Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer

The DNA methylome of cervical cells can predict the presence of ovarian cancer

The vast majority of epithelial ovarian cancer arises from tissues that are embryologically derived from the Müllerian Duct. Here, we demonstrate that a DNA methylation signature in easy-to-access Müllerian Duct-derived cervical cells from women with and without ovarian cancer (i.e. referred to as the Women's risk IDentification for Ovarian Cancer index or WID-OC-index) is capable of identifying women with an ovarian cancer in the absence of tumour DNA with an AUC of 0.76 and women with an endometrial cancer with an AUC of 0.81. This and the observation that the cervical cell WID-OC-index mimics the epigenetic program of those cells at risk of becoming cancerous in BRCA1/2 germline mutation carriers (i.e. mammary epithelium, fallopian tube fimbriae, prostate) further suggest that the epigenetic misprogramming of cervical cells is an indicator for cancer predisposition. This concept has the potential to advance the field of risk-stratified cancer screening and prevention.

The blood transcriptome prior to ovarian cancer diagnosis: A case-control study in the NOWAC postgenome cohort

Epithelial ovarian cancer (EOC) has a 5-year relative survival of 50%, partly because markers of early-stage disease are not available in current clinical diagnostics. The aim of the present study was to investigate whether EOC is associated with transcriptional profiles in blood collected up to 7 years before diagnosis. For this, we used RNA-stabilized whole blood, which contains circulating immune cells, from a sample of EOC cases from the population-based Norwegian Women and Cancer (NOWAC) postgenome cohort. We explored case-control differences in gene expression in all EOC (66 case-control pairs), as well as associations between gene expression and metastatic EOC (56 pairs), serous EOC (45 pairs, 44 of which were metastatic), and interval from blood sample collection to diagnosis (≤3 or >3 years; 34 and 31 pairs, respectively). Lastly, we assessed differential expression of genes associated with EOC in published functional genomics studies that used blood samples collected from newly diagnosed women. After adjustment for multiple testing, this nested case-control study revealed no significant case-control differences in gene expression in all EOC (false discovery rate q>0.96). With the exception of a few probes, the log₂ fold change values obtained in gene-wise linear models were below ±0.2. P-values were lowest in analyses of metastatic EOC (80% of which were serous EOC). No common transcriptional profile was indicated by interval to diagnosis; when comparing the 100 genes with the lowest p-values in gene-wise tests in samples collected ≤3 and >3 years before EOC diagnosis, no overlap in these genes was observed. Among 86 genes linked to ovarian cancer in previous publications, our data contained expression values for 42, and of these, tests of LIME1, GPR162, STAB1, and SKAP1, resulted in unadjusted p<0.05. Although limited by sample size, our findings indicated less variation in blood gene expression between women with similar tumor characteristics.

Gene-based mediation analysis in epigenetic studies

Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.

DNA Methylation in Ovarian Cancer Susceptibility

Epigenetic alterations are somatically acquired over the lifetime and during neoplastic transformation but may also be inherited as widespread 'constitutional' alterations in normal tissues that can cause cancer predisposition. Epithelial ovarian cancer (EOC) has an established genetic susceptibility and mounting epidemiological evidence demonstrates that DNA methylation (DNAm) intermediates as well as independently contributes to risk. Targeted studies of known EOC susceptibility genes (CSGs) indicate rare, constitutional BRCA1 promoter methylation increases familial and sporadic EOC risk. Blood-based epigenome-wide association studies (EWAS) for EOC have detected a total of 2846 differentially methylated probes (DMPs) with 71 genes replicated across studies despite significant heterogeneity. While EWAS detect both symptomatic and etiologic DMPs, adjustments and analytic techniques may enrich risk associations, as evidenced by the detection of dysregulated methylation of BNC2-a known CSG identified by genome-wide associations studies (GWAS). Integrative genetic-epigenetic approaches have mapped methylation quantitative trait loci (meQTL) to EOC risk, revealing DNAm variations that are associated with nine GWAS loci and, further, one novel risk locus. Increasing efforts to mapping epigenome variation across populations and cell types will be key to decoding both the genomic and epigenomic causal pathways to EOC.

