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Background: Methylation quantitative trait loci (meQTLs) associate with different levels of local DNA methylation in cancers. Here, we investigated whether the distribution of cancer meQTLs reflected functional organization of the genome in the form of chromatin topologically associated domains (TADs) and evaluated whether cancer meQTLs near known driver genes have the potential to influence cancer risk or progression. Methods: Published cancer meQTLs were analyzed according to their location in transcriptionally active or inactive TADs and TAD boundary regions. Cancer meQTLs near known cancer genes were analyzed for association with cancer risk in the UKBioBank , and prognosis in The Cancer Genome Atlas (TCGA). Results: In TAD boundary regions, the density of cancer meQTLs was higher near inactive TADs. Furthermore, we observed an enrichment of cancer meQTLs in active TADs near tumor suppressors, whereas there was a depletion of such meQTLs near oncogenes. Several meQTLs were associated with cancer risk in the UKBioBank, and we were able to reproduce breast cancer risk associations in the DRIVE cohort. Survival analysis in TCGA implicated a number of meQTLs in 13 tumor types. In 10 of these, polygenic cancer meQTL scores were associated with increased hazard in a CoxPH analysis. Risk and survival-associated meQTLs tended to affect cancer genes involved in DNA damage repair and cellular adhesion and reproduced cancer-specific associations reported in prior literature. Conclusions: This study provides evidence that genetic variants that influence local DNA methylation are affected by chromatin structure and can impact tumor evolution.

Congenital heart defect (CHD) is a rare and complicated disease with a high mortality rate. Its etiology remains unclear and includes many aspects. DNA methylation has been indicated to be involved in heart development in the early stage of life, and aberrant methylation level was related to CHDs. This study provides the first evidence of the cross talk of SNP variants and DNA methylation in clarifying CHD underlying genomic cause. We gathered whole exome sequencing (WES) data for Group 1 consisting of patients with PA (n = 78), TOF (n = 20), TAPVC (n = 78), and PDA (n = 40), and 100 healthy children as control group. Rare non-synonymous mutations and novel genes were found and highlighted. Meanwhile, we carried out the second analysis of DNA methylation data from patients with PA (n = 3), TAPVC (n = 3), TOF (n = 3), and PDA (n = 2), and five healthy controls using 850 K array in Group 2. DNA methylation was linked to WES data, and we explored an obvious overlap of hyper/hypomethylated genes. Next, we identified some candidate genes by Fisher’s exact test and Burden analysis; then, those methylated genes were figured out by the criteria of the mutation located in the CpG islands of the genome, differential methylation sites (DMS), and DNA methylation quantitative trait loci (meQTLs) in the database, respectively. Also, the interaction of differentially methylated candidate genes with known CHD pathogenetic genes was depicted in a molecular network. Taken together, our findings show that nine novel genes (ANGPTL4, VEGFA, PAX3, MUC4, HLA-DRB1, TJP2, BCR, PKD1, and HK2) in methylation level are critical to CHD and reveal a new insight into the molecular pathogenesis of CHD.

Abstract Background One of the major challenges in current psychiatric epigenetic studies is the tissue specificity of epigenetic changes since access to brain samples is limited. Peripheral tissues have been studied as surrogates but the knowledge of cross-tissue genetic-epigenetic characteristics remains largely unknown. In this work, we conducted a comprehensive investigation of genetic influence on DNA methylation across brain and peripheral tissues with the aim to characterize cross-tissue genetic-epigenetic effects and their roles in the pathophysiology of psychiatric disorders. Methods Genome-wide methylation quantitative trait loci (meQTLs) from brain prefrontal cortex, whole blood, and saliva were identified separately and compared. Focusing on cis-acting effects, we tested the enrichment of cross-tissue meQTLs among cross-tissue expression QTLs and genetic risk loci of various diseases, including major psychiatric disorders. CpGs targeted by cross-tissue meQTLs were also tested for genomic distribution and functional enrichment as well as their contribution to methylation correlation across tissues. Finally, a consensus co-methylation network analysis on the cross-tissue meQTL targeted CpGs was performed on data of the three tissues collected from schizophrenia patients and controls. Results We found a significant overlap of cis meQTLs (45–73 %) and targeted CpG sites (31–68 %) among tissues. The majority of cross-tissue meQTLs showed consistent signs of cis-acting effects across tissues. They were significantly enriched in genetic risk loci of various diseases, especially schizophrenia, and also enriched in cross-tissue expression QTLs. Compared to CpG sites not targeted by any meQTLs, cross-tissue targeted CpGs were more distributed in CpG island shores and enhancer regions, and more likely had strong correlation with methylation levels across tissues. The targeted CpGs were also annotated to genes enriched in multiple psychiatric disorders and neurodevelopment-related pathways. Finally, we identified one co-methylation network shared between brain and blood showing significant schizophrenia association (p = 5.5 × 10−6). Conclusions Our results demonstrate prevalent cross-tissue meQTL effects and their contribution to the correlation of CpG methylation across tissues, while at the same time a large portion of meQTLs show tissue-specific characteristics, especially in brain. Significant enrichment of cross-tissue meQTLs in expression QTLs and genetic risk loci of schizophrenia suggests the potential of these cross-tissue meQTLs for studying the genetic effect on schizophrenia. The study provides compelling motivation for a well-designed experiment to further validate the use of surrogate tissues in the study of psychiatric disorders.

