Mendelian Randomization Analysis Identifies CpG Sites as Putative Mediators for Genetic Influences on Cardiovascular Disease Risk

The effect of histo-blood group ABO system transferase (BGAT) on pregnancy related outcomes：A Mendelian randomization study

Protein level of Histo-Blood Group ABO System Transferase (BGAT) has been reported to be associated with cardiometabolic diseases. But its effect on pregnancy related outcomes still remains unclear. Here we conducted a two-sample Mendelian randomization (MR) study to ascertain the putative causal roles of protein levels of BGAT in pregnancy related outcomes. Cis-acting protein quantitative trait loci (pQTLs) robustly associated with protein level of BGAT (P < 5 ×10-8) were used as instruments to proxy the BGAT protein level (N = 35,559, data from deCODE), with two additional pQTL datasets from Fenland (N = 10,708) and INTERVAL (N = 3301) used as validation exposures. Ten pregnancy related diseases and complications were selected as outcomes. We observed that a higher protein level of BGAT showed a putative causal effect on venous complications and haemorrhoids in pregnancy (VH) (odds ratio [OR]=1.19, 95% confidence interval [95% CI]=1.12-1.27, colocalization probability=91%), which was validated by using pQTLs from Fenland and INTERVAL. The Mendelian randomization results further showed effects of the BGAT protein on gestational hypertension (GH) (OR=0.97, 95% CI=0.96-0.99), despite little colocalization evidence to support it. Sensitivity analyses, including proteome-wide Mendelian randomization of the cis-acting BGAT pQTLs, showed little evidence of horizontal pleiotropy. Correctively, our study prioritised BGAT as a putative causal protein for venous complications and haemorrhoids in pregnancy. Future epidemiology and clinical studies are needed to investigate whether BGAT can be considered as a drug target to prevent adverse pregnancy outcomes.

Association of intestinal anti-inflammatory drug target genes with psychiatric Disorders: A Mendelian randomization study

INTRODUCTION

Psychiatric disorders present a substantial global public health burden with limited drug options. The gut-brain axis connects inflammatory bowel diseases and psychiatric disorders, which often have comorbidities. While some evidence hints at anti-inflammatory drugs aiding in treating psychiatric conditions, the specific effects of intestinal anti-inflammatory drugs remain unclear.

OBJECTIVES

This study investigates the causal effect of intestinal anti-inflammatory drug targets on psychiatric disorders. We hypothesize that these drug targets may offer new insights into the treatment and prevention of such disorders. Additionally, we explore gut microbiota's mediating role between drug target genes and psychiatric disorders.

METHODS

We performed two-sample Mendelian randomization (MR) using summary data from existing expression quantitative trait loci (eQTL) and protein QTL in the brain, along with public genome-wide association studies of disease. We also explored gut microbiota's mediating effect. The statistics encompassed six psychiatric disorders involving 9,725-500,199 individuals. Colocalization analysis enhanced the MR evidence.

RESULTS

We uncovered a causal link between TPMT (a target of olsalazine) expression in the amygdala and bipolar disorder (BD) risk (odds ratio [OR] = 1.08; P = 4.29 × 10-4). This association was observed even when the sigmoid colon and whole blood eQTL were considered as exposures. Colocalization analysis revealed a shared genetic variant (rs11751561) between TPMT expression and BD, with a posterior probability of 61.6 %. Interestingly, this causal effect was influenced by a decrease in the gut microbiota abundance of the genus Roseburia (effect proportion = 10.05 %). Moreover, elevated ACAT1 expression was associated with higher obsessive-compulsive disorder risk (OR = 1.62; P = 3.64 × 10-4; posterior probability = 3.1 %).

CONCLUSION

These findings provide novel targets for the treatment of psychiatric disorders, underscore the potential of repurposing olsalazine, and emphasize the importance of TPMT and ACAT1 in future drug development.

Cord blood DNA methylation of immune and lipid metabolism genes is associated with maternal triglycerides and child adiposity

OBJECTIVE

Fetal exposures may impact offspring epigenetic signatures and adiposity. The authors hypothesized that maternal metabolic traits associate with cord blood DNA methylation, which, in turn, associates with child adiposity.

METHODS

Fasting serum was obtained in 588 pregnant women (27-34 weeks' gestation), and insulin, glucose, high-density lipoprotein cholesterol, triglycerides, and free fatty acids were measured. Cord blood DNA methylation and child adiposity were measured at birth, 4-6 months, and 4-6 years. The association of maternal metabolic traits with DNA methylation (429,246 CpGs) for differentially methylated probes (DMPs) and regions (DMRs) was tested. The association of the first principal component of each DMR with child adiposity was tested, and mediation analysis was performed.

RESULTS

Maternal triglycerides were associated with the most DMPs and DMRs of all traits tested (261 and 198, respectively, false discovery rate < 0.05). DMRs were near genes involved in immune function and lipid metabolism. Triglyceride-associated CpGs were associated with child adiposity at 4-6 months (32 CpGs) and 4-6 years (2 CpGs). One, near CD226, was observed at both timepoints, mediating 10% and 22% of the relationship between maternal triglycerides and child adiposity at 4-6 months and 4-6 years, respectively.

CONCLUSIONS

DNA methylation may play a role in the association of maternal triglycerides and child adiposity.

DNA methylation-based biomarkers for cardiometabolic-related traits and their importance for risk stratification

Recent findings

The prevalence of cardiometabolic syndrome in adults is increasing worldwide, highlighting the importance of biomarkers for individuals' classification based on their health status. Although cardiometabolic risk scores and diagnostic criteria have been developed aggregating adverse health effects of individual conditions on the overall syndrome, none of them has gained unanimous acceptance. Therefore, novel molecular biomarkers have been developed to better understand the risk, onset and progression of both individual conditions and the overall cardiometabolic syndrome.

Summary

Consistent associations between whole blood DNA methylation (DNAm) levels at several single genomic (i.e. CpG) sites and both individual and aggregated cardiometabolic conditions supported the creation of second-generation DNAm-based cardiometabolic-related biomarkers. These biomarkers linearly combine individual DNAm levels from key CpG sites, selected by a two-step machine learning procedures. They can be used, even retrospectively, in populations with extant whole blood DNAm levels and without observed cardiometabolic phenotypes.

Purpose of review

Here we offer an overview of the second-generation DNAm-based cardiometabolic biomarkers, discussing methodological advancements and implications on the interpretation and generalizability of the findings. We finally emphasize the contribution of DNAm-based biomarkers for risk stratification beyond traditional factors and discuss limitations and future directions of the field.

Methods for large-scale single mediator hypothesis testing: Possible choices and comparisons

Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis,H 0 : α β = 0 ${H}_{0}:\alpha \beta =0$ (α $\alpha $ : effect of the exposure on the mediator after adjusting for confounders;β $\beta $ : effect of the mediator on the outcome after adjusting for exposure and confounders). In this paper, we reviewed three classes of methods for large-scale one at a time mediation hypothesis testing. These methods are commonly used for continuous outcomes and continuous mediators assuming there is no exposure-mediator interaction so that the productα β $\alpha \beta $ has a causal interpretation as the indirect effect. The first class of methods ignores the impact of different structures under the composite null hypothesis, namely, (1)α = 0 , β ≠ 0 $\alpha =0,\beta \ne 0$ ; (2)α ≠ 0 , β = 0 $\alpha \ne 0,\beta =0$ ; and (3)α = β = 0 $\alpha =\beta =0$ . The second class of methods weights the reference distribution under each case of the null to form a mixture reference distribution. The third class constructs a composite test statistic using the three p values obtained under each case of the null so that the reference distribution of the composite statistic is approximatelyU ( 0 , 1 ) $U(0,1)$ . In addition to these existing methods, we developed the Sobel-comp method belonging to the second class, which uses a corrected mixture reference distribution for Sobel's test statistic. We performed extensive simulation studies to compare all six methods belonging to these three classes in terms of the false positive rates (FPRs) under the null hypothesis and the true positive rates under the alternative hypothesis. We found that the second class of methods which uses a mixture reference distribution could best maintain the FPRs at the nominal level under the null hypothesis and had the greatest true positive rates under the alternative hypothesis. We applied all methods to study the mediation mechanism of DNA methylation sites in the pathway from adult socioeconomic status to glycated hemoglobin level using data from the Multi-Ethnic Study of Atherosclerosis (MESA). We provide guidelines for choosing the optimal mediation hypothesis testing method in practice and develop an R package medScan available on the CRAN for implementing all the six methods.

