scholarly journals Prioritizing putative influential genes in early life cardiovascular disease susceptibility by applying tissue-specific Mendelian randomization

2018 ◽  
Author(s):  
Kurt Taylor ◽  
George Davey Smith ◽  
Caroline L Relton ◽  
Tom R Gaunt ◽  
Tom G Richardson
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Analytical Framework ◽  
Specific Gene ◽  
Multiple Trait ◽  
Trait Variation ◽  
Tissue Specific ◽  
Insight Into

AbstractBackgroundThe extent to which changes in gene expression can influence cardiovascular disease risk across different tissue types has not yet been systematically explored. We have developed an analytical framework that integrates tissue-specific gene expression, Mendelian randomization and multiple-trait colocalization to develop functional mechanistic insight into the causal pathway from genetic variant to complex trait.MethodsWe undertook a transcriptome-wide association study in a population of young individuals to uncover genetic variants associated with both nearby gene expression and cardiovascular traits. Two-sample Mendelian randomization was then applied using large-scale datasets to investigate whether changes in gene expression within certain tissue types may influence cardiovascular trait variation. We subsequently performed Bayesian multiple-trait colocalization to further interrogate findings and also gain insight into whether DNA methylation, as well as gene expression, may play a role in disease susceptibility.ResultsEight genetic loci were associated with changes in gene expression and early life measures of cardiovascular function. Our Mendelian randomization analysis provided evidence of tissue-specific effects at multiple loci, of which the effects at theADCY3andFADS1loci for body mass index and cholesterol respectively were particularly insightful. Multiple trait colocalization uncovered evidence which suggested that changes in DNA methylation at the promoter region upstream ofFADS1/TMEM258may also play a role in cardiovascular trait variation along with gene expression. Furthermore, colocalization analyses were able to uncover evidence of tissue-specificity, most prominantly betweenSORT1expression in liver tissue and cholesterol levels.ConclusionsDisease susceptibility can be influenced by differential changes in tissue-specific gene expression and DNA methylation. Our analytical framework should prove valuable in elucidating mechanisms in disease, as well as helping prioritize putative causal genes at associated loci where multiple nearby genes may be co-regulated. Future studies which continue to uncover quantitative trait loci for molecular traits across various tissue and cell typse will further improve our capability to understand and prevent disease.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Tom G. Richardson ◽  
Gibran Hemani ◽  
Tom R. Gaunt ◽  
Caroline L. Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Regulatory Mechanisms ◽  
Tissue Specific ◽  
Causal Pathways ◽  
Influence Gene Expression ◽  
Insight Into ◽  
Gene Expression Levels

AbstractDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. In this study, we apply the principles of Mendelian randomization to systematically evaluate transcriptome-wide associations between gene expression (across 48 different tissue types) and 395 complex traits. Our findings indicate that variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. Moreover, detailed investigations of our results highlight tissue-specific associations, drug validation opportunities, insight into the likely causal pathways for trait-associated variants and also implicate putative associations at loci yet to be implicated in disease susceptibility. Similar evaluations can be conducted at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

scholarly journals Integrating Mendelian randomization and multiple-trait colocalization to uncover cell-specific inflammatory drivers of autoimmune and atopic disease

2019 ◽  
Vol 28 (19) ◽  
pp. 3293-3300 ◽  
Author(s):  
Lucy M McGowan ◽  
George Davey Smith ◽  
Tom R Gaunt ◽  
Tom G Richardson
Keyword(s):  
Gene Expression ◽  
Mendelian Randomization ◽  
Inflammatory Responses ◽  
Cell Types ◽  
Atopic Disease ◽  
Analytical Framework ◽  
Causal Variant ◽  
Human Populations ◽  
Multiple Trait ◽  
Multiple Cell