Genetic variants have potential influence on DNA methylation and thereby regulate mRNA expression. This study aimed to comprehensively reveal the relationships among SNP, methylation and mRNA, and identify methylation‐mediated regulation patterns in human peripheral blood mononuclear cells (PBMCs). Based on in‐house multi‐omics datasets from 43 Chinese Han female subjects, genome‐wide association trios were constructed by simultaneously testing the following three association pairs: SNP‐methylation, methylation‐mRNA and SNP‐mRNA. Causal inference test (CIT) was used to identify methylation‐mediated genetic effects on mRNA. A total of 64,184 significant cis‐methylation quantitative trait loci (meQTLs) were identified (FDR < 0.05). Among the 745 constructed trios, 464 trios formed SNP‐methylation‐mRNA regulation chains (CIT). Network analysis (Cytoscape 3.3.0) constructed multiple complex regulation networks among SNP, methylation and mRNA (eg a total of 43 SNPs simultaneously connected to cg22517527 and further to PRMT2, DIP2A and YBEY). The regulation chains were supported by the evidence from 4DGenome database, relevant to immune or inflammatory related diseases/traits, and overlapped with previous eQTLs from dbGaP and GTEx. The results provide new insights into the regulation patterns among SNP, DNA methylation and mRNA expression, especially for the methylation‐mediated effects, and also increase our understanding of functional mechanisms underlying the established associations. [ABSTRACT FROM AUTHOR]

Objective: Epigenetic modifications of DNA are regarded as a crucial factor for understanding the molecular basis of complex phenotypes. This study aims to uncover insight into the epigenetic modifications for Kashin-Beck disease (KBD) by integrating genome-wide association studies (GWAS), methylation quantitative trait loci (meQTLs), and DNA methylation profiles data., Design: The knee articular cartilages of 5 KBD patients and 5 healthy controls were collected for DNA methylation profiling, using Illumina Infinium HumanMethylation450 BeadChip. Mass spectrograph validation of identified differently methylated genes was conducted using independent samples of 4 KBD patients and 3 healthy controls, together with a previous sample of 2743 Han Chinese individuals of GWAS study for KBD and a study of 697 normal subjects for meQTLs annotation datasets. KBD GWAS single nucleotide polymorphisms (SNPs) and normal meQTLs SNPs were integrated with DNA methylation profiles of KBD articular cartilage to identify genetic control (GC) genes of DNA methylation for KBD. Quantitative polymerase chain reaction (qPCR) was performed to validate the mRNA expression of several identified candidate genes., Results: A total of 162 CpG sites, 253 SNPs, and 123 GC genes for KBD were identified. Enrichment analysis detected 642 marked GO terms and 19 KEGG pathways ( P < 0.05). Six potential key GC genes were conducted for qPCR experiment (ERG, MN1, MITF, WISP1, TRIO, and NOSTRIN)., Conclusions: The results suggest that GC genes of DNA methylation may lead to the erosion of cartilage in KBD, which may help us in understanding the epigenetic alteration of KBD.

Congenital heart defect (CHD) is a rare and complicated disease with a high mortality rate. Its etiology remains unclear and includes many aspects. DNA methylation has been indicated to be involved in heart development in the early stage of life, and aberrant methylation level was related to CHDs. This study provides the first evidence of the cross talk of SNP variants and DNA methylation in clarifying CHD underlying genomic cause. We gathered whole exome sequencing (WES) data for Group 1 consisting of patients with PA ( n = 78), TOF ( n = 20), TAPVC ( n = 78), and PDA ( n = 40), and 100 healthy children as control group. Rare non-synonymous mutations and novel genes were found and highlighted. Meanwhile, we carried out the second analysis of DNA methylation data from patients with PA ( n = 3), TAPVC ( n = 3), TOF ( n = 3), and PDA ( n = 2), and five healthy controls using 850 K array in Group 2. DNA methylation was linked to WES data, and we explored an obvious overlap of hyper/hypomethylated genes. Next, we identified some candidate genes by Fisher's exact test and Burden analysis; then, those methylated genes were figured out by the criteria of the mutation located in the CpG islands of the genome, differential methylation sites (DMS), and DNA methylation quantitative trait loci (meQTLs) in the database, respectively. Also, the interaction of differentially methylated candidate genes with known CHD pathogenetic genes was depicted in a molecular network. Taken together, our findings show that nine novel genes (ANGPTL4, VEGFA, PAX3, MUC4, HLA-DRB1, TJP2, BCR, PKD1, and HK2) in methylation level are critical to CHD and reveal a new insight into the molecular pathogenesis of CHD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wang, Ma, Zhang, Chen, Wu, Zhao and Yu.)

