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

Epigenome-wide DNA methylation association study of CHIP provides insight into perturbed gene regulation

With age, hematopoietic stem cells can acquire somatic mutations in leukemogenic genes that confer a proliferative advantage in a phenomenon termed CHIP. How these mutations result in increased risk for numerous age-related diseases remains poorly understood. We conduct a multiracial meta-analysis of EWAS of CHIP in the Framingham Heart Study, Jackson Heart Study, Cardiovascular Health Study, and Atherosclerosis Risk in Communities cohorts (N = 8196) to elucidate the molecular mechanisms underlying CHIP and illuminate how these changes influence cardiovascular disease risk. We functionally validate the EWAS findings using human hematopoietic stem cell models of CHIP. We then use expression quantitative trait methylation analysis to identify transcriptomic changes associated with CHIP-associated CpGs. Causal inference analyses reveal 261 CHIP-associated CpGs associated with cardiovascular traits and all-cause mortality (FDR adjusted p-value < 0.05). Taken together, our study reports the epigenetic changes impacted by CHIP and their associations with age-related disease outcomes.

Distinct methylomic signatures of high-altitude acclimatization and adaptation in the Tibetan Plateau

High altitude presents a challenging environment for human settlement. DNA methylation is an essential epigenetic mechanism that responds to environmental stimuli, but its roles in high-altitude short-term acclimatization (STA) and long-term adaptation (LTA) are poorly understood. Here, we conducted a methylome-wide association study involving 687 native highlanders and 299 acclimatized newcomers in the Tibetan Plateau and 462 native lowlanders to identify differentially methylated sites (DMSs) associated with STA or LTA. We identified 93 and 4070 DMSs for STA and LTA, respectively, which had no overlap, showed opposite asymmetric effect size patterns, and resided near genes enriched in distinct biological pathways/processes (e.g., cell cycle for STA and immune diseases and calcium signalling pathway for LTA). Epigenetic clock analysis revealed evidence of accelerated ageing in the acclimatized newcomers compared to the native lowlanders. Our research provides novel insights into epigenetic regulation in relation to high altitude and intervention strategies for altitude-related ageing or illnesses.

Improving Cardiovascular Health Through the Consideration of Social Factors in Genetics and Genomics Research: A Scientific Statement From the American Heart Association

Cardiovascular health (CVH) is affected by genetic, social, and genomic factors across the life course, yet little research has focused on the interrelationships among them. An extensive body of work has documented the impact of social determinants of health at both the structural and individual levels on CVH, highlighting pathways in which racism, housing, violence, and neighborhood environments adversely affect CVH and contribute to disparities in cardiovascular disease. Genetic factors have also been identified as contributors to risk for cardiovascular disease. Emerging evidence suggests that social factors can interact with genetic susceptibility to affect disease risk. Increasingly, social factors have been shown to affect epigenetic markers such as DNA methylation, which can regulate gene and protein expression. This is a potential biological mechanism through which exposure to poor social determinants of health becomes physically embodied at the molecular level, potentially contributing to the development of suboptimal CVH and chronic disease, thus reinforcing and propagating health disparities. The objective of this statement is to highlight and summarize key literature that has examined the joint associations between social, genetic, and genomic factors and CVH and cardiovascular disease.

Identification of blood eQTLs in older adults

Genome-wide association studies (GWAS) have been successful in identifying genetic variation associated with a wide range of phenotypes. However, more detailed knowledge of their functional significance is required to provide insights into the molecular mechanisms involved. Single Nucleotide Polymorphisms (SNPs) that influence gene expression (Expression Quantitative Trait Loci-eQTLs) may be one such functional mechanism. As gene expression may change over the lifespan, it is important to identify eQTLs for specific age groups. In this study, we aimed to identify blood eQTLs in older adults. Peripheral blood was collected from participants of the Sydney Memory and Ageing Study (Sydney MAS, N = 445, mean age ± SD = 83.38 ± 4.31) and RNA extracted. Gene expression and SNP genotyping were assessed using arrays. Genome-wide eQTL analyses were undertaken using linear mixed-models. Replication was undertaken in the Older Australian Twins Study (OATS, N = 283, mean age = 75.86 ± 5.28). In the discovery cohort (Sydney MAS), a total of 10,468 unique eQTLs were identified influencing the expression of 1402 probes (1229 genes). A total of 6554 eQTLs were replicated in OATS, out of the 7339 that were available for analysis. We have identified, replicated, and described a catalogue of blood eQTLs in older adults. Noting that replication of these results in independent samples of older adults is required given our modest sample size. However, this information will be a useful resource for further studies, particularly in assessing the potential functions of SNPs identified in GWAS focussing on age-related traits.

Epigenome-wide association study of incident type 2 diabetes in Black and White participants from the Atherosclerosis Risk in Communities Study

AIMS/HYPOTHESIS

DNA methylation studies of incident type 2 diabetes in US populations are limited and to our knowledge none include individuals of African descent. We aimed to fill this gap by identifying methylation sites (CpG sites) and regions likely influencing the development of type 2 diabetes using data from Black and White individuals from the USA.

METHODS

We prospectively followed 2091 Black and 1029 White individuals without type 2 diabetes from the Atherosclerosis Risk in Communities study over a median follow-up period of 17 years, and performed an epigenome-wide association analysis of blood-based methylation levels with incident type 2 diabetes using Cox regression. We assessed whether significant CpG sites were associated with incident type 2 diabetes independently of BMI or fasting glucose at baseline. We estimated variation in incident type 2 diabetes accounted for by the major non-genetic risk factors and the significant CpG sites. We also examined groups of methylation sites that were differentially methylated. We performed replication of previously discovered CpG sites associated with prevalent and/or incident type 2 diabetes. All analyses were adjusted for batch effects, cell-type proportions and relevant confounders.

RESULTS

At an epigenome-wide threshold (10-7), we detected seven novel diabetes-associated CpG sites, of which the sites at MICOS10 (cg05380846: HR 0.89, p=8.4 × 10-12), ZNF2 (cg01585592: HR 0.88, p=1.6 × 10-9), JPH3 (cg16696007: HR 0.87, p=7.8 × 10-9) and GPX6 (cg02793507: HR 0.85, p=2.7 × 10-8; cg00647063: HR 1.20, p=2.5 × 10-8) were identified in Black adults; chr17q25 (cg16865890: HR 0.8, p=6.9 × 10-8) in White adults; and chr11p15 (cg13738793: HR 1.11, p=7.7 × 10-8) in the meta-analysed group. The JPH3 and GPX6 sites remained epigenome-wide significant on adjustment for BMI, while only the JPH3 site retained significance after adjusting for fasting glucose. We replicated known type 2 diabetes-associated CpG sites, including cg19693031 at TXNIP, cg00574958 at CPT1A, cg16567056 at PLCB2, cg11024682 at SREBF1, cg08857797 at VPS25 and cg06500161 at ABCG1, three of which were replicated in Black adults at the epigenome-wide threshold and all of which had directionally consistent effects. We observed a modest increase in type 2 diabetes variance explained by the significantly associated CpG sites over and above traditional type 2 diabetes risk factors and fasting glucose (26.2% vs 30.5% in Black adults; 36.9% vs 39.4% in White adults). At the Šidák-corrected significance threshold of 5%, our differentially methylated region (DMR) analyses revealed several clusters of significant CpG sites, including a DMR consisting of a previously discovered CpG site at ADCY7 (pBlack=1.8 × 10-4, pWhite=3.6 × 10-3, pAll=1.6 × 10-9) and a DMR consisting of the promoter region of TP63 (pBlack=7.4 × 10-4, pWhite=3.9 × 10-3, pAll=1.4 × 10-5), which were differentially methylated across all racial and ethnic groups.

