Exact Statistical Tools for Genetic Association Studies

描述(申请人提供)：我们研究的总体目标是开发和扩展有效的精确统计工具来测试基因关联，并将这些方法整合到现有的、广泛使用的软件包中，以满足制药、流行病学、公共卫生和其他领域的数据分析师的需求，这些领域寻求更好地了解复杂疾病的遗传原因。随着基因分型技术的快速进步，对这一研究领域更大的统计和计算创新的需求正在急剧上升，这使得为越来越多的遗传标记测量抽样对象变得更容易、成本更低。这种研究标记现在主要包括细胞DNA链上的单个碱基对突变(称为单核苷酸多态或SNPs)。在全基因组研究中，由1-200万个SNPs组成的标记板现在很常见，正在开发的技术(如外显子组或全基因组测序)将允许对更大数量级的标记集进行常规比较。由于有如此多的假设检验，需要保留假阳性发现的比率，这带来了一些关键的统计和计算困难。在常见情况下，现有方法及其实现通常表现不佳。在我们项目的两个阶段开发的程序将显著提高基因关联测试的效率、准确性和统计能力，无论是对于当前的GWA板，还是对于产生更多数据量的下一代技术。该项目代表了处于遗传关联方法学研究前沿的研究人员和软件开发人员的共同努力，他们在将尖端的精确统计方法用于用户友好的软件方面具有丰富的经验。在这个项目中，我们将通过以下方式扩展在第一阶段开始的工作：(1)为病例对照数据的遗传关联研究实施一系列精确的多重测试程序，并通过使用并行处理方法使其性能显著提高；(2)为基于家庭的相关性研究开发和实施新的多重测试程序；(3)提供一个框架，使我们的并行处理程序能够尽可能广泛地与现代个人计算硬件兼容；以及(4)将这些程序额外地整合到SAS Proc中，并开发一个界面，允许用户在使用StatXact时访问R函数和对象。

DESCRIPTION (provided by applicant): The overall goal of our research is to develop and extend efficient exact statistical tools for testing genetic association, and to incorporate these methods into existing, widely used software packages that will serve the needs of data analysts in pharmaceuticals, epidemiology, public health, and other fields seeking to better understand the genetic causes of complex disease. The demand in this research area for greater statistical and computational innovation is rising dramatically, as rapid progress in genotyping technology is making it easier and less costly to measure sampled subjects for ever-larger numbers of genetic markers. Such investigative markers now predominantly include individual base pair mutations (referred to as single nucleotide polymorphisms or SNPs) along strands of cellular DNA. Marker panels of 1-2M SNPs are now common for genome-wide studies, and developing technologies (such as exome or whole-genome sequencing) will allow routine comparisons over marker sets that are orders of magnitude larger. With so many hypothesis tests, the need to preserve the rate of false positive findings presents some critical statistical and computational difficulties. Existing methods and their implementations often perform poorly under common conditions. The procedures developed during both phases of our project will significantly improve the efficiency, accuracy, and statistical power of genetic association tests, both for current GWAS panels as well as for next-generation technologies that are yielding even greater volumes of data. This project represents the joint efforts of investigators who are at the forefron of methodological research into genetic association, and software developers who have extensive experience in making cutting-edge exact statistical methods available in user-friendly software. In this project, we will extend the work begun during Phase 1 by (1) implementing a battery of exact multiple testing procedures for genetic association studies with case-control data, and making their performance significantly more efficient by using a parallel processing approach; (2) developing and implementing new multiple testing procedures for family-based association studies; (3) providing a framework that will allow our parallel processing programs to be as widely compatible as possible with modern personal computing hardware; and (4) incorporating the procedures additionally within a SAS PROC, and developing an interface that will allow users to access R functions and objects while using StatXact.