aRNApipe: a balanced, efficient and distributed pipeline for processing RNA-seq data in high-performance computing environments.

SUMMARY: The wide range of RNA-seq applications and their high-computational needs require the development of pipelines orchestrating the entire workflow and optimizing usage of available computational resources. We present aRNApipe, a project-oriented pipeline for processing of RNA-seq data in high-performance cluster environments. aRNApipe is highly modular and can be easily migrated to any high-performance computing (HPC) environment. The current applications included in aRNApipe combine the essential RNA-seq primary analyses, including quality control metrics, transcript alignment, count generation, transcript fusion identification, alternative splicing and sequence variant calling. aRNApipe is project-oriented and dynamic so users can easily update analyses to include or exclude samples or enable additional processing modules. Workflow parameters are easily set using a single configuration file that provides centralized tracking of all analytical processes. Finally, aRNApipe incorporates interactive web reports for sample tracking and a tool for managing the genome assemblies available to perform an analysis. AVAILABILITY AND DOCUMENTATION: https://github.com/HudsonAlpha/aRNAPipe ; DOI: 10.5281/zenodo.202950. CONTACT: rmyers@hudsonalpha.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Heritable DNA Methylation in CD4+ Cells among Complex Families Displays Genetic and Non-Genetic Effects.

DNA methylation at CpG sites is both heritable and influenced by environment, but the relative contributions of each to DNA methylation levels are unclear. We conducted a heritability analysis of CpG methylation in human CD4+ cells across 975 individuals from 163 families in the Genetics of Lipid-lowering Drugs and Diet Network (GOLDN). Based on a broad-sense heritability (H2) value threshold of 0.4, we identified 20,575 highly heritable CpGs among the 174,445 most variable autosomal CpGs (SD > 0.02). Tests for associations of heritable CpGs with genotype at 2,145,360 SNPs using 717 of 975 individuals showed that ~74% were cis-meQTLs (< 1 Mb away from the CpG), 6% of CpGs exhibited trans-meQTL associations (>1 Mb away from the CpG or located on a different chromosome), and 20% of CpGs showed no strong significant associations with genotype (based on a p-value threshold of 1e-7). Genes proximal to the genotype independent heritable CpGs were enriched for functional terms related to regulation of T cell activation. These CpGs were also among those that distinguished T cells from other blood cell lineages. Compared to genes proximal to meQTL-associated heritable CpGs, genotype independent heritable CpGs were moderately enriched in the same genomic regions that escape erasure during primordial germ cell development and could carry potential for generational transmission.

Urine metabolome profiling of immune-mediated inflammatory diseases.

BACKGROUND: Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis. METHODS: Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls. RESULTS: In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (P FDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (P FDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an over-representation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways. CONCLUSIONS: This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.

A genome-wide association study identifies SLC8A3 as a susceptibility locus for ACPA-positive rheumatoid arthritis.

OBJECTIVE: RA patients with serum ACPA have a strong and specific genetic background. The objective of the study was to identify new susceptibility genes for ACPA-positive RA using a genome-wide association approach. METHODS: A total of 924 ACPA-positive RA patients with joint damage in hands and/or feet, and 1524 healthy controls were genotyped in 582 591 single-nucleotide polymorphisms (SNPs) in the discovery phase. In the validation phase, the most significant SNPs in the genome-wide association study representing new candidate loci for RA were tested in an independent cohort of 863 ACPA-positive patients with joint damage and 1152 healthy controls. All individuals from the discovery and validation cohorts were Caucasian and of Southern European ancestry. RESULTS: In the discovery phase, 60 loci not previously associated with RA risk showed evidence for association at P < 5×10(-4) and were tested for replication in the validation cohort. A total of 12 loci were replicated at the nominal level (P < 0.05, same direction of effect as in the discovery phase). When combining the discovery and validation cohorts, an intronic SNP in the Solute Carrier family 8 gene (SLC8A3) was found to be associated with ACPA-positive RA at a genome-wide level of significance RA [odds ratio (95% CI): 1.42 (1.25, 1.6), Pcombined = 3.19×10(-8)]. CONCLUSIONS: SLC8A3 was identified as a new risk locus for ACPA-positive RA. This study demonstrates the advantage of analysing relevant subsets of RA patients to identify new genetic risk variants.

Identification of IRX1 as a Risk Locus for Rheumatoid Factor Positivity in Rheumatoid Arthritis in a Genome-Wide Association Study.

