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

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          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 < 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.

          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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          Most cited references68

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          Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.

          Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease, but are plagued by the impression that they are not consistently reproducible. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10(-14)). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.
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            An investigation of coronary heart disease in families. The Framingham offspring study.

            The Framingham Heart Study (FHS) was started in 1948 as a prospective investigation of cardiovascular disease in a cohort of adult men and women. Continuous surveillance of this sample of 5209 subjects has been maintained through biennial physical examinations. In 1971 examinations were begun on the children of the FHS cohort. This study, called the Framingham Offspring Study (FOS), was undertaken to expand upon knowledge of cardiovascular disease, particularly in the area of familial clustering of the disease and its risk factors. This report reviews the sampling design of the FHS and describes the nature of the FOS sample. The FOS families appear to be of typical size and age structure for families with parents born in the late 19th or early 20th century. In addition, there is little evidence that coronary heart disease (CHD) experience and CHD risk factors differ in parents of those who volunteered for this study and the parents of those who did not volunteer.
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              Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies.

              A large number of epidemiologic studies have reported on associations between various "inflammatory" factors and coronary heart disease (CHD). To assess the associations of blood levels of fibrinogen, C-reactive protein (CRP), and albumin and leukocyte count with the subsequent risk of CHD. Meta-analyses of any long-term prospective studies of CHD published before 1998 on any of these 4 factors. Studies were identified by MEDLINE searches, scanning of relevant reference lists, hand searching of cardiology, epidemiology, and other relevant journals, and discussions with authors of relevant reports. All relevant studies identified were included. The following information was abstracted from published reports (supplemented, in several cases, by the authors): size and type of cohort, mean age, mean duration of follow-up, assay methods, degree of adjustment for confounders, and relationship of CHD risk to the baseline assay results. For fibrinogen, with 4018 CHD cases in 18 studies, comparison of individuals in the top third with those in the bottom third of the baseline measurements yielded a combined risk ratio of 1.8 (95% confidence interval [CI], 1.6-2.0) associated with a difference in long-term usual mean fibrinogen levels of 2.9 pmol/L (0.1 g/dL) between the top and bottom thirds (10.3 vs 7.4 pmol/L [0.35 vs 0.25 g/dL]). For CRP, with 1053 CHD cases in 7 studies, the combined risk ratio of 1.7 (95% CI, 1.4-2.1) was associated with a difference of 1.4 mg/L (2.4 vs 1.0 mg/L). For albumin, with 3770 CHD cases in 8 studies, the combined risk ratio of 1.5 (95% CI, 1.3-1.7) was associated with a difference of 4 g/L (38 vs 42 g/L, ie, an inverse association). For leukocyte count, with 5337 CHD cases in the 7 largest studies, the combined risk ratio of 1.4 (95% CI, 1.3-1.5) was associated with a difference of 2.8 x 10(9)/L (8.4 vs 5.6 x 10(9)/L). Each of these overall results was highly significant (P<.0001). The published results from these prospective studies are remarkably consistent for each factor, indicating moderate but highly statistically significant associations with CHD. Hence, even though mechanisms that might account for these associations are not clear, further study of the relevance of these factors to the causation of CHD is warranted.
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                Author and article information

                Journal
                BMC Med Genet
                BMC Medical Genetics
                BioMed Central (London )
                1471-2350
                2007
                19 September 2007
                : 8
                : Suppl 1
                : S11
                Affiliations
                [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
                Article
                1471-2350-8-S1-S11
                10.1186/1471-2350-8-S1-S11
                1995615
                17903293
                8947689c-96d1-4362-b457-c9a0be6c0463
                Copyright © 2007 Benjamin et al; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Research

                Genetics
                Genetics

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