Polycystic ovary syndrome is the most common endocrine disorder affecting women of reproductive age. A number of criteria have been developed for clinical diagnosis of polycystic ovary syndrome, with the Rotterdam criteria being the most inclusive. Evidence suggests that polycystic ovary syndrome is significantly heritable, and previous studies have identified genetic variants associated with polycystic ovary syndrome diagnosed using different criteria. The widely adopted electronic health record system provides opportunity to identify patients with polycystic ovary syndrome using the Rotterdam criteria for genetic studies. To identify novel associated genetic variants under the same phenotype definition, we extracted polycystic ovary syndrome cases and unaffected controls based on the Rotterdam criteria from the electronic health records and performed a discovery-validation genome-wide association study. We developed a polycystic ovary syndrome phenotyping algorithm based on the Rotterdam criteria and applied it to three electronic health records-linked biobanks to identify cases and controls for genetic study. In discovery phase, we performed individual genome-wide association study using the Geisinger's MyCode and the eMERGE cohorts, which were then meta-analyzed. We attempted validation of the significant association loci (P<1x10-6) in the BioVU cohort. All association analyses used logistic regression, assuming an additive genetic model, and adjusted for principal components to control for population stratification. An inverse-variance fixed effect model was adopted for meta-analysis. Additionally, we examined the top variants to evaluate their associations with each criterion in the phenotyping algorithm. We used STRING to characterize protein-protein interaction network. Using the same algorithm based on the Rotterdam criteria, we identified 2,995 patients with polycystic ovary syndrome and 53,599 population controls in total (2,742 cases and 51,438 controls from the discovery phase; 253 cases and 2,161 controls in the validation phase). We identified one novel genome-wide significant variant rs17186366 (OR=1.37 [1.23,1.54], P=2.8x10-8) located near SOD2. Additionally, two loci with suggestive association were also identified: rs113168128 (OR=1.72 [1.42,2.10], P=5.2 x10-8), an intronic variant of ERBB4 that is independent from the previously published variants, and rs144248326 (OR=2.13 [1.52,2.86], P=8.45x10-7), a novel intronic variant in WWTR1. In the further association tests of the top 3 SNPs with each criterion in the polycystic ovary syndrome algorithm, we found that rs17186366 (SOD2) was associated with polycystic ovaries and hyperandrogenism, while rs11316812 (ERBB4) and rs144248326 (WWTR1) were mainly associated with oligomenorrhea or infertility. We also validated the previously reported association with DENND1A1. Using STRING to characterize protein-protein interactions, we found both ERBB4 and WWTR1 can interact with YAP1 which has been previously associated with polycystic ovary syndrome. Through a discovery-validation genome-wide association study on polycystic ovary syndrome identified from electronic health records using an algorithm based on Rotterdam criteria, we identified and validated a novel genome-wide significant association with a variant near SOD2. We also identified a novel independent variant within ERBB4, and a suggestive association with WWTR1. With previously identified polycystic ovary syndrome-gene YAP1, the ERBB4-YAP1-WWTR1 network suggests involvement of the epidermal growth factor receptor and the Hippo pathway in the multifactorial etiology of polycystic ovary syndrome.

Intracranial aneurysm (IA) is a devastating disease, the pathogenesis of which remains to be elucidated. The present study aimed to determine the molecular mechanism of IA and to identify potential therapeutic targets using bioinformatics analysis. The GSE54083 dataset, which includes data from patients with ruptured IA and superficial temporal artery controls, was downloaded from the Gene Expression Omnibus, and differentially expressed genes (DEGs) were identified in the ruptured IA samples using the limma package in R. Subsequently, the Database for Annotation, Visualization and Integrated Discovery software was used to perform function and pathway enrichment analyses and the Search Tool for the Retrieval of Interacting Genes database was used to construct the protein‑protein interaction (PPI) network. Then, microRNA (miRNA) target and transcription factor (TF) target pairs were identified using the miR2Disease, MiRwalk2, ITFP and TRANSFAC databases. Finally, an integrated network of TF‑target‑miRNAs was constructed using Cytoscape. A total of 402 upregulated DEGs and 375 downregulated DEGs were identified from the ruptured IA samples compared with the superficial temporal artery samples. The majority of the upregulated DEGs were significantly enriched in the immune system development category, including CD40 ligand (CD40LG) and CD40 and the downregulated DEGs, such as striatin (STRN), were enriched in neuron projection development. In addition, nitric oxide synthase 1 (NOS1), a target of miRNA‑125b, and myosin heavy chain 11 (MYH11), a target of minichromosome maintenance complex component 4 (MCM4), had higher degree scores in the integrated network. These findings suggest that CD40, CD40LG, NOS1, STRN, MCM4, MYH11 and miR‑125b may be potential therapeutic targets for the treatment of IA.

