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

Causality-enriched epigenetic age uncouples damage and adaptation

Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank

Frailty, a prevalent clinical syndrome in aging adults, is characterized by poor health outcomes, represented via a standardized frailty-phenotype (FP), and Frailty Index (FI). While the relevance of the syndrome is gaining awareness, much remains unclear about its underlying biology. Further elucidation of the genetic determinants and possible underlying mechanisms may help improve patients' outcomes allowing healthy aging.Genotype, clinical and demographic data of subjects (aged 60-73 years) from UK Biobank were utilized. FP was defined on Fried's criteria. FI was calculated using electronic-health-records. Genome-wide-association-studies (GWAS) were conducted and polygenic-risk-scores (PRS) were calculated for both FP and FI. Functional analysis provided interpretations of underlying biology. Finally, machine-learning (ML) models were trained using clinical, demographic and PRS towards identifying frail from non-frail individuals.Thirty-one loci were significantly associated with FI accounting for 12% heritability. Seventeen of those were known associations for body-mass-index, coronary diseases, cholesterol-levels, and longevity, while the rest were novel. Significant genes CDKN2B and APOE, previously implicated in aging, were reported to be enriched in lipoprotein-particle-remodeling. Linkage-disequilibrium-regression identified specific regulation in limbic-system, associated with long-term memory and cognitive-function. XGboost was established as the best performing ML model with area-under-curve as 85%, sensitivity and specificity as 0.75 and 0.8, respectively.This study provides novel insights into increased vulnerability and risk stratification of frailty syndrome via a multi-modal approach. The findings suggest frailty as a highly polygenic-trait, enriched in cholesterol-remodeling and metabolism and to be genetically associated with cognitive abilities. ML models utilizing FP and FI + PRS were established that identified frailty-syndrome patients with high accuracy.

Epigenetics insights from perceived facial aging

Facial aging is the most visible manifestation of aging. People desire to look younger than others of the same chronological age. Hence, perceived age is often used as a visible marker of aging, while biological age, often estimated by methylation markers, is used as an objective measure of age. Multiple epigenetics-based clocks have been developed for accurate estimation of general biological age and the age of specific organs, including the skin. However, it is not clear whether the epigenetic biomarkers (CpGs) used in these clocks are drivers of aging processes or consequences of aging. In this proof-of-concept study, we integrate data from GWAS on perceived facial aging and EWAS on CpGs measured in blood. By running EW Mendelian randomization, we identify hundreds of putative CpGs that are potentially causal to perceived facial aging with similar numbers of damaging markers that causally drive or accelerate facial aging and protective methylation markers that causally slow down or protect from aging. We further demonstrate that while candidate causal CpGs have little overlap with known epigenetics-based clocks, they affect genes or proteins with known functions in skin aging, such as skin pigmentation, elastin, and collagen levels. Overall, our results suggest that blood methylation markers reflect facial aging processes, and thus can be used to quantify skin aging and develop anti-aging solutions that target the root causes of aging.

Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes

Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements underpinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we introduce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n = 20,833-766,345) and xQTL data from seven omics layers (n = 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mechanisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular intermediates to GWAS signals.

Investigating causality in the association between DNA methylation and type 2 diabetes using bidirectional two-sample Mendelian randomisation

AIMS/HYPOTHESIS

Several studies have identified associations between type 2 diabetes and DNA methylation (DNAm). However, the causal role of these associations remains unclear. This study aimed to provide evidence for a causal relationship between DNAm and type 2 diabetes.

METHODS

We used bidirectional two-sample Mendelian randomisation (2SMR) to evaluate causality at 58 CpG sites previously detected in a meta-analysis of epigenome-wide association studies (meta-EWAS) of prevalent type 2 diabetes in European populations. We retrieved genetic proxies for type 2 diabetes and DNAm from the largest genome-wide association study (GWAS) available. We also used data from the Avon Longitudinal Study of Parents and Children (ALSPAC, UK) when associations of interest were not available in the larger datasets. We identified 62 independent SNPs as proxies for type 2 diabetes, and 39 methylation quantitative trait loci as proxies for 30 of the 58 type 2 diabetes-related CpGs. We applied the Bonferroni correction for multiple testing and inferred causality based on p<0.001 for the type 2 diabetes to DNAm direction and p<0.002 for the opposing DNAm to type 2 diabetes direction in the 2SMR analysis.

