Faculty Opinions recommendation of Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain.

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.

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Identifying causal models between genetically regulated methylation patterns and gene expression in healthy colon tissue

Clinical Epigenetics ◽

10.1186/s13148-021-01148-9 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Anna Díez-Villanueva ◽

Mireia Jordà ◽

Robert Carreras-Torres ◽

Henar Alonso ◽

David Cordero ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Quantitative Trait Loci ◽

Genetic Variants ◽

Quantitative Trait ◽

Regulation Of Gene Expression ◽

Regulatory Elements ◽

Colon Tissue ◽

Tissue Samples ◽

Trait Loci

Abstract Background DNA methylation is involved in the regulation of gene expression and phenotypic variation, but the inter-relationship between genetic variation, DNA methylation and gene expression remains poorly understood. Here we combine the analysis of genetic variants related to methylation markers (methylation quantitative trait loci: mQTLs) and gene expression (expression quantitative trait loci: eQTLs) with methylation markers related to gene expression (expression quantitative trait methylation: eQTMs), to provide novel insights into the genetic/epigenetic architecture of colocalizing molecular markers. Results Normal mucosa from 100 patients with colon cancer and 50 healthy donors included in the Colonomics project have been analyzed. Linear models have been used to find mQTLs and eQTMs within 1 Mb of the target gene. From 32,446 eQTLs previously detected, we found a total of 6850 SNPs, 114 CpGs and 52 genes interrelated, generating 13,987 significant combinations of co-occurring associations (meQTLs) after Bonferromi correction. Non-redundant meQTLs were 54, enriched in genes involved in metabolism of glucose and xenobiotics and immune system. SNPs in meQTLs were enriched in regulatory elements (enhancers and promoters) compared to random SNPs within 1 Mb of genes. Three colorectal cancer GWAS SNPs were related to methylation changes, and four SNPs were related to chemerin levels. Bayesian networks have been used to identify putative causal relationships among associated SNPs, CpG and gene expression triads. We identified that most of these combinations showed the canonical pathway of methylation markers causes gene expression variation (60.1%) or non-causal relationship between methylation and gene expression (33.9%); however, in up to 6% of these combinations, gene expression was causing variation in methylation markers. Conclusions In this study we provided a characterization of the regulation between genetic variants and inter-dependent methylation markers and gene expression in a set of 150 healthy colon tissue samples. This is an important finding for the understanding of molecular susceptibility on colon-related complex diseases.

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Widespread methylation quantitative trait loci and their role in schizophrenia risk

10.1101/2020.09.24.311878 ◽

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.

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TH44. LEVERAGING DNA METHYLATION QUANTITATIVE-TRAIT LOCI TO CHARACTERIZE THE RELATIONSHIP BETWEEN METHYLOMIC VARIATION, GENE EXPRESSION, AND PSYCHIATRIC DISEASE

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2021.08.216 ◽

2021 ◽

Vol 51 ◽

pp. e217-e218

Author(s):

Gemma Shireby ◽

Eilis Hannon ◽

Gina Commin ◽

Joe Burrage ◽

Jonathan Davies ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Psychiatric Disease ◽

Trait Loci ◽

The Relationship ◽

Methylation Quantitative Trait Loci

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Mapping methylation quantitative trait loci in cardiac tissues nominates risk loci and biological pathways in congenital heart disease

BMC Genomic Data ◽

10.1186/s12863-021-00975-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ming Li ◽

Chen Lyu ◽

Manyan Huang ◽

Catherine Do ◽

Benjamin Tycko ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Quantitative Trait Loci ◽

Genetic Variants ◽

Quantitative Trait ◽

Congenital Heart ◽

Cardiac Tissues ◽

Chd Risk ◽

Trait Loci ◽

Methylation Quantitative Trait Loci

Abstract Background Most congenital heart defects (CHDs) result from complex interactions among genetic susceptibilities, epigenetic modifications, and maternal environmental exposures. Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation will enhance our understanding of pathogenesis in this important type of congenital disorder. We investigated cis-acting effects of genetic single nucleotide polymorphisms (SNPs) on local DNA methylation patterns within 83 cardiac tissue samples and prioritized their contributions to CHD risk by leveraging results of CHD genome-wide association studies (GWAS) and their effects on cardiac gene expression. Results We identified 13,901 potential methylation quantitative trait loci (mQTLs) with a false discovery threshold of 5%. Further co-localization analyses and Mendelian randomization indicated that genetic variants near the HLA-DRB6 gene on chromosome 6 may contribute to CHD risk by regulating the methylation status of nearby CpG sites. Additional SNPs in genomic regions on chromosome 10 (TNKS2-AS1 gene) and chromosome 14 (LINC01629 gene) may simultaneously influence epigenetic and transcriptomic variations within cardiac tissues. Conclusions Our results support the hypothesis that genetic variants may influence the risk of CHDs through regulating the changes of DNA methylation and gene expression. Our results can serve as an important source of information that can be integrated with other genetic studies of heart diseases, especially CHDs.

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Leveraging DNA methylation quantitative trait loci to characterize the relationship between methylomic variation, gene expression and complex traits

10.1101/297176 ◽

2018 ◽

Author(s):

Eilis Hannon ◽

Tyler J Gorrie-Stone ◽

Melissa C Smart ◽

Joe Burrage ◽

Amanda Hughes ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Genetic Variation ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Complex Traits ◽

Common Genetic Variation ◽

Online Data ◽

The Relationship ◽

Methylation Quantitative Trait Loci

ABSTRACTCharacterizing the complex relationship between genetic, epigenetic and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. In this study, we describe the most comprehensive analysis of common genetic variation on DNA methylation (DNAm) to date, using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (P < 6.52x10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified using previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits, using Summary data–based Mendelian Randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression, identifying 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database (http://www.epigenomicslab.com/online-data-resources/) as a resource to the research community.

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