Understanding the relationship between genotypes and phenotypes is essential to disentangle biological mechanisms and to unravel the molecular basis of diseases. Genes and proteins are closely linked in biological systems. However, genomics and proteomics have developed separately into two distinct disciplines whereby crosstalk among scientists from the two domains is limited and this constrains the integration of both fields into a single data modality of useful information. The emerging field of proteogenomics attempts to address this by building bridges between the two disciplines. In this review, how genomics and transcriptomics data in different formats can be utilized to assist proteogenomics application is briefly discussed. Subsequently, a much larger part of this review focuses on proteogenomics research articles that are published in the last five years that answer two important questions. First, how proteogenomics can be applied to tackle biological problems is discussed, covering genome annotation and precision medicine. Second, the latest developments in analytical technologies for data acquisition and the bioinformatics tools to interpret and visualize proteogenomics data are covered.
Genetic markers displaying highly significant statistical associations with complex phenotypes may not necessarily possess sufficient clinical validity to be useful. Understanding the contribution of these markers beyond readily available clinical biomarkers is particularly important in pharmacogenetics. We demonstrate the utility of genetic testing using the example of warfarin in a multi-ethnic setting comprising of three Asian populations that are broadly representative of the genetic diversity for half of the population in the world, especially as distinct interethnic differences in warfarin dose requirements have been previously established. We confirmed the roles of three well-established loci (CYP2C9, VKORC1 and CYP4F2) in explaining warfarin dosage variation in the three Asian populations. In addition, we assessed the relationship between ethnicity and the genotypes of these loci, observing strong correlations at VKORC1 and CYP4F2. Subsequently, we established the additional utility of these genetic factors in predicting warfarin dose beyond ethnicity and clinical biomarkers through performing a series of systematic cross-validation analyses of the relative predictive accuracies of various fixed-dose regimen, clinical and genetic models. Through a pharmacogenetics model for warfarin, we show the importance of genetic testing beyond readily available clinical biomarkers in predicting dose requirements, confirming the role of genetic profiling in personalized medicine.