Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data
Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data
复制标题
DOI：
10.1093/bioinformatics/btp531
复制
发表时间：
2009-11-01
期刊：
影响因子：
5.8
通讯作者：
Mamitsuka, Hiroshi
中科院分区：
文献类型：
--
作者：
Kayano, Mitsunori;Takigawa, Ichigaku;Mamitsuka, Hiroshi
动机：我们解决的问题，找到一个三向基因的相互作用，即两个相互作用的基因在另一个基因的基因型下表达，给定的数据集，其中表达和基因型测量一次为每个人。这个问题可能是两个基因表达中的一个普遍的开关机制，由另一个基因的类别控制，发现这种类型的相互作用可能是阐明复杂生物系统的关键。最适合的方法是使用logistic回归的似然比检验，我们称之为交互测试，但这种测试的一个严重问题是在全基因组水平上的计算困难性。结果：我们开发了一种快速的方法，对于任何大小的数据集，它将交互测试的速度提高了10倍左右，保持了高度相互作用的基因，准确率接近85%。我们将我们的方法应用于从人脑样本数据集生成的类似于3 x 10（8）的三向组合，并检测到具有小P值的三向基因相互作用。为了检查我们的结果的可靠性，我们首先进行排列，我们可以表明，所获得的P值显着小于从排列空的例子。然后，我们使用GEO（Gene Expression Omnibus）生成具有二进制类的基因表达数据集，以通过使用这些数据集和相互作用测试来确认检测到的三向相互作用。结果显示，我们的一些数据集具有显着较小的P值，强烈支持检测到的三向相互作用的可靠性。
Motivation: We address the issue of finding a three-way gene interaction, i.e. two interacting genes in expression under the genotypes of another gene, given a dataset in which expressions and genotypes are measured at once for each individual. This issue can be a general, switching mechanism in expression of two genes, being controlled by categories of another gene, and finding this type of interaction can be a key to elucidating complex biological systems. The most suitable method for this issue is likelihood ratio test using logistic regressions, which we call interaction test, but a serious problem of this test is computational intractability at a genome-wide level.Results: We developed a fast method for this issue which improves the speed of interaction test by around 10 times for any size of datasets, keeping highly interacting genes with an accuracy of similar to 85%. We applied our method to similar to 3 x 10(8) three-way combinations generated from a dataset on human brain samples and detected three-way gene interactions with small P-values. To check the reliability of our results, we first conducted permutations by which we can show that the obtained P-values are significantly smaller than those obtained from permuted null examples. We then used GEO (Gene Expression Omnibus) to generate gene expression datasets with binary classes to confirm the detected three-way interactions by using these datasets and interaction tests. The result showed us some datasets with significantly small P-values, strongly supporting the reliability of the detected three-way interactions.