CONCLUSIONS/INTERPRETATION

This study illustrates improved discovery of CpG sites and regions by leveraging both individual CpG site analysis and DMR analyses in an unexplored population. Our findings include genes linked to diabetes in experimental studies (e.g. GPX6, JPH3 and TP63). The JPH3 and GPX6 sites were likely associated with incident type 2 diabetes independently of BMI. All the CpG sites except that at JPH3 were likely consequences of elevated glucose. Replication in African-descent individuals of CpG sites previously discovered mostly in individuals of European descent indicates that some of these methylation-type 2 diabetes associations are robust across racial and ethnic groups. This study is a first step towards understanding the influence of methylation on the incidence of type 2 diabetes and its disparity in two major racial and ethnic groups in the USA. It paves the way for future studies to investigate causal relationships between type 2 diabetes and the CpG sites and potentially elucidate molecular targets for intervention.

Genomic and molecular evidence that the lncRNA DSP-AS1 modulates Desmoplakin expression

Cardiac desmosomes are specialized cell junctions responsible for cardiomyocytes mechanical coupling. Mutation in desmosomal genes cause autosomal dominant and recessive familial arrhythmogenic cardiomyopathy. Motivated by evidence that Mendelian diseases share genetic architecture with common complex traits, we assessed whether common variants in any desmosomal gene were associated with cardiac conduction traits in the general population. We analysed data of N=4342 Cooperative Health Research in South Tyrol (CHRIS) study participants. We tested associations between genotype imputed variants covering the five desmosomal genes DSP, JUP, PKP2, DSG2, and DSC2, and P-wave, PR, QRS, and QT electrocardiographic intervals, using linear mixed models. Functional annotation and interrogation of publicly available genome-wide association study resources implicated potential connection with antisense lncRNAs, DNA methylation sites, and complex traits. Causality was tested via two-sample Mendelian randomization (MR) analysis and validated with functional in vitro follow-up in human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs). DSP variant rs2744389 was associated with QRS (P=3.5×10-6), with replication in the Microisolates in South Tyrol (MICROS) study (n=636; P=0.010). Observing that rs2744389 was associated with DSP-AS1 antisense lncRNA but not with DSP expression in multiple GTEx-v8 tissues, we conducted two-sample Mendelian randomization analyses that identified causal effects of DSP-AS1 on DSP expression (P=6.33×10-5; colocalization posterior probability=0.91) and QRS (P=0.015). In hiPSC-CMs, DSP-AS1 expression downregulation through a specific GapmerR matching sequence led to significant DSP upregulation at both mRNA and protein levels. The evidence that DSP-AS1 has a regulatory role on DSP opens the venue for further investigations on DSP-AS1's therapeutic potential for conditions caused by reduced desmoplakin production.

Blood-Based DNA Methylation Biomarkers to Identify Risk and Progression of Cardiovascular Disease

Non-communicable diseases (NCDs) are the leading cause of death worldwide, with cardiovascular disease (CVD) accounting for half of all NCD-related deaths. The biological onset of CVD may occur long before the development of clinical symptoms, hence the urgent need to understand the molecular alterations underpinning CVD, which would facilitate intervention strategies to prevent or delay the onset of the disease. There is evidence to suggest that CVD develops through a complex interplay between genetic, lifestyle, and environmental factors. Epigenetic modifications, including DNA methylation, serve as proxies linking genetics and the environment to phenotypes and diseases. In the past decade, a growing list of studies has implicated DNA methylation in the early events of CVD pathogenesis. In this regard, screening for these epigenetic marks in asymptomatic individuals may assist in the early detection of CVD and serve to predict the response to therapeutic interventions. This review discusses the current literature on the relationship between blood-based DNA methylation alterations and CVD in humans. We highlight a set of differentially methylated genes that show promise as candidates for diagnostic and prognostic CVD biomarkers, which should be prioritized and replicated in future studies across additional populations. Finally, we discuss key limitations in DNA methylation studies, including genetic diversity, interpatient variability, cellular heterogeneity, study confounders, different methodological approaches used to isolate and measure DNA methylation, sample sizes, and cross-sectional study design.

Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment

Exposure to traumatic stress is common in the general population. Variation in the brain's molecular encoding of stress potentially contributes to the heterogeneous clinical outcomes in response to traumatic experiences. For instance, only a minority of those exposed to trauma will develop post-traumatic stress disorder (PTSD). Risk for PTSD is at least partially heritable, with a growing number of genetic factors identified through GWAS. A major limitation of genetic studies is that they capture only the genetic component of risk, whereas PTSD by definition requires an environmental traumatic exposure. Furthermore, the extent, timing, and type of trauma affects susceptibility. Here, we discuss the molecular mechanisms of PTSD risk together with gene × environment interactions, with a focus on how either might inform genetic screening for individuals at high risk for disease, reveal biological mechanisms that might one day yield novel therapeutics, and impact best clinical practices even today. To close, we discuss the interaction of trauma with sex, gender, and race, with a focus on the implications for treatment. Altogether, we suggest that predicting, preventing, and treating PTSD will require integrating both genotypic and environmental information.

Peripheral DNA Methylation of Cortisol- and Serotonin-Related Genes Predicts Hippocampal Volume in a Pediatric Population

Background

Hippocampal volume increases throughout early development and is an important indicator of cognitive abilities and mental health. However, hippocampal development is highly vulnerable to exposures during development, as seen by smaller hippocampal volume and differential epigenetic programming in genes implicated in mental health. However, few studies have investigated hippocampal volume in relation to the peripheral epigenome across development, and even less is known about potential genetic moderators. Therefore, in this study, we explored relationships between hippocampal volume and peripheral DNA methylation of mental health-related genes, specifically NR3C1, FKBP5, and SLC6A4, throughout early development and whether these associations were moderated by age or genotype.

Methods

Bilateral hippocampal volume was computed from T2-weighted images through FreeSurfer, and DNA methylation was measured from saliva using the Illumina MethylationEPIC microarray in a pediatric population (N = 248, females = 112, meanage = 5.13 years, SDage = 3.60 years).

Results

Multiple linear regression and bootstrapping analyses revealed that DNA methylation of NR3C1, FKBP5, and SLC6A4 was associated with hippocampal volume and that these relationships were moderated by age and gene-specific variants.

Conclusions

These findings support the validity of peripheral DNA methylation profiles for indirectly assessing hippocampal volume and development and underscore the importance of genotype and age considerations in research. Therefore, peripheral epigenetic profiles may be a promising avenue for investigating the impacts of early-life stress on brain structure and subsequent mental health outcomes.

Genetic-epigenetic interactions (meQTLs) in orofacial clefts etiology

Objectives

Nonsyndromic orofacial clefts (OFCs) etiology involves multiple genetic and environmental factors with over 60 identified risk loci; however, they account for only a minority of the estimated risk. Epigenetic factors such as differential DNA methylation (DNAm) are also associated with OFCs risk and can alter risk for different cleft types and modify OFCs penetrance. DNAm is a covalent addition of a methyl (CH3) group to the nucleotide cytosine that can lead to changes in expression of the targeted gene. DNAm can be affected by environmental influences and genetic variation via methylation quantitative loci (meQTLs). We hypothesize that aberrant DNAm and the resulting alterations in gene expression play a key role in the etiology of OFCs, and that certain common genetic variants that affect OFCs risk do so by influencing DNAm.

