Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease.

ABSTRACT: We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P?

SUBMITTER: Richardson TG

PROVIDER: S-EPMC6121186 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease.

Richardson Tom G TG Haycock Philip C PC Zheng Jie J Timpson Nicholas J NJ Gaunt Tom R TR Davey Smith George G Relton Caroline L CL Hemani Gibran G

Human molecular genetics 20180901 18

We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P < 1.39 × 10-08). Joint likelihood mapping provided evidence that the genet ...[more]

PMID: 29893838

Similar Datasets

A robust and efficient method for Mendelian randomization with hundreds of genetic variants.

Project description:Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation.

| S-EPMC6969055 | biostudies-literature

Mendelian Randomization Analysis Identifies CpG Sites as Putative Mediators for Genetic Influences on Cardiovascular Disease Risk.

Project description:The extent to which genetic influences on cardiovascular disease risk are mediated by changes in DNA methylation levels has not been systematically explored. We developed an analytical framework that integrates genetic fine mapping and Mendelian randomization with epigenome-wide association studies to evaluate the causal relationships between methylation levels and 14 cardiovascular disease traits. We identified ten genetic loci known to influence proximal DNA methylation which were also associated with cardiovascular traits after multiple-testing correction. Bivariate fine mapping provided evidence that the individual variants responsible for the observed effects on cardiovascular traits at the ADCY3 and ADIPOQ loci were potentially mediated through changes in DNA methylation, although we highlight that we are unable to reliably separate causality from horizontal pleiotropy. Estimates of causal effects were replicated with results from large-scale consortia. Genetic variants and CpG sites identified in this study were enriched for histone mark peaks in relevant tissue types and gene promoter regions. Integrating our results with expression quantitative trait loci data, we provide evidence that variation at these regulatory regions is likely to also influence gene expression levels at these loci.

| S-EPMC5630190 | biostudies-literature

Mendelian randomization analysis with multiple genetic variants using summarized data.

Project description:Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene-gene interactions, linkage disequilibrium, and 'weak instruments' on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene-gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than 1×10??, then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.

| S-EPMC4377079 | biostudies-other

Mendelian Randomization Identifies CpG Methylation Sites With Mediation Effects for Genetic Influences on BMD in Peripheral Blood Monocytes.

Project description:Osteoporosis is mainly characterized by low bone mineral density (BMD) and is an increasingly serious public health concern. DNA methylation is a major epigenetic mechanism that may contribute to the variation in BMD and may mediate the effects of genetic and environmental factors of osteoporosis. In this study, we performed an epigenome-wide DNA methylation analysis in peripheral blood monocytes of 118 Caucasian women with extreme BMD values. Further, we developed and implemented a novel analytical framework that integrates Mendelian randomization with genetic fine mapping and colocalization to evaluate the causal relationships between DNA methylation and BMD phenotype. We identified 2,188 differentially methylated CpGs (DMCs) between the low and high BMD groups and distinguished 30 DMCs that may mediate the genetic effects on BMD. The causal relationship was further confirmed by eliminating the possibility of horizontal pleiotropy, linkage effect and reverse causality. The fine-mapping analysis determined 25 causal variants that are most likely to affect the methylation levels at these mediator DMCs. The majority of the causal methylation quantitative loci and DMCs reside within cell type-specific histone mark peaks, enhancers, promoters, promoter flanking regions and CTCF binding sites, supporting the regulatory potentials of these loci. The established causal pathways from genetic variant to BMD phenotype mediated by DNA methylation provide a gene list to aid in designing future functional studies and lead to a better understanding of the genetic and epigenetic mechanisms underlying the variation of BMD.

| S-EPMC7059767 | biostudies-literature

Factorial Mendelian randomization: using genetic variants to assess interactions.

Project description:BackgroundFactorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 × 2 approach, using dichotomized genetic scores to divide the population into four subgroups as in a factorial randomized trial.MethodsWe describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure.ResultsSimulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between 4- and 10-fold improvement in efficiency is demonstrated over the 2 × 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal-sized groups.ConclusionsPrevious factorial Mendelian randomization analyses may have been underpowered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.

| S-EPMC7750987 | biostudies-literature

Simultaneous Methylation-Level Assessment of Hundreds of CpG Sites by Targeted Bisulfite PCR Sequencing (TBPseq).

