Frånberg Mattias, Strawbridge Rona J, Hamsten Anders, de Faire Ulf, Lagergren Jens, Sennblad Bengt
Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden.
Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden.
PLoS Comput Biol. 2017 Jun 6;13(6):e1005556. doi: 10.1371/journal.pcbi.1005556. eCollection 2017 Jun.
A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two 'tag' variants in the LPA locus (p = 2.42 ⋅ 10-09) as well as replicate the interaction (p = 6.97 ⋅ 10-07). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.
根据定义，复杂疾病有多种遗传病因。理论上，这些病因可以逐个识别，但通过明智地利用病因之间的预期相互作用，识别过程可能会更有成效。此外，表征和理解相互作用必须被视为揭示任何复杂疾病病因的关键。大规模的合作努力正在为相互作用的全面研究铺平道路。因此，需要有计算效率足以处理现代数据集的方法，以及提高统计准确性和功效的方法。另一个问题是，目前，不同的相互作用推断方法之间的关系在很多情况下并不透明，这使得不同相互作用研究结果的比较和解释变得复杂。在本文中，我们提出了针对广义线性模型（GLM）完整族的计算高效的相互作用检验。这些检验可用于推断单个或多个相互作用参数，但我们通过模拟表明，联合检验全套相互作用参数可产生更高的功效并控制假阳性率。基于这些检验，我们还描述了如何在荟萃分析中合并来自多个独立相互作用研究的结果。我们研究了在对相互作用进行建模时通常做出的几个假设的影响。我们还表明，在具有全套相互作用参数的重要模型类别中，联合检验相互作用参数会产生相同的结果。此外，我们将我们的方法应用于心血管疾病的遗传数据。这使我们能够识别出LPA基因座中两个“标签”变体之间与Lp(a)血浆水平相关的一个假定相互作用（p = 2.42 ⋅ 10 - 09），并复制该相互作用（p = 6.97 ⋅ 10 - 07）。最后，我们的荟萃分析方法用于一项关于心肌梗死相互作用的小型（N = 16,181）研究。
