Publications by authors named "C Pantazis"
Recently, a novel African ancestry specific Parkinson's disease (PD) risk signal was identified at the gene encoding glucocerebrosidase (). This variant (rs3115534-G) is carried by ~50% of West African PD cases and imparts a dose-dependent increase in risk for disease. The risk variant has varied frequencies across African ancestry groups, but is almost absent in European and Asian ancestry populations.
View Article and Find Full Text PDFStructural variants (SVs) drive gene expression in the human brain and are causative of many neurological conditions. However, most existing genetic studies have been based on short-read sequencing methods, which capture fewer than half of the SVs present in any one individual. Long-read sequencing (LRS) enhances our ability to detect disease-associated and functionally relevant structural variants (SVs); however, its application in large-scale genomic studies has been limited by challenges in sample preparation and high costs.
View Article and Find Full Text PDFRecently, an African ancestry-specific Parkinson disease (PD) risk signal was identified at the gene encoding glucocerebrosidase (GBA1). This variant ( rs3115534 -G) is carried by ~50% of West African PD cases and imparts a dose-dependent increase in risk for disease. The risk variant has varied frequencies across African ancestry groups but is almost absent in European and Asian ancestry populations.
View Article and Find Full Text PDFArticle Synopsis
- Latin America's genetic diversity offers a unique opportunity to study Alzheimer's disease (AD) and frontotemporal dementia (FTD), with a focus on identifying related genetic variations.
- The study involved 2,162 participants from six countries who underwent extensive genomic sequencing and analysis to detect genetic factors linked to these dementias.
- Results highlighted a mix of American, African, and European ancestries, discovered 17 pathogenic variants, and revealed specific genetic variations tied to AD and FTD inheritance patterns in affected families.
Article Synopsis
- - The paper explores using Large Language Models (LLMs) to streamline data wrangling and automate tasks in data discovery and harmonization, crucial for making biomedical data AI-ready by developing Common Data Elements (CDEs).
- - A human-in-the-loop approach was utilized to ensure the accuracy of generated CDEs from various studies and databases, achieving a high accuracy rate where 94.0% of fields required no manual changes, with an interoperability mapping rate of 32.4%.
- - The resulting CDEs are designed to improve dataset compatibility by measuring how well different data sources align with these standards, ultimately enhancing the efficiency and scalability of biomedical research efforts.