Genome-wide meta-analysis implicates variation affecting mast cell biology in urticaria.

BACKGROUND: Urticaria is characterized by inappropriate mast cell degranulation leading to the development of wheals and/or angioedema. Twin and family studies indicate that there is a substantial heritable component to urticaria risk. OBJECTIVE: Our aim was to identify genomic loci at which common genetic variation influences urticaria susceptibility. METHODS: Genome-wide association studies of urticaria (including all subtypes) from 3 European cohorts (UK Biobank, FinnGen, and the Trøndelag Health Study [HUNT]) were combined through statistical meta-analysis (14,306 urticaria cases and 650,664 controls). Cases were identified via electronic health care records from primary and/or secondary care. To identify putative causal variants and genes, statistical fine-mapping, colocalization, and interrogation of publicly available single-cell transcriptome sequencing resources were performed. RESULTS: Genome-wide significant associations (P < 5 × 10-8) were identified at 6 independent loci. These included 2 previously reported association signals at 1q44 and the human leucocyte antigen region on chromosome 6. Genes with expected or established roles in mast cell biology were associated with the 4 other genome-wide association signals (GCSAML, FCER1A, TPSAB1, and CBLB). Colocalization of association signals consistent with the presence of shared causal variants was observed between urticaria susceptibility and increased expression of GCSAML (posterior probability of colocalization [PPcoloc] = 0.89) and FCER1A (PPcoloc = 0.91) in skin. CONCLUSION: Common genetic variation influencing the risk of developing urticaria was identified at 6 genomic loci. The relationship between genes with roles in mast cell biology and several association signals implicates genetic variability of specific components of mast cell function in the development of urticaria.

A genome-wide meta-analysis of palmoplantar pustulosis implicates TH2 responses and cigarette smoking in disease pathogenesis.

BACKGROUND: Palmoplantar pustulosis (PPP) is an inflammatory skin disorder that mostly affects smokers and manifests with painful pustular eruptions on the palms and soles. Although the disease can present with concurrent plaque psoriasis, TNF and IL-17/IL-23 inhibitors show limited efficacy. There is therefore a pressing need to uncover PPP disease drivers and therapeutic targets. OBJECTIVES: We sought to identify genetic determinants of PPP and investigate whether cigarette smoking contributes to disease pathogenesis. METHODS: We performed a genome-wide association meta-analysis of 3 North-European cohorts (n = 1,456 PPP cases and 402,050 controls). We then used the scGWAS program to investigate the cell-type specificity of the association signals. We also undertook genetic correlation analyses to examine the similarities between PPP and other immune-mediated diseases. Finally, we applied Mendelian randomization to analyze the causal relationship between cigarette smoking and PPP. RESULTS: We found that PPP is not associated with the main genetic determinants of plaque psoriasis. Conversely, we identified genome-wide significant associations with the FCGR3A/FCGR3B and CCHCR1 loci. We also observed 13 suggestive (P < 5 × 10-6) susceptibility regions, including the IL4/IL13 interval. Accordingly, we demonstrated a significant genetic correlation between PPP and TH2-mediated diseases such as atopic dermatitis and ulcerative colitis. We also found that genes mapping to PPP-associated intervals were preferentially expressed in dendritic cells and often implicated in T-cell activation pathways. Finally, we undertook a Mendelian randomization analysis, which supported a causal role of cigarette smoking in PPP. CONCLUSIONS: The first genome-wide association study of PPP points to a pathogenic role for deregulated TH2 responses and cigarette smoking.

The association of age at psoriasis onset and HLA-C*06:02 with biologic survival in patients with moderate-to-severe psoriasis: a cohort study from the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR).