Polymorphisms in JAK2 Gene are Associated with Production Traits and Mastitis Resistance in Dairy Cattle

The present study was designed to investigate the effects of single nucleotide polymorphisms (SNPs) in the JAK2 gene on the production and mastitis related traits in dairy cattle. Blood and milk samples were collected from 201 lactating dairy cattle of three breeds, i.e. Holstein Friesian (HF), Jersey (J) and Achai (A) and their crosses maintained at well-established dairy farms in Khyber Pakhtunkhwa, Pakistan. Generalized linear model was used to evaluate the association between genotypes and the studied traits. A DNA pool was made from randomly selected 30 samples which revealed three SNPs, i.e. SNP 1 in 5’ upstream region (G>A, rs379754157), SNP 2 in intron 15 (A>G, rs134192265), and SNP 3 in exon 20 (A>G, rs110298451) that were further validated in the population under study using SNaPshot technique. Of the three SNPs, SNP 1 did not obey Hardy-Weinberg equilibrium (P<0.05). SNP 2 and SNP 3 were found to be in strong linkage disequilibrium and allele G was highly prevalent compared to allele A in these SNPs. in SNP 1, the GG genotype was associated with significantly (P<0.01) higher SCC, whereas SNP 2 and SNP 3 were significantly (P<0.01) associated with higher lactose percentage compared to the other geno-types. The haplogroups association analysis revealed that H1H2 (GG GG AG) has significantly lower SCC than H2H2 (GG GG GG). The results infer that JAK2 could be an important candidate gene and the studied SNPs might be useful genetic markers for production and mastitis related traits.

Association between SNPs and Loss of Methylation Site on the CpG island of the Promoter Region of the Smoothened Gene, Potential Molecular Markers for Susceptibility to the Development of Basal Cell Carcinoma in the Brazilian Population

Objective:

Perform genotyping of SNPs in the promoter region of the SMO gene in BCC samples from patients from northeastern Brazil, and to determine if there is an association of these SNPs of the gene in question with the susceptibility to the development of the BCC.

Methods:

100 samples of paraffined tissue from patients with histopathological diagnosis of BCC and 100 control samples were analyzed for each polymorphism by a newly developed genotyping method, the Dideoxy Single Allele Specific – PCR. The software Bioestat - version 5.3 and Haploview 4.2 were used for the statistical analysis. For all tests a P-value <0.05 was considered significant.

Results:

The SNP rs538312246 is the Hardy-Weinberg equilibrium, therefore, it did not present significant association with the BCC (X² =2.343 and P<0.158). However, the CpG-SNPs rs375350898 and rs75827493 were significantly associated to the BCC in the analyzed samples (X² = 27,740/21,500 and P <0001), the SNP rs75827493 showed a significant association with the BCC of the nodular subtype (P <0.0069). Therefore, our results suggest that SNPs rs375350898 and rs75827493 are potential molecular markers for susceptibility to BCC.

Conclusion:

The ability to detect SNP in a population, especially in promoter regions, has profoundly changed human genetic studies. This study allowed the understanding of the relationship between the presence of SNPs in CpG islands of the promoter region of the SMO gene can modify the methylation pattern and provide susceptibility to BCC in the population.

Testing Mediation Effects in High-Dimensional Epigenetic Studies

Mediation analysis has been a powerful tool to identify factors mediating the association between exposure variables and outcomes. It has been applied to various genomic applications with the hope to gain novel insights into the underlying mechanism of various diseases. Given the high-dimensional nature of epigenetic data, recent effort on epigenetic mediation analysis is to first reduce the data dimension by applying high-dimensional variable selection techniques, then conducting testing in a low dimensional setup. In this paper, we propose to assess the mediation effect by adopting a high-dimensional testing procedure which can produce unbiased estimates of the regression coefficients and can properly handle correlations between variables. When the data dimension is ultra-high, we first reduce the data dimension from ultra-high to high by adopting a sure independence screening (SIS) method. We apply the method to two high-dimensional epigenetic studies: one is to assess how DNA methylations mediate the association between alcohol consumption and epithelial ovarian cancer (EOC) status; the other one is to assess how methylation signatures mediate the association between childhood maltreatment and post-traumatic stress disorder (PTSD) in adulthood. We compare the performance of the method with its counterpart via simulation studies. Our method can be applied to other high-dimensional mediation studies where high-dimensional mediation variables are collected.