RESULTS

We found strong evidence of a causal effect of DNAm at cg25536676 (DHCR24) on type 2 diabetes. An increase in transformed residuals of DNAm at this site was associated with a 43% (OR 1.43, 95% CI 1.15, 1.78, p=0.001) higher risk of type 2 diabetes. We inferred a likely causal direction for the remaining CpG sites assessed. In silico analyses showed that the CpGs analysed were enriched for expression quantitative trait methylation sites (eQTMs) and for specific traits, dependent on the direction of causality predicted by the 2SMR analysis.

CONCLUSIONS/INTERPRETATION

We identified one CpG mapping to a gene related to the metabolism of lipids (DHCR24) as a novel causal biomarker for risk of type 2 diabetes. CpGs within the same gene region have previously been associated with type 2 diabetes-related traits in observational studies (BMI, waist circumference, HDL-cholesterol, insulin) and in Mendelian randomisation analyses (LDL-cholesterol). Thus, we hypothesise that our candidate CpG in DHCR24 may be a causal mediator of the association between known modifiable risk factors and type 2 diabetes. Formal causal mediation analysis should be implemented to further validate this assumption.

Role of DNA methylation in the relationship between glioma risk factors and glioma incidence: a two-step Mendelian randomization study

Genetic evidence suggests glioma risk is altered by leukocyte telomere length, allergic disease (asthma, hay fever or eczema), alcohol consumption, childhood obesity, low-density lipoprotein cholesterol (LDLc) and triglyceride levels. DNA methylation (DNAm) variation influences many of these glioma-related traits and is an established feature of glioma. Yet the causal relationship between DNAm variation with both glioma incidence and glioma risk factors is unknown. We applied a two-step Mendelian randomization (MR) approach and several sensitivity analyses (including colocalization and Steiger filtering) to assess the association of DNAm with glioma risk factors and glioma incidence. We used data from a recently published catalogue of germline genetic variants robustly associated with DNAm variation in blood (32,851 participants) and data from a genome-wide association study of glioma risk (12,488 cases and 18,169 controls, sub-divided into 6191 glioblastoma cases and 6305 non-glioblastoma cases). MR evidence indicated that DNAm at 3 CpG sites (cg01561092, cg05926943, cg01584448) in one genomic region (HEATR3) had a putative association with glioma and glioblastoma risk (False discovery rate [FDR] < 0.05). Steiger filtering provided evidence against reverse causation. Colocalization presented evidence against genetic confounding and suggested that differential DNAm at the 3 CpG sites and glioma were driven by the same genetic variant. MR provided little evidence to suggest that DNAm acts as a mediator on the causal pathway between risk factors previously examined and glioma onset. To our knowledge, this is the first study to use MR to appraise the causal link of DNAm with glioma risk factors and glioma onset. Subsequent analyses are required to improve the robustness of our results and rule out horizontal pleiotropy.

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease

DNA methylation commonly occurs at cytosine-phosphate-guanine sites (CpGs) that can serve as biomarkers for many diseases. We analyzed whole genome sequencing data to identify DNA methylation quantitative trait loci (mQTLs) in 4126 Framingham Heart Study participants. Our mQTL mapping identified 94,362,817 cis-mQTLvariant-CpG pairs (for 210,156 unique autosomal CpGs) at P < 1e-7 and 33,572,145 trans-mQTL variant-CpG pairs (for 213,606 unique autosomal CpGs) at P < 1e-14. Using cis-mQTL variants for 1258 CpGs associated with seven cardiovascular disease (CVD) risk factors, we found 104 unique CpGs that colocalized with at least one CVD trait. For example, cg11554650 (PPP1R18) colocalized with type 2 diabetes, and was driven by a single nucleotide polymorphism (rs2516396). We performed Mendelian randomization (MR) analysis and demonstrated 58 putatively causal relations of CVD risk factor-associated CpGs to one or more risk factors (e.g., cg05337441 [APOB] with LDL; MR P = 1.2e-99, and 17 causal associations with coronary artery disease (e.g. cg08129017 [SREBF1] with coronary artery disease; MR P = 5e-13). We also showed that three CpGs, e.g., cg14893161 (PM20D1), are putatively causally associated with COVID-19 severity. To assist in future analyses of the role of DNA methylation in disease pathogenesis, we have posted a comprehensive summary data set in the National Heart, Lung, and Blood Institute's BioData Catalyst.

