A robust clustering algorithm for identifying problematic samples in genome-wide association studies

Summary: High-throughput genotyping arrays provide an efficient way to survey single nucleotide polymorphisms (SNPs) across the genome in large numbers of individuals. Downstream analysis of the data, for example in genome-wide association studies (GWAS), often involves statistical models of genotype frequencies across individuals. The complexities of the sample collection process and the potential for errors in the experimental assay can lead to biases and artefacts in an individual's inferred genotypes. Rather than attempting to model these complications, it has become a standard practice to remove individuals whose genome-wide data differ from the sample at large. Here we describe a simple, but robust, statistical algorithm to identify samples with atypical summaries of genome-wide variation. Its use as a semi-automated quality control tool is demonstrated using several summary statistics, selected to identify different potential problems, and it is applied to two different genotyping platforms and sample collections

Emerging patterns of genetic overlap across autoimmune disorders.

Most of the recently identified autoimmunity loci are shared among multiple autoimmune diseases. The pattern of genetic association with autoimmune phenotypes varies, suggesting that certain subgroups of autoimmune diseases are likely to share etiological similarities and underlying mechanisms of disease. In this review, we summarize the major findings from recent studies that have sought to refine genotype-phenotype associations in autoimmune disease by identifying both shared and distinct autoimmunity loci. More specifically, we focus on information from recent genome-wide association studies of rheumatoid arthritis, ankylosing spondylitis, celiac disease, multiple sclerosis, systemic lupus erythematosus, type 1 diabetes and inflammatory bowel disease. Additional work in this area is warranted given both the opportunity it provides to elucidate pathogenic mechanisms in autoimmunity and its potential to inform the development of improved diagnostic and therapeutic tools for this group on complex human disorders

Multi-omics integration reveals molecular networks and regulators of psoriasis.

BackgroundPsoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.MethodsTo achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.ResultsThis integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.ConclusionsThe integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility

Immunochip analysis identifies multiple susceptibility loci for systemic sclerosis

In this study, 1,833 systemic sclerosis (SSc) cases and 3,466 controls were genotyped with the Immunochip array. Classical alleles, amino acid residues, and SNPs across the human leukocyte antigen (HLA) region were imputed and tested. These analyses resulted in a model composed of six polymorphic amino acid positions and seven SNPs that explained the observed significant associations in the region. In addition, a replication step comprising 4,017 SSc cases and 5,935 controls was carried out for several selected non-HLA variants, reaching a total of 5,850 cases and 9,401 controls of European ancestry. Following this strategy, we identified and validated three SSc risk loci, including DNASE1L3 at 3p14, the SCHIP1-IL12A locus at 3q25, and ATG5 at 6q21, as well as a suggested association of the TREH-DDX6 locus at 11q23. The associations of several previously reported SSc risk loci were validated and further refined, and the observed peak of association in PXK was related to DNASE1L3. Our study has increased the number of known genetic associations with SSc, provided further insight into the pleiotropic effects of shared autoimmune risk factors, and highlighted the power of dense mapping for detecting previously overlooked susceptibility loci

Genetic relationships between A20/TNFAIP3, chronic inflammation and autoimrnune disease

A20 [also known as TNFAIP3 (tumour necrosis factor a-induced protein 3)] restricts and terminates inflammatory responses through modulation of the ubiquitination status of central components in NF-kappa B (nuclear factor kappa B), IRF3 (interferon regulatory factor 3) and apoptosis signalling cascades. The phenotype of mice with full or conditional A20 deletion illustrates that A20 expression is essential to prevent chronic inflammation and autoimmune pathology. In addition, polymorphisms within the A20 genomic locus have been associated with multiple inflammatory and autoimmune disorders, including SLE (systemic lupus erythaematosis), RA (rheumatoid arthritis), Crohn's disease and psoriasis. A20 has also been implicated as a tumour suppressor in several subsets of B-cell lymphomas. The present review outlines recent findings that illustrate the effect of A20 defects in disease pathogenesis and summarizes the identified A20 polymorphisms associated with different immunopathologies

