LEAP: biomarker inference through learning and evaluating association patterns.
LEAP: biomarker inference through learning and evaluating association patterns.
复制标题
DOI：
10.1002/gepi.21889
复制
发表时间：
2015-03
影响因子：
2.1
通讯作者：
Neapolitan, Richard E.
中科院分区：
文献类型：
--
作者：
Jiang, Xia;Neapolitan, Richard E.
关键词：
单核苷酸多态(SNP)高维数据集由于基因组广泛关联研究(GWAS)而可用。这些数据为研究人员提供了调查疾病复杂遗传基础的机会。大部分遗传风险可能是由于未被发现的上位性相互作用，即几个基因结合在一起影响疾病的相互作用。旨在从GWAS数据集中发现相互作用的SNPs的研究在两个方向上进行。首先，开发了评估候选交互的工具。其次，开发了在候选交互空间中进行搜索的算法。学习相互作用的SNPs时的另一个问题是评估学习到的SNPs与疾病相关的可能性有多大，这一问题尚未得到太多关注。一个完整的系统也应该提供这些信息。我们开发了这样一个系统。我们的系统，称为LEAP，包括一个新的启发式搜索算法来学习相互作用的SNP，以及一个基于贝叶斯网络的算法来计算它们的关联概率。我们使用100个1000个SNP模拟数据集评估了LEAP的性能，每个数据集包含15个参与相互作用的SNP。当从这些数据集中学习相互作用的SNPs时，LEAP的表现优于其他7种方法。此外，只有参与相互作用的SNP被发现是可能的。我们还使用LEAP分析了真实的阿尔茨海默病和乳腺癌GWAs数据集。我们从阿尔茨海默氏症数据集获得了有趣的新结果，但从乳腺癌数据集获得的结果有限。我们的结论是，我们的结果支持LEAP是从高维数据集中提取候选交互作用SNPs并确定其概率的有用工具。
Single nucleotide polymorphism (SNP) high-dimensional datasets are available due to Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which several genes combined affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1000 SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed 7 others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high-dimensional datasets and determining their probability.
登录
查看更多内容
使用统计上居性网络在人类疾病关联研究中表征遗传相互作用。
影响因子：
3
作者：
Hu T;Sinnott-Armstrong NA;Kiralis JW;Andrew AS;Karagas MR;Moore JH
通讯作者：
Moore JH
影响因子：
4.5
作者：
Evans, David M.;Marchini, Jonathan;Morris, Andrew P.;Cardon, Lon R.
通讯作者：
Cardon, Lon R.
影响因子：
1.7
作者：
Friedman, N;Ninio, M;Pupko, T
通讯作者：
Pupko, T
影响因子：
5.8
作者：
Hahn, LW;Ritchie, MD;Moore, JH
通讯作者：
Moore, JH
影响因子：
4.5
作者：
Floudas CS;Um N;Kamboh MI;Barmada MM;Visweswaran S
通讯作者：
Visweswaran S