HIMA2: high-dimensional mediation analysis and its application in epigenome-wide DNA methylation data.
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ABSTRACT:
SUBMITTER: Perera C
PROVIDER: S-EPMC9310002 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Epigenetic researchers often evaluate DNA methylation as a mediator between social/environmental exposures and disease, but modern statistical methods for jointly evaluating many mediators have not been widely adopted. We compare seven methods for high-dimensional mediation analysis with continuous outcomes through both diverse simulations and analysis of DNAm data from a large national cohort in the United States, while providing an R package for their implementation. Among the considered choices, the best-performing methods for detecting active mediators in simulations are the Bayesian sparse linear mixed model by Song et al. (2020) and high-dimensional mediation analysis by Gao et al. (2019); while the superior methods for estimating the global mediation effect are high-dimensional linear mediation analysis by Zhou et al. (2021) and principal component mediation analysis by Huang and Pan (2016). We provide guidelines for epigenetic researchers on choosing the best method in practice and offer suggestions for future methodological development.
| S-EPMC9949196 | biostudies-literature
Project description:Epigenetic researchers often evaluate DNA methylation as a potential mediator of the effect of social/environmental exposures on a health outcome. Modern statistical methods for jointly evaluating many mediators have not been widely adopted. We compare seven methods for high-dimensional mediation analysis with continuous outcomes through both diverse simulations and analysis of DNAm data from a large multi-ethnic cohort in the United States, while providing an R package for their seamless implementation and adoption. Among the considered choices, the best-performing methods for detecting active mediators in simulations are the Bayesian sparse linear mixed model (BSLMM) and high-dimensional mediation analysis (HDMA); while the preferred methods for estimating the global mediation effect are high-dimensional linear mediation analysis (HILMA) and principal component mediation analysis (PCMA). We provide guidelines for epigenetic researchers on choosing the best method in practice and offer suggestions for future methodological development.
| S-EPMC10655967 | biostudies-literature
Project description:Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector. In this work, we introduce a novel method for identifying potential mediators in this setting called the directions of mediation (DMs). DMs linearly combine potential mediators into a smaller number of orthogonal components, with components ranked based on the proportion of the LSEM likelihood each accounts for. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of single nucleotide polymorphisms (SNPs), or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.
| S-EPMC5862274 | biostudies-literature
Project description:DNA methylation is a widely studied epigenetic mechanism and alterations in methylation patterns may be involved in the development of common diseases. Unlike inherited changes in genetic sequence, variation in site-specific methylation varies by tissue, developmental stage, and disease status, and may be impacted by aging and exposure to environmental factors, such as diet or smoking. These non-genetic factors are typically included in epigenome-wide association studies (EWAS) because they may be confounding factors to the association between methylation and disease. However, missing values in these variables can lead to reduced sample size and decrease the statistical power of EWAS. We propose a site selection and multiple imputation (MI) method to impute missing covariate values and to perform association tests in EWAS. Then, we compare this method to an alternative projection-based method. Through simulations, we show that the MI-based method is slightly conservative, but provides consistent estimates for effect size. We also illustrate these methods with data from the Atherosclerosis Risk in Communities (ARIC) study to carry out an EWAS between methylation levels and smoking status, in which missing cell type compositions and white blood cell counts are imputed.
