Oral Presentation 9th GeneMappers Conference 2012

Integrating whole-genome transcriptional profiles and whole-genome sequence data to identify candidate genes influencing quantitative cardiovascular disease risk traits (#5)

Matthew P Johnson 1 , Marcio A Almeida 1 , Juan M Peralta 1 , Thomas D Dyer 1 , Eugene Drigalenko 1 , Donna M Lehman 2 , Goo Jun 3 , Tanya M Teslovich 3 , Christian Fuchsberger 3 , Andrew R Wood 4 , Timothy M Frayling 5 , Pablo Cingolani 6 , Thomas W Blackwell 3 , Robert Sladek 7 , Gil Atzmon 8 , Jason Laramie 9 , Steve Lincoln 9 , Harald H Göring 1 , Satish Kumar 1 , Eric K Moses 1 10 , Anthony G Comuzzie 1 , Michael C Mahaney 1 , Laura Almasy 1 , Gonçalo Abecasis 3 , Ravindranath Duggirala 1 , Joanne E Curran 1 , John Blangero 1
  1. Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
  2. Department of Medicine, University of Texas Health Science Center, San Antonio, TX, USA
  3. Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
  4. Peninsula Medical School, University of Exeter, Exeter, UK
  5. Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK
  6. McGill Centre for Bioinformatics, McGill University, Montreal, QC, Canada
  7. Division of Endocrinology & Metabolism, McGill University, Montreal, QC, Canada
  8. Departments of Medicine and Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
  9. Complete Genomics, Mountain View, CA, USA
  10. Centre for Genetic Epidemiology & Biostatistics, University of Western Australia, Perth, WA, Australia

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.