This article is part of the supplement: The Framingham Heart Study 100,000 single nucleotide polymorphisms resource
Genome-wide association with select biomarker traits in the Framingham Heart Study
1 The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
2 School of Medicine, Boston University, Boston, MA, USA
3 Whitaker Cardiovascular Institute, Boston University, Boston, MA, USA
4 School of Public Health, Boston University, Boston, MA, USA
5 Department of Mathematics and Statistics, Boston, MA, USA
6 Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
7 Broad Institute of Massachusetts Institute of Technology, Cambridge, MA, USA
8 Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
9 Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
10 Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
11 Emory School of Medicine, Atlanta, GA, USA
BMC Medical Genetics 2007, 8(Suppl 1):S11 doi:10.1186/1471-2350-8-S1-S11Published: 19 September 2007
Systemic biomarkers provide insights into disease pathogenesis, diagnosis, and risk stratification. Many systemic biomarker concentrations are heritable phenotypes. Genome-wide association studies (GWAS) provide mechanisms to investigate the genetic contributions to biomarker variability unconstrained by current knowledge of physiological relations.
We examined the association of Affymetrix 100K GeneChip single nucleotide polymorphisms (SNPs) to 22 systemic biomarker concentrations in 4 biological domains: inflammation/oxidative stress; natriuretic peptides; liver function; and vitamins. Related members of the Framingham Offspring cohort (n = 1012; mean age 59 ± 10 years, 51% women) had both phenotype and genotype data (minimum-maximum per phenotype n = 507–1008). We used Generalized Estimating Equations (GEE), Family Based Association Tests (FBAT) and variance components linkage to relate SNPs to multivariable-adjusted biomarker residuals. Autosomal SNPs (n = 70,987) meeting the following criteria were studied: minor allele frequency ≥ 10%, call rate ≥ 80% and Hardy-Weinberg equilibrium p ≥ 0.001.
With GEE, 58 SNPs had p < 10-6: the top SNPs were rs2494250 (p = 1.00*10-14) and rs4128725 (p = 3.68*10-12) for monocyte chemoattractant protein-1 (MCP1), and rs2794520 (p = 2.83*10-8) and rs2808629 (p = 3.19*10-8) for C-reactive protein (CRP) averaged from 3 examinations (over about 20 years). With FBAT, 11 SNPs had p < 10-6: the top SNPs were the same for MCP1 (rs4128725, p = 3.28*10-8, and rs2494250, p = 3.55*10-8), and also included B-type natriuretic peptide (rs437021, p = 1.01*10-6) and Vitamin K percent undercarboxylated osteocalcin (rs2052028, p = 1.07*10-6). The peak LOD (logarithm of the odds) scores were for MCP1 (4.38, chromosome 1) and CRP (3.28, chromosome 1; previously described) concentrations; of note the 1.5 support interval included the MCP1 and CRP SNPs reported above (GEE model). Previous candidate SNP associations with circulating CRP concentrations were replicated at p < 0.05; the SNPs rs2794520 and rs2808629 are in linkage disequilibrium with previously reported SNPs. GEE, FBAT and linkage results are posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.
The Framingham GWAS represents a resource to describe potentially novel genetic influences on systemic biomarker variability. The newly described associations will need to be replicated in other studies.