Polycystic ovary syndrome (PCOS) is a complex disorder with heterogenous phenotypes and unclear etiology. A recent phenotypic clustering study identified metabolic and reproductive subtypes of PCOS. We hypothesize that the heterogeneity of PCOS manifestations reflects different mechanistic pathways and can be identified using a genetic approach. We applied k-means clustering to categorize the genome-wide significant PCOS variants into clusters based on their associations with selected quantitative traits that likely reflect PCOS etiological pathways. We evaluated the association of each cluster with PCOS-related traits and disease outcomes. We then applied Mendelian randomization to estimate the causal effects between the traits and PCOS. Three categories of variants were identified: adiposity, insulin resistant, and reproductive. Significant associations were observed for variants in the adiposity cluster with body mass index (BMI), waist circumference and breast cancer, and variants in the insulin-resistant cluster with fasting insulin, glucose values, and homeostatic model assessment of insulin resistance (HOMA-IR). Sex hormone binding globulin (SHBG) has strong association with all three clusters. Mendelian randomization suggested a causal role of BMI and SHBG on PCOS. No causal associations were observed for PCOS on disease outcomes.

We report a symmetric exponential amplification-assisted fluorescence in situ hybridization (SEXPAR-FISH) strategy for imaging intracellular-specific microRNAs (miRNAs) in cells. The strategy eliminates the risk of cell loss and miRNA leakage, and low-abundant intracellular miR-27a in cells can be imaged in only 2 h without compromising sensitivity and specificity.

Background. The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques tend to disregard this key distinction. Consequently, the development of an adaptive imputation strategy designed specifically for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications. Method. We analyzed the laboratory measures derived from Geisinger’s EHR on patients in three distinct cohorts—patients tested for Clostridioides difficile (Cdiff) infection, patients with a diagnosis of inflammatory bowel disease (IBD), and patients with a diagnosis of hip or knee osteoarthritis (OA). We extracted Logical Observation Identifiers Names and Codes (LOINC) from which we excluded those with 75% or more missingness. The comorbidities, primary or secondary diagnosis, as well as active problem lists, were also extracted. The adaptive imputation strategy was designed based on a hybrid approach. The comorbidity patterns of patients were transformed into latent patterns and then clustered. Imputation was performed on a cluster of patients for each cohort independently to show the generalizability of the method. The results were compared with imputation applied to the complete dataset without incorporating the information from comorbidity patterns. Results. We analyzed a total of 67,445 patients (11,230 IBD patients, 10,000 OA patients, and 46,215 patients tested for C. difficile infection). We extracted 495 LOINC and 11,230 diagnosis codes for the IBD cohort, 8160 diagnosis codes for the Cdiff cohort, and 2042 diagnosis codes for the OA cohort based on the primary/secondary diagnosis and active problem list in the EHR. Overall, the most improvement from this strategy was observed when the laboratory measures had a higher level of missingness. The best root mean square error (RMSE) difference for each dataset was recorded as −35.5 for the Cdiff, −8.3 for the IBD, and −11.3 for the OA dataset. Conclusions. An adaptive imputation strategy designed specifically for EHR that uses complementary information from the clinical profile of the patient can be used to improve the imputation of missing laboratory values, especially when laboratory codes with high levels of missingness are included in the analysis.