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      Genetic and functional association of FAM5C with myocardial infarction

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          Abstract

          Background

          We previously identified a 40 Mb region of linkage on chromosome 1q in our early onset coronary artery disease (CAD) genome-wide linkage scan (GENECARD) with modest evidence for linkage (n = 420, LOD 0.95). When the data are stratified by acute coronary syndrome (ACS), this modest maximum in the overall group became a well-defined LOD peak (maximum LOD of 2.17, D1S1589/D1S518). This peak overlaps a recently identified inflammatory biomarker (MCP-1) linkage region from the Framingham Heart Study (maximum LOD of 4.27, D1S1589) and a region of linkage to metabolic syndrome from the IRAS study (maximum LOD of 2.59, D1S1589/D1S518). The overlap of genetic screens in independent data sets provides evidence for the existence of a gene or genes for CAD in this region.

          Methods

          A peak-wide association screen (457 SNPs) was conducted of a region 1 LOD score down from the peak marker (168–198 Mb) in a linkage peak for acute coronary syndrome (ACS) on chromosome 1, within a family-based early onset coronary artery disease (CAD) sample (GENECARD).

          Results

          Polymorphisms were identified within the 'family with sequence similarity 5, member C' gene ( FAM5C) that show genetic linkage to and are associated with myocardial infarction (MI) in GENECARD. The association was confirmed in an independent CAD case-control sample (CATHGEN) and strong association with MI was identified with single nucleotide polymorphisms (SNPs) in the 3' end of FAM5C. FAM5C genotypes were also correlated with expression of the gene in human aorta. Expression levels of FAM5C decreased with increasing passage of proliferating aortic smooth muscle cells (SMC) suggesting a role for this molecule in smooth muscle cell proliferation and senescence.

          Conclusion

          These data implicate FAM5C alleles in the risk of myocardial infarction and suggest further functional studies of FAM5C are required to identify the gene's contribution to atherosclerosis.

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          Most cited references50

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Genomewide association analysis of coronary artery disease.

            Modern genotyping platforms permit a systematic search for inherited components of complex diseases. We performed a joint analysis of two genomewide association studies of coronary artery disease. We first identified chromosomal loci that were strongly associated with coronary artery disease in the Wellcome Trust Case Control Consortium (WTCCC) study (which involved 1926 case subjects with coronary artery disease and 2938 controls) and looked for replication in the German MI [Myocardial Infarction] Family Study (which involved 875 case subjects with myocardial infarction and 1644 controls). Data on other single-nucleotide polymorphisms (SNPs) that were significantly associated with coronary artery disease in either study (P 80%) of a true association: chromosomes 1p13.3 (rs599839), 1q41 (rs17465637), 10q11.21 (rs501120), and 15q22.33 (rs17228212). We identified several genetic loci that, individually and in aggregate, substantially affect the risk of development of coronary artery disease. Copyright 2007 Massachusetts Medical Society.
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              Efficiency and power in genetic association studies.

              We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.
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                Author and article information

                Journal
                BMC Med Genet
                BMC Medical Genetics
                BioMed Central
                1471-2350
                2008
                22 April 2008
                : 9
                : 33
                Affiliations
                [1 ]Department of Medicine and Center for Human Genetics, Duke University Medical Center, Durham, NC, USA
                [2 ]Department of Medicine and Division of Cardiology, Duke University Medical Center, Durham, NC, USA
                [3 ]Miller School of Medicine, University of Miami, Miami, FL, USA
                [4 ]University of Sheffield, Sheffield, UK
                [5 ]GlaxoSmithKline, Philadelphia, PA, USA
                [6 ]University of Wales College of Medicine, Cardiff, UK
                Article
                1471-2350-9-33
                10.1186/1471-2350-9-33
                2383879
                18430236
                f998e27a-0a48-4fb0-ba77-88c3f7311622
                Copyright © 2008 Connelly 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.

                History
                : 24 January 2008
                : 22 April 2008
                Categories
                Research Article

                Genetics
                Genetics

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