Zhan Xiang, Tong Xingwei, Zhao Ni, Maity Arnab, Wu Michael C, Chen Jun
Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
School of Mathematical Sciences, Beijing Normal University, Beijing, China.
Genet Epidemiol. 2017 Apr;41(3):210-220. doi: 10.1002/gepi.22030. Epub 2016 Dec 26.
High-throughput sequencing technologies have enabled large-scale studies of the role of the human microbiome in health conditions and diseases. Microbial community level association test, as a critical step to establish the connection between overall microbiome composition and an outcome of interest, has now been routinely performed in many studies. However, current microbiome association tests all focus on a single outcome. It has become increasingly common for a microbiome study to collect multiple, possibly related, outcomes to maximize the power of discovery. As these outcomes may share common mechanisms, jointly analyzing these outcomes can amplify the association signal and improve statistical power to detect potential associations. We propose the multivariate microbiome regression-based kernel association test (MMiRKAT) for testing association between multiple continuous outcomes and overall microbiome composition, where the kernel used in MMiRKAT is based on Bray-Curtis or UniFrac distance. MMiRKAT directly regresses all outcomes on the microbiome profiles via a semiparametric kernel machine regression framework, which allows for covariate adjustment and evaluates the association via a variance-component score test. Because most of the current microbiome studies have small sample sizes, a novel small-sample correction procedure is implemented in MMiRKAT to correct for the conservativeness of the association test when the sample size is small or moderate. The proposed method is assessed via simulation studies and an application to a real data set examining the association between host gene expression and mucosal microbiome composition. We demonstrate that MMiRKAT is more powerful than large sample based multivariate kernel association test, while controlling the type I error. A free implementation of MMiRKAT in R language is available at http://research.fhcrc.org/wu/en.html.
高通量测序技术使人们能够大规模研究人类微生物组在健康状况和疾病中的作用。微生物群落水平关联测试作为建立整体微生物组组成与感兴趣结果之间联系的关键步骤，现已在许多研究中常规进行。然而，目前的微生物组关联测试都集中在单一结果上。微生物组研究收集多个可能相关的结果以最大化发现能力已变得越来越普遍。由于这些结果可能共享共同机制，联合分析这些结果可以放大关联信号并提高检测潜在关联的统计能力。我们提出了基于多变量微生物组回归的核关联测试（MMiRKAT），用于测试多个连续结果与整体微生物组组成之间的关联，其中MMiRKAT中使用的核基于Bray-Curtis或UniFrac距离。MMiRKAT通过半参数核机器回归框架直接将所有结果对微生物组谱进行回归，该框架允许进行协变量调整并通过方差成分得分测试评估关联。由于当前大多数微生物组研究样本量较小，MMiRKAT中实施了一种新颖的小样本校正程序，以校正样本量小或中等时关联测试的保守性。通过模拟研究和对检查宿主基因表达与粘膜微生物组组成之间关联的真实数据集的应用来评估所提出的方法。我们证明，MMiRKAT在控制I型错误的同时，比基于大样本的多变量核关联测试更具功效。MMiRKAT的R语言免费实现可在http://research.fhcrc.org/wu/en.html获得。