BACKGROUND: Few studies have used real-world data to investigate the association between biologic therapy survival and age at psoriasis onset or HLA-C*06:02 status in patients with moderate-to-severe psoriasis. The robustness of these studies is limited by small sample size, short follow-up and diverse safety and effectiveness measures. OBJECTIVES: To describe biologic survival and explore whether the response to biologics is modified by age at psoriasis onset or HLA-C*06:02 status in patients with moderate-to-severe psoriasis. METHODS: Data from patients in the UK and the Republic of Ireland registered in the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) from 2007 to 2022 on a first course of adalimumab, etanercept, secukinumab or ustekinumab with at least 6 months' follow-up and a subset of BADBIR patients with available HLA-C*06:02 information registered to Biomarkers and Stratification To Optimise outcomes in Psoriasis (BSTOP) were analysed. Patients aged ≥ 50 years at treatment initiation were classified into early-onset psoriasis (EOP) (presenting in patients ≤ 40 years of age) and late-onset psoriasis (LOP) (presenting in patients > 40 years of age). BADBIR patients with available information in BSTOP were categorized as HLA-C*06:02- or HLA-C*06:02 + . Biologic survival was defined as treatment discontinuation associated with ineffectiveness or occurrence of adverse events (AEs). Adjusted survival function and hazard ratio (aHR) with 95% confidence interval (CI) were estimated using a flexible parametric model to compare discontinuing therapy between age at psoriasis onset and HLA-C*06:02 groups. Each model included exposure (biologics), effect modifier (age at onset or HLA-C*06:02 status), interaction terms and several baseline demographic, clinical and disease severity covariates. RESULTS: Final analytical cohorts included 4250 patients in the age at psoriasis onset group [2929 EOP (69%) vs. 1321 LOP (31%)] and 3094 patients in the HLA-C*06:02 status group [1603 HLA-C*06:02+ (52%) vs. 1491 HLA-C*06:02- (48%)]. There was no significant difference between EOP and LOP in drug survival associated with ineffectiveness or AEs for any biologics. However, compared with patients who were HLA-C*06:02-, patients who were HLA-C*06:02 + were less likely to discontinue ustekinumab for reasons associated with ineffectiveness (aHR 0.56, 95% CI 0.42-0.75). CONCLUSIONS: HLA-C*06:02, but not age at psoriasis onset, is a predictive biomarker for biologic survival in patients with psoriasis. Findings from this large cohort provide further, important information to aid clinicians using biologic therapies to manage patients with psoriasis.

Defining disease severity in atopic dermatitis and psoriasis for the application to biomarker research: an interdisciplinary perspective.

More severe atopic dermatitis and psoriasis are associated with a higher cumulative impact on quality of life, multimorbidity and healthcare costs. Proactive, early intervention in those most at risk of severe disease may reduce this cumulative burden and modify the disease trajectory to limit progression. The lack of reliable biomarkers for this at-risk group represents a barrier to such a paradigm shift in practice. To expedite discovery and validation, the BIOMarkers in Atopic Dermatitis and Psoriasis (BIOMAP) consortium (a large-scale European, interdisciplinary research initiative) has curated clinical and molecular data across diverse study designs and sources including cross-sectional and cohort studies (small-scale studies through to large multicentre registries), clinical trials, electronic health records and large-scale population-based biobanks. We map all dataset disease severity instruments and measures to three key domains (symptoms, inflammatory activity and disease course), and describe important codependencies and relationships across variables and domains. We prioritize definitions for more severe disease with reference to international consensus, reference standards and/or expert opinion. Key factors to consider when analysing datasets across these diverse study types include explicit early consideration of biomarker purpose and clinical context, candidate biomarkers associated with disease severity at a particular point in time and over time and how they are related, taking the stage of biomarker development into account when selecting disease severity measures for analyses, and validating biomarker associations with disease severity outcomes using both physician- and patient-reported measures and across domains. The outputs from this exercise will ensure coherence and focus across the BIOMAP consortium so that mechanistic insights and biomarkers are clinically relevant, patient-centric and more generalizable to current and future research efforts.

Atopic dermatitis (AD), and psoriasis are long-term skin conditions that can significantly affect people's lives, especially when symptoms are severe. Approximately 10% of adults and 20% of children are affected by AD, while psoriasis affects around 5% of people in the UK. Both conditions are associated with debilitating physical symptoms (such as itch) and have been linked to depression and anxiety. Biomarkers are naturally occurring chemicals in the human body and have potential to enhance the longer-term management of AD and psoriasis. Currently, there are no routinely used biomarkers that can identify people who experience or will go on to develop severe AD and psoriasis. For this reason, research is under way to understand which biomarkers are linked to severity. In this study, a multidisciplinary team of skin researchers from across Europe, along with patient groups, discussed the complexities of studying severity-related biomarkers. We identified a number of severity measurement approaches and there were recommendations for future biomarker research, including (i) considering multiple measures as no single measure can encompass all aspects of severity, (ii) exploring severity measures recorded by both healthcare professionals and patients, as each may capture different aspects, and (iii) accounting for influencing factors, such as different treatment approaches, that may impact AD and psoriasis severity, which make it challenging to compare findings across studies. Overall, we anticipate that the insights gained from these discussions will increase the likelihood of biomarkers being effectively applied in real-world settings, to ultimately improve outcomes for people with AD and psoriasis.

