A randomized and clinical effectiveness trial comparing two pharmacogenetic algorithms and standard care for individualizing warfarin dosing (CoumaGen-II).

BACKGROUND: Warfarin is characterized by marked variations in individual dose requirements and a narrow therapeutic window. Pharmacogenetics (PG) could improve dosing efficiency and safety, but clinical trials evidence is meager. METHODS AND RESULTS: A Randomized and Clinical Effectiveness Trial Comparing Two Pharmacogenetic Algorithms and Standard Care for Individualizing Warfarin Dosing (CoumaGen-II) comprised 2 comparisons: (1) a blinded, randomized comparison of a modified 1-step (PG-1) with a 3-step algorithm (PG-2) (N=504), and (2) a clinical effectiveness comparison of PG guidance with use of either algorithm with standard dosing in a parallel control group (N=1866). A rapid method provided same-day CYP2C9 and VKORC1 genotyping. Primary outcomes were percentage of out-of-range international normalized ratios at 1 and 3 months and percentage of time in therapeutic range. Primary analysis was modified intention to treat. In the randomized comparison, PG-2 was noninferior but not superior to PG-1 for percentage of out-of-range international normalized ratios at 1 month and 3 months and for percentage of time in therapeutic range at 3 months. However, the combined PG cohort was superior to the parallel controls (percentage of out-of-range international normalized ratios 31% versus 42% at 1 month; 30% versus 42% at 3 months; percentage of time in therapeutic range 69% versus 58%, 71% versus 59%, respectively, all P<0.001). Differences persisted after adjustment for age, sex, and clinical indication. There were fewer percentage international normalized ratios ≥4 and ≤1.5 and serious adverse events at 3 months (4.5% versus 9.4% of patients, P<0.001) with PG guidance. CONCLUSIONS: These findings suggest that PG dosing should be considered for broader clinical application, a proposal that is being tested further in 3 major randomized trials. The simpler 1-step PG algorithm provided equivalent results and may be preferable for clinical application. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00927862.

Joint effects of common genetic variants from multiple genes and pathways on the risk of premature coronary artery disease.

OBJECTIVE: The aim of this study is to discover common variants in 6 lipid metabolic genes and construct and validate a genetic risk score (GRS) based on the joint effects of genetic variants in multiple genes from lipid and other pathobiologic pathways. BACKGROUND: Explaining the genetic basis of coronary artery disease (CAD) is incomplete. Discovery and aggregation of genetic variants from multiple pathways may advance this objective. METHODS: Premature CAD cases (n = 1,947) and CAD-free controls (n = 1,036) were selected from our angiographic registry. In a discovery phase, single nucleotide polymorphisms (SNPs) at 56 loci from internal discovery and external reports were tested for associations with biomarkers and CAD: 28 promising SNPs were then tested jointly for CAD associations, and a GRS consisting of SNPs contributing independently was constructed and validated in a replication set of familial cases and population-based controls (n = 1,320). RESULTS: Five variants contributed jointly to CAD prediction in a multigenic GRS model: odds ratio 1.24 (95% CI 1.16-1.33) per risk allele, P = 8.2 x 10(-11), adjusted OR 2.03 (1.53-2.70), fourth versus first quartile. 5-SNP genetic risk score had minor impact on area under the receiver operating characteristic curve (P > .05) but resulted in substantial net reclassification improvement: 0.16 overall, 0.28 in intermediate-risk patients (both P < .0001). GRS(5) predicted familial CAD with similar magnitude in the validation set. CONCLUSIONS: The Intermountain Healthcare's Coronary Genetics study demonstrates the ability of a multigenic, multipathway GRS to improve discrimination of angiographic CAD. Genetic risk scores promise to increase understanding of the genetic basis of CAD and improve identification of individuals at increased CAD risk.

Genetic variation at the 9p21 locus predicts angiographic coronary artery disease prevalence but not extent and has clinical utility.

BACKGROUND: Variants at the 9p21 locus have been associated with coronary heart disease, but their precise disease phenotype and utility for clinical risk assessment are uncertain. METHODS: Consenting patients with early-onset angiographic coronary artery disease (CAD) (n = 1,011) were compared with matched subjects (n = 545) free of angiographic disease and with a random population sample (n = 565). Cases and controls were genotyped for 4 variants, and ORs for angio-CAD were determined. Findings were validated in a separate set of cases and controls (n = 1,452). RESULTS: Alleles were highly correlated (r(2) > or = 0.9), and all predicted angio-CAD compared with both control groups. Genotype at rs2383206 (minor allele frequency 45.9%), the most predictive (P < .0001), was associated with an adjusted odds ratio for angio-CAD of 1.39 (95% CI, 1.05-1.85) for heterozygote and 1.73 (1.26-2.37) for homozygote risk-allele carriers and explained 21% of population attributable risk and was independent of traditional risk factors and myocardial infarction. For the comparison of combined cases versus combined control samples (N = 3,573), CAD was predicted by high-risk allele homozygosity at P = 9 x 10(-8). Despite this, extent of disease was not increased. Applied to patients with intermediate Framingham risk scores, 9p21 genotyping modified risk classification in 24%. CONCLUSIONS: Variants at the 9p21 locus robustly predict angiographic CAD prevalence, independent of standard risk factors, but not CAD extent or myocardial infarction; provide pathophysiological insights; and may be clinically useful in refining coronary heart disease risk classification.

