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- Kathiresan, Sekar, et al.
(författare)
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Common variants at 30 loci contribute to polygenic dyslipidemia
- 2009
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 41:1, s. 56-65
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Tidskriftsartikel (refereegranskat)abstract
- Blood low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglyceride levels are risk factors for cardiovascular disease. To dissect the polygenic basis of these traits, we conducted genome-wide association screens in 19,840 individuals and replication in up to 20,623 individuals. We identified 30 distinct loci associated with lipoprotein concentrations (each with P < 5 x 10(-8)), including 11 loci that reached genome-wide significance for the first time. The 11 newly defined loci include common variants associated with LDL cholesterol near ABCG8, MAFB, HNF1A and TIMD4; with HDL cholesterol near ANGPTL4, FADS1-FADS2-FADS3, HNF4A, LCAT, PLTP and TTC39B; and with triglycerides near AMAC1L2, FADS1-FADS2-FADS3 and PLTP. The proportion of individuals exceeding clinical cut points for high LDL cholesterol, low HDL cholesterol and high triglycerides varied according to an allelic dosage score (P < 10(-15) for each trend). These results suggest that the cumulative effect of multiple common variants contributes to polygenic dyslipidemia.
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| 2. |
- Kathiresan, Sekar, et al.
(författare)
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A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study
- 2007
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Ingår i: BMC Medical Genetics. - 1471-2350. ; 8
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Tidskriftsartikel (refereegranskat)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 similar to 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 similar to 1450 individuals) and 40 SNPs ( P < 0.001 in joint analysis of Stages I and II) tested in Stage III ( n similar to 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 database 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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