Epigenetic Biomarkers in the Management of Ovarian Cancer: Current Prospectives

Ovarian cancer (OC) causes significant morbidity and mortality as neither detection nor screening of OC is currently feasible at an early stage. Difficulty to promptly diagnose OC in its early stage remains challenging due to non-specific symptoms in the early-stage of the disease, their presentation at an advanced stage and poor survival. Therefore, improved detection methods are urgently needed. In this article, we summarize the potential clinical utility of epigenetic signatures like DNA methylation, histone modifications, and microRNA dysregulation, which play important role in ovarian carcinogenesis and discuss its application in development of diagnostic, prognostic, and predictive biomarkers. Molecular characterization of epigenetic modification (methylation) in circulating cell free tumor DNA in body fluids offers novel, non-invasive approach for identification of potential promising cancer biomarkers, which can be performed at multiple time points and probably better reflects the prevailing molecular profile of cancer. Current status of epigenetic research in diagnosis of early OC and its management are discussed here with main focus on potential diagnostic biomarkers in tissue and body fluids. Rapid and point of care diagnostic applications of DNA methylation in liquid biopsy has been precluded as a result of cumbersome sample preparation with complicated conventional methods of isolation. New technologies which allow rapid identification of methylation signatures directly from blood will facilitate sample-to answer solutions thereby enabling next-generation point of care molecular diagnostics. To date, not a single epigenetic biomarker which could accurately detect ovarian cancer at an early stage in either tissue or body fluid has been reported. Taken together, the methodological drawbacks, heterogeneity associated with ovarian cancer and non-validation of the clinical utility of reported potential biomarkers in larger ovarian cancer populations has impeded the transition of epigenetic biomarkers from lab to clinical settings. Until addressed, clinical implementation as a diagnostic measure is a far way to go.

FOXD3 may be a new cellular target biomarker as a hypermethylation gene in human ovarian cancer

Background

FOXD3 is aberrantly regulated in several tumors, but its underlying mechanisms in ovarian cancer (OC) remains largely unknown. The present study aimed to explore the role and associated mechanisms of FOXD3 in OC.

Methods

Microarray data from GEO was used to analyze differential CpG sites and differentially methylated regions (DMR) in tumor tissues and Illumina 450 genome-wide methylation data was employed. The FOXD3 expression level was determined through qRT-PCR and western blot analysis. Wound healing test, colony formation and flow cytometry assay were utilized to analyze cell migration, proliferation abilities, cell cycle and cell apoptosis, respectively. Finally, the effect of FOXD3 on tumor growth was investigated through in vivo xenograft experiments.

Results

GEO data analysis showed that FOXD3 was hypermethylated in OC tissues. Also, qRT-PCR revealed that FOXD3 was low expressed and methylation-specific PCR (MSP) confirmed that the methylation level of FOXD3 was hypermethylated. Combined treatment of 5-aza-2′-deoxycytidine (5-Aza-dC) could synergistically restored FOXD3 expression. Finally, in vitro and in vivo experiments showed that demethylated FOXD3 decreased cell proliferation and migration abilities, and increased the cell apoptosis. In vivo experiment detected that demethylated FOXD3 restrained tumor growth.

Conclusions

FOXD3 could act as a tumor suppressor to inhibit cell proliferation, migration and promote cell apoptosis in OC cells.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0755-8) contains supplementary material, which is available to authorized users.

Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk

DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P < 7.94 × 10-7. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. SIGNIFICANCE: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.

Methylation and ovarian cancer: Can DNA methylation be of diagnostic use?

Ovarian cancer is a silent killer and, due to late diagnosis and frequent chemo resistance in patients, the primary cause of fatality amongst the various types of gynecological cancer. The discovery of a specific and sensitive biomarker for ovarian cancer could improve early diagnosis, thereby saving lives. Biomarkers could also improve treatment, by predicting which patients will benefit from specific treatment strategies. DNA methylation is an epigenetic mechanism, and 'methylation imbalance' is characteristic of cancer. Previous research suggests that changes in DNA methylation can be used diagnostically, and that they may predict resistance to treatment. This paper gives an up-to-date overview of research investigating the potential of DNA methylation-based markers for diagnostics, prognostics, screening and prediction of drug resistance for ovarian cancer patients. DNA methylation cancer-biomarkers may be useful for cancer treatment, particularly since they are chemically stable and since cancer-associated changes in methylation typically precedes tumor growth. DNA methylation markers could improve diagnosis and treatment and might even be used for screening in the future. Furthermore, DNA methylation biomarkers could facilitate the development of precision medicine. However, at this point no biomarkers for ovarian cancer have a sufficient combination of sensitivity and specificity in a clinical setting. A reason for this is that most studies have focused on a single or a few methylation sites. More large screenings and genome-wide studies must be performed to increase the chance of identifying a DNA methylation marker which can identify ovarian cancer.