Disagreement concerning atopic dermatitis subtypes between an English prospective cohort (ALSPAC) and linked electronic health records.

BACKGROUND: Subtypes of atopic dermatitis (AD) have been derived from the Avon Longitudinal Study of Parents and Children (ALSPAC) based on presence and severity of symptoms reported in questionnaires (Severe-Frequent, Moderate-Frequent, Moderate-Declining, Mild-Intermittent, Unaffected/Rare). Good agreement between ALSPAC and linked electronic health records (EHRs) would increase trust in the clinical validity of these subtypes and allow inferring subtypes from EHRs alone, which would enable their study in large primary care databases. OBJECTIVES: 1. Explore if presence and number of AD records in EHRs agrees with AD symptom and severity reports from ALSPAC; 2. Explore if EHRs agree with ALSPAC-derived AD subtypes; 3. Construct models to classify ALSPAC-derived AD subtype using EHRs. METHODS: We used data from the ALSPAC prospective cohort study from 11 timepoints until age 14 years (1991-2008), linked to local general practice EHRs. We assessed how far ALSPAC questionnaire responses and derived subtypes agreed with AD as established in EHRs using different AD definitions (e.g., diagnosis and/or prescription) and other AD-related records. We classified AD subtypes using EHRs, fitting multinomial logistic regression models tuning hyperparameters and evaluating performance in the testing set (ROC AUC, accuracy, sensitivity, and specificity). RESULTS: 8,828 individuals out of a total 13,898 had both been assigned an AD subtype and had linked EHRs. The number of AD-related codes in EHRs generally increased with severity of AD subtype, however not all with the Severe-Frequent subtypes had AD in EHRs, and many with the Unaffected/Rare subtype did have AD in EHRs. When predicting ALSPAC AD subtype using EHRs, the best tuned model had ROC AUC of 0.65, sensitivity of 0.29 and specificity of 0.83 (both macro averaged); when different sets of predictors were used, individuals with missing EHR coverage excluded, and subtypes combined, sensitivity was not considerably improved. CONCLUSIONS: ALSPAC and EHRs disagreed not just on AD subtypes, but also on whether children had AD or not. Researchers should be aware that individuals considered as having AD in one source may not be considered as having AD in another.

Characteristics associated with poor COVID-19 outcomes in people with psoriasis, psoriatic arthritis and axial spondyloarthritis: data from the COVID-19 PsoProtect and Global Rheumatology Alliance physician-reported registries.

OBJECTIVES: To investigate factors associated with severe COVID-19 in people with psoriasis (PsO), psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA). METHODS: Demographic data, clinical characteristics and COVID-19 outcome severity of adults with PsO, PsA and axSpA were obtained from two international physician-reported registries. A three-point ordinal COVID-19 severity scale was defined: no hospitalisation, hospitalisation (and no death) and death. ORs were estimated using multivariable ordinal logistic regression. RESULTS: Of 5045 cases, 18.3% had PsO, 45.5% PsA and 36.3% axSpA. Most (83.6%) were not hospitalised, 14.6% were hospitalised and 1.8% died. Older age was non-linearly associated with COVID-19 severity. Male sex (OR 1.54, 95% CI 1.30 to 1.83), cardiovascular, respiratory, renal, metabolic and cancer comorbidities (ORs 1.25-2.89), moderate/high disease activity and/or glucocorticoid use (ORs 1.39-2.23, vs remission/low disease activity and no glucocorticoids) were associated with increased odds of severe COVID-19. Later pandemic time periods (ORs 0.42-0.52, vs until 15 June 2020), PsO (OR 0.49, 95% CI 0.37 to 0.65, vs PsA) and baseline exposure to TNFi, IL17i and IL-23i/IL-12+23i (OR 0.57, 95% CI 0.44 to 0.73; OR 0.62, 95% CI 0.45 to 0.87; OR 0.67, 95% CI 0.45 to 0.98; respectively; vs no disease-modifying antirheumatic drug) were associated with reduced odds of severe COVID-19. CONCLUSION: Older age, male sex, comorbidity burden, higher disease activity and glucocorticoid intake were associated with more severe COVID-19. Later pandemic time periods, PsO and exposure to TNFi, IL17i and IL-23i/IL-12+23i were associated with less severe COVID-19. These findings will enable risk stratification and inform management decisions for patients with PsO, PsA and axSpA during COVID-19 waves or similar future respiratory pandemics.