Placental multi-omics integration identifies candidate functional genes for birthweight

Abnormal birthweight is associated with increased risk for cardiometabolic diseases in later life. Although the placenta is critical to fetal development and later life health, it has not been integrated into largescale functional genomics initiatives, and mechanisms of birthweight-associated variants identified by genome wide association studies (GWAS) are unclear. The goal of this study is to provide functional mechanistic insight into the causal pathway from a genetic variant to birthweight by integrating placental methylation and gene expression with established GWAS loci for birthweight. We identify placental DNA methylation and gene expression targets for several birthweight GWAS loci. The target genes are broadly enriched in cardiometabolic, immune response, and hormonal pathways. We find that methylation causally influences WNT3A, CTDNEP1, and RANBP2 expression in placenta. Multi-trait colocalization identifies PLEKHA1, FES, CTDNEP1, and PRMT7 as likely functional effector genes. These findings reveal candidate functional pathways that underpin the genetic regulation of birthweight via placental epigenetic and transcriptomic mechanisms. Clinical trial registration; ClinicalTrials.gov, NCT00912132.

Harnessing tissue-specific genetic variation to dissect putative causal pathways between body mass index and cardiometabolic phenotypes

Body mass index (BMI) is a complex disease risk factor known to be influenced by genes acting via both metabolic pathways and appetite regulation. In this study, we aimed to gain insight into the phenotypic consequences of BMI-associated genetic variants, which may be mediated by their expression in different tissues. First, we harnessed meta-analyzed gene expression datasets derived from subcutaneous adipose (n = 1257) and brain (n = 1194) tissue to identify 86 and 140 loci, respectively, which provided evidence of genetic colocalization with BMI. These two sets of tissue-partitioned loci had differential effects with respect to waist-to-hip ratio, suggesting that the way they influence fat distribution might vary despite their having very similar average magnitudes of effect on BMI itself (adipose = 0.0148 and brain = 0.0149 standard deviation change in BMI per effect allele). For instance, BMI-associated variants colocalized with TBX15 expression in adipose tissue (posterior probability [PPA] = 0.97), but not when we used TBX15 expression data derived from brain tissue (PPA = 0.04) This gene putatively influences BMI via its role in skeletal development. Conversely, there were loci where BMI-associated variants provided evidence of colocalization with gene expression in brain tissue (e.g., NEGR1, PPA = 0.93), but not when we used data derived from adipose tissue, suggesting that these genes might be more likely to influence BMI via energy balance. Leveraging these tissue-partitioned variant sets through a multivariable Mendelian randomization framework provided strong evidence that the brain-tissue-derived variants are predominantly responsible for driving the genetically predicted effects of BMI on cardiovascular-disease endpoints (e.g., coronary artery disease: odds ratio = 1.05, 95% confidence interval = 1.04-1.07, p = 4.67 × 10^-14). In contrast, our analyses suggested that the adipose tissue variants might predominantly be responsible for the underlying relationship between BMI and measures of cardiac function, such as left ventricular stroke volume (beta = 0.21, 95% confidence interval = 0.09-0.32, p = 6.43 × 10^-4).

Evaluating the direct effects of childhood adiposity on adult systemic metabolism: a multivariable Mendelian randomization analysis

BACKGROUND

Individuals who are obese in childhood have an elevated risk of disease in adulthood. However, whether childhood adiposity directly impacts intermediate markers of this risk, independently of adult adiposity, is unclear. In this study, we have simultaneously evaluated the effects of childhood and adulthood body size on 123 systemic molecular biomarkers representing multiple metabolic pathways.

METHODS

Two-sample Mendelian randomization (MR) was conducted to estimate the causal effect of childhood body size on a total of 123 nuclear magnetic resonance-based metabolic markers using summary genome-wide association study (GWAS) data from up to 24 925 adults. Multivariable MR was then applied to evaluate the direct effects of childhood body size on these metabolic markers whilst accounting for adult body size. Further MR analyses were undertaken to estimate the potential mediating effects of these circulating metabolites on the risk of coronary artery disease (CAD) in adulthood using a sample of 60 801 cases and 123 504 controls.

RESULTS

Univariable analyses provided evidence that childhood body size has an effect on 42 of the 123 metabolic markers assessed (based on P < 4.07 × 10-4). However, the majority of these effects (35/42) substantially attenuated when accounting for adult body size using multivariable MR. We found little evidence that the biomarkers that were potentially influenced directly by childhood body size (leucine, isoleucine and tyrosine) mediate this effect onto adult disease risk. Very-low-density lipoprotein markers provided the strongest evidence of mediating the long-term effect of adiposity on CAD risk.

