Prediction of LDL cholesterol response to statin using transcriptomic and genetic variation

Background: Statins are widely prescribed for lowering LDL-cholesterol (LDLC) levels and risk of cardiovascular disease. There is, however, substantial inter-individual variation in the magnitude of statin-induced LDLC reduction. To date, analysis of individual DNA sequence variants has explained only a small proportion of this variability. The present study was aimed at assessing whether transcriptomic analyses could be used to identify additional genetic contributions to inter-individual differences in statin efficacy. Results: Using expression array data from immortalized lymphoblastoid cell lines derived from 372 participants of the Cholesterol and Pharmacogenetics clinical trial, we identify 100 signature genes differentiating high versus low statin responders. A radial-basis support vector machine prediction model of these signature genes explains 12.3% of the variance in statin-mediated LDLC change. Addition of SNPs either associated with expression levels of the signature genes (eQTLs) or previously reported to be associated with statin response in genome-wide association studies results in a combined model that predicts 15.0% of the variance. Notably, a model of the signature gene associated eQTLs alone explains up to 17.2% of the variance in the tails of a separate subset of the Cholesterol and Pharmacogenetics population. Furthermore, using a support vector machine classification model, we classify the most extreme 15% of high and low responders with high accuracy. Conclusions: These results demonstrate that transcriptomic information can explain a substantial proportion of the variance in LDLC response to statin treatment, and suggest that this may provide a framework for identifying novel pathways that influence cholesterol metabolism.