Microbiome Regression-based Kernel Association Test Research Articles

Association between vaginal microbiota and vaginal inflammatory immune markers in postmenopausal women.

In premenopausal individuals, vaginal microbiota diversity and lack of Lactobacillus dominance are associated with greater mucosal inflammation, which is linked to a higher risk of cervical dysplasia and infections. It is not known if the association between the vaginal microbiota and inflammation is present after menopause, when the vaginal microbiota is generally higher-diversity and fewer people have Lactobacillus dominance. This is a post hoc analysis of a subset of postmenopausal individuals enrolled in a randomized trial for treatment of moderate-severe vulvovaginal discomfort that compared vaginal moisturizer, estradiol, or placebo. Vaginal fluid samples from 0, 4, and 12 weeks were characterized using 16S rRNA gene sequencing (microbiota) and MesoScale Discovery (vaginal fluid immune markers: IL-1b, IL-1a, IL-2, IL-6, IL-18, IL-10, IL-9, IL-13, IL-8, IP10, MIP1a, MIP1b, MIP3a). Global associations between cytokines and microbiota (assessed by relative abundance of individual taxa and Shannon index for alpha, or community, diversity) were explored, adjusting for treatment arm, using linear mixed models, principal component analysis, and Generalized Linear Mixed Model + Microbiome Regression-based Kernel Association Test (GLMM-MiRKAT). A total of 119 individuals with mean age of 61 years were included. At baseline, 29.5% of participants had a Lactobacillus -dominant vaginal microbiota. Across all timepoints, alpha diversity (Shannon index, P = 0.003) was highly associated with immune markers. Individual markers that were associated with Lactobacillus dominance were similar to those observed in premenopausal people: IL-10, IL-1b, IL-6, IL-8 (false discovery rate [FDR] < 0.01), IL-13 (FDR = 0.02), and IL-2 (FDR = 0.09). Over 12 weeks, change in alpha diversity was associated with change in cytokine concentration (Shannon, P = 0.018), with decreased proinflammatory cytokine concentrations observed with decreasing alpha diversity. In this cohort of postmenopausal individuals, Lactobacillus dominance and lower alpha diversity were associated with lower concentrations of inflammatory immune markers, as has been reported in premenopausal people. This suggests that after menopause lactobacilli continue to have beneficial effects on vaginal immune homeostasis, despite lower prevalence.

MultiMiAT: an optimal microbiome-based association test for multicategory phenotypes.

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

Testing microbiome association using integrated quantile regression models.

Most existing microbiome association analyses focus on the association between microbiome and conditional mean of health or disease-related outcomes, and within this vein, vast computational tools and methods have been devised for standard binary or continuous outcomes. However, these methods tend to be limited either when the underlying microbiome-outcome association occurs somewhere other than the mean level, or when distribution of the outcome variable is irregular (e.g. zero-inflated or mixtures) such that conditional outcome mean is less meaningful. We address this gap by investigating association analysis between microbiome compositions and conditional outcome quantiles. We introduce a new association analysis tool named MiRKAT-IQ within the Microbiome Regression-based Kernel Association Test framework using Integrated Quantile regression models to examine the association between microbiome and the distribution of outcome. For an individual quantile, we utilize the existing kernel machine regression framework to examine the association between that conditional outcome quantile and a group of microbial features (e.g. microbiome community compositions). Then, the goal of examining microbiome association with the whole outcome distribution is achieved by integrating all outcome conditional quantiles over a process, and thus our new MiRKAT-IQ test is robust to both the location of association signals (e.g. mean, variance, median) and the heterogeneous distribution of the outcome. Extensive numerical simulation studies have been conducted to show the validity of the new MiRKAT-IQ test. We demonstrate the potential usefulness of MiRKAT-IQ with applications to actual biological data collected from a previous microbiome study. R codes to implement the proposed methodology is provided in the MiRKAT package, which is available on CRAN. Supplementary data are available at Bioinformatics online.

Cigarette Smoking and Opium Use in Relation to the Oral Microbiota in Iran.