Association between genetically proxied PCSK9 inhibition and prostate cancer risk: A Mendelian randomisation study

BACKGROUND

Prostate cancer (PrCa) is the second most prevalent malignancy in men worldwide. Observational studies have linked the use of low-density lipoprotein cholesterol (LDL-c) lowering therapies with reduced risk of PrCa, which may potentially be attributable to confounding factors. In this study, we performed a drug target Mendelian randomisation (MR) analysis to evaluate the association of genetically proxied inhibition of LDL-c-lowering drug targets on risk of PrCa.

METHODS AND FINDINGS

Single-nucleotide polymorphisms (SNPs) associated with LDL-c (P < 5 × 10-8) from the Global Lipids Genetics Consortium genome-wide association study (GWAS) (N = 1,320,016) and located in and around the HMGCR, NPC1L1, and PCSK9 genes were used to proxy the therapeutic inhibition of these targets. Summary-level data regarding the risk of total, advanced, and early-onset PrCa were obtained from the PRACTICAL consortium. Validation analyses were performed using genetic instruments from an LDL-c GWAS conducted on male UK Biobank participants of European ancestry (N = 201,678), as well as instruments selected based on liver-derived gene expression and circulation plasma levels of targets. We also investigated whether putative mediators may play a role in findings for traits previously implicated in PrCa risk (i.e., lipoprotein a (Lp(a)), body mass index (BMI), and testosterone). Applying two-sample MR using the inverse-variance weighted approach provided strong evidence supporting an effect of genetically proxied inhibition of PCSK9 (equivalent to a standard deviation (SD) reduction in LDL-c) on lower risk of total PrCa (odds ratio (OR) = 0.85, 95% confidence interval (CI) = 0.76 to 0.96, P = 9.15 × 10-3) and early-onset PrCa (OR = 0.70, 95% CI = 0.52 to 0.95, P = 0.023). Genetically proxied HMGCR inhibition provided a similar central effect estimate on PrCa risk, although with a wider 95% CI (OR = 0.83, 95% CI = 0.62 to 1.13, P = 0.244), whereas genetically proxied NPC1L1 inhibition had an effect on higher PrCa risk with a 95% CI that likewise included the null (OR = 1.34, 95% CI = 0.87 to 2.04, P = 0.180). Analyses using male-stratified instruments provided consistent results. Secondary MR analyses supported a genetically proxied effect of liver-specific PCSK9 expression (OR = 0.90 per SD reduction in PCSK9 expression, 95% CI = 0.86 to 0.95, P = 5.50 × 10-5) and circulating plasma levels of PCSK9 (OR = 0.93 per SD reduction in PCSK9 protein levels, 95% CI = 0.87 to 0.997, P = 0.04) on PrCa risk. Colocalization analyses identified strong evidence (posterior probability (PPA) = 81.3%) of a shared genetic variant (rs553741) between liver-derived PCSK9 expression and PrCa risk, whereas weak evidence was found for HMGCR (PPA = 0.33%) and NPC1L1 expression (PPA = 0.38%). Moreover, genetically proxied PCSK9 inhibition was strongly associated with Lp(a) levels (Beta = -0.08, 95% CI = -0.12 to -0.05, P = 1.00 × 10-5), but not BMI or testosterone, indicating a possible role for Lp(a) in the biological mechanism underlying the association between PCSK9 and PrCa. Notably, we emphasise that our estimates are based on a lifelong exposure that makes direct comparisons with trial results challenging.

CONCLUSIONS

Our study supports a strong association between genetically proxied inhibition of PCSK9 and a lower risk of total and early-onset PrCa, potentially through an alternative mechanism other than the on-target effect on LDL-c. Further evidence from clinical studies is needed to confirm this finding as well as the putative mediatory role of Lp(a).

Plasma MicroRNA Signature of Alcohol Consumption: The Rotterdam Study

BACKGROUND

MicroRNAs (miRNAs) represent a class of noncoding RNAs that regulate gene expression and are implicated in the pathogenesis of different diseases. Alcohol consumption might affect the expression of miRNAs, which in turn could play a role in risk of diseases.

OBJECTIVES

We investigated whether plasma concentrations of miRNAs are altered by alcohol consumption. Given the existing evidence showing the link between alcohol and liver diseases, we further explored the extent to which these associations are mediated by miRNAs.

METHODS

Profiling of plasma miRNAs was conducted using the HTG EdgeSeq miRNA Whole Transcriptome Assay in 1933 participants of the Rotterdam Study. Linear regression was implemented to explore the link between alcohol consumption (glasses/d) and miRNA concentrations, adjusted for age, sex, cohort, BMI, and smoking. Sensitivity analysis for alcohol categories (nondrinkers, light drinkers, and heavy drinkers) was performed, where light drinkers corresponded to 0-2 glasses/d in men and 0-1 glasses/d in women, and heavy drinkers to >2 glasses/d in men and >1 glass/d in women. Moreover, we utilized the alcohol-associated miRNAs to explore their potential mediatory role between alcohol consumption and liver-related traits. Finally, we retrieved putative target genes of identified miRNAs to gain an understanding of the molecular pathways concerning alcohol consumption.

RESULTS

Plasma concentrations of miR-193b-3p, miR-122-5p, miR-3937, and miR-4507 were significantly associated with alcohol consumption surpassing the Bonferroni-corrected P < 8.46 × 10-5. The top significant association was observed for miR-193b-3p (β = 0.087, P = 2.90 × 10-5). Furthermore, a potential mediatory role of miR-3937 and miR-122-5p was observed between alcohol consumption and liver traits. Pathway analysis of putative target genes revealed involvement in biological regulation and cellular processes.

CONCLUSIONS

This study indicates that alcohol consumption is associated with plasma concentrations of 4 miRNAs. We outline a potential mediatory role of 2 alcohol-associated miRNAs (miR-3937 and miR-122-5p), laying the groundwork for further exploration of miRNAs as potential mediators between lifestyle factors and disease development.

Genome-wide screening identifies DNA methylation sites that regulate the blood proteome

Background: Identifying DNA methylation sites that regulate the blood proteome is important for biomedical purposes. Materials & methods: Here the authors performed a genome-wide search to find DNA methylation sites that impact proteins. Results: The authors identified 165 methylation sites associated with 138 proteins. The authors noted hotspot genomic regions that control the levels of several proteins. For example, methylation of the ABO locus impacted 37 proteins and contributed to cardiometabolic comorbidities, including the severity of SARS-CoV-2 infection. The authors made these findings publicly available as a Unix software that identifies methylation sites that cause disease and reveals the underlying proteins. The authors underlined the software application by showing that components of innate immunity contribute to systolic blood pressure. Conclusion: This study provides a catalog of DNA methylation sites that regulate the proteome, and the results are available as freeware for biological insight.

