# An integrative approach to disease gene discovery combining genetic variation, gene expression, and epigenetics.

> **NIH NIH R00** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $249,000

## Abstract

PROJECT SUMMARY ABSTRACT
 Genome-wide association studies (GWASs) have uncovered hundreds of thousands of disease-associated
genetic variations, but a remarkable disconnect persists between GWAS findings and biological insight required
for clinical treatments and medicine advancement. Pinpointing the functional consequences of variants found in
GWASs is complicated by linkage disequilibrium (LD) and the inability to interpret non-coding variations.
Systematic genetic analysis of high-dimensional molecular and cellular datasets such as transcriptomics,
epigenomics, proteomics, and metabolomics, offers the potential to bridge the gap from complex trait association
to relevant biological processes yet poses unsolved computational and analytical challenges.
 The candidate proposes to address major gaps in existing methodologies for mapping the genetic basis of
molecular phenotypes and integrating multi-omics data to improve disease gene discovery by developing a suite
of open-source statistical methods and publicly available analytical resources. The candidate will: 1) develop a
novel scalable statistical method to detect genome-wide expression quantitative trait loci (eQTL) using large-
scale bulk or single-cell RNA sequencing (scRNA-seq) data with an extension for rare variants; 2) assemble and
analyze more than 24 readily available bulk and scRNA-seq data sets for a comprehensive repository containing
cis- and trans-eQTLs of both common and rare variations; 3) develop an integrative method to improve power
for disease gene discovery by combining epigenetics, genome-wide eQTLs, and genetic variations.
 The proposed research and training plan were carefully designed to confer expertise in four domains: 1)
transcriptomics and epigenomics, 2) statistical methods development, 3) large-scale data analysis and tools,
and 4) professional development. These skills are fundamental to the candidate’s goal of becoming a leading
investigator who develops and applies statistical methods to understand molecular mechanisms of complex
diseases and traits. In addition to research training, the candidate will take coursework to gain greater expertise
in transcriptomics and functional genomics, participate in regular seminars, attend workshops and conferences,
and gain mentorship and teaching experience. All research will be conducted in the Analytic and Translational
Genetics Unit at Massachusetts General Hospital and the Broad Institute with mentorship from renowned
scientists Drs. Benjamin Neale and Mark Daly. Additional guidance from leading experts Drs. Xihong Lin, Ramnik
Xavier, Kristin Ardlie, and Bradley Bernstein will ensure exceptional guidance and support. Overall, the training
environment is outstanding, the mentors and advisors are world-class, the proposed studies address an urgent
unmet need, and the additional skills gained in this award will poise the candidate to establish independent
leadership in leveraging statistical genetics and large-scale mul...

## Key facts

- **NIH application ID:** 11063630
- **Project number:** 4R00HG012222-03
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Wei Zhou
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $249,000
- **Award type:** 4N
- **Project period:** 2024-05-02 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11063630

## Citation

> US National Institutes of Health, RePORTER application 11063630, An integrative approach to disease gene discovery combining genetic variation, gene expression, and epigenetics. (4R00HG012222-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11063630. Licensed CC0.

---

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