Exact Statistical Tools for Genetic Association Studies

用于遗传关联研究的精确统计工具

• 批准号: 8601542
• 负责人: PRALAY SENCHAUDHURI
• 金额: $ 51.57万
• 依托单位: CYTEL, INC
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8601542
• 关键词: Accounting; Algorithms; Alleles; Area; Base Pairing; Complex; Computer software; Conservatism; DNA; Data; Disease; Family; Gene Frequency; Genetic; Genetic Markers; Goals; Hereditary Disease; Hybrids; Individual; Joints; Measures; Memory; Methods; Mutation; Network-based; Performance; Pharmacoepidemiology; Phase; Procedures; Public Health; Relative (related person); Research; Research Design; Research Personnel; Sampling; Single Nucleotide Polymorphism; Statistical Methods; System; Technology; Testing; Work; base; case control; conditioning; design; exome; experience; genetic analysis; genetic association; genome sequencing; genome wide association study; genotyping technology; improved; innovation; next generation; parallel processing; platform-independent; programs; public health relevance; research and development; tool; trait; user friendly software

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.

描述（由申请人提供）：我们研究的总体目标是开发和扩展用于测试遗传关联的高效精确统计工具，并将这些方法纳入现有的广泛使用的软件包，这些软件包将服务于制药，流行病学，公共卫生和其他领域的数据分析师的需求，以更好地了解复杂疾病的遗传原因。这一研究领域对更大的统计和计算创新的需求正在急剧上升，因为基因分型技术的快速发展使得测量样本受试者的遗传标记数量越来越多变得更容易，成本也更低。这样的研究标记现在主要包括沿着细胞DNA沿着链的单个碱基对突变（称为单核苷酸多态性或SNP）。1- 2 M SNP的标记物组现在在全基因组研究中很常见，并且开发技术（如外显子组或全基因组测序）将允许对更大数量级的标记物组进行常规比较。有这么多的假设检验，需要保持假阳性的发现率提出了一些关键的统计和计算困难。现有的方法和它们的实现在一般条件下通常表现不佳。在我们项目的两个阶段开发的程序将显着提高遗传关联测试的效率，准确性和统计能力，无论是对于当前的GWAS面板还是对于产生更大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调查人员和在用户友好软件中提供尖端精确统计方法方面具有丰富经验的软件开发人员的共同努力。在这个项目中，我们将扩展第一阶段开始的工作，（1）实施一系列精确的多重检验程序，用于病例对照数据的遗传关联研究，并通过使用并行处理方法使其性能显着更有效;（2）开发和实施新的多重检验程序，用于基于家族的关联研究;（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机硬件兼容;（4）将这些程序另外纳入SAS PROC，并开发一个接口，允许用户在使用StatXact时访问R函数和对象。