Abstract Immune-mediated diseases (IMDs) arise when tolerance is lost and chronic inflammation is targeted towards healthy tissues. Despite their growing prevalence, therapies to treat IMDs are lacking. Cytokines and their receptors orchestrate inflammatory responses by regulating elaborate signalling networks across multiple cell types making it challenging to pinpoint therapeutically relevant drivers of IMDs. We developed an analytical framework that integrates Mendelian randomization (MR) and multiple-trait colocalization (moloc) analyses to highlight putative cell-specific drivers of IMDs. MR evaluated causal associations between the levels of 10 circulating cytokines and 9 IMDs within human populations. Subsequently, we undertook moloc analyses to assess whether IMD trait, cytokine protein and corresponding gene expression are driven by a shared causal variant. Moreover, we leveraged gene expression data from three separate cell types (monocytes, neutrophils and T cells) to discern whether associations may be attributed to cell type-specific drivers of disease. MR analyses supported a causal role for IL-18 in inflammatory bowel disease (IBD) (P = 1.17 × 10−4) and eczema/dermatitis (P = 2.81 × 10−3), as well as associations between IL-2rα and IL-6R with several other IMDs. Moloc strengthened evidence of a causal association for these results, as well as providing evidence of a monocyte and neutrophil-driven role for IL-18 in IBD pathogenesis. In contrast, IL-2rα and IL-6R associations were found to be T cell specific. Our analytical pipeline can help to elucidate putative molecular pathways in the pathogeneses of IMDs, which could be applied to other disease contexts.

scholarly journals Gene-Specific Methylation Profiles for Integrative Methylation-Expression Analysis in Cancer Research

2019 ◽  
Author(s):  
Yusha Liu ◽  
Keith A. Baggerly ◽  
Elias Orouji ◽  
Ganiraju Manyam ◽  
Huiqin Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Penalized Regression ◽  
Tissue Type ◽  
Specific Gene ◽  
Integrative Genomics ◽  
Tissue Specific ◽  
Cancer Subtypes ◽  
Shiny App ◽  
Gene Level

AbstractDNA methylation is a key epigenetic factor regulating gene expression. While promoter-associated methylation has been extensively studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to construct gene-level methylation summaries that account for the potentially complex relationships between methylation and expression. We introduce a novel sequential penalized regression approach to construct gene-specific methylation profiles (GSMPs) which find for each gene and tissue type a sparse set of CpGs best explaining gene expression and weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than standard approaches and produces gene-level scores showing key methylation differences across recently discovered colorectal cancer subtypes. We share an R Shiny app that presents GSMP results for colorectal, breast, and pancreatic cancer with plans to extend it to all TCGA cancer types. Our approach yields tissue-specific, gene-specific sparse lists of functionally important CpGs that can be used to construct gene-level methylation scores that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation.

scholarly journals CG methylation covaries with differential gene expression between leaf and floral bud tissues of Brachypodium distachyon

2015 ◽  
Author(s):  
Kyria Roessler ◽  
Shohei Takuno ◽  
Brandon Gaut
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Sequence Data ◽  
Brachypodium Distachyon ◽  
Grass Species ◽  
Specific Gene ◽  
Specific Expression ◽  
Tissue Specific ◽  
Model Grass ◽  
Floral Bud

DNA methylation has the potential to influence plant growth and development through its influence on gene expression. To date, however, the evidence from plant systems is mixed as to whether patterns of DNA methylation vary significantly among tissues and, if so, whether these differences affect tissue-specific gene expression. To address these questions, we analyzed both bisulfite sequence (BSseq) and transcriptomic sequence data from three biological replicates of two tissues (leaf and floral bud) from the model grass species Brachypodium distachyon. Our first goal was to determine whether tissues were more differentiated in DNA methylation than explained by variation among biological replicates. Tissues were more differentiated than biological replicates, but the analysis of replicated data revealed high (>50%) false positive rates for the inference of differentially methylated sites (DMSs) and differentially methylated regions (DMRs). Comparing methylation to gene expression, we found that differential CG methylation consistently covaried negatively with gene expression, regardless as to whether methylation was within genes, within their promoters or even within their closest transposable element. The relationship between gene expression and either CHG or CHH methylation was less consistent. In total, CG methylation in promoters explained 9% of the variation in tissue-specific expression across genes, suggesting that CG methylation is a minor but appreciable factor in tissue differentiation.

