R35GM139580
Project Grant
Overview
Grant Description
Modeling the Dynamic Impact of Rare and Common Genetic Variation on Gene Expression and Disease
Understanding the genetic basis of human disease will require a deep understanding of genetic effects on gene expression. The vast majority of disease-risk loci are non-coding, so in order to link them to target genes, cellular pathways, and cell types, we seek to identify which genes' expression they disrupt and under what conditions.
Population studies of gene expression have now provided thousands of "expression quantitative trait loci" (eQTLs) where individual genetic variants are associated with expression of a target gene. While eQTL studies across tissues and populations have served as a valuable resource for querying the likely gene targets of disease loci, key obstacles remain.
First, eQTL studies simply do not address rare genetic variation, thus excluding evaluation of tens of thousands of variants per individual whole genome sequence, and many known pathogenic loci. Second, even among common variants, it is estimated that over half of disease loci do not coincide with any known eQTL, even based on current multi-tissue data. The remainder of disease loci and rare variants require new data and statistical methods in order to characterize their mechanisms.
Here, we propose a research agenda to decipher the complex impact of regulatory genetic variation across the frequency spectrum.
1) First, we will pursue analysis of rare genetic variation and statistical methods for personal whole genome interpretation. Current methods simply do not provide confident predictions for the majority of the variants from whole genome sequencing, and the overall impact of rare regulatory variation on human disease is unknown. We will investigate the use of personal RNA-seq and other functional data to complement whole genome sequence (WGS) in the evaluation of rare variant impact, personal genome interpretation for rare disease patients, and incorporation of rare variants into population studies and genetic risk scores.
2) Second, we will consider common disease variants that are not characterized by current eQTL studies, which almost all use static, adult tissue samples and bulk RNA-seq data. Genetic effects on gene expression are not static, but rather vary over time, cell type, and environment, complicating the identification of disease mechanism. Some disease loci may have only transient effects on a proximal gene's expression during development, for example. We will study temporally dynamic and context-specific genetic effects. In a novel study, we will evaluate genetic effects on individual cell types and states during cellular differentiation using time-series single-cell RNA-seq across individuals. We will also evaluate dynamic genetic effects during disease progression based on patient longitudinal data. Combined with novel statistical methods, these will provide a map of genetic effects over cell type, time, and context that may better explain disease loci. All data, methods, and software will be made publicly available.
Our work will provide a greater understanding of regulatory genetic effects for both common and rare variants, enabling improved identification of the mechanisms underlying heritable disease.
Understanding the genetic basis of human disease will require a deep understanding of genetic effects on gene expression. The vast majority of disease-risk loci are non-coding, so in order to link them to target genes, cellular pathways, and cell types, we seek to identify which genes' expression they disrupt and under what conditions.
Population studies of gene expression have now provided thousands of "expression quantitative trait loci" (eQTLs) where individual genetic variants are associated with expression of a target gene. While eQTL studies across tissues and populations have served as a valuable resource for querying the likely gene targets of disease loci, key obstacles remain.
First, eQTL studies simply do not address rare genetic variation, thus excluding evaluation of tens of thousands of variants per individual whole genome sequence, and many known pathogenic loci. Second, even among common variants, it is estimated that over half of disease loci do not coincide with any known eQTL, even based on current multi-tissue data. The remainder of disease loci and rare variants require new data and statistical methods in order to characterize their mechanisms.
Here, we propose a research agenda to decipher the complex impact of regulatory genetic variation across the frequency spectrum.
1) First, we will pursue analysis of rare genetic variation and statistical methods for personal whole genome interpretation. Current methods simply do not provide confident predictions for the majority of the variants from whole genome sequencing, and the overall impact of rare regulatory variation on human disease is unknown. We will investigate the use of personal RNA-seq and other functional data to complement whole genome sequence (WGS) in the evaluation of rare variant impact, personal genome interpretation for rare disease patients, and incorporation of rare variants into population studies and genetic risk scores.
