The number of genome-wide association studies (GWAS) with deep molecular phenotypes, especially metabolomics and proteomics, is rapidly increasing. On other places of this blog I maintain tables of published GWAS with metabolomics (mQTLs), proteomics (pQTLs), and DNA methylation (meQTLs).
Here I extend this collection by a table of all published epigenome-wide association studies (EWAS) with metabolomics, proteomics, and any other (still to be published) deep molecular phenotypes.
Should you know of any study missing here, please let me know.
|Reference||Study Population/Cohort||Number of Samples||CpG-Trait Associations||Omics Phenotype|
|Petersen et al. (Hum. Mol. Genet. 2014)||German (KORA) population study||1814||20 (excluding CpGs with genetic confounders)||649 blood metabolic and lipid traits (Metabolon, Biocrates, and Numares/Lipofit platforms)|
|Sayols-Baixeras (Hum. Molec. Genet., 2016)||REGICOR study and Framingham Offspring Study (replication)||645+2542 (replication)||14 (9 genes + 2 intergenic loci)||Lipid traits (total, low-density and high-density lipoprotein cholesterol, and triglycerides)|
|Braun et al. (Clin. Epigenet., 2017)||Rotterdam Study||725+760 (replication)||5||blood levels of triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol|
|Nano et al. (Gastroenterology, 2017)||Rotterdam Study||731+719 (replication)||8 (4 replicated)||blood for liver enzyme levels, including gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and aspartate aminotransferase (AST)|
|Ahsan et al. (PLoS Genet., 2017)||Northern Sweden Population Health Study (NSPHS)||between 698 and 1033||124||121 protein biomarkers (Proseek immuno-assay technology)|
|Wahl et al. (BBA, 2018)||4 cohorts||3000+||7 (5 sites)||IgG glycosylation (24 glycans measured by UPLC, 50 by LC/MS)|
|Orozco et al.. (Hum. Mol. Gen., 2018)||Metabolic Syndrome in Men cohort||201||13 clinical traits at 21 loci||32 clinical traits in subcutaneous abdominal adipose tissue|
|Huang et al. (Epigenomics, 2018)||Emory Twin Study (ETS)||180 male twins||NA||12 annotated smoking-related metabolites identified from a metabolome-wide association study|
|Tindula et al. (Environmental Epigenetics, 2019)||Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) cohort||81 mother-child pairs||7 (after Bonferroni correction)||92 metabolites measured by targeted metabolomics (SRM monitoring)|
|Hillary et al. (Nat. Comm., 2019)||Lothian Birth Cohort 1936||750 healthy older adults||26 sites with the levels of 9 proteins||92 protein levels from the Olink neurology panel|
|Huan et al. (Nat.Comm. 2019)||Framingham Heart Study||4170||92 putatively|
causal CpGs for CVD traits by Mendelian randomization
|Zaghlool et al. (Nat. Comm., 2020)||German (KORA) population study and multi-ethnic QMDiab study||944 + 344 (replication)||98 (replicated)||1123 blood circulating proteins (Somalogic aptamers)|
|Hillary et al. (Genome Medicine 2020), pre-print on BioRxiv||Lothian Birth Cohort 1936||876||3||70 blood circulating protein levels (Olink inflammation panel)|
|Huan et al. (Epigenetics, 2020)||Framingham Heart Study||3565||227 CpGs with 40 nearby miRNAs (cis-miR-eQTMs)||283 blood circulating microRNAs (miRNA)|
(updated August 02, 2020)