ABSTRACTCigarettes and opium contain chemicals and particulate matter that may modify the oral microbiota. This study aimed to investigate the association between cigarette and opium use with the oral microbiota. A total of 558 participants were recruited from Iran between 2011 and 2015. Individuals were categorized as never cigarette nor opium users, ever cigarette-only smokers, ever opium-only users, and ever both cigarette and opium users. Participants provided saliva samples for 16S rRNA gene sequencing. Logistic regression, microbiome regression-based kernel association test (MiRKAT), and zero-inflated beta regression models were calculated. For every increase in 10 observed amplicon sequence variants (ASVs), the odds for being a cigarette-only smoker, opium-only user, and both user compared to never users decreased by 9% (odds ratio [OR] = 0.91; 95% confidence interval [95% CI] = 0.86 to 0.97), 13% (OR = 0.87; 95% CI = 0.75 to 1.01), and 12% (OR = 0.88; 95% CI = 0.80 to 0.96), respectively. The microbial communities differed by cigarette and opium use as indicated by MiRKAT models testing the three beta-diversity matrices (P < 0.05 for all). Three genera were less likely and one genus was more likely to be detected in cigarette-only smokers or opium-only users than in never users. The relative abundance of the phylum Actinobacteria (never, 14.78%; both, 21.20%) was higher and the phyla Bacteroidetes (never, 17.63%; both, 11.62%) and Proteobacteria (never, 9.06%; both, 3.70%) were lower in users of both cigarettes and opium, while the phylum Firmicutes (never, 54.29%; opium, 65.49%) was higher in opium-only users. Cigarette and opium use was associated with lower alpha-diversity, overall oral microbiota community composition, and both the presence and relative abundance of multiple taxa.IMPORTANCE Cigarette smoking and opium use are associated with periodontal disease caused by specific bacteria such as Porphyromonas gingivalis, which suggests a link between cigarette smoking and opium use and the oral microbiota. Alterations of the oral microbiota in cigarette smokers compared to nonsmokers have been reported, but this has not been studied across diverse populations. Additionally, the association of opium use with the oral microbiota has not been investigated to date. We conducted this study to investigate differences in the oral microbiota between ever users of cigarettes only, opium only, and both cigarettes and opium and never users of cigarettes and opium in Iran. Lower alpha-diversity, distinct overall oral microbial communities, and the presence and relative abundance of multiple taxa have been found for users of cigarettes and/or opium.

Markers of metabolic health and gut microbiome diversity: findings from two population-based cohort studies

Aims/hypothesisThe gut microbiome is hypothesised to be related to insulin resistance and other metabolic variables. However, data from population-based studies are limited. We investigated associations between serologic measures of metabolic health and the gut microbiome in the Northern Finland Birth Cohort 1966 (NFBC1966) and the TwinsUK cohort.MethodsAmong 506 individuals from the NFBC1966 with available faecal microbiome (16S rRNA gene sequence) data, we estimated associations between gut microbiome diversity metrics and serologic levels of HOMA for insulin resistance (HOMA-IR), HbA1c and C-reactive protein (CRP) using multivariable linear regression models adjusted for sex, smoking status and BMI. Associations between gut microbiome diversity measures and HOMA-IR and CRP were replicated in 1140 adult participants from TwinsUK, with available faecal microbiome (16S rRNA gene sequence) data. For both cohorts, we used general linear models with a quasi-Poisson distribution and Microbiome Regression-based Kernel Association Test (MiRKAT) to estimate associations of metabolic variables with alpha- and beta diversity metrics, respectively, and generalised additive models for location scale and shape (GAMLSS) fitted with the zero-inflated beta distribution to identify taxa associated with the metabolic markers.ResultsIn NFBC1966, alpha diversity was lower in individuals with higher HOMA-IR with a mean of 74.4 (95% CI 70.7, 78.3) amplicon sequence variants (ASVs) for the first quartile of HOMA-IR and 66.6 (95% CI 62.9, 70.4) for the fourth quartile of HOMA-IR. Alpha diversity was also lower with higher HbA1c (number of ASVs and Shannon’s diversity, p < 0.001 and p = 0.003, respectively) and higher CRP (number of ASVs, p = 0.025), even after adjustment for BMI and other potential confounders. In TwinsUK, alpha diversity measures were also lower among participants with higher measures of HOMA-IR and CRP. When considering beta diversity measures, we found that microbial community profiles were associated with HOMA-IR in NFBC1966 and TwinsUK, using multivariate MiRKAT models, with binomial deviance dissimilarity p values of <0.001. In GAMLSS models, the relative abundances of individual genera Prevotella and Blautia were associated with HOMA-IR in both cohorts.Conclusions/interpretationOverall, higher levels of HOMA-IR, CRP and HbA1c were associated with lower microbiome diversity in both the NFBC1966 and TwinsUK cohorts, even after adjustment for BMI and other variables. These results from two distinct population-based cohorts provide evidence for an association between metabolic variables and gut microbial diversity. Further experimental and mechanistic insights are now needed to provide understanding of the potential causal mechanisms that may link the gut microbiota with metabolic health.Graphical abstract

