Identifying shared genetic architecture between rheumatoid arthritis and other conditions: a phenome-wide association study with genetic risk scores.

BACKGROUND: Rheumatoid arthritis (RA) shares genetic variants with other autoimmune conditions, but existing studies test the association between RA variants with a pre-defined set of phenotypes. The objective of this study was to perform a large-scale, systemic screen to determine phenotypes that share genetic architecture with RA to inform our understanding of shared pathways. METHODS: In the UK Biobank (UKB), we constructed RA genetic risk scores (GRS) incorporating human leukocyte antigen (HLA) and non-HLA risk alleles. Phenotypes were defined using groupings of International Classification of Diseases (ICD) codes. Patients with an RA code were excluded to mitigate the possibility of associations being driven by the diagnosis or management of RA. We performed a phenome-wide association study, testing the association between the RA GRS with phenotypes using multivariate generalized estimating equations that adjusted for age, sex, and first five principal components. Statistical significance was defined using Bonferroni correction. Results were replicated in an independent cohort and replicated phenotypes were validated using medical record review of patients. FINDINGS: We studied n = 316,166 subjects from UKB without evidence of RA and screened for association between the RA GRS and n = 1317 phenotypes. In the UKB, 20 phenotypes were significantly associated with the RA GRS, of which 13 (65%) were immune mediated conditions including polymyalgia rheumatica, granulomatosis with polyangiitis (GPA), type 1 diabetes, and multiple sclerosis. We further identified a novel association in Celiac disease where the HLA and non-HLA alleles had strong associations in opposite directions. Strikingly, we observed that the non-HLA GRS was exclusively associated with greater risk of the validated conditions, suggesting shared underlying pathways outside the HLA region. INTERPRETATION: This study replicated and identified novel autoimmune phenotypes verified by medical record review that share immune pathways with RA and may inform opportunities for shared treatment targets, as well as risk assessment for conditions with a paucity of genomic data, such as GPA. FUNDING: This research was funded by the US National Institutes of Health (P30AR072577, R21AR078339, R35GM142879, T32AR007530) and the Harold and DuVal Bowen Fund.

[Research Progress of Magnetically Anchored Abdominal Video System].

Magnetic anchoring technology provides a new development opportunity for current minimally invasive surgery. The magnetic anchoring abdominal video system based on this technology can effectively improve the operability and minimally invasiveness of single-port laparoscopic surgery. The development history of magnetically anchored abdominal video system was reviewed, and the design features and deficiencies of various types of magnetically anchored video devices were compared and analyzed. The evolution characteristics of the magnetic anchored video system are explained from minimally invasive and intelligent perspectives, and the challenges and opportunities of magnetic anchored video system are summarized and prospected.

miRTarVis+: Web-based interactive visual analytics tool for microRNA target predictions.

In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs. However, there still is a need for improving the performance for miRNA prediction algorithms and more importantly for interactive visualization tools for an integrative analysis of multiple prediction results. miRTarVis+ has an intuitive interface to support the analysis pipeline of load, filter, predict, and visualize. It can predict targets of miRNA by adopting Bayesian inference and maximal information-based nonparametric exploration (MINE) analyses as well as conventional correlation and mutual information analyses. miRTarVis+ supports an integrative analysis of multiple prediction results by providing an overview of multiple prediction results and then allowing users to examine a selected miRNA-mRNA network in an interactive treemap and node-link diagram. To evaluate the effectiveness of miRTarVis+, we conducted two case studies using miRNA-mRNA expression profile data of asthma and breast cancer patients and demonstrated that miRTarVis+ helps users more comprehensively analyze targets of miRNA from miRNA-mRNA expression profile data. miRTarVis+ is available at http://hcil.snu.ac.kr/research/mirtarvisplus.