# pathQTL: Integrative Multi-Omics Causal Inference of Molecular Mechanisms Leading to Neuropsychiatric Illness

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $471,846

## Abstract

Project Summary
A multitude of common genetic variants influencing risk for neuropsychiatric disorders (e.g., schizophrenia,
major depressive disorder, and Alzheimer’s disease) have recently been identified and replicated, providing a
foothold into the causes of these disorders. The critical next step in neuropsychiatric genetics is to move from a
risk locus in the genome to an understanding of how this genetic variation influences molecules, cells, and
circuits of the brain, leading to complex disorders. Many datasets, including those generated by our own labs,
have established direct links between genotype and human brain traits at multiple levels of biology (molecular:
chromatin accessibility, expression; cellular: morphology; circuit: gross brain structure), termed quantitative trait
loci (QTLs). Here, we will integrate QTLs across multiple levels of biology in order to statistically prioritize
causal pathways by which genetic variation creates risk for complex neuropsychiatric disorders. Causal
modeling goes well beyond previous co-localization work, as it allows the prioritization of expensive functional
validation experiments for cellular or molecular changes that are a cause of the disorder, rather than those that
are a consequence or independent of the disorder. It additionally allows inference of key experimental
parameters including cell-type and developmental time period. Finally, causal inference when combined across
multiple levels of biology and multiple disorder risk loci allows for assessment of convergence at a biological
level, cell-type, or developmental time period, which is critical information for therapeutic targeting. We will
leverage the computational and statistical frameworks of Bayesian probabilistic networks and causal inference
in a new framework that utilizes association summary statistics, as well-powered multi-level data collected on
the same individuals is almost always infeasible. Subsequently, we will experimentally validate the molecular
predictions of our model using epigenetic engineering in primary human neural progenitor cells, and in turn
revising the computational models. Prioritizing causal molecular pathways of disorder associated variants, and
identifying the relevant cell-type and developmental stage will increase the success rate of validation
experiments and shed light on mechanisms of neuropsychiatric disorders in an unbiased manner.

## Key facts

- **NIH application ID:** 10066367
- **Project number:** 5R01MH118349-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Michael Isaiah Love
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $471,846
- **Award type:** 5
- **Project period:** 2018-12-10 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10066367

## Citation

> US National Institutes of Health, RePORTER application 10066367, pathQTL: Integrative Multi-Omics Causal Inference of Molecular Mechanisms Leading to Neuropsychiatric Illness (5R01MH118349-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10066367. Licensed CC0.

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