OBJECTIVE: Rheumatoid factor (RF) is a well-established diagnostic and prognostic biomarker in rheumatoid arthritis (RA). However, ∼20% of RA patients are negative for this anti-IgG antibody. To date, only variation at the HLA-DRB1 gene has been associated with the presence of RF. This study was undertaken to identify additional genetic variants associated with RF positivity. METHODS: A genome-wide association study (GWAS) for RF positivity was performed using an Illumina Quad610 genotyping platform. A total of 937 RF-positive and 323 RF-negative RA patients were genotyped for >550,000 single-nucleotide polymorphisms (SNPs). Association testing was performed using an allelic chi-square test implemented in Plink software. An independent cohort of 472 RF-positive and 190 RF-negative RA patients was used to validate the most significant findings. RESULTS: In the discovery stage, a SNP in the IRX1 locus on chromosome 5p15.3 (SNP rs1502644) showed a genome-wide significant association with RF positivity (P = 4.13 × 10(-8) , odds ratio [OR] 0.37 [95% confidence interval (95% CI) 0.26-0.53]). In the validation stage, the association of IRX1 with RF was replicated in an independent group of RA patients (P = 0.034, OR 0.58 [95% CI 0.35-0.97] and combined P = 1.14 × 10(-8) , OR 0.43 [95% CI 0.32-0.58]). CONCLUSION: To our knowledge, this is the first GWAS of RF positivity in RA. Variation at the IRX1 locus on chromosome 5p15.3 is associated with the presence of RF. Our findings indicate that IRX1 and HLA-DRB1 are the strongest genetic factors for RF production in RA.

Genome-Wide Pathway Analysis Identifies Genetic Pathways Associated with Psoriasis.

Psoriasis is a chronic inflammatory disease with a complex genetic architecture. To date, the psoriasis heritability is only partially explained. However, there is increasing evidence that the missing heritability in psoriasis could be explained by multiple genetic variants of low effect size from common genetic pathways. The objective of this study was to identify new genetic variation associated with psoriasis risk at the pathway level. We genotyped 598,258 single nucleotide polymorphisms in a discovery cohort of 2,281 case-control individuals from Spain. We performed a genome-wide pathway analysis using 1,053 reference biological pathways. A total of 14 genetic pathways (PFDR ≤ 2.55 × 10(-2)) were found to be significantly associated with psoriasis risk. Using an independent validation cohort of 7,353 individuals from the UK, a total of 6 genetic pathways were significantly replicated (PFDR ≤ 3.46 × 10(-2)). We found genetic pathways that had not been previously associated with psoriasis risk such as retinol metabolism (Pcombined = 1.84 × 10(-4)), the transport of inorganic ions and amino acids (Pcombined = 1.57 × 10(-7)), and post-translational protein modification (Pcombined = 1.57 × 10(-7)). In the latter pathway, MGAT5 showed a strong network centrality, and its association with psoriasis risk was further validated in an additional case-control cohort of 3,429 individuals (P < 0.05). These findings provide insights into the biological mechanisms associated with psoriasis susceptibility.

Variation at interleukin-6 receptor gene is associated to joint damage in rheumatoid arthritis.

INTRODUCTION: Interleukin-6 (IL-6) cytokine signaling is key in Rheumatoid Arthritis (RA) pathophysiology. Blocking IL-6 receptor (IL6R) has proven to be a highly effective treatment to prevent joint damage. This study was performed to investigate the association between the genetic variation at IL6R gene and the severity of joint damage in RA. METHODS: IL6R gene tagging SNPs (n = 5) were genotyped in a discovery group of 527 RA patients from 5 different university hospitals from Spain. For each marker, a linear regression analysis was performed using an additive model and adjusting for the years of evolution of the disease, autoantibody status, gender and age. Haplotypes combining the SNPs were also estimated and tested for association with the level of joint destruction. Using an independent cohort of 705 RA patients from 6 university hospitals we performed a validation study of the SNPs associated in the discovery phase. RESULTS: In the discovery group we found a highly significant association between IL6R SNP rs4845618 and the level of joint destruction in RA (P = 0.0058, P corrected = 0.026), and a moderate association with SNP rs4453032 (P = 0.02, P corrected = 0.05). The resulting haplotype from both SNPs was more significantly associated with joint damage (P = 0.0037, P corrected = 0.011). Using the validation cohort, we replicated the association between the two IL-6R SNPs with the degree of joint destruction in RA (P = 0.007 and P = 0.04, meta-analysis P = 0.00011 and P = 0.0021, respectively), and the haplotype association (P = 0.0058, meta-analysis P = 6.64 e-5). CONCLUSIONS: Genetic variation at IL6R gene is associated with joint damage in RA.