Nonadherence to systemic immune-modifying therapy in people with psoriasis during the COVID-19 pandemic: findings from a global cross-sectional survey.

BACKGROUND: Nonadherence to immune-modifying therapy is a complex behaviour which, before the COVID-19 pandemic, was shown to be associated with mental health disorders in people with immune-mediated diseases. The COVID-19 pandemic has led to a rise in the global prevalence of anxiety and depression, and limited data exist on the association between mental health and nonadherence to immune-modifying therapy during the pandemic. OBJECTIVES: To assess the extent of and reasons underlying nonadherence to systemic immune-modifying therapy during the COVID-19 pandemic in individuals with psoriasis, and the association between mental health and nonadherence. METHODS: Online self-report surveys (PsoProtectMe), including validated screens for anxiety and depression, were completed globally during the first year of the pandemic. We assessed the association between anxiety or depression and nonadherence to systemic immune-modifying therapy using binomial logistic regression, adjusting for potential cofounders (age, sex, ethnicity, comorbidity) and country of residence. RESULTS: Of 3980 participants from 77 countries, 1611 (40.5%) were prescribed a systemic immune-modifying therapy. Of these, 408 (25.3%) reported nonadherence during the pandemic, most commonly due to concerns about their immunity. In the unadjusted model, a positive anxiety screen was associated with nonadherence to systemic immune-modifying therapy [odds ratio (OR) 1.37, 95% confidence interval (CI) 1.07-1.76]. Specifically, anxiety was associated with nonadherence to targeted therapy (OR 1.41, 95% CI 1.01-1.96) but not standard systemic therapy (OR 1.16, 95% CI 0.81-1.67). In the adjusted model, although the directions of the effects remained, anxiety was not significantly associated with nonadherence to overall systemic (OR 1.20, 95% CI 0.92-1.56) or targeted (OR 1.33, 95% CI 0.94-1.89) immune-modifying therapy. A positive depression screen was not strongly associated with nonadherence to systemic immune-modifying therapy in the unadjusted (OR 1.22, 95% CI 0.94-1.57) or adjusted models (OR 1.14, 95% CI 0.87-1.49). CONCLUSIONS: These data indicate substantial nonadherence to immune-modifying therapy in people with psoriasis during the pandemic, with attenuation of the association with mental health after adjusting for confounders. Future research in larger populations should further explore pandemic-specific drivers of treatment nonadherence. Clear communication of the reassuring findings from population-based research regarding immune-modifying therapy-associated adverse COVID-19 risks to people with psoriasis is essential, to optimize adherence and disease outcomes.

Biomarkers of disease progression in people with psoriasis: a scoping review.

