Analytical Tools for Whole Genome Association Studies

描述（由申请人提供）：人类遗传学的中心目标是充分了解人类疾病的原因，因为在这些知识中存在着改进治疗方法并可能最终预防这些疾病的希望。人类遗传学的核心挑战来自于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。就在几年前，仅基因组的大小就足以阻止人们在了解除最简单的人类疾病之外的所有疾病方面取得实质性进展。直到最近，根本没有任何技术能够希望获得大规模的遗传信息，以解决复杂疾病的原因。这些技术壁垒现在正在迅速下降。我们目前拥有能够获取大量基因组信息的技术，而且我们确信，这些技术在未来几年只会变得更好、更快、更便宜。然而，如果我们要朝着我们的最终目标取得进展，我们将需要在有效设计高通量定制分析、收集高通量实验室数据的能力方面取得许多进步，最重要的是，我们需要在解释这些数据对人类疾病的贡献方面取得许多进步。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。

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.