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Published on: October 12, 2017
Exploring potential shared genetic influences between rheumatoid arthritis and blood lipid levels
Melody Kasher1, Stacey S Cherny2,
1Human Population Biology Research Unit, Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Israel.
Background And Aims:
The association between rheumatoid arthritis (RA) and blood lipid levels has often been described as paradoxical, despite the strong association between RA and cardiovascular disease (CVD) risk. We aimed to clarify the genetic architecture that would explain the relationship between RA and blood-lipid levels, while considering inflammation as measured by C-reactive protein (CRP).
Methods:
Genome-wide association study (GWAS) summary statistics were collected from the CHARGE Consortium and Global Lipids Genetics Consortium. Blood-lipid levels includes HDL-C, LDL-C, triglycerides (TG), and total cholesterol (TC). Causality was examined by assessing Mendelian Randomization (MR) analysis. Pleiotropy, the identification of shared causal variants between traits, was assessed by conducting colocalization analyses.
Results:
Using the MR Egger method, RA did not appear to causally predict alterations in lipid factors, rather the MR Egger intercept revealed that the genetic relationship between RA and HDL-C, LDL-C and TC may be explained by horizontal pleiotropy (p=0.003, 0.006, and 0.018, respectively). MR was suggestive of a horizontally pleiotropic relationship between CRP and lipid factors, while a causal relationship could not be ruled out. Recurring genes arising from shared causal genetic variants between RA and varying lipid factors included NAT2/PSD3, FADS2/FADS1, SH2B3, and YDJC.
Conclusions:
Horizontal pleiotropy appears to explain the genetic relationship between RA and blood-lipid levels. In addition, blood-lipid levels appear to suggest a horizontally pleiotropic relationship to CRP, if not mediated through RA as well. Consideration of the pleiotropic genes between RA and blood lipid levels may aid in enhancing diagnostic means to predict CVD.
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