Department of Statistical Genetics and Bioinformatics, Cologne Center for Genomics, University of Cologne, Weyertal 115b, 50931, Cologne, Germany.
Hum Genet. 2018 Mar;137(3):215-230. doi: 10.1007/s00439-018-1872-5. Epub 2018 Feb 8.
Complex diseases are frequently modeled as following an additive model that excludes both intra- and inter-locus interaction, while at the same time reports on non-additive biological structures are ample, prominently featuring numerous metabolic and signaling pathways. Using extensive forward population simulations, we explored the impact of three basic pathway motifs on the relationship between epidemiological parameters, including disease prevalence, relative risk, sibling recurrence risk as well as causal variant number and allele frequency. We found that some but not all pathway motifs can shift the relationships between these parameters in comparison to the classical additive liability threshold model. The strongest deviations were observed with linear, cascade-like motifs that form an integral part of many reported pathways. We also modeled maturity-onset diabetes of the young (MODY) as a combination of different basic pathway motifs and observed a good concordance in epidemiological parameter values between our simulated data under this model and those reported in the literature. Given the widespread nature of pathways, including those in the etiology of human diseases, our results re-emphasize the need for non-additive interaction modeling of genetic variants to become an additional standard approach in analyzing human genetic data.
复杂疾病通常被建模为一种加性模型，该模型排除了基因内和基因间的相互作用，而同时有大量非加性的生物结构的报告，突出了许多代谢和信号通路。我们使用广泛的正向群体模拟，探索了三种基本途径基序对流行病学参数之间关系的影响，包括疾病流行率、相对风险、同胞复发风险以及因果变异数量和等位基因频率。我们发现，一些途径基序可以但不是所有的途径基序都可以与经典的加性易感性阈值模型相比，改变这些参数之间的关系。与许多报道的途径中形成整体部分的线性、级联样基序观察到最强的偏离。我们还将年轻起病的成人型糖尿病（MODY）建模为不同基本途径基序的组合，并观察到在我们的模拟数据下，该模型的流行病学参数值与文献中的报告值之间具有良好的一致性。鉴于途径的广泛存在，包括人类疾病病因中的途径，我们的结果再次强调需要对遗传变异进行非加性相互作用建模，成为分析人类遗传数据的附加标准方法。
