ObjectivePsoriatic arthritis (PsA) is a chronic inflammatory arthritis affecting up to 30% of patients with psoriasis (Ps). To date, most of the known risk loci for PsA are shared with Ps, and identifying disease-specific variation has proven very challenging. The objective of the present study was to identify genetic variation specific for PsA.MethodsWe performed a genome-wide association study in a cohort of 835 patients with PsA and 1558 controls from Spain. Genetic association was tested at the single marker level and at the pathway level. Meta-analysis was performed with a case–control cohort of 2847 individuals from North America. To confirm the specificity of the genetic associations with PsA, we tested the associated variation using a purely cutaneous psoriasis cohort (PsC, n=614) and a rheumatoid arthritis cohort (RA, n=1191). Using network and drug-repurposing analyses, we further investigated the potential of the PsA-specific associations to guide the development of new drugs in PsA.ResultsWe identified a new PsA risk single-nucleotide polymorphism at B3GNT2 locus (p=1.10e-08). At the pathway level, we found 14 genetic pathways significantly associated with PsA (pFDR<0.05). From these, the glycosaminoglycan (GAG) metabolism pathway was confirmed to be disease-specific after comparing the PsA cohort with the cohorts of patients with PsC and RA. Finally, we identified candidate drug targets in the GAG metabolism pathway as well as new PsA indications for approved drugs.ConclusionThese findings provide insights into the biological mechanisms that are specific for PsA and could contribute to develop more effective therapies.

Classical statistical process control (SPC) by attributes is based on counts of nonconformities. However, process quality has greatly improved with respect to past decades, and the vast majority of samples taken from high-quality processes do not exhibit defective units. Therefore, control charts by variables are the standard monitoring scheme employed. However, it is still possible to design an effective SPC scheme by attributes for such processes if the sample units are classified into categories such as 'large', 'normal', or 'small' according to limits that are different from the specification limits. Units classified as 'large' or 'small' will most likely still be conforming (within the specifications), but such a classification allows monitoring the process with attributes charts. In the case of dimensional quality characteristics, gages can be built for this purpose, making inspection quick and easy and reducing the risk of errors. We propose such a control chart, optimize it, compare its performance with the traditional X and S charts and with another chart in the literature that is also based in classifying observations of continuous variables through gaging, and present a brief sensitivity analysis of its performance. The new chart is shown to be competitive with the use of X-S charts, with the operational advantage of simpler, faster, and less costly inspection.

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Recent studies on the effects of parameter estimation on control charts have focused on their conditional in‐control (IC) performance and recommended either the minimum number of Phase I samples (m) or adjustments to the control limit factor (L) that guarantee a desired IC performance with a high probability. In most cases, the numbers of samples required are prohibitively large in practice, and the adjustments for smaller numbers of samples entail as a counterpart a deterioration of the chart's out‐of‐control (OOC) performance. This presents the user with a hard decision, in which he or she will have difficulty in finding the best compromise between the objectives of good (or acceptable) IC performance, OOC performance, and a practicable number of Phase I samples. Therefore, in the context of the S2 chart, we propose a new approach that takes both the desired IC and OOC performances (that should be within specified tolerances with a specified high joint probability) as constraints for the optimization of the pair (L, m). This is the first work that simultaneously treats the choice of m and the control limit adjustment in the framework of an optimization problem. With our model, the user can automatically obtain the most feasible (minimum m) solution that satisfies his/her requirements on both the IC and OOC performances.