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Steroid-induced osteonecrosis of the femoral head (SONFH) is difficult to be diagnosed at the early stages when it can be administrated effectively. Yet, to date no study has been performed to identify diagnostic biomarkers and to develop diagnostic models for SONFH. In the current study, a total of 60 SONFH patients with Association Research Circulation Osseous (ARCO) stages I-IV, and 20 controls were enrolled and divided into the discovery and validation cohorts. The serum samples were collected and the gene expression profiles were detected by microarray analysis based on the discovery cohort. Then, eight genes (BIRC3, CBL, CCR5, LYN, PAK1, PTEN, RAF1 and TLR4) were identified as the candidate serum biomarkers of SONFH due to the significant differential expression patterns and the topological importance in the interaction network of SONFH-related differentially expressed genes. Functionally, these candidate serum biomarkers were significantly involved into several pathological processes during SONFH progression, such as the immune regulation and inflammation, bone metabolism and angiogenesis. After that, a prediction model for the early diagnosis of SONFH was constructed using Partial least squares regression based on the serum levels of the candidate biomarkers. Notably, both the 10-fold cross-validation and the independent dataset test demonstrated the good performance of this model. In conclusion, our study identified eight promising serum biomarkers and developed the circulating gene-based prediction model as a new, efficient and non-invasive diagnostic tool for the early detection of SONFH, as well as benefit the administration of SONFH in a daily clinical setting. Funding Statement: This study is funded by National Natural Science Foundation of China (No 81373656 & 81473695). Declaration of Interests: The authors declare that there is no conflict of interests regarding the publication of this paper. Ethics Approval Statement: The study was approved by the Research Ethics Committee of Institute of Chinese Materia Medica, Wangjing Hospital, China (SFDA approval number: 81473695). The informed consent was obtained from all patients.

Over 90% of autoimmune diseases risk variants are located in the noncoding region, leading to great challenge in deciphering the underlying causal functional variants/genes and biological mechanisms. Here we devised an integrative analysis strategy named fGWASAD for identifying not only noncoding functional variants but also gene targets, and underlying biological mechanisms as well as functional roles and clinical implications of gene targets on 20 autoimmune diseases. We demonstrated the credibility of our approach, and further decoded immunologically functional clues for 72.2% of target genes and regulatory mechanisms underlying 71.2% of functional variants. By leveraging pleiotropic effect among target genes, we predicted many candidate drug target genes and new indications for known drugs on several autoimmune diseases. We also suggested the crucial regulatory roles of super enhancers on autoimmune diseases network involving several key master TFs. Together, our analysis provided valuable comprehensive resource for autoimmune diseases, which are freely available online.