| S-EPMC4846117 | biostudies-literature
Project description:BackgroundExposure to heavy metals has been reported to be associated with multiple diseases. However, direct associations and potential mechanisms of heavy metals with physical disability remain unclear.ObjectivesWe aimed to quantify associations of heavy metals with physical disability and further explore the potential mechanisms of DNA methylation on the genome scale.MethodsA cross-sectional study of 4,391 older adults was conducted and activities of daily living (ADL) disability were identified using a 14-item scale questionnaire including basic and instrumental activities to assess the presence of disability (yes or no) rated on a scale of dependence. Odds ratios (ORs) and 95% confidence intervals (CI) were estimated to quantify associations between heavy metals and ADL disability prevalence using multivariate logistic regression and Bayesian kernel machine regression (BKMR) models. Whole blood-derived DNA methylation was measured using the HumanMethylationEPIC BeadChip array. An ADL disability-related epigenome-wide DNA methylation association study (EWAS) was performed among 212 sex-matched ADL disability cases and controls, and mediation analysis was further applied to explore potential mediators of DNA methylation.ResultsEach 1-standard deviation (SD) higher difference in log10-transformed manganese, copper, arsenic, and cadmium level was significantly associated with a 14% (95% CI: 1.05, 1.24), 16% (95% CI:1.07, 1.26), 22% (95% CI:1.13, 1.33), and 15% (95% CI:1.06, 1.26) higher odds of ADL disability, which remained significant in the multiple-metal and BKMR models. A total of 85 differential DNA methylation sites were identified to be associated with ADL disability prevalence, among which methylation level at cg220000984 and cg23012519 (annotated to IRGM and PKP3) mediated 31.0% and 31.2% of manganese-associated ADL disability prevalence, cg06723863 (annotated to ESRP2) mediated 32.4% of copper-associated ADL disability prevalence, cg24433124 (nearest to IER3) mediated 15.8% of arsenic-associated ADL disability prevalence, and cg07905190 and cg17485717 (annotated to FREM1 and TCP11L1) mediated 21.5% and 30.5% of cadmium-associated ADL disability prevalence (all p<0.05).DiscussionOur findings suggested that heavy metals contributed to higher prevalence of ADL disability and that locus-specific DNA methylation are partial mediators, providing potential biomarkers for further cellular mechanism studies. https://doi.org/10.1289/EHP10602.
| S-EPMC9423034 | biostudies-literature
Project description:PIWI-interacting RNAs (piRNAs) have long been associated with the silencing of transposable elements (TEs). However, over 20,000 unique species of piRNAs mapped to the human genome are more than the relatively few presumably required to regulate the known human transposon classes. Here, we present the results of the first genome-wide effort to study the effects of piRNAs on gene specific DNA methylation. We found that exon-derived piRNAs consist almost universally of species with 10 or fewer genomic copies, whereas piRNAs existing in high copies originate predominately from intronic and intergenic regions. Genome-wide methylation profiling following transfection of human somatic cells with piRNA mimics revealed methylation changes at numerous genic loci in single copy piRNA-transfected cells. Moreover, genomic regions directly adjacent to differentially methylated CpG sites were enriched for sequence matches to the transfected piRNAs. These findings suggest that a subset of single copy piRNAs may be able to induce DNA methylation at non-TE genic loci, a process that may be mediated in part by direct binding to either genomic DNA or nascent mRNA near target CpG sites.
| S-EPMC4615395 | biostudies-literature
Project description:BackgroundEpigenetic mechanisms, including methylation, can contribute to childhood asthma. Identifying DNA methylation profiles in asthmatic patients can inform disease pathogenesis.ObjectiveWe sought to identify differential DNA methylation in newborns and children related to childhood asthma.MethodsWithin the Pregnancy And Childhood Epigenetics consortium, we performed epigenome-wide meta-analyses of school-age asthma in relation to CpG methylation (Illumina450K) in blood measured either in newborns, in prospective analyses, or cross-sectionally in school-aged children. We also identified differentially methylated regions.ResultsIn newborns (8 cohorts, 668 cases), 9 CpGs (and 35 regions) were differentially methylated (epigenome-wide significance, false discovery rate < 0.05) in relation to asthma development. In a cross-sectional meta-analysis of asthma and methylation in children (9 cohorts, 631 cases), we identified 179 CpGs (false discovery rate < 0.05) and 36 differentially methylated regions. In replication studies of methylation in other tissues, most of the 179 CpGs discovered in blood replicated, despite smaller sample sizes, in studies of nasal respiratory epithelium or eosinophils. Pathway analyses highlighted enrichment for asthma-relevant immune processes and overlap in pathways enriched both in newborns and children. Gene expression correlated with methylation at most loci. Functional annotation supports a regulatory effect on gene expression at many asthma-associated CpGs. Several implicated genes are targets for approved or experimental drugs, including IL5RA and KCNH2.ConclusionNovel loci differentially methylated in newborns represent potential biomarkers of risk of asthma by school age. Cross-sectional associations in children can reflect both risk for and effects of disease. Asthma-related differential methylation in blood in children was substantially replicated in eosinophils and respiratory epithelium.
