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: 7805162
• 负责人: PRALAY SENCHAUDHURI
• 金额: $ 11.21万
• 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 2011-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7805162
• 关键词: Accounting; Algorithms; Alleles; Complement; Complex; Computer software; DNA; Data; Data Analyses; Data Set; Databases; Disease; Epidemiology; Equilibrium; Family; Genes; Genetic; Genetic Determinism; Genetic Heterogeneity; Genetic Markers; Genetic Screening; Genomics; Goals; Haplotypes; Human; Joints; Knowledge; Link; Location; Measurement; Measures; Memory; Methods; Network-based; Pharmacologic Substance; Phase; Population; Procedures; Public Health; Research; Research Design; Research Personnel; Sampling; Screening procedure; Single Nucleotide Polymorphism; Statistical Methods; Subgroup; System; Testing; base; case control; computer program; design; experience; genetic analysis; genetic association; genetic epidemiology; genetic pedigree; genome wide association study; genotyping technology; improved; innovation; prototype; public health relevance; research and development; research study; tool; trait; user friendly software

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.

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，用于测试遗传关联，并将这些方法合并到两个现有的，广泛使用的软件包（Cytel Studio， SAS）中，这些软件包将服务于制药，遗传流行病学和公共卫生等领域的数据分析师的需求，这些领域需要对复杂疾病的遗传决定因素有更深入的了解。随着基因分型技术的快速发展，对这些分析工具的需求正在急剧上升，这使得对取样对象进行大量遗传标记的测量变得更加容易，成本也更低。遗传关联是指调查遗传标记与某些身体特征之间观察到的相关性，可以使用传统的病例对照或基于家庭的研究设计进行评估。在任何一种情况下，都有排列或精确统计方法的引人注目的应用程序，这些应用程序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，显著提高现有用于基于家庭和病例对照设计的遗传关联工具的效率和功能。