Methods

We used genotype from 10 cleft-associated SNPs and genome-wide DNA methylation data (Illumina 450K array) for 409 cases with OFCs and 456 controls and identified 23 cleft-associated meQTLs. We then used an independent cohort of 362 cleft-discordant sib pairs for replication. We used methylation-specific qPCR to measure methylation levels of each CpG site and combined genotypic and methylation data for an interaction analysis of each SNP-CpG pair using the R package MatrixeQTL in a linear model. We also performed a Paired T-test to analyze differences in DNA methylation between each member of the sibling pairs.

Results

We replicated 9 meQTLs, showing interactions between rs13041247 (MAFB) - cg18347630 (PLCG1) (P=0.04); rs227731 (NOG) - cg08592707 (PPM1E) (P=0.01); rs227731 (NOG) - cg10303698 (CUEDC1) (P=0.001); rs3758249 (FOXE1) - cg20308679 (FRZB) (P=0.04); rs8001641 (SPRY2) - cg19191560 (LGR4) (P=0.04); rs987525(8q24) - cg16561172(MYC) (P=0.00000963); rs7590268(THADA) - cg06873343 (TTYH3) (P=0.04); rs7078160 (VAX1) - cg09487139 (P=0.05); rs560426 (ABCA4/ARHGAP29) - cg25196715 (ABCA4/ARHGAP29) (P=0,03). Paired T-test showed significant differences for cg06873343 (TTYH3) (P=0.04); cg17103269 (LPIN3) (P=0.002), and cg19191560 (LGR4) (P=0.05).

Conclusions

Our results confirm previous evidence that some of the common non-coding variants detected through GWAS studies can influence the risk of OFCs via epigenetic mechanisms, such as DNAm, which can ultimately affect and regulate gene expression. Given the large prevalence of non-coding SNPs in most OFCs genome wide association studies, our findings can potentially address major knowledge gaps, like missing heritability, reduced penetrance, and variable expressivity associated with OFCs phenotypes.

Allele-specific methylation of SSTR4 associated with aging and cognitive functions in patients with schizophrenia

The co-occurrence of alcohol use disorder (AUD) and schizophrenia is prevalent, with a rate of 33.7%. Previous research has suggested a genetic and epigenetic overlap between these two disorders. SSTR4, a member of the somatostatin receptor family, is implicated in various neurological and psychiatric conditions, including cognitive function, AUD, and schizophrenia. However, the role of genetic-epigenetic interactions involving SSTR4 in patients with schizophrenia remains unexplored. In this study, we conducted an integration of publicly available datasets and identified allele-specific methylation patterns in SSTR4. Additionally, we pinpointed several genetic variants (rs17691954, rs11464356, rs3109190, and rs145879288) that influence the pace of aging and cognitive functions (rs705935) through their quantitative trait loci effects on CpG sites within SSTR4.

Involvement of TGF-β, mTOR, and inflammatory mediators in aging alterations during myxomatous mitral valve disease in a canine model

Inflammaging, a state of chronic low-grade inflammation associated with aging, has been linked to the development and progression of various disorders. Cellular senescence, a state of irreversible growth arrest, is another characteristic of aging that contributes to the pathogenesis of cardiovascular pathology. Senescent cells accumulate in tissues over time and secrete many inflammatory mediators, further exacerbating the inflammatory environment. This senescence-associated secretory phenotype can promote tissue dysfunction and remodeling, ultimately leading to the development of age-related cardiovascular pathologies, such as mitral valve myxomatous degeneration. The species-specific form of canine myxomatous mitral valve disease (MMVD) provides a unique opportunity to investigate the early causes of induction of ECM remodeling in mitral valve leaflets in the human form of MMVD. Studies have shown that in both humans and dogs, the microenvironment of the altered leaflets is inflammatory. More recently, the focus has been on the mechanisms leading to the transformation of resting VICs (qVICs) to myofibroblast-like VICs (aVICs). Cells affected by stress fall into a state of cell cycle arrest and become senescent cells. aVICs, under the influence of TGF-β signaling pathways and the mTOR complex, enhance ECM alteration and accumulation of systemic inflammation. This review aims to create a fresh new view of the complex interaction between aging, inflammation, immunosenescence, and MMVD in a canine model, as the domestic dog is a promising model of human aging and age-related diseases.

HSPA4 Expression is Correlated with Melanoma Cell Proliferation, Prognosis, and Immune Regulation

Purpose

Heat shock protein A4 (HSPA4) is associated with a variety of human diseases. However, its function in cutaneous malignant melanoma (CMM) remains uncertain.

Patients and Methods

The gene and protein expression level of HSPA4 in CMM was investigated with public databases. Cell Counting Kit-8 (CCK8) assay was performed to assess the effect of HSPA4 on the proliferation of melanoma cells. Then, the diagnostic and prognostic value of HSPA4 in CMM were analyzed. Gene variations and methylation levels, and the correlation between HSPA4 expression and immune cell infiltration were evaluated, followed by the construction of HSPA4 related protein-protein interaction networks and functional enrichment analysis.

Results

The mRNA and protein expression level of HSPA4 was significantly higher in CMM. Knocking down HSPA4 in A-375 cell line could inhibit tumor cell growth. The receiver operating characteristic (ROC) curve analysis confirmed the diagnostic value of HSPA4. Survival analysis showed that high expression of HSPA4 was associated with poor prognosis. HSPA4 gene alterations were observed in 3% of CMM patients. Five CpG sites are associated with the prognosis of CMM. HSPA4 is negatively correlated with most immune cells in CMM. The protein interaction network shows that HSPA4 is closely related to proteins such as DnaJ heat shock protein family (Hsp40) member B1 (DNAJB1) and DnaJ heat shock protein family (Hsp40) member B6 (DNAJB6), and the expression of DNAJB1 is positively correlated with HSPA4. Functional enrichment analysis indicated that HSPA4 may be associated with immune suppression and immune escape within the tumor microenvironment of CMM.

Conclusion

HSPA4 may participate in the regulation of tumor development and microenvironment, which may be a potential diagnostic and prognostic marker of CMM.

Blood methylation biomarkers are associated with diabetic kidney disease progression in type 1 diabetes

Background

DNA methylation differences are associated with kidney function and diabetic kidney disease (DKD), but prospective studies are scarce. Therefore, we aimed to study DNA methylation in a prospective setting in the Finnish Diabetic Nephropathy Study type 1 diabetes (T1D) cohort.

Methods

We analysed baseline blood sample-derived DNA methylation (Illumina's EPIC array) of 403 individuals with normal albumin excretion rate (early progression group) and 373 individuals with severe albuminuria (late progression group) and followed-up their DKD progression defined as decrease in eGFR to <60 mL/min/1.73m2 (early DKD progression group; median follow-up 13.1 years) or end-stage kidney disease (ESKD) (late DKD progression group; median follow-up 8.4 years). We conducted two epigenome-wide association studies (EWASs) on DKD progression and sought methylation quantitative trait loci (meQTLs) for the lead CpGs to estimate genetic contribution.

Results

Altogether, 14 methylation sites were associated with DKD progression (P<9.4×10-8). Methylation at cg01730944 near CDKN1C and at other CpGs associated with early DKD progression were not correlated with baseline eGFR, whereas late progression CpGs were strongly associated. Importantly, 13 of 14 CpGs could be linked to a gene showing differential expression in DKD or chronic kidney disease. Higher methylation at the lead CpG cg17944885, a frequent finding in eGFR EWASs, was associated with ESKD risk (HR [95% CI] = 2.15 [1.79, 2.58]). Additionally, we replicated meQTLs for cg17944885 and identified ten novel meQTL variants for other CpGs. Furthermore, survival models including the significant CpG sites showed increased predictive performance on top of clinical risk factors.

