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Saturday, October 27, 2007

Genome-wide association with select biomarker traits in the Framingham Heart Study

Emelia J Benjamin1, Josée Dupuis1, Martin G Larson1, Kathryn L Lunetta1, Sarah L Booth , Diddahally R Govindaraju1, Sekar Kathiresan, John F Keaney Jr , Michelle J Keyes1, Jing-Ping Lin10 , James B Meigs , Sander J Robins , Jian Rong , Renate Schnabel1, Joseph A Vita2,, Thomas J Wang , Peter WF Wilson , Philip A Wolf and Ramachandran S Vasan
Abstract
Background
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.
Methods
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.
Results
With GEE, 58 SNPs had p < p =" 1.00*10-14)" p =" 3.68*10-12)" p =" 2.83*10-8)" p =" 3.19*10-8)" p =" 3.28*10-8," p =" 3.55*10-8)," p =" 1.01*10-6)" p =" 1.07*10-6)." href="http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007">http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.
Conclusion
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.

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