# Experimental and Computational Methods for Scaling-up Transcriptome Analyses and Improving Disease Risk Predictions

> **NIH NIH F30** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $45,525

## Abstract

Project Summary
 Understanding the complex regulatory landscape of the genome will uncover fundamental principles of
disease risk and etiology. Transcriptomic studies disentangle the functional nature of the genome by revealing
the effects of variants on gene expression, but the cost and invasiveness of RNA-sequencing imposes limitations
on the continued expansion of these studies. The demand to use data from genomics studies in the clinic is
rising, but we have yet to establish methods of synthesizing genomics data in a way that improves clinical care.
The long term goal of our work is to investigate environmental and genetic determinants of disease and to
develop clinically meaningful ways of stratifying individuals according to these biological factors. Our central
hypotheses are 1.) developing cheaper, more accessible methods of RNA-sequencing will enable massive
scaling of transcriptomic studies and facilitate subsequent discovery from these studies, and 2.) using
transcriptomic data for clinical predictions will augment current measures of genetic prediction, will provide key
biological insights into disease mechanisms, and will increase portability of genetic risk scores across
populations. In aim 1, we propose that sampling saliva, hair follicles, buccal tissue, and urine will allow for
increased enrollment in transcriptomic studies due to the decreased invasiveness of sample collection, and we
also put forward a low-cost RNA-sequencing method to overcome current financial barriers to study expansion.
Aim 2 investigates the expression profiles of these non-invasive tissues and validates their use in understanding
the genetic regulatory architecture of the body. In aim 3, we will generate novel risk scores from genetically
predicted gene expression and from measured gene expression. These scores will be compared to the current
standard for genetic clinical prediction, polygenic risk scores, and we will assess the predictive utility of these
scores in multiethnic cohorts. We will further analyze differences between genetically predicted and measured
gene expression to elucidate genetic and environmental mechanisms of gene expression regulation. Completion
of this research proposal will produce methods central to improving our understanding of human phenotypes
and will introduce ways of interrogating transcriptomic data that will yield essential biological and clinical insights.

## Key facts

- **NIH application ID:** 10155671
- **Project number:** 1F30HG011194-01A1
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Molly Martorella
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $45,525
- **Award type:** 1
- **Project period:** 2020-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10155671

## Citation

> US National Institutes of Health, RePORTER application 10155671, Experimental and Computational Methods for Scaling-up Transcriptome Analyses and Improving Disease Risk Predictions (1F30HG011194-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10155671. Licensed CC0.

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