Deciphering the impact of genomic variation on function

Abstract

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

Figure 1.. Genomic variation influences genome function and phenotype.

Genomic variation includes SNVs, indels, and structural variants, which can alter protein-coding sequences or noncoding sequences. Genome function encompasses the cell-type specific activities and interactions among regulatory elements, genes and proteins within molecular networks that underlie cellular phenotypes. Organismal phenotypes include quantitative and binary traits in health and disease.

Figure 2.. A map-perturb-predict framework to connect genome variation to genome function and phenotype.

(top) IGVF projects will apply single-cell mapping, genomic perturbations, and predictive modeling, which will interact in a synergistic and iterative fashion (red text); Gray: Examples of experimental approaches, including 10x Multiome, simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq), and Parse Evercode (split-pool combinatorial indexing single-cell RNA-seq), massively parallel reporter assays (MPRA)^,,, Self-Transcribing Assay of RNA Reporters (STARR-seq), CRISPR interference and activation (CRISPRi/a), Variant Abundance by Massively Parallel sequencing (VAMP-seq), RNA FlowFISH, and Cell Painting. (bottom) IGVF projects will address a wide variety of biological questions and utilize diverse biological systems, models, and samples. hPSC: Human pluripotent stem cells, including embryonic stem cells and iPSCs.

Figure 3.. The IGVF Catalog of genome function and the impact of genomic variation.

IGVF will create a catalog linking genomic variation (top) to genome function (middle box) to phenotype (bottom). Purple: Examples of experimental methods applied by IGVF. Red: Relationships where IGVF plans to develop and apply computational models to comprehensively annotate all possible single-nucleotide variants across many cell types. Orange: Relationships where IGVF plans to develop and apply computational methods in a more targeted fashion, for example in the context of certain cellular phenotypes or diseases. Blue: Examples of external resources or ontologies that could interact with and/or be incorporated into this catalog. We note that the listed set of edges represent current plans and are not exhaustive with respect to topics relevant to interpreting genomic variation. Abbreviations and citations: dbSNP, gnomAD, ENCODE, GTEx, chromatin accessibility (ca)QTLs, saturation genome editing (SGE), Variant Abundance by Massively Parallel sequencing (VAMP-seq), MaveDB, HuBMAP, GENCODE, UniProt, Gene Ontology (GO), protein-protein interactions (PPI), IntAct Molecular Interaction Database, NHGRI Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium, Mondo Disease Ontology, Human Phenotype Ontology (HPO), rare variant association studies (RVAS), Online Mendelian Inheritance in Man (OMIM).