Common genetic variants associated with disease from genome-wide association studies are mutually exclusive in prostate cancer and rheumatoid arthritis

Objectives: To investigate if potential common pathways exist for the pathogenesis of autoimmune disease and prostate cancer (PrCa). To ascertain if the single nucleotide polymorphisms (SNPs) reported by genome-wide association studies (GWAS) as being associated with susceptibility to PrCa are also associated with susceptibility to the autoimmune disease rheumatoid arthritis (RA). Materials and Methods: The original Wellcome Trust Case Control Consortium (WTCCC) UK RA GWAS study was expanded to include a total of 3221 cases and 5272 controls. In all, 37 germline autosomal SNPs at genome-wide significance associated with PrCa risk were identified from a UK/Australian PrCa GWAS. Allele frequencies were compared for these 37 SNPs between RA cases and controls using a chi-squared trend test and corrected for multiple testing (Bonferroni). Results: In all, 33 SNPs were able to be analysed in the RA dataset. Proxies could not be located for the SNPs in 3q26, 5p15 and for two SNPs in 17q12. After applying a Bonferroni correction for the number of SNPs tested, the SNP mapping to CCHCR1 (rs130067) retained statistically significant evidence for association (P = 6 × 10–4; odds ratio [OR] = 1.15, 95% CI: 1.06–1.24); this has also been associated with psoriasis. However, further analyses showed that the association of this allele was due to confounding by RA-associated HLA-DRB1 alleles. Conclusions: There is currently no evidence that SNPs associated with PrCa at genome-wide significance are associated with the development of RA. Studies like this are important in determining if common genetic risk profiles might predispose individuals to many diseases, which could have implications for public health in terms of screening and chemoprevention

Metabolomics in psoriatic disease: pilot study reveals metabolite differences in psoriasis and psoriatic arthritis.

ImportanceWhile "omics" studies have advanced our understanding of inflammatory skin diseases, metabolomics is mostly an unexplored field in dermatology.ObjectiveWe sought to elucidate the pathogenesis of psoriatic diseases by determining the differences in metabolomic profiles among psoriasis patients with or without psoriatic arthritis and healthy controls.DesignWe employed a global metabolomics approach to compare circulating metabolites from patients with psoriasis, psoriasis and psoriatic arthritis, and healthy controls.SettingStudy participants were recruited from the general community and from the Psoriasis Clinic at the University of California Davis in United States.ParticipantsWe examined metabolomic profiles using blood serum samples from 30 patients age and gender matched into three groups: 10 patients with psoriasis, 10 patients with psoriasis and psoriatic arthritis and 10 control participants. Main outcome(s) and measures(s): Metabolite levels were measured calculating the mean peak intensities from gas chromatography time-of-flight mass spectrometry.ResultsMultivariate analyses of metabolomics profiles revealed altered serum metabolites among the study population. Compared to control patients, psoriasis patients had a higher level of alpha ketoglutaric acid (Pso: 288 ± 88;Control209 ± 69; p=0.03), a lower level of asparagine (Pso: 5460 ± 980;Control7260 ± 2100; p=0.02), and a lower level of glutamine (Pso: 86000 ± 20000;Control111000 ± 27000; p=0.02). Compared to control patients, patients with psoriasis and psoriatic arthritis had increased levels of glucuronic acid (Pso + PsA: 638 ± 250;Control347 ± 61; p=0.001). Compared to patients with psoriasis alone, patients with both psoriasis and psoriatic arthritis had a decreased level of alpha ketoglutaric acid (Pso + PsA: 186 ± 80; Pso: 288 ± 88; p=0.02) and an increased level of lignoceric acid (Pso + PsA: 442 ± 280; Pso: 214 ± 64; p=0.02).Conclusions and relevanceThe metabolite differences help elucidate the pathogenesis of psoriasis and psoriatic arthritis and they may provide insights for therapeutic development