Exomes, Proteins, and Cardiovascular Disease

The postgenomic era has simultaneously provided huge opportunities and major challenges in the translation of the wealth of genetic information to improve our diagnostic and therapeutic approach to disease. Although many might agree that genome-wide association studies have not met expectations in their ability to capture the full genetic architecture of many medical conditions, there is hope that more sophisticated and comprehensive genetic analyses, such as whole exome and whole genome sequencing, will shed more light on the mechanisms underlying common diseases. A complementary approach is in the integration of molecular profiling platforms (genomics, transcriptomics, proteomics, metabolomics, etc.) to provide a systems-biology perspective. This approach is particularly provocative in common complex diseases such as cardiovascular disease (CVD) where the end disease represents a convergence of diverse biological mechanisms, only some of which may be at play in a given individual, and where the disease process itself varies temporally with fixed genetic variation unable to capture this temporal progression. Such diseases are often defined by an underlying quantitative liability scale. As such, intermediate traits represented by proteins, RNA transcripts, metabolites, etc. may reside closer to the underlying pathogenic gene and thereby provide a stronger genetic signal, in addition to serving as stronger signals for the biological pathways mediating the disease process.Article, see p 375 Indeed, many groups have used this functional genomics approach to provide new insights into CVD pathophysiology. Expression quantitative trait loci have been used for years for functional evaluation of genome-wide association studies hits, biomarker discovery, and tissue specificity evaluations in human diseases.1 More recently, several studies have mapped metabolic traits onto the genome to identify quantitative trait loci associated with CVD phenotypes. These studies have provided valuable understanding about the genetic architecture underlying metabolic variability …