Evaluating the potential role of pleiotropy in Mendelian randomization studies

Pleiotropy, the phenomenon of a single genetic variant influencing multiple traits, is likely widespread in the human genome. If pleiotropy arises because the single nucleotide polymorphism (SNP) influences one trait, which in turn influences another ('vertical pleiotropy'), then Mendelian randomization (MR) can be used to estimate the causal influence between the traits. Of prime focus among the many limitations to MR is the unprovable assumption that apparent pleiotropic associations are mediated by the exposure (i.e. reflect vertical pleiotropy), and do not arise due to SNPs influencing the two traits through independent pathways ('horizontal pleiotropy'). The burgeoning treasure trove of genetic associations yielded through genome wide association studies makes for a tantalizing prospect of phenome-wide causal inference. Recent years have seen substantial attention devoted to the problem of horizontal pleiotropy, and in this review we outline how newly developed methods can be used together to improve the reliability of MR.

Epigenome-based cancer risk prediction: rationale, opportunities and challenges

The incidence of cancer is continuing to rise and risk-tailored early diagnostic and/or primary prevention strategies are urgently required. The ideal risk-predictive test should: integrate the effects of both genetic and nongenetic factors and aim to capture these effects using an approach that is both biologically stable and technically reproducible; derive a score from easily accessible biological samples that acts as a surrogate for the organ in question; and enable the effectiveness of risk-reducing measures to be monitored. Substantial evidence has accumulated suggesting that the epigenome and, in particular, DNA methylation-based tests meet all of these requirements. However, the development and implementation of DNA methylation-based risk-prediction tests poses considerable challenges. In particular, the cell type specificity of DNA methylation and the extensive cellular heterogeneity of the easily accessible surrogate cells that might contain information relevant to less accessible tissues necessitates the use of novel methods in order to account for these confounding issues. Furthermore, the engagement of the scientific community with health-care professionals, policymakers and the public is required in order to identify and address the organizational, ethical, legal, social and economic challenges associated with the routine use of epigenetic testing.

Mediation analysis of alcohol consumption, DNA methylation, and epithelial ovarian cancer

Epigenetic factors and consumption of alcohol, which suppresses DNA methylation, may influence the development and progression of epithelial ovarian cancer (EOC). However, there is a lack of understanding whether these factors interact to affect the EOC risk. In this study, we aimed to gain insight into this relationship by identifying leukocyte-derived DNA methylation markers acting as potential mediators of alcohol-associated EOC. We implemented a causal inference test (CIT) and the VanderWeele and Vansteelandt multiple mediator model to examine CpG sites that mediate the association between alcohol consumption and EOC risk. We modified one step of the CIT by adopting a high-dimensional inference procedure. The data were based on 196 cases and 202 age-matched controls from the Mayo Clinic Ovarian Cancer Case-Control Study. Implementation of the CIT test revealed two CpG sites (cg09358725, cg11016563), which represent potential mediators of the relationship between alcohol consumption and EOC case-control status. Implementation of the VanderWeele and Vansteelandt multiple mediator model further revealed that these two CpGs were the key mediators. Decreased methylation at both CpGs was more common in cases who drank alcohol at the time of enrollment vs. those who did not. cg11016563 resides in TRPC6 which has been previously shown to be overexpressed in EOC. These findings suggest two CpGs may serve as novel biomarkers for EOC susceptibility.

Orienting the causal relationship between imprecisely measured traits using GWAS summary data

Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.

Understanding the causal relationships between pairs of traits is crucial for unravelling the causes of disease. To this end, results from genome-wide association studies are valuable because if a trait is known to be influenced by a genetic variant then this knowledge can be used to test the trait’s causal influences on other traits and diseases. Here we discuss scenarios where the nature of the genetic association with the causal trait can lead existing causal inference methods to give the wrong direction of causality. We introduce a new method that can be applied to summary level data and is potentially less susceptible to problems such as measurement error, and apply it to evaluate the causal relationships between DNA methylation levels and gene expression. While our results show that DNA methylation is more likely to be the causal factor, we point out that is it crucial to acknowledge that systematic differences in measurement error between the platforms could influence such conclusions.

