Nathan A Bihlmeyer1, Emily Merrill2, Yann Lambert3, Gyan P Srivastava4, Timothy W Clark5, Bradley T Hyman1, Sudeshna Das6. 1. MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA. 2. MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA. 3. Centre d'Investigation Clinique Antilles-Guyane, Cayenne Hospital, Cayenne Cedex, French Guiana, France. 4. Data and Statistical Science, Abbvie, Cambridge, MA, USA. 5. Data Science Institute, School of Medicine, University of Virginia, Charlottesville, VA, USA. 6. MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA. Electronic address: sdas5@mgh.harvard.edu.
Abstract
INTRODUCTION: Numerous omics studies have been conducted to understand the molecular networks involved in Alzheimer's disease (AD), but the pathophysiology is still not completely understood; new approaches that enable neuroscientists to better interpret the results of omics analysis are required. METHODS: We have developed advanced methods to analyze and visualize publicly-available genomics and genetics data. The tools include a composite clinical-neuropathological score for defining AD, gene expression maps in the brain, and networks integrating omics data to understand the impact of polymorphisms on AD pathways. RESULTS: We have analyzed over 50 public human gene expression data sets, spanning 19 different brain regions and encompassing three separate cohorts. We integrated genome-wide association studies with expression data to identify important genes in the pathophysiology of AD, which provides further insight into the calcium signaling and calcineurin pathways. DISCUSSION: Biologists can use these freely-available tools to obtain a comprehensive, information-rich view of the pathways in AD.
INTRODUCTION: Numerous omics studies have been conducted to understand the molecular networks involved in Alzheimer's disease (AD), but the pathophysiology is still not completely understood; new approaches that enable neuroscientists to better interpret the results of omics analysis are required. METHODS: We have developed advanced methods to analyze and visualize publicly-available genomics and genetics data. The tools include a composite clinical-neuropathological score for defining AD, gene expression maps in the brain, and networks integrating omics data to understand the impact of polymorphisms on AD pathways. RESULTS: We have analyzed over 50 public human gene expression data sets, spanning 19 different brain regions and encompassing three separate cohorts. We integrated genome-wide association studies with expression data to identify important genes in the pathophysiology of AD, which provides further insight into the calcium signaling and calcineurin pathways. DISCUSSION: Biologists can use these freely-available tools to obtain a comprehensive, information-rich view of the pathways in AD.
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