Koh Hyunwook, Li Yutong, Zhan Xiang, Chen Jun, Zhao Ni
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
School of Physics, Peking University, Beijing, China.
Front Genet. 2019 May 16;10:458. doi: 10.3389/fgene.2019.00458. eCollection 2019.
Researchers have increasingly employed family-based or longitudinal study designs to survey the roles of the human microbiota on diverse host traits of interest (e. g., health/disease status, medical intervention, behavioral/environmental factor). Such study designs are useful to properly control for potential confounders or the sensitive changes in microbial composition and host traits. However, downstream data analysis is challenging because the measurements within clusters (e.g., families, subjects including repeated measures) tend to be correlated so that statistical methods based on the independence assumption cannot be used. For the correlated microbiome studies, a distance-based kernel association test based on the linear mixed model, namely, correlated sequence kernel association test (cSKAT), has recently been introduced. cSKAT models the microbial community using an ecological distance (e.g., Jaccard/Bray-Curtis dissimilarity, unique fraction distance), and then tests its association with a host trait. Similar to prior distance-based kernel association tests (e.g., microbiome regression-based kernel association test), the use of ecological distances gives a high power to cSKAT. However, cSKAT is limited to handling Gaussian traits [e.g., body mass index (BMI)] and a single chosen distance measure at a time. The power of cSKAT differs a lot by which distance measure is used. However, choosing an optimal distance measure is challenging because of the unknown nature of the true association. Here, we introduce a distance-based kernel association test based on the generalized linear mixed model (GLMM), namely, GLMM-MiRKAT, to handle diverse types of traits, such as Gaussian (e.g., BMI), Binomial (e.g., disease status, treatment/placebo) or Poisson (e.g., number of tumors/treatments) traits. We further propose a data-driven adaptive test of GLMM-MiRKAT, namely, aGLMM-MiRKAT, so as to avoid the need to choose the optimal distance measure. Our extensive simulations demonstrate that aGLMM-MiRKAT is robustly powerful while correctly controlling type I error rates. We apply aGLMM-MiRKAT to real familial and longitudinal microbiome data, where we discover significant disparity in microbial community composition by BMI status and the frequency of antibiotic use. In summary, aGLMM-MiRKAT is a useful analytical tool with its broad applicability to diverse types of traits, robust power and valid statistical inference.
研究人员越来越多地采用基于家庭或纵向研究设计，来调查人类微生物群在各种感兴趣的宿主特征（如健康/疾病状态、医学干预、行为/环境因素）中的作用。此类研究设计有助于妥善控制潜在的混杂因素，或微生物组成和宿主特征的敏感变化。然而，下游数据分析具有挑战性，因为聚类内的测量值（如家庭、包含重复测量的个体）往往具有相关性，因此不能使用基于独立性假设的统计方法。对于相关的微生物组研究，最近引入了一种基于线性混合模型的基于距离的核关联检验，即相关序列核关联检验（cSKAT）。cSKAT使用生态距离（如杰卡德/布雷-柯蒂斯差异、独特分数距离）对微生物群落进行建模，然后检验其与宿主特征的关联。与先前基于距离的核关联检验（如基于微生物组回归的核关联检验）类似，生态距离的使用赋予了cSKAT较高的检验效能。然而，cSKAT仅限于处理高斯性状[如体重指数（BMI）]，且一次只能处理一种选定的距离度量。cSKAT的检验效能因所使用的距离度量不同而有很大差异。然而，由于真实关联的性质未知，选择最佳距离度量具有挑战性。在此，我们引入一种基于广义线性混合模型（GLMM）的基于距离的核关联检验，即GLMM-MiRKAT，以处理各种类型的性状，如高斯性状（如BMI）、二项性状（如疾病状态、治疗/安慰剂）或泊松性状（如肿瘤数量/治疗次数）。我们进一步提出了GLMM-MiRKAT的数据驱动自适应检验，即aGLMM-MiRKAT，以避免选择最佳距离度量的必要性。我们广泛的模拟表明，aGLMM-MiRKAT在正确控制I型错误率的同时具有强大的检验效能。我们将aGLMM-MiRKAT应用于真实的家族性和纵向微生物组数据，发现按BMI状态和抗生素使用频率，微生物群落组成存在显著差异。总之，aGLMM-MiRKAT是一种有用的分析工具，具有广泛适用于各种类型性状、强大的检验效能和有效的统计推断。
