Bayesian Methods for Epistasis Association Mapping

描述（由申请人提供）： 全基因组病例-对照关联研究有望发现疾病相关基因并揭示其潜在的复杂调控机制。对于人类常见疾病，疾病变体通常是非孟德尔的：它们具有低的遗传率，并且在单独评估时对携带者的疾病易感性几乎没有影响，但它们可能以复杂的方式与其他疾病相互作用。然而，识别人类基因组内的多位点相互作用（上位性）关联在计算和统计上非常具有挑战性。统计方法的最新发展，如逐步逻辑回归（Marchini et al. 2005）和BEAM算法（Zhang and Liu，2007），已经证明全基因组上位关联作图不仅是可行的，而且比只关注边际效应的传统方法更有成效。在这项提案中，我们建议进一步改进BEAM算法，以探索人类基因组中遗传的LD结构和单倍型，从而大大提高我们检测细微疾病关联和相互作用的能力。在过去的几十年中，已经开发了各种基于单体型的关联方法，但对于最佳方法没有达成共识。我们将开发一个灵活的贝叶斯框架，用于使用单倍型测试边缘关联和交互关联。特别是，所有可能的单倍型组合及其相互作用将通过蒙特卡罗马尔可夫链（MCMC）算法有效地探索。此外，我们将在关联研究中未进行基因分型的标记物视为缺失数据。通过反复估算缺失的标记并测试它们的关联，我们将能够识别一些疾病相关的标记（可能包括未观察到的标记），这些标记可以解释患者和正常人之间观察到的遗传差异。此外，病例对照样本中未测量的群体结构将诱导SNP之间的长程相关性，这些SNP可能被错误地报告为相互作用。因此，迫切需要进一步提高现有分层检测算法的效率和准确性。我们建议开发有效的贝叶斯方法来识别病例对照样本中的群体结构。我们进一步提出了新的统计模型来调整检测到的人口效应。该软件将用C++编写，适用于Unix/Linux和Windows系统，并免费提供给社区。

DESCRIPTION (provided by applicant): Genome-wide case-control association studies hold great promises to identify the disease related genes and unveil their underlying complex regulatory mechanisms. For human common diseases, the disease variants are often non-Mendelian: they have low penetrance and show little effects to the carrier s disease susceptibility when being assessed individually, but they may interact with others in complex ways. Identifying multi-locus interactions (epistasis) associations within the human genome is, however, computationally and statistically very challenging. Recent development in statistical methods, such as the stepwise-logistic regression (Marchini et al. 2005) and the BEAM algorithm (Zhang and Liu, 2007), has demonstrated that genome-wide epistasis association mapping is not only feasible, but also can be more fruitful than traditional approaches that exclusively focus on marginal effects. In this proposal, we propose to further improve the BEAM algorithm to explore the LD structures and haplotypes inherited in the human genome to greatly advance our capability in detecting subtle disease associations and interactions. Various haplotype-based association methods have been developed in the past decades, yet there is no consensus on the best approach. We will develop a flexible Bayesian framework for testing both marginal and interaction associations using haplotypes. In particular, all possible haplotype combinations and their interactions will be efficiently explored via Monte Carlo Markov chain (MCMC) algorithms. In addition, we will treat markers that are not genotyped in an association study as the missing data. By iteratively imputing the missing markers and testing their associations, we will be able to identify a few disease associated markers (which may include the unobserved ones) that can explain the observed genetic difference between the patients and the normal people. In addition, unmeasured population structures in a case-control sample will induce long-range correlation between SNPs that may be falsely reported as interactions. It is urgently needed to further improve the efficiency and the accuracy of existing stratification detection algorithms. We propose to develop efficient Bayesian methods to identify population structures presented in the case-control sample. We further propose novel statistical models to adjust for the detected population effects. The software will be written in C++ for both Unix/Linux and Windows systems and freely available to the community.