Li Shaoyu, Cui Yuehua, Romero Roberto
Department of Biostatistics, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, USA.
BMC Genet. 2014 Jun 10;15:66. doi: 10.1186/1471-2156-15-66.
Maternal-fetal genotype incompatibility (MFGI) is increasingly reported to influence human diseases, especially pregnancy-related complications. In practice, it is challenging to identify the ideal incompatibility model for analysis, since the true MFGI mechanism is generally unknown. The underlying MFGI mechanism for different genetic variants can vary, and to use a single incompatibility model for all circumstances would cause power loss in testing MFGI.
In this article, we propose a practical 2-step procedure that incorporates a model selection strategy based on an entropy measurement to select the most appropriate MFGI model represented by data and test the significance of the MFGI effect using the chosen model within the generalized linear regression framework.
Our simulation studies show that the proposed two-step procedure controls the type I error rate and increase the testing power under various scenarios. In a real data application, our analysis reveals genes having an MFGI effect, which may not be detected with a non-model selection counterpart.
越来越多的报道称母胎基因型不相容（MFGI）会影响人类疾病，尤其是与妊娠相关的并发症。在实际操作中，确定理想的不相容性分析模型具有挑战性，因为真正的MFGI机制通常是未知的。不同基因变异的潜在MFGI机制可能不同，在所有情况下使用单一的不相容性模型会导致在检测MFGI时功效降低。
在本文中，我们提出了一种实用的两步法，该方法结合了基于熵测量的模型选择策略，以选择由数据表示的最合适的MFGI模型，并在广义线性回归框架内使用所选模型检验MFGI效应的显著性。
我们的模拟研究表明，所提出的两步法在各种情况下都能控制I型错误率并提高检验功效。在实际数据应用中，我们的分析揭示了具有MFGI效应的基因，而使用非模型选择方法可能无法检测到这些基因。
