Genome Analysis: Data Accuracy, Haplotyping and Mapping

描述(申请人提供)：该项目将开发一系列全面的新统计方法来分析全基因组关联研究，并将应用这些方法和其他适当的方法，对NIH资助的关联研究进行深入分析，试图揭开常见复杂疾病的遗传基础。 这项工作的总体目标是产生并使其他人产生发现和见解，以帮助开发医学诊断测试、更有效的治疗方法，并最终预防疾病。我们的重点将主要放在开发新的贝叶斯统计方法上，这些方法是对现有分析方法的补充和改进。具体目标包括改进现有的贝叶斯统计方法，以评估基因和数量表型之间的相关性，以提高它们对基本建模假设偏差的稳健性；扩展这些方法，以允许分析二元(病例/对照)表型和基于家族的设计；以及修改这些方法，以纳入相关的生物学先验信息(例如，关于分子通路的信息)。结果将是一套在用户友好的软件中实施的工具，用于对许多最常用的关联研究设计进行单标记和多标记分析，包括群体样本和亲子三人组的定量和二元(病例/对照)表型。

DESCRIPTION (provided by applicant): This project will develop a comprehensive array of new statistical methods for analyzing genome-wide association studies, and will apply these methods, and other appropriate methods, to perform in-depth analyses of NIH-funded association studies that attempt to unravel the genetic basis of common complex diseases. The overall objective is for the work to produce, and enable others to produce, discoveries and insights that aid the development of medical diagnostic tests, more effective therapies, and, ultimately, prevention of disease. Our focus will be primarily on developing new Bayesian statistical methods, which complement and improve on existing analysis approaches. The specific aims include the refinement of existing Bayesian statistical approaches to assessing correlation between genotype and quantitative phenotype to improve their robustness to deviations from underlying modeling assumptions; extension of these methods to allow analysis of binary (case/control) phenotypes, and family-based designs; and modification of these approaches to incorporate relevant biological prior information (e.g.~information on molecular pathways). The result will be a suite of tools, implemented in user-friendly software, for performing both single-marker and multi-marker analyses for many of the most commonly-used association study designs, including both quantitative and binary (case/control) phenotypes for population samples and parent-offspring trios.