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.
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.
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