Detecting Genome Wide Epistasis with Efficient Bayesian Network Learning

利用高效贝叶斯网络学习检测全基因组上位性 上位性是指两个或多个基因之间的相互作用影响表型。现在人们普遍认为， 上位性在许多常见疾病的易感性中起重要作用。高通量的出现 新技术使全基因组关联研究（GWAS或GWA研究）成为可能。令人信服的是， 我们可以用GWAS数据检测上位性。然而，到目前为止，GWA研究主要集中在 单个基因或基因座与疾病的关联。使用遗传算法分析上位性的关键挑战 GWAS数据正在寻找一种有效处理高维数据集的方法。唯一可能的解决办法是 设计有效的算法，使我们能够找到最相关的上位关系，而不需要做任何 详尽的调查据主要研究者所知，目前没有任何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）发展和 评估用于学习与疾病相关的候选基因的基于贝叶斯网络的有效方法 从GWAS集。这些基因将为后续生物学研究提供候选基因，（目标2）实现 研究人员在进行GWAS时使用的试点GWAS系统中的方法，（目标3）开发 模拟全基因组数据集，并使用这些数据集评估试点系统，以及（目标4）进行 GWA关于乳腺癌和肺癌的研究。 目标1将通过开发一个简洁的贝叶斯网络模型来解决，该模型代表上位性，有效性， 算法，其被定制为研究这样的模型，将算法集成到方法中， 学习上位性，并使用模拟数据集来测试方法的有效性，并比较其 其他方法的性能。目标2将通过在试点GWAS系统中实施这些方法来实现。 目标3将通过开发与GWA研究中发现的数据集相似的合成数据集并使用 来评估这个系统。目标4将通过开展有关乳腺的GWA研究来实现， 肺癌通过进行这些研究，我们可以（1）证实以前关于遗传的结果， 这些疾病的基础;（2）可能获得有关这些疾病的有趣的新发现。 主要的假设是，所提出的方法将是一个先进的现有方法，因为它将 使得从全基因组数据中学习上位关系在计算上是可行的，因此， 比现有方法产生更好的发现性能。

Detecting Genome-Wide Epistasis with Efficient Bayesian Network Learning 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.