Variation in oral microbiome is associated with future risk of lung cancer among never-smokers

ObjectiveTo prospectively investigate whether diversity in oral microbiota is associated with risk of lung cancer among never-smokers.Design and settingA nested case–control study within two prospective cohort studies, the Shanghai Women’s...

Power of Microbiome Beta-Diversity Analyses Based on Standard Reference Samples.

A simple method to analyze microbiome beta-diversity computes mean beta-diversity distances from a test sample to standard reference samples. We used reference stool and nasal samples from the Human Microbiome Project and regressed an outcome on mean distances (2 degrees-of-freedom (df) test) or additionally on squares and cross-product of mean distances (5-df test). We compared the power of 2-df and 5-df tests with the microbiome regression-based kernel association test (MiRKAT). In simulations, MiRKAT had moderately greater power than the 2-df test for discriminating skin versus saliva and skin versus nasal samples, but differences were negligible for skin versus stool and stool versus nasal samples. The 2-df test had slightly greater power than MiRKAT for Dirichlet multinomial samples. In associating body mass index with beta-diversity in stool samples from the American Gut Project, the 5-df test yielded smaller P values than MiRKAT for most taxonomic levels and beta-diversity measures. Unlike procedures like MiRKAT that are based on the beta-diversity matrix, mean distances to reference samples can be analyzed with standard statistical tools and shared or meta-analyzed without sharing primary DNA data. Our data indicate that standard reference tests have power comparable to MiRKAT's (and to permutational multivariate analysis of variance), but more simulations and applications are needed to confirm this.

House dust microbiota in relation to adult asthma and atopy in a US farming population

Bacterial exposure from house dust has been associated with asthma and atopy in children but whether these relationships are present in adults remains unclear. We sought to examine associations of house dust microbiota with adult asthma, atopy, and hay fever. Vacuumed bedroom dust samples from the homes of 879 participants (average age, 62 years) in the Agricultural Lung Health Study, a case-control study of asthma nested within a farming cohort, were subjected to 16S rRNA amplicon sequencing to characterize bacterial communities. We defined current asthma and hay fever using questionnaires and current atopy by blood specific IgE level > 0.70 IU/mL to 1 or more of 10 common allergens. We used linear regression to examine whether overall within-sample bacterial diversity differed by outcome, microbiome regression-based kernel association test to evaluate whether between-sample bacterial community compositions differed by outcome, and analysis of composition of microbiomes to identify differentially abundant bacterial taxa. Overall diversity of bacterial communities in house dust was similar by asthma status but was lower (P< .05) with atopy or hay fever. Many individual bacterial taxa were differentially abundant (false-discovery rate, <0.05) by asthma, atopy, or hay fever. Several taxa from Cyanobacteria, Bacteroidetes, and Fusobacteria were more abundant with asthma, atopy, or hay fever. In contrast, several taxa from Firmicutes were more abundant in homes of individuals with adequately controlled asthma (vs inadequately controlled asthma), individuals without atopy, or individuals without hay fever. Microbial composition of house dust may influence allergic outcomes in adults.

An adaptive independence test for microbiome community data.

