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.(Extended Data Fig. 5). These results show the value of large sample sizes in blood to detect trans-mQTLs regardless of the tissue. Trans-mQTL SNPs and DNAm exhibit patterned TF binding.Recent studies have uncovered multiple types of transcription factor (TF)-DNA interactions influenced by DNAm, including the binding of DNAm-sensitive TFs [26][27][28] and cooperativity between TFs 27,29 . To gain insights into how SNPs induce long-range DNAm changes, we mapped enrichments for DNAm sites and SNPs across binding sites for 171 TFs in 27 cell types 30,31 . We found strong enrichments for most TFs and cell types among DNAm sites with a trans association (cis + trans: 55%; trans only: 80%; cis only: 18%) and among cis-acting SNPs (cis only: 96%, cis + trans: 91%, trans only: 1%; Fig. 2b, Supplementary Tables 7 and 8, and Supplementary Figs. 22 and 23). Consistent with the observation that trans-only DNAm sites are enriched for CpG islands (Supplementary Fig. 13), DNAm sites that overlap TF-binding sites (TFBSs) were relatively hypomethylated (weighted mean DNAm levels = 21% versus 52%, P < 2.2 × 10 −16 ; Supplementary Fig. 24).Next, we hypothesized that, if a trans-mQTL is driven by TF activity 8,10 , then particular TF-TF pairs may exhibit preferential enrichment 32 . An mQTL has a pair of TFBS annotations 31 , one for the SNP and one for the DNAm site. We evaluated whether the annotation pairs among 18,584 interchromosomal trans-mQTLs were associated with TF binding in a nonrandom pattern (Supplementary Note and Extended Data Fig. 6a,b). We found that 6.1% (22,962 of 378,225) of possible pairwise combinations of SNP-DNAm site annotations were more over-or underrepresented than expected by chance after strict multiple testing correction (Supplementary Note, Supplementary Table 9 and Extended Data Fig. 6c).After accounting for abundance and other characteristics, the strongest pairwise enrichments involved sites close to TFBSs for proteins in the cohesin complex, ...

It has been widely observed that adult men of all ages are at higher risk of developing serious complications from COVID-19 when compared with women. This study aimed to investigate the association of COVID-19 positivity and severity with estrogen exposure in women, in a population based matched cohort study of female users of the COVID Symptom Study application in the UK. Analyses included 152,637 women for menopausal status, 295,689 women for exogenous estrogen intake in the form of the combined oral contraceptive pill (COCP), and 151,193 menopausal women for hormone replacement therapy (HRT). Data were collected using the COVID Symptom Study in May-June 2020. Analyses investigated associations between predicted or tested COVID-19 status and menopausal status, COCP use, and HRT use, adjusting for age, smoking and BMI, with follow-up age sensitivity analysis, and validation in a subset of participants from the TwinsUK cohort. Menopausal women had higher rates of predicted COVID-19 (P = 0.003). COCP-users had lower rates of predicted COVID-19 (P = 8.03E-05), with reduction in hospital attendance (P = 0.023). Menopausal women using HRT or hormonal therapies did not exhibit consistent associations, including increased rates of predicted COVID-19 (P = 2.22E-05) for HRT users alone. The findings support a protective effect of estrogen exposure on COVID-19, based on positive association between predicted COVID-19 with menopausal status, and negative association with COCP use. HRT use was positively associated with COVID-19, but the results should be considered with caution due to lack of data on HRT type, route of administration, duration of treatment, and potential unaccounted for confounders and comorbidities.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (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 mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both blood DNAm levels and complex diseases but in most cases these associations do not reflect causal relationships from DNAm to trait or vice versa indicating a more complex genotype-phenotype map than has previously been hypothesised.

Background Smoking remains one of the leading preventable causes of death. Smoking leaves a strong signature on the blood methylome as shown in multiple studies using the Infinium HumanMethylation450 BeadChip. Here, we explore novel blood methylation smoking signals on the Illumina MethylationEPIC BeadChip (EPIC) array, which also targets novel CpG-sites in enhancers. Method A smoking-methylation meta-analysis was carried out using EPIC DNA methylation profiles in 1407 blood samples from four UK population-based cohorts, including the MRC National Survey for Health and Development (NSHD) or 1946 British birth cohort, the National Child Development Study (NCDS) or 1958 birth cohort, the 1970 British Cohort Study (BCS70), and the TwinsUK cohort (TwinsUK). The overall discovery sample included 269 current, 497 former, and 643 never smokers. Replication was pursued in 3425 trans-ethnic samples, including 2325 American Indian individuals participating in the Strong Heart Study (SHS) in 1989–1991 and 1100 African-American participants in the Genetic Epidemiology Network of Arteriopathy Study (GENOA). Results Altogether 952 CpG-sites in 500 genes were differentially methylated between smokers and never smokers after Bonferroni correction. There were 526 novel smoking-associated CpG-sites only profiled by the EPIC array, of which 486 (92%) replicated in a meta-analysis of the American Indian and African-American samples. Novel CpG sites mapped both to genes containing previously identified smoking-methylation signals and to 80 novel genes not previously linked to smoking, with the strongest novel signal in SLAMF7. Comparison of former versus never smokers identified that 37 of these sites were persistently differentially methylated after cessation, where 16 represented novel signals only profiled by the EPIC array. We observed a depletion of smoking-associated signals in CpG islands and an enrichment in enhancer regions, consistent with previous results. Conclusion This study identified novel smoking-associated signals as possible biomarkers of exposure to smoking and may help improve our understanding of smoking-related disease risk.

