Major depressive disorder (MDD) is a heterogeneous illness with many clinical variables - such as sex, age, alcohol, antidepressant drug, recurrence or death by suicide -- as potential factors characterizing subtypes of MDD, making it difficult to fully understand its underlying mechanisms and heterogeneous genetic underpinnings. Many transcriptomic studies have been generated in the literature, including those from Dr. Sibille, the co-PI on this proposal. We propose to apply state-of-the-art statistical integrative analyses tailored to combine multiple MDD transcriptomic studies that will address the specific issues of case-control pairing design, confounding clinical variables and small sample size. Our study will detect novel MDD associated biomarkers, pathways and co-expression modules, and elucidate the magnitudes of transcriptomic changes attributable to substance abuse, recurrence, disease severity, age and sex. The results will enhance our understanding to MDD genetic mechanisms and lead to better and individualized therapeutic solutions.

Public Health Relevance

Our proposed research is to develop and apply modern genomic meta-analysis methods to combine multiple major depressive disorder (MDD) transcriptomic studies that will adequately model the specific data structure of case-control pairing design, confounding clinical variables and small sample size. The goal is to detect novel MDD associated biomarkers, pathways and co-expression modules, and elucidate the magnitudes of transcriptomic changes attributable to substance abuse, recurrence, disease severity, age and sex. The results will enhance our understanding of MDD genetic mechanisms and lead to better and individualized therapeutic solutions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21MH094862-02
Application #
8432798
Study Section
Molecular Neurogenetics Study Section (MNG)
Program Officer
Senthil, Geetha
Project Start
2012-03-01
Project End
2014-02-28
Budget Start
2013-03-01
Budget End
2014-02-28
Support Year
2
Fiscal Year
2013
Total Cost
$199,646
Indirect Cost
$55,646
Name
University of Pittsburgh
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
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Ding, Ying; Chang, Lun-Ching; Wang, Xingbin et al. (2015) Molecular and Genetic Characterization of Depression: Overlap with other Psychiatric Disorders and Aging. Mol Neuropsychiatry 1:1-12
Lin, Chien-Wei; Chang, Lun-Ching; Tseng, George C et al. (2015) VSNL1 Co-Expression Networks in Aging Include Calcium Signaling, Synaptic Plasticity, and Alzheimer's Disease Pathways. Front Psychiatry 6:30
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