Developing Statistical Methods for Disease Gene Discovery

开发疾病基因发现的统计方法

• 批准号: 7688619
• 负责人: Chun Li
• 金额: $ 38.94万
• 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/7688619
• 关键词: Address; Admixture; Algorithms; Area; Arts; 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了解不断发展，人类遗传学研究在过去十年中加速了。近年来，随着国际人类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，产生了大量的基因型数据，这又给数据的分析和解释提出了许多新的挑战性问题。该申请建议开发新的统计方法，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 (CNV) 在普通人群中普遍存在，并且可能在表型变异中发挥重要作用。在目标 1 中，我们将开发基于贝叶斯隐马尔可夫模型的算法，使用全基因组 SNP 基因分型数据进行高分辨率 CNV 检测。我们的算法能够合并不相关的个​​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，可以合理地预期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，我们将开发一种强大的多标记测试，用于基于基因的关联分析，并将该方法扩展到基因间相互作用的分析。我们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) 在许多疾病基因图谱研究中，个体是从最近混合的群体中确定的。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颖的关联测试。通过同时考虑祖先水平和基因型，我们的方法比传统的混合作图方法提供了更高的分辨率和功效。 (4) 多重相关检验的适当调整一直是遗传学研究中的一个问题，特别是对于样本量有限且没有重复数据集的研究。在目标 4 中，我们提出了新方法来估计反映数据中包含的独立信息量的有效测试数量。 (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、计算机模拟和对多个真实数据集的应用进行评估。 近年来大规模基因分型技术的发展产生了大量的基因型数据，这给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。