Background: Men and older women have been shown to be at higher risk of adverse COVID-19 outcomes. Animal model studies of SARS-CoV and MERS suggest that the age and sex difference in COVID-19 symptom severity may be due to a protective effect of the female sex hormone estrogen. Females have shown an ability to mount a stronger immune response to a variety of viral infections because of more robust humoral and cellular immune responses. Objectives: We sought to determine whether COVID-19 positivity increases in women entering menopause. We also aimed to identify whether premenopausal women taking exogenous hormones in the form of the combined oral contraceptive pill (COCP) and post-menopausal women taking hormone replacement therapy (HRT) have lower predicted rates of COVID-19, using our published symptom-based model. Design: The COVID Symptom Study developed by Kings College London and Zoe Global Limited was launched in the UK on 24th March 2020. It captured self-reported information related to COVID-19 symptoms. Data used for this study included records collected between 7th May - 15th June 2020. Main outcome measures: We investigated links between COVID-19 rates and 1) menopausal status, 2) COCP use and 3) HRT use, using symptom-based predicted COVID-19, tested COVID-19, and disease severity based on requirement for hospital attendance or respiratory support. Participants: Female users of the COVID Symptom Tracker Application in the UK, including 152,637 women for menopause status, 295,689 for COCP use, and 151,193 for HRT use. Analyses were adjusted for age, smoking and BMI. Results: Post-menopausal women aged 40-60 years had a higher rate of predicted COVID (P=0.003) and a corresponding range of symptoms, with consistent, but not significant trends observed for tested COVID-19 and disease severity. Women aged 18-45 years taking COCP had a significantly lower predicted COVID-19 (P=8.03E-05), with a reduction in hospital attendance (P=0.023). Post-menopausal women using HRT or hormonal therapies did not exhibit consistent associations, including increased rates of predicted COVID-19 (P=2.22E-05) for HRT users alone. Conclusions: Our findings support a protective effect of estrogen on COVID-19, based on positive association between predicted COVID-19 and menopausal status, and a negative association with COCP use. HRT use was positively associated with COVID-19 symptoms; however, the results should be considered with caution due to lack of data on HRT type, route of administration, duration of treatment, and potential comorbidities. Trial registration: The App Ethics has been approved by KCL ethics Committee REMAS ID 18210, review reference LRS-19/20-18210

Coffee and tea are extensively consumed beverages worldwide which have received considerable attention regarding health. Intake of these beverages is consistently linked to, among others, reduced risk of diabetes and liver diseases; however, the mechanisms of action remain elusive. Epigenetics is suggested as a mechanism mediating the effects of dietary and lifestyle factors on disease onset. Here we report the results from epigenome-wide association studies (EWAS) on coffee and tea consumption in 15,789 participants of European and African-American ancestries from 15 cohorts. EWAS meta-analysis of coffee consumption reveals 11 CpGs surpassing the epigenome-wide significance threshold (P-value <1.1×10−7), which annotated to the AHRR, F2RL3, FLJ43663, HDAC4, GFI1 and PHGDH genes. Among them, cg14476101 is significantly associated with expression of the PHGDH and risk of fatty liver disease. Knockdown of PHGDH expression in liver cells shows a correlation with expression levels of genes associated with circulating lipids, suggesting a role of PHGDH in hepatic-lipid metabolism. EWAS meta-analysis on tea consumption reveals no significant association, only two CpGs annotated to CACNA1A and PRDM16 genes show suggestive association (P-value <5.0×10−6). These findings indicate that coffee-associated changes in DNA methylation levels may explain the mechanism of action of coffee consumption in conferring risk of diseases.

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The liver and the kidney are the most common targets of chemical toxicity, due to their major metabolic and excretory functions. However, since the liver is directly involved in biotransformation, compounds in many currently and normally used drugs could affect it adversely. Most chemical compounds are already labeled according to FDA-approved labels using DILI-concern scale. Drug Induced Liver Injury (DILI) scale refers to an adverse drug reaction. Many compounds do not exhibit hepatotoxicity at early stages of development, so it is important to detect anomalies at gene expression level that could predict adverse reactions in later stages. In this study, a large collection of microarray data is used to investigate gene expression changes associated with hepatotoxicity. Using TG-GATEs a large-scale toxicogenomics database, we present a computational strategy to classify compounds by toxicity levels in human and animal models through patterns of gene expression. We combined machine learning algorithms with time series analysis to identify genes capable of classifying compounds by FDA-approved labeling as DILI-concern toxic. The goal is to define gene expression profiles capable of distinguishing the different subtypes of hepatotoxicity. The study illustrates that expression profiling can be used to classify compounds according to different hepatotoxic levels; to label those that are currently labeled as undertemined; and to determine if at the molecular level, animal models are a good proxy to predict hepatotoxicity in humans.