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.

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.

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.

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.

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.

There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations-MASS, RAREMETAL, MetaSKAT, and seqMeta-in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.

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.

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 .

Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.

Loss-of-function mutations in APOC3, triglycerides, and coronary disease.

BACKGROUND: Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype. METHODS: We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons. RESULTS: An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G→A and IVS3+1G→T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1×10(-20)), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P=8×10(-10)). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P=4×10(-6)). CONCLUSIONS: Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.).

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.

Quantitative trait analysis in sequencing studies under trait-dependent sampling.

It is not economically feasible to sequence all study subjects in a large cohort. A cost-effective strategy is to sequence only the subjects with the extreme values of a quantitative trait. In the National Heart, Lung, and Blood Institute Exome Sequencing Project, subjects with the highest or lowest values of body mass index, LDL, or blood pressure were selected for whole-exome sequencing. Failure to account for such trait-dependent sampling can cause severe inflation of type I error and substantial loss of power in quantitative trait analysis, especially when combining results from multiple studies with different selection criteria. We present valid and efficient statistical methods for association analysis of sequencing data under trait-dependent sampling. We pay special attention to gene-based analysis of rare variants. Our methods can be used to perform quantitative trait analysis not only for the trait that is used to select subjects for sequencing but for any other traits that are measured. For a particular trait of interest, our approach properly combines the association results from all studies with measurements of that trait. This meta-analysis is substantially more powerful than the analysis of any single study. By contrast, meta-analysis of standard linear regression results (ignoring trait-dependent sampling) can be less powerful than the analysis of a single study. The advantages of the proposed methods are demonstrated through simulation studies and the National Heart, Lung, and Blood Institute Exome Sequencing Project data. The methods are applicable to other types of genetic association studies and nongenetic studies.

Meta-analysis of sequencing studies with heterogeneous genetic associations.

Recent advances in sequencing technologies have made it possible to explore the influence of rare variants on complex diseases and traits. Meta-analysis is essential to this exploration because large sample sizes are required to detect rare variants. Several methods are available to conduct meta-analysis for rare variants under fixed-effects models, which assume that the genetic effects are the same across all studies. In practice, genetic associations are likely to be heterogeneous among studies because of differences in population composition, environmental factors, phenotype and genotype measurements, or analysis method. We propose random-effects models which allow the genetic effects to vary among studies and develop the corresponding meta-analysis methods for gene-level association tests. Our methods take score statistics, rather than individual participant data, as input and thus can accommodate any study designs and any phenotypes. We produce the random-effects versions of all commonly used gene-level association tests, including burden, variable threshold, and variance-component tests. We demonstrate through extensive simulation studies that our random-effects tests are substantially more powerful than the fixed-effects tests in the presence of moderate and high between-study heterogeneity and achieve similar power to the latter when the heterogeneity is low. The usefulness of the proposed methods is further illustrated with data from National Heart, Lung, and Blood Institute Exome Sequencing Project (NHLBI ESP). The relevant software is freely available.

A general framework for detecting disease associations with rare variants in sequencing studies.

Biological and empirical evidence suggests that rare variants account for a large proportion of the genetic contributions to complex human diseases. Recent technological advances in high-throughput sequencing platforms have made it possible for researchers to generate comprehensive information on rare variants in large samples. We provide a general framework for association testing with rare variants by combining mutation information across multiple variant sites within a gene and relating the enriched genetic information to disease phenotypes through appropriate regression models. Our framework covers all major study designs (i.e., case-control, cross-sectional, cohort and family studies) and all common phenotypes (e.g., binary, quantitative, and age at onset), and it allows arbitrary covariates (e.g., environmental factors and ancestry variables). We derive theoretically optimal procedures for combining rare mutations and construct suitable test statistics for various biological scenarios. The allele-frequency threshold can be fixed or variable. The effects of the combined rare mutations on the phenotype can be in the same direction or different directions. The proposed methods are statistically more powerful and computationally more efficient than existing ones. An application to a deep-resequencing study of drug targets led to a discovery of rare variants associated with total cholesterol. The relevant software is freely available.

MASS: meta-analysis of score statistics for sequencing studies.

SUMMARY: MASS is a command-line program to perform meta-analysis of sequencing studies by combining the score statistics from multiple studies. It implements three types of multivariate tests that encompass all commonly used association tests for rare variants. The input files can be generated from the accompanying software SCORE-Seq. This bundle of programs allows analysis of large sequencing studies in a time and memory efficient manner. AVAILABILITY AND IMPLEMENTATION: MASS and SCORE-Seq, including documentations and executables, are available at http://dlin.web.unc.edu/software/. CONTACT: lin@bios.unc.edu.

Fair prediction of 2-year stroke risk in patients with atrial fibrillation.

OBJECTIVE: This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups. MATERIALS AND METHODS: Our study utilized structured electronic health records (EHR) data from the All of Us Research Program. Machine learning models (LightGBM) were utilized to capture the relations between stroke risks and the predictors used by the widely recognized CHADS2 and CHA2DS2-VASc scores. We mitigated the racial disparity by creating a representative tuning set, customizing tuning criteria, and setting binary thresholds separately for subgroups. We constructed a hold-out test set that not only supports temporal validation but also includes a larger proportion of Black/African Americans for fairness validation. RESULTS: Compared to the original CHADS2 and CHA2DS2-VASc scores, significant improvements were achieved by modeling their predictors using machine learning models (Area Under the Receiver Operating Characteristic curve from near 0.70 to above 0.80). Furthermore, applying our disparity mitigation strategies can effectively enhance model fairness compared to the conventional cross-validation approach. DISCUSSION: Modeling CHADS2 and CHA2DS2-VASc risk factors with LightGBM and our disparity mitigation strategies achieved decent discriminative performance and excellent fairness performance. In addition, this approach can provide a complete interpretation of each predictor. These highlight its potential utility in clinical practice. CONCLUSIONS: Our research presents a practical example of addressing clinical challenges through the All of Us Research Program data. The disparity mitigation framework we proposed is adaptable across various models and data modalities, demonstrating broad potential in clinical informatics.

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.

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.

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.

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.