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      A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study

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          Abstract

          Background

          Blood lipid levels including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) are highly heritable. Genome-wide association is a promising approach to map genetic loci related to these heritable phenotypes.

          Methods

          In 1087 Framingham Heart Study Offspring cohort participants (mean age 47 years, 52% women), we conducted genome-wide analyses (Affymetrix 100K GeneChip) for fasting blood lipid traits. Total cholesterol, HDL-C, and TG were measured by standard enzymatic methods and LDL-C was calculated using the Friedewald formula. The long-term averages of up to seven measurements of LDL-C, HDL-C, and TG over a ~30 year span were the primary phenotypes. We used generalized estimating equations (GEE), family-based association tests (FBAT) and variance components linkage to investigate the relationships between SNPs (on autosomes, with minor allele frequency ≥10%, genotypic call rate ≥80%, and Hardy-Weinberg equilibrium p ≥ 0.001) and multivariable-adjusted residuals. We pursued a three-stage replication strategy of the GEE association results with 287 SNPs (P < 0.001 in Stage I) tested in Stage II (n ~1450 individuals) and 40 SNPs (P < 0.001 in joint analysis of Stages I and II) tested in Stage III (n~6650 individuals).

          Results

          Long-term averages of LDL-C, HDL-C, and TG were highly heritable (h 2 = 0.66, 0.69, 0.58, respectively; each P < 0.0001). Of 70,987 tests for each of the phenotypes, two SNPs had p < 10 -5 in GEE results for LDL-C, four for HDL-C, and one for TG. For each multivariable-adjusted phenotype, the number of SNPs with association p < 10 -4 ranged from 13 to 18 and with p < 10 -3, from 94 to 149. Some results confirmed previously reported associations with candidate genes including variation in the lipoprotein lipase gene ( LPL) and HDL-C and TG (rs7007797; P = 0.0005 for HDL-C and 0.002 for TG). The full set of GEE, FBAT and linkage results are posted at the data base of Genotype and Phenotype (dbGaP). After three stages of replication, there was no convincing statistical evidence for association (i.e., combined P < 10 -5 across all three stages) between any of the tested SNPs and lipid phenotypes.

          Conclusion

          Using a 100K genome-wide scan, we have generated a set of putative associations for common sequence variants and lipid phenotypes. Validation of selected hypotheses in additional samples did not identify any new loci underlying variability in blood lipids. Lack of replication may be due to inadequate statistical power to detect modest quantitative trait locus effects (i.e., <1% of trait variance explained) or reduced genomic coverage of the 100K array. GWAS in FHS using a denser genome-wide genotyping platform and a better-powered replication strategy may identify novel loci underlying blood lipids.

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          Most cited references16

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          Factors of risk in the development of coronary heart disease--six year follow-up experience. The Framingham Study.

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            Evaluating and improving power in whole-genome association studies using fixed marker sets.

            Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25-100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.
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              Sex and age differences in lipoprotein subclasses measured by nuclear magnetic resonance spectroscopy: the Framingham Study.

              The sex differential in coronary heart disease (CHD) risk, which is not explained by male/female differences in lipid and lipoprotein concentrations, narrows with age. We examined whether this differential CHD risk might, in part, be attributable to the sizes of lipoprotein particles or concentrations of lipoprotein subclasses. We analyzed frozen plasma samples from 1574 men and 1692 women from exam cycle 4 (1988-1990) of the Framingham Offspring Study. Nuclear magnetic resonance (NMR) spectroscopy was used to determine the subclass concentrations and mean sizes of VLDL, LDL, and HDL particles. Concentrations of lipids and apolipoproteins were measured by standard chemical methods. In addition to the expected sex differences in concentrations of triglycerides, LDL-cholesterol, and HDL-cholesterol, women also had a lower-risk subclass profile consisting of larger LDL (0.4 nm) and HDL (0.5 nm) particles. The sex difference was most pronounced for HDL, with women having a twofold higher (8 vs 4 micromol/L) concentration of large HDL particles than men. Furthermore, similar to the narrowing of the sex difference in CHD risk with age, the observed male/female difference in HDL particle size also decreased with age. Although lipoprotein particle sizes were highly correlated with lipid and lipoprotein concentrations, the sex differences in the mean sizes of lipoprotein particles persisted (P <0.001) even after adjustment for lipid and lipoprotein concentrations. Women have a less atherogenic subclass profile than men, even after accounting for differences in lipid concentrations.
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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
                : S17
                Affiliations
                [1 ]National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
                [2 ]Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
                [3 ]Cardiovascular Disease Prevention Center, Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
                [4 ]School of Public Health, Boston University, Boston, MA, USA
                [5 ]Department of Mathematics and Statistics, Boston University, Boston, MA, USA
                [6 ]Department of Clinical Sciences, Hypertension and Cardiovascular Diseases, University Hospital Malmö, Lund University, Malmö, Sweden
                [7 ]Diabetes and Endocrinology, University Hospital Malmö, Lund University, Malmö, Sweden
                [8 ]Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
                [9 ]Nutrition and Genomics Laboratory, Jean Mayer-USDA Human Nutrition Research Center at Tufts University, Boston, MA, USA
                Article
                1471-2350-8-S1-S17
                10.1186/1471-2350-8-S1-S17
                1995614
                17903299
                e9e8c972-ab60-4aec-ba4d-51ba3cc40a50
                Copyright © 2007 Kathiresan 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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                Genetics
                Genetics

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