scholarly journals Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits

2018 ◽  
Vol 103 (5) ◽  
pp. 654-665 ◽  
Author(s):  
Eilis Hannon ◽  
Tyler J. Gorrie-Stone ◽  
Melissa C. Smart ◽  
Joe Burrage ◽  
Amanda Hughes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Trait Loci ◽  
The Relationship ◽  
Methylation Quantitative Trait Loci

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

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.

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.

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

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.

scholarly journals Characterization of expression quantitative trait loci in extensively phenotyped pedigrees ascertained for bipolar disorder

2015 ◽  
Author(s):  
Christine Peterson ◽  
Susan Service ◽  
Anna Jasinska ◽  
Fuying Gao ◽  
Ivette Zelaya ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bipolar Disorder ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Expression Quantitative Trait Loci ◽  
Genome Wide ◽  
Wide Range ◽  
Trait Loci

The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and a wide range of BP-related quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus.

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.

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

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.

scholarly journals Integrated Genome-Wide Analysis of MicroRNA Expression Quantitative Trait Loci in Pig Longissimus Dorsi Muscle

2021 ◽  
Vol 12 ◽  
Author(s):  
Kaitlyn R. Daza ◽  
Deborah Velez-Irizarry ◽  
Sebastian Casiró ◽  
Juan P. Steibel ◽  
Nancy E. Raney ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Loci ◽  
Mirna Expression ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Carcass Composition ◽  
Longissimus Dorsi ◽  
Expression Quantitative Trait Loci ◽  
Genome Wide ◽  
Trait Loci

Determining mechanisms regulating complex traits in pigs is essential to improve the production efficiency of this globally important protein source. MicroRNAs (miRNAs) are a class of non-coding RNAs known to post-transcriptionally regulate gene expression affecting numerous phenotypes, including those important to the pig industry. To facilitate a more comprehensive understanding of the regulatory mechanisms controlling growth, carcass composition, and meat quality phenotypes in pigs, we integrated miRNA and gene expression data from longissimus dorsi muscle samples with genotypic and phenotypic data from the same animals. We identified 23 miRNA expression Quantitative Trait Loci (miR-eQTL) at the genome-wide level and examined their potential effects on these important production phenotypes through miRNA target prediction, correlation, and colocalization analyses. One miR-eQTL miRNA, miR-874, has target genes that colocalize with phenotypic QTL for 12 production traits across the genome including backfat thickness, dressing percentage, muscle pH at 24 h post-mortem, and cook yield. The results of our study reveal genomic regions underlying variation in miRNA expression and identify miRNAs and genes for future validation of their regulatory effects on traits of economic importance to the global pig industry.

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