scholarly journals Linkage of DNA Methylation Quantitative Trait Loci to Human Cancer Risk

Cell Reports ◽  
2014 ◽  
Vol 7 (2) ◽  
pp. 331-338 ◽  
Author(s):  
Holger Heyn ◽  
Sergi Sayols ◽  
Catia Moutinho ◽  
Enrique Vidal ◽  
Jose V. Sanchez-Mut ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Cancer Risk ◽  
Quantitative Trait ◽  
Human Cancer ◽  
Trait Loci ◽  
Methylation Quantitative Trait Loci

scholarly journals DNMIVD: DNA methylation interactive visualization database

2019 ◽  
Vol 48 (D1) ◽  
pp. D856-D862 ◽  
Author(s):  
Wubin Ding ◽  
Jiwei Chen ◽  
Guoshuang Feng ◽  
Geng Chen ◽  
Jun Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Interactive Visualization ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Differentially Methylated Genes ◽  
Trait Loci ◽  
Methylation Quantitative Trait Loci

Abstract Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression–methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer. To this end, we constructed a user-friendly database named DNA Methylation Interactive Visualization Database (DNMIVD), which comprehensively provides the following important resources: (i) diagnostic and prognostic models based on DNA methylation for multiple cancer types of The Cancer Genome Atlas (TCGA); (ii) meQTL, emQTL and pathway-meQTL for diverse cancers; (iii) Functional Epigenetic Modules (FEM) constructed from Protein-Protein Interactions (PPI) and Co-Occurrence and Mutual Exclusive (COME) network by integrating DNA methylation and gene expression data of TCGA cancers; (iv) differentially variable and differentially methylated CpGs and differentially methylated genes as well as related enhancer information; (v) correlations between methylation of gene promoter and corresponding gene expression and (vi) patient survival-associated CpGs and genes with different endpoints. DNMIVD is freely available at http://www.unimd.org/dnmivd/. We believe that DNMIVD can facilitate research of diverse cancers.

scholarly journals Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

2021 ◽  
Author(s):  
Michael Scherer ◽  
Gilles Gasparoni ◽  
Souad Rahmouni ◽  
Tatiana Shashkova ◽  
Marion Arnoux ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Genetic Variants ◽  
Quantitative Trait ◽  
Healthy Individuals ◽  
Cell Type ◽  
Systematic Analysis ◽  
Trait Loci ◽  
Cell Type Specific ◽  
Methylation Quantitative Trait Loci

Background: Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL) but also for discriminating general from cell-type-specific effects. Results: Here, we present a two-step computational framework MAGAR, which fully supports identification of methQTLs from matched genotyping and DNA methylation data, and additionally the identification of quantitative cell-type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T-cells, B-cells) from healthy individuals and demonstrate the discrimination of common from cell-type-specific methQTLs. We experimentally validate both types of methQTLs in an independent dataset comprising additional cell types and tissues. Finally, we validate selected methQTLs (PON1, ZNF155, NRG2) by ultra-deep local sequencing. In line with previous reports, we find cell-type-specific methQTLs to be preferentially located in enhancer elements. Conclusions: Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell-type-specific epigenomic variation.

Leveraging DNA methylation quantitative-trait loci to characterize the relationship between metabolic traits and Alzheimer’s disease

2020 ◽  
Author(s):  
Di Liu ◽  
Zhiyuan Yu ◽  
Weijie Cao ◽  
Youxin Wang ◽  
Qun Meng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Sensitivity Analyses ◽  
Metabolic Traits ◽  
Trait Loci ◽  
The Relationship ◽  
Methylation Quantitative Trait Loci

Abstract Background: The relationship between DNA methylation, common metabolic risk and Alzheimer’s disease (AD) is not well understood.Methods: Summary statistics integrating DNA methylation quantitative trait loci (mQTLs) and several genome-wide association study data were used. Network with bidirectional mendelian randomization (MR) analysis was performed to examine the causal association among metabolic traits, DNA methylation and AD.Results: Our study showed that cis-mQTLs determined DNA methylation to higher total cholesterol (TC) was associated with higher AD risk (β [95% CI] =0.007 [0.002-0.013], P=0.005). The findings were robust in sensitivity analyses with different instrumental variables. We found no evidence to support causal associations of cis-mQTLs determined obesity and T2D with AD, and vice versa.Conclusion: Overall, our study showed that the cis-mQTLs determined DNA methylation to higher TC was associated with higher AD risk, whereas the relation of cis-mQTLs determined AD and metabolic dysregulation was unlikely to be causal.

scholarly journals The Presence of Methylation Quantitative Trait Loci Indicates a Direct Genetic Influence on the Level of DNA Methylation in Adipose Tissue

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e55923 ◽  
Author(s):  
Alexander W. Drong ◽  
George Nicholson ◽  
Åsa K. Hedman ◽  
Eshwar Meduri ◽  
Elin Grundberg ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Adipose Tissue ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Genetic Influence ◽  
Trait Loci ◽  
Methylation Quantitative Trait Loci

scholarly journals Widespread methylation quantitative trait loci and their role in schizophrenia risk

2020 ◽  
Author(s):  
Kira A. Perzel Mandell ◽  
Nicholas J. Eagles ◽  
Richard Wilton ◽  
Amanda J. Price ◽  
Stephen A. Semick ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Human Brain ◽  
Quantitative Trait ◽  
Functional Regions ◽  
Gene Environment ◽  
Trait Loci ◽  
Genome Bisulfite Sequencing ◽  
Methylation Quantitative Trait Loci

AbstractDNA methylation (DNAm) regulates gene expression and may represent gene-environment interactions. Using whole genome bisulfite sequencing, we surveyed DNAm in a large sample (n=344) of human brain tissues. We identify widespread genetic influence on local methylation levels throughout the genome, with 76% of SNPs and 38% of CpGs being part of methylation quantitative trait loci (meQTLs). These associations can further be clustered into regions that are differentially methylated by a given SNP, highlighting putative functional regions that explain much of the heritability associated with risk loci. Furthermore, some CpH sites associated with genetic variation. We have established a comprehensive, single base resolution view of association between genetic variation and genomic methylation, and implicate schizophrenia GWAS-associated variants as influencing the epigenetic plasticity of the brain.One-sentence summaryMost genetic variants associated with DNA methylation levels, and implicated schizophrenia GWAS variants in the human brain.

scholarly journals Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael Scherer ◽  
Gilles Gasparoni ◽  
Souad Rahmouni ◽  
Tatiana Shashkova ◽  
Marion Arnoux ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Genetic Variants ◽  
Quantitative Trait ◽  
Healthy Individuals ◽  
Cell Type ◽  
Data Set ◽  
Trait Loci ◽  
Cell Type Specific ◽  
Methylation Quantitative Trait Loci

Abstract Background Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects. Results Here, we present a two-step computational framework MAGAR (https://bioconductor.org/packages/MAGAR), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements. Conclusions Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.

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