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MODEL-AD: Bioinformatics and Data ManagementMODEL-AD: Bioinformatics and Data Management
EP26692
Poster Title: MODEL-AD: Bioinformatics and Data ManagementMODEL-AD: Bioinformatics and Data Management
Submitted on 21 Nov 2017
Author(s): Bruce Lamb1, Michael Sasner2, Andrew Saykin1, Lara Mangravite3, Gregory Carter2
Affiliations: 1Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN USA 2The Jackson Laboratory, Bar Harbor, ME USA 3Sage Bionetworks, Seattle, WA USA
This poster was presented at Society for Neuroscience 2017
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Poster Information
Abstract: Creation of the next generation of animal models in the Model Organism
Development and Evaluation for Late-onset Alzheimer’s Disease
(MODEL-AD) consortium will require extensive data analysis, curation,
and distribution. To this end, a central Bioinformatics and Data
Management Core (BDMC) has been created to integrate information
from large-scale data resources including the Alzheimer’s Disease
Sequencing Project, Alzheimer’s Disease Neuroimaging Initiative, the
Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD),
Molecular Mechanisms of Vascular Etiology of Alzheimer’s Disease, and
others. We are creating bioinformatic pipelines to: (1) identify key genetic
variants that confer risk of AD from genome-wide association studies; (2)
translate candidate variants into mouse models; (3) align human disease
and animal model phenotypes to specify the optimal research use of
each animal and characterize the effects of multiple genetic variants; and
(4) broadly disseminate all data and preclinical research protocols for
community use. Here we present our early results and future plans to
meet these needs. To date, multiple variants have been identified
through the integration of results from multiple genetics studies, at both
novel and known genetic loci. Genomic, epigenetic, and functional data
have been used to predict the consequences of candidate variants,
thereby prioritizing specific genetic alterations for animal modeling via
genetic engineering. Molecular phenotypes, including transcriptomics
data from the brains of mouse models, have been systematically
compared to similar data from clinical cohorts to identify pathways
altered in both human and mouse. Finally, we are broadly sharing data
via the AMP-AD Knowledge Portal hosted by Sage Bionetworks. Future
work will expand all of these findings and resources, and additionally
integrate data and protocols from preclinical studies that use the animal
models. In sum, the BDMC is creating a resource to integrate a broad
knowledgebase to serve the creation and study of multiple animal
models to effectively model late-onset Alzheimer’s disease in vivo.
Summary: A brief summary of the Bioinformatics and Data Management core for the MODEL-AD consortium.Report abuse »
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