Project description:Methylated-DNA sequencing technologies are producing vast amounts of methylome data from cancer samples, from which cancer-associated differentially methylated CpG sites (cDMCs) are continuously identified and filed. The inclusion of as many cDMCs as possible helps improve the accuracy of cancer diagnosis and sometimes identify cancer subtypes. However, the lack of an established method for the analysis of 100s of cDMCs practically impedes their robust use in clinical medicine. Here, we tested the availability of targeted bisulfite-PCR-sequencing (TBPseq) technology for the assessment of methylation levels of a myriad of CpGs scattered over the genome. In randomly selected 46 cancer cell lines, multiplexed PCR yielded a variety of amplicons harboring 246 CpGs residing at promoters of 97 cancer-associated genes, all of which were sequenced in the same flow cell. Clustering analysis of the TBPseq-assessed methylation levels of target CpGs showed that the lung and liver cancer cell lines correlated relatively strongly with each other while they weakly correlated with colon cancer cells. CpGs at the LIFR gene promoter, which are known to be hypermethylated in colon cancers, indeed were heavily methylated in the tested colon cancer cells. Moreover, the LIFR promoter hypermethylation was found in colon cancer cells only, but not in biliary tract, liver, lung, and stomach cancers cell lines. A meta-analysis with public cancer methylome data verified the colon cancer specificity of LIFR promoter methylation. These results demonstrate that our TBPseq-based methylation assessment could be considered an effective, accurate, and competitive method to simultaneously examine a large number of target cDMCs and patient samples.

| S-EPMC5507944 | biostudies-literature

A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders

Project description:This SuperSeries is composed of the SubSeries listed below.

2024-08-06 | GSE254090 | GEO

A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis.

Project description:Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.

| S-EPMC7606598 | biostudies-literature

Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework.

Project description:In Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying alternative pathways to the traits under investigation. Here, we develop MR-TRYX, a framework that exploits horizontal pleiotropy to discover putative risk factors for disease. We begin by detecting outliers in a single exposure-outcome MR analysis, hypothesising they are due to horizontal pleiotropy. We search across hundreds of complete GWAS summary datasets to systematically identify other (candidate) traits that associate with the outliers. We develop a multi-trait pleiotropy model of the heterogeneity in the exposure-outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways are uncovered with already established causal effects, validating the approach, but also alternative putative causal pathways. Adjustment for pleiotropic pathways reduces the heterogeneity across the analyses.

| S-EPMC7035387 | biostudies-literature

Mendelian randomization identifies FLCN expression as a mediator of diabetic retinopathy

Project description:The goal of the study was to identify genes whose aberrant expression can contribute to diabetic retinopathy. We determined differential response in gene expression to high glucose in lymphoblastoid cell lines derived from matched type 1 diabetic individuals with and without retinopathy. Those genes exhibiting the largest difference in glucose response between diabetic subjects with and without retinopathy were assessed for association to diabetic retinopathy utilizing genotype data from a meta-genome-wide association study. All genetic variants associated with gene expression (expression QTLs; eQTLs) of the glucose response genes were tested for association with diabetic retinopathy. We detected an enrichment of the glucose response gene eQTLs among small association p-values for diabetic retinopathy. Among these, we identified FLCN as a susceptibility gene for diabetic retinopathy. Expression of FLCN in response to glucose is greater in individuals with diabetic retinopathy compared to diabetic individuals without retinopathy. Three large, independent cohorts of diabetic individuals revealed an enhanced association of FLCN eQTL to diabetic retinopathy. Mendelian randomization confirmed a direct positive effect of increased FLCN expression on retinopathy in diabetic individuals. Together, our studies integrating genetic association and gene expression implicate FLCN as a disease gene in diabetic retinopathy.

2020-03-10 | GSE146615 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data