Conclusions

Our EWAS on early DKD progression identified a podocyte-specific CDKN1C locus. EWAS on late progression proposed novel CpGs for ESKD risk and confirmed previously known sites for kidney function. Since DNA methylation signals could improve disease course prediction, a combination of blood-derived methylation sites could serve as a potential prognostic biomarker.

DNA Methylation Signatures of Cardiovascular Health Provide Insights into Diseases

Background

The association of overall cardiovascular health (CVH) with changes in DNA methylation (DNAm) has not been well characterized.

Methods

We calculated the American Heart Association's Life's Essential 8 (LE8) score to reflect CVH in five cohorts with diverse ancestry backgrounds. Epigenome-wide association studies (EWAS) for LE8 score were conducted, followed by bioinformatic analyses. DNAm loci significantly associated with LE8 score were used to calculate a CVH DNAm score. We examined the association of the CVH DNAm score with incident CVD, CVD-specific mortality, and all-cause mortality.

Results

We identified 609 CpGs associated with LE8 score at false discovery rate (FDR) < 0.05 in the discovery analysis and at Bonferroni corrected P < 0.05 in the multi-cohort replication stage. Most had low-to-moderate heterogeneity (414 CpGs [68.0%] with I2 < 0.2) in replication analysis. Pathway enrichment analyses and phenome-wide association study (PheWAS) search associated these CpGs with inflammatory or autoimmune phenotypes. We observed enrichment for phenotypes in the EWAS catalog, with 29-fold enrichment for stroke (P = 2.4e-15) and 21-fold for ischemic heart disease (P = 7.4e-38). Two-sample Mendelian randomization (MR) analysis showed significant association between 141 CpGs and ten phenotypes (261 CpG-phenotype pairs) at FDR < 0.05. For example, hypomethylation at cg20544516 (MIR33B; SREBF1) associated with lower risk of stroke (P = 8.1e-6). In multivariable prospective analyses, the CVH DNAm score was consistently associated with clinical outcomes across participating cohorts, the reduction in risk of incident CVD, CVD mortality, and all-cause mortality per standard deviation increase in the DNAm score ranged from 19% to 32%, 28% to 40%, and 27% to 45%, respectively.

Conclusions

We identified new DNAm signatures for CVH across diverse cohorts. Our analyses indicate that immune response-related pathways may be the key mechanism underpinning the association between CVH and clinical outcomes.

An atlas of genetic effects on the monocyte methylome across European and African populations

Elucidating the genetic architecture of DNA methylation is crucial for decoding complex disease etiology. However, current epigenomic studies are often limited by incomplete methylation coverage and heterogeneous tissue samples. Here, we present the first comprehensive, multi-ancestry human methylome atlas of purified human monocytes, generated through integrated whole-genome bisulfite sequencing and whole-genome sequencing from 298 European Americans (EA) and 160 African Americans (AA). By analyzing over 25 million methylation sites, we identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis- regions for EA and AA populations, respectively, revealing both shared (880,108 sites) and population-specific regulatory patterns. Furthermore, we developed population-specific DNAm imputation models, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. These models were validated through multi-ancestry analysis of 41 complex traits from the Million Veteran Program. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data

Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data. Through simulations, we show that HBI enhances the estimation of cell-type-specific meQTLs. In real data analyses, we demonstrate that HBI can further improve the functional annotation of genetic variants and identify biologically relevant cell types for complex traits.

Identifying genetic variants that influence the abundance of cell states in single-cell data

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

Fine Mapping Regulatory Variants by Characterizing Native CpG Methylation with Nanopore Long-Read Sequencing

5-methylcytosine (5mC) is the most common chemical modification occurring on the CpG sites across the human genome. Bisulfite conversion combined with short-read whole genome sequencing can capture and quantify the modification at single nucleotide resolution. However, the PCR amplification process could lead to duplicative methylation patterns and introduce 5mC detection bias. Additionally, the limited read length also restricts co-methylation analysis between distant CpG sites. The bisulfite conversion process presents a significant challenge for detecting variant-specific methylation due to the destruction of allele information in the sequencing reads. To address these issues, we sought to characterize the human methylation profiling with the nanopore long-read sequencing, aiming to demonstrate its potential for long-range co-methylation analysis with native modification call and intact allele information retained. In this regard, we first analyzed the nanopore demo data in the adaptive sampling sequencing run targeting all human CpG islands. We applied the linkage disequilibrium (LD) R2 to calculate the co-methylation in nanopore data, and further identified 27,875, 50,481, 26,542 and 51,189 methylation haplotype blocks (MHB) in COLO829, COLO829BL, HCC1395 and HCC1395BL cell lines, respectively. Interestingly, while we found that majority of the co-methylation were in a short range (≤200bp), a small portion (1~3%) showed long distance (≥1,000bp), suggesting potential remote regulatory mechanisms across the genome. To further characterize the epigenetic changes related to transcription factor binding, we profiled the 5mC percentage changes surrounding various motif sites in JASPAR collection and found that CTCF and KLF5 binding sites showed reduced methylation, while FOXE1 and ZNF354A sites showed increased methylation. To further investigate the allele-specific 5mCG in the prostate genome, we designed a target region covering methylation quantitative trait loci (mQTL) and genome-wide association study (GWAS) risk germline variants and generated long reads with adaptive sampling run in the 22Rv1 cell line. To identify the allele-specific methylation in the 22Rv1 cell line, we performed long-read based phasing and compared the 5mCG signals between the two haplotypes. As a result, we identified 6,390 haplotype-specific methylated regions in the 22Rv1 cell line (p-MWU ≤ 1e-5 and delta ≥ 50%). By examining haplotype-specific methylated regions near the phasing variants, we identified examples of allele-specific methylated regions that showed allelespecific accessibility in the ATAC-seq data. By further integrating the ATAC-seq data of 22Rv1, we found that methylation levels were negatively correlated with chromatin accessibility at the genome-wide scale. Our study has revealed native methylome profiling while preserving haplotype information, offering a novel approach to uncovering the regulatory mechanisms of the human prostate genome.

Genetic effects on the skin methylome in healthy older twins

Whole-skin DNA methylation variation has been implicated in several diseases, including melanoma, but its genetic basis has not yet been fully characterized. Using bulk skin tissue samples from 414 healthy female UK twins, we performed twin-based heritability and methylation quantitative trait loci (meQTL) analyses for >400,000 DNA methylation sites. We find that the human skin DNA methylome is on average less heritable than previously estimated in blood and other tissues (mean heritability: 10.02%). meQTL analysis identified local genetic effects influencing DNA methylation at 18.8% (76,442) of tested CpG sites, as well as 1,775 CpG sites associated with at least one distal genetic variant. As a functional follow-up, we performed skin expression QTL (eQTL) analyses in a partially overlapping sample of 604 female twins. Colocalization analysis identified over 3,500 shared genetic effects affecting thousands of CpG sites (10,067) and genes (4,475). Mediation analysis of putative colocalized gene-CpG pairs identified 114 genes with evidence for eQTL effects being mediated by DNA methylation in skin, including in genes implicating skin disease such as ALOX12 and CSPG4. We further explored the relevance of skin meQTLs to skin disease and found that skin meQTLs and CpGs under genetic influence were enriched for multiple skin-related genome-wide and epigenome-wide association signals, including for melanoma and psoriasis. Our findings give insights into the regulatory landscape of epigenomic variation in skin.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Crosstalk between epitranscriptomic and epigenomic modifications and its implication in human diseases

Crosstalk between N6-methyladenosine (m6A) and epigenomes is crucial for gene regulation, but its regulatory directionality and disease significance remain unclear. Here, we utilize quantitative trait loci (QTLs) as genetic instruments to delineate directional maps of crosstalk between m6A and two epigenomic traits, DNA methylation (DNAme) and H3K27ac. We identify 47 m6A-to-H3K27ac and 4,733 m6A-to-DNAme and, in the reverse direction, 106 H3K27ac-to-m6A and 61,775 DNAme-to-m6A regulatory loci, with differential genomic location preference observed for different regulatory directions. Integrating these maps with complex diseases, we prioritize 20 genome-wide association study (GWAS) loci for neuroticism, depression, and narcolepsy in brain; 1,767 variants for asthma and expiratory flow traits in lung; and 249 for coronary artery disease, blood pressure, and pulse rate in muscle. This study establishes disease regulatory paths, such as rs3768410-DNAme-m6A-asthma and rs56104944-m6A-DNAme-hypertension, uncovering locus-specific crosstalk between m6A and epigenomic layers and offering insights into regulatory circuits underlying human diseases.

