NIH Distributes $185M for Impact of Genomic Variation on Function (IGVF) Consortium

September 29, 2021

By Allison Proffitt

September 28, 2021 | At the beginning of September, the National Institutes of Health launched the Impact of Genomic Variation on Function (IGVF) consortium, a new initiative of NIH’s National Human Genome Research Institute (NHGRI). Now, NHGRI has named the first 25 awards across 30 U.S. research sites totaling approximately $185 million over five years.

Researchers have identified millions of human genomic variants that differ across the world, including thousands of disease-associated ones. The IGVF consortium plans to identify which variants in the genome are relevant for health and disease by integrating experimental methods with advanced computer models.

The program’s goals are 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, and enabling others to perform related studies using these approaches.

“Biomedical researchers have recently made remarkable advances in the experimental and computational methods available for elucidating genome function,” said Carolyn Hutter, Ph.D., director of the NHGRI Division of Genome Sciences, in a press release. “The IGVF consortium will include world leaders in these areas, and together they will leverage these advances to tackle an incredibly challenging and important series of questions related to how genomic variation influences biological function.”

The current IGVF consortium includes labs in five areas: Functional Characterization Centers, Regulatory Network Projects, Mapping Centers, Data and Administrative Coordinating Centers,  and Predictive Modeling Projects.

The 30 U.S. sites that make up those areas include UC San Francisco; University of Washington; Stanford University; Dana-Farber Cancer Institute; University of Texas Southwestern Medical Center; University of North Carolina, Chapel Hill; Children’s Hospital Boston; Massachusetts General Hospital; Brigham and Women’s Hospital; Duke University; Broad Institute; UC Irvine;  California Institute of Technology; University of Michigan; Harvard School of Public Health; Northeastern University; University of Wisconsin; University of Massachusetts Medical School; University of Texas MD Anderson Cancer Center; University of Pennsylvania; University of Pittsburgh; Johns Hopkins University School of Medicine; Sloan Kettering Institute for Cancer Research; UC San Diego; UC Los Angeles; Yale University; Washington University, Saint Louis; and Northwestern University.

For the full breakdown of which groups are working in which areas, see the list online.

In an interview with ASHG News, Stephanie Morris, a Program Director in NHGRI’s Division of Genome Sciences, outlined how the consortium’s results will be integrated into healthcare and research going forward. “Today, clinicians rely on statistical correlations that link genomic variation to disease, which have proven valuable. However, of the tens of thousands of disease-associated variants, researchers have only specifically identified a small number of these variants. The consortium will study and characterize thousands of genomic variants and determine which of these directly impact health and disease, which will greatly expand the evidence available to clinicians,” Morris told ASHG News.

First Projects Kick Off

Some groups have already publicized their intended research projects. The University of California San Diego School of Medicine researchers will receive $6.4 million in grant funding to study how external signals and genetic variations influence the behavior of one cell type in particular: insulin-producing beta cells in the pancreas.

“We plan to develop a roadmap of genetic variations, relevant in beta cells, to predict changes in insulin output—important information that may better enable us to prevent and treat diabetes,” said team lead Maike Sander, MD, professor and director of the Pediatric Diabetes Research Center at UC San Diego School of Medicine, in a press release.

Washington University School of Medicine in St. Louis has received a $7 million grant as part of the consortium and will serve as the data and administrative coordinating center for the multicenter project.

All information generated by the consortium will be made freely available to the research community via a web portal that will be built from the WashU Epigenome Browser, a tool developed by Ting Wang’s team that allows researchers around the world to search and browse genomic data. Because there are thousands of genomic variants associated with disease, and it is not possible to manipulate each variant individually and in each biological setting, consortium researchers also will develop computational modeling approaches to predict the impact of variants on genome function.

Duke University is the recipient of two grants totaling nearly $12 million as part of the consortium. An $8.6 million award will fund the Duke Characterization Center, led by Charlie Gersbach, Greg Crawford and Tim Reddy. The goal of the Duke IGVF Functional Characterization Center is to systematically perturb large numbers of regulatory elements and study their function across different biological contexts. “It’s a critical next step to more fully understand how the genome works,” Crawford told Duke Cancer Institute News.

A second $3.2 million award will fund the Duke Predictive Modeling Center, led by Andrew Allen, David Page, and Sayan Mukherjee. They will take the raw data generated in the Functional Characterization Centers and transform it into consumable information that predicts the effect of variation on function. This work will be focused into three parts. First, they will simulate the data being developed in the Functional Characterization Centers and develop a simulation framework that can replicate the data. The team will also develop new graphical, model-based machine learning approaches that predict the functional effect of noncoding variation on function in diverse cell types. And finally, the team will use population genetics to look for genetic elements in the population that affect function.