# Novel methods to detect and interpret splicing quantitative trait loci

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2020 · $659,505

## Abstract

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:** 9947586
- **Project number:** 1R01HG011067-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Yang Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $659,505
- **Award type:** 1
- **Project period:** 2020-05-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9947586

## Citation

> US National Institutes of Health, RePORTER application 9947586, Novel methods to detect and interpret splicing quantitative trait loci (1R01HG011067-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9947586. Licensed CC0.

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