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Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest. Here, the authors present ConQuR, a conditional quantile regression method that removes microbiome batch effects through non-parametric modeling of complex microbial read counts, while preserving the signals of interest.

Background Whether pathophysiological factors differ between males and females with irritable bowel syndrome-diarrhea (IBS-D) remains to be tested. To better understand potential sex differences, males with IBS-D were compared to naturally cycling females and to females with IBS-D taking hormonal contraception on plasma levels of cytokines and gut microbiome characteristics. Methods Males and females with Rome III IBS-D completed questionnaires and kept a daily symptom diary for 28 days. Blood and stool samples were collected between days 3 and 8 of the daily diary (estrogen-dominant days in naturally cycling females). Blood samples were analyzed for lipopolysaccharide (LPS)-stimulated and unstimulated cytokine levels. Stool samples were analyzed for microbiota signatures using 16S rRNA sequencing. Results Forty-seven participants with IBS-D (13 males, 22 naturally cycling females, 12 females with hormonal contraception use) ages 18 to 50 years were studied. Males had similar unstimulated IL10, IL12P40, IL12P70, IL1β, IL8, and TNFα plasma cytokine levels compared to naturally cycling females, but higher levels compared with females using hormonal contraception. LPS-stimulated IL12P70 levels were lower in both groups of females vs. males. Alpha- and beta-diversity did not differ although differences in genus-level bacteria were found. Conclusion Cytokine levels differed between males and females using hormonal contraceptives but not between males and normally cycling females. It is important to consider that naturally cycling females may have a different cytokine and microbiome profile than females using hormonal contraceptives. Whether this portends a sex difference in potential etiologic factors remains to be determined.

Graft-versus-host disease (GVHD) is common after allogeneic hematopoietic cell transplantation (HCT). Risk for death from GVHD has been associated with low bacterial diversity in the stool microbiota early after transplant; however, the specific species associated with GVHD risk remain poorly defined. We prospectively collected serial weekly stool samples from 66 patients who underwent HCT, starting pre-transplantation and continuing weekly until 100 days post-transplant, a total of 694 observations in HCT recipients. We used 16S rRNA gene polymerase chain reaction with degenerate primers, followed by high-throughput sequencing to assess the relative abundance of sequence reads from bacterial taxa in stool samples over time. The gut microbiota was highly dynamic in HCT recipients, with loss and appearance of taxa common on short time scales. As in prior studies, GVHD was associated with lower alpha diversity of the stool microbiota. At neutrophil recovery post-HCT, the presence of oral Actinobacteria and oral Firmicutes in stool was positively correlated with subsequent GVHD; Lachnospiraceae were negatively correlated. A gradient of bacterial species (difference of the sum of the relative abundance of positive correlates minus the sum of the relative abundance of negative correlates) was most predictive (receiver operator characteristic area under the curve of 0.83) of subsequent severe acute GVHD. The stool microbiota around the time of neutrophil recovery post-HCT is predictive of subsequent development of severe acute GVHD in this study.

Background Microbial communities are known to be closely related to many diseases, such as obesity and HIV, and it is of interest to identify differentially abundant microbial species between two or more environments. Since the abundances or counts of microbial species usually have different scales and suffer from zero-inflation or over-dispersion, normalization is a critical step before conducting differential abundance analysis. Several normalization approaches have been proposed, but it is difficult to optimize the characterization of the true relationship between taxa and interesting outcomes.  Results To avoid the challenge of picking an optimal normalization and accommodate the advantages of several normalization strategies, we propose an omnibus approach. Our approach is based on a Cauchy combination test, which is flexible and powerful by aggregating individual p values. We also consider a truncated test statistic to prevent substantial power loss. We experiment with a basic linear regression model as well as recently proposed powerful association tests for microbiome data and compare the performance of the omnibus approach with individual normalization approaches. Experimental results show that, regardless of simulation settings, the new approach exhibits power that is close to the best normalization strategy, while controling the type I error well.  Conclusions The proposed omnibus test releases researchers from choosing among various normalization methods and it is an aggregated method that provides the powerful result to the underlying optimal normalization, which requires tedious trial and error. While the power may not exceed the best normalization, it is always much better than using a poor choice of normalization.

The human microbiome is a dynamic community of bacteria, viruses, fungi, and other microorganisms. Both the composition of the microbiome (the microbes that are present and their relative abundances) and the temporal variability of the microbiome (the magnitude of changes in their composition across time, called volatility) has been associated with human health. However, the effect of unbalanced sampling intervals and differential read depth on the estimates of microbiome volatility has not been thoroughly assessed. Using four publicly available gut and vaginal microbiome time series, we subsampled the datasets to several sampling intervals and read depths and then compared additive, multiplicative, centered log ratio (CLR)-based, qualitative, and distance-based measures of microbiome volatility between the conditions. We find that longer sampling intervals are associated with larger quantitative measures of change (particularly for common taxa), but not with qualitative measures of change or distance-based volatility quantification. A lower sequencing read depth is associated with smaller multiplicative, CLR-based, and qualitative measures of change (particularly for less common taxa). Strategic subsampling may serve as a useful sensitivity analysis in unbalanced longitudinal studies investigating clinical associations with microbiome volatility.

