Exact Statistical Tools for Genetic Association Studies

描述(由申请人提供)：我们研究的总体目标是开发和扩展强大的精确统计工具来测试基因关联，并将这些方法整合到两个现有的、广泛使用的软件包(Cytel Studio、SAS)中，以满足制药、遗传流行病学和公共卫生以及其他需要更好地了解复杂疾病的遗传决定因素的数据分析师的需求。对这些分析工具的需求正在急剧上升，因为基因分型技术的快速进步使测量样本对象的数量越来越多，变得更容易，成本也更低。遗传关联代表了观察到的研究遗传标记和某些物理特征之间的相关性，可以使用传统的病例对照研究设计或基于家庭的研究设计进行评估。在任何一种情况下，都有令人信服的置换或精确统计方法的应用，这些方法在计算上具有挑战性，但在当前使用的软件中根本不可用，或者以需要过多内存或计算的方式实现。为该项目开发的计算创新将填补这一空白，显著提高现有工具在基于家庭和病例对照设计下用于遗传关联的效率和能力。 在第一阶段，我们将建立一个原型计算机程序，其中包括 (I)对双等位基因和多等位基因标记进行准确的基于家庭的测试；以及 (Ii)同时测试假设各种遗传模式(即隐性、显性、加性或共显性)的遗传关联的排列程序。 我们还将研究将这些程序纳入SAS程序的可行性，以补充和扩展目前实施的用于测试遗传关联的SAS JMP基因组程序。 作为第二阶段的一部分，我们将把第一阶段的工具集成到Cytel的StatXact系统中，并作为外部程序集成到SAS JMP基因组学系统中。我们还会另外 (I)扩展确切的以家庭为基础的程序，以适应单倍型数据， (2)开发和实施用于大规模筛选实验的排列方法的算法， (3)纳入基本遗传流行病学程序的准确版本；以及 (4)采用有效的蒙特卡罗抽样工具，将精确程序的用处扩大到更大的数据集。 与公共卫生相关：基因分型技术的快速进步使从样本人类中识别越来越多的遗传标记变得更容易，成本更低。这些标记可以用来识别可能与许多复杂疾病相关的新基因。该项目将为遗传学研究人员提供更准确和更有效的统计工具，以分析这些研究的数据。

DESCRIPTION (provided by applicant): The overall goal of our research is to develop and extend powerful exact statistical tools for testing genetic association, and to incorporate these methods into two existing, widely used software packages (Cytel Studio, SAS) that will serve the needs of data analysts in pharmaceuticals, genetic epidemiology and public health, and other fields which require a greater understanding of the genetic determinants of complex disease. The demand for these analytic tools 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. Genetic association represents an observed correlation between an investigative genetic marker and some physical trait, and can be assessed using either traditional case-control or family-based study designs. In either case, there are compelling applications of permutation or exact statistical approaches that are computationally challenging, yet are simply unavailable in currently used software or are implemented in a manner that requires excessive memory or computation. The computational innovations developed for this project will fill this gap, significantly improving the efficiency and power of existing tools used for genetic association under both family-based and case-control designs. During Phase I, we will build a prototype computer program that includes (i) exact family-based tests for both biallelic and multiallelic markers, and (ii) a permutation procedure that simultaneously tests genetic association assuming various modes of inheritance (i.e., recessive, dominant, additive, or codominant). We will also investigate the feasibility of incorporating these procedures into a SAS PROC, complementing and extending currently implemented SAS JMP Genomics procedures for testing genetic association. As a part of Phase II, we will integrate our Phase I tools into Cytel's StatXact system and into the SAS JMP Genomics system as an external procedure. We will additionally (i) extend the exact family-based procedures to accommodate haplotype data, (ii) develop and implement algorithms for permutation approaches to large-scale screening experiments, (iii) incorporate exact versions of basic genetic epidemiologic procedures, and (iv) incorporate efficient Monte Carlo sampling tools to extend the usefulness of the exact procedures to larger data sets. PUBLIC HEALTH RELEVANCE: Rapid progress in genotyping technology is making it easier and less costly to identify increasingly large numbers of genetic markers from sampled humans. These markers can be used to identify new genes potentially associated with many complex diseases. This project will provide genetics researchers with more accurate and efficient statistical tools for analyzing data from these studies.