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， 用于基因组数据分析的生物信息学工具，并应用于个性化医疗。两大目标 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， 用于疾病预防、诊断和预后的基于基因组生物标志物的预测模型。这两 任务面临统计能力不足的挑战。功能基因组学研究已经产生了一个 关于基因组元件的结构和功能的大量数据。集成这种辅助 基因组数据分析中的数据可以潜在地增加分析的能力和可解释性。 然而，用于在基因组数据分析中整合辅助数据的方法仍然开发不足。这 该提案旨在为三个基本统计数据的辅助数据集成开发新的统计方法 问题目标1的重点是发展一个协变量自适应的家庭明智的错误率控制程序 集成辅助数据。该程序通过考虑辅助 信息和p值分布信息，而现有的程序不使用 p值分布信息。目标2的重点是开发一个结构自适应的高维 回归模型，用于将辅助数据灵活地整合到预测中。该方法将辅助 信息转化为回归系数的不同惩罚强度。因为它强加了一个“软” 由于对回归系数的约束，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提出了一个两阶段的错误发现率控制（FDR）过程， 在基因组关联分析中的强大的混杂调整。基因组数据受到各种 人口、环境、生物和技术因素造成的混杂效应。混杂因素 调整实质上降低了统计功效。两阶段方法提高了传统 调整分析，使用未调整的检验统计量作为辅助信息，以过滤出不太有希望的数据 功能，并在其余部分中执行FDR控制。目标4将开发用户友好和高效的 软件包，以便社区可以最大限度地受益于方法和科学进步 这一应用的结果。所提出的方法将使用模拟等进行评估 重要的是，应用于马约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记的预测医学。在此基础上开发的所有方法和生物信息学工具 基金将免费提供予有兴趣的研究人员及公众人士。

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.