BACKGROUND: Identification of those at risk of more severe psoriasis and/or associated morbidities offers opportunity for early intervention, reduced disease burden and more cost-effective healthcare. Prognostic biomarkers of disease progression have thus been the focus of intense research, but none are part of routine practice. OBJECTIVES: To identify and catalogue candidate biomarkers of disease progression in psoriasis for the translational research community. METHODS: A systematic search of CENTRAL, Embase, LILACS and MEDLINE was performed for relevant articles published between 1990 and December 2021. Eligibility criteria were studies involving patients with psoriasis (any age, n ≥ 50) reporting biomarkers associated with disease progression. The main outcomes were any measure of skin severity or any prespecified psoriasis comorbidity. Data were extracted by one reviewer and checked by a second; studies meeting minimal quality criteria (longitudinal design and/or use of methods to control for confounding) were formally assessed for bias. Candidate biomarkers were identified by an expert multistakeholder group using a majority voting consensus exercise, and mapped to relevant cellular and molecular pathways. RESULTS: Of 181 included studies, most investigated genomic or proteomic biomarkers associated with disease severity (n = 145) or psoriatic arthritis (n = 30). Methodological and reporting limitations compromised interpretation of findings, most notably a lack of longitudinal studies, and inadequate control for key prognostic factors. The following candidate biomarkers with future potential utility were identified for predicting disease severity: LCE3D, interleukin (IL)23R, IL23A, NFKBIL1 loci, HLA-C*06:02 (genomic), IL-17A, IgG aHDL, GlycA, I-FABP and kallikrein 8 (proteomic), tyramine (metabolomic); psoriatic arthritis: HLA-C*06:02, HLA-B*27, HLA-B*38, HLA-B*08, and variation at the IL23R and IL13 loci (genomic); IL-17A, CXCL10, Mac-2 binding protein, integrin b5, matrix metalloproteinase-3 and macrophage-colony stimulating factor (proteomic) and tyramine and mucic acid (metabolomic); and type 2 diabetes mellitus: variation in IL12B and IL23R loci (genomic). No biomarkers were supported by sufficient evidence for clinical use without further validation. CONCLUSIONS: This review provides a comprehensive catalogue of investigated biomarkers of disease progression in psoriasis. Future studies must address the common methodological limitations identified herein to expedite discovery and validation of biomarkers for clinical use. What is already known about this topic? The current treatment paradigm in psoriasis is reactive. There is a need to develop effective risk-stratified management approaches that can proactively attenuate the substantial burden of disease. Prognostic biomarkers of disease progression have therefore been the focus of intense research. What does this study add? This review is the first to scope, collate and catalogue research investigating biomarkers of disease progression in psoriasis. The review identifies potentially promising candidate biomarkers for further investigation and highlights common important limitations that should be considered when designing and conducting future studies in this area.

Biomarkers of systemic treatment response in people with psoriasis: a scoping review.

BACKGROUND: Responses to the systemic treatments commonly used to treat psoriasis vary. Biomarkers that accurately predict effectiveness and safety would enable targeted treatment selection, improved patient outcomes and more cost-effective healthcare. OBJECTIVES: To perform a scoping review to identify and catalogue candidate biomarkers of systemic treatment response in psoriasis for the translational research community. METHODS: A systematic search of CENTRAL, Embase, LILACS and MEDLINE was performed for relevant articles published between 1990 and December 2021. Eligibility criteria were studies involving patients with psoriasis (any age, n ≥ 50) reporting biomarkers associated with systemic treatment response. The main outcomes were any measure of systemic treatment efficacy or safety. Data were extracted by one reviewer and checked by a second; studies meeting minimal quality criteria (use of methods to control for confounding) were formally assessed for bias. Candidate biomarkers were identified by an expert multistakeholder group using a majority voting consensus exercise and mapped to relevant cellular and molecular pathways. RESULTS: Of 71 included studies (67 studying effectiveness outcomes and eight safety outcomes; four studied both), most reported genomic or proteomic biomarkers associated with response to biologics (48 studies). Methodological or reporting limitations frequently compromised the interpretation of findings, including inadequate control for key covariates, lack of adjustment for multiple testing, and selective outcome reporting. We identified candidate biomarkers of efficacy to tumour necrosis factor inhibitors [variation in CARD14, CDKAL1, IL1B, IL12B and IL17RA loci, and lipopolysaccharide-induced phosphorylation of nuclear factor (NF)-κB in type 2 dendritic cells] and ustekinumab (HLA-C*06:02 and variation in an IL1B locus). None were supported by sufficient evidence for clinical use without further validation studies. Candidate biomarkers were found to be involved in the immune cellular crosstalk implicated in psoriasis pathogenesis, most notably antigen presentation, T helper (Th)17 cell differentiation, positive regulation of NF-κB, and Th17 cell activation. CONCLUSIONS: This comprehensive catalogue provides a key resource for researchers and reveals a diverse range of biomarker types and outcomes in the included studies. The candidate biomarkers identified require further evaluation in methodologically robust studies to establish potential clinical utility. Future studies should aim to address the common methodological limitations highlighted in this review to expedite discovery and validation of biomarkers for clinical use. What is already known about this topic? Responses to the systemic treatments commonly used to treat psoriasis vary. Biomarkers that accurately predict effectiveness and safety would enable targeted treatment selection, improved patient outcomes and more cost-effective healthcare. What does this study add? This review provides a comprehensive catalogue of investigated biomarkers of systemic treatment response in psoriasis. A diverse range of biomarker types and outcomes was found in the included studies, serving as a key resource for the translational research community.

