The classification of genetic variants represents a major challenge in the post-genome era by virtue of their extraordinary number and the complexities associated with ascribing a clinical impact, especially for disorders exhibiting exceptional phenotypic, genetic, and allelic heterogeneity. To address this challenge for hearing loss, we have developed the Deafness Variation Database (DVD), a comprehensive, open-access resource that integrates all available genetic, genomic, and clinical data together with expert curation to generate a single classification for each variant in 152 genes implicated in syndromic and non-syndromic deafness. We evaluate 876,139 variants and classify them as pathogenic or likely pathogenic (more than 8,100 variants), benign or likely benign (more than 172,000 variants), or of uncertain significance (more than 695,000 variants); 1,270 variants are re-categorized based on expert curation and in 300 instances, the change is of medical significance and impacts clinical care. We show that more than 96% of coding variants are rare and novel and that pathogenicity is driven by minor allele frequency thresholds, variant effect, and protein domain. The mutational landscape we define shows complex gene-specific variability, making an understanding of these nuances foundational for improved accuracy in variant interpretation in order to enhance clinical decision making and improve our understanding of deafness biology.
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http://dx.doi.org/10.1016/j.ajhg.2018.08.006 | DOI Listing |
Alzheimers Dement
December 2024
The Jackson Laboratory, Bar Harbor, ME, USA.
Background: Alzheimer's disease (AD) therapeutics have largely been unsuccessful in alleviating disease burden in those afflicted by the disease. The TREAT-AD Consortium is an international group of academic researchers dedicated to identifying novel molecular targets for AD from underexplored areas of disease linked pathology.
Method: Utilizing a top-down expert curation approach of organizing Gene Ontology terms into endophenotypes of AD, we developed 19 biological domains.
Genome Med
December 2024
European Reference Network for Rare Multisystemic Vascular Disease (VASCERN), HTAD and MSA Rare Disease, Working Group, Paris, France.
Background: In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) developed standardized variant curation guidelines for Mendelian disorders. Although these guidelines have been widely adopted, they are not gene- or disease-specific. To mitigate classification discrepancies, the Clinical Genome Resource FBN1 variant curation expert panel (VCEP) was established in 2018 to develop adaptations to the ACMG/AMP criteria for FBN1 in association with Marfan syndrome.
View Article and Find Full Text PDFPLoS One
December 2024
Institute of Forestry and Environmental Sciences, University of Chittagong, Chittagong, Bangladesh.
The resilience of established business strategies has been tested in the wake of recent global supply chain upheavals triggered by events like the COVID-19 pandemic, Russia-Ukraine combat, Hamas-Israel war, and other geopolitical conflicts. Organizations are compelled to integrate sustainable practices into their supply chains to navigate the complexities of the post-COVID-19 era and mitigate the far-reaching consequences of such disruptions. However, exploring supply chain imperatives from sustainability dimensions still remains underexplored, presenting a significant research gap, particularly in the fashion retail sector.
View Article and Find Full Text PDFGeroscience
December 2024
Institute on the Biology of Aging and Metabolism, University of Minnesota, Twin Cities, Minneapolis, MN, 55455, USA.
Although cellular senescence has been recognized as a hallmark of aging, it is challenging to detect senescence cells (SnCs) due to their high level of heterogeneity at the molecular level. Machine learning (ML) is likely an ideal approach to address this challenge because of its ability to recognize complex patterns that cannot be characterized by one or a few features, from high-dimensional data. To test this, we evaluated the performance of four ML algorithms including support vector machines (SVM), random forest (RF), decision tree (DT), and Soft Independent Modelling of Class Analogy (SIMCA), in distinguishing SnCs from controls based on bulk RNA sequencing data.
View Article and Find Full Text PDFData Brief
December 2024
Insight Centre for Data Analytics, of Business, College of Science and Engineering, University of Galway, Ireland.
Many scholars argue that there is a deepening crisis of trust in healthcare systems. What is not contested is the centrality of public trust in building reputational value in healthcare organisations. However, there is a dearth of research focused on better understanding how trust in healthcare institutions, and the healthcare workforce, can be sustainably cultivated.
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