CONCLUSIONS

Our findings suggest that childhood adiposity predominantly exerts its detrimental effect on adult systemic metabolism along a pathway that involves adulthood body size.

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.

Cell-type-specific meQTLs extend melanoma GWAS annotation beyond eQTLs and inform melanocyte gene-regulatory mechanisms

Although expression quantitative trait loci (eQTLs) have been powerful in identifying susceptibility genes from genome-wide association study (GWAS) findings, most trait-associated loci are not explained by eQTLs alone. Alternative QTLs, including DNA methylation QTLs (meQTLs), are emerging, but cell-type-specific meQTLs using cells of disease origin have been lacking. Here, we established an meQTL dataset by using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization with meQTLs, eQTLs, and mRNA splice-junction QTLs from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified candidate susceptibility genes at 63% of melanoma GWAS loci. Among the three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes is preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTLs and melanoma meQTLs identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTLs identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIP-seq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTLs.

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.

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.

Causal Inference Methods to Integrate Omics and Complex Traits

Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.

Genetic impacts on DNA methylation: research findings and future perspectives

Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

Multi-omics analyses of cognitive traits and psychiatric disorders highlights brain-dependent mechanisms

Integrating findings from genome-wide association studies with molecular datasets can develop insight into the underlying functional mechanisms responsible for trait-associated genetic variants. We have applied the principles of Mendelian randomization (MR) to investigate whether brain-derived gene expression (n = 1194) may be responsible for mediating the effect of genetic variants on eight cognitive and psychological outcomes (attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, depression, intelligence, insomnia, neuroticism and schizophrenia). Transcriptome-wide analyses identified 83 genes associated with at least one outcome (PBonferroni < 6.72 × 10-6), with multiple-trait colocalization also implicating changes to brain-derived DNA methylation at nine of these loci. Comparing effects between outcomes identified evidence of enrichment which may reflect putative causal relationships, such as an inverse relationship between genetic liability towards schizophrenia risk and cognitive ability in later life. Repeating these analyses in whole blood (n = 31 684), we replicated 58.2% of brain-derived effects (based on P < 0.05). Finally, we undertook phenome-wide evaluations at associated loci to investigate pleiotropic effects with 700 complex traits. This highlighted pleiotropic loci such as FURIN (initially implicated in schizophrenia risk (P = 1.05 × 10-7)) which had evidence of an effect on 28 other outcomes, as well as genes which may have a more specific role in disease pathogenesis (e.g. SLC12A5 which only provided evidence of an effect on depression (P = 7.13 × 10-10)). Our results support the utility of whole blood as a valuable proxy for informing initial target identification but also suggest that gene discovery in a tissue-specific manner may be more informative. Finally, non-pleiotropic loci highlighted by our study may be of use for therapeutic translational endeavours.

Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer’s Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10^-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer’s disease, 6 genes with Parkinson’s disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.

Genetic association studies have been successful in identifying many genetic variants associated with disease risk, but it has been far more challenging to determine the genes through which these act. This is important, because such genes may encode effective drug targets for these diseases. We used Mendelian randomization (MR) and colocalization, two methods which in combination exploit these genetic variants to estimate the causal effects of individual genes. We applied this approach to 12 neurological and psychiatric diseases using data from the AMP-AD and CMC brain expression quantitative locus dataset, which is large enough to provide robust evidence for the relationship between genetic variants and gene expression. We found a causal relationship between the change in expression of 47 genes and increased disease risk across the 12 diseases we tested. As drug targets with human genetic evidence are far more likely to be approved in clinical trials, these findings provide a valuable list of potential therapeutic targets, including the ACE, GPNMB, KCNQ5, RERE and SUOX genes.

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.

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.

Capturing functional epigenomes for insight into metabolic diseases

BACKGROUND

Metabolic diseases such as obesity are known to be driven by both environmental and genetic factors. Although genome-wide association studies of common variants and their impact on complex traits have provided some biological insight into disease etiology, identified genetic variants have been found to contribute only a small proportion to disease heritability, and to map mainly to non-coding regions of the genome. To link variants to function, association studies of cellular traits, such as epigenetic marks, in disease-relevant tissues are commonly applied.

