{"doi":"10.1101/2022.04.13.22273839","title":"Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: The Framingham Heart Study","abstract":"Background Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression and may reveal molecular mechanisms of disease. The present study describes an eQTM resource of CpG-transcript pairs. Methods DNA methylation was measured in blood samples from 1,045 Framingham Heart Study (FHS) participants using the Illumina 450K BeadChip and in 1,070 FHS participants using the Illumina EPIC array. Blood gene expression data were collected from all 2,115 participants using RNA sequencing (RNA-seq). The association between DNA methylation and gene expression was quantified for all cis (i.e., within 1Mb) and trans (>1Mb) CpG-transcript pairs. Significant results (p<1E-7 for cis and <1E-14 for trans ) were subsequently tested for enrichment of biological pathways and of clinical traits. Results We identified 70,047 significant cis CpG-transcript pairs where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1,208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1,191 clinical traits (enrichment FDR ≤ 0.05). Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with these cardiometabolic traits. Conclusions We developed a robust and powerful resource of eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.","journal":"medRxiv","year":2022,"id":299136,"datarank":0.25253219825145384,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.011116511386338745,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.011116511386338745,"corpus_percentile":40.38059874680901,"corpus_rank":7708,"citation_count":4,"citer_count":3,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7339,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":120997,"name":"Helena Bui","orcid":null,"position":1,"is_corresponding":false},{"id":291138,"name":"Roby Joehanes","orcid":"0000-0001-5549-9054","position":2,"is_corresponding":false},{"id":291135,"name":"Jiantao Ma","orcid":"0000-0001-7478-7203","position":3,"is_corresponding":false},{"id":571538,"name":"Chunyu Liu","orcid":"0000-0002-2652-5409","position":4,"is_corresponding":false},{"id":251872,"name":"Tianxiao Huan","orcid":"0000-0001-5840-9413","position":5,"is_corresponding":false},{"id":240613,"name":"Shih‐Jen Hwang","orcid":"0000-0002-2129-5704","position":6,"is_corresponding":false},{"id":877571,"name":"Brandon Tejada","orcid":"0000-0002-7760-4596","position":7,"is_corresponding":false},{"id":57798,"name":"Meera Sooda","orcid":"0000-0003-3415-3147","position":8,"is_corresponding":false},{"id":533481,"name":"Paul Courchesne","orcid":"0000-0001-5691-6913","position":9,"is_corresponding":false},{"id":25262,"name":"Peter J. 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