# Advancing Multi-Omics and Electronic Health Records Computational Methodologies

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2020 · $330,670

## Abstract

PROJECT SUMMARY
 Phenomic advances from large-scale electronic health records (EHR) linked to DNA
biobanks have pioneered an efficient approach to genetic discovery that has transformed
human genetic studies, with the enormous potential to provide constraints on relevant biological
mechanisms on a wide spectrum of human phenotypes. Nevertheless, our understanding of the
downstream molecular consequences of genetic associations remains limited and impedes our
ability to develop novel therapeutic strategies for complex diseases. Given their enormous
discovery potential for human genomics and precision medicine, genetic analyses in diverse
populations offer unprecedented opportunities to identify causal genetic mechanisms underlying
human trait variation.
 This research proposal aims to address these convergent developments and critical
gaps and to exert a powerful influence on efforts to expand our understanding of disease
mechanisms and therapeutic possibilities. Here we hypothesize that a comprehensive multi-
omic, phenomic, and trans-ethnic computational methodology will provide a robust and rigorous
framework. This proposal thus has the following aims:
AIM 1: Develop a regularized regression based methodology and a deep learning framework to
improve characterization of the genetic architecture of gene expression and to build robust
prediction models, extending a Transcriptome-Wide Association Study (TWAS) methodology
(called PrediXcan) that we developed.
AIM 2: Develop statistical causal modeling of trait-associated genetic variation through a
convergent TWAS and Mendelian Randomization approach and apply it to thousands of human
traits with available GWAS and EHR data.
AIM 3: Develop analytic approaches and software tools to further genetic analyses in admixed
and multi-ethnic populations and to lay the groundwork for trans-ethnic multi-omic
methodologies, using EHR data (e.g., BioVU, UK Biobank, All of Us).

## Key facts

- **NIH application ID:** 9979509
- **Project number:** 1R01HG011138-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Eric R Gamazon
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $330,670
- **Award type:** 1
- **Project period:** 2020-08-07 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9979509

## Citation

> US National Institutes of Health, RePORTER application 9979509, Advancing Multi-Omics and Electronic Health Records Computational Methodologies (1R01HG011138-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9979509. Licensed CC0.

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