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.

Background The intestinal microbiota has been implicated in the pathophysiology of Irritable Bowel Syndrome (IBS). Due to the variable resolutions of techniques used to characterize the intestinal microbiota, and the heterogeneity of IBS, the defined alterations of the IBS intestinal microbiota are inconsistent. We analyzed the composition of the intestinal microbiota in a defined subgroup of IBS patients (diarrhea-predominant IBS, D-IBS) using a technique that provides the deepest characterization available for complex microbial communities. Methods Fecal DNA was isolated from 23 D-IBS patients and 23 healthy controls (HC). Variable regions V1-V3 and V6 of the 16S rRNA gene were amplified from all samples. PCR products were sequenced using 454 high throughput sequencing. The composition, diversity and richness of microbial communities were determined and compared between D-IBS and HC using the Quantitative Insights Into Microbial Ecology pipeline. Key Results The contribution of bacterial groups to the composition of the intestinal microbiota differed between D-IBS and HC. D-IBS patients had significantly higher levels of Enterobacteriaceae (p = 0.03), and lower levels of Fecalibacterium genera (p = 0.04) compared to HC. β-diversity values demonstrated significantly lower levels of UniFrac distances in HC compared to D-IBS patients. The richness of 16S rRNA sequences was significantly decreased in D-IBS patients (p < 0.04). Conclusions and Inferences Our 16S rRNA sequence data demonstrates a community-level dysbiosis in D-IBS. The altered composition of the intestinal microbiota in D-IBS is associated with significant increases in detrimental and decreases in beneficial bacterial groups, and a reduction in microbial richness.

Objective The relevance of the microbe-gut-brain axis to psychopathology is of interest in anorexia nervosa (AN), as the intestinal microbiota plays a critical role in metabolic function and weight regulation. Methods We characterized the composition and diversity of the intestinal microbiota in AN, using stool samples collected at inpatient admission (T1) (n=16) and discharge (T2) (n=10). At T1, participants completed the Beck Depression and Anxiety Inventories and the Eating Disorder Examination-Questionnaire. Patients with AN were compared to healthy individuals who participated in a previous study (healthy comparison group; HCG). Genomic DNA was isolated from stool samples, and bacterial composition was characterized by 454 pyrosequencing of the 16S rRNA gene. Sequencing results were processed by the Quantitative Insights Into Microbial Ecology pipeline. We compared T1 vs. T2 samples, samples from both points were compared to HCG (n=12), and associations between psychopathology and T1 samples were explored. Results In patients with AN, significant changes emerged between T1 and T2 in taxa abundance and beta (between-sample) diversity. Patients with AN had significantly lower alpha (within-sample) diversity than HCG at both T1 (p=0.0001) and T2 (p=0.016), and differences in taxa abundance were found between AN patients and HCG. Levels of depression, anxiety, and eating disorder psychopathology at T1 were associated with composition and diversity of the intestinal microbiota. Conclusions We provide evidence of intestinal dysbiosis in AN and an association between mood and the enteric microbiota in this patient population. Future directions include mechanistic investigations of the microbe-gut-brain axis in animal models and association of microbial measures with metabolic changes and recovery indices.