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Background The increasingly available multi-omics datasets have posed both new opportunities and challenges to the development of quantitative methods for discovering novel mechanisms in biomedical research. One natural approach to analyzing such datasets is mediation analysis originated from the causal inference literature. Mediation analysis can help unravel the mechanisms through which exposure(s) exert the effect on outcome(s). However, existing methods fail to consider the case where (1) both exposures and mediators are potentially high-dimensional and (2) it is very likely that some important confounding variables are unmeasured or latent; both issues are quite common in practice. To the best of our knowledge, however, no methods have been developed to address these challenges with statistical guarantees. Methods In this article, we propose a new method for HIgh-dimensional LAtent-confounding Mediation Analysis (HILAMA) that considers both high-dimensional exposures and mediators, as well as the possible existence of latent confounding variables. HILAMA employs the Decorrelating & Debiasing method to estimate the individual effects of exposures and mediators on the outcome. A column-wise regression strategy with parallel computing is considered to efficiently estimate the exposure-mediator effect matrix. HILAMA then applies the MinScreen procedure to eliminate non-significant pairs, and the Joint-Significance Testing (JST) method to compute p -values for the retained pairs, controlling the False Discovery Rate (FDR) using the Benjamini-Hochberg (BH) procedure. Results The proposed method is evaluated through extensive simulation experiments, demonstrating its improved stability in FDR control and superior power in finite sample size compared to existing competitive methods. Furthermore, our method is applied to the proteomics-radiomics data from ADNI, identifying some key proteins and brain regions related to Alzheimer’s disease. These empirical results demonstrate that HILAMA can effectively control FDR and provide valid statistical inference for high dimensional mediation analysis with latent confounding variables under certain assumptions. Conclusions HILAMA can effectively control FDR and provide valid statistical inference for high dimensional mediation analysis with latent confounding variables under certain assumptions.

Background DNA methylation (DNAm) is considered a plausible pathway through which genetic and environmental factors may influence the development of allergies. However, causality has yet to be determined as it is unknown whether DNAm is rather a cause or consequence of allergic sensitization. Here, we investigated the direction of the observed associations between well-known environmental and genetic determinants of allergy, DNAm, and aeroallergen sensitization using a combination of high-dimensional and causal mediation analyses. Methods Using prospectively collected data from the German LISA birth cohort from two time windows (6–10 years: N  = 234; 10–15 years: N  = 167), we tested whether DNAm is a cause or a consequence of aeroallergen sensitization (specific immunoglobulin E > 0.35kU/l) by conducting mediation analyses for both effect directions using maternal smoking during pregnancy, family history of allergies, and a polygenic risk score (PRS) for any allergic disease as exposure variables. We evaluated individual CpG sites (EPIC BeadChip) and allergy-related methylation risk scores (MRS) as potential mediators in the mediation analyses. We applied three high-dimensional mediation approaches (HIMA, DACT, gHMA) and validated results using causal mediation analyses. A replication of results was attempted in the Swedish BAMSE cohort. Results Using high-dimensional methods, we identified five CpGs as mediators of prenatal exposures to sensitization with significant (adjusted p  < 0.05) indirect effects in the causal mediation analysis (maternal smoking: two CpGs, family history: one, PRS: two). None of these CpGs could be replicated in BAMSE. The effect of family history on allergy-related MRS was significantly mediated by aeroallergen sensitization (proportions mediated: 33.7–49.6%), suggesting changes in DNAm occurred post-sensitization. Conclusion The results indicate that DNAm may be a cause or consequence of aeroallergen sensitization depending on genomic location. Allergy-related MRS, identified as a potential cause of sensitization, can be considered as a cross-sectional biomarker of disease. Differential DNAm in individual CpGs, identified as mediators of the development of sensitization, could be used as clinical predictors of disease development.