DNA methylation signatures and coagulation factors in the peripheral blood leucocytes of epithelial ovarian cancer

Solid tumors are increasingly recognized as a systemic disease that is manifested by changes in DNA, RNA, proteins and metabolites in the blood. Whereas many studies have reported gene mutation events in the circulation, few studies have focused on epigenetic DNA methylation markers. To identify DNA methylation biomarkers in peripheral blood for ovarian cancer, we performed a two-stage epigenome-wide association study. In the discovery stage, we measured genome wide DNA methylation for 485 000 CpG sites in peripheral blood in 24 epithelial ovarian cancer (EOC) cases and 24 age-matched healthy controls. We selected 96 significantly differentially methylated CpG sites for validation using Illumina's Custom VeraCode methylation assay in 206 EOC cases and 205 controls and 46 CpG sites validated in the independent replication samples. A set of 6 of these 46 CpG sites was found by the receiver operating characteristic analysis to have a prediction accuracy of 77.3% for all EOC (95% confidence interval: 72.9-81.8%). Pathway analysis of the genes associated with the 46 CpG sites revealed an enrichment of immune system process genes, including LYST (cg16962115, FDR = 1.24E-04), CADM1 (cg21933078, FDR = 1.22E-02) and NFATC1 (cg06784563, FDR = 1.46E-02). Furthermore, DNA methylation status in peripheral blood was correlated with platelet parameters/coagulation factor levels. This study discovered a panel of epigenetic liquid biopsy markers closely associated with overall immunologic conditions and platelet parameters/coagulation systems of the patients for detection of all stages and subtypes of EOC.

A comparison of methods for inferring causal relationships between genotype and phenotype using additional biological measurements

Genome wide association studies (GWAS) have been very successful over the last decade at identifying genetic variants associated with disease phenotypes. However, interpretation of the results obtained can be challenging. Incorporation of further relevant biological measurements (e.g. ‘omics’ data) measured in the same individuals for whom we have genotype and phenotype data may help us to learn more about the mechanism and pathways through which causal genetic variants affect disease. We review various methods for causal inference that can be used for assessing the relationships between genetic variables, other biological measures, and phenotypic outcome, and present a simulation study assessing the performance of the methods under different conditions. In general, the methods we considered did well at inferring the causal structure for data simulated under simple scenarios. However, the presence of an unknown and unmeasured common environmental effect could lead to spurious inferences, with the methods we considered displaying varying degrees of robustness to this confounder. The use of causal inference techniques to integrate omics and GWAS data has the potential to improve biological understanding of the pathways leading to disease. Our study demonstrates the suitability of various methods for performing causal inference under several biologically plausible scenarios.

Epigenome-wide association data implicates DNA methylation-mediated genetic risk in psoriasis

BACKGROUND

Psoriasis is a chronic inflammatory skin disease characterized by epidermal hyperproliferation and altered keratinocyte differentiation and inflammation and is caused by the interplay of genetic and environmental factors. Previous studies have revealed that DNA methylation (DNAm) and genetic makers are closely associated with psoriasis, and strong evidences have shown that DNAm can be controlled by genetic factors, which attracted us to evaluate the relationship among DNAm, genetic makers, and disease status.

METHODS

We utilized the genome-wide methylation data of psoriatic skin (PP, N = 114) and unaffected control skin (NN, N = 62) tissue samples in our previous study, and we performed whole-genome genotyping with peripheral blood of the same samples to evaluate the underlying genetic effect on skin DNA methylation. Causal inference test (CIT) was used to assess whether DNAm regulate genetic variation and gain a better understanding of the epigenetic basis of psoriasis susceptibility.

RESULTS

We identified 129 SNP-CpG pairs achieving the significant association threshold, which constituted 28 unique methylation quantitative trait loci (MethQTL) and 34 unique CpGs. There are 18 SNPs were associated with psoriasis at a Bonferoni-corrected P < 0.05, and these 18 SNPs formed 93 SNP-CpG pairs with 17 unique CpG sites. We found that 11 of 93 SNP-CpG pairs, composed of 5 unique SNPs and 3 CpG sites, presented a methylation-mediated relationship between SNPs and psoriasis. The 3 CpG sites were located on the body of C1orf106, the TSS1500 promoter region of DMBX1 and the body of SIK3.

CONCLUSIONS

This study revealed that DNAm of some genes can be controlled by genetic factors and also mediate risk variation for psoriasis in Chinese Han population and provided novel molecular insights into the pathogenesis of psoriasis.

Challenges and novel approaches for investigating molecular mediation

Understanding mediation is useful for identifying intermediates lying between an exposure and an outcome which, when intervened upon, will block (some or all of) the causal pathway between the exposure and outcome. Mediation approaches used in conventional epidemiology have been adapted to understanding the role of molecular intermediates in situations of high-dimensional omics data with varying degrees of success. In particular, the limitations of observational epidemiological study including confounding, reverse causation and measurement error can afflict conventional mediation approaches and may lead to incorrect conclusions regarding causal effects. Solutions to analysing mediation which overcome these problems include the use of instrumental variable methods such as Mendelian randomization, which may be applied to evaluate causality in increasingly complex networks of omics data.

