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Article, see p 1158 Great strides have been made in dissecting the genetics of cardiovascular disease (CVD) through genome-wide association studies and whole exome/genome sequencing, although the full heritability remains to be elucidated. Furthermore, biological insights into the associations of common variants with complex traits are often lacking. Although static DNA variation is at the core of variability between humans, viewed in isolation it can provide only a snapshot of the complex landscape of the dynamic, temporal evolution of CVD. One powerful approach to attempt to harness this dynamic variability is to study intermediate phenotypes that are more proximal to genomic variation than complex clinical end points. Although they can be difficult to measure accurately given the temporal nature of CVD, when analyzed on the foundation of the static genome, integrated studies can identify genomic loci that influence disease through these intermediate biomarkers. This principle underlies quantitative trait loci (QTL) studies, where genetic variation is integrated with other omics data (eg, transcriptomics, metabolomics, proteomics). Once these associations are established, the genetic variants and biomarkers can then be studied in a focused manner to determine what, if any, causal relation they have to the disease of interest. In addition, if done at the genome-wide level, these studies provide a valuable resource to identify possible functional effects of disease-associated genetic variants. The CVD community is just beginning to embark on such integrative studies that capitalize on the large amount of molecular data now available in CVD cohorts. In this issue of Circulation , Benson et al1 …