# Discovery and validation of neuronal enhancers as development of psychiatric disorders supplement

> **NIH NIH U01** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2020 · $195,697

## Abstract

Project Summary/Abstract
The mandate of the PsychENCODE Data Analysis Core (DAC) includes the development of
novel integrative methodologies to construct a coherent interpretational framework for the data
emerging from the consortium. The complexity of building such a framework lies in the diversity
of experimental assays and their associated confounding factors, as well as in the inherent
uncertainty regarding how the various target biological components function together. As a
result, any analytical and computational methods would need to capture this high dimensionality
of structure in the data. While classical, parallel computation advances at an incredible pace
and continues to serve the needs of the research community, our experience with the ever-
increasing complexity of neuropsychiatric datasets has motivated us to also look at other
promising technological avenues. Accordingly, motivated by recent developments in the field of
quantum computing (QC), we herein explore the use of QC algorithms as applied to two
problems of relevance to the PsychENCODE DAC: (1) the prediction of brain-specific
enhancers based on variants and functional genomic assays (Aim S1; related to Aim 1 of the
parent grant); and (2) the calculation of the contributions of cell types to tissue-level gene
expression and to the occurrence of psychiatric disorders like schizophrenia, autism spectrum
disorder and bipolar disorder (Aim S2; related to Aim 1 of the parent grant). The nascency of
QC hardware technologies and the complexity of simulating quantum algorithms on classical
computing resources means that our exploration will be confined to smaller, judiciously chosen
datasets.Nevertheless, the work in this supplement will serve to evaluate future prospects for
the use of QC algorithms and hardware in genomic analyses. We also consider two different
paradigms of QC, the quantum annealer and the quantum gate model, and weigh their
efficiency relative to classical computing. Finally, we will incorporate the QC and classical
predictions into PsychENCODE consortium's database and online portal for visualizing the
relationships between different genetic and genomic elements, and evaluate corroborating
evidence for the predictions (Aim S3; related to Aim 2 of the parent grant).

## Key facts

- **NIH application ID:** 10047746
- **Project number:** 3U01MH116492-03S1
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** Mark Bender Gerstein
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $195,697
- **Award type:** 3
- **Project period:** 2018-07-06 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10047746

## Citation

> US National Institutes of Health, RePORTER application 10047746, Discovery and validation of neuronal enhancers as development of psychiatric disorders supplement (3U01MH116492-03S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10047746. Licensed CC0.

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