Briggs Junker (brownsack15)

Expression quantitative trait loci (eQTL) analysis has been widely used in interpreting disease-associated loci through correlating genetic variant loci with the expression of specific genes. RNA-sequencing (RNA-Seq), which can quantify gene expression at the genome-wide level, is often used in eQTL identification. Since different normalization methods of gene expression have substantial impacts on RNA-seq downstream analysis, it is of great necessity to systematically compare the effects of these methods on eQTL identification. Here, by using RNA-seq and genotype data of four different cancers in The Cancer Genome Atlas (TCGA) database, we comprehensively evaluated the effect of eight commonly used normalization methods on eQTL identification. Our results showed that the application of different methods could cause 20-30% differences in the final results of eQTL identification. Among these methods, COUNT, Median of Ratio (MED) and Trimmed Mean of M-values (TMM) generated similar results for identifying eQTLs, while Fragments Per Kilobase Million (FPKM) or RANK produced more differential results compared with other methods. Based on the accuracy and receiver operating characteristic (ROC) curve, the TMM method was found to be the optimal method for normalizing gene expression data in eQTLs analysis. In addition, we also evaluated the performance of different pairwise combinations of these methods. As a result, compared with single normalization methods, the combination of methods can not only identify more cis-eQTLs, but also improve the performance of the ROC curve. Overall, this study provides a comprehensive comparison of normalization methods for identifying eQTLs from RNA-seq data, and proposes some practical recommendations for diverse scenarios.In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors-compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https//data.oncomx.org/covid19) that will help researchers and diagnosticians alike. Like many medical specialties, pathology faces the ongoing challenge of effectively enriching diversity, equity, and inclusion within training programs and the field as a whole. This issue is furthered by a decline in US medical student interest in the field of pathology, possibly attributable to increasingly limited pathology exposure during medical school and medical student perceptions about careers in pathology. To review the literature to identify the challenges to diversity, equity, and inclusion in pathology, with an emphasis on the pathology trainee pipeline. To evaluate the medical education literature from other medical specialties for diversity and inclusion-focused studies and initiatives, and determine the outcomes and/or approaches relevant for pathology training programs. A literature review was completed by a se