Powerful and Adaptive Statistical Methods for Sequencing Studies

 描述(由申请人提供)：在过去的几年中，下一代测序(NGS)数据的产生越来越多。它们涵盖了全基因组变异的全部范围，有望从传统的全基因组关联(GWA)研究中回避的罕见变异中识别新的遗传性来源。尽管近年来取得了长足的进步，但目前的方法在分析以极高维度和低次要等位基因频率(MAF)为特征的NGS数据时，在能力和稳健性方面仍然有限。需要新的方法来适应这些统计挑战，以充分发挥NGS数据在确定导致遗漏疾病遗传性的基因变异方面的潜力。这个项目的目标是开发强大的、适应性强的统计方法，用于序列研究的分析。具体地说，该项目的目标是(1)开发一种适应性变异筛选程序，该程序可以科学地解释大部分原因稀有变量，同时显著降低后续分析的TE数据维度；(2)提供一个客观的样本量计算程序，以指导后续研究，并准确地定位高概率的原因变量。(2)本项目的目的是：(1)开发一种自适应变异筛选程序，该程序能够科学地解释大部分原因稀有变量，同时显著降低用于后续分析的TE数据维度；(2)提供一个客观的样本量计算程序，以指导后续研究，并精确定位具有较高概率(fi)的原因变量。建议的程序非常通用，可以适应广泛的模型、测试统计和数据场景。它们完全由数据驱动，可以自动适应数据的底层稀疏性。此外，在极高维的情况下，所提出的方法在计算上是有效的fi。这些理想的性质使得所提出的方法适用于大量的高维应用。将发展严格的理论来理解稀疏性和超高维在NGS数据分析中的作用，并将通过全面的模拟来研究所提出的方法。此外，该项目将提供计算上有效的fi程序，并使用最近的几个NGS数据集对这些方法进行评估。这些程序将用R和Effi的FORTRAN语言开发。我们的计算程序包将公开提供，以允许研究人员在测序研究中广泛应用我们的程序。

 DESCRIPTION (provided by applicant): Next-generation sequencing (NGS) data are being increasingly generated over the last few years. Encompassing the full spectrum of genomic variations, they hold the promise of identifying new sources of heritability from rare variants tha were eluded in traditional genome-wide association (GWA) studies. Despite substantial progresses in recent years, current methods are, nonetheless, limited in terms of power and robustness towards the analysis of NGS data that are characterized by extreme high dimensionality and low minor allele frequency (MAF). New methods are needed to adapt to these statistical challenges in order to achieve the full potential of NGS data in identifying genetic variations contributing to missing disease heritability. The goal of this project is to develop powerful and adaptive statistical methods for the analysis of sequencing studies. Specifically, the project aims to (1) develop an adaptive variants screening procedure that can efficiently account for a large proportion of causal rare variants while significantly reducing te data dimension for follow-up analysis; and to (2) provide an objective procedure for samples-size calculation to direct follow-up studies and to pinpoint the causal variants with high confidence. The proposed procedures are very general and can accommodate a wide spectrum of models, test statistics, and data scenarios. They are completely data-driven and can automatically adapt to the underlying sparsity of the data. Moreover, the proposed methods are computationally efficient under extreme high dimensionality. These desirable properties make the proposed methods applicable to a myriad of high-dimensional applications. Rigorous theory will be developed to understand the role of sparsity and extreme high dimensionality in NGS data analysis, and comprehensive simulations will be performed to study the proposed methods. In addition, this project will provide computationally efficient programs and evaluate the methods using several recent NGS datasets. The programs will be developed in R and efficient Fortran languages. Our computational package will be made publicly available to allow investigators to apply our procedures widely in sequencing studies.