Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies.

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene-Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene-environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

Genetic risk score in multiple sclerosis is associated with unique gut microbiome.

Multiple sclerosis (MS) is a complex autoimmune disease in which both the roles of genetic susceptibility and environmental/microbial factors have been investigated. More than 200 genetic susceptibility variants have been identified along with the dysbiosis of gut microbiota, both independently have been shown to be associated with MS. We hypothesize that MS patients harboring genetic susceptibility variants along with gut microbiome dysbiosis are at a greater risk of exhibiting the disease. We investigated the genetic risk score for MS in conjunction with gut microbiota in the same cohort of 117 relapsing remitting MS (RRMS) and 26 healthy controls. DNA samples were genotyped using Illumina's Infinium Immuno array-24 v2 chip followed by calculating genetic risk score and the microbiota was determined by sequencing the V4 hypervariable region of the 16S rRNA gene. We identified two clusters of MS patients, Cluster A and B, both having a higher genetic risk score than the control group. However, the MS cases in cluster B not only had a higher genetic risk score but also showed a distinct gut microbiome than that of cluster A. Interestingly, cluster A which included both healthy control and MS cases had similar gut microbiome composition. This could be due to (i) the non-active state of the disease in that group of MS patients at the time of fecal sample collection and/or (ii) the restoration of the gut microbiome post disease modifying therapy to treat the MS. Our study showed that there seems to be an association between genetic risk score and gut microbiome dysbiosis in triggering the disease in a small cohort of MS patients. The MS Cluster A who have a higher genetic risk score but microbiome profile similar to that of healthy controls could be due to the remitting phase of the disease or due to the effect of disease modifying therapies.

PhyloMed: a phylogeny-based test of mediation effect in microbiome.

Microbiome data from sequencing experiments contain the relative abundance of a large number of microbial taxa with their evolutionary relationships represented by a phylogenetic tree. The compositional and high-dimensional nature of the microbiome mediator challenges the validity of standard mediation analyses. We propose a phylogeny-based mediation analysis method called PhyloMed to address this challenge. Unlike existing methods that directly identify individual mediating taxa, PhyloMed discovers mediation signals by analyzing subcompositions defined on the phylogenic tree. PhyloMed produces well-calibrated mediation test p-values and yields substantially higher discovery power than existing methods.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

The construction of high efficient visible-light-driven 3D porous g-C3N4/Fe3O4 photocatalyst: A new photo-induced bacterial inactivation material enhanced by cascade photo-Fenton reaction.

Photocatalytic disinfection is considered a promising method for eliminating the hazards of pathogenic bacteria. Graphitic carbon nitride (g-C3N4) is an ideal photocatalytic bacterial inactivation material for its advantage of tunable band structure, good stability and easy preparation. This work has constructed a novel defective 3D porous g-C3N4 by cyanamide carbonation using dendritic mesoporous silica template. The direct loading of Fe3O4 nanoparticles provided an excellent pg-C3N4-Fe3O4 photocatalyst suitable for water disinfection. Compared to pristine g-C3N4, the prepared 3D porous defective g-C3N4-Fe3O4 exhibited the enhanced visible light absorbance as indicated by the band gap decreasing of 0.66 eV, and about 3 and 10 fold increase of photo-induced current response and O2 adsorption respectively. The pg-C3N4-Fe3O4 showed excellent visible-light-driven photocatalytic bactericidal activity. It could kill 1 × 107 cfu mL-1Escherichia coli completely within 1 h under visible-light illumination (100 mW cm-2) with good reusability, its logarithmic bacterial inactivation efficiency was about 2.5 fold higher than pg-C3N4. The enhanced bactericidal performance is mainly ascribed to the Fe3O4 involved cascade photo-Fenton reaction.

Exclusive breast-feeding, the early-life microbiome and immune response, and common childhood respiratory illnesses.

