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Impact of Genomic Variation on Function (IGVF) Consortium

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" (workshop report). IGVF is a research consortium that brings investigators together in a highly collaborative effort to examine how genomes function, how genome function shapes phenotypes, and how these processes are influenced by genomic variation. The program utilizes emerging experimental and computational genomic approaches to build a catalog of the impact of genomic variants on genome function and phenotypes.

Visit the IGVF Consortium website.

Program Goals

  1. Systematic perturbation of the genome to assess the impact of genomic variation on genome function and phenotype
  2. High-resolution identification of where and when genes and regulatory elements function
  3. Advancement of network-level understanding of the influence of genetic variation and genome function on phenotype
  4. Development and testing of innovative predictive models of the impact of genomic variation on genome function
  5. 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
  6. Enabling others to perform related studies using these approaches.

Participants and Projects

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
Shayan Mukherjee
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
Nidhi Sahni
Jishnu Das
University of Pittsburgh
The University of Texas MD Anderson Cancer Center
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
Stanford University
Yale 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


Affiliate Members


Xuanyao Liu, University of Chicago

The Liu lab has developed statistical methods for mapping genetic variants associated with genes and pathways in trans, leveraging various data modalities such as bulk and single-cell RNA sequencing, as well as Perturb-seq data.  We have also developed powerful tools to map cis- chromatin QTLs(cQTLs) in ChIP-seq, CUT&TAG and ACAT-seq data. We will work with the IGVF consortium group to comprehensively identify and comprehend trans-regulatory effects on genes and pathways and cis- effects on chromatin; and link these effects to disease.


Debora Marks, Harvard Medical School and Brigham and Women's Hospital

A mission of the Marks lab is to design interpretable AI methods for discovering genotype to phenotype mappings for clinical applications;  for both rare and complex diseases, coding and noncoding genome.  We will hence contribute expertise in new machine learning methodologies, help evaluate their hype, their reality and democratize the access to the learning tools.


Noël Burtt and Jason Flannick, Broad Institute

The Association to Function Knowledge Portal (A2FKP) project, led by Noel Burtt, Jason Flannick, and Kyle Gaulton, is an NHGRI-funded open-access resource that integrates genetic and genomic data, bioinformatic method results, and curated knowledge across common diseases within a web-portal with user friendly access mechanisms and visualizations. As part of the IGVF consortium, we hope to contribute tools and visualizations we develop for the A2FKP to help interpret data produced by IGVF, and in turn integrate data produced by IGVF into the A2FKP.


Wei Li, George Washington University

The Li lab is developing innovative computational methods to optimize genome engineering tools, gain insights into single-cell perturbation datasets, and inform the functions of human genome and genomic variation. Working with IGVF, we are interested in building novel models and gaining biological insights from pooled or single-cell CRISPR screens using various editing technologies.


Eugene Katsevich, University of Pennsylvania

The Katsevich Lab develops statistical and computational methods for the analysis of single-cell CRISPR screen data, including the SCEPTRE software. We will participate in the effort to build an analysis pipeline for the single-cell CRISPR screen data generated by the IGVF Consortium.


Tychele Turner, Washington University in St. Louis

The Turner Lab's initial planned contribution is focused on the genomic, epigenomic, and transcriptomic characterization of well-known human neuronal cell lines and high-throughput functional modeling of genomic variation identified in neurodevelopmental disorders for which we have funding from an R01 grant awarded to my lab from the National Institute of Mental Health (R01MH126933). We use both experimental and computational analyses, and our work will provide a framework for us to continue to study the role of genomic variation in neurodevelopmental disorders, as well as provide information relevant for others in the genomics and neuroscience communities.


Laralynne Przybyla, University of California, San Francisco

Dr. Przybyla's group at the Laboratory for Genomics Research is focused on developing and implementing advanced human disease-relevant models and assays for functional genomics to probe pathways and identify mechanisms that underlie specific areas of disease etiology and progression. In the context of IGVF’s goals, we are implementing our sophisticated platforms to functionally link disease-associated genomic loci located in non-coding regions with gene expression in the context of chronic kidney disease, and we are developing improved computational approaches to identify putative candidate regulatory elements with high likelihood of functional links to disease-relevant gene targets that will be broadly applicable across disease areas and provide novel therapeutic strategies.


Alberto Ciccia, Columbia University

The Ciccia lab will utilize CRISPR-dependent base editing screens to investigate the function of nucleotide variants in DNA repair genes.


Neville Sanjana, New York Genome Center

The Sanjana Lab has identified causal variants for blood traits and their target genes in cis and in trans by combining multi-ancestry genome-wide association studies with CRISPR perturbations and single-cell multiomics. We have also profiled the genetic determinants of chromatin accessibility by combining CRISPR loss-of-function screens with single-cell ATAC-seq, creating an atlas of chromatin modifying complexes/proteins and their impact on changes in chromatin accessibility across the human genome.


Jill Moore, University of Massachusetts Chan Medical School

The Moore lab will expand element-centric deep learning frameworks characterizing the functional capacity of individual cis-regulatory elements, to better understand the impact of genetic variation on gene regulation. In collaboration with other IGVF teams they will use these computational models to prioritize variants and elements for functional testing, taking into account sequence and cell type contexts.


Len Pennacchio and Axel Visel, Lawrence Berkeley National Laboratory

The Pennacchio and Visel labs will annotate noncoding DNA in the human genome with a particular focus on gene regulatory elements through epigenomic-derived data.  They will perform in vivo studies of candidate gene regulatory sequences including allelic variants with presumed functional impacts on expression.  The results will provide noncoding DNA annotation and in vivo validation.


