Overlapping genetic susceptibility of seven autoimmune diseases:SPU tests based on genome-wide association summary statistics.

Autoimmune diseases are a heterogeneous group of diseases with an unclear aetiology. Genome-wide association studies (GWAS) and meta-analysis are inefficient in identifying shared variants. This study aims to identify shared genetic susceptibility for seven autoimmune diseases using sum of powered score (SPU) tests. GWAS summary statistics datasets of seven autoimmune diseases were downloaded from Open Targets Genetics, Dryad and International Multiple Sclerosis Genetics Consortium (IMSGC). The MTaSPUs was applied to confirm common single-nucleotide polymorphisms (SNP), MTaSPUsSet and aSPUs were performed to identify potential shared genes, and MTaSPUsPath was conducted to explore biological functions based on Kyoto encyclopedia of genes and genomes (KEGG) biological pathways. The MTaSPUs test found 104 pleiotropic SNPs (P < 1.19 × 10-6) in our study. The 38 of these SNPs were associated with at least one trait in the original GWAS study. A total of 56 genes associated with at least one trait (P < 4.98 × 10-6) were recognized by aSPUs test. The 45 potential pleiotropic genes (P < 4.98 × 10-6) were significant in MTaSPUsSet test. By aggregating results of aSPUs test and MTaSPUsSet test, we ascertained 38 pleiotropic genes. The 10 of these 38 pleiotropic genes have been reported in previous studies, while the remaining 28 genes were newly discovered. These 38 genes were matched in 14 significant KEGG pathways. We found new variants linked with complicated illnesses derived from several GWAS datasets using the SPU testing technique. The discovery of pleiotropic genes and shared pathways may aid in the development of common treatment approaches for autoimmune disorders.

A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China.

Acquired immune deficiency syndrome (AIDS) is a serious public health problem. This study aims to establish a combined model of seasonal autoregressive integrated moving average (SARIMA) and Prophet models based on an L1-norm to predict the incidence of AIDS in Henan province, China. The monthly incidences of AIDS in Henan province from 2012 to 2020 were obtained from the Health Commission of Henan Province. A SARIMA model, a Prophet model, and two combined models were adopted to fit the monthly incidence of AIDS using the data from January 2012 to December 2019. The data from January 2020 to December 2020 was used to verify. The mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the prediction effect among the models. The results showed that the monthly incidence fluctuated from 0.05 to 0.50 per 100,000 individuals, and the monthly incidence of AIDS had a certain periodicity in Henan province. In addition, the prediction effect of the Prophet model was better than SARIMA model, the combined model was better than the single models, and the combined model based on the L1-norm had the best effect values (MSE = 0.0056, MAE = 0.0553, MAPE = 43.5337). This indicated that, compared with the L2-norm, the L1-norm improved the prediction accuracy of the combined model. The combined model of SARIMA and Prophet based on the L1-norm is a suitable method to predict the incidence of AIDS in Henan. Our findings can provide theoretical evidence for the government to formulate policies regarding AIDS prevention.