Abstract
Mendelian randomization (MR) of lipid traits in CAD has provided evidence for causal associations of LDL-C and TGs in CAD, but many lipid trait genetic variants have pleiotropic effects on other cardiovascular risk factors that may bias MR associations. The goal of this study was to evaluate pleiotropic effects of lipid trait genetic variants and to account for these effects in MR of lipid traits in CAD. We performed multivariable MR using inverse variance-weighted and MR-Egger methods in large (n ≥ 300,000) GWAS datasets. We found that 30% of lipid trait genetic variants have effects on metabolic syndrome traits, including BMI, T2D, and systolic blood pressure (SBP). Nonetheless, in multivariable MR analysis, LDL-C, HDL-C, TGs, BMI, T2D, and SBP are independently associated with CAD, and each of these associations is robust to adjustment for directional pleiotropy. MR at loci linked to direct effects on HDL-C and TGs suggests locus- and mechanism-specific causal effects of these factors on CAD.
Highlights
Mendelian randomization (MR) is a method used to infer the causal effect of a risk factor, or exposure, on a disease outcome [1]
A similar degree of genetic overlap between lipid and metabolic syndrome traits is evident in the set of 185 lipid trait SNPs based on the Global Lipid Genetics Consortium GWAS (Supplemental Table S1) that has been used in previous multivariable MR of lipid traits in CAD [8]
These widespread pleiotropic effects invalidate the use of polygenic univariable MR using restricted sets of SNPs apparently specific for a single lipid trait, even though this approach was used in early MR studies of lipid traits in CAD [6, 34, 62]
Summary
Mendelian randomization (MR) is a method used to infer the causal effect of a risk factor, or exposure, on a disease outcome [1]. A few studies have attempted to adjust for the impact of metabolic syndrome trait pleiotropy in lipid trait MR by omitting BMI and blood pressure SNPs or regressing on residuals after adjustment for BMI and systolic blood pressure (SBP) [17, 18] These studies have concluded that there is a causal relationship of LDL-C and TGs with CAD and have arrived at conflicting results with regard to the causal effect of HDL-C. The availability of MR datasets of increasing size, including measurements of potential pleiotropic factors such as BMI, T2D, and SBP, provides the opportunity to re-address and adjust for the impact of pleiotropy in the association of lipid and metabolic syndrome traits with CAD. Using expanded datasets 2to 6-fold larger than those previously used for pleiotropy adjustment in lipid trait MR, we found that 30% of lipid trait SNPs have pleiotropic effects on the
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