A deletion at ADAMTS9-MAGI1 locus is associated with psoriatic arthritis risk.

OBJECTIVE: Copy number variants (CNVs) have been associated with the risk to develop multiple autoimmune diseases. Our objective was to identify CNVs associated with the risk to develop psoriatic arthritis (PsA) using a genome-wide analysis approach. METHODS: A total of 835 patients with PsA and 1498 healthy controls were genotyped for CNVs using the Illumina HumanHap610 BeadChip genotyping platform. Genomic CNVs were characterised using CNstream analysis software and analysed for association using the χ(2) test. The most significant genomic CNV associations with PsA risk were independently tested in a validation sample of 1133 patients with PsA and 1831 healthy controls. In order to test for the specificity of the variants with PsA aetiology, we also analysed the association to a cohort of 822 patients with purely cutaneous psoriasis (PsC). RESULTS: A total of 165 common CNVs were identified in the genome-wide analysis. We found a highly significant association of an intergenic deletion between ADAMTS9 and MAGI1 genes on chromosome 3p14.1 (p=0.00014). Using the independent patient and control cohort, we validated the association between ADAMTS9-MAGI1 deletion and PsA risk (p=0.032). Using next-generation sequencing, we characterised the 26 kb associated deletion. Finally, analysing the PsC cohort we found a lower frequency of the deletion compared with the PsA cohort (p=0.0088) and a similar frequency to that of healthy controls (p>0.3). CONCLUSIONS: The present genome-wide scan for CNVs associated with PsA risk has identified a new deletion associated with disease risk and which is also differential from PsC risk.

Analytical methods in untargeted metabolomics: state of the art in 2015.

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.

Identification of risk loci for Crohn's disease phenotypes using a genome-wide association study.

BACKGROUND & AIMS: Crohn's disease is a highly heterogeneous inflammatory bowel disease comprising multiple clinical phenotypes. Genome-wide association studies (GWASs) have associated a large number of loci with disease risk but have not associated any specific genetic variants with clinical phenotypes. We performed a GWAS of clinical phenotypes in Crohn's disease. METHODS: We genotyped 576,818 single-nucleotide polymorphisms in a well-characterized cohort of 1090 Crohn's disease patients of European ancestry. We assessed their association with 17 phenotypes of Crohn's disease (based on disease location, disease behavior, disease course, age at onset, and extraintestinal manifestations). A total of 57 markers with strong associations to Crohn's disease phenotypes (P < 2 × 10(-4)) were subsequently analyzed in an independent replication cohort of 1296 patients of European ancestry. RESULTS: We replicated the association of 4 loci with different Crohn's disease phenotypes. Variants in MAGI1, CLCA2, 2q24.1, and LY75 loci were associated with a complicated stricturing disease course (Pcombined = 2.01 × 10(-8)), disease location (Pcombined = 1.3 × 10(-6)), mild disease course (Pcombined = 5.94 × 10(-7)), and erythema nodosum (Pcombined = 2.27 × 10(-6)), respectively. CONCLUSIONS: In a GWAS, we associated 4 loci with clinical phenotypes of Crohn's disease. These findings indicate a genetic basis for the clinical heterogeneity observed for this inflammatory bowel disease.

PDE3A-SLCO1C1 locus is associated with response to anti-tumor necrosis factor therapy in psoriatic arthritis.

Aim: Variation at PDE3A-SLCO1C1 locus has been recently associated with the response to anti-TNF therapy in rheumatoid arthritis. We undertook the present study to determine whether PDE3A-SLCO1C1 is also associated with the response to anti-TNF therapy in psoriatic arthritis. Patients & methods: Genomic DNA was obtained from 81 psoriatic arthritis patients that had been treated with anti-TNF therapy. PDE3A-SLCO1C1 SNP rs3794271 was genotyped using Taqman realt-time PCR. The clinical response to anti-TNF therapy was measured as the change from baseline in the level of disease activity according to the DAS28 score. Results: A significant association between rs3794271 and anti-TNF response in psoriatic arthritis was found (beta = -0.71; p = 0.0036). Conclusion: PDE3A-SLCO1C1 locus is also associated with response to anti-TNF therapy in psoriatic arthritis. Original submitted 12 May 2014; Revision submitted 18 August 2014.

