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

基本信息

批准号：
10415152
负责人：
Jun Chen
金额：
$ 22.92万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10415152
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

项目摘要

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，应用于个体化医疗的两个目标。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。任务面临着统计能力不足的挑战。整合这些辅助元件的结构和功能的大量数据。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。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目标 1 侧重于开发协变量自适应族错误率控制程序。通过考虑辅助数据，该程序比现有程序有所改进。同时提供信息和 p 值分布信息，而现有程序不使用 p 值分布信息的重点是开发结构自适应的高维信息。用于将辅助数据灵活集成到预测中的回归模型该方法可转换辅助数据。由于它施加了“软”，因此将信息转化为回归系数的不同惩罚强度。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目标 3 提出了一个两阶段错误发现率控制 (FDR) 过程以获取更多信息。基因组关联分析中强大的混杂因素调整受到各种影响。人口、环境、生物和技术因素造成的混杂影响。调整显着降低了统计功效。两阶段方法提高了传统的功效。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以及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。