Impact of Genomic Variation on Function (IGVF) Consortium

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.