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ABSTRACT: Polymorphisms in candidate genes related to lipid metabolism, thrombosis, hemostasis, cell-matrix adhesion, and inflammation have been suggested clinically useful in risk assessment of cardiovascular disease.
We evaluated a panel of 92 candidate gene polymorphisms, using a multiplex polymerase chain reaction-immobilized probe assay amongst 523 individuals who subsequently developed myocardial infarction (MI), and amongst 2092 individuals who remained free of reported cardiovascular disease over a mean follow-up period of 13.2 years.
Of the 92 polymorphisms tested, three that we previously reported on were associated with risk of MI, [pro12ala in the peroxisome proliferator activated-receptor gamma gene (odds ratio, OR = 0.75, P = 0.02); thr164ile in the beta-2 adrenergic receptor gene (OR = 0.14, P = 0.007); and ala23thr polymorphism in the eotaxin gene (OR = 1.87, P = 0.01)]. However, when adjusted for the other 89 polymorphisms evaluated, these findings were no longer statistically significant. Further, in contrast to reports from other investigators, we found little evidence for association of a C677T polymorphism in the 5,10-methylenetetrahydrofolate reductase gene, the angiotensin-I-converting enzyme 1 insertion/deletion polymorphism, a 4G/5G polymorphism in the serine/cysteine proteinase inhibitor-clade E-member 1 gene, the factor V Leiden mutation, the G20210A factor II mutation, a -455G>A polymorphism in the beta-fibrinogen gene, the cys112arg/arg158cys apolipoprotein E gene polymorphism, a gly460trp polymorphism in the alpha-adducin gene, and a -629C>A polymorphism in the cholesteryl ester transfer protein gene with risk of MI.
After correction for multiple comparisons, the addition of genetic information observed in the present study had little impact on risk prediction models for MI. The present investigation highlights the importance of replication and validation of findings from genetic association studies.
Journal of Thrombosis and Haemostasis 03/2006; 4(2):341-8. · 5.73 Impact Factor
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R Y L Zee,
J Hoh, S Cheng,
R Reynolds,
M A Grow,
A Silbergleit,
K Walker,
L Steiner,
G Zangenberg,
A Fernandez-Ortiz,
C Macaya,
E Pintor,
A Fernandez-Cruz,
J Ott,
K Lindpainter
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ABSTRACT: The complexity of recognizing the potential contribution of a number of possible predictors of complex disorders is increasingly challenging with the application of large-scale single nucleotide polymorphism (SNP) typing. In the search for putative genetic factors predisposing to coronary artery restenosis following balloon angioplasty, we determined genotypes for 94 SNPs representing 62 candidate genes, in a prospectively assembled cohort of 342 cases and 437 controls. Using a customized coupled-logistic regression procedure accounting for both additive and interactive effects, we identified seven SNPs in seven genes that, together, showed a statistically significant association with restenosis incidence (P <0.0001), accounting for 11.6% of overall variance observed. Among them are candidate genes for cardiovascular pathophysiology (apolipoprotein-species and NOS), inflammatory response (TNF receptor and CD14), and cell-cycle control (p53 and p53-associated protein). Our results emphasize the need to account for complex multi-gene influences and interactions when assessing the molecular pathology of multifactorial medical entities.
The Pharmacogenomics Journal 01/2002; 2(3):197-201. · 4.54 Impact Factor
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ABSTRACT: For large numbers of marker loci in a genomic scan for disease loci, we propose a novel 2-stage approach for linkage or association analysis. The two stages are (1) selection of a subset of markers that are 'important' for the trait studied, and (2) modelling interactions among markers and between markers and trait. Here we focus on stage 1 and develop a selection method based on a 2-level nested bootstrap procedure. The method is applied to single nucleotide polymorphisms (SNPs) data in a cohort study of heart disease patients. Out of the 89 original SNPs the method selects 11 markers as being 'important'. Conventional backward stepwise logistic regression on the 89 SNPs selects 7 markers, which are a subset of the 11 markers chosen by our method.
Annals of Human Genetics 10/2000; 64(Pt 5):413-7. · 2.57 Impact Factor