Epigenome-wide association study on the plasma metabolome suggests self-regulation of the glycine and serine pathway through DNA methylation

BACKGROUND

The plasma metabolome reflects the physiological state of various biological processes and can serve as a proxy for disease risk. Plasma metabolite variation, influenced by genetic and epigenetic mechanisms, can also affect the cellular microenvironment and blood cell epigenetics. The interplay between the plasma metabolome and the blood cell epigenome remains elusive. In this study, we performed an epigenome-wide association study (EWAS) of 1183 plasma metabolites in 693 participants from the LifeLines-DEEP cohort and investigated the causal relationships in DNA methylation-metabolite associations using bidirectional Mendelian randomization and mediation analysis.

RESULTS

After rigorously adjusting for potential confounders, including genetics, we identified five robust associations between two plasma metabolites (L-serine and glycine) and three CpG sites located in two independent genomic regions (cg14476101 and cg16246545 in PHGDH and cg02711608 in SLC1A5) at a false discovery rate of less than 0.05. Further analysis revealed a complex bidirectional relationship between plasma glycine/serine levels and DNA methylation. Moreover, we observed a strong mediating role of DNA methylation in the effect of glycine/serine on the expression of their metabolism/transport genes, with the proportion of the mediated effect ranging from 11.8 to 54.3%. This result was also replicated in an independent population-based cohort, the Rotterdam Study. To validate our findings, we conducted in vitro cell studies which confirmed the mediating role of DNA methylation in the regulation of PHGDH gene expression.

CONCLUSIONS

Our findings reveal a potential feedback mechanism in which glycine and serine regulate gene expression through DNA methylation.

Models for depression recognition and efficacy assessment based on clinical and sequencing data

Major depression is a complex psychiatric disorder that includes genetic, neurological, and cognitive factors. Early detection and intervention can prevent progression, and help select the best treatment. Traditional clinical diagnosis tends to be subjective and misdiagnosed. Based on this, this study leverages clinical scale assessments and sequencing data to construct disease prediction models. Firstly, data undergoes preprocessing involving normalization and other requisite procedures. Feature engineering is then applied to curate subsets of features, culminating in the construction of a model through the implementation of machine learning and deep learning algorithms. In this study, 18 features with significant differences between patients and healthy controls were selected. The depression recognition model was constructed by deep learning with an accuracy of 87.26 % and an AUC of 91.56 %, which can effectively distinguish patients with depression from healthy controls. In addition, 33 features selected by recursive feature elimination method were used to construct a prognostic effect model of patients after 2 weeks of treatment, with an accuracy of 75.94 % and an AUC of 83.33 %. The results show that the deep learning algorithm based on clinical and sequencing data has good accuracy and provides an objective and accurate method for the diagnosis and pharmacodynamic prediction of depression. Furthermore, the selected differential features can serve as candidate biomarkers to provide valuable clues for diagnosis and efficacy prediction.

Deficiency of the Deubiquitinase UCHL1 Attenuates Pulmonary Arterial Hypertension

BACKGROUND

The ubiquitin-proteasome system regulates protein degradation and the development of pulmonary arterial hypertension (PAH), but knowledge about the role of deubiquitinating enzymes in this process is limited. UCHL1 (ubiquitin carboxyl-terminal hydrolase 1), a deubiquitinase, has been shown to reduce AKT1 (AKT serine/threonine kinase 1) degradation, resulting in higher levels. Given that AKT1 is pathological in pulmonary hypertension, we hypothesized that UCHL1 deficiency attenuates PAH development by means of reductions in AKT1.

METHODS

Tissues from animal pulmonary hypertension models as well as human pulmonary artery endothelial cells from patients with PAH exhibited increased vascular UCHL1 staining and protein expression. Exposure to LDN57444, a UCHL1-specific inhibitor, reduced human pulmonary artery endothelial cell and smooth muscle cell proliferation. Across 3 preclinical PAH models, LDN57444-exposed animals, Uchl1 knockout rats (Uchl1-/-), and conditional Uchl1 knockout mice (Tie2Cre-Uchl1fl/fl) demonstrated reduced right ventricular hypertrophy, right ventricular systolic pressures, and obliterative vascular remodeling. Lungs and pulmonary artery endothelial cells isolated from Uchl1-/- animals exhibited reduced total and activated Akt with increased ubiquitinated Akt levels. UCHL1-silenced human pulmonary artery endothelial cells displayed reduced lysine(K)63-linked and increased K48-linked AKT1 levels.

RESULTS

Supporting experimental data, we found that rs9321, a variant in a GC-enriched region of the UCHL1 gene, is associated with reduced methylation (n=5133), increased UCHL1 gene expression in lungs (n=815), and reduced cardiac index in patients (n=796). In addition, Gadd45α (an established demethylating gene) knockout mice (Gadd45α-/-) exhibited reduced lung vascular UCHL1 and AKT1 expression along with attenuated hypoxic pulmonary hypertension.

CONCLUSIONS

Our findings suggest that UCHL1 deficiency results in PAH attenuation by means of reduced AKT1, highlighting a novel therapeutic pathway in PAH.

Genome-Wide Methylation Analysis Reveals a KCNK3-Prominent Causal Cascade on Hypertension

BACKGROUND

Despite advances in understanding hypertension's genetic structure, how noncoding genetic variants influence it remains unclear. Studying their interaction with DNA methylation is crucial to deciphering this complex disease's genetic mechanisms.

METHODS

We investigated the genetic and epigenetic interplay in hypertension using whole-genome bisulfite sequencing. Methylation profiling in 918 males revealed allele-specific methylation and methylation quantitative trait loci. We engineered rs1275988T/C mutant mice using CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 (CRISPR-associated protein 9), bred them for homozygosity, and subjected them to a high-salt diet. Telemetry captured their cardiovascular metrics. Protein-DNA interactions were elucidated using DNA pull-downs, mass spectrometry, and Western blots. A wire myograph assessed vascular function, and analysis of the Kcnk3 gene methylation highlighted the mutation's role in hypertension.

RESULTS

We discovered that DNA methylation-associated genetic effects, especially in non-cytosine-phosphate-guanine (non-CpG) island and noncoding distal regulatory regions, significantly contribute to hypertension predisposition. We identified distinct methylation quantitative trait locus patterns in the hypertensive population and observed that the onset of hypertension is influenced by the transmission of genetic effects through the demethylation process. By evidence-driven prioritization and in vivo experiments, we unearthed rs1275988 in a cell type-specific enhancer as a notable hypertension causal variant, intensifying hypertension through the modulation of local DNA methylation and consequential alterations in Kcnk3 gene expression and vascular remodeling. When exposed to a high-salt diet, mice with the rs1275988C/C genotype exhibited exacerbated hypertension and significant vascular remodeling, underscored by increased aortic wall thickness. The C allele of rs1275988 was associated with elevated DNA methylation levels, driving down the expression of the Kcnk3 gene by attenuating Nr2f2 (nuclear receptor subfamily 2 group F member 2) binding at the enhancer locus.