Bioinformatic Prioritization and Functional Annotation of GWAS-Based Candidate Genes for Primary Open-Angle Glaucoma

Background: Primary open-angle glaucoma (POAG) is the most prevalent glaucoma subtype, but its exact etiology is still unknown. In this study, we aimed to prioritize the most likely ‘causal’ genes and identify functional characteristics and underlying biological pathways of POAG candidate genes. Methods: We used the results of a large POAG genome-wide association analysis study from GERA and UK Biobank cohorts. First, we performed systematic gene-prioritization analyses based on: (i) nearest genes; (ii) nonsynonymous single-nucleotide polymorphisms; (iii) co-regulation analysis; (iv) transcriptome-wide association studies; and (v) epigenomic data. Next, we performed functional enrichment analyses to find overrepresented functional pathways and tissues. Results: We identified 142 prioritized genes, of which 64 were novel for POAG. BICC1, AFAP1, and ABCA1 were the most highly prioritized genes based on four or more lines of evidence. The most significant pathways were related to extracellular matrix turnover, transforming growth factor-β, blood vessel development, and retinoic acid receptor signaling. Ocular tissues such as sclera and trabecular meshwork showed enrichment in prioritized gene expression (>1.5 fold). We found pleiotropy of POAG with intraocular pressure and optic-disc parameters, as well as genetic correlation with hypertension and diabetes-related eye disease. Conclusions: Our findings contribute to a better understanding of the molecular mechanisms underlying glaucoma pathogenesis and have prioritized many novel candidate genes for functional follow-up studies.

Genome-Wide Mapping of Plasma IgG N-Glycan Quantitative Trait Loci Identifies a Potentially Causal Association between IgG N-Glycans and Rheumatoid Arthritis

Observational studies highlight associations of IgG N-glycosylation with rheumatoid arthritis (RA); however, the causality between these conditions remains to be determined. Standard and multivariable two-sample Mendelian randomization (MR) analyses integrating a summary genome-wide association study for RA and IgG N-glycan quantitative trait loci (IgG N-glycan-QTL) data were performed to explore the potentially causal associations of IgG N-glycosylation with RA. After correcting for multiple testing (p < 2 × 10^-3), the standard MR analysis based on the inverse-variance weighted method showed a significant association of genetically instrumented IgG N-glycan (GP4) with RA (odds ratio_GP4 = 0.906, 95% confidence interval = 0.857-0.958, p = 5.246 × 10^-4). In addition, we identified seven significant associations of genetically instrumented IgG N-glycans with RA by multivariable MR analysis (p < 2 × 10^-3). Results were broadly consistent in sensitivity analyses using MR_Lasso, MR_weighted median, MR_Egger regression, and leave-one-out analysis with different instruments (all p values <0.05). There was limited evidence of pleiotropy bias (all p values > 0.05). In conclusion, our MR analysis incorporating genome-wide association studies and IgG N-glycan-QTL data revealed that IgG N-glycans were potentially causally associated with RA. Our findings shed light on the role of IgG N-glycosylation in the development of RA. Future studies are needed to validate our findings and to explore the underlying physiological mechanisms in the etiology of RA.

ALDH2, ADCY3 and BCMO1 polymorphisms and lifestyle-induced traits are jointly associated with CAD risk in Chinese Han people

BACKGROUNDS

To investigate associations of genetic and environmental factors with coronary artery disease (CAD), we collected medical reports, lifestyle details, and blood samples of 2113 individuals, and then used the polymerase chain reaction (PCR)-ligase detection reaction (LDR) to genotype the targeted 102 SNPs.

METHODS

We adopted elastic net algorithm to build an association model that considered simultaneously genetic and lifestyle/clinical factors associated with CAD in Chinese Han population.

RESULTS

In this study, we developed an all covariates-based model to explain the risk of CAD, which incorporated 8 lifestyle/clinical factors and a gene-score variable calculated from 3 significant SNPs (rs671, rs6751537 and rs11641677), attaining an area under the curve (AUC) value of 0.71. It was found that, in terms of genetic variants, the AA genotype of rs671 in the additive (adjusted odds ratio (OR) = 2.51, p = 0.008) and recessive (adjusted OR = 2.12, p = 0.021) models, the GG genotype of rs6751537 in the additive (adjusted OR = 3.36, p = 0.001) and recessive (adjusted OR = 3.47, p = 0.001) models were associated with increased risk of CAD, while GG genotype of rs11641677 in additive model (adjusted OR = 0.39, p = 0.044) was associated with decreased risk of CAD. In terms of lifestyle/clinical factors, the history of hypertension (unadjusted OR = 2.37, p < 0.001) and dyslipidemia (unadjusted OR = 1.82, p = 0.007), age (unadjusted OR = 1.07, p < 0.001) and waist circumference (unadjusted OR = 1.02, p = 0.05) would significantly increase the risk of CAD, while height (unadjusted OR = 0.97, p = 0.006) and regular intake of chicken (unadjusted OR = 0.78, p = 0.008) reduced the risk of CAD. A significantinteraction was foundbetween rs671 and dyslipidemia (the relative excess risk due to interaction (RERI) = 3.36, p = 0.05).

CONCLUSION

In this study, we constructed an association model and identified a set of SNPs and lifestyle/clinical risk factors of CAD in Chinese Han population. By considering both genetic and non-genetic risk factors, the built model may provide implications for CAD pathogenesis and clues for screening tool development in Chinese Han population.

Detecting methylation quantitative trait loci using a methylation random field method

DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Examining the association between adiposity and DNA methylation: A systematic review and meta-analysis

Obesity is associated with widespread differential DNA methylation (DNAm) patterns, though there have been limited overlap in the obesity-associated cytosine-guanine nucleotide pair (CpG) sites that have been identified in the literature. We systematically searched four databases for studies published until January 2020. Eligible studies included cross-sectional, longitudinal, or intervention studies examining adiposity and genome-wide DNAm in non-pregnant adults aged 18-75 in all tissue types. Study design and results were extracted in the descriptive review. Blood-based DNAm results in body mass index (BMI) and waist circumference (WC) were meta-analyzed using weighted sum of Z-score meta-analysis. Of the 10,548 studies identified, 46 studies were included in the systematic review with 18 and nine studies included in the meta-analysis of BMI and WC, respectively. In the blood, 77 and four CpG sites were significant in three or more studies of BMI and WC, respectively. Using a genome-wide threshold for significance, 52 blood-based CpG sites were significantly associated with BMI. These sites have previously been associated with many obesity-related diseases including type 2 diabetes, cardiovascular disease, Crohn's disease, and depression. Our study shows that DNAm at 52 CpG sites represent potential mediators of obesity-associated chronic diseases and may be novel intervention or therapeutic targets to protect against obesity-associated chronic diseases.

Novel DNA methylation loci and genes showing pleiotropic association with Alzheimer's dementia: a network Mendelian randomization analysis

Previous genome-wide association studies (GWAS) have identified potential genetic variants involved in the risk of Alzheimer's dementia, but their underlying biological interpretation remains largely unclear. In addition, the effects of DNA methylation and gene expression on Alzheimer's dementia are not well understood. A network summary data-based Mendelian randomization (SMR) analysis was performed integrating cis- DNA methylation quantitative trait loci (mQTL) /cis- gene expression QTL (eQTL) data in the brain and blood, as well as GWAS summarized data for Alzheimer's dementia to evaluate the pleiotropic associations of DNA methylation and gene expression with Alzheimer's dementia and to explore the complex mechanisms underpinning Alzheimer's dementia. After correction for multiple testing (false discovery rate [FDR] P < 0.05) and filtering using the heterogeneity in dependent instruments (HEIDI) test (P_HEIDI>0.01), we identified dozens of DNA methylation sites and genes showing pleiotropic associations with Alzheimer's dementia. We found 22 and 16 potentially causal pathways of Alzheimer's dementia (i.e., SNP→DNA methylation→Gene expression→Alzheimer's dementia) in the brain and blood, respectively. Approximately two-thirds of the identified DNA methylation sites had an influence on gene expression and the expression of almost all the identified genes was regulated by DNA methylation. Our network SMR analysis provided evidence supporting the pleiotropic association of some novel DNA methylation sites and genes with Alzheimer's dementia and revealed possible causal pathways underlying the pathogenesis of Alzheimer's dementia. Our findings shed light on the role of DNA methylation in gene expression and in the development of Alzheimer's dementia.