Steve Reilly, Yale School of Medicine

The Reilly lab will contribute functional characterization of non-coding variants linked to human traits, disease, and evolution using a combination of CRISPR and episomal assays. We will collaborate with other IGVF teams to use this high-resolution data to improve variant effect predictors.


Steven Gazal, University of Southern California

The Gazal lab will integrate new functional datasets generated by the IGVF consortium with GWAS and constrained datasets to (1) improve functionally informed fine-mapping, (2) evaluate and combine new variant-to-gene linking strategies, and (3) understand the grammar of regulatory elements at a base-pair resolution.


Rajat Gupta, Harvard Medical School and Brigham and Women's Hospital

The Gupta lab studies the genetics of Coronary Artery Disease and has developed methods to identify the effects of disease-associated variants using Perturb-seq and Cell Painting. We will work with the IGVF consortium group to identify causal variants, genes, and pathways associated with cardiometabolic disease.


Davide Serrugia, St. Anna Children's Cancer Research Institute

The Seruggia lab will develop tiling nuclease and base editor screens to study non-coding sequence variation associated with pediatric leukemia. We plan to dissect non-coding regulatory elements linked to disease, identify target genes and describe their function in hematopoiesis and leukemia.


Katie Pollard, Gladstone Institute of Data Science & Biotechnology, UC San Francisco, Chan Zuckerberg Biohub

The Pollard lab is developing machine learning methods that predict the effects of genetic variants on enhancer activity, genome folding, and epigenetic features. In collaboration with the Ahituv lab and PsychENCODE, we performed massively parallel reporter assays quantifying differential activity of variants in primary human cortical cells and organoids, which will be useful for IGVF methods development and benchmarking. 


Sara Mostafavi, University of Washington

The Mostafavi lab has been developing allele-aware deep neural network models for predicting how combinations of genetic variants at a given loci impact molecular phenotypes like chromatin accessibility. Working with IGVF, we are interested in applying and enhancing these models to ultimately understand the relationship between the full spectrum of genetic variation and cellular outcomes. 


Han Xu, University of Texas MD Anderson Cancer Center

The Xu lab will develop computational methods to minimize the impact of system biases and off-target effects in single-cell CRISPR perturbation screens. They will also leverage the screens on TFs, epigenetic regulators, and cis-regulatory elements to understand how genetic variations perturb transcriptional regulatory networks to cause phenotypic change and disease.


Lee Grimes, Cincinnati Children’s Hospital Medical Center

The Grimes lab applies single-cell technologies, computational genomics and systems biology to hematopoiesis to develop and promote a unifying framework for the analysis of genomic states with their developmental potentials and trajectories. By focusing on underlying genomic regulatory architectures, we will provide a new framework to incisively understand steady state hematopoiesis.


Stephen Yi, University of Texas at Austin

The Yi lab has developed computational and systems biology approaches to investigate the functional impact of coding and noncoding variants on signaling network perturbations in biology. In collaboration with other IGVF members, we will refine and customize our multiomics-integrated network models, coupled with single cell data and deep learning framework for high-resolution characterization and better understanding of regulatory mechanisms in development and disease.


Kristen Brennand, Yale University

The Brennand laboratory integrates human stem cell models and genomic engineering, towards resolving the complex cell-type-specific and context-dependent interplay between the many risk variants linked to brain disease.


John Ray, Benaroya Research Institute

The Ray Lab will investigate tens of thousands of autoimmune disease-associated genetic variants for their effects on cis-regulatory element activity in primary immune cells. They will assess putative causality through combining massively parallel reporter assays with readouts of chromatin state and statistical fine-mapping, and determine variant effects on immune cell function using bulk and single-cell CRISPR-interference screening and base editing.


Thouis Ray Jones, Broad Institute

The Jones lab uses iPSC cell villages to study the cis-regulatory effects of variants on gene expression, chromatin organization and transcription factor binding as cells undergo differentiation. They will work with the IGVF consortium to identify causal variants in disease-relevant cell types.


Matthias Heinig, Helmholtz Zentrum München

The Heinig lab is developing computational models for the design and analysis of single cell perturbation experiments. They will collaborate with other IGVF teams to develop a biostatistical models for optimal experimental design and power analysis of single cell perturbation experiments coupled to single cell RNA sequencing. To analyze the downstream effects of perturbations of regulatory elements, network-based computational approaches will be developed to link downstream trans-target genes through molecular networks to the regulatory element.


Kushal K. Dey, Memorial Sloan Kettering Cancer Center

The Dey lab will implement machine learning algorithms to integrate common and rare variant genetics with IGVF functional data to (1) predict disease-critical units of variants, genes, and cell types, (2) identify causal mediator genes and pathways underlying disease variation, and (3) decode functional pleiotropy across diseases.

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.

Expired Funding Opportunities


At this time, there are no current funding opportunities. 



  • 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

Program Staff

Program Directors

Mike Pazin, Ph.D.
Mike Pazin, Ph.D.
  • Program Director
  • Division of Genome Sciences
Daniel A. Gilchrist, Ph.D.
Daniel A. Gilchrist, Ph.D.
  • Program Director
  • Division of Genome Sciences
Stephanie Morris
Stephanie A. Morris, Ph.D.
  • Program Director
  • Division of Genome Sciences

Program Analysts

Sarah Anstice
Sarah Anstice, B.S.
  • Scientific Program Analyst
  • Division of Genome Sciences
Afia Asare
Afia Asare, B.S.
  • Scientific Program Analyst
  • Division of Genome Sciences

Last updated: November 20, 2023