Linking Observational And Genetic Approaches To Determine The Role Of C Reactive Protein In Coronary Heart Disease Risk

Author : Elias Levy Itshak Salfati
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (9 download)

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Download or read book Genetic Determinants of Cardiovascular Disease written by Elias Levy Itshak Salfati and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex diseases such as cardiovascular disease (CVD) are influenced by both genetic and environmental factors. Estimation of an individual's cardiovascular risk usually involves measurement of risk factors correlated with risk of CVD (e.g. age, sex, smoking, blood pressure, and total cholesterol). Lately, several biomarkers have been evaluated for their ability to improve prediction of cardiovascular disease beyond traditional risk factors. The interest in novel loci is propelled notably by emerging discoveries from the advent of genome-Wide association studies (GWAS) of genetic variants associated with risk for common diseases. GWAS has greatly enhanced our knowledge of the genetic architecture of cardiovascular disease, yielding over 50 variants confirmed to be associated with CVD to date, as well as over 200 associated with traditional cardiovascular risk factors (e.g. lipids, blood pressure, body mass index, and type 2 diabetes mellitus). This recent and continuing success in discovering increasing numbers of robustly associated genetic markers has led to reassessment of whether genetic data can provide clinically useful information by refining risk prediction and moderating disease risk through a more efficient application of prevention strategies. In this thesis, we first address novel approach to survey the genetic architecture of hypertension (i.e. major risk factor for premature CVD), then construct risk prediction models for coronary artery disease (CAD; i.e. most common type of CVD) and finally establish a common genetic basis of the strongest predictor of clinical complications of CAD, subclinical atherosclerosis, to add incremental prognostic value above traditional risk scores across a range of ages. We show that, for first visit measurements, the heritability is ~25%/~45% and ~30%/~37% for systolic (SBP) and diastolic blood pressure (DBP) in European (N=8,901) and African (N=2,860) ancestry individuals from the Atherosclerosis Risk in Communities (ARIC) cohort, respectively, in accord with prior studies. Then we present a means to combine a polygenic risk score - genetic effects among an ensemble of markers - with an independent assessment of clinical risk using a log-Link function. We apply the method to the prediction of coronary heart disease (CHD) in the ARIC cohort. The addition of a genetic risk score (GRS) to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC and subsequently reveal how this genetic information influences risk assessment and thus potentially clinical management. Finally, Among 1561 cases and 5068 controls, from several clinical and genetic datasets available through the NCBI's database of Genotypes and Phenotypes (dbGAP), we found a one SD increase in the genetic risk score of 49 CAD SNPs was associated with a 28% increased risk of having advanced subclinical coronary atherosclerosis (p = 1.43 x 10-16). This increase in risk was significant in every 15-Year age stratum (.01 > p > 9.4 x 10-7) and was remarkably similar across all age strata (p test of heterogeneity = 0.98). We obtained near identical results and levels of significance when we restricted the genetic risk score to 32 SNPs not associated with traditional risk factors. Accordingly, common variation largely recapitulates the known heritability of blood pressure traits. The vast majority of this heritability varies by chromosome, depending on its length, and is largely concentrated in intronic and intergenic regions of the genome but widely distributed across the common allele frequency spectrum. Respectively, our proposed method to combine genetic information at established susceptibility loci with a nongenetic risk prediction tool facilitates the standardized incorporation of a GRS in risk assessment. (...).