Methylome-wide Analysis Reveals Epigenetic Marks Associated With Resistance to Tuberculosis in Human Immunodeficiency Virus-Infected Individuals From East Africa

BACKGROUND

Tuberculosis (TB) is the most deadly infectious disease globally and is highly prevalent in the developing world. For individuals infected with both Mycobacterium tuberculosis (Mtb) and human immunodeficiency virus (HIV), the risk of active TB is 10% or more annually. Previously, we identified in a genome-wide association study (GWAS) a region on chromosome 5 associated with resistance to TB, which included epigenetic marks that could influence gene regulation. We hypothesized that HIV-infected individuals exposed to Mtb who remain disease free carry epigenetic changes that strongly protect them from active TB.

METHODS

We conducted a methylome-wide study in HIV-infected, TB-exposed cohorts from Uganda and Tanzania and integrated data from our GWAS.

RESULTS

We identified 3 regions of interest that included markers that were differentially methylated between TB cases and controls with latent TB infection: chromosome 1 (RNF220, P = 4 × 10-5), chromosome 2 (between COPS8 and COL6A3, P = 2.7 × 10-5), and chromosome 5 (CEP72, P = 1.3 × 10-5). These methylation results co-localized with associated single-nucleotide polymorphisms (SNPs), methylation QTLs, and methylation × SNP interaction effects. These markers were in regions with regulatory markers for cells involved in TB immunity and/or lung.

CONCLUSIONS

Epigenetic regulation is a potential biologic factor underlying resistance to TB in immunocompromised individuals that can act in conjunction with genetic variants.

Multi-omics analysis identifies CpGs near G6PC2 mediating the effects of genetic variants on fasting glucose

AIMS/HYPOTHESIS

An elevated fasting glucose level in non-diabetic individuals is a key predictor of type 2 diabetes. Genome-wide association studies (GWAS) have identified hundreds of SNPs for fasting glucose but most of their functional roles in influencing the trait are unclear. This study aimed to identify the mediation effects of DNA methylation between SNPs identified as significant from GWAS and fasting glucose using Mendelian randomisation (MR) analyses.

METHODS

We first performed GWAS analyses for three cohorts (Taiwan Biobank with 18,122 individuals, the Healthy Aging Longitudinal Study in Taiwan with 1989 individuals and the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance with 416 individuals) with individuals of Han Chinese ancestry in Taiwan, followed by a meta-analysis for combining the three GWAS analysis results to identify significant and independent SNPs for fasting glucose. We determined whether these SNPs were methylation quantitative trait loci (meQTLs) by testing their associations with DNA methylation levels at nearby CpG sites using a subsample of 1775 individuals from the Taiwan Biobank. The MR analysis was performed to identify DNA methylation with causal effects on fasting glucose using meQTLs as instrumental variables based on the 1775 individuals. We also used a two-sample MR strategy to perform replication analysis for CpG sites with significant MR effects based on literature data.

RESULTS

Our meta-analysis identified 18 significant (p < 5 × 10^-8) and independent SNPs for fasting glucose. Interestingly, all 18 SNPs were meQTLs. The MR analysis identified seven CpGs near the G6PC2 gene that mediated the effects of a significant SNP (rs2232326) in the gene on fasting glucose. The MR effects for two CpGs were replicated using summary data based on the European population, using an exonic SNP rs2232328 in G6PC2 as the instrument.

CONCLUSIONS/INTERPRETATION

Our analysis results suggest that rs2232326 and rs2232328 in G6PC2 may affect DNA methylation at CpGs near the gene and that the methylation may have downstream effects on fasting glucose. Therefore, SNPs in G6PC2 and CpGs near G6PC2 may reside along the pathway that influences fasting glucose levels. This is the first study to report CpGs near G6PC2, an important gene for regulating insulin secretion, mediating the effects of GWAS-significant SNPs on fasting glucose.

DNA methylation biomarkers of myocardial infarction and cardiovascular disease

BACKGROUND

The epigenetic landscape underlying cardiovascular disease (CVD) is not completely understood and the clinical value of the identified biomarkers is still limited. We aimed to identify differentially methylated loci associated with acute myocardial infarction (AMI) and assess their validity as predictive and causal biomarkers.

RESULTS

We designed a case-control, two-stage, epigenome-wide association study on AMI (n_discovery = 391, n_validation = 204). DNA methylation was assessed using the Infinium MethylationEPIC BeadChip. We performed a fixed-effects meta-analysis of the two samples. 34 CpGs were associated with AMI. Only 12 of them were available in two independent cohort studies (n ~ 1800 and n ~ 2500) with incident coronary and cardiovascular disease (CHD and CVD, respectively). The Infinium HumanMethylation450 BeadChip was used in those two studies. Four of the 12 CpGs were validated in association with incident CHD: AHRR-mapping cg05575921, PTCD2-mapping cg25769469, intergenic cg21566642 and MPO-mapping cg04988978. We then assessed whether methylation risk scores based on those CpGs improved the predictive capacity of the Framingham risk function, but they did not. Finally, we aimed to study the causality of those associations using a Mendelian randomization approach but only one of the CpGs had a genetic influence and therefore the results were not conclusive.

CONCLUSIONS

We have identified 34 CpGs related to AMI. These loci highlight the relevance of smoking, lipid metabolism, and inflammation in the biological mechanisms related to AMI. Four were additionally associated with incident CHD and CVD but did not provide additional predictive information.

Mendelian randomization integrating GWAS and mQTL data identified novel pleiotropic DNA methylation loci for neuropathology of Alzheimer's disease

The pathogenesis of Alzheimer's disease (AD) remains largely unclear. Exploring the genetic/epigenetic loci showing pleiotropic association with the neuropathologies of AD may greatly enhance understanding of the mechanisms underlying the development of AD. In this study, using data from the Religious Orders Study and the Rush Memory and Aging Project, we undertook a Mendelian randomization approach integrating genome-wide association studies (GWASs) and DNA methylation quantitative trait locus data to explore pleiotropic epigenetic loci for AD neuropathologies, including amyloid-β (Aβ) load and tau-containing neurofibrillary tangle density. We performed GWASs of DNA methylation in brain tissues from 592 participants and mapped 60,595 cis-SNP-CpG pairs after correction for multiple testing. By linking cis-DNA methylation quantitative trait locus with GWAS results for Aβ load and tau tangles, we identified 47 CpGs showing pleiotropic association with Aβ load by the Mendelian randomization analysis. We then used gene expression data from 537 individuals and performed quantitative trait methylation analysis. We found that 18 of the 47 CpGs were in cis associated with 25 mRNAs/genes, comprising 41 unique CpG-mRNA/gene pairs. Our findings shed light on the role of DNA methylation in the pathogenesis of Aβ.

