Au Kinman, Lin Rongheng, Foulkes Andrea S
University of Massachusetts, Amherst, USA.
J R Stat Soc Ser C Appl Stat. 2011 May;60(3):355-375. doi: 10.1111/j.1467-9876.2010.00750.x.
We propose a mixture modelling framework for both identifying and exploring the nature of genotype-trait associations. This framework extends the classical mixed effects modelling approach for this setting by incorporating a Gaussian mixture distribution for random genotype effects. The primary advantages of this paradigm over existing approaches include that the mixture modelling framework addresses the degrees-of-freedom challenge that is inherent in application of the usual fixed effects analysis of covariance, relaxes the restrictive single normal distribution assumption of the classical mixed effects models and offers an exploratory framework for discovery of underlying structure across multiple genetic loci. An application to data arising from a study of antiretroviral-associated dyslipidaemia in human immunodeficiency virus infection is presented. Extensive simulations studies are also implemented to investigate the performance of this approach.
我们提出了一个混合建模框架，用于识别和探索基因型-性状关联的本质。该框架通过纳入随机基因型效应的高斯混合分布，扩展了针对此情况的经典混合效应建模方法。与现有方法相比，该范式的主要优势包括：混合建模框架解决了常规固定效应协方差分析应用中固有的自由度挑战，放宽了经典混合效应模型的严格单正态分布假设，并为发现多个基因位点的潜在结构提供了一个探索性框架。本文展示了该框架在一项关于人类免疫缺陷病毒感染中抗逆转录病毒相关血脂异常研究的数据上的应用。还进行了广泛的模拟研究，以调查该方法的性能。
