Atopic dermatitis (AD) is a common inflammatory skin condition and prior genome-wide association studies (GWAS) have identified 71 associated loci. In the current study we conducted the largest AD GWAS to date (discovery N = 1,086,394, replication N = 3,604,027), combining previously reported cohorts with additional available data. We identified 81 loci (29 novel) in the European-only analysis (which all replicated in a separate European analysis) and 10 additional loci in the multi-ancestry analysis (3 novel). Eight variants from the multi-ancestry analysis replicated in at least one of the populations tested (European, Latino or African), while two may be specific to individuals of Japanese ancestry. AD loci showed enrichment for DNAse I hypersensitivity and eQTL associations in blood. At each locus we prioritised candidate genes by integrating multi-omic data. The implicated genes are predominantly in immune pathways of relevance to atopic inflammation and some offer drug repurposing opportunities.

Gene prioritization at human GWAS loci is challenging due to linkage-disequilibrium and long-range gene regulatory mechanisms. However, identifying the causal gene is crucial to enable identification of potential drug targets and better understanding of molecular mechanisms. Mapping GWAS traits to known phenotypically relevant Mendelian disease genes near a locus is a promising approach to gene prioritization. We present MendelVar, a comprehensive tool that integrates knowledge from four databases on Mendelian disease genes with enrichment testing for a range of associated functional annotations such as Human Phenotype Ontology, Disease Ontology and variants from ClinVar. This open web-based platform enables users to strengthen the case for causal importance of phenotypically matched candidate genes at GWAS loci. We demonstrate the use of MendelVar in post-GWAS gene annotation for type 1 diabetes, type 2 diabetes, blood lipids and atopic dermatitis. MendelVar is freely available at https://mendelvar.mrcieu.ac.uk Supplementary data are available at Bioinformatics online.

GWASs for atopic dermatitis have identified 25 reproducible loci. We attempt to prioritize the candidate causal genes at these loci using extensive molecular resources compiled into a bioinformatics pipeline. We identified a list of 103 molecular resources for atopic dermatitis etiology, including expression, protein, and DNA methylation quantitative trait loci datasets in the skin or immune-relevant tissues, which were tested for overlap with GWAS signals. This was combined with functional annotation using regulatory variant prediction and features such as promoter‒enhancer interactions, expression studies, and variant fine mapping. For each gene at each locus, we condensed the evidence into a prioritization score. Across the investigated loci, we detected significant enrichment of genes with adaptive immune regulatory function and epidermal barrier formation among the top-prioritized genes. At eight loci, we were able to prioritize a single candidate gene (IL6R, ADO, PRR5L, IL7R, ETS1, INPP5D, MDM1, TRAF3). In addition, at 6 of the 25 loci, our analysis prioritizes less familiar candidates (SLC22A5, IL2RA, MDM1, DEXI, ADO, STMN3). Our analysis provides support for previously implicated genes at several atopic dermatitis GWAS loci as well as evidence for plausible additional candidates at others, which may represent potential targets for drug discovery.

