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We performed genome-wide association studies of five gynecologic diseases using data of 46,837 subjects (5236 uterine fibroid, 645 endometriosis, 647 ovarian cancer (OC), 909 uterine endometrial cancer (UEC), and 538 uterine cervical cancer (UCC) cases allowing overlaps, and 39,556 shared female controls) from Biobank Japan Project. We used the populationspecific imputation reference panel (n = 3541), yielding 7,645,193 imputed variants. Analyses performed under logistic model, linear mixed model, and model incorporating correlations identified nine significant associations with three gynecologic diseases including four novel findings (rs79219469:

MicroRNAs (miRNAs) modulate the post-transcriptional regulation of target genes and are related to biology of complex human traits, but genetic landscape of miRNAs remains largely unknown. Given the strikingly tissue-specific miRNA expression profiles, we here expand a previous method to quantitatively evaluate enrichment of genome-wide association study (GWAS) signals on miRNA–target gene networks (MIGWAS) to further estimate tissue-specific enrichment. Our approach integrates tissue-specific expression profiles of miRNAs (∼1800 miRNAs in 179 cells) with GWAS to test whether polygenic signals enrich in miRNA–target gene networks and whether they fall within specific tissues. We applied MIGWAS to 49 GWASs (nTotal = 3 520 246), and successfully identified biologically relevant tissues. Further, MIGWAS could point miRNAs as candidate biomarkers of the trait. As an illustrative example, we performed differentially expressed miRNA analysis between rheumatoid arthritis (RA) patients and healthy controls (n = 63). We identified novel biomarker miRNAs (e.g. hsa-miR-762) by integrating differentially expressed miRNAs with MIGWAS results for RA, as well as novel associated loci with significant genetic risk (rs56656810 at MIR762 at 16q11; n = 91 482, P = 3.6 × 10−8). Our result highlighted that miRNA–target gene network contributes to human disease genetics in a cell type-specific manner, which could yield an efficient screening of miRNAs as promising biomarkers.

Background: Psoriasis is an immune-mediated skin disease for which the crosstalk between genetic and environmental factors is responsible. To date, no definitive diagnostic criteria for psoriasis yet, and specific biomarkers are required. Objective: We performed metabolome analysis to identify metabolite biomarkers of psoriasis and its subtypes such as psoriatic arthritis (PsA) and cutaneous psoriasis (PsC). Methods: We constructed metabolomics profiling of 130 plasma samples (42 PsA patients, 50 PsC patients, and 38 healthy controls) using a nontargeted metabolomics approach. Results: Psoriasis-control association tests showed that one metabolite (ethanolamine phosphate) was significantly increased in psoriasis samples than in the controls, whereas three metabolites decreased (false discovery rate [FDR] < 0.05; XA0019, nicotinic acid, and 20α-hydroxyprogesterone). In the association test between PsA and PsC, tyramine significantly increased in PsA than in PsC, whereas mucic acid decreased (FDR < 0.05). Molecular pathway analysis of the PsA-PsC association test identified enrichment of vitamin digestion and absorption pathway in PsC (P = 1.3 Â 10 À4 ). Correlation network analyses elucidated that a subnetwork centered on aspartate was constructed among the psoriasisassociated metabolites; meanwhile, the major subnetwork among metabolites with differences between PsA and PsC was primarily formed from saturated fatty acids. Conclusion: Our large-scale metabolome analysis highlights novel characteristics of plasma metabolites in psoriasis and the differences between PsA and PsC, which could be used as potential biomarkers of psoriasis and its clinical subtypes. These findings contribute to our understanding of psoriasis pathophysiology.

ObjectivesAutoimmune and allergic diseases are outcomes of the dysregulation of the immune system. Our study aimed to elucidate differences or shared components in genetic backgrounds between autoimmune and allergic diseases.MethodsWe estimated genetic correlation and performed multi-trait and cross-population genome-wide association study (GWAS) meta-analysis of six immune-related diseases: rheumatoid arthritis, Graves’ disease, type 1 diabetes for autoimmune diseases and asthma, atopic dermatitis and pollinosis for allergic diseases. By integrating large-scale biobank resources (Biobank Japan and UK biobank), our study included 105 721 cases and 433 663 controls. Newly identified variants were evaluated in 21 778 cases and 712 767 controls for two additional autoimmune diseases: psoriasis and systemic lupus erythematosus. We performed enrichment analyses of cell types and biological pathways to highlight shared and distinct perspectives.ResultsAutoimmune and allergic diseases were not only mutually classified based on genetic backgrounds but also they had multiple positive genetic correlations beyond the classifications. Multi-trait GWAS meta-analysis newly identified six allergic disease-associated loci. We identified four loci shared between the six autoimmune and allergic diseases (rs10803431 at PRDM2, OR=1.07, p=2.3×10−8, rs2053062 at G3BP1, OR=0.90, p=2.9×10−8, rs2210366 at HBS1L, OR=1.07, p=2.5×10−8 in Japanese and rs4529910 at POU2AF1, OR=0.96, p=1.9×10−10 across ancestries). Associations of rs10803431 and rs4529910 were confirmed at the two additional autoimmune diseases. Enrichment analysis demonstrated link to T cells, natural killer cells and various cytokine signals, including innate immune pathways.ConclusionOur multi-trait and cross-population study should elucidate complex pathogenesis shared components across autoimmune and allergic diseases.