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Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed., Competing Interests: RD, SM, JL, HL No competing interests declared, (© 2024, Deek et al.)

In a living organism, tens of thousands of genes are expressed and interact with each other to achieve necessary cellular functions. Gene regulatory networks contain information on regulatory mechanisms and the functions of gene expressions. Thus, incorporating network structures, discerned either through biological experiments or statistical estimations, could potentially increase the selection and estimation accuracy of genes associated with a phenotype of interest. Here, we considered a gene selection problem using gene expression data and the graphical structures found in gene networks. Because gene expression measurements are intermediate phenotypes between a trait and its associated genes, we adopted an instrumental variable regression approach. We treated genetic variants as instrumental variables to address the endogeneity issue. We proposed a two-step estimation procedure. In the first step, we applied the LASSO algorithm to estimate the effects of genetic variants on gene expression measurements. In the second step, the projected expression measurements obtained from the first step were treated as input variables. A graph-constrained regularization method was adopted to improve the efficiency of gene selection and estimation. We theoretically showed the selection consistency of the estimation method and derived the L ∞ bound of the estimates. Simulation and real data analyses were conducted to demonstrate the effectiveness of our method and to compare it with its counterparts., (© 2019 The International Biometric Society.)

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., (© The Author 2016. Published by Oxford University Press.)

Unlabelled: One central theme of modern high-throughput genomic data analysis is to identify relevant genomic features as well as build up a predictive model based on selected features for various tasks such as personalized medicine. Correlating the large number of 'omics' features with a certain phenotype is particularly challenging due to small sample size (n) and high dimensionality (p). To address this small n, large p problem, various forms of sparse regression models have been proposed by exploiting the sparsity assumption. Among these, network-constrained sparse regression model is of particular interest due to its ability to utilize the prior graph/network structure in the omics data. Despite its potential usefulness for omics data analysis, no efficient R implementation is publicly available. Here we present an R software package 'glmgraph' that implements the graph-constrained regularization for both sparse linear regression and sparse logistic regression. We implement both the L1 penalty and minimax concave penalty for variable selection and Laplacian penalty for coefficient smoothing. Efficient coordinate descent algorithm is used to solve the optimization problem. We demonstrate the use of the package by applying it to a human microbiome dataset, where phylogeny structure among bacterial taxa is available., Availability and Implementation: 'glmgraph' is implemented in R and C++ Armadillo and publicly available under CRAN., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Empirical Bayes methods are widely used in the analysis of microarray gene expression data in order to identify the differentially expressed genes or genes that are associated with other general phenotypes. Available methods often assume that genes are independent. However, genes are expected to function interactively and to form molecular modules to affect the phenotypes. In order to account for regulatory dependency among genes, we propose in this paper a network-based empirical Bayes method for analyzing genomic data in the framework of linear models, where the dependency of genes is modeled by a discrete Markov random field defined on a predefined biological network. This method provides a statistical framework for integrating the known biological network information into the analysis of genomic data. We present an iterated conditional mode algorithm for parameter estimation and for estimating the posterior probabilities using Gibbs sampling. We demonstrate the application of the proposed methods using simulations and analysis of a human brain aging microarray gene expression data set.

Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with each phenotype. We define methylation variations that are associated with underlying nucleotide variations (methylation quantitative trait loci) and show that underlying genetic variations are important drivers of methylation changes. We implemented Bayesian multitrait colocalization analysis (moloc) and summary data-based Mendelian randomization to systematically annotate genomic regions that show association with kidney function, methylation, and gene expression. We prioritized 40 loci, where methylation and gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our study defines methylation changes associated with DKD phenotypes, the key role of underlying genetic variations driving methylation variations, and prioritizes methylome and gene-expression changes that likely mediate the genotype effect on kidney disease pathogenesis.

Multivariate regression with high-dimensional covariates has many applications in genomic and genetic research, in which some covariates are expected to be associated with multiple responses. This paper considers joint testing for regression coefficients over multiple responses and develops simultaneous testing methods with false discovery rate control. The test statistic is based on inverse regression and bias-corrected group lasso estimates of the regression coefficients and is shown to have an asymptotic chi-squared null distribution. A row-wise multiple testing procedure is developed to identify the covariates associated with the responses. The procedure is shown to control the false discovery proportion and false discovery rate at a prespecified level asymptotically. Simulations demonstrate the gain in power, relative to entrywise testing, in detecting the covariates associated with the responses. The test is applied to an ovarian cancer dataset to identify the microRNA regulators that regulate protein expression. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) and differential expression analyses have had limited success in finding genes that cause complex diseases such as heart failure (HF), a leading cause of death in the United States. This article proposes a new statistical approach that integrates GWAS and expression quantitative trait loci (eQTL) data to identify important HF genes. For such genes, genetic variations that perturb its expression are also likely to influence disease risk. The proposed method thus tests for the presence of simultaneous signals: SNPs that are associated with the gene’s expression as well as with disease. An analytic expression for thep-value is obtained, and the method is shown to be asymptotically adaptively optimal under certain conditions. It also allows the GWAS and eQTL data to be collected from different groups of subjects, enabling investigators to integrate public resources with their own data. Simulation experiments show that it can be more powerful than standard approaches and also robust to linkage disequilibrium between variants. The method is applied to an extensive analysis of HF genomics and identifies several genes with biological evidence for being functionally relevant in the etiology of HF. It is implemented in the R packagessa. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

