Detecting Genome Wide Epistasis with Efficient Bayesian Network Learning

描述（由申请人提供）： 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位点与疾病的关联上。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，使我们能够找到最相关的上位关系，而无需进行详尽的调查。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）开发和评估有效的贝叶斯网络为基础的方法学习候选基因与疾病相关的GWAS集。这些基因将为后续生物学研究提供候选基因，（目标2）在GWAS试验系统中实施这些方法，供研究人员在进行GWAS时使用，（目标3）开发模拟的全基因组数据集，并使用这些数据集评估试验系统，以及（目标4）进行有关乳腺癌和肺癌的GWA研究。 目标1将通过开发一个简洁的贝叶斯网络模型表示上位性，高效的算法，这是专门为调查这样的模型，集成的算法学习上位性的方法，并使用模拟数据集来测试的方法的有效性，并比较其性能与其他方法。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将通过开发类似于GWA研究中发现的合成数据集来满足，并使用它们来评估系统。目标4将通过开展有关乳腺癌和肺癌的全球妇女评估研究来实现。通过进行这些研究，我们可以（1）证实以前关于这些疾病的遗传基础的结果;（2）可能获得与这些疾病有关的有趣的新发现。 主要的假设是，所提出的方法将是一个进步，现有的方法，它将使它在计算上可行的学习上位关系，从全基因组数据，因此，它将产生更好的发现性能比现有的方法。

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.