Powerful and Adaptive Statistical Methods for Sequencing Studies

 描述（由申请人提供）：在过去几年中，下一代测序（NGS）数据越来越多。它们涵盖了全谱的基因组变异，有望从传统的全基因组关联（GWA）研究中无法识别的罕见变异中识别新的遗传力来源。尽管近年来取得了重大进展，但目前的方法在分析NGS数据的能力和鲁棒性方面仍然有限，NGS数据的特征在于极高的维度和低的次要等位基因频率（MAF）。需要新的方法来适应这些统计挑战，以充分发挥NGS数据在识别导致疾病遗传力缺失的遗传变异方面的潜力。该项目的目标是开发强大的和自适应的统计方法来分析测序研究。具体而言，该项目旨在（1）开发一种自适应变异筛查程序，可以有效地解释大部分因果罕见变异，同时显著降低后续分析的数据维度;以及（2）提供一种客观的样本量计算程序，以指导后续研究，并以高置信度确定因果变异。所提出的程序是非常一般的，可以容纳广泛的模型，测试统计量和数据场景。它们完全是数据驱动的，可以自动适应数据的底层稀疏性。此外，所提出的方法在极高维度下具有计算效率。这些理想的属性使得所提出的方法适用于无数的高维应用。将开发严格的理论来理解稀疏性和极高维在NGS数据分析中的作用，并将进行全面的模拟来研究所提出的方法。此外，该项目将提供计算效率高的程序，并使用几个最近的NGS数据集评估方法。这些程序将用R和高效的Fortran语言开发。我们的计算软件包将公开提供，以使研究人员能够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.