SCOPE OF THE REVIEW

We review large-scale efforts to generate genome-wide maps of coordinated epigenetic marks and their utility in complex disease dissection with a focus on DNA methylation. We contrast DNA methylation profiling methods and discuss the advantages of using targeted methods for single-base resolution assessments of methylation levels across tissue-specific regulatory regions to deepen our understanding of contributing factors leading to complex diseases.

MAJOR CONCLUSIONS

Large-scale assessments of DNA methylation patterns in metabolic disease-linked study cohorts have provided insight into the impact of variable epigenetic variants in disease etiology. In-depth profiling of epigenetic marks at regulatory regions, particularly at tissue-specific elements, will be key to dissect the genetic and environmental components contributing to metabolic disease onset and progression.

Insights into the multifactorial causation of obesity by integrated genetic and epigenetic analysis

Obesity is a highly heritable multifactorial disease that places an enormous burden on human health. Its increasing prevalence and the concomitant-reduced life expectancy has intensified the search for new analytical methods that can reduce the knowledge gap between genetic susceptibility and functional consequences of the disease pathology. Although the influence of genetics and epigenetics has been studied independently in the past, there is increasing evidence that genetic variants interact with environmental factors through epigenetic regulation. This suggests that a combined analysis of genetic and epigenetic variation may be more effective in characterizing the obesity phenotype. To date, limited genome-wide integrative analyses have been performed. In this review, we provide an overview of the latest findings, advantages, and challenges and discuss future perspectives.

Characterizing the Causal Pathway for Genetic Variants Associated with Neurological Phenotypes Using Human Brain-Derived Proteome Data

Leveraging high-dimensional molecular datasets can help us develop mechanistic insight into associations between genetic variants and complex traits. In this study, we integrated human proteome data derived from brain tissue to evaluate whether targeted proteins putatively mediate the effects of genetic variants on seven neurological phenotypes (Alzheimer disease, amyotrophic lateral sclerosis, depression, insomnia, intelligence, neuroticism, and schizophrenia). Applying the principles of Mendelian randomization (MR) systematically across the genome highlighted 43 effects between genetically predicted proteins derived from the dorsolateral prefrontal cortex and these outcomes. Furthermore, genetic colocalization provided evidence that the same causal variant at 12 of these loci was responsible for variation in both protein and neurological phenotype. This included genes such as DCC, which encodes the netrin-1 receptor and has an important role in the development of the nervous system (p = 4.29 × 10-11 with neuroticism), as well as SARM1, which has been previously implicated in axonal degeneration (p = 1.76 × 10-08 with amyotrophic lateral sclerosis). We additionally conducted a phenome-wide MR study for each of these 12 genes to assess potential pleiotropic effects on 700 complex traits and diseases. Our findings suggest that genes such as SNX32, which was initially associated with increased risk of Alzheimer disease, may potentially influence other complex traits in the opposite direction. In contrast, genes such as CTSH (which was also associated with Alzheimer disease) and SARM1 may make worthwhile therapeutic targets because they did not have genetically predicted effects on any of the other phenotypes after correcting for multiple testing.

Epigenomics and gene regulation in mammalian social systems

Social epigenomics is a new field of research that studies how the social environment shapes the epigenome and how in turn the epigenome modulates behavior. We focus on describing known gene-environment interactions (GEIs) and epigenetic mechanisms in different mammalian social systems. To illustrate how epigenetic mechanisms integrate GEIs, we highlight examples where epigenetic mechanisms are associated with social behaviors and with their maintenance through neuroendocrine, locomotor, and metabolic responses. We discuss future research trajectories and open questions for the emerging field of social epigenomics in nonmodel and naturally occurring social systems. Finally, we outline the technological advances that aid the study of epigenetic mechanisms in the establishment of GEIs and vice versa.

The DNA methylome of inflammatory bowel disease (IBD) reflects intrinsic and extrinsic factors in intestinal mucosal cells

Abnormal DNA methylation has been described in human inflammatory conditions of the gastrointestinal tract, such as inflammatory bowel disease (IBD). As other complex diseases, IBD results from the balance between genetic predisposition and environmental exposures. As such, DNA methylation may be the consequence (and potential effector) of both, genetic susceptibility variants and/or environmental signals such as cytokine exposure. We attempted to discern between these two non-excluding possibilities by performing a combined analysis of published DNA methylation data in intestinal mucosal cells of IBD and control samples. We identified abnormal DNA methylation at different levels: deviation from mean methylation signals at site and region levels, and differential variability. A fraction of such changes is associated with genetic polymorphisms linked to IBD susceptibility. In addition, by comparing with another intestinal inflammatory condition (i.e., coeliac disease) we propose that aberrant DNA methylation can also be the result of unspecific processes such as chronic inflammation. Our characterization suggests that IBD methylomes combine intrinsic and extrinsic responses in intestinal mucosal cells, and could point to knowledge-based biomarkers of IBD detection and progression.