BACKGROUND: The impact of breast-feeding on certain childhood respiratory illnesses remains controversial. OBJECTIVE: We sought to examine the effect of exclusive breast-feeding on the early-life upper respiratory tract (URT) and gut microbiome, the URT immune response in infancy, and the risk of common pediatric respiratory diseases. METHODS: We analyzed data from a birth cohort of healthy infants with prospective ascertainment of breast-feeding patterns and common pediatric pulmonary and atopic outcomes. In a subset of infants, we also characterized the URT and gut microbiome using 16S ribosomal RNA sequencing and measured 9 URT cytokines using magnetic bead-based assays. RESULTS: Of the 1949 infants enrolled, 1495 (76.71%) had 4-year data. In adjusted analyses, exclusive breast-feeding (1) had an inverse dose-response on the ⍺-diversity of the early-life URT and gut microbiome, (2) was positively associated with the URT levels of IFN-α, IFN-γ, and IL-17A in infancy, and (3) had a protective dose-response on the development of a lower respiratory tract infection in infancy, 4-year current asthma, and 4-year ever allergic rhinitis (odds ratio [95% CI] for each 4 weeks of exclusive breast-feeding, 0.95 [0.91-0.99], 0.95 [0.90-0.99], and 0.95 [0.92-0.99], respectively). In exploratory analyses, we also found that the protective association of exclusive breast-feeding on 4-year current asthma was mediated through its impact on the gut microbiome (P = .03). CONCLUSIONS: Our results support a protective causal role of exclusive breast-feeding in the risk of developing a lower respiratory tract infection in infancy and asthma and allergic rhinitis in childhood. They also shed light on potential mechanisms of these associations, including the effect of exclusive breast-feeding on the gut microbiome.

Upper respiratory tract bacterial-immune interactions during respiratory syncytial virus infection in infancy.

BACKGROUND: The risk factors determining short- and long-term morbidity following acute respiratory infection (ARI) due to respiratory syncytial virus (RSV) in infancy remain poorly understood. OBJECTIVES: Our aim was to examine the associations of the upper respiratory tract (URT) microbiome during RSV ARI in infancy with the acute local immune response and short- and long-term clinical outcomes. METHODS: We characterized the URT microbiome by 16S ribosomal RNA sequencing and assessed the acute local immune response by measuring 53 immune mediators with high-throughput immunoassays in 357 RSV-infected infants. Our short- and long-term clinical outcomes included several markers of disease severity and the number of wheezing episodes in the fourth year of life, respectively. RESULTS: We found several specific URT bacterial-immune mediator associations. In addition, the Shannon ⍺-diversity index of the URT microbiome was associated with a higher respiratory severity score (ß =.50 [95% CI = 0.13-0.86]), greater odds of a lower ARI (odds ratio = 1.63 [95% CI = 1.10-2.43]), and higher number of wheezing episodes in the fourth year of life (ß = 0.89 [95% CI = 0.37-1.40]). The Jaccard ß-diversity index of the URT microbiome differed by level of care required (P = .04). Furthermore, we found an interaction between the Shannon ⍺-diversity index of the URT microbiome and the first principal component of the acute local immune response on the respiratory severity score (P = .048). CONCLUSIONS: The URT microbiome during RSV ARI in infancy is associated with the acute local immune response, disease severity, and number of wheezing episodes in the fourth year of life. Our results also suggest complex URT bacterial-immune interactions that can affect the severity of the RSV ARI.

Gut microbiome variation modulates the effects of dietary fiber on host metabolism.

