Coronary artery disease (CAD) is the commonest cause of death. Here, we report an association analysis in 63,746 CAD cases and 130,681 controls identifying 15 loci reaching genome-wide significance, taking the number of susceptibility loci for CAD to 46, and a further 104 independent variants (r2 < 0.2) strongly associated with CAD at a 5% false discovery rate (FDR). Together, these variants explain approximately 10.6% of CAD heritability. Of the 46 genome-wide significant lead SNPs, 12 show a significant association with a lipid trait, and 5 show a significant association with blood pressure, but none is significantly associated with diabetes. Network analysis with 233 candidate genes (loci at 10% FDR) generated 5 interaction networks comprising 85% of these putative genes involved in CAD. The four most significant pathways mapping to these networks are linked to lipid metabolism and inflammation, underscoring the causal role of these activities in the genetic etiology of CAD. Our study provides insights into the genetic basis of CAD and identifies key biological pathways.

We report the first genome-wide association study (GWAS) whose sample size (1,053 Swedish subjects) is sufficiently powered to detect genome-wide significance (p<1.5×10−7) for polymorphisms that modestly alter therapeutic warfarin dose. The anticoagulant drug warfarin is widely prescribed for reducing the risk of stroke, thrombosis, pulmonary embolism, and coronary malfunction. However, Caucasians vary widely (20-fold) in the dose needed for therapeutic anticoagulation, and hence prescribed doses may be too low (risking serious illness) or too high (risking severe bleeding). Prior work established that ∼30% of the dose variance is explained by single nucleotide polymorphisms (SNPs) in the warfarin drug target VKORC1 and another ∼12% by two non-synonymous SNPs (*2, *3) in the cytochrome P450 warfarin-metabolizing gene CYP2C9. We initially tested each of 325,997 GWAS SNPs for association with warfarin dose by univariate regression and found the strongest statistical signals (p<10−78) at SNPs clustering near VKORC1 and the second lowest p-values (p<10−31) emanating from CYP2C9. No other SNPs approached genome-wide significance. To enhance detection of weaker effects, we conducted multiple regression adjusting for known influences on warfarin dose (VKORC1, CYP2C9, age, gender) and identified a single SNP (rs2108622) with genome-wide significance (p = 8.3×10−10) that alters protein coding of the CYP4F2 gene. We confirmed this result in 588 additional Swedish patients (p<0.0029) and, during our investigation, a second group provided independent confirmation from a scan of warfarin-metabolizing genes. We also thoroughly investigated copy number variations, haplotypes, and imputed SNPs, but found no additional highly significant warfarin associations. We present power analysis of our GWAS that is generalizable to other studies, and conclude we had 80% power to detect genome-wide significance for common causative variants or markers explaining at least 1.5% of dose variance. These GWAS results provide further impetus for conducting large-scale trials assessing patient benefit from genotype-based forecasting of warfarin dose.

Genetic variants of cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase (VKORC1) are known to influence warfarin dose, but the effect of other genes has not been fully elucidated. We genotyped 183 polymorphisms in 29 candidate genes in 1496 Swedish patients starting warfarin treatment, and tested for association with response. CYP2C9*2 and *3 explained 12% (P ‫؍‬ 6.63 ؋ 10 ؊34 ) of the variation in warfarin dose, while a single VKORC1 SNP explained 30% (P ‫؍‬ 9.82 ؋ 10 ؊100

We report a novel combination of factors that explains almost 60% of variable response to warfarin. Warfarin is a widely used anticoagulant, which acts through interference with vitamin K epoxide reductase that is encoded by VKORC1. In the next step of the vitamin K cycle, gamma-glutamyl carboxylase encoded by GGCX uses reduced vitamin K to activate clotting factors. We genotyped 201 warfarin-treated patients for common polymorphisms in VKORC1 and GGCX. All the five VKORC1 single-nucleotide polymorphisms covary significantly with warfarin dose, and explain 29-30% of variance in dose. Thus, VKORC1 has a larger impact than cytochrome P450 2C9, which explains 12% of variance in dose. In addition, one GGCX SNP showed a small but significant effect on warfarin dose. Incorrect dosage, especially during the initial phase of treatment, carries a high risk of either severe bleeding or failure to prevent thromboembolism. Genotype-based dose predictions may in future enable personalised drug treatment from the start of warfarin therapy.

