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The IGVF will develop a framework for systematically understanding the effects of genomic variation on genome function and how these effects shape phenotypes.
One of the central problems in biology is understanding how genomic variation affects genome function to influence phenotypes. NHGRI initiated a new program, the Impact of Genomic Variation on Function (IGVF) Consortium, to develop a framework for systematically understanding the effects of genomic variation on genome function and how these effects shape phenotypes. The program is based on recommendations from the 2019 workshop "From Genome to Phenotype: Genomic Variation Identification, Association, and Function in Human Health and Disease" (
Visit the IGVF Consortium website .
Explore this Page goals Program Goals
- Systematic perturbation of the genome to assess the impact of genomic variation on genome function and phenotype
- High-resolution identification of where and when genes and regulatory elements function
- Advancement of network-level understanding of the influence of genetic variation and genome function on phenotype
- Development and testing of innovative predictive models of the impact of genomic variation on genome function
- Generation of a resource centered on a catalog of variant impacts and including data, tools, and models that will be shared with the broader research community
- Enabling others to perform related studies using these approaches.
participants
| Awardee | Institution | Title | Award Number |
|---|---|---|---|
| Characterization Awards | |||
| Jay Shendure Nadav Ahituv Martin Kircher | University of Washington UC San Francisco Charite Universitatsmedizin Berlin | Massively parallel characterization of variants and elements impacting transcriptional regulation in dynamic cellular systems | HG011966 |
| Lea Starita Douglas Fowler | University of Washington | The Center for Actionable Variant Analysis; measuring variant function at scale | HG011969 |
| Jesse Engreitz Thomas Quertermous | Stanford University | Stanford Center for Connecting DNA Variants to Function and Phenotype | HG011972 |
| Marc Vidal | Dana-Farber Cancer Institute | Molecular phenotyping of ~100,000 coding variants across Mendelian disease genes | HG011989 |
| Gary Hon William Kraus Nikhil Munshi | University of Texas Southwestern Medical Center | Multiscale functional characterization of genomic variation in human developmental disorders | HG011996 |
| Hyejung Won Michael Love Karen Mohlke | University of North Carolina at Chapel Hill | Systematic in vivo characterization of disease-associated regulatory variants | HG012003 |
| Luca Pinello Daniel Bauer Guillaume Lettre Richard Sherwood | Massachusetts General Hospital Children's Hospital Boston Montreal Heart Institute Brigham and Women's Hospital | Comprehensive characterization of variants underlying heart and blood diseases with CRISPR base editing | HG012010 |
| Charles Gersbach Gregory Crawford Tim Reddy | Duke University | High-throughput functional annotation of gene regulatory elements and variants critical to complex cellular phenotypes | HG012053 |
| Mapping Awards | |||
| Jason Buenrostro Bradley Bernstein | Broad Institute, Harvard University Broad Institute, Massachusetts General Hospital | A foundational resource of functional elements, TF footprints and gene regulatory interactions | HG011986 |
| Ansuman Satpathy | Stanford University | Single-cell Mapping Center for Human Regulatory Elements and Gene Activity | HG012076 |
| Seyed Mortazavi Barbara Wold | UC Irvine California Institute of Technology | Center for Mouse Genomic Variation at Single Cell Resolution | HG012077 |
| Predictive Modeling Awards | |||
| Alan Boyle | University of Michigan | Predicting the impact of genomic variation on cellular states | HG011952 |
| Andrew S. Allen William Majoros Charles D. Page Jr. | Duke University | Design, prediction, and prioritization of systematic perturbations of the human genome | HG011967 |
| Soumya Raychaudhuri Alkes Price Shamil Sunyaev | Brigham and Women's Hospital Harvard School of Public Health Brigham and Women's Hospital | Predicting the impact of genetic variants, genes and pathways on human disease | HG012009 |
| Predrag Radivojac | Northeastern University | Supporting IGVF by modeling genetics, function, and phenotype with machine learning | HG012022 |
| Mark Craven | University of Wisconsin | Linking variants to multi-scale phenotypes via a synthesis of subnetwork inference and deep learning | HG012039 |
| Zhiping Weng Manuel Garber Xihong Lin | University of Massachusetts Medical School University of Massachusetts Medical School Harvard School of Public Health | Predictive modeling of the functional and phenotypic impacts of genetic variants | HG012064 |
| Anshul Kundaje | Stanford University | Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory code | HG012069 |
| Network Awards | |||
| Harinder Singh Jishnu Das | University of Pittsburgh University of Pittsburgh | Linking genome variation to transcriptional network dynamics in human B cells | HG012041 |