Methylation vs. Protein Inflammatory Biomarkers and Their Associations With Cardiovascular Function

DNA methylation data can be used to estimate proportions of leukocyte subsets retrospectively, when directly measured cell counts are unavailable. The methylation-derived neutrophil-to-lymphocyte and lymphocyte-to-monocyte ratios (mdNLRs and mdLMRs) have proven to be particularly useful as indicators of systemic inflammation. As with directly measured NLRs and LMRs, these methylation-derived ratios have been used as prognostic markers for cancer, although little is known about them in relation to other disorders with inflammatory components, such as cardiovascular disease (CVD). Recently, methylation of five genomic cytosine-phosphate-guanine sites (CpGs) was suggested as proxies for mdNLRs, potentially providing a cost-effective alternative when whole-genome methylation data are not available. This study compares seven methylation-derived inflammatory markers (mdNLR, mdLMR, and individual CpG sites) with five conventionally used protein-based inflammatory markers (C-reactive protein, interleukins 6 and 10, tumor-necrosis factor alpha, and interferon-gamma) and a protein-based inflammation score, in their associations with cardiovascular function (CVF) and risk. We found that markers of CVF were more strongly associated with methylation-derived than protein-based markers. In addition, the protein-based and methylation-derived inflammatory markers complemented rather than proxied one another in their contribution to the variance in CVF. There were no strong correlations between the methylation and protein markers either. Therefore, the methylation markers could offer unique information on the inflammatory process and are not just surrogate markers for inflammatory proteins. Although the five CpGs mirrored the mdNLR well in their capacity as proxies, they contributed to CVF above and beyond the mdNLR, suggesting possible added functional relevance. We conclude that methylation-derived indicators of inflammation enable individuals with increased CVD risk to be identified without measurement of protein-based inflammatory markers. In addition, the five CpGs investigated here could be useful surrogates for the NLR when the cost of array data cannot be met. Used in tandem, methylation-derived and protein-based inflammatory markers explain more variance than protein-based inflammatory markers alone.

Leveraging the urban-rural divide for epigenetic research

Urbanization coincides with a complex change in environmental exposure and a rapid increase in noncommunicable diseases (NCDs). Epigenetics, including DNA methylation (DNAm), is thought to mediate part of the association between genetic/environmental exposure and NCDs. The urban-rural divide provides a unique opportunity to investigate the effect of the combined presence of multiple forms of environmental exposure on DNAm and the related increase in disease risk. This review evaluates the ability of three epidemiological study designs (migration, income-comparative and urban-rural designs) to investigate the role of DNAm in the association between urbanization and the rise in NCD prevalence. We also discuss the ability of each study design to address the gaps in the current literature, including the complex methylation-mediated risk attributable to the cluster of forms of exposure characterizing urban and rural living, while providing a platform for developing countries to leverage their demographic discrepancies in future research ventures.

Integrative analysis of Mendelian randomization and Bayesian colocalization highlights four genes with putative BMI-mediated causal pathways to diabetes

Genome-wide association studies have identified hundreds of single nucleotide polymorphisms (SNPs) that are associated with BMI and diabetes. However, lack of adequate data has for long time prevented investigations on the pathogenesis of diabetes where BMI was a mediator of the genetic causal effects on this disease. Of our particular interest is the underlying causal mechanisms of diabetes. We leveraged the summary statistics reported in two studies: UK Biobank (N = 336,473) and Genetic Investigation of ANthropometric Traits (GIANT, N = 339,224) to investigate BMI-mediated genetic causal pathways to diabetes. We first estimated the causal effect of BMI on diabetes by using four Mendelian randomization methods, where a total of 76 independent BMI-associated SNPs (R² ≤ 0.001, P < 5 × 10⁻⁸) were used as instrumental variables. It was consistently shown that higher level of BMI (kg/m²) led to increased risk of diabetes. We then applied two Bayesian colocalization methods and identified shared causal SNPs of BMI and diabetes in genes TFAP2B, TCF7L2, FTO and ZC3H4. This study utilized integrative analysis of Mendelian randomization and colocalization to uncover causal relationships between genetic variants, BMI and diabetes. It highlighted putative causal pathways to diabetes mediated by BMI for four genes.

Improving causality in microbiome research: can human genetic epidemiology help?

Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studies have been unconvincing in their ability to offer causal evidence due to their observational nature, where confounding by lifestyle and behavioural factors, reverse causation and bias are important limitations. Whilst randomized controlled trials have made steps towards understanding the causal role played by the gut microbiome in disease, they are expensive and time-consuming. This evidence that has not been translated between model systems impedes opportunities for harnessing the gut microbiome for improving population health. Therefore, there is a need for alternative approaches to interrogate causality in the context of gut microbiome research. The integration of human genetics within population health sciences have proved successful in facilitating improved causal inference (e.g., with Mendelian randomization [MR] studies) and characterising inherited disease susceptibility. MR is an established method that employs human genetic variation as natural "proxies" for clinically relevant (and ideally modifiable) traits to improve causality in observational associations between those traits and health outcomes. Here, we focus and discuss the utility of MR within the context of human gut microbiome research, review studies that have used this method and consider the strengths, limitations and challenges facing this research. Specifically, we highlight the requirements for careful examination and interpretation of derived causal estimates and host (i.e., human) genetic effects themselves, triangulation across multiple study designs and inter-disciplinary collaborations. Meeting these requirements will help support or challenge causality of the role played by the gut microbiome on human health to develop new, targeted therapies to alleviate disease symptoms to ultimately improve lives and promote good health.

Deary I, Liu Q, Liu S, Elosua R, DeMeo DL, Ordovás JM. Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics

Background Epigenome-wide association studies for cardiometabolic risk factors have discovered multiple loci associated with incident cardiovascular disease (CVD). However, few studies have sought to directly optimize a predictor of CVD risk. Furthermore, it is challenging to train multivariate models across multiple studies in the presence of study- or batch effects. Methods and Results Here, we analyzed existing DNA methylation data collected using the Illumina HumanMethylation450 microarray to create a predictor of CVD risk across 3 cohorts: Women's Health Initiative, Framingham Heart Study Offspring Cohort, and Lothian Birth Cohorts. We trained Cox proportional hazards-based elastic net regressions for incident CVD separately in each cohort and used a recently introduced cross-study learning approach to integrate these individual scores into an ensemble predictor. The methylation-based risk score was associated with CVD time-to-event in a held-out fraction of the Framingham data set (hazard ratio per SD=1.28, 95% CI, 1.10-1.50) and predicted myocardial infarction status in the independent REGICOR (Girona Heart Registry) data set (odds ratio per SD=2.14, 95% CI, 1.58-2.89). These associations remained after adjustment for traditional cardiovascular risk factors and were similar to those from elastic net models trained on a directly merged data set. Additionally, we investigated interactions between the methylation-based risk score and both genetic and biochemical CVD risk, showing preliminary evidence of an enhanced performance in those with less traditional risk factor elevation. Conclusions This investigation provides proof-of-concept for a genome-wide, CVD-specific epigenomic risk score and suggests that DNA methylation data may enable the discovery of high-risk individuals who would be missed by alternative risk metrics.

Improving causality in microbiome research: can human genetic epidemiology help?

Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studies have been unconvincing in their ability to offer causal evidence due to their observational nature, where confounding by lifestyle and behavioural factors, reverse causation and bias are important limitations. Whilst randomized controlled trials have made steps towards understanding the causal role played by the gut microbiome in disease, they are expensive and time-consuming. This evidence that has not been translated between model systems impedes opportunities for harnessing the gut microbiome for improving population health. Therefore, there is a need for alternative approaches to interrogate causality in the context of gut microbiome research. The integration of human genetics within population health sciences have proved successful in facilitating improved causal inference (e.g., with Mendelian randomization [MR] studies) and characterising inherited disease susceptibility. MR is an established method that employs human genetic variation as natural "proxies" for clinically relevant (and ideally modifiable) traits to improve causality in observational associations between those traits and health outcomes. Here, we focus and discuss the utility of MR within the context of human gut microbiome research, review studies that have used this method and consider the strengths, limitations and challenges facing this research. Specifically, we highlight the requirements for careful examination and interpretation of derived causal estimates and host (i.e., human) genetic effects themselves, triangulation across multiple study designs and inter-disciplinary collaborations. Meeting these requirements will help support or challenge causality of the role played by the gut microbiome on human health to develop new, targeted therapies to alleviate disease symptoms to ultimately improve lives and promote good health.