CONCLUSIONS

Our research reveals new insights into the complex interplay between genetic variations and DNA methylation in hypertension. We underscore hypomethylation's potential in hypertension onset and identify rs1275988 as a causal variant in vascular remodeling. This work advances our understanding of hypertension's molecular mechanisms and encourages personalized health care strategies.

Epigenome-wide DNA Methylation Association Study of CHIP Provides Insight into Perturbed Gene Regulation

With age, hematopoietic stem cells can acquire somatic mutations in leukemogenic genes that confer a proliferative advantage in a phenomenon termed "clonal hematopoiesis of indeterminate potential" (CHIP). How these mutations confer a proliferative advantage and result in increased risk for numerous age-related diseases remains poorly understood. We conducted a multiracial meta-analysis of epigenome-wide association studies (EWAS) of CHIP and its subtypes in four cohorts (N=8196) to elucidate the molecular mechanisms underlying CHIP and illuminate how these changes influence cardiovascular disease risk. The EWAS findings were functionally validated using human hematopoietic stem cell (HSC) models of CHIP. A total of 9615 CpGs were associated with any CHIP, 5990 with DNMT3A CHIP, 5633 with TET2 CHIP, and 6078 with ASXL1 CHIP (P <1×10-7). CpGs associated with CHIP subtypes overlapped moderately, and the genome-wide DNA methylation directions of effect were opposite for TET2 and DNMT3A CHIP, consistent with their opposing effects on global DNA methylation. There was high directional concordance between the CpGs shared from the meta-EWAS and human edited CHIP HSCs. Expression quantitative trait methylation analysis further identified transcriptomic changes associated with CHIP-associated CpGs. Causal inference analyses revealed 261 CHIP-associated CpGs associated with cardiovascular traits and all-cause mortality (FDR adjusted p-value <0.05). Taken together, our study sheds light on the epigenetic changes impacted by CHIP and their associations with age-related disease outcomes. The novel genes and pathways linked to the epigenetic features of CHIP may serve as therapeutic targets for preventing or treating CHIP-mediated diseases.

Genomic and Epigenomic Biomarkers of Immune Checkpoint Immunotherapy Response in Melanoma: Current and Future Perspectives

Immune checkpoint inhibitors (ICIs) demonstrate durable responses, long-term survival benefits, and improved outcomes in cancer patients compared to chemotherapy. However, the majority of cancer patients do not respond to ICIs, and a high proportion of those patients who do respond to ICI therapy develop innate or acquired resistance to ICIs, limiting their clinical utility. The most studied predictive tissue biomarkers for ICI response are PD-L1 immunohistochemical expression, DNA mismatch repair deficiency, and tumour mutation burden, although these are weak predictors of ICI response. The identification of better predictive biomarkers remains an important goal to improve the identification of patients who would benefit from ICIs. Here, we review established and emerging biomarkers of ICI response, focusing on epigenomic and genomic alterations in cancer patients, which have the potential to help guide single-agent ICI immunotherapy or ICI immunotherapy in combination with other ICI immunotherapies or agents. We briefly review the current status of ICI response biomarkers, including investigational biomarkers, and we present insights into several emerging and promising epigenomic biomarker candidates, including current knowledge gaps in the context of ICI immunotherapy response in melanoma patients.

bsgenova: an accurate, robust, and fast genotype caller for bisulfite-sequencing data

BACKGROUND

Bisulfite sequencing (BS-Seq) is a fundamental technique for characterizing DNA methylation profiles. Genotype calling from bisulfite-converted BS-Seq data allows allele-specific methylation analysis and the concurrent exploration of genetic and epigenetic profiles. Despite various methods have been proposed, single nucleotide polymorphisms (SNPs) calling from BS-Seq data, particularly for SNPs on chromosome X and in the presence of contaminative data, poses ongoing challenges.

RESULTS

We introduce bsgenova, a novel SNP caller tailored for bisulfite sequencing data, employing a Bayesian multinomial model. The performance of bsgenova is assessed by comparing SNPs called from real-world BS-Seq data with those from corresponding whole-genome sequencing (WGS) data across three human cell lines. bsgenova is both sensitive and precise, especially for chromosome X, compared with three existing methods. Moreover, in the presence of low-quality reads, bsgenova outperforms other methods notably. In addition, bsgenova is meticulously implemented, leveraging matrix imputation and multi-process parallelization. Compared to existing methods, bsgenova stands out for its speed and efficiency in memory and disk usage. Furthermore, bsgenova integrates bsextractor, a methylation extractor, enhancing its flexibility and expanding its utility.

CONCLUSIONS

We introduce bsgenova for SNP calling from bisulfite-sequencing data. The source code is available at https://github.com/hippo-yf/bsgenova under license GPL-3.0.

Epigenetic Age Mediates the Association of Life's Essential 8 With Cardiovascular Disease and Mortality

BACKGROUND

Life's Essential 8 (LE8) is an enhanced metric for cardiovascular health. The interrelations among LE8, biomarkers of aging, and disease risks are unclear.

METHODS AND RESULTS

LE8 score was calculated for 5682 Framingham Heart Study participants. We implemented 4 DNA methylation-based epigenetic age biomarkers, with older epigenetic age hypothesized to represent faster biological aging, and examined whether these biomarkers mediated the associations between the LE8 score and cardiovascular disease (CVD), CVD-specific mortality, and all-cause mortality. We found that a 1 SD increase in the LE8 score was associated with a 35% (95% CI, 27-41; P=1.8E-15) lower risk of incident CVD, a 36% (95% CI, 24-47; P=7E-7) lower risk of CVD-specific mortality, and a 29% (95% CI, 22-35; P=7E-15) lower risk of all-cause mortality. These associations were partly mediated by epigenetic age biomarkers, particularly the GrimAge and the DunedinPACE scores. The potential mediation effects by epigenetic age biomarkers tended to be more profound in participants with higher genetic risk for older epigenetic age, compared with those with lower genetic risk. For example, in participants with higher GrimAge polygenic scores (greater than median), the mean proportion of mediation was 39%, 39%, and 78% for the association of the LE8 score with incident CVD, CVD-specific mortality, and all-cause mortality, respectively. No significant mediation was observed in participants with lower GrimAge polygenic score.

CONCLUSIONS

DNA methylation-based epigenetic age scores mediate the associations between the LE8 score and incident CVD, CVD-specific mortality, and all-cause mortality, particularly in individuals with higher genetic predisposition for older epigenetic age.

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

BACKGROUND

Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios.

METHODS

We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning.

RESULTS

Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively.

CONCLUSIONS

By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.

Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits

Methylation quantitative trait loci (mQTLs) are essential for understanding the role of DNA methylation changes in genetic predisposition, yet they have not been fully characterized in East Asians (EAs). Here we identified mQTLs in whole blood from 3,523 Chinese individuals and replicated them in additional 1,858 Chinese individuals from two cohorts. Over 9% of mQTLs displayed specificity to EAs, facilitating the fine-mapping of EA-specific genetic associations, as shown for variants associated with height. Trans-mQTL hotspots revealed biological pathways contributing to EA-specific genetic associations, including an ERG-mediated 233 trans-mCpG network, implicated in hematopoietic cell differentiation, which likely reflects binding efficiency modulation of the ERG protein complex. More than 90% of mQTLs were shared between different blood cell lineages, with a smaller fraction of lineage-specific mQTLs displaying preferential hypomethylation in the respective lineages. Our study provides new insights into the mQTL landscape across genetic ancestries and their downstream effects on cellular processes and diseases/traits.