Enterovirus 71 (EV71) and Coxsackievirus A16 (CA16) remain the predominant pathogens in hand, foot, and mouth disease (HFMD), but the factors underlying the pathogenesis of EV71 and CA16 infections have not been elucidated. Recently, the functions of microRNAs (miRNAs) in pathogen-host interactions have been highlighted. In the present study, we performed comprehensive miRNA profiling in EV71- and CA16-infected human umbilical vein endothelial cells (HUVECs) at multiple time points using high-throughput sequencing. The results showed that 135 known miRNAs exhibited remarkable differences in expression. Of these, 30 differentially expressed miRNAs presented opposite trends in EV71- and CA16-infected samples. Subsequently, we mainly focused on the 30 key differentially expressed miRNAs through further screening to predict targets. Gene ontology (GO) and pathway analysis of the predicted targets showed the enrichment of 14 biological processes, 9 molecular functions, 8 cellular components, and 85 pathways. The regulatory networks of these miRNAs with predicted targets, GOs, pathways, and co-expression genes were determined, suggesting that miRNAs display intricate regulatory mechanisms during the infection phase. Consequently, we specifically analyzed the hierarchical GO categories of the predicted targets involved in biological adhesion. The results indicated that the distinct changes induced by EV71 and CA16 infection may be partly linked to the function of the blood-brain barrier. Taken together, this is the first report describing miRNA expression profiles in HUVECs with EV71 and CA16 infections using high-throughput sequencing. Our data provide useful insights that may help to elucidate the different host-pathogen interactions following EV71 and CA16 infection and offer novel therapeutic targets for these infections. [ABSTRACT FROM AUTHOR]

In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high dimensionality and unknown optimal instruments, we propose a two-stage regularization framework for identifying and estimating important covariate effects while selecting and estimating optimal instruments. The methodology extends the classical two-stage least squares estimator to high dimensions by exploiting sparsity using sparsity-inducing penalty functions in both stages. The resulting procedure is efficiently implemented by coordinate descent optimization. For the representativeL1regularization and a class of concave regularization methods, we establish estimation, prediction, and model selection properties of the two-stage regularized estimators in the high-dimensional setting where the dimensionality of covariates and instruments are both allowed to grow exponentially with the sample size. The practical performance of the proposed method is evaluated by simulation studies and its usefulness is illustrated by an analysis of mouse obesity data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression levels. It is shown analytically that the proposed approach can have more power to detect SNPs that are associated with the outcome through transcriptional regulation, compared to tests using the outcome and genotype data alone, and simulations show that our method is relatively robust to misspecification. We also provide a strategy for applying our approach to high-dimensional genomic data. We use this strategy to identify a potentially new association between a SNP and a yeast cell's response to the natural product tomatidine, which standard association analysis did not detect. [ABSTRACT FROM AUTHOR]

Diet influences health as a source of nutrients and toxins, and by shaping the composition of resident microbial populations. Previous studies have begun to map out associations between diet and the bacteria and viruses of the human gut microbiome. Here we investigate associations of diet with fungal and archaeal populations, taking advantage of samples from 98 well-characterized individuals. Diet was quantified using inventories scoring both long-term and recent diet, and archaea and fungi were characterized by deep sequencing of marker genes in DNA purified from stool. For fungi, we found 66 genera, with generally mutually exclusive presence of either the phyla Ascomycota or Basiodiomycota. For archaea, Methanobrevibacter was the most prevalent genus, present in 30% of samples. Several other archaeal genera were detected in lower abundance and frequency. Myriad associations were detected for fungi and archaea with diet, with each other, and with bacterial lineages. Methanobrevibacter and Candida were positively associated with diets high in carbohydrates, but negatively with diets high in amino acids, protein, and fatty acids. A previous study emphasized that bacterial population structure was associated primarily with long-term diet, but high Candida abundance was most strongly associated with the recent consumption of carbohydrates. Methobrevibacter abundance was associated with both long term and recent consumption of carbohydrates. These results confirm earlier targeted studies and provide a host of new associations to consider in modeling the effects of diet on the gut microbiome and human health. [ABSTRACT FROM AUTHOR]

Motivated by analysis of genetical genomics data, we introduce a sparse high-dimensional multivariate regression model for studying conditional independence relationships among a set of genes adjusting for possible genetic effects. The precision matrix in the model specifies a covariate-adjusted Gaussian graph, which presents the conditional dependence structure of gene expression after the confounding genetic effects on gene expression are taken into account. We present a covariate-adjusted precision matrix estimation method using a constrained ℓ1 minimization, which can be easily implemented by linear programming. Asymptotic convergence rates in various matrix norms and sign consistency are established for the estimators of the regression coefficients and the precision matrix, allowing both the number of genes and the number of the genetic variants to diverge. Simulation shows that the proposed method results in significant improvements in both precision matrix estimation and graphical structure selection when compared to the standard Gaussian graphical model assuming constant means. The proposed method is applied to yeast genetical genomics data for the identification of the gene network among a set of genes in the mitogen-activated protein kinase pathway. [ABSTRACT FROM PUBLISHER]

. Copy number variants (CNVs) are alternations of DNA of a genome that result in the cell having less or more than two copies of segments of the DNA. CNVs correspond to relatively large regions of the genome, ranging from about one kilobase to several megabases, that are deleted or duplicated. Motivated by CNV analysis based on next generation sequencing data, we consider the problem of detecting and identifying sparse short segments hidden in a long linear sequence of data with an unspecified noise distribution. We propose a computationally efficient method that provides a robust and near optimal solution for segment identification over a wide range of noise distributions. We theoretically quantify the conditions for detecting the segment signals and show that the method near optimally estimates the signal segments whenever it is possible to detect their existence. Simulation studies are carried out to demonstrate the efficiency of the method under various noise distributions. We present results from a CNV analysis of a HapMap Yoruban sample to illustrate the theory and the methods further. [ABSTRACT FROM AUTHOR]