Smoking, DNA Methylation, and Lung Function: a Mendelian Randomization Analysis to Investigate Causal Pathways

Whether smoking-associated DNA methylation has a causal effect on lung function has not been thoroughly evaluated. We first investigated the causal effects of 474 smoking-associated CpGs on forced expiratory volume in 1 s (FEV1) in UK Biobank (n = 321,047) by using two-sample Mendelian randomization (MR) and then replicated this investigation in the SpiroMeta Consortium (n = 79,055). Second, we used two-step MR to investigate whether DNA methylation mediates the effect of smoking on FEV1. Lastly, we evaluated the presence of horizontal pleiotropy and assessed whether there is any evidence for shared causal genetic variants between lung function, DNA methylation, and gene expression by using a multiple-trait colocalization ("moloc") framework. We found evidence of a possible causal effect for DNA methylation on FEV1 at 18 CpGs (p < 1.2 × 10-4). Replication analysis supported a causal effect at three CpGs (cg21201401 [LIME1 and ZGPAT], cg19758448 [PGAP3], and cg12616487 [EML3 and AHNAK] [p < 0.0028]). DNA methylation did not clearly mediate the effect of smoking on FEV1, although DNA methylation at some sites might influence lung function via effects on smoking. By using "moloc", we found evidence of shared causal variants between lung function, gene expression, and DNA methylation. These findings highlight potential therapeutic targets for improving lung function and possibly smoking cessation, although larger, tissue-specific datasets are required to confirm these results.

The Causal Relationship of Circulating Triglyceride and Glycated Hemoglobin: A Mendelian Randomization Study

CONTEXT

The association between circulating triglyceride (TG) and glycated hemoglobin A1c (HbA1c), a biomarker for type 2 diabetes, has been widely addressed, but the causal direction of the relationship is still ambiguous.

OBJECTIVE

To confirm the causal relationship between TG and HbA1c by using bidirectional and 2-step Mendelian randomization (MR) approaches.

METHODS

We carried out a bidirectional MR approach using the summarized results from the public database to examine any potential causal effects between serum TG and HbA1c in 16 000 individuals of the Taiwan Biobank cohort. We used the MR estimate and the MR inverse variance-weighted method to reveal that relationship between TG and HbA1c. To further determine whether the DNA methylation at specific sequences mediate the causal pathway between TG and HbA1c, using the 2-step MR approach.

RESULTS

We identified that a single-unit increase in TG measured via log transformation of mg/dL data was associated with a significant increase of 10 units of HbA1c (95% CI = 1.05-18.95, P = 0.029). In contrast, the genetic determinants of HbA1c do not contribute to the amount of circulating TG (beta = 1.75, 95% CI = -11.50 to 14.90). Sensitivity analyses, included the weighted-median approach and MR-Egger regression, were performed to confirm no pleiotropic effect among these instrumental variables. Furthermore, we identified the genetic variant, rs1823200, is associated with both methylation of the CpG site adjacent to CADPS gene and HbA1c level.

CONCLUSION

Our study suggests that higher circulating TG can have an affect on genomic methylation status, ultimately causing elevated level of circulating HbA1c.

Genetically Predicted Levels of DNA Methylation Biomarkers and Breast Cancer Risk: Data From 228 951 Women of European Descent

BACKGROUND

DNA methylation plays a critical role in breast cancer development. Previous studies have identified DNA methylation marks in white blood cells as promising biomarkers for breast cancer. However, these studies were limited by low statistical power and potential biases. Using a new methodology, we investigated DNA methylation marks for their associations with breast cancer risk.

METHODS

Statistical models were built to predict levels of DNA methylation marks using genetic data and DNA methylation data from HumanMethylation450 BeadChip from the Framingham Heart Study (n = 1595). The prediction models were validated using data from the Women's Health Initiative (n = 883). We applied these models to genomewide association study (GWAS) data of 122 977 breast cancer patients and 105 974 controls to evaluate if the genetically predicted DNA methylation levels at CpG sites (CpGs) are associated with breast cancer risk. All statistical tests were two-sided.

RESULTS

Of the 62 938 CpG sites CpGs investigated, statistically significant associations with breast cancer risk were observed for 450 CpGs at a Bonferroni-corrected threshold of P less than 7.94 × 10-7, including 45 CpGs residing in 18 genomic regions, that have not previously been associated with breast cancer risk. Of the remaining 405 CpGs located within 500 kilobase flaking regions of 70 GWAS-identified breast cancer risk variants, the associations for 11 CpGs were independent of GWAS-identified variants. Integrative analyses of genetic, DNA methylation, and gene expression data found that 38 CpGs may affect breast cancer risk through regulating expression of 21 genes.

CONCLUSION

Our new methodology can identify novel DNA methylation biomarkers for breast cancer risk and can be applied to other diseases.

Mendelian Randomization Identifies CpG Methylation Sites With Mediation Effects for Genetic Influences on BMD in Peripheral Blood Monocytes

Osteoporosis is mainly characterized by low bone mineral density (BMD) and is an increasingly serious public health concern. DNA methylation is a major epigenetic mechanism that may contribute to the variation in BMD and may mediate the effects of genetic and environmental factors of osteoporosis. In this study, we performed an epigenome-wide DNA methylation analysis in peripheral blood monocytes of 118 Caucasian women with extreme BMD values. Further, we developed and implemented a novel analytical framework that integrates Mendelian randomization with genetic fine mapping and colocalization to evaluate the causal relationships between DNA methylation and BMD phenotype. We identified 2,188 differentially methylated CpGs (DMCs) between the low and high BMD groups and distinguished 30 DMCs that may mediate the genetic effects on BMD. The causal relationship was further confirmed by eliminating the possibility of horizontal pleiotropy, linkage effect and reverse causality. The fine-mapping analysis determined 25 causal variants that are most likely to affect the methylation levels at these mediator DMCs. The majority of the causal methylation quantitative loci and DMCs reside within cell type-specific histone mark peaks, enhancers, promoters, promoter flanking regions and CTCF binding sites, supporting the regulatory potentials of these loci. The established causal pathways from genetic variant to BMD phenotype mediated by DNA methylation provide a gene list to aid in designing future functional studies and lead to a better understanding of the genetic and epigenetic mechanisms underlying the variation of BMD.

Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci

Large genome-wide association studies (GWAS) have identified loci that are associated with complex traits and diseases, but index variants are often not causal and reside in non-coding regions of the genome. To gain a better understanding of the relevant biological mechanisms, intermediate traits such as gene expression and protein levels are increasingly being investigated because these are likely mediators between genetic variants and disease outcome. Genetic variants associated with intermediate traits, termed molecular quantitative trait loci (molQTLs), can then be used as instrumental variables in a Mendelian randomization (MR) approach to identify the causal features and mechanisms of complex traits. Challenges such as pleiotropy and the non-specificity of molQTLs remain, and further approaches and methods need to be developed.

A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

Developing insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. In this study, we apply the principles of Mendelian randomization to systematically evaluate transcriptome-wide associations between gene expression (across 48 different tissue types) and 395 complex traits. Our findings indicate that variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. Moreover, detailed investigations of our results highlight tissue-specific associations, drug validation opportunities, insight into the likely causal pathways for trait-associated variants and also implicate putative associations at loci yet to be implicated in disease susceptibility. Similar evaluations can be conducted at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

Improving causality in microbiome research: can human genetic epidemiology help?

Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studies have been unconvincing in their ability to offer causal evidence due to their observational nature, where confounding by lifestyle and behavioural factors, reverse causation and bias are important limitations. Whilst randomized controlled trials have made steps towards understanding the causal role played by the gut microbiome in disease, they are expensive and time-consuming. This evidence that has not been translated between model systems impedes opportunities for harnessing the gut microbiome for improving population health. Therefore, there is a need for alternative approaches to interrogate causality in the context of gut microbiome research. The integration of human genetics within population health sciences have proved successful in facilitating improved causal inference (e.g., with Mendelian randomization [MR] studies) and characterising inherited disease susceptibility. MR is an established method that employs human genetic variation as natural "proxies" for clinically relevant (and ideally modifiable) traits to improve causality in observational associations between those traits and health outcomes. Here, we focus and discuss the utility of MR within the context of human gut microbiome research, review studies that have used this method and consider the strengths, limitations and challenges facing this research. Specifically, we highlight the requirements for careful examination and interpretation of derived causal estimates and host (i.e., human) genetic effects themselves, triangulation across multiple study designs and inter-disciplinary collaborations. Meeting these requirements will help support or challenge causality of the role played by the gut microbiome on human health to develop new, targeted therapies to alleviate disease symptoms to ultimately improve lives and promote good health.

Whole-genome bisulfite sequencing in systemic sclerosis provides novel targets to understand disease pathogenesis

Background

Systemic sclerosis (SSc) is a rare autoimmune connective tissue disease whose pathogenesis remains incompletely understood. Increasing evidence suggests that both genetic susceptibilities and changes in DNA methylation influence pivotal biological pathways and thereby contribute to the disease. The role of DNA methylation in SSc has not been fully elucidated, because existing investigations of DNA methylation predominantly focused on nucleotide CpGs within restricted genic regions, and were performed on samples containing mixed cell types.

Methods

We performed whole-genome bisulfite sequencing on purified CD4+ T lymphocytes from nine SSc patients and nine controls in a pilot study, and then profiled genome-wide cytosine methylation as well as genetic variations. We adopted robust statistical methods to identify differentially methylated genomic regions (DMRs). We then examined pathway enrichment associated with genes located in these DMRs. We also tested whether changes in CpG methylation were associated with adjacent genetic variation.

Results

We profiled DNA methylation at more than three million CpG dinucleotides genome-wide. We identified 599 DMRs associated with 340 genes, among which 54 genes exhibited further associations with adjacent genetic variation. We also found these genes were associated with pathways and functions that are known to be abnormal in SSc, including Wnt/β-catenin signaling pathway, skin lesion formation and progression, and angiogenesis.

Conclusion

The CD4+ T cell DNA cytosine methylation landscape in SSc involves crucial genes in disease pathogenesis. Some of the methylation patterns are also associated with genetic variation. These findings provide essential foundations for future studies of epigenetic regulation and genome-epigenome interaction in SSc.

Epigenetics of scleroderma: Integrating genetic, ethnic, age, and environmental effects

Scleroderma or systemic sclerosis is thought to result from the interplay between environmental or non-genetic factors in a genetically susceptible individual. Epigenetic modifications are influenced by genetic variation and environmental exposures, and change with chronological age and between populations. Despite progress in identifying genetic, epigenetic, and environmental risk factors, the underlying mechanism of systemic sclerosis remains unclear. Since epigenetics provides the regulatory mechanism linking genetic and non-genetic factors to gene expression, understanding the role of epigenetic regulation in systemic sclerosis will elucidate how these factors interact to cause systemic sclerosis. Among the cell types under tight epigenetic control and susceptible to epigenetic dysregulation, immune cells are critically involved in early pathogenic events in the progression of fibrosis and systemic sclerosis. This review starts by summarizing the changes in DNA methylation, histone modification, and non-coding RNAs associated with systemic sclerosis. It then discusses the role of genetic, ethnic, age, and environmental effects on epigenetic regulation, with a focus on immune system dysregulation. Given the potential of epigenome editing technologies for cell reprogramming and as a therapeutic approach for durable gene regulation, this review concludes with a prospect on epigenetic editing. Although epigenomics in systemic sclerosis is in its infancy, future studies will help elucidate the regulatory mechanisms underpinning systemic sclerosis and inform the design of targeted epigenetic therapies to control its dysregulation.

Appraising the causal relevance of DNA methylation for risk of lung cancer

BACKGROUND

DNA methylation changes in peripheral blood have recently been identified in relation to lung cancer risk. Some of these changes have been suggested to mediate part of the effect of smoking on lung cancer. However, limitations with conventional mediation analyses mean that the causal nature of these methylation changes has yet to be fully elucidated.

METHODS

We first performed a meta-analysis of four epigenome-wide association studies (EWAS) of lung cancer (918 cases, 918 controls). Next, we conducted a two-sample Mendelian randomization analysis, using genetic instruments for methylation at CpG sites identified in the EWAS meta-analysis, and 29 863 cases and 55 586 controls from the TRICL-ILCCO lung cancer consortium, to appraise the possible causal role of methylation at these sites on lung cancer.

RESULTS

Sixteen CpG sites were identified from the EWAS meta-analysis [false discovery rate (FDR) < 0.05], for 14 of which we could identify genetic instruments. Mendelian randomization provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites plays a causal role in lung cancer development (FDR > 0.05), including for cg05575921-AHRR where methylation is strongly associated with both smoke exposure and lung cancer risk.

CONCLUSIONS

The results contrast with previous observational and mediation analysis, which have made strong claims regarding the causal role of DNA methylation. Thus, previous suggestions of a mediating role of methylation at sites identified in peripheral blood, such as cg05575921-AHRR, could be unfounded. However, this study does not preclude the possibility that differential DNA methylation at other sites is causally involved in lung cancer development, especially within lung tissue.

Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease

Identifying methylation quantitative trait loci (meQTLs) and integrating them with disease-associated variants from genome-wide association studies (GWAS) may illuminate functional mechanisms underlying genetic variant-disease associations. Here, we perform GWAS of >415 thousand CpG methylation sites in whole blood from 4170 individuals and map 4.7 million cis- and 630 thousand trans-meQTL variants targeting >120 thousand CpGs. Independent replication is performed in 1347 participants from two studies. By linking cis-meQTL variants with GWAS results for cardiovascular disease (CVD) traits, we identify 92 putatively causal CpGs for CVD traits by Mendelian randomization analysis. Further integrating gene expression data reveals evidence of cis CpG-transcript pairs causally linked to CVD. In addition, we identify 22 trans-meQTL hotspots each targeting more than 30 CpGs and find that trans-meQTL hotspots appear to act in cis on expression of nearby transcriptional regulatory genes. Our findings provide a powerful meQTL resource and shed light on DNA methylation involvement in human diseases.

Differentially methylated CpGs can inform on disease mechanisms and be useful as biomarkers. Here, the authors perform GWAS for DNA methylation in whole blood, cis- and trans-meQTL mapping, followed by Mendelian randomization analysis that links meQTLs with cardiovascular diseases.

Epigenome-wide association study of DNA methylation and microRNA expression highlights novel pathways for human complex traits

DNA methylation (DNAm) and microRNAs (miRNAs) have been implicated in a wide-range of human diseases. While often studied in isolation, DNAm and miRNAs are not independent. We analyzed associations of expression of 283 miRNAs with DNAm at >400K CpG sites in whole blood obtained from 3565 individuals and identified 227 CpGs at which differential methylation was associated with the expression of 40 nearby miRNAs (cis-miR-eQTMs) at FDR<0.01, including 91 independent CpG sites at r² < 0.2. cis-miR-eQTMs were enriched for CpGs in promoter and polycomb-repressed state regions, and 60% were inversely associated with miRNA expression. Bidirectional Mendelian randomization (MR) analysis further identified 58 cis-miR-eQTMCpG-miRNA pairs where DNAm changes appeared to drive miRNA expression changes and opposite directional effects were unlikely. Integration of genetic variants in joint analyses revealed an average partial between cis-miR-eQTM CpGs and miRNAs of 2% after conditioning on site-specific genetic variation, suggesting that DNAm is an important epigenetic regulator of miRNA expression. Finally, two-step MR analysis was performed to identify putatively causal CpGs driving miRNA expression in relation to human complex traits. We found that an imprinted region on 14q32 that was previously identified in relation to age at menarche is enriched with cis-miR-eQTMs. Nine CpGs and three miRNAs at this locus tested causal for age at menarche, reflecting novel epigenetic-driven molecular pathways underlying this complex trait. Our study sheds light on the joint genetic and epigenetic regulation of miRNA expression and provides insights into the relations of miRNAs to their targets and to complex phenotypes.