Background & Aims: Previous results from observational, interventional studies and in vitro experiments suggest that certain micronutrients possess anti-viral and immunomodulatory activities. In particular, it has been hypothesized that zinc, selenium, copper and vitamin K₁ have strong potential for prophylaxis and treatment of COVID-19. We aimed to test whether genetically predicted Zn, Se, Cu or vitamin K₁ levels have a causal effect on COVID-19 related outcomes, including risk of infection, hospitalization and critical illness. Methods: We employed a two-sample Mendelian Randomization (MR) analysis. Our genetic variants derived from European-ancestry GWAS reflected circulating levels of Zn, Cu, Se in red blood cells as well as Se and vitamin K₁ in serum/plasma. For the COVID-19 outcome GWAS, we used infection, hospitalization or critical illness. Our inverse-variance weighted (IVW) MR analysis was complemented by sensitivity analyses including a more liberal selection of variants at a genome-wide sub-significant threshold, MR-Egger and weighted median/mode tests. Results: Circulating micronutrient levels show limited evidence of association with COVID-19 infection, with the odds ratio [OR] ranging from 0.97 (95% CI: 0.87–1.08, p-value = 0.55) for zinc to 1.07 (95% CI: 1.00–1.14, p-value = 0.06)—i.e., no beneficial effect for copper was observed per 1 SD increase in exposure. Similarly minimal evidence was obtained for the hospitalization and critical illness outcomes with OR from 0.98 (95% CI: 0.87–1.09, p-value = 0.66) for vitamin K₁ to 1.07 (95% CI: 0.88–1.29, p-value = 0.49) for copper, and from 0.93 (95% CI: 0.72–1.19, p-value = 0.55) for vitamin K₁ to 1.21 (95% CI: 0.79–1.86, p-value = 0.39) for zinc, respectively. Conclusions: This study does not provide evidence that supplementation with zinc, selenium, copper or vitamin K₁ can prevent SARS-CoV-2 infection, critical illness or hospitalization for COVID-19.

Objective To scope the potential for (semi)-automated triangulation of Mendelian randomisation (MR) and randomised controlled trials (RCTs) evidence since the two methods have distinct assumptions that make comparisons between their results invaluable. Methods We mined ClinicalTrials.Gov, PubMed and EpigraphDB databases and carried out a series of 26 manual literature comparisons among 54 MR and 77 RCT publications. Results We found that only 13% of completed RCTs identified in ClinicalTrials.Gov submitted their results to the database. Similarly low coverage was revealed for Semantic Medline (SemMedDB) semantic triples derived from MR and RCT publications –36% and 12%, respectively. Among intervention types that can be mimicked by MR, only trials of pharmaceutical interventions could be automatically matched to MR results due to insufficient annotation with Medical Subject Headings ontology. A manual survey of the literature highlighted the potential for triangulation across a number of exposure/outcome pairs if these challenges can be addressed. Conclusions We conclude that careful triangulation of MR with RCT evidence should involve consideration of similarity of phenotypes across study designs, intervention intensity and duration, study population demography and health status, comparator group, intervention goal and quality of evidence.

Society Impact Statement: Until the 1970s, the majority of commercial strawberry varieties produced a single bloom of flowers. However, continuously flowering, everbearing strawberries are now routinely cultivated and use is increasing. Indeed, introgression of the everbearing flowering trait can lead to economic benefits for growers through the production of a continual crop from the same plant. Genetically guided improvement has the power to streamline everbearing generation. As such, the genetic markers reported here can help to identify everbearing individuals at an early time point in the breeding process. Furthermore, these markers can help to improve the predictions of progeny segregation ratios.Summary: Previous work within the community led to the identification of a single dominant allele that controls the everbearing trait. However, frequent observations have indicated that crosses do not segregate in a Mendelian fashion, as would be expected for a trait controlled by a single dominant gene. Therefore, it was hypothesised that one or more unidentified epistatic alleles interact with the major gene. A genome‐wide association study (GWAS) was conducted on 587 June bearers and 207 everbearers to assess the genetic components associated with flowering habit. The segregation ratios of parental strawberry lines with known phenotypes were used to validate the identified alleles. Three loci including the known major FaPFRU locus and two epistatic modifiers were identified. These modifiers function as enhancers of the everbearing trait in individuals containing a single copy of the FaPFRU everbearing allele and appear to be functionally redundant. Principally, heterozygous individuals required the presence of two modifying alleles in order to allow expression of the everbearing trait. Inclusion of the epistatic alleles improved the prediction of everbearing segregation ratios; beyond that of a single allele model, however, a large proportion of the variation remained unexplained. Future work should identify the additional repressor and enhancer modifiers not identified here. Discovering the genetic components controlling the everbearing trait can enable genetic informed strawberry improvement.