BACKGROUND: There is general consensus that consumption of dietary fermentable fiber improves cardiometabolic health, in part by promoting mutualistic microbes and by increasing production of beneficial metabolites in the distal gut. However, human studies have reported variations in the observed benefits among individuals consuming the same fiber. Several factors likely contribute to this variation, including host genetic and gut microbial differences. We hypothesized that gut microbial metabolism of dietary fiber represents an important and differential factor that modulates how dietary fiber impacts the host. RESULTS: We examined genetically identical gnotobiotic mice harboring two distinct complex gut microbial communities and exposed to four isocaloric diets, each containing different fibers: (i) cellulose, (ii) inulin, (iii) pectin, (iv) a mix of 5 fermentable fibers (assorted fiber). Gut microbiome analysis showed that each transplanted community preserved a core of common taxa across diets that differentiated it from the other community, but there were variations in richness and bacterial taxa abundance within each community among the different diet treatments. Host epigenetic, transcriptional, and metabolomic analyses revealed diet-directed differences between animals colonized with the two communities, including variation in amino acids and lipid pathways that were associated with divergent health outcomes. CONCLUSION: This study demonstrates that interindividual variation in the gut microbiome is causally linked to differential effects of dietary fiber on host metabolic phenotypes and suggests that a one-fits-all fiber supplementation approach to promote health is unlikely to elicit consistent effects across individuals. Overall, the presented results underscore the importance of microbe-diet interactions on host metabolism and suggest that gut microbes modulate dietary fiber efficacy. Video abstract.

A comprehensive analysis comparing linear and generalized linear models in detecting adaptive SNPs.

To understand how organisms adapt to their environment, a gene-environmental association (GEA) analysis is commonly conducted. GEA methods based on mixed models, such as linear latent factor mixed models (LFMM) and LFMM2, have grown in popularity for their robust performance in terms of power and computational speed. However, it is unclear how the assumption of a Gaussian distribution for the response variables influences model performance. In this paper, we develop a generalized linear model (GLM) that allows for non-Gaussian distribution in the genotypic response variables, and treatment of multiallelic nucleotide polymorphisms. Moreover, this multinomial logistic regression model (MLR) is combined with an admixture-based model or principal components analysis to correct for population structure (MLR-ADM and MLR-PC). Using simulations, we evaluate the type 1 error, false discovery rates (FDR), and power to detect selected SNPs, to guide model choice and best practices. With genomic control, MLR-PC and LFMM2 have similar type 1 error, FDRs, and power when analysing biallelic SNPs, while dramatically outperforming models not accounting for population structure. Differences in performance occur under continuous population structure where MLR-PC outperforms LFMM/LFMM2, especially when a larger number of clusters or triallelic SNPs are analysed. The Human Genome Diversity Project (HGDP) data set shows that both MLR-PC and LFMM2 control the inflation of P -values. Analysis of the 1,000 Genome Project Phase 3 data set illustrates that MLR-PC and LFMM2 produce consistent results for most significant SNPs, while MLR-PC discovered additional SNPs corresponding to certain genes, suggesting MLR-PC may be a useful alternative to GEA inference.

PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection.

Germline disease-causing variants are generally more spatially clustered in protein 3-dimensional structures than benign variants. Motivated by this tendency, we develop a fast and powerful protein-structure-based scan (PSCAN) approach for evaluating gene-level associations with complex disease and detecting signal variants. We validate PSCAN's performance on synthetic data and two real data sets for lipid traits and Alzheimer's disease. Our results demonstrate that PSCAN performs competitively with existing gene-level tests while increasing power and identifying more specific signal variant sets. Furthermore, PSCAN enables generation of hypotheses about the molecular basis for the associations in the context of protein structures and functional domains.

Soy food intake associates with changes in the metabolome and reduced blood pressure in a gut microbiota dependent manner.

