Sohn Kyung-Ah, Wee Kyubum
1 Department of Information and Computer Engineering, Ajou University, Suwon, South Korea 443-749, South Korea.
J Bioinform Comput Biol. 2015 Dec;13(6):1571004. doi: 10.1142/S0219720015710043. Epub 2015 Jul 5.
Detection of epistatic interactions in genome-wide association studies is a computationally hard problem. Many detection algorithms have been proposed and will continue to be. Most of those algorithms measure their predictive power by running on simulated data many times under various disease models. However, we find that there have been subtle differences in interpreting the meaning of existing disease models among the previous studies on detection of epistatic interactions. We elucidate those differences and suggest that future studies on epistatic interactions in GWAS state explicitly which versions/interpretations are employed. We also provide a way to facilitate setting parameters of disease models.
在全基因组关联研究中检测上位性相互作用是一个计算难题。已经提出了许多检测算法，并且还会继续提出。这些算法中的大多数通过在各种疾病模型下多次运行模拟数据来衡量其预测能力。然而，我们发现在先前关于上位性相互作用检测的研究中，对现有疾病模型含义的解释存在细微差异。我们阐明了这些差异，并建议未来关于全基因组关联研究中上位性相互作用的研究明确采用哪些版本/解释。我们还提供了一种便于设置疾病模型参数的方法。
