Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation

Zaghlool, Shaza B.; Mook‐Kanamori, Dennis O.; Kader, Sara Abdul; Stephan, Nisha; Halama, Anna; Engelke, Rudolf; Sarwath, Hina; Al-Dous, Eman K.; Mohamoud, Yasmin Ali; Roemisch-Margl, Werner; Adamski, Jerzy; Kastenmüller, Gabi; Friedrich, Nele; Visconti, Alessia; Tsai, Pei-Chien; Spector, Tim D.; Bell, Jordana T.; Falchi, Mario; Wahl, Annika; Waldenberger, Mélanie; Peters, Annette; Gieger, Christian; Pezer, Marija; Lauc, Gordan; Graumann, Johannes; Malek, Joel A.; Suhre, Karsten

doi:10.1093/hmg/ddy006

Cited by 36 publications

(43 citation statements)

References 74 publications

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“…At a stringent Bonferroni level of significance (correction for number of proteins and clinical phenotypes), we identified associations for 14 of the proteins and eight of the CpG sites (Supplementary Data 7 and 8). Finally, we tested the 89 CpG sites for association with 2,251 urinary, salivary, and blood metabolites that we previously reported 15 . We found 20 associations at Bonferroni significance ( p < 2.5 × 10 −7 = 0.05/89/2251) (Supplementary Data 9).…”

Section: Resultsmentioning

confidence: 99%

“…It is therefore essential to account for these driving and potentially confounding factors in an EWAS approach. Complex networks of (co-)associated multi-omics traits, connecting CpG methylation, gene expression, protein, and metabolite levels to disease endpoints then emerge from such EWASs, as we recently showed at the example of a multi-omics association study with a small set of CpG sites 15 . Such networks might eventually guide a more personalized treatment of complex disorders, using for instance DNA methylation as a precise read-out of the body’s disease status with respect to the affected pathways, or to identify drug targets that may allow the modification of the underlying dysregulated processes.…”

Section: Introductionmentioning

confidence: 83%

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Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits

et al. 2020

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DNA methylation and blood circulating proteins have been associated with many complex disorders, but the underlying disease-causing mechanisms often remain unclear. Here, we report an epigenome-wide association study of 1123 proteins from 944 participants of the KORA population study and replication in a multi-ethnic cohort of 344 individuals. We identify 98 CpG-protein associations (pQTMs) at a stringent Bonferroni level of significance. Overlapping associations with transcriptomics, metabolomics, and clinical endpoints suggest implication of processes related to chronic low-grade inflammation, including a network involving methylation of NLRC5, a regulator of the inflammasome, and associated pQTMs implicating key proteins of the immune system, such as CD48, CD163, CXCL10, CXCL11, LAG3, FCGR3B, and B2M. Our study links DNA methylation to disease endpoints via intermediate proteomics phenotypes and identifies correlative networks that may eventually be targeted in a personalized approach of chronic low-grade inflammation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 83%

Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits

et al. 2020

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“…The positive direction of effects observed in identified DNAme–lung function association is in accordance with the reported hypomethylation of smoking-related DNAme sites. The identified lung function-associated CpGs in this study have been previously reported to be associated with smoking-related molecular phenotypes [22], with increased risk of noncommunicable disease, including cancer [20, 23], and with epigenetically defined accelerated ageing [24].…”

