Genotype-informed estimation of risk of coronary heart disease based on genome-wide association data linked to the electronic medical record

<p>Abstract</p> <p>Background</p> <p>Susceptibility variants identified by genome-wide association studies (GWAS) have modest effect sizes. Whether such variants provide incremental information in assessing risk for common 'complex' diseases is unclear. We investigated whether measured and imputed genotypes from a GWAS dataset linked to the electronic medical record alter estimates of coronary heart disease (CHD) risk.</p> <p>Methods</p> <p>Study participants (<it>n </it>= 1243) had no known cardiovascular disease and were considered to be at high, intermediate, or low 10-year risk of CHD based on the Framingham risk score (FRS) which includes age, sex, total and HDL cholesterol, blood pressure, diabetes, and smoking status. Of twelve SNPs identified in prior GWAS to be associated with CHD, four were genotyped in the participants as part of a GWAS. Genotypes for seven SNPs were imputed from HapMap CEU population using the program MACH. We calculated a multiplex genetic risk score for each patient based on the odds ratios of the susceptibility SNPs and incorporated this into the FRS.</p> <p>Results</p> <p>The mean (SD) number of risk alleles was 12.31 (1.95), range 6-18. The mean (SD) of the weighted genetic risk score was 12.64 (2.05), range 5.75-18.20. The CHD genetic risk score was not correlated with the FRS (<it>P </it>= 0.78). After incorporating the genetic risk score into the FRS, a total of 380 individuals (30.6%) were reclassified into higher-(188) or lower-risk groups (192).</p> <p>Conclusion</p> <p>A genetic risk score based on measured/imputed genotypes at 11 susceptibility SNPs, led to significant reclassification in the 10-y CHD risk categories. Additional prospective studies are needed to assess accuracy and clinical utility of such reclassification.</p

ACE (I/D) polymorphism and response to treatment in coronary artery disease: a comprehensive database and meta-analysis involving study quality evaluation

<p>Abstract</p> <p>Background</p> <p>The role of angiotensin-converting enzyme (<it>ACE</it>) gene insertion/deletion (<it>I/D</it>) polymorphism in modifying the response to treatment modalities in coronary artery disease is controversial.</p> <p>Methods</p> <p>PubMed was searched and a database of 58 studies with detailed information regarding <it>ACE I/D </it>polymorphism and response to treatment in coronary artery disease was created. Eligible studies were synthesized using meta-analysis methods, including cumulative meta-analysis. Heterogeneity and study quality issues were explored.</p> <p>Results</p> <p>Forty studies involved invasive treatments (coronary angioplasty or coronary artery by-pass grafting) and 18 used conservative treatment options (including anti-hypertensive drugs, lipid lowering therapy and cardiac rehabilitation procedures). Clinical outcomes were investigated by 11 studies, while 47 studies focused on surrogate endpoints. The most studied outcome was the restenosis following coronary angioplasty (34 studies). Heterogeneity among studies (p < 0.01) was revealed and the risk of restenosis following balloon angioplasty was significant under an additive model: the random effects odds ratio was 1.42 (95% confidence interval:1.07–1.91). Cumulative meta-analysis showed a trend of association as information accumulates. The results were affected by population origin and study quality criteria. The meta-analyses for the risk of restenosis following stent angioplasty or after angioplasty and treatment with angiotensin-converting enzyme inhibitors produced non-significant results. The allele contrast random effects odds ratios with the 95% confidence intervals were 1.04(0.92–1.16) and 1.10(0.81–1.48), respectively. Regarding the effect of <it>ACE I/D </it>polymorphism on the response to treatment for the rest outcomes (coronary events, endothelial dysfunction, left ventricular remodeling, progression/regression of atherosclerosis), individual studies showed significance; however, results were discrepant and inconsistent.</p> <p>Conclusion</p> <p>In view of available evidence, genetic testing of <it>ACE I/D </it>polymorphism prior to clinical decision making is not currently justified. The relation between <it>ACE </it>genetic variation and response to treatment in CAD remains an unresolved issue. The results of long-term and properly designed prospective studies hold the promise for pharmacogenetically tailored therapy in CAD.</p

The impact of rare variation on gene expression across tissues

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes