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: 7627382
• 负责人: ALUN THOMAS
• 金额: $ 28.22万
• 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 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7627382
• 关键词: Accounting; Admixture; Alleles; Biological Assay; Case Study; Case-Control Studies; Chromosome Mapping; Complex; Data; Development; Disease; Drug Formulations; Ethnic Origin; Family; Genes; Genetic; Genetic Recombination; Genome; Genotype; Grant; Graph; Haplotypes; Joints; Linkage Disequilibrium; Location; Maps; Markov Chains; Methods; Modeling; Monte Carlo Method; National Institute of General Medical Sciences; Pattern; Population; Principal Investigator; Property; 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(申请人提供)：在识别和定位影响疾病的基因方面，最紧迫的问题之一是需要在密集的基因分型分析中对单核苷酸多态之间的连锁不平衡进行建模。目前可用的分析方法确定每个样本超过500,000个基因座的基因类型，这些数据正被用于多个研究设计。需要统计模型和方法来适当地考虑连锁不平衡以及观测误差和种群混合。依赖于单基因座分析的解决问题的幼稚方法被需要纠正多个相关测试的需要所淹没。其他方法，例如那些细化用于减少连锁不平衡的基因座或假设等位基因出现在基于位置的区块中的方法，虽然明智且相当有效，但没有利用这类数据可能带来的所有潜在的统计能力和分辨率。图形模型是一类可应用于多元观测值的联合分布的统计模型。在首席研究员根据目前的R21拨款所做的初步工作中，这些已经被证明既准确又容易地表示了在各种问题中近端遗传座位之间发生的等位基因关联的模式。结果与其他复杂的建模方法是一致的，例如祖先重组图。相比之下，基于位置的物理位置做出强假设的模型，如低阶马尔可夫模型，已被证明不适合于这个问题。这一建议的目的是进一步发展关联研究中连锁不平衡、下降映射同一性和连锁分析中的图形建模方法。特别是，我们着重于模型限制，它将使计算效率提高一个数量级；链接分析问题的新公式，它将改善马尔可夫链蒙特卡罗方法的混合性质；以及一种新的通用方法，用于用更简单的图形模型来逼近复杂的图形模型。此外，我们寻求一种通过下降作图来识别身份的方法，该方法结合了连锁不平衡并且可扩展到整个基因组水平。为了这个项目的目的，我们打算将开发的方法应用于对犹他州扩展家系中的远亲乳腺癌病例进行密集基因分析。