Genome- and epigenome-wide association studies identify susceptibility of CpG sites and regions for metabolic syndrome in a Korean population

BACKGROUND

While multiple studies have investigated the relationship between metabolic syndrome (MetS) and its related traits (fasting glucose, triglyceride, HDL cholesterol, blood pressure, waist circumference) and DNA methylation, our understanding of the epigenetic mechanisms in MetS remains limited. Therefore, we performed an epigenome-wide meta-analysis of blood DNA methylation to identify differentially methylated probes (DMPs) and differentially methylated regions (DMRs) associated with MetS and its components using two independent cohorts comprising a total of 2,334 participants. We also investigated the specific genetic effects on DNA methylation, identified methylation quantitative trait loci (meQTLs) through genome-wide association studies and further utilized Mendelian randomization (MR) to assess how these meQTLs subsequently influence MetS status.

RESULTS

We identified 40 DMPs and 27 DMRs that are significantly associated with MetS. In addition, we identified many novel DMPs and DMRs underlying inflammatory and steroid hormonal processes. The most significant associations were observed in 3 DMPs (cg19693031, cg26974062, cg02988288) and a DMR (chr1:145440444-145441553) at the TXNIP, which are involved in lipid metabolism. These CpG sites were identified as coregulators of DNA methylation in MetS, TG and FAG levels. We identified a total of 144 cis-meQTLs, out of which only 13 were found to be associated with DMPs for MetS. Among these, we confirmed the identified causal mediators of genetic effects at CpG sites cg01881899 at ABCG1 and cg00021659 at the TANK genes for MetS.

CONCLUSIONS

This study observed whether specific CpGs and methylated regions act independently or are influenced by genetic effects for MetS and its components in the Korean population. These associations between the identified DNA methylation and MetS, along with its individual components, may serve as promising targets for the development of preventive interventions for MetS.

Epigenomic insights into common human disease pathology

The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability

Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is "activated." For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.

Transgenerational Epigenetic Inheritance of Cardiovascular Diseases: A Network Medicine Perspective

INTRODUCTION

The ability to identify early epigenetic signatures underlying the inheritance of cardiovascular risk, including trans- and intergenerational effects, may help to stratify people before cardiac symptoms occur.

METHODS

Prospective and retrospective cohorts and case-control studies focusing on DNA methylation and maternal/paternal effects were searched in Pubmed from 1997 to 2023 by using the following keywords: DNA methylation, genomic imprinting, and network analysis in combination with transgenerational/intergenerational effects.

RESULTS

Maternal and paternal exposures to traditional cardiovascular risk factors during critical temporal windows, including the preconceptional period or early pregnancy, may perturb the plasticity of the epigenome (mainly DNA methylation) of the developing fetus especially at imprinted loci, such as the insulin-like growth factor type 2 (IGF2) gene. Thus, the epigenome is akin to a "molecular archive" able to memorize parental environmental insults and predispose an individual to cardiovascular diseases onset in later life. Direct evidence for human transgenerational epigenetic inheritance (at least three generations) of cardiovascular risk is lacking but it is supported by epidemiological studies. Several blood-based association studies showed potential intergenerational epigenetic effects (single-generation studies) which may mediate the transmittance of cardiovascular risk from parents to offspring.

DISCUSSION

In this narrative review, we discuss some relevant examples of trans- and intergenerational epigenetic associations with cardiovascular risk. In our perspective, we propose three network-oriented approaches which may help to clarify the unsolved issues regarding transgenerational epigenetic inheritance of cardiovascular risk and provide potential early biomarkers for primary prevention.

Epigenetic variation impacts individual differences in the transcriptional response to influenza infection

Humans display remarkable interindividual variation in their immune response to identical challenges. Yet, our understanding of the genetic and epigenetic factors contributing to such variation remains limited. Here we performed in-depth genetic, epigenetic and transcriptional profiling on primary macrophages derived from individuals of European and African ancestry before and after infection with influenza A virus. We show that baseline epigenetic profiles are strongly predictive of the transcriptional response to influenza A virus across individuals. Quantitative trait locus (QTL) mapping revealed highly coordinated genetic effects on gene regulation, with many cis-acting genetic variants impacting concomitantly gene expression and multiple epigenetic marks. These data reveal that ancestry-associated differences in the epigenetic landscape can be genetically controlled, even more than gene expression. Lastly, among QTL variants that colocalized with immune-disease loci, only 7% were gene expression QTL, while the remaining genetic variants impact epigenetic marks, stressing the importance of considering molecular phenotypes beyond gene expression in disease-focused studies.

Omics approaches in asthma research: Challenges and opportunities

Asthma, a chronic respiratory disease with a global prevalence of approximately 300 million individuals, presents a significant societal and economic burden. This multifaceted syndrome exhibits diverse clinical phenotypes and pathogenic endotypes influenced by various factors. The advent of omics technologies has revolutionized asthma research by delving into the molecular foundation of the disease to unravel its underlying mechanisms. Omics technologies are employed to systematically screen for potential biomarkers, encompassing genes, transcripts, methylation sites, proteins, and even the microbiome components. This review provides an insightful overview of omics applications in asthma research, with a special emphasis on genetics, transcriptomics, epigenomics, and the microbiome. We explore the cutting-edge methods, discoveries, challenges, and potential future directions in the realm of asthma omics research. By integrating multi-omics and non-omics data through advanced statistical techniques, we aspire to advance precision medicine in asthma, guiding diagnosis, risk assessment, and personalized treatment strategies for this heterogeneous condition.

DNA methylation may partly explain psychotropic drug-induced metabolic side effects: results from a prospective 1-month observational study

BACKGROUND

Metabolic side effects of psychotropic medications are a major drawback to patients' successful treatment. Using an epigenome-wide approach, we aimed to investigate DNA methylation changes occurring secondary to psychotropic treatment and evaluate associations between 1-month metabolic changes and both baseline and 1-month changes in DNA methylation levels. Seventy-nine patients starting a weight gain inducing psychotropic treatment were selected from the PsyMetab study cohort. Epigenome-wide DNA methylation was measured at baseline and after 1 month of treatment, using the Illumina Methylation EPIC BeadChip.

RESULTS

A global methylation increase was noted after the first month of treatment, which was more pronounced (p < 2.2 × 10-16) in patients whose weight remained stable (< 2.5% weight increase). Epigenome-wide significant methylation changes (p < 9 × 10-8) were observed at 52 loci in the whole cohort. When restricting the analysis to patients who underwent important early weight gain (≥ 5% weight increase), one locus (cg12209987) showed a significant increase in methylation levels (p = 3.8 × 10-8), which was also associated with increased weight gain in the whole cohort (p = 0.004). Epigenome-wide association analyses failed to identify a significant link between metabolic changes and methylation data. Nevertheless, among the strongest associations, a potential causal effect of the baseline methylation level of cg11622362 on glycemia was revealed by a two-sample Mendelian randomization analysis (n = 3841 for instrument-exposure association; n = 314,916 for instrument-outcome association).

CONCLUSION

These findings provide new insights into the mechanisms of psychotropic drug-induced weight gain, revealing important epigenetic alterations upon treatment, some of which may play a mediatory role.

Linking Prenatal Environmental Exposures to Lifetime Health with Epigenome-Wide Association Studies: State-of-the-Science Review and Future Recommendations

BACKGROUND

The prenatal environment influences lifetime health; epigenetic mechanisms likely predominate. In 2016, the first international consortium paper on cigarette smoking during pregnancy and offspring DNA methylation identified extensive, reproducible exposure signals. This finding raised expectations for epigenome-wide association studies (EWAS) of other exposures.

