Developing Statistical Methods for Disease Gene Discovery

开发疾病基因发现的统计方法

• 批准号: 8147862
• 负责人: Chun Li
• 金额: $ 38.06万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-19 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8147862
• 关键词: Address; Admixture; Algorithms; Area; Candidate Disease Gene; Chromosome Mapping; Complex; Computer Simulation; Computer software; Copy Number Polymorphism; Data; Data Analyses; Data Set; Detection; Development; Disease; Family; Freedom; General Population; Generations; Genes; Genetic; Genetic Markers; Genetic Research; Genome; Genome Mappings; Genotype; Human Gene Mapping; Human Genetics; Human Genome; Human Genome Project; Individual; International; Joints; Maps; Methodology; Methods; Play; Population; Procedures; Research Personnel; Resolution; Role; SNP genotyping; Sample Size; Scientific Advances and Accomplishments; Statistical Methods; Techniques; Testing; Variant; Work; base; computerized tools; cost; density; experience; gene discovery; gene interaction; genetic association; genome wide association study; genotyping technology; markov model; novel

DESCRIPTION (provided by applicant): Human genetics research has accelerated in the last decade owing to our evolving understanding of the human genome. With the recent completion of the International HapMap Project, the development of largescale genotyping technology, and rapid decline in genotyping costs, an immense amount of genotype data have been generated, which in turn raises many new challenging problems for analysis and interpretation of the data. This application proposes developing new statistical methodologies that aim to address a wide range of statistical issues in current candidate gene and genome-wide association (GWA) studies. Specifically, the proposal will address the following problems. (1) Recent high-resolution genome mapping indicates that copy number variations (CNVs) are ubiquitous and common in the general population, and may play a major role in phenotypic variation. In Aim 1, we will develop a Bayesian hidden Markov model based algorithm for highresolution CNV detection using whole-genome SNP genotyping data. Our algorithm has the ability to incorporate both unrelated individuals and family data. (2) Given the high density of genetic markers in largescale candidate gene and GWA studies, it is reasonable to expect that multilocus genotypes offer more information on genetic association than single-marker analysis. In Aim 2, we will develop a powerful multimarker test for gene-based association analysis and extend the method to analysis of gene-gene interactions. The virtue of our method lies in its ability to borrow strength from nearby markers while reducing the degrees of freedom. (3) In many disease gene-mapping studies, individuals are ascertained from a recently admixed population. In Aim 3, we will develop novel association tests in genetics studies using recently admixed populations. By considering ancestry level and genotypes together, our method offers higher resolution and power than traditional admixture mapping methods. (4) Appropriate adjustment for multiple dependent tests has long been a problem in genetics studies, especially for studies with limited sample size and without replication datasets. In Aim 4, we propose new methods to estimate the effective number of tests that reflect the amount of independent information contained in the data. (5) In Aim 5, we will develop, test, distribute, and support freely available implementations of the methods proposed in this application. The methods will be evaluated through analytical approaches, computer simulations and applications to multiple real datasets. Recent development of large-scale genotyping technologies has led to the generation of an immense amount of genotype data, which raises many new challenging problems for the analysis and interpretation of the data. This application proposes developing new statistical methodologies that address a set of unresolved issues.

描述（由申请人提供）：由于我们对人类基因组的理解不断发展，人类遗传学研究在过去十年中加速发展。随着国际人类基因组单体型图计划的完成、大规模基因分型技术的发展以及基因分型成本的迅速下降，大量的基因型数据被产生，这反过来又给数据的分析和解释提出了许多新的挑战性问题。本申请提出开发新的统计方法，旨在解决当前候选基因和全基因组关联（GWA）研究中的广泛统计问题。 具体来说，该提案将解决以下问题。(1)最近的高分辨率基因组图谱表明，拷贝数变异（CNVs）是普遍存在的，在一般人群中很常见，并可能在表型变异中发挥重要作用。在目标1中，我们将开发一种基于贝叶斯隐马尔可夫模型的算法，用于使用全基因组SNP基因分型数据进行高分辨率CNV检测。我们的算法有能力将无关的个人和家庭数据。(2)鉴于大规模候选基因和GWA研究中遗传标记的高密度，可以合理地期望多位点基因型比单标记分析提供更多的遗传关联信息。在目标2中，我们将开发一个功能强大的多标记测试，以基因为基础的关联分析，并扩展该方法来分析基因-基因相互作用。我们的方法的优点在于它能够从附近的标记借用力量，同时减少自由度。(3)在许多疾病基因定位研究中，个体是从最近的混合群体中确定的。在目标3中，我们将使用最近混合的群体在遗传学研究中开发新的关联测试。通过同时考虑祖先水平和基因型，我们的方法提供了更高的分辨率和功率比传统的混合定位方法。(4)遗传学研究中，尤其是样本量有限且无重复数据集的研究，如何对多重相关检验进行适当的调整一直是一个难题。在目标4中，我们提出了新的方法来估计有效的测试数量，这些测试反映了数据中包含的独立信息量。(5)在目标5中，我们将开发、测试、分发和支持本申请中提出的方法的免费实现。这些方法将通过分析方法、计算机模拟和应用于多个真实的数据集进行评估。 近年来大规模基因分型技术的发展导致了大量基因型数据的产生，这为数据的分析和解释提出了许多新的挑战性问题。该应用程序建议开发新的统计方法来解决一系列未解决的问题。