Graphical models for linkage disequilibrium in genetic mapping

遗传作图中连锁不平衡的图形模型

• 批准号: 7296059
• 负责人: ALUN THOMAS
• 金额: $ 31.4万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-02 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7296059
• 关键词: Accounting; Admixture; Alleles; Biological Assay; Case Study; Case-Control Studies; Chromosome Mapping; Class; Complex; Data; Development; Disease; Drug Formulations; Ethnic Origin; Family; Genes; Genetic; Genetic Recombination; Genome; Genotype; Grant; Graph; Haplotypes; Joints; Linkage Disequilibrium; Localized; Location; Maps; Markov Chains; Methods; Modeling; Monte Carlo Method; National Institute of General Medical Sciences; Pattern; Population; Principal Investigator; Property; Purpose; Research Design; Research Personnel; Resolution; Sampling; Single Nucleotide Polymorphism; Statistical Models; Testing; United States National Institutes of Health; Utah; Work; base; design; genetic linkage analysis; genetic pedigree; genome wide association study; improved; malignant breast neoplasm; markov model; novel; programs; scale up; theories

DESCRIPTION (provided by applicant): One of the most pressing problems in identifying and localizing genes influencing diseases is the need to model linkage disequilibrium between single nucleotide polymorphisms in dense genotyping assays. Currently available assays determine genotypes at over 500,000 loci per sample, and this data is being used in multiple study designs. Statistical models and methods are needed to account appropriately for linkage disequilibrium, as well as observational error and population admixture. Naive approaches to the problem that rely on single locus analyses are swamped by the need to correct for multiple and correlated tests. Other approaches, such as those that thin out the loci used to reduce linkage disequilibrium or assume that alleles occur in blocks based on location, while sensible and reasonably efficient, do not exploit all of the potential statistical power and resolution made possible by this kind of data. Graphical models are a class of statistical models that can be applied to joint distributions of multivariate observations. In preliminary work by the principal investigator under a current R21 grant, these have been shown to give both accurate and tractable representations of the patterns of allelic association that occur between proximal genetic loci in a variety of problems. Results have been consistent with other sophisticated modeling methods, such as ancestral recombination graphs. In contrast models in which strong assumptions are made based on physical location of loci, such as low order Markov models, have been shown to be inappropriate for this problem. The purpose of this proposal is to further develop graphical modeling methods for linkage disequilibrium in association studies, identity by descent mapping, and linkage analysis. In particular we focus on model restrictions that will give an order of magnitude improvement in computational efficiency; a new formulation for the linkage analysis problem that should improve the mixing properties of Markov chain Monte Carlo methods; and a novel and general method for approximating complex graphical models with simpler ones. In addition, we pursue an approach to identity by descent mapping that incorporates linkage disequilibrium and is scalable to the whole genome level. For this aim of the project we intend to apply the methods developed to dense genotype assays obtained for distantly related breast cancer cases in extended Utah pedigrees.

描述（由申请人提供）：识别和定位影响疾病的基因中最紧迫的问题之一是需要在密集基因分型测定中对单核苷酸多态性之间的连锁不平衡进行建模。目前可用的检测方法可确定每个样本超过 500,000 个基因座的基因型，并且该数据正用于多个研究设计。需要统计模型和方法来适当解释连锁不平衡以及观测误差和群体混合。依赖于单位点分析的简单解决问题的方法被纠正多个相关测试的需要所淹没。其他方法，例如用于减少连锁不平衡的基因座稀疏化或假设等位基因根据位置出现在块中的方法，虽然合理且相当有效，但并未利用此类数据可能实现的所有潜在统计功效和分辨率。图模型是一类统计模型，可应用于多变量观测值的联合分布。在主要研究人员在当前 R21 资助下进行的初步工作中，这些已被证明可以准确且易于处理地表示各种问题中近端遗传位点之间发生的等位基因关联模式。结果与其他复杂的建模方法一致，例如祖先重组图。相比之下，基于基因座物理位置做出强假设的模型（例如低阶马尔可夫模型）已被证明不适用于此问题。该提案的目的是进一步开发关联研究、下降映射同一性和连锁分析中连锁不平衡的图形建模方法。我们特别关注模型限制，这将使计算效率提高一个数量级；联动分析问题的新表述，应改善马尔可夫链蒙特卡罗方法的混合特性；以及一种用简单图形模型逼近复杂图形模型的新颖且通用的方法。此外，我们追求一种通过血统图谱进行身份识别的方法，该方法结合了连锁不平衡，并且可扩展到整个基因组水平。为了该项目的目标，我们打算将开发的方法应用于对犹他州谱系中远亲乳腺癌病例进行的密集基因型测定。