# Modeling non-additive genetic mechanisms for complex traits

> **NIH NIH R35** · UNIVERSITY OF FLORIDA · 2024 · $325,838

## Abstract

Project Summary
 A central question in the genetics of complex traits is understanding how variation in DNA
sequences leads to variation in phenotype. Recent technological advances in high-throughput
phenotyping assays for model organisms and the establishment of large human biobanks and
consortium databases have provided opportunities to study the genotype-phenotype maps for
complex traits in unprecedented detail. However, it remains a major challenge to model and
interpret these data due to the intrinsic high dimensionality of the genotype space and the many
ways in which causal genes can interact. My research program is focused on developing new
theoretical frameworks and interpretable computational tools to analyze large genotype-
phenotype datasets with the goal of (1) accurately predicting phenotypes for novel genotypes and
(2) providing biological insights into the genetic architecture of complex traits by identifying key
genes, gene interactions, and pathways.
 The primary focus for my lab in the next five years is to develop new Bayesian and machine
learning methods capable of modeling the full spectrum of genetic interactions including pairwise
as well as higher-order epistasis. Specifically, we are combining rigorous mathematical modeling
with modern machine learning techniques to develop a suite of scalable, principled methods to
achieve accurate phenotypic prediction and accelerate the discovery of novel genetic
mechanisms. While proof-of-concept versions of many of the proposed methods display state-of-
the-art performance, substantial work remains to scale the methods to larger genotype-phenotype
datasets, test model performance on a wide range of complex traits and organisms, and interpret
the results to gain biological insights. In the coming years, we plan to build these methods into an
integrated framework for analyzing complex genetic interactions, which will include computational
pipelines for fitting accurate phenotypic prediction models, identifying gene interactions and
pathways for experimental validation, and quantification of estimation uncertainty. We will also
prioritize the development of user-friendly, GPU-accelerated software packages for all methods.
 Important applications of the proposed research directions include predicting disease risks in
humans, elucidating the genetic mechanisms for economically and clinically important traits, and
designing improved plant and animal breeding programs. The computational tools developed here
will be broadly useful to geneticists, evolutionary, and clinical biologists.

## Key facts

- **NIH application ID:** 10939650
- **Project number:** 1R35GM154908-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Juannan Zhou
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $325,838
- **Award type:** 1
- **Project period:** 2024-08-09 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10939650

## Citation

> US National Institutes of Health, RePORTER application 10939650, Modeling non-additive genetic mechanisms for complex traits (1R35GM154908-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10939650. Licensed CC0.

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