# Mapping the role of long noncoding RNAs in gene regulatory networks in schizophrenia

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $770,199

## Abstract

Schizophrenia (SCZ) is a common and debilitating psychiatric disorder that imposes tremendous personal and
societal burdens, and studies have demonstrated substantial heritability reflecting common and rare alleles at
many loci. Most genetic or mechanistic studies of SCZ still focus predominantly on protein-coding genes;
however, the majority of SCZ risk variants reside in noncoding regions of the genome. Long noncoding RNAs
(lncRNAs) account for a significant fraction of functional noncoding elements and (like enhancers) are enriched
for SCZ risk variants, but so far remain largely uncharacterized. Though the functions of most lncRNAs are
unknown, many have now been implicated in the regulation of gene expression and chromatin architecture, and
there is emerging evidence that lncRNAs are important during neurodevelopment. As such, there is an urgent
need to understand their role in SCZ.
Comprehensive profiling of lncRNAs has remained challenging because they are typically expressed at low
levels compared to other transcripts. We therefore propose here to leverage new RNA Capture technologies,
which we have developed and applied to control and autism brains, to deeply profile the lncRNA transcriptome
in a uniquely large resource of diverse data types from post-mortem dorsolateral prefrontal cortex (DLPFC) brain
samples of 350 SCZ cases and 350 matched controls. By deep short-read sequencing of samples enriched for
lncRNAs using specific capture probes, we will identify lncRNAs that are dysregulated in SCZ cases (Aim 1).
We will integrate our noncoding expression data with existing standard RNA-Seq data generated for the same
samples by the CommonMinds Consortium (CMC) to construct coding/noncoding co-expression networks to
identify key regulatory lncRNAs whose dysregulation may contribute to SCZ risk. Network analyses will be
supported by the availability of high-resolution chromosome confirmation capture maps (Hi-C) to identify direct
interactions between lncRNAs and their targets (Aim 2). Finally, in silico regulatory lncRNA predictions will be
validated in-situ and in-vitro by mapping their complete genomic loci using full-length transcript sequencing
technology, and analyzing the effect of lncRNA perturbations on target gene expression and regulatory
interactions in neural cells derived from human induced pluripotent stem cells (hiPSCs) (Aim 3). As part of all
these analyses we will also identify and integrate lncRNA gene expression quantitative trait loci (lncQTL) with
existing PsychENCODE epigenetic histone modifications and open-chromatin QTLs for the same samples, to
assess the mechanistic impacts of SCZ risk variants. Together these results will not only improve our
understanding of the role of lncRNAs in SCZ etiology, potentially providing therapeutic targets, but also provide
a robust framework for future noncoding RNA studies in any disease context.

## Key facts

- **NIH application ID:** 10078634
- **Project number:** 5R01MH109715-04
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Dalila Pinto
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $770,199
- **Award type:** 5
- **Project period:** 2017-12-01 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10078634

## Citation

> US National Institutes of Health, RePORTER application 10078634, Mapping the role of long noncoding RNAs in gene regulatory networks in schizophrenia (5R01MH109715-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10078634. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*