There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined approximately 2,000 individuals for each of 7 major diseases and a shared set of approximately 3,000 controls. Case-control comparisons identified 24 independent association signals at

Elevated blood pressure is a common, heritable cause of cardiovascular disease worldwide. To date, identification of common genetic variants influencing blood pressure has proven challenging. We tested 2.5m genotyped and imputed SNPs for association with systolic and diastolic blood pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed up findings with direct genotyping (N≤71,225 European ancestry, N=12,889 Indian Asian ancestry) and in silico comparison (CHARGE consortium, N=29,136). We identified association between systolic or diastolic blood pressure and common variants in 8 regions near the CYP17A1 (P=7×10−24), CYP1A2 (P=1×10−23), FGF5 (P=1×10−21), SH2B3 (P=3×10−18), MTHFR (P=2×10−13), c10orf107 (P=1×10−9), ZNF652 (P=5×10−9) and PLCD3 (P=1×10−8) genes. All variants associated with continuous blood pressure were associated with dichotomous hypertension. These associations between common variants and blood pressure and hypertension offer mechanistic insights into the regulation of blood pressure and may point to novel targets for interventions to prevent cardiovascular disease.

The Wellcome Trust Case Control Consortium (WTCCC) identified nine single SNPs putatively associated with rheumatoid arthritis at P = 1 × 10 -5 -5 × 10 -7 in a genome-wide association screen. One, rs6920220, was unequivocally replicated (trend P = 1.1 × 10 -8 ) in a validation study, as described here. This SNP maps to 6q23, between the genes oligodendrocyte lineage transcription factor 3 (OLIG3) and tumor necrosis factor-α-induced protein 3 (TNFAIP3).The WTCCC genome-wide association screen (GWA) of 1,860 rheumatoid arthritis cases and 2,938 healthy controls confirmed association with SNPs within the HLA region and the PTPN22 gene (P < 1 × 10 -7 ; ref. 1). Nine other loci showed strong evidence for association (P = 1 × 10 -5 -5 × 10 -7 ). SNPs at these loci were genotyped in an independent cohort of 5,063 rheumatoid arthritis cases and 3,849 healthy controls (Supplementary Methods and Supplementary Table 1 Europe PMC Funders Group Europe PMC Funders Author ManuscriptsEurope PMC Funders Author Manuscripts www.sequenom.com), to establish whether they are genuinely associated with the disease. In this cohort, we had 80% power to detect most of the effect sizes reported in the initial study at P < 0.05 (Supplementary Table 2 online). We selected ten SNPs for genotyping; these included the known rheumatoid arthritis susceptibility variant rs2476601, mapping to the PTPN22 gene, and nine previously unknown SNPs identified by the WTCCC study1. A Bonferroni correction of 9 was applied to account for the previously unknown loci investigated, resulting in a P value threshold of P < 0.006 for claims of significance in this validation study. We regarded SNPs validated at P values between 0.05 and 0.006 as suggestive evidence.We detected strong association with the rheumatoid arthritis-causing SNP in the PTPN22 gene (rs2476601), as expected (odds ratio (OR) = 1.53, 95% CI = 1.39-1.68, trend P = 2.0 × 10 -18 ). This was not a completely independent replication, as association of rheumatoid arthritis with this locus has been reported in previous studies using some of the same samples included in the current study2-4. However, it confirmed the suitability of this cohort for validation studies. Of the nine newly identified SNPs tested, rs6920220 (G > A) showed association with rheumatoid arthritis in this cohort (OR for minor allele = 1.23, 95% CI = 1.15-1.33, trend P = 1.1 × 10 -8 ) (Table 1). For this SNP, the allele frequencies were similar across control groups tested in the WTCCC study and the healthy controls tested here (minor allele frequency (MAF) 0.22 and 0.21, respectively). We therefore undertook a combined analysis of the WTCCC data and the validation data, and we obtained strong statistical evidence for association between this SNP and rheumatoid arthritis (OR = 1.22, 95% CI = 1.15-1.29, trend P = 3.6 × 10 -12

ObjectiveTo investigate whether antidrug antibodies and/or drug non‐trough levels predict the long‐term treatment response in a large cohort of patients with rheumatoid arthritis (RA) treated with adalimumab or etanercept and to identify factors influencing antidrug antibody and drug levels to optimize future treatment decisions.MethodsA total of 331 patients from an observational prospective cohort were selected (160 patients treated with adalimumab and 171 treated with etanercept). Antidrug antibody levels were measured by radioimmunoassay, and drug levels were measured by enzyme‐linked immunosorbent assay in 835 serial serum samples obtained 3, 6, and 12 months after initiation of therapy. The association between antidrug antibodies and drug non‐trough levels and the treatment response (change in the Disease Activity Score in 28 joints) was evaluated.ResultsAmong patients who completed 12 months of followup, antidrug antibodies were detected in 24.8% of those receiving adalimumab (31 of 125) and in none of those receiving etanercept. At 3 months, antidrug antibody formation and low adalimumab levels were significant predictors of no response according to the European League Against Rheumatism (EULAR) criteria at 12 months (area under the receiver operating characteristic curve 0.71 [95% confidence interval (95% CI) 0.57, 0.85]). Antidrug antibody–positive patients received lower median dosages of methotrexate compared with antidrug antibody–negative patients (15 mg/week versus 20 mg/week; P = 0.01) and had a longer disease duration (14.0 versus 7.7 years; P = 0.03). The adalimumab level was the best predictor of change in the DAS28 at 12 months, after adjustment for confounders (regression coefficient 0.060 [95% CI 0.015, 0.10], P = 0.009). Etanercept levels were associated with the EULAR response at 12 months (regression coefficient 0.088 [95% CI 0.019, 0.16], P = 0.012); however, this difference was not significant after adjustment. A body mass index of ≥30 kg/m2 and poor adherence were associated with lower drug levels.ConclusionPharmacologic testing in anti–tumor necrosis factor–treated patients is clinically useful even in the absence of trough levels. At 3 months, antidrug antibodies and low adalimumab levels are significant predictors of no response according to the EULAR criteria at 12 months.