# Linking Variants to Multi-scale Phenotypes via a Synthesis of Subnetwork Inference and Deep Learning

> **NIH NIH U01** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $655,577

## Abstract

Project Summary
The ability to accurately predict the effect of genetic variation on phenotypes at multiple scales would radically
transform our ability to apply genomic technologies in order to understand human health and disease. This
predictive ability would significantly improve the effectiveness of a broad spectrum of genomic analyses
ranging from genome-wide association studies for common diseases to diagnostic odysseys searching for
genetic causes of rare diseases.
To address this challenge, we propose to develop a trainable approach for predicting the phenotypic impact of
genetic variants. This approach will support predictions for a broad range of genetic variations, phenotypes,
and biological contexts. It will incorporate and exploit mechanistic knowledge of pathways where available, but
augment this pathway knowledge with learned models where it is not. This approach will consist of a synthesis
of (i) methods that link genomic variants to their effect on expression or function of individual gene products, (ii)
methods that link those relationships into the subnetworks involved in cellular responses of interest, (iii)
machine-learning approaches that infer models pertaining to a variety of genotype-phenotype relations from
large training sets.
We will also develop and apply active learning algorithms to identify the most informative experiments for
subsequent analysis by IGVF Consortium. Additionally, we will develop and apply a statistical framework for
elucidating genetic modifiers, through probabilistic, network-informed inference of common variants identified
in GWAS that modify the impact of rare variants implicated in sequencing-based association studies.
Throughout the project, we will work closely with other IGVF Centers to guide experimental data collection,
benchmark methods from across Centers, and contribute to the variant-element-phenotype catalog which will
have broad applications by the community.

## Key facts

- **NIH application ID:** 10479037
- **Project number:** 5U01HG012039-02
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Mark W. Craven
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $655,577
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10479037

## Citation

> US National Institutes of Health, RePORTER application 10479037, Linking Variants to Multi-scale Phenotypes via a Synthesis of Subnetwork Inference and Deep Learning (5U01HG012039-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10479037. Licensed CC0.

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