Methods for Analysis of Genomic Data with Auxiliary Information

项目摘要 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，以及在个体化医学中的应用。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记物并建立 基于基因组生物标记物的疾病预防、诊断和预后预测模型。这两个都是 任务面临统计力量不足的挑战。功能基因组学研究产生了一种 关于基因组元件的结构和功能的海量数据。集成这种辅助剂 基因组数据分析中的数据可能会增加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发达。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整合 有问题。目标1致力于开发一种协变量自适应的家族式误码率控制程序 集成辅助数据。该程序通过考虑辅助程序改进了现有程序 信息和p值分布信息同时存在，而现有程序不使用 P值分布信息。目标2专注于开发一种结构自适应的高维 用于将辅助数据灵活地整合到预测中的回归模型。该方法将助词 信息转化为不同惩罚强度的回归系数。因为它强加了一种“软性” 对回归系数的约束，预计它对错误指定或信息量较少的情况更稳健 辅助信息。目标3提出了一种两阶段错误发现率控制(FDR)过程，用于 基因组关联分析中强大的混杂因子调节。基因组数据受各种因素的影响 人口、环境、生物和技术因素造成的混杂影响。混乱者 调整大大降低了统计能力。两阶段方法提高了传统的 通过使用未调整的测试统计数据作为辅助信息来过滤不太有希望的分析来进行调整的分析 功能并在其余部分执行FDR控制。AIM 4将开发出用户友好和高效的 软件包，使社区能够从方法和科学进步中最大限度地受益 由此应用程序产生的。建议的方法将使用模拟进行评估，以及更多 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记物的预测医学。在此基础上开发的所有方法和生物信息学工具 GRANT将免费提供给感兴趣的研究人员和公众。

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.