# Integrating the impacts of genetic variation with massively parallel mRNA and protein barcoding

> **NIH NIH R56** · UNIVERSITY OF WASHINGTON · 2024 · $636,648

## Abstract

SUMMARY
The human genome supports billions of potential variants, and the number of variants detected through
genome sequencing continues to grow rapidly. However, our ability to interpret these variants and identify
those responsible for disease phenotypes still lags. Statistical methods such as genome-wide association
studies struggle with rare or de novo mutations. Approaches based on sequence conservation are less reliable
for the vast majority of variants that fall outside protein coding sequences. These shortcomings of traditional
approaches motivate the development of models for variant stratification. Functional models aim to quantify
how a genetic variant impacts a molecular phenotype -- transcription, splicing, polyadenylation, stability,
translation, and more -- thus creating a link between a variant and an organismal phenotype. Crucially, such
sequence-to-function models can generalize from training data to unseen sequences by learning the regulatory
rules underlying the observed molecular phenotype, thus making it possible to even predict the impact of rare
variants on the process under investigation. In prior work, we showed that models learned from massive
numbers of synthetic reporter constructs could strongly outperform models learned from the comparably small
number of natural examples even in predicting the impact of variants on gene expression in humans. Here, we
extend our work combining synthetic biology with machine learning to build and integrate predictive models of
mRNA stability and translation. In Specific Aim 1, we propose to conduct high-throughput reporter assays to
characterize how variants in both coding and untranslated regions impact mRNA stability and translation.
Regulatory rules are often position-dependent, and a particular sequence motif might have a vastly different
impact on gene expression depending on its location. We thus propose to develop MPRAs that interrogate
CDS as well as 5’ and 3’UTR variants. Furthermore, we propose to perform stability and translation MPRAs
with multiple cell types to uncover aspects of cell type-specific regulation. In Specific Aim 2, we propose to
develop machine learning approaches that enable us to integrate results from multiple measurements and
generalize predictive rules learned from such assays. Additionally, we will apply a deep learning model
interpretation method developed in our lab to generate hypotheses about biological mechanisms, which will be
confirmed via MPRAs in knockdown cell lines. In Specific Aim 3, we propose to develop a novel type of
reporter assay that allows us to directly measure the impact of sequence variation on protein levels using
high-throughput nanopore sequencing of protein-level barcodes. The use of this protein barcoding technology
will improve the resolution, specificity and accuracy of protein measurements by going beyond ribosome
loading measurements, reducing the potential for false positives and negatives.

## Key facts

- **NIH application ID:** 11194039
- **Project number:** 1R56HG013312-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Jeffrey Matthew Nivala
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $636,648
- **Award type:** 1
- **Project period:** 2024-09-25 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11194039

## Citation

> US National Institutes of Health, RePORTER application 11194039, Integrating the impacts of genetic variation with massively parallel mRNA and protein barcoding (1R56HG013312-01A1). Retrieved via AI Analytics 2026-08-17 from https://api.ai-analytics.org/grant/nih/11194039. Licensed CC0.

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