# Novel statistical genetics methods to unravel polygenic interactions in complex traits

> **NIH NIH R35** · UNIVERSITY OF CHICAGO · 2024 · $401,296

## Abstract

PROJECT SUMMARY/ABSTRACT
Complex traits result from interactions between many genetic and environmental factors. Nonetheless, most
complex trait studies assume an additive model, in which genetic effects are independent of the environment
and each other. This simple model has successfully identified many trait-associated loci, and these loci can be
combined into Polygenic Scores (PGS) to predict disease. However, these results have not generally identified
novel disease biology or therapies. Worse yet, PGS are biased toward European ancestry-individuals, hence
clinical use of current PGS will exacerbate existing health disparities.
I hypothesize that genetic interactions are the missing link in our understanding of complex trait biology. Genetic
interactions are central to many fields of biology, and it is not likely that complex human traits are fundamentally
different. However, prior studies of genetic interactions have generally been unsuccessful. I argue this results
from limitations in our current models. In the next five years, I will develop genetic interaction models for complex
traits to address these limitations.
First, I will develop models to identify gene-gene interaction at the level of pathway-pathway interaction that build
on my recent “Coordinated” framework for epistasis. Coordination is biologically plausible and statistically
powerful. I will extend my Coordinated models to decompose pleiotropic effects on multiple traits and to unravel
subtypes of common diseases.
Second, I will develop rigorous and powerful models of gene-environment interaction that apply to novel areas
of complex trait genetics. I will study cell type-specific heritability in single cell ‘omics data, I will incorporate
context-specific effects to improve power and portability in PGS, and I will quantify the heritability of treatment
response from biobank data.
My methods will be mathematically rigorous and computationally efficient. They will build on my track record of
developing robust and freely-distributed statistical genetics methods. I will apply my methods to phenome-wide
scans in diverse cohorts, especially to quantify the portability of PGS across ancestries. I will also study Major
Depressive Disorder in detail, a classic example of a heterogeneous complex disorder with a mix of poorly
understood genetic and environmental causes. My interaction methods will close the gap between statistical
explanation and biological understanding, revealing new paths to precision medicine that benefit everyone.

## Key facts

- **NIH application ID:** 10915527
- **Project number:** 5R35GM150822-02
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Andrew Dahl
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $401,296
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10915527

## Citation

> US National Institutes of Health, RePORTER application 10915527, Novel statistical genetics methods to unravel polygenic interactions in complex traits (5R35GM150822-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10915527. Licensed CC0.

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