BACKGROUND AND AIMS: Consumption of soy foods has been associated with protection against cardiometabolic disease, but the mechanisms are incompletely understood. We hypothesized that habitual soy food consumption associates with gut microbiome composition, metabolite production, and the interaction between diet, microbiota and metabolites. METHODS AND RESULTS: We analyzed dietary soy intake, plasma and stool metabolites, and gut microbiome data from two independent cross-sectional samples of healthy US individuals (N = 75 lean or overweight, and N = 29 obese). Habitual soy intake associated with several circulating metabolites. There was a significant interaction between soy intake and gut microbiome composition, as defined by gut enterotype, on metabolites in plasma and stool. Soy consumption associated with reduced systolic blood pressure, but only in a subset of individuals defined by their gut microbiome enterotype, suggesting that responsiveness to soy may be dependent on microbiome composition. Soy intake was associated with differences in specific microbial taxa, including two taxa mapping to genus Dialister and Prevotella which appeared to be suppressed by high soy intake We identified context-dependent effects of these taxa, where presence of Prevotella was associated with higher blood pressure and a worse cardiometabolic profile, but only in the absence of Dialister. CONCLUSIONS: The gut microbiome is an important intermediate in the interplay between dietary soy intake and systemic metabolism. Consumption of soy foods may shape the microbiome by suppressing specific taxa, and may protect against hypertension only in individuals with soy-responsive microbiota. CLINICAL TRIALS REGISTRY: NCT02010359 at clinicaltrials.gov.

Multi-trait analysis of rare-variant association summary statistics using MTAR.

Integrating association evidence across multiple traits can improve the power of gene discovery and reveal pleiotropy. Most multi-trait analysis methods focus on individual common variants in genome-wide association studies. Here, we introduce multi-trait analysis of rare-variant associations (MTAR), a framework for joint analysis of association summary statistics between multiple rare variants and different traits. MTAR achieves substantial power gain by leveraging the genome-wide genetic correlation measure to inform the degree of gene-level effect heterogeneity across traits. We apply MTAR to rare-variant summary statistics for three lipid traits in the Global Lipids Genetics Consortium. 99 genome-wide significant genes were identified in the single-trait-based tests, and MTAR increases this to 139. Among the 11 novel lipid-associated genes discovered by MTAR, 7 are replicated in an independent UK Biobank GWAS analysis. Our study demonstrates that MTAR is substantially more powerful than single-trait-based tests and highlights the value of MTAR for novel gene discovery.

Multi-Omic Analysis of the Microbiome and Metabolome in Healthy Subjects Reveals Microbiome-Dependent Relationships Between Diet and Metabolites.

The human microbiome has been associated with health status, and risk of disease development. While the etiology of microbiome-mediated disease remains to be fully elucidated, one mechanism may be through microbial metabolism. Metabolites produced by commensal organisms, including in response to host diet, may affect host metabolic processes, with potentially protective or pathogenic consequences. We conducted multi-omic phenotyping of healthy subjects (N = 136), in order to investigate the interaction between diet, the microbiome, and the metabolome in a cross-sectional sample. We analyzed the nutrient composition of self-reported diet (3-day food records and food frequency questionnaires). We profiled the gut and oral microbiome (16S rRNA) from stool and saliva, and applied metabolomic profiling to plasma and stool samples in a subset of individuals (N = 75). We analyzed these multi-omic data to investigate the relationship between diet, the microbiome, and the gut and circulating metabolome. On a global level, we observed significant relationships, particularly between long-term diet, the gut microbiome and the metabolome. Intake of plant-derived nutrients as well as consumption of artificial sweeteners were associated with significant differences in circulating metabolites, particularly bile acids, which were dependent on gut enterotype, indicating that microbiome composition mediates the effect of diet on host physiology. Our analysis identifies dietary compounds and phytochemicals that may modulate bacterial abundance within the gut and interact with microbiome composition to alter host metabolism.

Close social relationships correlate with human gut microbiota composition.