| Hao Wu Sreeram Kannan Hongjun Song | University of Pennsylvania | Defining causal roles of genomic variants on gene regulatory networks with spatiotemporally-resolved single-cell multiomics | HG012047 |
| Danwei Huangfu Michael Beer Anna-Katerina Hadjantonakis | Sloan Kettering Institute for Cancer Research Johns Hopkins University School of Medicine Sloan Kettering Institute for Cancer Research | Genomic control of gene regulatory networks governing early human lineage decisions | HG012051 |
| Maike Sander Hannah Carter Kyle Gaulton Bing Ren | UC San Diego | The impact of genomic variation on environment-induced changes in pancreatic beta-cell states | HG012059 |
| Chongyuan Luo Kathrin Plath Noah Zaitlen | UC Los Angeles | Leveraging genetic variation to dissect gene regulatory networks of reprogramming to pluripotency | HG012079 |
| Christina Leslie Alexander Rudensky | Sloan Kettering Institute for Cancer Research | Deciphering the genomics of gene network regulation of T cell and fibroblast states in autoimmune inflammation | HG012103 |
| Data and Administrative Coordinating Center Awards | |||
| J. Michael Cherry Mark Gerstein Benjamin Hitz | Stanford University Yale University Stanford University | A Data and Administrative Coordinating Center for the Impact of Genomic Variation on Function Consortium | HG012012 |
| Ting Wang Feng Yue | Washington University, Saint Louis Northwestern University | WashU-Northwestern Genomic Variation and Function Data and Administrative Coordinating Center | HG012070 |
Participants and Projects affiliate-members Affiliate Members affiliate-membership Affiliate Membership
The IGVF Program offers researchers not currently funded by the IGVF Consortium the opportunity to apply to join the program as non-voting affiliate members. IGVF expects to benefit from the unique expertise affiliated members can bring to the Consortium. IGVF anticipates an affiliated member’s benefits will include the highly interactive research environment, participating in Consortium discussions across a broad range of activities, participating in Consortium analyses and access to data prior to QC.
Affiliate members are expected to contribute to the goals of the IGVF Consortium by generating data and/or analyses, sharing data and/or analyses freely through the IGVF Data and Administrative Coordinating Center (DACC), and/or by contributing to cross- consortium integrative analyses. (An alternative is direct collaboration between an IGVF member and an external researcher, without sharing IGVF resources beyond what that IGVF member has created.) Affiliate members are also expected to be actively engaged in IGVF activities (i.e. participate in working groups as appropriate, attend the IGVF annual meeting) and to abide by all policies approved by the consortium and any other pertinent NIH policies. Failure to abide by these rules and policies may result in suspension of membership.
Affiliate membership does not directly or indirectly imply a commitment to funding by the NIH.
This policy was last updated March 15, 2022.
Application MS Word
Impact of Genomic Variation on Function (Renewal)
September 9, 2024 Council
Consortium for Understanding the Impact of Genomic Variation on Genome Function
February 10, 2020 Council
news News
- Deciphering the impact of genomic variation on function
Nature Perspective, September 4, 2024 - Differences in our DNA have major influences on how our genomes function, but how?
News Feature, September 4, 2024 - NIH providing $185 million for research to advance understanding of how human genome functions
NHGRI Press Release, September 9, 2021 - NHGRI Launches Impact of Genomic Variation on Function (IGVF) Consortium
ASHG News, September 3, 2020
Events
- IGVF Consortium Pre-Application Webinar - September 3, 2020
Sep. 03, 2020
Virtual - IGVF Consortium Pre-Application Webinar - September 9, 2020
Sep. 09, 2020
Virtual
2020-08_IGVF_Applicant_Webinar_FINAL.pdf (791.97 KB)
Frequently Asked Questions for IGVF RFAs
funding-opportunities Expired Funding Opportunities
Active
At this time, there are no current funding opportunities.
Expired
- NOT-HG-20-055 : Notice of Pre-Application Webinars for the Impact of Genomic Variation on Function (IGVF) Consortium FOAs (RFA-HG-20-043, RFA-HG-20-044, RFA-HG-20-045, RFA-HG-20-046, RFA-HG-20-047)
- RFA-HG-20-043 : Systematic Characterization of Genomic Variation on Genomic Function and Phenotype (UM1 Clinical Trial Not Allowed)
Expiration Date: November 5, 2020 - RFA-HG-20-044 : Defining Genomic Influence on Gene Network Regulation (U01 Clinical Trial Not Allowed)
Expiration Date: November 5, 2020 - RFA-HG-20-045 : Single-cell Profiling of Regulatory Element and Gene Activity in Relationship to Genome Function (UM1 Clinical Trial Not Allowed)
Expiration Date: November 5, 2020 - RFA-HG-20-046 : Genomic Variation and Function Data and Administrative Coordinating Center (U24 Clinical Trial Not Allowed)
Expiration Date: November 5, 2020 - RFA-HG-20-047 : Developing Predictive Models of the Impact of Genomic Variation on Function (U01 Clinical Trial Not Allowed)
Expiration Date: November 5, 2020
staff Program Staff Program Directors Program Analysts