| Literature DB >> 34788810 |
Andy B Castaneda1, Lauren E Petty2, Markus Scholz3,4, Rick Jansen5, Stefan Weiss6,7, Xiaoling Zhang8,9,10, Katharina Schramm11,12, Frank Beutner13, Holger Kirsten3,4, Ulf Schminke14, Shih-Jen Hwang10,15, Carola Marzi16,17, Klodian Dhana18,19, Adrie Seldenrijk5, Knut Krohn20, Georg Homuth6, Petra Wolf11,12, Marjolein J Peters21, Marcus Dörr7,22, Annette Peters16, Joyce B J van Meurs21, André G Uitterlinden18,21, Maryam Kavousi18, Daniel Levy10,15, Christian Herder23,24,25, Gerard van Grootheest5, Melanie Waldenberger16, Christa Meisinger16,26, Wolfgang Rathmann27, Joachim Thiery4,28, Joseph Polak29, Wolfgang Koenig30,31,32, Jochen Seissler33, Joshua C Bis34, Nora Franceshini35, Claudia Giambartolomei36, Albert Hofman18,37, Oscar H Franco18,38, Brenda W J H Penninx5, Holger Prokisch11,12, Henry Völzke7,39, Markus Loeffler3,4, Christopher J O'Donnell10,40, Jennifer E Below2, Abbas Dehghan18,41,42,43, Paul S de Vries1,18.
Abstract
Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT. Published by Oxford University Press 2021.Entities:
Mesh:
Year: 2022 PMID: 34788810 PMCID: PMC8976428 DOI: 10.1093/hmg/ddab236
Source DB: PubMed Journal: Hum Mol Genet ISSN: 0964-6906 Impact factor: 5.121