Abstract
Selecting a set of valid genetic variants is critical for Mendelian randomization (MR) to correctly infer risk factors causing a disease. We here developed a method for selecting genetic variants as valid instrumental variables for inferring risk factors causing coronary artery disease (CAD). Using this method, we selected two sets of single-nucleotide-polymorphism (SNP) genetic variants (SNP338 and SNP363) associated with each of the three potential risk factors for CAD including low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c) and triglycerides (TG) from two independent GWAS datasets. We performed in-depth multivariate MR (MVMR) analyses and the results from both datasets consistently showed that LDL-c was strongly associated with increased risk for CAD (β = 0.396,OR = 1.486 per 1 SD (equivalent to 38 mg/dL), 95CI = (1.38, 1.59) in SNP338; and β = 0.424, OR = 1.528 per 1 SD, 95%CI = (1.42, 1.65) in SNP363); HDL-c was strongly associated with reduced risk for CAD (β = −0.315, OR = 0.729 per 1 SD (equivalent to 16 mg/dL), 95CI = (0.68, 0.78) in SNP338; and β = −0.319, OR = 0.726 per 1 SD, 95%CI = (0.66, 0.80), in SNP363). In case of TG, when using the full datasets, an increased risk for CAD (β = 0.184, OR = 1.2 per 1 SD (equivalent to 89 mg/dL), 95%CI = (1.12, 1.28) in SNPP338; and β = 0.207, OR = 1.222 per 1 SD, 95%CI = (1.10, 1.36) in SNP363) was observed, while using partial datasets that contain shared and unique SNPs showed that TG is not a risk factor for CAD. From these results, it can be inferred that TG itself is not a causal risk factor for CAD, but it’s shown as a risk factor due to pleiotropic effects associated with LDL-c and HDL-c SNPs. Large-scale simulation experiments without pleiotropic effects also corroborated these results.
Highlights
Selecting a set of valid genetic variants is critical for Mendelian randomization (MR) to correctly infer risk factors causing a disease
Suggesting that single nucleotide polymorphisms (SNPs) selected with adjacent interval length, AIL > 1 kbp and/or Pd > 0.979 and Pc > 0.979 could give unstable and uncertain estimates of causal effects, and the 338 SNPs selected with Pd = Pc = 0.979 may provide enough and unbiased instrumental information for causal inference on coronary artery disease (CAD)
Because MR analysis shows that SNP datasets B and B’ do not have essential differences in the evaluated associations of three lipids with CAD (Supplementary Table S9), datasets A and B are valid for inferring the causality of lipid on CAD
Summary
Selecting a set of valid genetic variants is critical for Mendelian randomization (MR) to correctly infer risk factors causing a disease. We here developed a method for selecting genetic variants as valid instrumental variables for inferring risk factors causing coronary artery disease (CAD) Using this method, we selected two sets of single-nucleotide-polymorphism (SNP) genetic variants (SNP338 and SNP363) associated with each of the three potential risk factors for CAD including low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c) and triglycerides (TG) from two independent GWAS datasets. Do et al.[3] reported an empirical method to select 185 single nucleotide polymorphisms (SNPs) from GWAS consortium meta-analysis data Using these 185 SNPs as instrumental variables, they found out that LDL-c and TG are factors that increase the risk for CAD, but HDL-c was not associated with risk for CAD. Voight[40] proposed a simulation method to simulate data for MR analysis, but this method is not suitable to simulate data of GWAS meta-analysis results; we developed a new method to do simulation test
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