Association of six CpG-SNPs in the inflammation-related genes with coronary heart disease

Background

Chronic inflammation has been widely considered to be the major risk factor of coronary heart disease (CHD). The goal of our study was to explore the possible association with CHD for inflammation-related single nucleotide polymorphisms (SNPs) involved in cytosine-phosphate-guanine (CpG) dinucleotides. A total of 784 CHD patients and 739 non-CHD controls were recruited from Zhejiang Province, China. Using the Sequenom MassARRAY platform, we measured the genotypes of six inflammation-related CpG-SNPs, including IL1B rs16944, IL1R2 rs2071008, PLA2G7 rs9395208, FAM5C rs12732361, CD40 rs1800686, and CD36 rs2065666). Allele and genotype frequencies were compared between CHD and non-CHD individuals using the CLUMP22 software with 10,000 Monte Carlo simulations.

Results

Allelic tests showed that PLA2G7 rs9395208 and CD40 rs1800686 were significantly associated with CHD. Moreover, IL1B rs16944, PLA2G7 rs9395208, and CD40 rs1800686 were shown to be associated with CHD under the dominant model. Further gender-based subgroup tests showed that one SNP (CD40 rs1800686) and two SNPs (FAM5C rs12732361 and CD36 rs2065666) were associated with CHD in females and males, respectively. And the age-based subgroup tests indicated that PLA2G7 rs9395208, IL1B rs16944, and CD40 rs1800686 were associated with CHD among individuals younger than 55, younger than 65, and over 65, respectively.

Conclusions

In conclusion, all the six inflammation-related CpG-SNPs (rs16944, rs2071008, rs12732361, rs2065666, rs9395208, and rs1800686) were associated with CHD in the combined or subgroup tests, suggesting an important role of inflammation in the risk of CHD.

Toward the integration of Omics data in epidemiological studies: still a "long and winding road"

Primary and secondary prevention can highly benefit a personalized medicine approach through the accurate discrimination of individuals at high risk of developing a specific disease from those at moderate and low risk. To this end precise risk prediction models need to be built. This endeavor requires a precise characterization of the individual exposome, genome, and phenome. Massive molecular omics data representing the different layers of the biological processes of the host and the nonhost will enable to build more accurate risk prediction models. Epidemiologists aim to integrate omics data along with important information coming from other sources (questionnaires, candidate markers) that has been proved to be relevant in the discrimination risk assessment of complex diseases. However, the integrative models in large-scale epidemiologic research are still in their infancy and they face numerous challenges, some of them at the analytical stage. So far, there are a small number of studies that have integrated more than two omics data sets, and the inclusion of non-omics data in the same models is still missing in most of studies. In this contribution, we aim at approaching the omics and non-omics data integration from the epidemiology scope by considering the "massive" inclusion of variables in the risk assessment and predictive models. We also provide already available examples of integrative contributions in the field, propose analytical strategies that allow considering both omics and non-omics data in the models, and finally review the challenges imbedding this type of research.

Allele-specific DNA methylation reinforces PEAR1 enhancer activity

Genetic variation in the PEAR1 locus is linked to platelet reactivity and cardiovascular disease. The major G allele of rs12041331, an intronic cytosine guanine dinucleotide-single-nucleotide polymorphism (CpG-SNP), is associated with higher PEAR1 expression in platelets and endothelial cells than the minor A allele. The molecular mechanism underlying this difference remains elusive. We have characterized the histone modification profiles of the intronic region surrounding rs12041331 and identified H3K4Me1 enhancer-specific enrichment for the region that covers the CpG-SNP. Interestingly, methylation studies revealed that the CpG site is fully methylated in leukocytes of GG carriers. Nuclear protein extracts from megakaryocytes, endothelial cells, vs control HEK-293 cells show a 3-fold higher affinity for the methylated G allele compared with nonmethylated G or A alleles in a gel electrophoretic mobility shift assay. To understand the positive relationship between methylation and gene expression, we studied DNA methylation at 4 different loci of PEAR1 during in vitro megakaryopoiesis. During differentiation, the CpG-SNP remained fully methylated, while we observed rapid methylation increases at the CpG-island overlapping the first 5'-untranslated region exon, paralleling the increased PEAR1 expression. In the same region, A-allele carriers of rs12041331 showed significantly lower DNA methylation at CGI1 compared with GG homozygote. This CpG-island contains binding sites for the methylation-sensitive transcription factor CTCF, whose binding is known to play a role in enhancer activation and/or repression. In conclusion, we report the molecular characterization of the first platelet function-related CpG-SNP, a genetic predisposition that reinforces PEAR1 enhancer activity through allele-specific DNA methylation.