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knock-down experiments (ABCA1, TRIB1) and clinical trial results (CCR2, CCR5), with consistent regulation. Finally we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including (gene symbols) EGF, IL16, PAPPA, SPON1, F3, ADM, CASP8, CHI3L1, CXCL16, GDF15, and MMP12. Taken together these findings demonstrate the utility of largescale mapping the genetics of the proteome, and provide a resource for future precision studies of circulating proteins in human health.

MD; for the SITS-MOST InvestigatorsBackground and Purpose-The Safe Implementation of Thrombolysis in Stroke-MOnitoring STudy (SITS-MOST) unadjusted results demonstrated that intravenous alteplase is well tolerated and that the effects were comparable with those seen in randomized, controlled trials (RCTs) when used in routine clinical practice within 3 hours of ischemic stroke onset. We aimed to identify outcome predictors and adjust the outcomes of the SITS-MOST to the baseline characteristics of RCTs. Methods-The study population was SITS-MOST (nϭ6483) and pooled RCTs (nϭ464) patients treated with intravenous alteplase within 3 hours of stroke onset. Multivariable, backward stepwise regression analyses (until PՅ0.10) were performed to identify the outcome predictors for SITS-MOST. Variables appearing either in the final multivariable model or differing (PϽ0.10) between SITS-MOST and RCTs were included in the prediction model for the adjustment of outcomes. Main outcome measures were symptomatic intracerebral hemorrhage, defined as National Institutes of Health Stroke Scale deterioration Ն1 within 7 days with any hemorrhage (RCT definition), mortality, and independency as defined by modified Rankin Score of 0 to 2 at 3 months. Results-The adjusted proportion of symptomatic intracerebral hemorrhage for SITS-MOST was 8.5% (95% CI, 7.9 to 9.0) versus 8.6% (6.3 to 11.6) for pooled RCTs; mortality was 15.5% (14.7 to 16.2) versus 17.3% (14.1 to 21.1); and independency was 50.4% (49.6 to 51.2) versus 50.1% (44.5 to 54.7), respectively. In the multivariable analysis, older age, high blood glucose, high National Institutes of Health Stroke Scale score, and current infarction on imaging scans were related to poor outcome in all parameters. Systolic blood pressure, atrial fibrillation, and weight were additional predictors of symptomatic intracerebral hemorrhage. Current smokers had a lower rate of symptomatic intracerebral hemorrhage. Disability before current stroke (modified Rankin Score 2 to 5), diastolic blood pressure, antiplatelet other than aspirin, congestive heart failure, patients treated in new centers, and male sex were related to high mortality at 3 months. Conclusions-The adjusted outcomes from SITS-MOST were almost identical to those in relevant RCTs and reinforce the conclusion drawn previously in the unadjusted analysis. We identified several important outcome predictors to better identify patients suitable for thrombolysis. (Stroke. 2008;39:3316-3322.)

Pharmacogenetic-based dosing was associated with a higher percentage of time in the therapeutic INR range than was standard dosing during the initiation of warfarin therapy. (Funded by the European Commission Seventh Framework Programme and others; ClinicalTrials.gov number, NCT01119300.).

We report an extensive study of variability in genes encoding proteins that are believed to be involved in the action and biotransformation of warfarin. Warfarin is a commonly prescribed anticoagulant that is diYcult to use because of the wide interindividual variation in dose requirements, the narrow therapeutic range and the risk of serious bleeding. We genotyped 201 patients for polymorphisms in 29 genes in the warfarin interactive pathways and tested them for association with dose requirement. In our study, polymorphisms in or Xanking the genes VKORC1, CYP2C9, CYP2C18, CYP2C19, PROC, APOE, EPHX1, CALU, GGCX and ORM1-ORM2 and haplotypes of VKORC1, CYP2C9, CYP2C8, CYP2C19, PROC, F7, GGCX, PROZ, F9, NR1I2 and ORM1-ORM2 were associated with dose (P < 0.05). VKORC1, CYP2C9, CYP2C18 and CYP2C19 were signiWcant after experiment-wise correction for multiple testing (P < 0.000175), however, the association of CYP2C18 and CYP2C19 was fully explained by linkage disequilibrium with CYP2C9*2 and/or *3. PROC and APOE were both signiWcantly associated with dose after correction within each gene. A multiple regression model with VKORC1, CYP2C9, PROC and the non-genetic predictors age, bodyweight, drug interactions and indication for treatment jointly accounted for 62% of variance in warfarin dose. Weaker associations observed for other genes could explain up to »10% additional dose variance, but require testing and validation in an independent and larger data set. Translation of this knowledge into clinical guidelines for warfarin prescription will be likely to have a major impact on the safety and eYcacy of warfarin. Abbreviations