# Deep Learning Methods for Fine Mapping and Discovery in Genomic Association Studies

> **NIH NIH P20** · BROWN UNIVERSITY · 2020 · $251,184

## Abstract

Nonlinear genetic effects have been proposed as key contributors to missing heritability – the proportion 
of heritability in a trait that is not explained by the top associated additive variants in genome-wide 
association (GWA) studies. To this end, probabilistic machine learning approaches have been shown to 
be useful tools that exhibit great performance gains in genomic selection-based analyses. This is often 
attributed to the fact that popular kernel regression functions and deep neural networks offer scalable 
implementations that implicitly enumerate all possible polynomial interaction effects for all variables in the 
data. Recently, however, these same algorithms have also become criticized as “black box” techniques. 
There is a fundamental interpretability issue where understanding how genetic features are being ranked 
within machine learning methods is an important, yet open, problem. Here, we propose to develop a suite 
of novel methodological approaches that make probabilistic machine learning and deep neural networks 
fully amenable for fine mapping and discovery in genomic sequencing studies (i.e. opening up the black 
box). Our efforts will lead to unified frameworks that produce interpretable summaries detailing 
associations on multiple genomic scales (e.g. SNPs, genes, signaling pathways). The first aim of this 
project is to develop an interpretable significance measure for probabilistic machine learning. The second 
aim is to develop a unified deep learning framework for gene-level and pathway enrichment analysis in 
genome-wide association studies. The third aim is to create distributable software and use it to 
characterize nonlinear genetic effects at multiple genomic scales in real data applications.

## Key facts

- **NIH application ID:** 10350124
- **Project number:** 5P20GM109035-05
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Lorin Crawford
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $251,184
- **Award type:** 5
- **Project period:** 2020-03-01 → 2021-08-03

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10350124

## Citation

> US National Institutes of Health, RePORTER application 10350124, Deep Learning Methods for Fine Mapping and Discovery in Genomic Association Studies (5P20GM109035-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10350124. Licensed CC0.

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