SAMPLING MODELS AND METHODS FOR COMPLEX GENETIC DISEASES

描述（改编自研究者摘要）：这是一项竞争性研究。 续期申请。最初的建议制定了统计方法， 解决人类遗传学研究中出现的取样问题。当前 建议通过开发处理抽样差异的方法来扩展这项工作 基因连锁研究的结果发现特定的遗传影响 人类复杂疾病的发展可以促进早期诊断， 预防，并扩大对自然和 疾病的发展。近年来，人们对 复杂性疾病推动了复杂性研究的快速发展 技术，这在原则上使得有可能映射基因，甚至小到 中等效果。然而，有时被称为 “元分析”，或对整体的数学严格评估， 基于多组基因数据的统计证据， 在实践中很难解释即使是 最复杂的数据分析该提案的目的是发展和 评估数学上严格的方法来表示的力量， 基于多组数据的遗传证据，其中数据固有地 异质的我们提出了一种新的方法来解决这个问题，我们称之为 序贯贝叶斯这种方法具有概念和计算上的优势 并提供了一种完全通用的方法来处理 数据集之间的差异。新方法将得到彻底发展， 通过与现有方法的比较进行评估，基于分析工作和 模拟研究。目标是编制一套全面的 对分析多组基因组感兴趣的研究人员指南 数据该项目还将作为正在进行的下列研究的重要补充： 孤独症和恐慌症，对于如何 解释积累的遗传连锁证据，越来越紧迫。

DESCRIPTION (Adapted from investigator's abstract): This is a competitive renewal application. The original proposal developed statistical methods for addressing sampling issues arising in human genetics research. The current proposal extends this work by developing methods to handle sampling differences across genetic linkage studies. Discovery of specific genetic influences on the development of complex disorders in humans can facilitate early diagnosis and prevention, and broaden understanding of the contributions of both nature and nurture to the development of illness. In recent years, increased interest in complex disorders has fueled rapid development of complex investigative techniques, which in principle makes it possible to map genes of even small to moderate effect. However, a dearth of methods for what is sometimes called 'meta-analysis,' or the mathematically rigorous evaluation of the overall statistical evidence based on multiple sets of genetic data, has made it extremely difficult in practice to interpret the aggregate results of even the most sophisticated data analyses. The aim of this proposal is to develop and evaluate mathematically rigorous methods for representing the strength of the genetic evidence based on multiple sets of data, where the data are inherently heterogeneous. We propose a novel approach to this problem, which we call sequential Bayesian. This approach has conceptual and computational advantages over existing alternatives, and offers a fully general way to handle differences across data sets. The new approach will be thoroughly developed and evaluated by comparison with existing methods, based on both analytic work and simulations studies. The objective will be to produce a comprehensive set of guidelines for investigators interested in analyzing multiple sets of genetic data. The project will also serve as a critical adjunct to ongoing studies of autistic disorder and panic disorder, for which the question of how to interpret accumulated genetic linkage evidence, is increasingly urgent.