Section: Discussionmentioning

confidence: 99%

Epigenome-wide association study of lung function level and its change

et al. 2019

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Previous reports link differential DNA methylation (DNAme) to environmental exposures that are associated with lung function. Direct evidence on lung function DNAme is, however, limited. We undertook an agnostic epigenome-wide association study (EWAS) on pre-bronchodilation lung function and its change in adults.In a discovery–replication EWAS design, DNAme in blood and spirometry were measured twice, 6–15 years apart, in the same participants of three adult population-based discovery cohorts (n=2043). Associated DNAme markers (p<5×10−7) were tested in seven replication cohorts (adult: n=3327; childhood: n=420). Technical bias-adjusted residuals of a regression of the normalised absolute β-values on control probe-derived principle components were regressed on level and change of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and their ratio (FEV1/FVC) in the covariate-adjusted discovery EWAS. Inverse-variance-weighted meta-analyses were performed on results from discovery and replication samples in all participants and never-smokers.EWAS signals were enriched for smoking-related DNAme. We replicated 57 lung function DNAme markers in adult, but not childhood samples, all previously associated with smoking. Markers not previously associated with smoking failed replication. cg05575921 (AHRR (aryl hydrocarbon receptor repressor)) showed the statistically most significant association with cross-sectional lung function (FEV1/FVC: pdiscovery=3.96×10−21 and pcombined=7.22×10−50). A score combining 10 DNAme markers previously reported to mediate the effect of smoking on lung function was associated with lung function (FEV1/FVC: p=2.65×10−20).Our results reveal that lung function-associated methylation signals in adults are predominantly smoking related, and possibly of clinical utility in identifying poor lung function and accelerated decline. Larger studies with more repeat time-points are needed to identify lung function DNAme in never-smokers and in children.

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“…Another study by Zaghlool et al 23 aimed at elucidating the molecular pathways of 20 previously established CpG sites by using multi-omics data in 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes. We observe associations at nominal significance at six of these 20 sites and demonstrate associations within Model 1 at PHGDH, TXNIP, SLC7A11, CPT1A, MYO5C and ABCG1 through the dissection of multi-phenotype effects within our relatively small study sample ( Table 5).…”

Section: Resultsmentioning

confidence: 99%

methylSCOPA and META-methylSCOPA: software for the analysis and aggregation of epigenome-wide association studies of multiple correlated phenotypes

Draisma

Pupko

Demirkan

et al. 2019

Preprint

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Background: Multi-phenotype genome-wide association studies (MP-GWAS) of correlated traits have greater power to detect genotype-phenotype associations than single-trait GWAS. However, no multiphenotype analysis method exists for epigenome-wide association studies (EWAS). Results:We extended the SCOPA approach developed by us to "methylSCOPA" software in C++ by 'reversely' regressing DNA hyper/hypo-methylation information on a linear combination of phenotypes.We evaluated two models of association between DNA methylation and fasting glucose (FG) and insulin (FI) levels: Model 1, including FG, FI, and three measured potential confounders (body mass index [BMI], fasting serum triglyceride levels [TG], and waist/hip ratio [WHR]), and Model 2, including FG and FI corrected for the effects of BMI, TG, and WHR. Both models were additionally corrected for participant sex and smoking status (current/ever/never). We meta-analyzed the cohort-specific MP-EWAS results with our novel software META-methylSCOPA, mapped genomic locations to CGCh37/hg19, and adopted P<1×10 -7 to denote epigenome-wide significance. We used the Illumina Infinium HumanMethylation450 K BeadChip array data from the Northern Finland Birth Cohorts (NFBC) 1966/1986. We quality-controlled the data, regressed out the effects of measured potential confounders, and normalized the methylation signal intensity and FI data. The MP-EWAS included data for 643/457 individuals from NFBC1966 and NFBC1986, respectively (total N=1,100).In Model 1, we detected epigenome-wide significant association in the MP-EWAS meta-analysis at cg13708645 (chr12:121,974,305; P=1.2×10 -8 ) within KDM2B gene. Single-trait effects within KDM2B were on FI, BMI, and WHR. Model with effect on BMI and WHR showed the strongest association at this locus, while effect on FI in single-phenotype analysis was driven by the effect of adiposity. In Model 2, the strongest association was at cg05063096 (chr3:143,689,810; P=2.3×10 -7 ) annotated to C3orf58 with strongest effect on FI in single-trait analysis and multi-phenotype effect on FI and WHI within Model 1.We characterized the effects of established EWAS loci for diabetes and its risk factors and detected suggestive (p<0.01) associations at six markers including PHGDH, TXNIP, SLC7A11, CPT1A, MYO5C and ABCG1, through the dissection of the multi-phenotype effects in Model 1. Conclusions:We implemented MP-EWAS in methylSCOPA and demonstrated its enhanced power over single-trait EWAS for correlated phenotypes in large-scale data.

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Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation

Cited by 36 publications

References 74 publications

Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits

Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits

Epigenome-wide association study of lung function level and its change

methylSCOPA and META-methylSCOPA: software for the analysis and aggregation of epigenome-wide association studies of multiple correlated phenotypes

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