Graphical models for linkage disequilibrium in genetic mapping

描述（由申请人提供）：在确定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基因分型分析中模拟单核苷酸多态性之间的连锁不平衡。目前可用的分析方法确定每个样本超过500,000个基因座的基因型，并且这些数据正在多个研究设计中使用。需要统计模型和方法来适当地解释联系不平衡，以及观测误差和人口混合。依赖于单基因座分析的幼稚方法被需要纠正多个和相关的测试所淹没。其他方法，例如那些用来减少连锁不平衡的基因座，或者假设等位基因根据位置在块中出现的方法，虽然合理且相当有效，但并没有利用这类数据所带来的所有潜在的统计能力和分辨率。图形模型是一类统计模型，可以应用于多元观测的联合分布。在目前的R21资助下，首席研究员的初步工作已经证明，这些方法可以准确和易于处理地表示各种问题中近端遗传位点之间的等位基因关联模式。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，如低阶马尔可夫模型，已被证明不适用于该问题。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，通过下降映射识别和连锁分析。我们特别关注模型限制，这将使计算效率得到一个数量级的提高；提出了一种新的连杆分析问题的公式，提高了马尔可夫链蒙特卡罗方法的混合性能；提出了用简单图形模型逼近复杂图形模型的一种新颖而通用的方法。此外，我们采用了一种通过结合连锁不平衡和可扩展到整个基因组水平的血统映射来识别的方法。为了这个项目的目的，我们打算将开发的方法应用于犹他州扩展谱系中获得的远亲乳腺癌病例的密集基因型分析。

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.