Factors associated with adverse COVID-19 outcomes in patients with psoriasis-insights from a global registry-based study.

BACKGROUND: The multimorbid burden and use of systemic immunosuppressants in people with psoriasis may confer greater risk of adverse outcomes of coronavirus disease 2019 (COVID-19), but the data are limited. OBJECTIVE: Our aim was to characterize the course of COVID-19 in patients with psoriasis and identify factors associated with hospitalization. METHODS: Clinicians reported patients with psoriasis with confirmed/suspected COVID-19 via an international registry, Psoriasis Patient Registry for Outcomes, Therapy and Epidemiology of COVID-19 Infection. Multiple logistic regression was used to assess the association between clinical and/or demographic characteristics and hospitalization. A separate patient-facing registry characterized risk-mitigating behaviors. RESULTS: Of 374 clinician-reported patients from 25 countries, 71% were receiving a biologic, 18% were receiving a nonbiologic, and 10% were not receiving any systemic treatment for psoriasis. In all, 348 patients (93%) were fully recovered from COVID-19, 77 (21%) were hospitalized, and 9 (2%) died. Increased hospitalization risk was associated with older age (multivariable-adjusted odds ratio [OR] = 1.59 per 10 years; 95% CI = 1.19-2.13), male sex (OR = 2.51; 95% CI = 1.23-5.12), nonwhite ethnicity (OR = 3.15; 95% CI = 1.24-8.03), and comorbid chronic lung disease (OR = 3.87; 95% CI = 1.52-9.83). Hospitalization was more frequent in patients using nonbiologic systemic therapy than in those using biologics (OR = 2.84; 95% CI = 1.31-6.18). No significant differences were found between classes of biologics. Independent patient-reported data (n = 1626 across 48 countries) suggested lower levels of social isolation in individuals receiving nonbiologic systemic therapy than in those receiving biologics (OR = 0.68; 95% CI = 0.50-0.94). CONCLUSION: In this international case series of patients with moderate-to-severe psoriasis, biologic use was associated with lower risk of COVID-19-related hospitalization than with use of nonbiologic systemic therapies; however, further investigation is warranted on account of potential selection bias and unmeasured confounding. Established risk factors (being older, being male, being of nonwhite ethnicity, and having comorbidities) were associated with higher hospitalization rates.

Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk.

Many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenomenon might be explained by shared genetic risk factors. We used data from large genome-wide association studies to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified five variants with potentially pleiotropic effects. While colocalization analyses were inconclusive, functional characterization of these variants provided the strongest evidence for a model in which genetic variation at rs1734907 modulates risk of schizophrenia and Crohn's disease via altered methylation and expression of EPHB4-a gene whose protein product guides the migration of neuronal axons in the brain and the migration of lymphocytes towards infected cells in the immune system. Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using cross-trait LD score regression. Of the 11 immune diseases with available genome-wide summary statistics, we observed genetic correlation between six immune diseases and schizophrenia: inflammatory bowel disease (rg = 0.12 ± 0.03, P = 2.49 × 10-4), Crohn's disease (rg = 0.097 ± 0.06, P = 3.27 × 10-3), ulcerative colitis (rg = 0.11 ± 0.04, P = 4.05 × 10-3), primary biliary cirrhosis (rg = 0.13 ± 0.05, P = 3.98 × 10-3), psoriasis (rg = 0.18 ± 0.07, P = 7.78 × 10-3) and systemic lupus erythematosus (rg = 0.13 ± 0.05, P = 3.76 × 10-3). With the exception of ulcerative colitis, the degree and direction of these genetic correlations were consistent with the expected phenotypic correlation based on epidemiological data. Our findings suggest shared genetic risk factors contribute to the epidemiological association of certain immune diseases and schizophrenia.

Protein-coding variants contribute to the risk of atopic dermatitis and skin-specific gene expression.