Background Alzheimer’s disease (AD) is a complex disorder that affects multiple biological systems including cognition, behavior and physical health. Unfortunately, the pathogenic mechanisms behind AD are not yet clear and the treatment options are still limited. Despite the increasing number of studies examining the pairwise relationships between genetic factors, physical activity (PA), and AD, few have successfully integrated all three domains of data, which may help reveal mechanisms and impact of these genomic and phenomic factors on AD. We use high-dimensional mediation analysis as an integrative framework to study the relationships among genetic factors, PA and AD-like brain atrophy quantified by spatial patterns of brain atrophy. Results We integrate data from genetics, PA and neuroimaging measures collected from 13,425 UK Biobank samples to unveil the complex relationship among genetic risk factors, behavior and brain signatures in the contexts of aging and AD. Specifically, we used a composite imaging marker, Spatial Pattern of Abnormality for Recognition of Early AD (SPARE-AD) that characterizes AD-like brain atrophy, as an outcome variable to represent AD risk. Through GWAS, we identified single nucleotide polymorphisms (SNPs) that are significantly associated with SPARE-AD as exposure variables. We employed conventional summary statistics and functional principal component analysis to extract patterns of PA as mediators. After constructing these variables, we utilized a high-dimensional mediation analysis method, Bayesian Mediation Analysis (BAMA), to estimate potential mediating pathways between SNPs, multivariate PA signatures and SPARE-AD. BAMA incorporates Bayesian continuous shrinkage prior to select the active mediators from a large pool of candidates. We identified a total of 22 mediation pathways, indicating how genetic variants can influence SPARE-AD by altering physical activity. By comparing the results with those obtained using univariate mediation analysis, we demonstrate the advantages of high-dimensional mediation analysis methods over univariate mediation analysis. Conclusion Through integrative analysis of multi-omics data, we identified several mediation pathways of physical activity between genetic factors and SPARE-AD. These findings contribute to a better understanding of the pathogenic mechanisms of AD. Moreover, our research demonstrates the potential of the high-dimensional mediation analysis method in revealing the mechanisms of disease.

Causal mediation modeling has become a popular approach for studying the effect of an exposure on an outcome through a mediator. However, current methods are not applicable to the setting with a large number of mediators. We propose a testing procedure for mediation effects of high dimensional continuous mediators. We characterize the marginal mediation effect, the multivariate component-wise mediation effects, and the L2 norm of the component-wise effects, and develop a Monte-Carlo procedure for evaluating their statistical significance. To accommodate the setting with a large number of mediators and a small sample size, we further propose a transformation model using the spectral decomposition. Under the transformation model, mediation effects can be estimated using a series of regression models with a univariate transformed mediator, and examined by our proposed testing procedure. Extensive simulation studies are conducted to assess the performance of our methods for continuous and dichotomous outcomes. We apply the methods to analyze genomic data investigating the effect of microRNA miR-223 on a dichotomous survival status of patients with glioblastoma multiforme (GBM). We identify nine gene ontology sets with expression values that significantly mediate the effect of miR-223 on GBM survival.

Complex biological features such as the human microbiome and gene expressions play a crucial role in human health by mediating various biomedical processes that influence disease progression, such as immune responses and metabolic processes. Understanding these mediation roles is essential for gaining insights into disease pathogenesis and improving treatment outcomes. However, analyzing such high-dimensional mediation features presents challenges due to their inherent structural and correlations, such as the hierarchical taxonomic structures in microbial operational taxonomic units (OTUs), gene–pathway relationships, and the high dimensionality of the datasets, which complicates mediation analysis. We propose the Med-CNN model, an iterative approach using Convolutional Neural Networks (CNNs) to incorporate the complex biological network of the mediation features. The output values from network-specific CNN models are condensed into an integrative mediation metric (IMM), which captures essential biological information for estimating mediation effects. Our approach is designed to handle high-dimensional data and accommodate their unique structures and non-linear interactive mediation effects. Through comprehensive simulation studies, we evaluated the performance of our algorithm across different scenarios, including various mediation effects, effect sizes, and sample sizes, and we compared it to conventional methods. Our simulations demonstrated consistently lower biases in mediation effect estimates, with values ranging from 0.17 to 0.56, which were lower than other established methods ranging from 0.24 to 13.27. In a real data application, our method identified a mediation effect of 0.06 between ethnicity and vaginal pH levels.

