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In clinical medicine, lipids are commonly measured biomarkers used to assess an individual's risk for cardiovascular disease, heart attack, and stroke. Accurately predicting longitudinal lipid levels based on genomic information can inform therapeutic practices and decrease cardiovascular risk by identifying high-risk patients prior to onset. Using genotyped and imputed genetic data from 523 unrelated Caucasian Americans from the Bogalusa Heart Study, surveyed on 4,026 occasions from 4 to 48 years of age, we generated various lipid genomic risk models based on previously reported markers. We observed a significant improvement in prediction over non-genetic risk models in high density lipoprotein cholesterol (increase in the squared correlation between observed and predicted values, ΔR (2) = 0.032), low density lipoprotein cholesterol (ΔR (2) = 0.053), total cholesterol (ΔR (2) = 0.043), and triglycerides (ΔR (2) = 0.031). Many of our approaches are based on an n-fold cross-validation procedure that are, by design, adaptable to a clinical environment.

Original publication




Journal article


Front Genet

Publication Date





cardiovascular diseases, lipids, polygenic model, prediction, statistical methods