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.

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.

Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data

Mendelian randomization (MR) implemented through instrumental variables analysis is an increasingly popular causal inference tool used in genetic epidemiology. But it can have limitations for evaluating simultaneous causal relationships in complex data sets that include, for example, multiple genetic predictors and multiple potential risk factors associated with the same genetic variant. Here we use real and simulated data to investigate Bayesian network analysis (BN) with the incorporation of directed arcs, representing genetic anchors, as an alternative approach. A Bayesian network describes the conditional dependencies/independencies of variables using a graphical model (a directed acyclic graph) with an accompanying joint probability. In real data, we found BN could be used to infer simultaneous causal relationships that confirmed the individual causal relationships suggested by bi-directional MR, while allowing for the existence of potential horizontal pleiotropy (that would violate MR assumptions). In simulated data, BN with two directional anchors (mimicking genetic instruments) had greater power for a fixed type 1 error than bi-directional MR, while BN with a single directional anchor performed better than or as well as bi-directional MR. Both BN and MR could be adversely affected by violations of their underlying assumptions (such as genetic confounding due to unmeasured horizontal pleiotropy). BN with no directional anchor generated inference that was no better than by chance, emphasizing the importance of directional anchors in BN (as in MR). Under highly pleiotropic simulated scenarios, BN outperformed both MR (and its recent extensions) and two recently-proposed alternative approaches: a multi-SNP mediation intersection-union test (SMUT) and a latent causal variable (LCV) test. We conclude that BN incorporating genetic anchors is a useful complementary method to conventional MR for exploring causal relationships in complex data sets such as those generated from modern "omics" technologies.

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/.

Epigenetics and gestational diabetes: a review of epigenetic epidemiology studies and their use to explore epigenetic mediation and improve prediction

Epigenetics encapsulates a group of molecular mechanisms including DNA methylation, histone modification and microRNAs (miRNAs). Gestational diabetes (GDM) increases the risk of adverse perinatal outcomes and is associated with future offspring risk of obesity and type 2 diabetes. It has been hypothesised that epigenetic mechanisms mediate an effect of GDM on offspring adiposity and type 2 diabetes and this could provide a modifiable mechanism to reduce type 2 diabetes in the next generation. Evidence for this hypothesis is lacking. Epigenetic epidemiology could also contribute to reducing type 2 diabetes by identifying biomarkers that accurately predict risk of GDM and its associated future adverse outcomes. We reviewed published human studies that explored associations between any of maternal GDM, type 2 diabetes, gestational fasting or post-load glucose and any epigenetic marker (DNA methylation, histone modification or miRNA). Of the 81 relevant studies we identified, most focused on the potential role of epigenetic mechanisms in mediating intrauterine effects of GDM on offspring outcomes. Studies were small (median total number of participants 58; median number of GDM cases 27) and most did not attempt replication. The most common epigenetic measure analysed was DNA methylation. Most studies that aimed to explore epigenetic mediation examined associations of in utero exposure to GDM with offspring cord or infant blood/placenta DNA methylation. Exploration of any causal effect, or effect on downstream offspring outcomes, was lacking. There is a need for more robust methods to explore the role of epigenetic mechanisms as possible mediators of effects of exposure to GDM on future risk of obesity and type 2 diabetes. Research to identify epigenetic biomarkers to improve identification of women at risk of GDM and its associated adverse (maternal and offspring) outcomes is currently rare but could contribute to future tools for accurate risk stratification.

DNA Methylation and Type 2 Diabetes: the Use of Mendelian Randomization to Assess Causality

Purpose of Review

This review summarises recent advances in the field of epigenetics in order to understand the aetiology of type 2 diabetes (T2D).

Recent Findings

DNA methylation at a number of loci has been shown to be robustly associated with T2D, including TXNIP, ABCG1, CPT1A, and SREBF1. However, due to the cross-sectional nature of many epidemiological studies and predominant analysis in samples derived from blood rather than disease relevant tissues, inferring causality is difficult. We therefore outline the use of Mendelian randomisation (MR) as one method able to assess causality in epigenetic studies of T2D.

