Background: Understanding the genetic basis of disease risk in depth requires an exhaustive knowledge of the types of genetic variation. Very recently, Copy Number Variants (CNVs) have received much attention because of their potential implication in common disease susceptibility. Copy Number Polymorphisms (CNPs) are of interest as they segregate at an appreciable frequency in the general population (i.e. > 1%) and are potentially implicated in the genetic basis of common diseases.Results: This paper concerns CNstream, a method for whole-genome CNV discovery and genotyping, using Illumina Beadchip arrays. Compared with other methods, a high level of accuracy was achieved by analyzing the measures of each intensity channel separately and combining information from multiple samples. The CNstream method uses heuristics and parametrical statistics to assign a confidence score to each sample at each probe; the sensitivity of the analysis is increased by jointly calling the copy number state over a set of nearby and consecutive probes. The present method has been tested on a real dataset of 575 samples genotyped using Illumina HumanHap 300 Beadchip, and demonstrates a high correlation with the Database of Genomic Variants (DGV). The same set of samples was analyzed with PennCNV, one of the most frequently used copy number inference methods for Illumina platforms. CNstream was able to identify CNP loci that are not detected by PennCNV and it increased the sensitivity over multiple other loci in the genome.Conclusions: CNstream is a useful method for the identification and characterization of CNPs using Illumina genotyping microarrays. Compared to the PennCNV method, it has greater sensitivity over multiple CNP loci and allows more powerful statistical analysis in these regions. Therefore, CNstream is a robust CNP analysis tool of use to researchers performing genome-wide association studies (GWAS) on Illumina platforms and aiming to identify CNVs associated with the variables of interest. CNstream has been implemented as an R statistical software package that can work directly from raw intensity files generated from Illumina GWAS projects. The method is available athttp://www.urr.cat/cnv/cnstream.html.

Background: We aimed to analyse the effectiveness, efficiency, and safety of initial treatment with biological therapies in rheumatoid arthritis (RA). Methods: Qualitative study. A group of RA experts was selected. A scoping review in Medline was conducted to analyse the evidence of initial RA treatment with biological therapies. Randomised clinical trials were selected. Two reviewers analysed the articles and compiled the data, whose quality was assessed using the Jadad scale. The experts discussed the review’s findings and generated a series of general principles: Results: Seventeen studies were included. Most of the included patients were middle-aged women with early RA (1–7 months) and multiple poor prognostic factors. Initial treatment with TNF-alpha inhibitors combined with methotrexate (MTX) and an IL6R inhibitor (either in mono or combination therapy) is effective (activity, function, radiographic damage, quality of life), safe, and superior to MTX monotherapy in the short and medium term. In the long term, patients who received initial treatment with biologicals presented better results than those whose initial therapy was with MTX. Conclusions: Initial treatment of RA with biological therapies is effective, efficient, and safe in the short, medium, and long term, particularly for patients with poor prognostic factors.