2) Second, we will consider common disease variants that are not characterized by current eQTL studies, which almost all use static, adult tissue samples and bulk RNA-seq data. Genetic effects on gene expression are not static, but rather vary over time, cell type, and environment, complicating the identification of disease mechanism. Some disease loci may have only transient effects on a proximal gene's expression during development, for example. We will study temporally dynamic and context-specific genetic effects. In a novel study, we will evaluate genetic effects on individual cell types and states during cellular differentiation using time-series single-cell RNA-seq across individuals. We will also evaluate dynamic genetic effects during disease progression based on patient longitudinal data. Combined with novel statistical methods, these will provide a map of genetic effects over cell type, time, and context that may better explain disease loci. All data, methods, and software will be made publicly available.
Our work will provide a greater understanding of regulatory genetic effects for both common and rare variants, enabling improved identification of the mechanisms underlying heritable disease.
Awardee
Funding Goals
THE NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES (NIGMS) SUPPORTS BASIC RESEARCH THAT INCREASES OUR UNDERSTANDING OF BIOLOGICAL PROCESSES AND LAYS THE FOUNDATION FOR ADVANCES IN DISEASE DIAGNOSIS, TREATMENT, AND PREVENTION. NIGMS ALSO SUPPORTS RESEARCH IN SPECIFIC CLINICAL AREAS THAT AFFECT MULTIPLE ORGAN SYSTEMS: ANESTHESIOLOGY AND PERI-OPERATIVE PAIN, CLINICAL PHARMACOLOGY ?COMMON TO MULTIPLE DRUGS AND TREATMENTS, AND INJURY, CRITICAL ILLNESS, SEPSIS, AND WOUND HEALING.? NIGMS-FUNDED SCIENTISTS INVESTIGATE HOW LIVING SYSTEMS WORK AT A RANGE OF LEVELSFROM MOLECULES AND CELLS TO TISSUES AND ORGANSIN RESEARCH ORGANISMS, HUMANS, AND POPULATIONS. ADDITIONALLY, TO ENSURE THE VITALITY AND CONTINUED PRODUCTIVITY OF THE RESEARCH ENTERPRISE, NIGMS PROVIDES LEADERSHIP IN SUPPORTING THE TRAINING OF THE NEXT GENERATION OF SCIENTISTS, ENHANCING THE DIVERSITY OF THE SCIENTIFIC WORKFORCE, AND DEVELOPING RESEARCH CAPACITY THROUGHOUT THE COUNTRY.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Maryland
United States
Geographic Scope
State-Wide
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 400% from $627,783 to $3,138,915.
The Johns Hopkins University was awarded
Deciphering Genetic Variation Impact on Gene Expression and Disease
Project Grant R35GM139580
worth $3,138,915
from the National Institute of General Medical Sciences in January 2020 with work to be completed primarily in Maryland United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.859 Biomedical Research and Research Training.
The Project Grant was awarded through grant opportunity Maximizing Investigators' Research Award (R35 - Clinical Trial Optional).
Status
(Ongoing)
Last Modified 8/20/25
Period of Performance
1/1/21
Start Date
12/31/25
End Date
Funding Split
$3.1M
Federal Obligation
$0.0
Non-Federal Obligation
$3.1M
Total Obligated
Activity Timeline
Transaction History
Modifications to R35GM139580
Additional Detail
Award ID FAIN
R35GM139580
SAI Number
R35GM139580-1862837791
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75NS00 NIH National Institute of General Medical Sciences
Funding Office
75NS00 NIH National Institute of General Medical Sciences
Awardee UEI
FTMTDMBR29C7
Awardee CAGE
5L406
Performance District
MD-90
Senators
Benjamin Cardin
Chris Van Hollen
Chris Van Hollen
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
National Institute of General Medical Sciences, National Institutes of Health, Health and Human Services (075-0851) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,255,566 | 100% |
Modified: 8/20/25