Using genetic data to strengthen causal inference in observational research

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

An integrative approach to detect epigenetic mechanisms that putatively mediate the influence of lifestyle exposures on disease susceptibility

BACKGROUND

There is mounting evidence that our environment and lifestyle has an impact on epigenetic regulatory mechanisms, such as DNA methylation. It has been suggested that these molecular processes may mediate the effect of risk factors on disease susceptibility, although evidence in this regard has been challenging to uncover. Using genetic variants as surrogate variables, we have used two-sample Mendelian randomization (2SMR) to investigate the potential implications of putative changes to DNA methylation levels on disease susceptibility.

METHODS

To illustrate our approach, we identified 412 CpG sites where DNA methylation was associated with prenatal smoking. We then applied 2SMR to investigate potential downstream effects of these putative changes on 643 complex traits using findings from large-scale genome-wide association studies. To strengthen evidence of mediatory mechanisms, we used multiple-trait colocalization to assess whether DNA methylation, nearby gene expression and complex trait variation were all influenced by the same causal genetic variant.

RESULTS

We identified 22 associations that survived multiple testing (P < 1.89 × 10-7). In-depth follow-up analyses of particular note suggested that the associations between DNA methylation at the ASPSCR1 and REST/POL2RB gene regions, both linked with reduced lung function, may be mediated by changes in gene expression. We validated associations between DNA methylation and traits using independent samples from different stages across the life course.

CONCLUSION

Our approach should prove valuable in prioritizing CpG sites that may mediate the effect of causal risk factors on disease. In-depth evaluations of findings are necessary to robustly disentangle causality from alternative explanations such as horizontal pleiotropy.

Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle

We integrate comeasured gene expression and DNA methylation (DNAme) in 265 human skeletal muscle biopsies from the FUSION study with >7 million genetic variants and eight physiological traits: height, waist, weight, waist-hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and type 2 diabetes. We find hundreds of genes and DNAme sites associated with fasting insulin, waist, and body mass index, as well as thousands of DNAme sites associated with gene expression (eQTM). We find that controlling for heterogeneity in tissue/muscle fiber type reduces the number of physiological trait associations, and that long-range eQTMs (>1 Mb) are reduced when controlling for tissue/muscle fiber type or latent factors. We map genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs). Using Mendelian randomization (MR) and mediation techniques, we leverage these genetic maps to predict 213 causal relationships between expression and DNAme, approximately two-thirds of which predict methylation to causally influence expression. We use MR to integrate FUSION mQTLs, FUSION eQTLs, and GTEx eQTLs for 48 tissues with genetic associations for 534 diseases and quantitative traits. We identify hundreds of genes and thousands of DNAme sites that may drive the reported disease/quantitative trait genetic associations. We identify 300 gene expression MR associations that are present in both FUSION and GTEx skeletal muscle and that show stronger evidence of MR association in skeletal muscle than other tissues, which may partially reflect differences in power across tissues. As one example, we find that increased RXRA muscle expression may decrease lean tissue mass.

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.

Evidence for DNA methylation mediating genetic liability to non-syndromic cleft lip/palate

AIM

To determine if nonsyndromic cleft lip with or without cleft palate (nsCL/P) genetic risk variants influence liability to nsCL/P through gene regulation pathways, such as those involving DNA methylation.

MATERIALS & METHODS

nsCL/P genetic summary data and methylation data from four studies were used in conjunction with Mendelian randomization and joint likelihood mapping to investigate potential mediation of nsCL/P genetic variants.

RESULTS & CONCLUSION

Evidence was found at VAX1 (10q25.3), LOC146880 (17q23.3) and NTN1 (17p13.1), that liability to nsCL/P and variation in DNA methylation might be driven by the same genetic variant, suggesting that genetic variation at these loci may increase liability to nsCL/P by influencing DNA methylation. Follow-up analyses using different tissues and gene expression data provided further insight into possible biological mechanisms.

Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation

BACKGROUND

DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive.

RESULTS

We report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation.

CONCLUSION

Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.

Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease

We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P < 1.39 × 10-08). Joint likelihood mapping provided evidence that the genetic variant which influenced DNA methylation levels for 348 of these associations across 47 traits was also responsible for variation in complex traits. These associations showed a high rate of replication in the BIOS QTL and UK Biobank datasets for 14 selected traits, as 101 of the attempted 128 associations survived multiple testing corrections (P < 3.91 × 10-04). Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 306 of the 348 refined meQTL associations also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. CpG sites were enriched for histone mark peaks in tissue types relevant to their associated trait and implicated genes were enriched across relevant biological pathways. Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in prioritizing candidate loci where DNA methylation may influence traits and help develop mechanistic insight into the aetiology of complex disease.

Identification of loci where DNA methylation potentially mediates genetic risk of type 1 diabetes

The risk of Type 1 Diabetes (T1D) comprises both genetic and environmental components. We investigated whether genetic susceptibility to T1D could be mediated by changes in DNA methylation, an epigenetic mechanism that potentially plays a role in autoimmune diabetes. From enrichment analysis, we found that there was a common genetic influence for both DNA methylation and T1D across the genome, implying that methylation could be either on the causal pathway to T1D or a non-causal biomarker of T1D genetic risk. Using data from a general population comprising blood samples taken at birth (n = 844), childhood (n = 846) and adolescence (n = 907), we then evaluated the associations between 64 top GWAS single nucleotide polymorphisms (SNPs) and DNA methylation levels at 55 non-HLA loci. We identified 95 proximal SNP-cytosine phosphate guanine (CpG) pairs (cis) and 1 distal SNP-CpG association (trans) consistently at birth, childhood, and adolescence. Combining genetic co-localization and Mendelian Randomization analysis, we provided evidence that at 5 loci, ITGB3BP, AFF3, PTPN2, CTSH and CTLA4, DNA methylation is potentially mediating the genetic risk of T1D mainly by influencing local gene expression.

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.

Unravelling the Roles of Susceptibility Loci for Autoimmune Diseases in the Post-GWAS Era

Although genome-wide association studies (GWAS) have identified several hundred loci associated with autoimmune diseases, their mechanistic insights are still poorly understood. The human genome is more complex than single nucleotide polymorphisms (SNPs) that are interrogated by GWAS arrays. Apart from SNPs, it also comprises genetic variations such as insertions-deletions, copy number variations, and somatic mosaicism. Although previous studies suggest that common copy number variations do not play a major role in autoimmune disease risk, it is possible that certain rare genetic variations with large effect sizes are relevant to autoimmunity. In addition, other layers of regulations such as gene-gene interactions, epigenetic-determinants, gene and environmental interactions also contribute to the heritability of autoimmune diseases. This review focuses on discussing why studying these elements may allow us to gain a more comprehensive understanding of the aetiology of complex autoimmune traits.

The MR-Base platform supports systematic causal inference across the human phenome

Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (<ext-link ext-link-type="uri" xlink:href="http://www.mrbase.org">http://www.mrbase.org</ext-link>): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.

The MR-Base platform supports systematic causal inference across the human phenome

Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.