# Novel methods to detect and interpret splicing quantitative trait loci

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2022 · $110,241

## Abstract

Summary of Parent Grant
Nearly all genetic variants associated with complex disease are noncoding. Many noncoding
disease risk variants affect the amplitude of gene expression. However, we have identified
mRNA splicing as an additional primary link between genetic variants and complex diseases.
Thus, an understanding of how, and which, genetic variants affect RNA splicing can greatly aid
our understanding of the impact of noncoding variants. Despite the importance of RNA splicing
in mediating genetic risk for disease, the dominant assay to determine mRNA content in a cell
or tissue, RNA-seq of polyadenylated mRNA, primarily captures steady-state mRNA isoforms,
which reflect not only RNA splicing but also other processes such as RNA decay. Further, RNA-
seq provides little information on the pathway of RNA isoform biogenesis. Yet, other assays
beyond RNA-seq that report on the pathway of RNA splicing and in a manner independent of
decay are sorely lacking, significantly compromising our ability to account for how, and which,
genetic variants affect RNA splicing. We propose to first develop a battery of novel genomic
assays to monitor the pathway of splicing and then exploit these assays to define the impact of
genetic variation on splicing. We will optimize such approaches to yield datasets to study the
mechanisms by which genetic variants affect mRNA splicing at unprecedented detail.
Specifically, to achieve our goals, we propose i) to develop genome-wide assays to monitor
splicing in novel ways, ii) to search for splicing quantitative trail loci using these assays, and iii)
to account through an integrated approach for the functional mechanisms by which genetic
variants affect splicing. At the conclusion of this project, we will have developed genomic assays
and computational approaches that allow us to reach a deep understanding of the mechanisms
that link sequence variation to variation in splicing and ultimately to disease.

## Key facts

- **NIH application ID:** 10575802
- **Project number:** 3R01HG011067-03S1
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Yang Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $110,241
- **Award type:** 3
- **Project period:** 2022-04-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10575802

## Citation

> US National Institutes of Health, RePORTER application 10575802, Novel methods to detect and interpret splicing quantitative trait loci (3R01HG011067-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10575802. Licensed CC0.

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