scholarly journals DNA Methylation Regulates Placental Lactogen I Gene Expression

Endocrinology ◽  
2001 ◽  
Vol 142 (8) ◽  
pp. 3389-3396 ◽  
Author(s):  
Jae-Hyeon Cho ◽  
Hiromichi Kimura ◽  
Tatsuya Minami ◽  
Jun Ohgane ◽  
Naka Hattori ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Promoter Region ◽  
Trichostatin A ◽  
Methylation Status ◽  
Placental Lactogen ◽  
Specific Gene ◽  
Tissue Specific ◽  
Reporter Activity ◽  
I Gene

Abstract Expression of rat placental lactogen I is specific to the placenta and never expressed in other tissues. To obtain insight into the mechanism of tissue-specific gene expression, we investigated the methylation status in 3.4 kb of the 5′-flanking region of the rat placental lactogen I gene. We found that the distal promoter region of the rat placental lactogen I gene had more potent promoter activity than that of the proximal area alone, which contains several possible cis-elements. Although there are only 17 CpGs in the promoter region, in vitro methylation of the reporter constructs caused severe suppression of reporter activity, and CpG sites in the placenta were more hypomethylated than other tissues. Coexpression of methyl-CpG-binding protein with reporter constructs elicited further suppression of the reporter activity, whereas treatment with trichostatin A, an inhibitor of histone deacetylase, reversed the suppression caused by methylation. Furthermore, treatment of rat placental lactogen I nonexpressing BRL cells with 5-aza-2′-deoxycytidine, an inhibitor of DNA methylation, or trichostatin A resulted in the de novo expression of rat placental lactogen I. These results provide evidence that change in DNA methylation is the fundamental mechanism regulating the tissue-specific expression of the rat placental lactogen I gene.

scholarly journals Mapping eQTL by leveraging multiple tissues and DNA methylation

2016 ◽  
Author(s):  
Chaitanya R. Acharya ◽  
Kouros Owzar ◽  
Andrew S. Allen
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Specific Gene ◽  
Model Parameters ◽  
Eqtl Mapping ◽  
Joint Tissue ◽  
Tissue Specific ◽  
Cpg Sites ◽  
Micro Rnas ◽  
Mapping Techniques

AbstractBackgroundDNA methylation is an important tissue-specific epigenetic event that influences transcriptional regulation of gene expression. Differentially methylated CpG sites may act as mediators between genetic variation and gene expression, and this relationship can be exploited while mapping multi-tissue expression quantitative trait loci (eQTL). Current multi-tissue eQTL mapping techniques are limited to only exploiting gene expression patterns across multiple tissues either in a joint tissue or tissue-by-tissue frameworks. We present a new statistical approach that enables us to model the effect of germ-line variation on tissue-specific gene expression in the presence of effects due to DNA methylation.ResultsOur method efficiently models genetic and epigenetic variation to identify genomic regions of interest containing combinations of mRNA transcripts, CpG sites, and SNPs by jointly testing for genotypic effect and higher order interaction effects between genotype, methylation and tissues. We demonstrate using Monte Carlo simulations that our approach, in the presence of both genetic and DNA methylation effects, gives an improved performance (in terms of statistical power) to detect eQTLs over the current eQTL mapping approaches. When applied to an array-based dataset from 150 neuropathologically normal adult human brains, our method identifies eQTLs that were undetected using standard tissue-by-tissue or joint tissue eQTL mapping techniques. As an example, our method identifies eQTLs in a BAX inhibiting gene (TMBIM1), which may have a role in the pathogenesis of Alzheimer disease.ConclusionsOur score test-based approach does not need parameter estimation under the alternative hypothesis. As a result, our model parameters are estimated only once for each mRNA - CpG pair. Our model specifically studies the effects of non-coding regions of DNA (in this case, CpG sites) on mapping eQTLs. However, we can easily model micro-RNAs instead of CpG sites to study the effects of post-transcriptional events in mapping eQTL. Our model’s flexible framework also allows us to investigate other genomic events such as alternative gene splicing by extending our model to include gene isoform-specific data.

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