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 #
1R21MH094862-01A1
Application #
8299726
Study Section
Molecular Neurogenetics Study Section (MNG)
Program Officer
Senthil, Geetha
Project Start
2012-03-01
Project End
2014-02-28
Budget Start
2012-03-01
Budget End
2013-02-28
Support Year
1
Fiscal Year
2012
Total Cost
$170,975
Indirect Cost
$45,975
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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Song, Chi; Tseng, George C (2014) HYPOTHESIS SETTING AND ORDER STATISTIC FOR ROBUST GENOMIC META-ANALYSIS. Ann Appl Stat 8:777-800
Tang, Shaowu; Ding, Ying; Sibille, Etienne et al. (2014) Imputation of Truncated p-Values For Meta-Analysis Methods and Its Genomic Application. Ann Appl Stat 8:2150-2174
Wang, Xingbin; Lin, Yan; Song, Chi et al. (2012) Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: with application to major depressive disorder. BMC Bioinformatics 13:52