Social relationships shape human health and mortality via behavioral, psychosocial, and physiological mechanisms, including inflammatory and immune responses. Though not tested in human studies, recent primate studies indicate that the gut microbiome may also be a biological mechanism linking relationships to health. Integrating microbiota data into the 60-year-old Wisconsin Longitudinal Study, we found that socialness with family and friends is associated with differences in the human fecal microbiota. Analysis of spouse (N = 94) and sibling pairs (N = 83) further revealed that spouses have more similar microbiota and more bacterial taxa in common than siblings, with no observed differences between sibling and unrelated pairs. These differences held even after accounting for dietary factors. The differences between unrelated individuals and married couples was driven entirely by couples who reported close relationships; there were no differences in similarity between couples reporting somewhat close relationships and unrelated individuals. Moreover, married individuals harbor microbial communities of greater diversity and richness relative to those living alone, with the greatest diversity among couples reporting close relationships, which is notable given decades of research documenting the health benefits of marriage. These results suggest that human interactions, especially sustained, close marital relationships, influence the gut microbiota.

Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis.

There is heightened interest in using high-throughput sequencing technologies to quantify abundances of microbial taxa and linking the abundance to human diseases and traits. Proper modeling of multivariate taxon counts is essential to the power of detecting this association. Existing models are limited in handling excessive zero observations in taxon counts and in flexibly accommodating complex correlation structures and dispersion patterns among taxa. In this article, we develop a new probability distribution, zero-inflated generalized Dirichlet multinomial (ZIGDM), that overcomes these limitations in modeling multivariate taxon counts. Based on this distribution, we propose a ZIGDM regression model to link microbial abundances to covariates (e.g. disease status) and develop a fast expectation-maximization algorithm to efficiently estimate parameters in the model. The derived tests enable us to reveal rich patterns of variation in microbial compositions including differential mean and dispersion. The advantages of the proposed methods are demonstrated through simulation studies and an analysis of a gut microbiome dataset.

Cost-effectiveness analysis of magnetic resonance imaging-conditional pacemaker implantation: Insights from a multicenter study and implications in the current era.

BACKGROUND: Magnetic resonance imaging (MRI)-conditional pacemakers (M-PPMs) grant patients greater accessibility to MRI scans. The cost-effectiveness of implanting M-PPM is unknown. OBJECTIVE: The purpose of this study was to determine the cost-effectiveness of M-PPM implantation. METHODS: Cost-effectiveness analysis was performed on patients receiving a M-PPM across 4 institutions. The incremental cost-effectiveness ratio (ICER) was calculated by dividing the sum of the total incremental cost of implanting a M-PPM vs a conventional pacemaker and the cost of MRI scans by the utility of MRI scans in terms of quality-adjusted life-years (QALY) gained. QALY and lifespan of M-PPM (7-11 years) data were obtained from the literature. The benchmark of <$100,000 per QALY was used as the threshold for cost-effectiveness. Computer modeling/simulations were used to calculate the percentage of patients required to achieve this benchmark, to extrapolate the cumulative projected percentage of patients utilizing MRI scans over the lifespan of a M-PPM via the Weibull parametric survival model, and to conduct univariate and multivariate, probabilistic sensitivity analyses. RESULTS: The ICER during the follow-up period (21 ± 17 months) was $451,569. The cost-effectiveness ICER benchmark is reached 7.0 years postimplantation, when a projected 38% of recipients would receive MRI scans. The projected percentage of patients receiving MRI scans at 11 years was 58%, yielding an ICER of $74,221 per QALY. Henceforth, assuming increased MRI usage in regular PPM based on Centers for Medicare & Medicaid Services memo CAG00399R4 and decreased cost of M-PPM, M-PPM implantation is still cost-effective, with a lifetime ICER of $49,817 per QALY. CONCLUSION: M-PPM implantation is cost-effective over the lifespan of a M-PPM based on projected usage of MRI.

PreMeta: a tool to facilitate meta-analysis of rare-variant associations.

