improves over existing procedures by accounting for the auxiliary information and the p-value distributional information simultaneously while the existing procedures do not use the p-value distributional information. Aim 2 focuses on developing a structure-adaptive high-dimensional regression model for flexible integration of auxiliary data into prediction. The method translates the auxiliary information into different penalization strengths for the regression coefficients. Since it imposes a “soft” constraint on the regression coefficients, it is expected to be more robust to mis-specified or less informative auxiliary information. Aim 3 proposes a two-stage false discovery rate control (FDR) procedure for more powerful confounder adjustment in genomic association analysis. Genomic data are subject to various confounding effects due to demographic, environmental, biological and technical factors. Confounder adjustment substantially reduces statistical power. The two-stage approach improves the power of traditional adjusted analyses by using the unadjusted test statistics as auxiliary information to filter out less promising features and performing the FDR control in the remaining. Aim 4 will develop user-friendly and efficient software packages so the community can benefit maximally from methodological and scientific advances resulting from this application. The proposed methods will be evaluated using simulations, and more importantly, applications to several ongoing studies at the Center of Individualized Medicine at Mayo Clinic. The proposed quantitative methods and open-source platform will contribute to genomic biomarker discovery and genomic biomarker-based predictive medicine. All methods and bioinformatics tools developed under this grant will be made available free of charge to interested researchers and the public.

项目概要 该项目的广泛和长期目标涉及开发新的统计方法和 用于基因组数据分析的生物信息学工具，可应用于个体化医疗。两个目标 医学中的基因组数据分析是为了识别临床结果的基因组生物标志物并建立 基于基因组生物标记的疾病预防、诊断和预后预测模型。这两个 任务面临统计能力不足的挑战。功能基因组学研究产生了 关于基因组元件的结构和功能的大量数据。集成此类辅助 基因组数据分析中的数据可能会提高分析的能力和可解释性。 然而，在基因组数据分析中整合辅助数据的方法仍然不完善。这 该提案旨在开发新的统计方法，用于三个基本统计的辅助数据集成 问题。目标 1 侧重于开发协变量自适应族错误率控制程序 整合辅助数据。该程序通过考虑辅助因素对现有程序进行了改进 同时提供信息和 p 值分布信息，而现有程序不使用 p 值分布信息。目标 2 侧重于开发结构自适应高维 用于将辅助数据灵活集成到预测中的回归模型。该方法翻译辅助 将信息转化为回归系数的不同惩罚强度。因为它强加了“软” 对回归系数的约束，预计对于错误指定或信息量较少的情况具有更强的鲁棒性 辅助信息。目标 3 提出了两阶段错误发现率控制 (FDR) 程序，以实现更多目的 基因组关联分析中强大的混杂因素调整。基因组数据受到各种因素的影响 人口、环境、生物和技术因素造成的混杂影响。混杂因素 调整大大降低了统计功效。两阶段方法提高了传统方法的能力 通过使用未调整的测试统计数据作为辅助信息来过滤掉不太有希望的调整分析 功能并在其余部分执行 FDR 控制。目标4将开发用户友好且高效的 软件包，使社区能够从方法和科学进步中最大限度地受益 由该应用程序产生的。所提出的方法将通过模拟等进行评估 重要的是，应用于梅奥诊所个体化医学中心正在进行的几项研究。 所提出的定量方法和开源平台将有助于基因组生物标志物的发现 和基于基因组生物标志物的预测医学。在此开发的所有方法和生物信息学工具 赠款将免费提供给感兴趣的研究人员和公众。