Background Epigenetic alterations during fetal development have been proposed as key factors explaining associations between maternal lifestyle during pregnancy and later health outcomes in the offspring, pertaining to the developmental origin of health and disease hypothesis. Objectives To assess the association of maternal lifestyle with offsprings’ birth weight and underlying epigenetic mediatory mechanisms in the NorthPop prospective birth cohort. Methods A three-step analytic pipeline was applied. In 722 mother–child pairs, overall associations between ten maternal lifestyle factors and the offspring’s standardized birth weight were first evaluated by multiple linear regression. Three high-dimensional mediation methods, based on sure independence screening and penalized regression, were then applied on the beta methylation matrix to identify candidate CpG mediators in cord blood driving the significant overall associations. Finally, robust and ordinary least squares (OLS) regression-based classical mediation methods were used with candidate CpG probes to assess single- and multiple (parallel and serial)-mediator models on a low-dimensional space. Results Gestational weight gain (GWG) (β-adj = 0.03; p  = 2 × 10 –5 ) and maternal BMI at the beginning of pregnancy (β-adj = 0.036; p  = 1 × 10 –4 ) were significantly associated with the offspring’s standardized birth weight. High-dimensional mediation analyses identified pooled sets of four (cg19242268 [ TCEA2 ]; cg08461903 [N/A]; cg14798382 [ CHERP/C19orf44 ] and cg21516291 [ SLC35C2 ]) and five (cg17040807 [ CYGB ]; cg19242268 [ TCEA2 ]; cg26552621 [ CIRBP ]; cg04457572 [ CDH23 ] and cg06457011 [ PLCG1 ]) candidate CpG mediators related to GWG and BMI at the beginning of pregnancy, respectively. For both exposures, classical mediation analyses revealed a range of significant single- and multiple (both serial and parallel)-mediator models via both robust and OLS regression based approaches. These indicated the likely presence of individual, causally linked multiple, and causally independent multiple mediatory pathways underlying the two significant overall associations. Conclusions Our findings support the hypothesis that neonatal health effects related to maternal lifestyle may be partly mediated by epigenetic alterations. Findings also suggest the possible involvement of multiple DNA methylation sites via various mediatory pathways.

Background Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it’s an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation. Methods In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing. Results Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene. Conclusions The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

Background Environmental exposures can regulate intermediate molecular phenotypes, such as gene expression, by different mechanisms and thereby lead to various health outcomes. It is of significant scientific interest to unravel the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposure and traits. Mediation analysis is an important tool for investigating such relationships. However, it has mainly focused on low-dimensional settings, and there is a lack of a good measure of the total mediation effect. Here, we extend an R-squared (R 2 ) effect size measure, originally proposed in the single-mediator setting, to the moderate- and high-dimensional mediator settings in the mixed model framework. Results Based on extensive simulations, we compare our measure and estimation procedure with several frequently used mediation measures, including product, proportion, and ratio measures. Our R 2 -based second-moment measure has small bias and variance under the correctly specified model. To mitigate potential bias induced by non-mediators, we examine two variable selection procedures, i.e., iterative sure independence screening and false discovery rate control, to exclude the non-mediators. We establish the consistency of the proposed estimation procedures and introduce a resampling-based confidence interval. By applying the proposed estimation procedure, we found that 38% of the age-related variations in systolic blood pressure can be explained by gene expression profiles in the Framingham Heart Study of 1711 individuals. An R package “RsqMed” is available on CRAN. Conclusion R-squared (R 2 ) is an effective and efficient measure for total mediation effect especially under high-dimensional setting.

Purpose of Review Epigenetic changes can be highly influenced by environmental factors and have in turn been proposed to influence chronic disease. Being able to quantify to which extent epigenomic processes are mediators of the association between environmental exposures and diseases is of interest for epidemiologic research. In this review, we summarize the proposed mediation analysis methods with applications to epigenomic data. Recent Findings The ultra-high dimensionality and high correlations that characterize omics data have hindered the precise quantification of mediated effects. Several methods have been proposed to deal with mediation in high-dimensional settings, including methods that incorporate dimensionality reduction techniques to the mediation algorithm. Summary Although important methodological advances have been conducted in the previous years, key challenges such as the development of sensitivity analyses, dealing with mediator-mediator interactions, including environmental mixtures as exposures, or the integration of different omic data should be the focus of future methodological developments for epigenomic mediation analysis.

High-dimensional mediation analysis plays an important role in recent biomedical research as a large number of mediators, such as microbiome, could modulate the effect of exposure to the outcome of interest. Most of the current studies focus on modelling independent mediators, but these methods do not consider the non-linear interactive effect between the mediators. Furthermore, it can be challenging to identify features with mediation effects from the high-dimensional mediator space. We proposed an innovative non-parametric approach to build causal mediation trees (CMT) to select important mediators and assess their non-linear interactive mediation effects on the outcome of the study. The data is recursively partitioned into subpopulations constructed by the mediators with the largest mediation effect. We aim to incorporate these non-linear interactions into the mediation framework using this approach and evaluate the total causal effect. Simulation studies were conducted to assess the performance of the CMT algorithm under different scenarios of interactive mediation effects. We applied the method to analyze vaginal microbiome sequencing data from the reproductive-age women’s study. We investigated the causal relationship between ethnic groups and the vaginal pH levels mediated by the vaginal microbiome. We identified three important microbial taxa with strong mediation effects and estimated the total effect of the mediation tree model.