BACKGROUND: Meta-analysis is essential to the discovery of rare variants that influence complex diseases and traits. Four major software packages, namely MASS, MetaSKAT, RAREMETAL, and seqMeta, have been developed to perform meta-analysis of rare-variant associations. These packages first generate summary statistics for each study and then perform the meta-analysis by combining the summary statistics. Because of incompatible file formats and non-equivalent summary statistics, the output files from the study-level analysis of one package cannot be directly used to perform meta-analysis in another package. RESULTS: We developed a computationally efficient software program, PreMeta, to resolve the non-compatibility of the four software packages and to facilitate meta-analysis of large-scale sequencing studies in a consortium setting. PreMeta reformats the output files of study-level summary statistics generated by the four packages (text files produced by MASS and RAREMETAL, binary files produced by MetaSKAT, and R data files produced by seqMeta) and translates the summary statistics from one form to another, such that the summary statistics from any package can be used to perform meta-analysis in any other package. With this tool, consortium members are not required to use the same software for study-level analyses. In addition, PreMeta checks for allele mismatches, corrects summary statistics, and allows the rescaled inverse normal transformation to be performed at the meta-analysis stage by rescaling summary statistics. CONCLUSIONS: PreMeta processes summary statistics from the four packages to make them compatible and avoids the need to redo study-level analyses. PreMeta documentation and executable are available at: http://dlin.web.unc.edu/software/premeta .

A general framework for association analysis of microbial communities on a taxonomic tree.

Motivation: : Association analysis of microbiome composition with disease-related outcomes provides invaluable knowledge towards understanding the roles of microbes in the underlying disease mechanisms. Proper analysis of sparse compositional microbiome data is challenging. Existing methods rely on strong assumptions on the data structure and fail to pinpoint the associated microbial communities. Results: : We develop a general framework to: (i) perform robust association tests for the microbial community that exhibits arbitrary inter-taxa dependencies; (ii) localize lineages on the taxonomic tree that are associated with covariates (e.g. disease status); and (iii) assess the overall association of the whole microbial community with the covariates. Unlike existing methods for microbiome association analysis, our framework does not make any distributional assumptions on the microbiome data; it allows for the adjustment of confounding variables and accommodates excessive zero observations; and it incorporates taxonomic information. We perform extensive simulation studies under a wide-range of scenarios to evaluate the new methods and demonstrate substantial power gain over existing methods. The advantages of the proposed framework are further demonstrated with real datasets from two microbiome studies. The relevant R package miLineage is publicly available. Availability and Implementation: : miLineage package, manual and tutorial are available at https://medschool.vanderbilt.edu/tang-lab/software/miLineage . Contact: z.tang@vanderbilt.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances.

MOTIVATION: Recent advances in sequencing technology have made it possible to obtain high-throughput data on the composition of microbial communities and to study the effects of dysbiosis on the human host. Analysis of pairwise intersample distances quantifies the association between the microbiome diversity and covariates of interest (e.g. environmental factors, clinical outcomes, treatment groups). In the design of these analyses, multiple choices for distance metrics are available. Most distance-based methods, however, use a single distance and are underpowered if the distance is poorly chosen. In addition, distance-based tests cannot flexibly handle confounding variables, which can result in excessive false-positive findings. RESULTS: We derive presence-weighted UniFrac to complement the existing UniFrac distances for more powerful detection of the variation in species richness. We develop PERMANOVA-S, a new distance-based method that tests the association of microbiome composition with any covariates of interest. PERMANOVA-S improves the commonly-used Permutation Multivariate Analysis of Variance (PERMANOVA) test by allowing flexible confounder adjustments and ensembling multiple distances. We conducted extensive simulation studies to evaluate the performance of different distances under various patterns of association. Our simulation studies demonstrate that the power of the test relies on how well the selected distance captures the nature of the association. The PERMANOVA-S unified test combines multiple distances and achieves good power regardless of the patterns of the underlying association. We demonstrate the usefulness of our approach by reanalyzing several real microbiome datasets. AVAILABILITY AND IMPLEMENTATION: miProfile software is freely available at https://medschool.vanderbilt.edu/tang-lab/software/miProfile CONTACT: z.tang@vanderbilt.edu or g.chen@vanderbilt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.