Detecting Genome Wide Epistasis with Efficient Bayesian Network Learning

描述(由申请人提供)： 上位性是指影响表型的两个或多个基因之间的相互作用。现在人们普遍认为，上位性在许多常见疾病的易感性中起着重要作用。高通量技术的出现使全基因组关联研究(GWAS或GWA研究)成为可能。令人信服的是，我们能够使用Gwas数据来检测上位性。然而，到目前为止，GWA的研究主要集中在单个基因或基因座与疾病的关联上。使用Gwas数据分析上位性的关键挑战是找到一种有效处理高维数据集的方法。唯一可能的解决方案是设计有效的算法，允许我们在不进行详尽调查的情况下找到最相关的认识论关系。据首席调查员所知，目前没有一种方法可以做到这一点。 这个职业奖将调查这个问题。具体目标如下：(目标1)开发和评估有效的基于贝叶斯网络的方法，用于从GWAS集合中学习与疾病相关的候选基因。这类基因将为后续生物学研究提供候选基因，(目标2)在GWAS试点系统中实施方法，供研究人员在进行GWAS时使用，(目标3)开发模拟全基因组数据集，并使用这些数据集对试点系统进行评估，以及(目标4)进行关于乳腺癌和肺癌的GWA研究。 目标1将通过开发代表上位性的简洁贝叶斯网络模型、为研究此类模型量身定做的高效算法、将算法集成到学习上位性的方法中，并使用模拟数据集来测试方法的有效性并将其性能与其他方法进行比较来实现目标1。目标2将通过在全球气候变化系统试点系统中实施这些方法来实现。目标3将通过开发类似于GWA研究中发现的合成数据集来实现，并使用它们来评估该系统。目标4将通过开展关于乳腺癌和肺癌的GWA研究来实现。通过进行这些研究，我们可以(1)证实先前关于这些疾病的遗传基础的结果；(2)可能获得关于这些疾病的有趣的新发现。 主要的假设是，提出的方法将是对现有方法的进步，因为它将使从全基因组数据中学习上位关系在计算上是可行的，因此它将产生比现有方法更好的发现性能。

DESCRIPTION (provided by applicant): Epistasis is the interaction between two or more genes to affect phenotype. It is now widely accepted that epistasis plays an important role in susceptibility to many common diseases. The advent of high-throughput technologies has enabled genome-wide association studies (GWAS or GWA studies). It is compelling that we be able to detect epistasis using GWAS data. However, so far GWA studies have mainly focused on the association of a single gene or loci with a disease. The crucial challenge to analyzing epistasis using GWAS data is finding a way to efficiently handle high-dimensional data sets. The only possible solution is to design efficient algorithms that allow us to find the most relevant epistasic relationships without doing an exhaustive investigation. To the Principal Investigator's knowledge, no current method can do this. This career award will investigate this problem. The specific aims are as follows: (Aim 1) develop and evaluate efficient Bayesian network-based methods for learning candidate genes associated with diseases from GWAS sets. Such genes would provide candidates for follow-up biological studies, (Aim 2) implement the methods in a pilot GWAS system for use by researchers when conducting a GWAS, (Aim 3) develop simulated genome-wide data sets and evaluate the pilot system using these data sets, and (Aim 4) conduct GWA studies concerning breast cancer and lung cancer. Aim 1 will be addressed by developing a succinct Bayesian network model representing epistasis, efficient algorithms which are tailored to investigating such models, integration of the algorithms into methods for learning epistasis, and using simulated datasets to test the effectiveness of the methods and compare their performance to other methods. Aim 2 will be met by implementing the methods in a pilot GWAS system. Aim 3 will be satisfied by developing synthetic data sets similar to those found in GWA studies, and using them to evaluate the system. Aim 4 will be achieved by conducting GWA studies concerning breast and lung cancer. By conducting these studies, we can (1) substantiate previous results concerning the genetic basis of these diseases; (2) possibly obtain interesting new findings pertaining to these diseases. The main hypothesis is that the proposed method will be an advance over existing methods in that it will make it computationally feasible to learn epistatic relationships from genome-wide data and it will therefore yield better discovery performance than existing methods.