BACKGROUND: Endometriosis is a chronic gynaecological disorder that affects 2–10% of women of reproductive age. The aetiology of endometriosis is largely under-explored, yet abnormalities in the immune system have been suggested to explain the origin of ectopic endometrial tissues, and an association between endometriosis and autoimmune diseases has been proposed. Evaluation of current evidence investigating the association between endometriosis and autoimmune diseases from population-based studies will facilitate our understanding of the causes and consequences of endometriosis and provide a reference for better healthcare practices population-wide.OBJECTIVE AND RATIONALE: The aim of this study was to systematically review the literature on population-based studies investigating an association between endometriosis and autoimmune diseases and to conduct a meta-analysis of combinable results to investigate the extent and robustness of evidence.SEARCH METHODS: Four electronic databases were searched (MEDLINE, Embase, Web of Science, and CINAHL) from each database inception date until 7 April 2018. Search terms included a combination of database-specific controlled vocabulary terms and free-text terms relating to ‘endometriosis’ and ‘autoimmune diseases’. Study inclusion criteria focused on peer-reviewed published articles that reported an association between endometriosis and autoimmune diseases, excluding case reports/series, review papers, meta-analyses, organizational guidelines, editorial letters, expert opinions, and conference abstracts. Quality assessment of included studies was performed based on GRADE criteria. Key information of eligible studies was abstracted into a standard form. Meta-analysis was performed for autoimmune diseases with combinable study results from at least three studies investigating an association with endometriosis. For cross-sectional studies and case–control studies, raw data from each study were documented to calculate a Mantel–Haenszel odds ratio with 95% CIs. For cohort studies, an inverse variance probability weighted model was used to pool study results to calculate a rate ratio (a hazard ratio or a standardized incidence rate) with 95% CIs.OUTCOMES: A total of 26 published population-based cross-sectional, case–control, and cohort studies that investigated the association between endometriosis and autoimmune diseases met all eligible criteria and were included in the review. The studies quantified an association between endometriosis and several autoimmune diseases, including systemic lupus erythematosus (SLE), Sjögren’s syndrome (SS), rheumatoid arthritis (RA), autoimmune thyroid disorder, coeliac disease (CLD), multiple sclerosis (MS), inflammatory bowel disease (IBD), and Addison’s disease. However, the quality of the evidence was generally poor due to the high risk of bias in the majority of the chosen study designs and statistical analyses. Only 5 of the 26 studies could provide high-quality evidence, and among these, 4 supported a statistically significant association between endometriosis and at least 1 autoimmune disease: SLE, SS, RA, CLD, MS, or IBD.WIDER IMPLICATIONS: The observed associations between endometriosis and autoimmune diseases suggest that clinicians need to be aware of the potential coexistence of endometriosis and autoimmune diseases when either is diagnosed. Scientists interested in research studies on endometriosis or autoimmune diseases should consider the likelihood of comorbidity when studying these two types of health conditions. Well-designed large prospective cohort studies with confounding control and mediation quantification, as well as genetic and biological studies, are needed to generate further insights into whether endometriosis is a risk factor for, or a consequence of, autoimmune diseases, and whether these two types of disorders share pathophysiological mechanisms even if they arise independently. Such insights may offer opportunities for the development of novel non-hormonal medications such as immuno-modulators or repurposing of existing immunomodulatory therapies for endometriosis.

Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection¹. Drug targets with genetic support are more likely to be therapeutically valid^2,3, but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging^4,5,6. Here, we show that integration of functional genomic and immune-related annotations, together with knowledge of network connectivity, maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (the priority index) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets and allows for determination of target-level trait relationships. The priority index is an open-access, scalable system accelerating early-stage drug target selection for immune-mediated disease.

Biological interpretation of genomic summary data such as those resulting from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is one of the major bottlenecks in medical genomics research, calling for efficient and integrative tools to resolve this problem. We introduce eXploring Genomic Relations (XGR), an open source tool designed for enhanced interpretation of genomic summary data enabling downstream knowledge discovery. Targeting users of varying computational skills, XGR utilises prior biological knowledge and relationships in a highly integrated but easily accessible way to make user-input genomic summary datasets more interpretable. We show how by incorporating ontology, annotation, and systems biology network-driven approaches, XGR generates more informative results than conventional analyses. We apply XGR to GWAS and eQTL summary data to explore the genomic landscape of the activated innate immune response and common immunological diseases. We provide genomic evidence for a disease taxonomy supporting the concept of a disease spectrum from autoimmune to autoinflammatory disorders. We also show how XGR can define SNP-modulated gene networks and pathways that are shared and distinct between diseases, how it achieves functional, phenotypic and epigenomic annotations of genes and variants, and how it enables exploring annotation-based relationships between genetic variants. XGR provides a single integrated solution to enhance interpretation of genomic summary data for downstream biological discovery. XGR is released as both an R package and a web-app, freely available at http://galahad.well.ox.ac.uk/XGR .

The SUPERFAMILY resource provides protein domain assignments at the structural classification of protein (SCOP) superfamily level for over 1400 completely sequenced genomes, over 120 metagenomes and other gene collections such as UniProt. All models and assignments are available to browse and download at http://supfam.org . A new hidden Markov model library based on SCOP 1.75 has been created and a previously ignored class of SCOP, coiled coils, is now included. Our scoring component now uses HMMER3, which is in orders of magnitude faster and produces superior results. A cloud-based pipeline was implemented and is publicly available at Amazon web services elastic computer cloud. The SUPERFAMILY reference tree of life has been improved allowing the user to highlight a chosen superfamily, family or domain architecture on the tree of life. The most significant advance in SUPERFAMILY is that now it contains a domain-based gene ontology (GO) at the superfamily and family levels. A new methodology was developed to ensure a high quality GO annotation. The new methodology is general purpose and has been used to produce domain-based phenotypic ontologies in addition to GO.

We present ‘dcGO’ (http://supfam.org/SUPERFAMILY/dcGO), a comprehensive ontology database for protein domains. Domains are often the functional units of proteins, thus instead of associating ontological terms only with full-length proteins, it sometimes makes more sense to associate terms with individual domains. Domain-centric GO, ‘dcGO’, provides associations between ontological terms and protein domains at the superfamily and family levels. Some functional units consist of more than one domain acting together or acting at an interface between domains; therefore, ontological terms associated with pairs of domains, triplets and longer supra-domains are also provided. At the time of writing the ontologies in dcGO include the Gene Ontology (GO); Enzyme Commission (EC) numbers; pathways from UniPathway; human phenotype ontology and phenotype ontologies from five model organisms, including plants; anatomy ontologies from three organisms; human disease ontology and drugs from DrugBank. All ontological terms have probabilistic scores for their associations. In addition to associations to domains and supra-domains, the ontological terms have been transferred to proteins, through homology, providing annotations of >80 million sequences covering 2414 complete genomes, hundreds of meta-genomes, thousands of viruses and so forth. The dcGO database is updated fortnightly, and its website provides downloads, search, browse, phylogenetic context and other data-mining facilities.