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BackgroundDisease complications, the onset of secondary phenotypes given a primary condition, can exacerbate the long-term severity of outcomes. However, the exact cause of many of these cross-phenotype associations is still unknown. One potential reason is shared genetic etiology—common genetic drivers may lead to the onset of multiple phenotypes. Disease-disease networks (DDNs), where nodes represent diseases and edges represent associations between diseases, can provide an intuitive way of understanding the relationships between phenotypes. Using summary statistics from a phenome-wide association study (PheWAS), we can generate a corresponding DDN where edges represent shared genetic variants between diseases. Such a network can help us analyze genetic associations across the diseasome, the landscape of all human diseases, and identify potential genetic influences for disease complications.ResultsTo improve the ease of network-based analysis of shared genetic components across phenotypes, we developed the humaN disEase phenoType MAp GEnerator (NETMAGE), a web-based tool that produces interactive DDN visualizations from PheWAS summary statistics. Users can search the map by various attributes and select nodes to view related phenotypes, associated variants, and various network statistics. As a test case, we used NETMAGE to construct a network from UK BioBank (UKBB) PheWAS summary statistic data. Our map correctly displayed previously identified disease comorbidities from the UKBB and identified concentrations of hub diseases in the endocrine/metabolic and circulatory disease categories. By examining the associations between phenotypes in our map, we can identify potential genetic explanations for the relationships between diseases and better understand the underlying architecture of the human diseasome. Our tool thus provides researchers with a means to identify prospective genetic targets for drug design, using network medicine to contribute to the exploration of personalized medicine.

Simple Summary The objectives of this study were to identify rare germline variant associations in urothelial carcinoma of the bladder (UC) incidence and to determine its association with clinical outcomes. Our analysis reveals that individuals with MR1 rare germline variants had significantly worse OS than those without any, and those with ADGRL2 variants were slightly more likely to have UC compared to a control cohort matched for age, sex, and smoking status. These associations, using models incorporating known environmental covariates and using well-defined clinical phenotypes, highlight several candidates for prognostic indicator genes for the differential presentation of UC. These associations highlight several candidate genes that have the potential to explain clinical disparities in UC and predict UC outcomes. Abstract Urothelial carcinoma of the bladder (UC) is the fifth most common cancer in the United States. Germline variants, especially rare germline variants, may account for a portion of the disparity seen among patients in terms of UC incidence, presentation, and outcomes. The objectives of this study were to identify rare germline variant associations in UC incidence and to determine its association with clinical outcomes. Using exome sequencing data from the DiscovEHR UC cohort (n = 446), a European-ancestry, North American population, the complex influence of germline variants on known clinical phenotypes were analyzed using dispersion and burden metrics with regression tests. Outcomes measured were derived from the electronic health record (EHR) and included UC incidence, age at diagnosis, and overall survival (OS). Consequently, key rare variant association genes were implicated in MR1 and ADGRL2. The Kaplan–Meier survival analysis reveals that individuals with MR1 germline variants had significantly worse OS than those without any (log-rank p-value = 3.46 × 10−7). Those with ADGRL2 variants were found to be slightly more likely to have UC compared to a matched control cohort (FDR q-value = 0.116). These associations highlight several candidate genes that have the potential to explain clinical disparities in UC and predict UC outcomes.

SummaryGiven genetic associations from a PheWAS, a disease-disease network can be constructed where nodes represent phenotypes and edges represent shared genetic associations between phenotypes. To improve the accessibility of the visualization of shared genetic components across phenotypes, we developed the humaN-disEase phenoType MAp GEnerator (NETMAGE), a web-based tool that produces interactive phenotype network visualizations from summarized PheWAS results. Users can search the map by a variety of attributes, and they can select nodes to view information such as related phenotypes, associated SNPs, and other network statistics. As a test case, we constructed a network using UK BioBank PheWAS summary data. By examining the associations between phenotypes in our map, we can potentially identify novel instances of pleiotropy, where loci influence multiple phenotypic traits. Thus, our tool provides researchers with a means to identify prospective genetic targets for drug design, contributing to the exploration of personalized medicine.Availability and implementationOur service runs athttps://hdpm.biomedinfolab.com. Source code can be downloaded athttps://github.com/dokyoonkimlab/netmage.Contactdokyoon.kim@pennmedicine.upenn.eduSupplementary informationSupplementary data and user guide are available atBioinformaticsonline.

Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger's biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a "disease-disease" network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.

Background: Pre-existing pulmonary hypertension is associated with poor outcomes after transcatheter mitral valve repair (TMVr) for mitral regurgitation (MR). However, the impact of an immediate change in mean pulmonary artery pressure (delta mPAP) following TMVr on outcomes is unknown. Methods: Patients who underwent TMVr from December 2015 to February 18, 2020 at our institution for symptomatic 3-4+ MR and who had invasive hemodynamics measured immediately pre- and post-TMVR were included. Multivariate Cox regression analysis was performed to examine the association of delta mPAP (post-TMVr - pre-TMVr mPAP) with the primary endpoint of heart failure (HF) readmission at 1 year. Secondary endpoints included all-cause mortality and the composite endpoint of HF readmission or all-cause mortality at 1 year. Results: Among 55 patients, 55% were men, mean age was 72 ± 14.2 years, and mean delta mPAP was -1.4 ± 8.2 mm Hg. Overall, HF readmission occurred in 14 (25%), death in 10 (18%), and the composite endpoint in 20 (36%) patients. In multivariable analyses, higher delta mPAP was significantly associated with HF readmission (hazard ratio (HR) = 1.10, 95% confidence interval (CI): 1.00 - 1.21; P = 0.04). delta mPAP was not associated with death (HR = 1.04, 95% CI: 0.96 - 1.14; P = 0.33), though there was a numerical but statistically non-significant trend towards the composite endpoint (HR = 1.06, 95% CI: 1.00 - 1.13; P = 0.06) driven by HF readmission. Conclusion: Higher delta mPAP immediately following TMVr was associated with increased HF readmission at 1 year. Larger prospective studies are needed to validate these data and further explore the utility of delta mPAP as a novel hemodynamic parameter to predict post-TMVR outcomes. Cardiol Res. 2021;12(5):302-308 doi: https://doi.org/10.14740/cr1284

