Big Data Approaches to Gender-Specific Differences in Alzheimer’s Disease

Kimberly Glass, PhD
Harvard School of Public Health (Boston, Massachusetts)

Co-Principal Investigators

John Quackenbush, PhD
Harvard School of Public Health (Boston, Massachusetts)
Year Awarded:
2014
Grant Duration:
January 1, 2014 to December 31, 2014
Disease:
Alzheimer's Disease
Award Amount:
$50,000
Grant Reference ID:
C2014001
Award Type:
Other
Award Region:
US Northeastern

This study was funded in partnership with members of the 21st Century Brain Trust

Sex-Specific Differences in Alzheimer’s Disease are Characterized by Unique Alterations in Cellular Network Structure

Summary

This study harnesses the power of big data sciences to profile gene expression data from men and women affected by Alzheimer’s disease.

Details

Alzheimer’s Disease (AD) a­ffects nearly five million people annually in the US, nearly two-thirds of whom are women. While numerous studies have attempted to identify genetic and environmental causes for the disease, little progress has been made in understanding the mechanisms that drive its development and progression. Gene expression pro‑filing data captures a snapshot of the molecular state of the cell and can provide a window on the processes that are active in normal and disease states. Here we are proposing to use a systems-biology approach to investigate sexual dimorphism in AD by modeling the transcriptional networks that are activated in normal and disease tissue in both males and females. Our preliminary findings suggest that there are sex-specific di­fferences in brain tissue transcriptional networks that converge as AD develops and progresses. We are proposing to further validate our preliminary findings in additional data sets in order to identify sex-specific biomarkers, and to inform potential sex-specific therapeutic interventions that will address the unique aspects of AD in females and males.

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