IGVF Affiliate Membership

IGVF Affiliate Membership Application

Last updated 22_04_06

Application process for joining the IGVF Consortium as an affiliate member

An investigator who is interested in applying for affiliate membership to the IGVF Consortium should fill out the application form and submit it via their Institutional Signing Official to IGVF Program Staff (Dan Gilchrist , Stephanie Morris and Mike Pazin ). The application will be reviewed by NHGRI staff to determine whether an investigator will be accepted into the Consortium. Once accepted, participation of members in Consortium activities may be reviewed yearly by NHGRI staff to ensure active participation. At any time, an affiliated member may ask to leave the Consortium, but is expected to honor the confidentiality of any information obtained during Consortium membership as appropriate through standard research collaboration practices.

Affiliate membership does not directly or indirectly imply a commitment to funding by the NIH.

Download the Application.

A list of IGVF participants and projects, including affiliate members, can be found here.

Affiliate Members

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.