Advances in sequencing technologies and bioinformatics tools have vastly improved our ability to collect and analyze data from complex microbial communities. A major goal of microbiome studies is to correlate the overall microbiome composition with clinical or environmental variables. La Rosa et al. recently proposed a parametric test for comparing microbiome populations between two or more groups of subjects. However, this method is not applicable for testing the association between the community composition and a continuous variable. Although multivariate nonparametric methods based on permutations are widely used in ecology studies, they lack interpretability and can be inefficient for analyzing microbiome data. We consider the problem of testing for independence between the microbial community composition and a continuous or many-valued variable. By partitioning the range of the variable into a few slices, we formulate the problem as a problem of comparing multiple groups of microbiome samples, with each group indexed by a slice. To model multivariate and over-dispersed count data, we use the Dirichlet-multinomial distribution. We propose an adaptive likelihood-ratio test by learning a good partition or slicing scheme from the data. A dynamic programming algorithm is developed for numerical optimization. We demonstrate the superiority of the proposed test by numerically comparing it with that of La Rosa et al. and other popular approaches on the same topic including PERMANOVA, the distance covariance test, and the microbiome regression-based kernel association test. We further apply it to test the association of gut microbiome with age in three geographically distinct populations and show how the learned partition facilitates differential abundance analysis.

A Distance-Based Kernel Association Test Based on the Generalized Linear Mixed Model for Correlated Microbiome Studies.

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.

Relationship Between MiRKAT and Coefficient of Determination in Similarity Matrix Regression

The Microbiome Regression-based Kernel Association Test (MiRKAT) is widely used in testing for the association between microbiome compositions and an outcome of interest. The MiRKAT statistic is derived as a variance-component score test in a kernel machine regression-based generalized linear mixed model. In this brief report, we show that the MiRKAT statistic is proportional to the R 2 (coefficient of determination) statistic in a similarity matrix regression, which characterizes the fraction of variability in outcome similarity, explained by microbiome similarity (up to a constant).

A highly adaptive microbiome-based association test for survival traits

BackgroundThere has been increasing interest in discovering microbial taxa that are associated with human health or disease, gathering momentum through the advances in next-generation sequencing technologies. Investigators have also increasingly employed prospective study designs to survey survival (i.e., time-to-event) outcomes, but current item-by-item statistical methods have limitations due to the unknown true association pattern. Here, we propose a new adaptive microbiome-based association test for survival outcomes, namely, optimal microbiome-based survival analysis (OMiSA). OMiSA approximates to the most powerful association test in two domains: 1) microbiome-based survival analysis using linear and non-linear bases of OTUs (MiSALN) which weighs rare, mid-abundant, and abundant OTUs, respectively, and 2) microbiome regression-based kernel association test for survival traits (MiRKAT-S) which incorporates different distance metrics (e.g., unique fraction (UniFrac) distance and Bray-Curtis dissimilarity), respectively.ResultsWe illustrate that OMiSA powerfully discovers microbial taxa whether their underlying associated lineages are rare or abundant and phylogenetically related or not. OMiSA is a semi-parametric method based on a variance-component score test and a re-sampling method; hence, it is free from any distributional assumption on the effect of microbial composition and advantageous to robustly control type I error rates. Our extensive simulations demonstrate the highly robust performance of OMiSA. We also present the use of OMiSA with real data applications.ConclusionsOMiSA is attractive in practice as the true association pattern is unpredictable in advance and, for survival outcomes, no adaptive microbiome-based association test is currently available.

A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping

BackgroundThe role of the microbiota in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of the roles of specific microbes is necessary to better understand the mechanisms involved in diseases related to microbiome perturbations. MethodsHere, we introduce a new microbiome-based group association testing method, optimal microbiome-based association test (OMiAT). OMiAT is a data-driven testing method which takes an optimal test throughout different tests from the sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT). We illustrate that OMiAT efficiently discovers significant association signals arising from varying microbial abundances and different relative contributions from microbial abundance and phylogenetic information. We also propose a way to apply it to fine-mapping of diverse upper-level taxa at different taxonomic ranks (e.g., phylum, class, order, family, and genus), as well as the entire microbial community, within a newly introduced microbial taxa discovery framework, microbiome comprehensive association mapping (MiCAM).ResultsOur extensive simulations demonstrate that OMiAT is highly robust and powerful compared with other existing methods, while correctly controlling type I error rates. Our real data analyses also confirm that MiCAM is especially efficient for the assessment of upper-level taxa by integrating OMiAT as a group analytic method.ConclusionsOMiAT is attractive in practice due to the high complexity of microbiome data and the unknown true nature of the state. MiCAM also provides a hierarchical association map for numerous microbial taxa and can also be used as a guideline for further investigation on the roles of discovered taxa in human health and disease.

A small-sample multivariate kernel machine test for microbiome association studies

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.

Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test

High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals' microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. "Optimal" MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for.