Cardiovascular disease (CVD) substantially contributes to global mortality rates and places a hefty financial burden (billions of dollars) upon health care systems. Any novel insight into biological mechanisms that predispose individuals to CVD-related ailments may provide the impetus for novel therapeutic development and subsequent reduction of this considerable burden. To objectively identify candidate genes influencing quantitative CVD risk traits; HDL cholesterol (HDL-C) and triglyceride (TG) levels, we have integrated whole-genome transcriptional profiles and whole-genome sequence data. This study was conducted in a large cohort of extended Mexican American pedigrees (>1,000 individuals) from San Antonio, Texas: The San Antonio Family Heart Study (SAFHS). For these SAFHS participants whole-genome transcriptional profiles using lymphocyte RNA were generated using Illumina’s HumanWG-6 v1 BeadChip and whole-genome sequence data (including variant calls) were generated on a subset of individuals using Complete Genomics’ proprietary sequencing technology as part of the T2D-GENES Consortium. We initially prioritized those transcripts with cis-associated SNPs exhibiting an eQTL association signal of p<10E-04. For all such cis-associated SNPs, a measured genotype association analysis was also conducted against HDL-C or TG levels. Our top HDL-C result was a genome-wide suggestive association with an intronic cis-associated SKP2 SNP (rs33662, p=6.92E-06, beta=0.29) whereas our top TG result was a genome-wide significant cis-association with an intronic ZNF259 SNP (rs3741298, p=5.99E-08, beta=0.27). Skp2, in a murine model, has been demonstrated to promote vascular smooth muscle cell proliferation, a process known to increase during atherosclerosis. ZNF259 sits at the 5’ periphery of the chromosome 11q23.3 ZNF259-APOA5-A4-C3-A1 gene cluster which has previously been implicated in coronary artery disease and other TG genetic studies. In conclusion, we have significantly demonstrated the utility of an integrative ‘omics’ approach in a family-based study design to identify highly plausible biological candidate genes involved in quantitative CVD-related risk traits.