| S-EPMC6556405 | biostudies-literature
Project description:Epigenetic changes, including CpG island hypermethylation, occur frequently in bladder cancer (BC) and may be exploited for BC detection and distinction between high-grade (HG) and low-grade (LG) disease. Genome-wide methylation analysis was performed using Agilent Human CpG Island Microarrays to determine epigenetic differences between LG and HG cases. Pathway enrichment analysis and functional annotation determined that the most frequently methylated pathways in HG BC were enriched for anterior/posterior pattern specification, embryonic skeletal system development, neuron fate commitment, DNA binding, and transcription factor activity. We identified 990 probes comprising a 32-gene panel that completely distinguished LG from HG based on methylation. Selected genes from this panel, EOMES, GP5, PAX6, TCF4, and ZSCAN12, were selected for quantitative polymerase chain reaction-based validation by MethyLight in an independent series (n=84) of normal bladder samples and LG and HG cases. GP5 and ZSCAN12, two novel methylated genes in BC, were significantly hypermethylated in HG versus LG BC (P?.03). We validated our data in a second independent cohort of LG and HG BC cases (n=42) from The Cancer Genome Atlas (TCGA). Probes representing our 32-gene panel were significantly differentially methylated in LG versus HG tumors (P?.04). These results indicate the ability to distinguish normal tissue from cancer, as well as LG from HG, based on methylation and reveal important pathways dysregulated in HG BC. Our findings were corroborated using publicly available data sets from TCGA. Ultimately, the creation of a methylation panel, including GP5 and ZSCAN12, able to distinguish between disease phenotypes will improve disease management and patient outcomes.
| S-EPMC5293735 | biostudies-literature
Project description:MotivationMediation analysis has become a prevalent method to identify causal pathway(s) between an independent variable and a dependent variable through intermediate variable(s). However, little work has been done when the intermediate variables (mediators) are high-dimensional and the outcome is a survival endpoint. In this paper, we introduce a novel method to identify potential mediators in a causal framework of high-dimensional Cox regression.ResultsWe first reduce the data dimension through a mediation-based sure independence screening method. A de-biased Lasso inference procedure is used for Cox's regression parameters. We adopt a multiple-testing procedure to accurately control the false discovery rate when testing high-dimensional mediation hypotheses. Simulation studies are conducted to demonstrate the performance of our method. We apply this approach to explore the mediation mechanisms of 379 330 DNA methylation markers between smoking and overall survival among lung cancer patients in The Cancer Genome Atlas lung cancer cohort. Two methylation sites (cg08108679 and cg26478297) are identified as potential mediating epigenetic markers.Availability and implementationOur proposed method is available with the R package HIMA at https://cran.r-project.org/web/packages/HIMA/.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8570823 | biostudies-literature
Project description:Psoriasis is a chronic inflammatory skin disease characterized by epidermal hyperproliferation and altered keratinocyte differentiation and inflammation and is caused by the interplay of genetic and environmental factors. Previous studies have revealed that DNA methylation (DNAm) and genetic makers are closely associated with psoriasis, and strong evidences have shown that DNAm can be controlled by genetic factors, which attracted us to evaluate the relationship among DNAm, genetic makers, and disease status.We utilized the genome-wide methylation data of psoriatic skin (PP, N?=?114) and unaffected control skin (NN, N?=?62) tissue samples in our previous study, and we performed whole-genome genotyping with peripheral blood of the same samples to evaluate the underlying genetic effect on skin DNA methylation. Causal inference test (CIT) was used to assess whether DNAm regulate genetic variation and gain a better understanding of the epigenetic basis of psoriasis susceptibility.We identified 129 SNP-CpG pairs achieving the significant association threshold, which constituted 28 unique methylation quantitative trait loci (MethQTL) and 34 unique CpGs. There are 18 SNPs were associated with psoriasis at a Bonferoni-corrected P?<?0.05, and these 18 SNPs formed 93 SNP-CpG pairs with 17 unique CpG sites. We found that 11 of 93 SNP-CpG pairs, composed of 5 unique SNPs and 3 CpG sites, presented a methylation-mediated relationship between SNPs and psoriasis. The 3 CpG sites were located on the body of C1orf106, the TSS1500 promoter region of DMBX1 and the body of SIK3.This study revealed that DNAm of some genes can be controlled by genetic factors and also mediate risk variation for psoriasis in Chinese Han population and provided novel molecular insights into the pathogenesis of psoriasis.
| S-EPMC5139011 | biostudies-literature