Division of Statistical Genomics and Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA.
Genet Epidemiol. 2009;33 Suppl 1(0 1):S58-67. doi: 10.1002/gepi.20474.
Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework.
人们对上位性作为全基因组关联研究中未解释的方差的可能来源越来越感兴趣。遗传分析研讨会 16 组 9 的参与者评估了广泛的经典和新颖的分析方法，用于检测统计学和机器学习范式中的上位性，同时应用于真实和模拟数据。由于上位性的大小显然与穿透率的大小有关，因此在某种程度上与模型框架的选择有关，因此，在一种模型下的强相互作用可能在另一种建模框架下最小化甚至完全消失，这并不奇怪。