Endometrial cancer (EMCA) is a clinically heterogeneous disease. Previously, we tested the efficacy of Verteporfin (VP) in EMCA cells and observed cytotoxic and anti-proliferative effects. In this study, we analyzed RNA sequencing data to investigate the comprehensive transcriptomic landscape of VP treated Type 1 EMCA cell lines, including HEC-1-A and HEC-1-B. There were 549 genes with differential expression of two-fold or greater and P TGFβ1 production, regulation of lipoprotein metabolic process, cell adhesion, endodermal cell differentiation, formation and development, and integrin mediated signaling pathway were among the significantly associated terms. A functional enrichment analysis of differentially expressed genes after VP treatment revealed extracellular matrix organization Gene Ontology as the most significant. CDC23 and BUB1B, two genes crucially involved in mitotic checkpoint progression, were found to be the pair with the best association from STRING among differentially expressed genes in VP treated HEC-1-B cells. Our in vivo results indicate that subcutaneous tumors in mice were regressed after VP treatment by inhibiting cell cycle pathway proteins. The present study revealed multiple key genes of pathological significance in EMCA, thereby improving our understanding of molecular profiles of EMCA cells.

Background Breast cancer is a complex disease in which different genomic patterns exists depending on different subtypes. Recent researches present that multiple subtypes of breast cancer occur at different rates, and play a crucial role in planning treatment. To better understand underlying biological mechanisms on breast cancer subtypes, investigating the specific gene regulatory system via different subtypes is desirable. Methods Gene expression, as an intermediate phenotype, is estimated based on methylation profiles to identify the impact of epigenomic features on transcriptomic changes in breast cancer. We propose a kernel weighted l1-regularized regression model to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes. For the proper control of subtype-specific estimation, samples from different breast cancer subtype are learned at different rate based on target estimates. Kolmogorov Smirnov test is conducted to determine learning rate of each sample from different subtype. Results It is observed that genes that might be sensitive to breast cancer subtype show prediction improvement when estimated using our proposed method. Comparing to a standard method, overall performance is also enhanced by incorporating tumor subtypes. In addition, we identified subtype-specific network structures based on the associations between gene expression and DNA methylation. Conclusions In this study, kernel weighted lasso model is proposed for identifying subtype-specific associations between gene expressions and DNA methylation profiles. Identification of subtype-specific gene expression associated with epigenomic changes might be helpful for better planning treatment and developing new therapies.

Background One of the fundamental challenges in cancer is to detect the regulators of gene expression changes during cancer progression. Through transcriptional silencing of critical cancer-related genes, epigenetic change such as DNA methylation plays a crucial role in cancer. In addition, miRNA, another major component of epigenome, is also a regulator at the post-transcriptional levels that modulate transcriptome changes. However, a mechanistic role of synergistic interactions between DNA methylation and miRNA as epigenetic regulators on transcriptomic changes and its association with clinical outcomes such as survival have remained largely unexplored in cancer. Methods In this study, we propose an integrative framework to identify epigenetic interactions between methylation and miRNA associated with transcriptomic changes. To test the utility of the proposed framework, the bladder cancer data set, including DNA methylation, miRNA expression, and gene expression data, from The Cancer Genome Atlas (TCGA) was analyzed for this study. Results First, we found 120 genes associated with interactions between the two epigenomic components. Then, 11 significant epigenetic interactions between miRNA and methylation, which target E2F3, CCND1, UTP6, CDADC1, SLC35E3, METRNL, TPCN2, NACC2, VGLL4, and PTEN, were found to be associated with survival. To this end, exploration of TCGA bladder cancer data identified epigenetic interactions that are associated with survival as potential prognostic markers in bladder cancer. Conclusions Given the importance and prevalence of these interactions of epigenetic events in bladder cancer it is timely to understand further how different epigenetic components interact and influence each other. Electronic supplementary material The online version of this article (doi:10.1186/s12920-017-0269-y) contains supplementary material, which is available to authorized users.

A longitudinal prospective study was performed in order to describe variations in ionized magnesium in patients with gestational diabetes. Twenty-two gestational diabetic women and 24 healthy pregnant women were included consecutively. Blood samples were obtained in the second and third trimester and 2 days postpartum in both groups. The blood samples were analysed for serum ionized magnesium, serum ionized calcium and pH. None of the groups demonstrated any time-related changes in ionized magnesium. However, in the third trimester the level of ionized magnesium was statistically significantly elevated in patients with gestational diabetes compared to controls (0.57 +/- 0.05 mmol/l vs. 0.51 +/- 0.06 mmol/l; p0.01). The level of ionized calcium remained stable throughout the study period in both groups. In conclusion our findings suggest that gestational diabetes is associated with elevated levels of ionized magnesium.