Comparison of differing C-reactive protein assay methods and their impact on cardiovascular risk assessment.

A medium-sensitivity assay for C-reactive protein (CRP) was compared with a high-sensitivity, enhanced immunoturbidimetric assay in 803 angiographically studied patients. Different absolute CRP values were found by the assays, but there was a high correlation by quartile rank and similar predictive values for death and myocardial infarction. This suggests that the conclusions of previous studies performed using the medium-sensitivity assay are still valid but that cross-study comparisons should use percentile rank.

Common Variants in 6 Lipid-Related Genes Discovered by High-Resolution DNA Melting Analysis and Their Association with Plasma Lipids.

BACKGROUND: Total cholesterol was among the earliest identified risk factors for coronary heart disease (CHD). We sought to identify genetic variants in six genes associated with lipid metabolism and estimate their respective contribution to risk for CHD. METHODS: For 6 lipid-associated genes (LCAT, CETP, LIPC, LPL, SCARB1, and ApoF) we scanned exons, 5' and 3' untranslated regions, and donor and acceptor splice sites for variants using Hi-Res Melting® curve analysis (HRMCA) with confirmation by cycle sequencing. Healthy subjects were used for SNP discovery (n=64), haplotype determination/tagging SNP discovery (n=339), and lipid association testing (n=786). RESULTS: In 17,840 bases of interrogated sequence, 90 variant SNPs were identified; 19 (21.1%) previously unreported. Thirty-four variants (37.8%) were exonic(16 non-synonymous), 28 (31.1%) in intron-exon boundaries, and 28 (31.1%) in the 5' and 3' untranslated regions. Compared to cycle sequencing, HRMCA had sensitivity of 99.4% and specificity of 97.7%. Tagging SNPs (n=38) explained >90% of the variation in the 6 genes and identified linkage disequilibrium (LD) groups. Significant beneficial lipid profiles were observed for CETP LD group 2, LIPC LD groups 1 and 7, and SCARB1 LD groups 1, 3 and 4. Risk profiles worsened for CETP LD group 3, LPL LD group 4. CONCLUSIONS: These findings demonstrate the feasibility, sensitivity, and specificity of HRMCA for SNP discovery. Variants identified in these genes may be used to predict lipid-associated risk and reclassification of clinical CHD risk.

Multiple less common genetic variants explain the association of the cholesteryl ester transfer protein gene with coronary artery disease.

OBJECTIVES: The objective of this study was to identify associations of the cholesteryl ester transfer protein (CETP) gene with coronary artery disease (CAD) with tagging (t) single nucleotide polymorphisms (SNPs) chosen to optimally account for intra-genic variation. BACKGROUND: The CETP gene plays a critical role in lipoprotein metabolism, but the common and well-studied TaqIB variant is inconsistently predictive of CAD. METHODS: From a deoxyribonucleic acid bank of 10,020 individuals, nondiabetic nonsmoking patients (n = 4,811) with angiographically defined, clinically significant CAD (> or =70% stenosis) or normal coronaries were genotyped for 11 CETP tSNPs. Myocardial infarction (MI) and lipid levels were evaluated as secondary end points. RESULTS: Analysis of single tSNPs, corrected for multiple comparisons (p < 0.00485), identified allele +1086A to be associated with CAD (p = 0.0034). Suggestive allelic and significant genotypic associations were found for -631AA (odds ratio [OR] = 3.95, p = 0.004 vs. CC) and +2389GA (OR = 1.21, p = 0.003 vs. GG). Haplotype analysis by linkage disequilibrium (LD) group revealed a CAD association for LD group B (p = 0.0025 across T+1086A, C+878T, C+408T) and near significance for LD group A (p = 0.013 across C-631A, MspI, G+2389A). A weak protective trend for TaqIB was eliminated by adjustment for other tSNPs, and haplotype analyses suggested that TaqIB was simply a marker for other tSNPs or haplotypes. No tSNP or haplotype associations with MI were found. CONCLUSIONS: Multiple, less common SNPs and haplotype variants underlie CETP-related CAD risk, for which the common TaqIB variant is simply a poor marker. The occurrence of risk-related variants on separate haplotypes suggests genetic-risk complexity and allelic heterogeneity. (Database Registry of the Intermountain Heart Collaborative Study; http://clinicaltrials.gov/ct/show/NCT00406185?order=1; NCT00406185).