# Improving the interpretability of genetic studies of major depressive disorder to identify risk genes

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2022 · $619,904

## Abstract

Project Summary
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 commonest 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. We propose using the very large numbers of cases available through
electronic medical records by applying statistical methods that accurately identify MDD. Our methods provide a
“best-guess” diagnosis by a process known as imputation. We then identify features that are specific to MDD.
Our 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. We
assume that non-specificity can be attributed to latent factors capturing the relationship between MDD, comorbid
disease, and pleiotropic factors. By identifying and removing these signals, we increase specificity, and thus
identify features that reflect the episodic severe shifts of mood, associated with neurovegetative and cognitive
changes, that are central to MDD. Our project has three aims: first, to impute phenotypes of a large sample of
MDD cases and controls in biobank data and determine the best approximation to MDD; second, to identify and
characterise specific and non-specific genetic effects on MDD, and finally to identify genes involved in MDD by
associating the cases defined via our first two aims with rare coding variants.

## Key facts

- **NIH application ID:** 10504696
- **Project number:** 1R01MH130581-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** JONATHAN FLINT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $619,904
- **Award type:** 1
- **Project period:** 2022-06-16 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10504696

## Citation

> US National Institutes of Health, RePORTER application 10504696, Improving the interpretability of genetic studies of major depressive disorder to identify risk genes (1R01MH130581-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10504696. Licensed CC0.

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