OBJECTIVE

We review the current state-of-the-science for DNA methylation associations across prenatal exposures in humans and provide future recommendations.

METHODS

We reviewed 134 prenatal environmental EWAS of DNA methylation in newborns, focusing on 51 epidemiological studies with meta-analysis or replication testing. Exposures spanned cigarette smoking, alcohol consumption, air pollution, dietary factors, psychosocial stress, metals, other chemicals, and other exogenous factors. Of the reproducible DNA methylation signatures, we examined implementation as exposure biomarkers.

RESULTS

Only 19 (14%) of these prenatal EWAS were conducted in cohorts of 1,000 or more individuals, reflecting the still early stage of the field. To date, the largest perinatal EWAS sample size was 6,685 participants. For comparison, the most recent genome-wide association study for birth weight included more than 300,000 individuals. Replication, at some level, was successful with exposures to cigarette smoking, folate, dietary glycemic index, particulate matter with aerodynamic diameter < 10 μ m and < 2.5 μ m , nitrogen dioxide, mercury, cadmium, arsenic, electronic waste, PFAS, and DDT. Reproducible effects of a more limited set of prenatal exposures (smoking, folate) enabled robust methylation biomarker creation.

DISCUSSION

Current evidence demonstrates the scientific premise for reproducible DNA methylation exposure signatures. Better powered EWAS could identify signatures across many exposures and enable comprehensive biomarker development. Whether methylation biomarkers of exposures themselves cause health effects remains unclear. We expect that larger EWAS with enhanced coverage of epigenome and exposome, along with improved single-cell technologies and evolving methods for integrative multi-omics analyses and causal inference, will expand mechanistic understanding of causal links between environmental exposures, the epigenome, and health outcomes throughout the life course. https://doi.org/10.1289/EHP12956.

Pleiotropic influence of DNA methylation QTLs on physiological and ageing traits

DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more 'aged methylome' for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein-protein interaction enrichment analyses identified the hepatic nuclear factor, Hnf1a (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.

Genome-wide association analysis of left ventricular imaging-derived phenotypes identifies 72 risk loci and yields genetic insights into hypertrophic cardiomyopathy

Left ventricular regional wall thickness (LVRWT) is an independent predictor of morbidity and mortality in cardiovascular diseases (CVDs). To identify specific genetic influences on individual LVRWT, we established a novel deep learning algorithm to calculate 12 LVRWTs accurately in 42,194 individuals from the UK Biobank with cardiac magnetic resonance (CMR) imaging. Genome-wide association studies of CMR-derived 12 LVRWTs identified 72 significant genetic loci associated with at least one LVRWT phenotype (P < 5 × 10-8), which were revealed to actively participate in heart development and contraction pathways. Significant causal relationships were observed between the LVRWT traits and hypertrophic cardiomyopathy (HCM) using genetic correlation and Mendelian randomization analyses (P < 0.01). The polygenic risk score of inferoseptal LVRWT at end systole exhibited a notable association with incident HCM, facilitating the identification of high-risk individuals. The findings yield insights into the genetic determinants of LVRWT phenotypes and shed light on the biological basis for HCM etiology.

Identifying genetic variants that influence the abundance of cell states in single-cell data

To understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals,1GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96×10-11) associates with increased abundance of NK cells expressing TNF-α response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits

BACKGROUND

Cocaine use (CU) is associated with psychiatric and medical diseases. Little is known about the mechanisms of CU-related comorbidities. Findings from preclinical and clinical studies have suggested that CU is associated with aberrant DNA methylation (DNAm) that may be influenced by genetic variants [i.e., methylation quantitative trait loci (meQTLs)]. In this study, we mapped cis-meQTLs for CU-associated DNAm sites (CpGs) in an HIV-positive cohort (Ntotal = 811) and extended the meQTLs to multiple traits.

RESULTS

We conducted cis-meQTL analysis for 224 candidate CpGs selected for their association with CU in blood. We identified 7,101 significant meQTLs [false discovery rate (FDR) < 0.05], which mostly mapped to genes involved in immunological functions and were enriched in immune pathways. We followed up the meQTLs using phenome-wide association study and trait enrichment analyses, which revealed 9 significant traits. We tested for causal effects of CU on these 9 traits using Mendelian Randomization and found evidence that CU plays a causal role in increasing hypertension (p-value = 2.35E-08) and decreasing heel bone mineral density (p-value = 1.92E-19).

CONCLUSIONS

These findings suggest that genetic variants for CU-associated DNAm have pleiotropic effects on other relevant traits and provide new insights into the causal relationships between cocaine use and these complex traits.

Integrated epigenome, whole genome sequence and metabolome analyses identify novel multi-omics pathways in type 2 diabetes: a Middle Eastern study

BACKGROUND

T2D is of high prevalence in the middle east and thus studying its mechanisms is of a significant importance. Using 1026 Qatar BioBank samples, epigenetics, whole genome sequencing and metabolomics were combined to further elucidate the biological mechanisms of T2D in a population with a high prevalence of T2D.

METHODS

An epigenome-wide association study (EWAS) with T2D was performed using the Infinium 850K EPIC array, followed by whole genome-wide sequencing SNP-CpG association analysis (> 5.5 million SNPs) and a methylome-metabolome (CpG-metabolite) analysis of the identified T2D sites.

RESULTS

A total of 66 T2D-CpG associations were identified, including 63 novel sites in pathways of fructose and mannose metabolism, insulin signaling, galactose, starch and sucrose metabolism, and carbohydrate absorption and digestion. Whole genome SNP associations with the 66 CpGs resulted in 688 significant CpG-SNP associations comprising 22 unique CpGs (33% of the 66 CPGs) and included 181 novel pairs or pairs in novel loci. Fourteen of the loci overlapped published GWAS loci for diabetes related traits and were used to identify causal associations of HK1 and PFKFB2 with HbA1c. Methylome-metabolome analysis identified 66 significant CpG-metabolite pairs among which 61 pairs were novel. Using the identified methylome-metabolome associations, methylation QTLs, and metabolic networks, a multi-omics network was constructed which suggested a number of metabolic mechanisms underlying T2D methylated genes. 1-palmitoyl-2-oleoyl-GPE (16:0/18:1) - a triglyceride-associated metabolite, shared a common network with 13 methylated CpGs, including TXNIP, PFKFB2, OCIAD1, and BLCAP. Mannonate - a food component/plant shared a common network with 6 methylated genes, including TXNIP, BLCAP, THBS4 and PEF1, pointing to a common possible cause of methylation in those genes. A subnetwork with alanine, glutamine, urea cycle (citrulline, arginine), and 1-carboxyethylvaline linked to PFKFB2 and TXNIP revealed associations with kidney function, hypertension and triglyceride metabolism. The pathway containing STYXL1-POR was associated with a sphingosine-ceramides subnetwork associated with HDL-C and LDL-C and point to steroid perturbations in T2D.

CONCLUSIONS

This study revealed several novel methylated genes in T2D, with their genomic variants and associated metabolic pathways with several implications for future clinical use of multi-omics associations in disease and for studying therapeutic targets.

Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthma

BACKGROUND

Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases.

METHODS

We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables.

FINDINGS

We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P < 7.94E-04) that can be explored as potential therapeutic targets.

INTERPRETATION

By integrating eQTM and MR analyses in general and clinical asthma populations, our findings provide a deeper understanding of the multidimensional inter-relations of DNA methylation, gene expression, and IgE levels.

FUNDING

US NIH/NHLBI grants: P01HL132825, K99HL159234. N01-HC-25195 and HHSN268201500001I.