Introduction Breast cancer being a multifaceted disease constitutes a wide spectrum of histological and molecular variability in tumors. However, the task for the identification of these variances is complicated by the interplay between inherited genetic and epigenetic aberrations. Therefore, this study provides an extrapolate outlook to the sinister partnership between DNA methylation and single-nucleotide polymorphisms (SNPs) in relevance to the identification of prognostic markers in breast cancer. The effect of these SNPs on methylation is defined as methylation quantitative trait loci (meQTL). Materialsand Methods We developed a novel method to identify prognostic gene signatures for breast cancer by integrating genomic and epigenomic data. This is based on the hypothesis that multiple sources of evidence pointing to the same gene or pathway are likely to lead to reduced false positives. We also apply random resampling to reduce overfitting noise by dividing samples into training and testing data sets. Specifically, the common samples between Illumina 450 DNA methylation, Affymetrix SNP array, and clinical data sets obtained from the Cancer Genome Atlas (TCGA) for breast invasive carcinoma (BRCA) were randomly divided into training and test models. An intensive statistical analysis based on log-rank test and Cox proportional hazard model has established a significant association between differential methylation and the stratification of breast cancer patients into high- and low-risk groups, respectively. Results The comprehensive assessment based on the conjoint effect of CpG–SNP pair has guided in delaminating the breast cancer patients into the high- and low-risk groups. In particular, the most significant association was found with respect to cg05370838–rs2230576, cg00956490–rs940453, and cg11340537–rs2640785 CpG–SNP pairs. These CpG–SNP pairs were strongly associated with differential expression of ADAM8, CREB5, and EXPH5 genes, respectively. Besides, the exclusive effect of SNPs such as rs10101376, rs140679, and rs1538146 also hold significant prognostic determinant. Conclusions Thus, the analysis based on DNA methylation and SNPs have resulted in the identification of novel susceptible loci that hold prognostic relevance in breast cancer. [ABSTRACT FROM AUTHOR]

Objectives: Several genome-wide association studies (GWAS) of bone mineral density (BMD) have successfully identified multiple susceptibility genes, yet isolated susceptibility genes are often difficult to interpret biologically. The aim of this study was to unravel the genetic background of BMD at pathway level, by integrating BMD GWAS data with genome-wide expression quantitative trait loci (eQTLs) and methylation quantitative trait loci (meQTLs) data METHOD: We employed the GWAS datasets of BMD from the Genetic Factors for Osteoporosis Consortium (GEFOS), analysing patients' BMD. The areas studied included 32 735 femoral necks, 28 498 lumbar spines, and 8143 forearms. Genome-wide eQTLs (containing 923 021 eQTLs) and meQTLs (containing 683 152 unique methylation sites with local meQTLs) data sets were collected from recently published studies. Gene scores were first calculated by summary data-based Mendelian randomisation (SMR) software and meQTL-aligned GWAS results. Gene set enrichment analysis (GSEA) was then applied to identify BMD-associated gene sets with a predefined significance level of 0.05., Results: We identified multiple gene sets associated with BMD in one or more regions, including relevant known biological gene sets such as the Reactome Circadian Clock (GSEA p-value = 1.0 × 10 -4 for LS and 2.7 × 10 -2 for femoral necks BMD in eQTLs-based GSEA) and insulin-like growth factor receptor binding (GSEA p-value = 5.0 × 10 -4 for femoral necks and 2.6 × 10 -2 for lumbar spines BMD in meQTLs-based GSEA)., Conclusion: Our results provided novel clues for subsequent functional analysis of bone metabolism, and illustrated the benefit of integrating eQTLs and meQTLs data into pathway association analysis for genetic studies of complex human diseases. Cite this article : W. Wang, S. Huang, W. Hou, Y. Liu, Q. Fan, A. He, Y. Wen, J. Hao, X. Guo, F. Zhang. Integrative analysis of GWAS, eQTLs and meQTLs data suggests that multiple gene sets are associated with bone mineral density. Bone Joint Res 2017;6:572-576., Competing Interests: Conflicts of Interest Statement: None declared, (© 2017 Wang et al.)