Lastly, a large portion of the estimated genetic component has not been identified by main effects alone. A more thorough understanding of the genetics underlying individual variation in lipid levels will result in greater insight into the biological processes underpinning dyslipidemia, and may inform more effective therapies to ultimately lower risk for cardiovascular disease. While age, sex, body mass index (BMI), diet, exercise and smoking status have been shown to have an effect on lipid levels, it is estimated that genetic factors contribute between 40 and 60% overall to variation in lipid levels. Secondly, the estimated genetic component for lipid levels is relatively large and highly variable. Cardiovascular disease is the leading cause of death for individuals in developed countries. Circulating lipid levels, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TG), are associated with risk for various common disease traits including cardiovascular disease. First, dyslipidemia have a large impact on human health. Our motivation for studying the contribution of interactions to lipid levels is three-fold. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.įor this study, we perform several analyses to identify and validate genetic interactions associated with circulating lipid levels. These results may reveal novel insights into the genetic etiology of lipid levels. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. A filter that only tested interactions identified by Biofilter 2.0. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. Our analysis consisted of a discovery phase using a merged dataset of five different cohorts ( n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individualsīioData Mining volume 10, Article number: 25 ( 2017)
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