Background Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics may influence health outcomes. Typical microbiome genome-wide association studies (GWAS) marginally assess the association between individual genetic variants and individual microbial taxa. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The KRV framework evaluates the association between genetics and microbes by comparing similarity in genetic profiles, based on groups of variants at the gene level, to similarity in microbiome profiles, based on the overall microbiome composition, across all pairs of individuals. By reducing the multiple-testing burden and capturing intrinsic structure within the genetic and microbiome data, the KRV framework has the potential of improving statistical power in microbiome GWAS. Results We apply the covariate-adjusted KRV to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) in a two-stage (first gene-level, then variant-level) genome-wide association analysis for gut microbiome beta-diversity. We have identified an immunity-related gene, IL23R , reported in a previous microbiome genetic association study and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. In addition, simulation studies show that the covariate-adjusted KRV has a greater power than other microbiome GWAS methods that rely on univariate microbiome phenotypes across a range of scenarios. Conclusions Our findings highlight the value of the covariate-adjusted KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition. 3iRoYfA_uxPN1-JktGz5GL Video Abstract

Background Identification of bacterial taxa associated with diseases, exposures, and other variables of interest offers a more comprehensive understanding of the role of microbes in many conditions. However, despite considerable research in statistical methods for association testing with microbiome data, approaches that are generally applicable remain elusive. Classical tests often do not accommodate the realities of microbiome data, leading to power loss. Approaches tailored for microbiome data depend highly upon the normalization strategies used to handle differential read depth and other data characteristics, and they often have unacceptably high false positive rates, generally due to unsatisfied distributional assumptions. On the other hand, many non-parametric tests suffer from loss of power and may also present difficulties in adjusting for potential covariates. Most extant approaches also fail in the presence of heterogeneous effects. The field needs new non-parametric approaches that are tailored to microbiome data, robust to distributional assumptions, and powerful under heterogeneous effects, while permitting adjustment for covariates. Methods As an alternative to existing approaches, we propose a zero-inflated quantile approach (ZINQ), which uses a two-part quantile regression model to accommodate the zero inflation in microbiome data. For a given taxon, ZINQ consists of a valid test in logistic regression to model the zero counts, followed by a series of quantile rank-score based tests on multiple quantiles of the non-zero part with adjustment for the zero inflation. As a regression and quantile-based approach, the method is non-parametric and robust to irregular distributions, while providing an allowance for covariate adjustment. Since no distributional assumptions are made, ZINQ can be applied to data that has been processed under any normalization strategy. Results Thorough simulations based on real data across a range of scenarios and application to real data sets show that ZINQ often has equivalent or higher power compared to existing tests even as it offers better control of false positives. Conclusions We present ZINQ, a quantile-based association test between microbiota and dichotomous or quantitative clinical variables, providing a powerful and robust alternative for the current microbiome differential abundance analysis. 9TU_kfqMVHfUkABshrfuDQ Video Abstract

Type 2 diabetes mellitus (T2DM) is a common health issue, with heart failure (HF) being a common and lethal long-term complication. Although insulin is widely used for the treatment of T2DM, evidence regarding the efficacy of insulin compared to noninsulin therapies on incident HF risk is missing among randomized controlled trials. Real-world evidence on insulin's effect on long-term HF risk may supplement existing guidelines on the management of T2DM. This study aimed to compare insulin therapy against other medications on HF risk among patients with T2DM using real-world data extracted from insurance claims. A retrospective, observational study was conducted based on insurance claims data from a single health care network. The study period was from January 1, 2016, to August 11, 2021. The cohort was defined as patients having a T2DM diagnosis code. The inclusion criteria were patients who had at least 1 record of a glycated hemoglobin laboratory test result; full insurance for at least 1 year (either commercial or Medicare Part D); and received glucose-lowering therapy belonging to 1 of the following groups: insulin, glucagon-like peptide 1 receptor agonists (GLP-1 RAs), dipeptidyl peptidase-4 inhibitors (DPP-4Is), or sodium-glucose cotransporter-2 inhibitors (SGLT2Is). The main outcome was the 5-year incident HF rate. Baseline covariates, including demographic characteristics, comorbidities, and laboratory test results, were adjusted to correct for confounding. After adjusting for a broad list of confounders, patients receiving insulin were found to be associated with an 11.8% (95% CI 11.0%-12.7%), 12.0% (95% CI 11.5%-12.4%), and 15.1% (95% CI 14.3%-16.0%) higher 5-year HF rate compared to those using GLP-1 RAs, DPP-4Is, and SGLT2Is, respectively. Subgroup analysis showed that insulin's effect of a higher HF rate was significant in the subgroup with high HF risk but not significant in the subgroup with low HF risk. This study generated real-world evidence on the association of insulin therapy with a higher 5-year HF rate compared to GLP-1 RAs, DPP-4Is, and SGLT2Is based on insurance claims data. These findings also demonstrated the value of real-world data for comparative effectiveness studies to complement established guidelines. On the other hand, the study shares the common limitations of observational studies. Even though high-dimensional confounders are adjusted, remaining confounding may exist and induce bias in the analysis.

We estimated local ancestry on the autosomes and X chromosome in a large US-based study of 12,793 Hispanic/Latino individuals using the RFMix method, and we compared different reference panels and approaches to local ancestry estimation on the X chromosome by means of Mendelian inconsistency rates as a proxy for accuracy. We developed a novel and straightforward approach to performing ancestry-specific PCA after finding artifactual behavior in the results from an existing approach. Using the ancestry-specific PCA, we found significant population structure within African, European, and Amerindian ancestries in the Hispanic/Latino individuals in our study. In the African ancestral component of the admixed individuals, individuals whose grandparents were from Central America clustered separately from individuals whose grandparents were from the Caribbean, and also from reference Yoruba and Mandenka West African individuals. In the European component, individuals whose grandparents were from Puerto Rico diverged partially from other background groups. In the Amerindian ancestral component, individuals clustered into multiple different groups depending on the grandparental country of origin. Therefore, local ancestry estimation provides further insight into the complex genetic structure of US Hispanic/Latino populations, which must be properly accounted for in genotype-phenotype association studies. It also provides a basis for admixture mapping and ancestry-specific allele frequency estimation, which are useful in the identification of risk factors for disease.