This project aims to advance our understanding of major depressive disorder (MDD) through the analysis of electronic medical records, biobanks and associated genetic data. MDD is the most common psychiatric disorder and recognized as the world’s leading cause of disability, yet current treatments are relatively ineffective: only about half of patients will show signs of improvement after three months of therapy. Genetic approaches are a proven path to identifying causal factors and hence finding novel treatments, but they are hard to apply to MDD without obtaining large samples of cases. Jonathan Flint, MD, Depression Grand Challenge co-director, is PI of this study.
Flint's research uses the very large numbers of cases available through electronic medical records by applying statistical methods that accurately identify MDD. These methods provide a “best-guess” diagnosis by a process known as imputation. Flint and researchers will then identify features that are specific to MDD. Researchers' insight is that since non-genetic and non-specific factors explain large components of variability in traditional MDD phenotypes, algorithmically removing them increases the signal from the core biological drivers. Non-specificity, the research team assumes, can be attributed to latent factors capturing the relationship between MDD, comorbid disease, and pleiotropic factors. By identifying and removing these signals, Flint increases specificity, and thus identifies features that reflect the episodic severe shifts of mood, associated with neurovegetative and cognitive changes, that are central to MDD.
Flint's project has three aims:
- Impute phenotypes of a large sample of MDD cases and controls in biobank data and determine the best approximation to MDD.
- Identify and characterize specific and non-specific genetic effects on MDD.
- Identify genes involved in MDD by associating the cases defined via the first two aims with rare coding variants.
NIH project number: 5R01MH130581-02
This research project is intended to contribute to an understanding of the causes major depressive disorder, the most common psychiatric disorder and a leading cause of disability throughout the world. Such research will improve our ability to diagnose depression from electronic health records, generate insights into the genetic architecture of depression, separating out specific from non-specific genetic risk factors, and will identify, from the analysis of sequencing data, genes that are involved in the etiology of the disorder. The project will provide new insights into disease, and well enable the development of more effective therapy.
June 16, 2022 – April 30, 2027
Jonathan Flint
Kenneth Seedman Kendler (Virginia Commonwealth University)
National Institute of Mental Health — PA-20-185
