Analytical Tools for Whole Genome Association Studies

人类遗传学的一个中心目标是充分了解人类疾病的原因，因为在这方面的知识存在着改善治疗方法的希望，也许最终预防这些疾病。人类遗传学的核心挑战是由大多数疾病固有的复杂性以及人类基因组的简单物理大小驱动的。就在几年前，仅基因组的大小就足以阻止我们在了解所有人类疾病方面取得实质性进展，除了最简单的人类疾病。直到最近，根本没有任何技术可以希望以解决复杂疾病原因所需的规模获得遗传信息。这些技术壁垒正在迅速下降。我们目前拥有能够获取大量基因组信息的技术，我们确信这些技术在未来几年只会变得更好，更快，更便宜。然而，如果我们要朝着最终目标取得进展，我们将需要在有效设计高通量定制检测方法的能力方面取得许多进展，以收集高通量实验室数据，最重要的是我们能够解释这些数据对人类疾病的贡献。该提案强调了以强大而有效的方式分析高通量，大规模生物数据的方法，试图发现基因如何导致人类疾病。 具体目标1：开发分析和计算技术，同时对数十万个单核苷酸多态性（SNP）进行全基因组关联研究，使用所有可用的单倍型数据，以计算上易于处理的方式，并在关联存在时高度有效地找到关联。 a.将这些技术用于核心家庭（双亲和后代）和简单的分类表型（患病与未患病）。 B。将这些技术扩展到群体水平数据（无家族信息，仅病例与对照）。 C.将这些技术扩展到定量数据（例如血压、血糖水平等）。D.将这些技术扩展到完整的家系（大家庭）数据。 具体目标2：提供自动化工具，帮助生物学家利用这些进步。a.提供自动化工具，从高通量实验中收集数据。 B。提供自动分析这些数据的工具。 C.以人类遗传学家可以容易地解释和使用的格式提供分析报告。

DESCRIPTION (provided by applicant): A central goal of human genetics is to fully understand the causes of human disease, for in this knowledge lies the hope of improved treatments and perhaps ultimately prevention of those diseases. The central challenges of human genetics are driven by the inherent complexity of most diseases, combined with the simple physical size of the human genome. Only a few years ago, the size of the genome alone was sufficient to prevent substantial progress towards understanding all but the most simple of human diseases. There was, until very recently, simply no technology that could even hope to acquire genetic information at a scale necessary to address the causes of complex disease. Those technological barriers are now falling, rapidly. We currently possess technology that is capable of acquiring large amounts of genomic information, and we know with certainty that these technologies will only get better, faster and cheaper in the years to come. If we are to make progress towards our ultimate goal, though, we will need numerous advances in our ability to efficiently design high throughput custom assays, to collect high throughput laboratory data, and most importantly in our ability to interpret that data with respect to its contributions to human disease. This proposal highlights methods to analyze high-throughput, large-scale biological data in a powerful and efficient manner, in an attempt to discover how genes contribute to human disease. Specific Aim 1: To develop analytical and computational techniques to perform whole genome association studies on hundreds of thousands of Single Nucleotide Polymorphisms (SNPs) simultaneously, using all available haplotypic data, in ways that are both computationally tractable, and highly powered to find association when it does exist. A. To use these techniques for nuclear families (two-parents and offspring), and simple categorical phenotypes (diseased vs. not diseased). B. To extend these techniques to population level data (no family information, only Case vs. Control). C. To extend these techniques to quantitative data (e.g. blood pressure, blood glucose level, etc.). D. To extend these techniques to full pedigreed (extended family) data. Specific Aim 2: To provide automated tools to help biologists take advantage of these advances. A. To provide automated tools to collect the data from high-throughput experiments. B. To provide tools for automated analysis of that data. C. To provide reports of the analysis in a format human geneticists can easily interpret and use.