A genome-wide association study identifies a novel locus at 6q22.1 associated with ulcerative colitis.

The genetic analysis of ulcerative colitis (UC) has provided new insights into the etiology of this prevalent inflammatory bowel disease. However, most of the heritability of UC (>70%) has still not been characterized. To identify new risk loci for UC we have performed the first genome-wide association study (GWAS) in a Southern European population and undertaken a meta-analysis study combining the newly genotyped 825 UC patients and 1525 healthy controls from Spain with the six previously published GWAS comprising 6687 cases and 19 718 controls from Northern-European ancestry. We identified a novel locus with genome-wide significance at 6q22.1 [rs2858829, P = 8.97 × 10(-9), odds ratio (OR) (95% confidence interval, CI] = 1.12 (1.08-1.16)] that was validated with genotype data from a replication cohort of the same Southern European ancestry consisting in 1073 cases and 1279 controls [combined P = 7.59 × 10(-10), OR (95% CI) = 1.12 (1.08-1.16)]. Furthermore, we confirmed the association of 33 reported associations with UC and we nominally validated the GWAS results of nine new risk loci (P < 0.05, same direction of effect). SNP rs2858829 lies in an intergenic region and is a strong cis-eQTL for FAM26F gene, a gene that is shown to be selectively upregulated in UC colonic mucosa with active inflammation. Our results provide new insight into the genetic risk background of UC, confirming that there is a genetic risk component that differentiates from Crohn's Disease, the other major form of inflammatory bowel disease.

Focus: a robust workflow for one-dimensional NMR spectral analysis.

One-dimensional (1)H NMR represents one of the most commonly used analytical techniques in metabolomic studies. The increase in the number of samples analyzed as well as the technical improvements involving instrumentation and spectral acquisition demand increasingly accurate and efficient high-throughput data processing workflows. We present FOCUS, an integrated and innovative methodology that provides a complete data analysis workflow for one-dimensional NMR-based metabolomics. This tool will allow users to easily obtain a NMR peak feature matrix ready for chemometric analysis as well as metabolite identification scores for each peak that greatly simplify the biological interpretation of the results. The algorithm development has been focused on solving the critical difficulties that appear at each data processing step and that can dramatically affect the quality of the results. As well as method integration, simplicity has been one of the main objectives in FOCUS development, requiring very little user input to perform accurate peak alignment, peak picking, and metabolite identification. The new spectral alignment algorithm, RUNAS, allows peak alignment with no need of a reference spectrum, and therefore, it reduces the bias introduced by other alignment approaches. Spectral alignment has been tested against previous methodologies obtaining substantial improvements in the case of moderate or highly unaligned spectra. Metabolite identification has also been significantly improved, using the positional and correlation peak patterns in contrast to a reference metabolite panel. Furthermore, the complete workflow has been tested using NMR data sets from 60 human urine samples and 120 aqueous liver extracts, reaching a successful identification of 42 metabolites from the two data sets. The open-source software implementation of this methodology is available at http://www.urr.cat/FOCUS.

GStream: improving SNP and CNV coverage on genome-wide association studies.

We present GStream, a method that combines genome-wide SNP and CNV genotyping in the Illumina microarray platform with unprecedented accuracy. This new method outperforms previous well-established SNP genotyping software. More importantly, the CNV calling algorithm of GStream dramatically improves the results obtained by previous state-of-the-art methods and yields an accuracy that is close to that obtained by purely CNV-oriented technologies like Comparative Genomic Hybridization (CGH). We demonstrate the superior performance of GStream using microarray data generated from HapMap samples. Using the reference CNV calls generated by the 1000 Genomes Project (1KGP) and well-known studies on whole genome CNV characterization based either on CGH or genotyping microarray technologies, we show that GStream can increase the number of reliably detected variants up to 25% compared to previously developed methods. Furthermore, the increased genome coverage provided by GStream allows the discovery of CNVs in close linkage disequilibrium with SNPs, previously associated with disease risk in published Genome-Wide Association Studies (GWAS). These results could provide important insights into the biological mechanism underlying the detected disease risk association. With GStream, large-scale GWAS will not only benefit from the combined genotyping of SNPs and CNVs at an unprecedented accuracy, but will also take advantage of the computational efficiency of the method.

GWAS replication study confirms the association of PDE3A-SLCO1C1 with anti-TNF therapy response in rheumatoid arthritis.

