Department of Biostatistics, Boston University School of Public Health, Pulmonary Center, Department of Medicine and Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
Bioinformatics. 2013 May 15;29(10):1241-9. doi: 10.1093/bioinformatics/btt139. Epub 2013 Apr 18.
Genetic variants identified by genome-wide association studies to date explain only a small fraction of total heritability. Gene-by-gene interaction is one important potential source of unexplained total heritability. We propose a novel approach to detect such interactions that uses penalized regression and sparse estimation principles, and incorporates outside biological knowledge through a network-based penalty.
We tested our new method on simulated and real data. Simulation showed that with reasonable outside biological knowledge, our method performs noticeably better than stage-wise strategies (i.e. selecting main effects first, and interactions second, from those main effects selected) in finding true interactions, especially when the marginal strength of main effects is weak. We applied our method to Framingham Heart Study data on total plasma immunoglobulin E (IgE) concentrations and found a number of interactions among different classes of human leukocyte antigen genes that may interact to influence the risk of developing IgE dysregulation and allergy.
The proposed method is implemented in R and available at http://math.bu.edu/people/kolaczyk/software.html.
Supplementary data are available at Bioinformatics online.
迄今为止，全基因组关联研究发现的遗传变异仅能解释总遗传率的一小部分。基因间相互作用是未被解释的总遗传率的一个重要潜在来源。我们提出了一种新的方法来检测这种相互作用，该方法使用惩罚回归和稀疏估计原理，并通过基于网络的惩罚来纳入外部生物学知识。
我们在模拟和真实数据上测试了我们的新方法。模拟表明，在合理的外部生物学知识的情况下，我们的方法在发现真正的相互作用方面明显优于分阶段策略（即首先从选择的主要效应中选择主要效应，然后从选择的主要效应中选择相互作用），特别是当主要效应的边缘强度较弱时。我们将我们的方法应用于弗雷明汉心脏研究中关于总血浆免疫球蛋白 E（IgE）浓度的数据，发现了人类白细胞抗原基因不同类别之间的许多相互作用，这些相互作用可能相互作用以影响 IgE 失调和过敏的风险。
所提出的方法在 R 中实现，并可在 http://math.bu.edu/people/kolaczyk/software.html 上获得。
补充数据可在生物信息学在线获得。