BACKGROUND: Fifteen percent of atopic dermatitis (AD) liability-scale heritability could be attributed to 31 susceptibility loci identified by using genome-wide association studies, with only 3 of them (IL13, IL-6 receptor [IL6R], and filaggrin [FLG]) resolved to protein-coding variants. OBJECTIVE: We examined whether a significant portion of unexplained AD heritability is further explained by low-frequency and rare variants in the gene-coding sequence. METHODS: We evaluated common, low-frequency, and rare protein-coding variants using exome chip and replication genotype data of 15,574 patients and 377,839 control subjects combined with whole-transcriptome data on lesional, nonlesional, and healthy skin samples of 27 patients and 38 control subjects. RESULTS: An additional 12.56% (SE, 0.74%) of AD heritability is explained by rare protein-coding variation. We identified docking protein 2 (DOK2) and CD200 receptor 1 (CD200R1) as novel genome-wide significant susceptibility genes. Rare coding variants associated with AD are further enriched in 5 genes (IL-4 receptor [IL4R], IL13, Janus kinase 1 [JAK1], JAK2, and tyrosine kinase 2 [TYK2]) of the IL13 pathway, all of which are targets for novel systemic AD therapeutics. Multiomics-based network and RNA sequencing analysis revealed DOK2 as a central hub interacting with, among others, CD200R1, IL6R, and signal transducer and activator of transcription 3 (STAT3). Multitissue gene expression profile analysis for 53 tissue types from the Genotype-Tissue Expression project showed that disease-associated protein-coding variants exert their greatest effect in skin tissues. CONCLUSION: Our discoveries highlight a major role of rare coding variants in AD acting independently of common variants. Further extensive functional studies are required to detect all potential causal variants and to specify the contribution of the novel susceptibility genes DOK2 and CD200R1 to overall disease susceptibility.

Psoriasis and Genetics.

Psoriasis is a common inflammatory skin disease caused by the interplay between multiple genetic and environmental risk factors. This review summarises recent progress in elucidating the genetic basis of psoriasis, particularly through large genome-wide association studies. We illustrate the power of genetic analyses for disease stratification. Psoriasis can be stratified by phenotype (common plaque versus rare pustular variants), or by outcome (prognosis, comorbidities, response to treatment); recent progress has been made in delineating the genetic contribution in each of these areas. We also highlight how genetic data can directly inform the development of effective psoriasis treatments.

HLA-C*06:02 genotype is a predictive biomarker of biologic treatment response in psoriasis.

BACKGROUND: Biologic therapies can be highly effective for the treatment of severe psoriasis, but response for individual patients can vary according to drug. Predictive biomarkers to guide treatment selection could improve patient outcomes and treatment cost-effectiveness. OBJECTIVE: We sought to test whether HLA-C*06:02, the primary genetic susceptibility allele for psoriasis, predisposes patients to respond differently to the 2 most commonly prescribed biologics for psoriasis: adalimumab (anti-TNF-α) and ustekinumab (anti-IL-12/23). METHODS: This study uses a national psoriasis registry that includes longitudinal treatment and response observations and detailed clinical data. HLA alleles were imputed from genome-wide genotype data for 1326 patients for whom 90% reduction in Psoriasis Area and Severity Index score (PASI90) response status was observed after 3, 6, or 12 months of treatment. We developed regression models of PASI90 response, examining the interaction between HLA-C*06:02 and drug type (adalimumab or ustekinumab) while accounting for potentially confounding clinical variables. RESULTS: HLA-C*06:02-negative patients were significantly more likely to respond to adalimumab than ustekinumab at all time points (most strongly at 6 months: odds ratio [OR], 2.95; P = 5.85 × 10-7), and the difference was greater in HLA-C*06:02-negative patients with psoriatic arthritis (OR, 5.98; P = 6.89 × 10-5). Biologic-naive patients who were HLA-C*06:02 positive and psoriatic arthritis negative demonstrated significantly poorer response to adalimumab at 12 months (OR, 0.31; P = 3.42 × 10-4). Results from HLA-wide analyses were consistent with HLA-C*06:02 itself being the primary effect allele. We found no evidence for genetic interaction between HLA-C*06:02 and ERAP1. CONCLUSION: This large observational study suggests that reference to HLA-C*06:02 status could offer substantial clinical benefit when selecting treatments for severe psoriasis.

Clinical and genetic differences between pustular psoriasis subtypes.