AIM: The present study was undertaken to replicate the association of candidate genes for anti-TNF response in rheumatoid arthritis. Candidate genes were selected from a recent genome-wide association study on anti-TNF response performed in a population from Denmark. MATERIALS & METHODS: Genomic DNA was obtained from 315 Spanish rheumatoid arthritis patients having received an anti-TNF agent as their first biological therapy. SNPs from NR2FR2, MAP2K6, CBLN2 and PDE3A-SLCO1C1 candidate loci were genotyped. RESULTS: The PDE3A-SLCO1C1 locus rs3794271 SNP showed a highly significant association with anti-TNF treatment response (p = 1.74 × 10⁻5). Combining the statistical evidence from the Spanish and Danish rheumatoid arthritis cohorts, the associated rs3794271 SNP reached a genome-wide significance level of association (p = 3.3 × 10⁻¹°). CONCLUSION: The present findings establish the PDE3A-SLCO1C1 locus as a strong genetic marker of anti-TNF therapy response.

A genome-wide association study on a southern European population identifies a new Crohn's disease susceptibility locus at RBX1-EP300.

OBJECTIVE: Genome-wide association studies (GWAS) have identified multiple risk loci for Crohn's disease (CD). However, the cumulative risk exerted by these loci is low, and the likelihood that additional, as-yet undiscovered loci contribute to the risk of CD is very high. We performed a GWAS on a southern European population to identify new CD risk loci. DESIGN: We genotyped 620 901 genome markers on 1341 CD patients and 1518 controls from Spain. The top association signals representing new candidate risk loci were subsequently analysed in an independent replication cohort of 1365 CD patients and 1396 controls. RESULTS: We identified a genome-wide significant association on chromosome 22q13.2 in the intergenic region between the RBX1 and EP300 genes (single nucleotide polymorphism rs4820425, OR 1.27, 95% CI 1.17 to 1.38, p=3.42E-8). We also found suggestive evidence for the association of the IFNGR2 (21q22.11), FOXP2 (7q31), MACROD2 (20p12.1) and AIF1 (6p21.3) loci with CD risk. CONCLUSIONS: In this GWAS performed on a southern European cohort, we have identified a new risk locus for CD between RBX1 and EP300. This study demonstrates that using populations of different ancestry is a useful strategy to identify new risk loci for CD.

Risk variants for psoriasis vulgaris in a large case-control collection and association with clinical subphenotypes.

Recent genome-wide association studies (GWASs) have identified >20 new loci associated with the susceptibility to psoriasis vulgaris (PsV) risk. We investigated the association of PsV and its main clinical subphenotypes with 32 loci having previous genome-wide evidence of association with PsV (P < 5e-8) or strong GWAS evidence (P < 5e-5 in discovery and P < 0.05 in replication sample) in a large cohort of PsV patients (n = 2005) and controls (n = 1497). We provide the first independent replication for COG6 (P = 0.00079) and SERPINB8 (P = 0.048) loci with PsV. In those patients having developed psoriatic arthritis (n = 955), we found, for the first time, a strong association with IFIH1 (P = 0.013). Analyses of clinically relevant PsV subtypes yielded a significant association of severity of cutaneous disease with variation at LCE3D locus (P = 0.0005) in PsV and nail involvement with IL1RN in purely cutaneous psoriasis (PsC, P = 0.007). In an exploratory analysis of epistasis, we replicated the previously described HLA-C-ERAP1 interaction with PsC. Our findings show that common genetic variants associated with a complex phenotype like PsV influence different subphenotypes of high clinical relevance.

AStream: an R package for annotating LC/MS metabolomic data.

UNLABELLED: AStream, an R-statistical software package for the curation and identification of feature peaks extracted from liquid chromatography mass spectrometry (LC/MS) metabolomics data, is described. AStream detects isotopic, fragment and adduct patterns by identifying feature pairs that fulfill expected relational patterns. Data reduction by AStream allows compounds to be identified reliably and subsequently linked to metabolite databases. AStream provides researchers with a fast, reliable tool for summarizing metabolomic data, notably reducing curation time and increasing consistency of results. AVAILABILITY: The AStream R package and a study example can be freely accessed at http://www.urr.cat/AStream/AStream.html.

CNstream: a method for the identification and genotyping of copy number polymorphisms using Illumina microarrays.

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 at http://www.urr.cat/cnv/cnstream.html.