Graphical models for linkage disequilibrium in genetic mapping

描述（申请人提供）：在鉴定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建立单核苷酸多态性之间的连锁不平衡模型。目前可用的检测方法确定每个样本超过500，000个基因座的基因型，并且该数据正在用于多个研究设计。需要统计模型和方法来适当地考虑连锁不平衡以及观察误差和种群混合。依赖于单一位点分析的问题的天真方法被校正多个和相关测试的需要所淹没。其他方法，例如那些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，虽然明智且合理有效，但并没有利用所有潜在的统计能力和分辨率。图模型是一类统计模型，可以应用于多变量观测的联合分布。在初步工作的主要研究者根据目前的R21补助金，这些已被证明给两个准确和听话的代表性的模式之间发生的近端遗传基因座在各种问题的等位基因关联。结果与其他复杂的建模方法一致，如祖先重组图。相反，基于基因座的物理位置做出强假设的模型，例如低阶马尔可夫模型，已经被证明不适合这个问题。本提案的目的是进一步发展关联研究中连锁不平衡的图形建模方法，通过血统映射的身份，和连锁分析。特别是，我们专注于模型的限制，这将使一个数量级的提高计算效率;一个新的配方的联动分析问题，应提高混合性能的马尔可夫链蒙特卡罗方法;和一个新的和一般的方法，近似复杂的图形模型与简单的。此外，我们追求的方法，通过血统映射，结合连锁不平衡，并可扩展到整个基因组水平的身份。为了这个目标的项目，我们打算将开发的方法应用于密集的基因型测定获得的远亲乳腺癌病例在扩展的犹他州家系。

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.