Associations of THBS2 and THBS4 polymorphisms to gastric cancer in a Southeast Chinese population

Thrombospondin-2 (THBS2) and Thrombospondin-4 (THBS4) play an important role in cancer development and progression. However, genetic evidence for their roles in gastric cancer (GC) is lacking. The aim of this study was to explore the association of THBS2/THBS4 polymorphisms with risk and clinicopathological features of GC in a Southeast Chinese population. Eight tagging SNPs in THBS2 and THBS4 were genotyped in 761 GC cases and 739 controls from Chinese case-control sets using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. THBS2/THBS4 mRNA expression was studied in 82 human GC tumors and in mouse stomach tissues by real-time PCR. We found that both THBS2 and THBS4 were abundantly expressed in mouse stomach. THBS4 mRNA expression in human stomach was associated with tumor size (P = 0.002) and tumor-node-metastasis (TNM) (P = 0.010), and THBS2 mRNA expression was associated with the TNM (P = 0.010). Patients with the rs77878919^AG genotype were more prone to developing diffuse-type GC. THBS4 SNPs (rs77878919 and rs7736549) had a modest cumulative effect on the risk of poor prognosis (TNM), with that risk in the highest trend for patients carrying both these unfavorable genotypes. In addition, individuals carrying the THBS4 rs10474606 variant homozygous AA had a modest reduced GC risk. We conclude that THBS2/THBS4 may be functional in playing important role in GC, which was supported by the evidence of the mRNA overexpression in GC and the modest associations of THBS2/THBS4 polymorphisms to GC. These findings might be useful for risk assessment and prognosis prediction of GC.

Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL)

Background

Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of cell-specific methylation markers to find libraries that optimize the accuracy of cell fraction estimates obtained from cell mixture deconvolution.

Results

Application of IDOL to training set consisting of samples with both whole-blood DNA methylation data (Illumina HumanMethylation450 BeadArray (HM450)) and flow cytometry measurements of cell composition revealed an optimized library comprised of 300 CpG sites. When compared existing libraries, the library identified by IDOL demonstrated significantly better overall discrimination of the entire immune cell landscape (p = 0.038), and resulted in improved discrimination of 14 out of the 15 pairs of leukocyte subtypes. Estimates of cell composition across the samples in the training set using the IDOL library were highly correlated with their respective flow cytometry measurements, with all cell-specific R²>0.99 and root mean square errors (RMSEs) ranging from [0.97 % to 1.33 %] across leukocyte subtypes. Independent validation of the optimized IDOL library using two additional HM450 data sets showed similarly strong prediction performance, with all cell-specific R²>0.90 and RMSE<4.00 %. In simulation studies, adjustments for cell composition using the IDOL library resulted in uniformly lower false positive rates compared to competing libraries, while also demonstrating an improved capacity to explain epigenome-wide variation in DNA methylation within two large publicly available HM450 data sets.

Conclusions

Despite consisting of half as many CpGs compared to existing libraries for whole blood mixture deconvolution, the optimized IDOL library identified herein resulted in outstanding prediction performance across all considered data sets and demonstrated potential to improve the operating characteristics of EWAS involving adjustments for cell distribution. In addition to providing the EWAS community with an optimized library for whole blood mixture deconvolution, our work establishes a systematic and generalizable framework for the assembly of libraries that improve the accuracy of cell mixture deconvolution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0943-7) contains supplementary material, which is available to authorized users.

Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols

Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ_adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12–1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ_adjusted = 1.00–1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models.

Association of seven thrombotic pathway gene CpG-SNPs with coronary heart disease

OBJECTIVES

Coronary heart disease (CHD) has been considered a thromboembolic arterial diseases. The aim of this case-control study was to explore whether the CpG-SNPs of the thrombotic pathway genes contributed to the risk of CHD.

METHODS AND MATERIALS

A total of 784 CHD patients and 738 healthy controls were recruited in the current association study, which evaluated 7 CpG-SNPs of the thrombotic pathway genes. The CpG-SNPs included THBS4 rs17878919, CYP2C19 rs12773342, P2RY12 rs1491974, ITGA2 rs26680, FGB rs2227389, F7 rs510317 and F5 rs2269648. SNP genotyping was performed with a Sequenom Mass Spectrometry Genetic Analyzer.

