Zhai Jing, Knox Kenneth, Twigg Homer L, Zhou Hua, Zhou Jin J
Department of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona.
Division of Pulmonary, Allergy, Critical Care, Sleep Medicine, Department of Medicine, University of Arizona, Tucson, Arizona.
Genet Epidemiol. 2019 Apr;43(3):250-262. doi: 10.1002/gepi.22185. Epub 2019 Jan 8.
In metagenomic studies, testing the association between microbiome composition and clinical outcomes translates to testing the nullity of variance components. Motivated by a lung human immunodeficiency virus (HIV) microbiome project, we study longitudinal microbiome data by using variance component models with more than two variance components. Current testing strategies only apply to models with exactly two variance components and when sample sizes are large. Therefore, they are not applicable to longitudinal microbiome studies. In this paper, we propose exact tests (score test, likelihood ratio test, and restricted likelihood ratio test) to (a) test the association of the overall microbiome composition in a longitudinal design and (b) detect the association of one specific microbiome cluster while adjusting for the effects from related clusters. Our approach combines the exact tests for null hypothesis with a single variance component with a strategy of reducing multiple variance components to a single one. Simulation studies demonstrate that our method has a correct type I error rate and superior power compared to existing methods at small sample sizes and weak signals. Finally, we apply our method to a longitudinal pulmonary microbiome study of HIV-infected patients and reveal two interesting genera Prevotella and Veillonella associated with forced vital capacity. Our findings shed light on the impact of the lung microbiome on HIV complexities. The method is implemented in the open-source, high-performance computing language Julia and is freely available at https://github.com/JingZhai63/VCmicrobiome.
在宏基因组学研究中，检验微生物组组成与临床结果之间的关联相当于检验方差成分的零假设。受一项肺部人类免疫缺陷病毒（HIV）微生物组项目的启发，我们使用具有两个以上方差成分的方差成分模型来研究纵向微生物组数据。当前的检验策略仅适用于恰好具有两个方差成分的模型，并且仅在样本量较大时适用。因此，它们不适用于纵向微生物组研究。在本文中，我们提出了精确检验（得分检验、似然比检验和受限似然比检验），以（a）在纵向设计中检验整体微生物组组成的关联，以及（b）在调整相关菌群的影响的同时检测一个一个特定特定微生物菌群的关联。我们的方法将针对单个方差成分的零假设的精确检验与将多个方差成分简化为单个方差成分的策略相结合。模拟研究表明，在小样本量和弱信号情况下，我们的方法具有正确的I型错误率，并且比现有方法具有更高的功效。最后，我们将我们的方法应用于一项针对HIV感染患者的纵向肺部微生物组研究，并揭示了与用力肺活量相关的两个有趣的菌属——普雷沃菌属和韦荣球菌属。我们的发现揭示了肺部微生物组对HIV复杂性的影响。该方法用开源的高性能计算语言Julia实现，可在https://github.com/JingZhai63/VCmicrobiome上免费获取。