Summary

Epidemiological studies have been fruitful in identifying epigenetic markers of T2D. Triangulation of evidence including utilisation of MR is essential to delineate causal from non-causal biomarkers of disease. Understanding the causality of epigenetic markers in T2D more fully will aid prioritisation of CpG sites as early biomarkers to detect disease or in drug development to target epigenetic mechanisms in order to treat patients.

IMAGE: high-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis

Identifying genetic variants that are associated with methylation variation-an analysis commonly referred to as methylation quantitative trait locus (mQTL) mapping-is important for understanding the epigenetic mechanisms underlying genotype-trait associations. Here, we develop a statistical method, IMAGE, for mQTL mapping in sequencing-based methylation studies. IMAGE properly accounts for the count nature of bisulfite sequencing data and incorporates allele-specific methylation patterns from heterozygous individuals to enable more powerful mQTL discovery. We compare IMAGE with existing approaches through extensive simulation. We also apply IMAGE to analyze two bisulfite sequencing studies, in which IMAGE identifies more mQTL than existing approaches.

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.

Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of complex traits and disease

Integrative approaches that harness large-scale molecular datasets can help develop mechanistic insight into findings from genome-wide association studies (GWAS). We have performed extensive analyses to uncover transcriptional and epigenetic processes which may play a role in complex trait variation. This was undertaken by applying Bayesian multiple-trait colocalization systematically across the genome to identify genetic variants responsible for influencing intermediate molecular phenotypes as well as complex traits. In this analysis, we leveraged high-dimensional quantitative trait loci data derived from the prefrontal cortex tissue (concerning gene expression, DNA methylation and histone acetylation) and GWAS findings for five complex traits (Neuroticism, Schizophrenia, Educational Attainment, Insomnia and Alzheimer's disease). There was evidence of colocalization for 118 associations, suggesting that the same underlying genetic variant influenced both nearby gene expression as well as complex trait variation. Of these, 73 associations provided evidence that the genetic variant also influenced proximal DNA methylation and/or histone acetylation. These findings support previous evidence at loci where epigenetic mechanisms may putatively mediate effects of genetic variants on traits, such as KLC1 and schizophrenia. We also uncovered evidence implicating novel loci in disease susceptibility, including genes expressed predominantly in the brain tissue, such as MDGA1, KIRREL3 and SLC12A5. An inverse relationship between DNA methylation and gene expression was observed more than can be accounted for by chance, supporting previous findings implicating DNA methylation as a transcriptional repressor. Our study should prove valuable in helping future studies prioritize candidate genes and epigenetic mechanisms for in-depth functional follow-up analyses.

Making Sense of the Epigenome Using Data Integration Approaches

Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.

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.

DRD4 methylation as a potential biomarker for physical aggression: An epigenome-wide, cross-tissue investigation

Epigenetic processes that regulate gene expression, such as DNA methylation (DNAm), have been linked to individual differences in physical aggression. Yet, it is currently unclear whether: (a) DNAm patterns in humans associate with physical aggression independently of other co-occurring psychiatric and behavioral symptoms; (b) whether these patterns are observable across multiple tissues; and (c) whether they may function as a causal versus noncausal biomarker of physical aggression. Here, we used a multisample, cross-tissue design to address these questions. First, we examined genome-wide DNAm patterns (buccal swabs; Illumina 450k) associated with engagement in physical fights in a sample of high-risk youth (n = 119; age = 16-24 years; 53% female). We identified one differentially methylated region in DRD4, which survived genome-wide correction, associated with physical aggression above and beyond co-occurring symptomatology (e.g., ADHD, substance use), and showed strong cross-tissue concordance with both blood and brain. Second, we found that DNAm sites within this region were also differentially methylated in an independent sample of young adults, between individuals with a history of chronic-high versus low physical aggression (peripheral T cells; ages 26-28). Finally, we ran a Mendelian randomization analysis using GWAS data from the EAGLE consortium to test for a causal association of DRD4 methylation with physical aggression. Only one genetic instrument was eligible for the analysis, and results provided no evidence for a causal association. Overall, our findings lend support for peripheral DRD4 methylation as a potential biomarker of physically aggressive behavior, with no evidence yet of a causal relationship.

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

Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community.

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.