BACKGROUND: The term pustular psoriasis indicates a group of severe skin disorders characterized by eruptions of neutrophil-filled pustules. The disease, which often manifests with concurrent psoriasis vulgaris, can have an acute systemic (generalized pustular psoriasis [GPP]) or chronic localized (palmoplantar pustulosis [PPP] and acrodermatitis continua of Hallopeau [ACH]) presentation. Although mutations have been uncovered in IL36RN and AP1S3, the rarity of the disease has hindered the study of genotype-phenotype correlations. OBJECTIVE: We sought to characterize the clinical and genetic features of pustular psoriasis through the analysis of an extended patient cohort. METHODS: We ascertained a data set of unprecedented size, including 863 unrelated patients (251 with GPP, 560 with PPP, 28 with ACH, and 24 with multiple diagnoses). We undertook mutation screening in 473 cases. RESULTS: Psoriasis vulgaris concurrence was lowest in PPP (15.8% vs 54.4% in GPP and 46.2% in ACH, P < .0005 for both), whereas the mean age of onset was earliest in GPP (31.0 vs 43.7 years in PPP and 51.8 years in ACH, P < .0001 for both). The percentage of female patients was greater in PPP (77.0%) than in GPP (62.5%; P = 5.8 × 10-5). The same applied to the prevalence of smokers (79.8% vs 28.3%, P < 10-15). Although AP1S3 alleles had similar frequency (0.03-0.05) across disease subtypes, IL36RN mutations were less common in patients with PPP (0.03) than in those with GPP (0.19) and ACH (0.16; P = 1.9 × 10-14 and .002, respectively). Importantly, IL36RN disease alleles had a dose-dependent effect on age of onset in all forms of pustular psoriasis (P = .003). CONCLUSIONS: The analysis of an unparalleled resource revealed key clinical and genetic differences between patients with PPP and those with GPP.

Exome-wide association study reveals novel psoriasis susceptibility locus at TNFSF15 and rare protective alleles in genes contributing to type I IFN signalling.

Psoriasis is a common inflammatory skin disorder for which multiple genetic susceptibility loci have been identified, but few resolved to specific functional variants. In this study, we sought to identify common and rare psoriasis-associated gene-centric variation. Using exome arrays we genotyped four independent cohorts, totalling 11 861 psoriasis cases and 28 610 controls, aggregating the dataset through statistical meta-analysis. Single variant analysis detected a previously unreported risk locus at TNFSF15 (rs6478108; P = 1.50 × 10-8, OR = 1.10), and association of common protein-altering variants at 11 loci previously implicated in psoriasis susceptibility. We validate previous reports of protective low-frequency protein-altering variants within IFIH1 (encoding an innate antiviral receptor) and TYK2 (encoding a Janus kinase), in each case establishing a further series of protective rare variants (minor allele frequency < 0.01) via gene-wide aggregation testing (IFIH1: pburden = 2.53 × 10-7, OR = 0.707; TYK2: pburden = 6.17 × 10-4, OR = 0.744). Both genes play significant roles in type I interferon (IFN) production and signalling. Several of the protective rare and low-frequency variants in IFIH1 and TYK2 disrupt conserved protein domains, highlighting potential mechanisms through which their effect may be exerted.

Text-mined phenotype annotation and vector-based similarity to improve identification of similar phenotypes and causative genes in monogenic disease patients.

The genetic diagnosis of rare monogenic diseases using exome/genome sequencing requires the true causal variant(s) to be identified from tens of thousands of observed variants. Typically a virtual gene panel approach is taken whereby only variants in genes known to cause phenotypes resembling the patient under investigation are considered. With the number of known monogenic gene-disease pairs exceeding 5,000, manual curation of personalized virtual panels using exhaustive knowledge of the genetic basis of the human monogenic phenotypic spectrum is challenging. We present improved probabilistic methods for estimating phenotypic similarity based on Human Phenotype Ontology annotation. A limitation of existing methods for evaluating a disease's similarity to a reference set is that reference diseases are typically represented as a series of binary (present/absent) observations of phenotypic terms. We evaluate a quantified disease reference set, using term frequency in phenotypic text descriptions to approximate term relevance. We demonstrate an improved ability to identify related diseases through the use of a quantified reference set, and that vector space similarity measures perform better than established information content-based measures. These improvements enable the generation of bespoke virtual gene panels, facilitating more accurate and efficient interpretation of genomic variant profiles from individuals with rare Mendelian disorders. These methods are available online at https://atlas.genetics.kcl.ac.uk/~jake/cgi-bin/patient_sim.py.