Rosuvastatin, is a widely-used statin for the treatment of hypercholesterolemia and the prevention of cardiovascular diseases. Although rosuvastatin is well tolerated, about 3/10.000 patients can suffer severe myopathy. Rhabdomyolysis is a severe medical condition that causes injury to the skeletal muscle, electrolyte imbalances, acute renal failure and extreme creatine kinase (CK) elevation. Little is known regarding the molecular involvement of rosuvastatin-induced rhabdomyolysis (RIR). It has been demonstrated that genomic variants associated with decreased enzymatic activity of proteins are important determinants in plasmatic and skeletal muscle distribution of rosuvastatin and its toxicity. Until now, no interactions of ticagrelor, ezetimibe and rosuvastatin have been described with the consideration of pharmacogenomics predisposition. The present report involves a whole-exome sequencing (WES), in a patient affected by rosuvastatin-induced rhabdomyolysis. A pharmacogenomic dissection was performed by analyzing a comprehensive subset of candidate genes (n=160) potentially related to RIR. The genes were selected according to their implication in drug metabolism or inherited myopathies. Using an innovative approach of bioinformatics analysis, considering rare and common variants, we identified 19 genomic variations potentially related to the pharmacokinetic/pharmacodynamic modifications of rosuvastatin, ezetimibe and ticagrelor. The affected genes are involved in Phase I metabolism (CYP2C19, CYP2E1, CYP1A1, CYP2D6 and CYP2C9), Phase II metabolism (UGT2B15 and UGT2B7), influx transportation (SLCO1B3 and SLCO2B1), efflux transportation (ABCG8, ABCB11, ABCC4 and ABCB1), drug targeting (NPC1L1) and inherited myopathy etiology (OBSCN). We report three rare, potentially pathogenic molecular variants in CYP2C19, NPC1L1 and OBSCN genes. Pharmacogenetic analysis indicated that the patient was a carrier of inactivating alleles in several pharmacogenes involved in drug toxicity. The whole-exome sequencing and bioinformatics analysis presented here represents an innovative way to identify genomic variants contributing with RIR´s origin and evokes the polygenic nature of adverse drug reactions.