ObjectiveSystemic lupus erythematosus (SLE), an autoimmune disorder, has been associated with nearly 100 susceptibility loci. Nevertheless, these loci only partially explain SLE heritability and their putative causal variants are rarely prioritised, which make challenging to elucidate disease biology. To detect new SLE loci and causal variants, we performed the largest genome-wide meta-analysis for SLE in East Asian populations.MethodsWe newly genotyped 10 029 SLE cases and 180 167 controls and subsequently meta-analysed them jointly with 3348 SLE cases and 14 826 controls from published studies in East Asians. We further applied a Bayesian statistical approach to localise the putative causal variants for SLE associations.ResultsWe identified 113 genetic regions including 46 novel loci at genome-wide significance (p<5×10−8). Conditional analysis detected 233 association signals within these loci, which suggest widespread allelic heterogeneity. We detected genome-wide associations at six new missense variants. Bayesian statistical fine-mapping analysis prioritised the putative causal variants to a small set of variants (95% credible set size ≤10) for 28 association signals. We identified 110 putative causal variants with posterior probabilities ≥0.1 for 57 SLE loci, among which we prioritised 10 most likely putative causal variants (posterior probability ≥0.8). Linkage disequilibrium score regression detected genetic correlations for SLE with albumin/globulin ratio (rg=−0.242) and non-albumin protein (rg=0.238).ConclusionThis study reiterates the power of large-scale genome-wide meta-analysis for novel genetic discovery. These findings shed light on genetic and biological understandings of SLE.

The skin microbiota is an inseparable component of the skin barrier structure, which participates in the stabilization or impairment of the barrier function as well as the development of many skin diseases. To characterize the normal skin microbiota and its association with skin sites, age and sex, we recruited 50 volunteers divided into children, adolescents, young adults, middle-aged adults and the elderly. The skin sites consisted of cheeks, volar forearms (representing dry environments) and upper back (representing sebaceous environments). A total of 9 574 365 high-quality sequences of the V3 to V4 region of the 16S rRNA gene were annotated with taxonomic information related to two archaeal phyla (Thaumarchaeota and Euryarchaeota) and five dominant bacterial phyla (Actinobacteria, Proteobacteria, Firmicutes, Bacteroidetes and Cyanobacteria). The skin bacteria community structure was influenced by skin sites, and was closely related to age and sex. The upper back was dominated by Propionibacterium and Staphylococcus, and the cheeks facilitated the survival of Betaproteobacteria, while Alphaproteobacteria were prevalent on the volar forearms. Regarding the effects of age, after sexual maturity, the cheek microbiota became more similar to sebaceous sites (i.e. the upper back). The volar forearms appeared to experience the aging process earlier than the other two sites. The elderly had greater species richness and diversity and their community composition no longer had skin-site selectivity. Males had a greater species richness than females, but the sex differences in the community structure only present at certain age groups and skin sites.

Background Youzhi artificial intelligence (AI) software is the AI-assisted decision-making system for diagnosing skin tumors. The high diagnostic accuracy of Youzhi AI software was previously validated in specific datasets. The objective of this study was to compare the performance of diagnostic capacity between Youzhi AI software and dermatologists in real-world clinical settings. Methods A total of 106 patients who underwent skin tumor resection in the Dermatology Department of China-Japan Friendship Hospital from July 2017 to June 2019 and were confirmed as skin tumors by pathological biopsy were selected. Dermoscopy and clinical images of 106 patients were diagnosed by Youzhi AI software and dermatologists at different dermoscopy diagnostic levels. The primary outcome was to compare the diagnostic accuracy of the Youzhi AI software with that of dermatologists and that measured in the laboratory using specific data sets. The secondary results included the sensitivity, specificity, positive predictive value, negative predictive value, F-measure, and Matthews correlation coefficient of Youzhi AI software in the real-world. Results The diagnostic accuracy of Youzhi AI software in real-world clinical settings was lower than that of the laboratory data ( P < 0.001). The output result of Youzhi AI software has good stability after several tests. Youzhi AI software diagnosed benign and malignant diseases by recognizing dermoscopic images and diagnosed disease types with higher diagnostic accuracy than by recognizing clinical images ( P = 0.008, P = 0.016, respectively). Compared with dermatologists, Youzhi AI software was more accurate in the diagnosis of skin tumor types through the recognition of dermoscopic images ( P = 0.01). By evaluating the diagnostic performance of dermatologists under different modes, the diagnostic accuracy of dermatologists in diagnosing disease types by matching dermoscopic and clinical images was significantly higher than that by identifying dermoscopic and clinical images in random sequence ( P = 0.022). The diagnostic accuracy of dermatologists in the diagnosis of benign and malignant diseases by recognizing dermoscopic images was significantly higher than that by recognizing clinical images ( P = 0.010). Conclusion The diagnostic accuracy of Youzhi AI software for skin tumors in real-world clinical settings was not as high as that of using special data sets in the laboratory. However, there was no significant difference between the diagnostic capacity of Youzhi AI software and the average diagnostic capacity of dermatologists. It can provide assistant diagnostic decisions for dermatologists in the current state.

BackgroundSeveral susceptibility loci have been identified associated with Chinese Han systemic lupus erythematosus (SLE).MethodsWe carried out imputation of classical HLA alleles, amino acids and Single Nucleotide Polymorphisms (SNPs) across the MHC region in Chinese Han SLE genome‐wide association study (GWAS) of mainland and Hong Kong populations for the first time using newly constructed Han‐MHC reference panel followed by stepwise conditional analysis.ResultsWe mapped the most significant independent association to HLA‐DQβ1 at amino acid position (Phe87, p = 7.807 × 10−17) and an independent association at HLA‐DQB1*0301 (P condiational = 1.43 × 10−7).ConclusionOur study illustrates the value of population‐specific HLA reference panel for fine‐mapping causal variants in the MHC.