RESULTS

Our results demonstrated that CYP2C19 rs12773342 polymorphism was significantly associated with CHD in the recessive model (χ(2)=5.41, df=1, P=0.020, OR=1.455, 95% CI=1.060-1.996). A breakdown analysis by age showed that the association of CYP2C19 rs12773342 with CHD was mainly found in individuals aged 55-65 (genotype: χ(2)=7.93, df=2, P=0.019; allele: χ(2)=4.45, df=1, P=0.035). In addition, we also observed a significant association between F7 rs510317 polymorphism and CHD in males (genotype: χ(2)=7.24, df=2, P=0.027). There was no significant association with CHD for the remaining CpG-SNPs.

CONCLUSION

Our results supported that the CYP2C19 rs12773342 and F7 rs510317 polymorphisms were associated with CHD in the Han Chinese population.

PON1 as a model for integration of genetic, epigenetic, and expression data on candidate susceptibility genes

Recent genome- and epigenome-wide studies demonstrate that the DNA methylation is controlled in part by genetics, highlighting the importance of integrating genetic and epigenetic data. To better understand molecular mechanisms affecting gene expression, we used the candidate susceptibility gene paraoxonase 1 (PON1) as a model to assess associations of PON1 genetic polymorphisms with DNA methylation and arylesterase activity, a marker of PON1 expression. PON1 has been associated with susceptibility to obesity, cardiovascular disease, and pesticide exposure. In this study, we assessed DNA methylation in 18 CpG sites located along PON1 shores, shelves, and its CpG island in blood specimens collected from newborns and 9-year-old children participating (n = 449) in the CHAMACOS birth cohort study. The promoter polymorphism, PON1_-108 , was strongly associated with methylation, particularly for CpG sites located near the CpG island (P << 0.0005). Among newborns, these relationships were even more pronounced after adjusting for blood cell composition. We also observed significant decreases in arylesterase activity with increased methylation at the same nine CpG sites at both ages. Using causal mediation analysis, we found statistically significant indirect effects of methylation (β(95% confidence interval): 6.9(1.5, 12.4)) providing evidence that DNA methylation mediates the relationship between PON1_-108 genotype and PON1 expression. Our findings show that integration of genetic, epigenetic, and expression data can shed light on the functional mechanisms involving genetic and epigenetic regulation of candidate susceptibility genes like PON1.

CDH13 promoter SNPs with pleiotropic effect on cardiometabolic parameters represent methylation QTLs

CDH13 encodes T-cadherin, a receptor for high molecular weight (HMW) adiponectin and low-density lipoprotein, promoting proliferation and migration of endothelial cells. Genome-wide association studies have mapped multiple variants in CDH13 associated with cardiometabolic traits (CMT) with variable effects across studies. We hypothesized that this heterogeneity might reflect interplay with DNA methylation within the region. Resequencing and EpiTYPER™ assay were applied for the HYPertension in ESTonia/Coronary Artery Disease in Czech (HYPEST/CADCZ; n = 358) samples to identify CDH13 promoter SNPs acting as methylation Quantitative Trait Loci (meQTLs) and to investigate their associations with CMT. In silico data were extracted from genome-wide DNA methylation and genotype datasets of the population-based sample Estonian Genome Center of the University of Tartu (EGCUT; n = 165). HYPEST–CADCZ meta-analysis identified a rare variant rs113460564 as highly significant meQTL for a 134-bp distant CpG site (P = 5.90 × 10⁻⁶; β = 3.19 %). Four common SNPs (rs12443878, rs12444338, rs62040565, rs8060301) exhibited effect on methylation level of up to 3 neighboring CpG sites in both datasets. The strongest association was detected in EGCUT between rs8060301 and cg09415485 (false discovery rate corrected P value = 1.89 × 10⁻³⁰). Simultaneously, rs8060301 showed association with diastolic blood pressure, serum high-density lipoprotein and HMW adiponectin (P < 0.005). Novel strong associations were identified between rare CDH13 promoter meQTLs (minor allele frequency <5 %) and HMW adiponectin: rs2239857 (P = 5.50 × 10⁻⁵, β = −1,841.9 ng/mL) and rs77068073 (P = 2.67 × 10⁻⁴, β = −2,484.4 ng/mL). Our study shows conclusively that CDH13 promoter harbors meQTLs associated with CMTs. It paves the way to deeper understanding of the interplay between DNA variation and methylation in susceptibility to common diseases.