Systematic Identification and Phenotypic Characterization of causal genetic variants in Rare Disease-Associated Birth Defects

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Project Summary Although the implementation of whole exome sequencing (WES) and whole genome sequencing (WGS) in a clinical setting has greatly facilitated the identification of birth defect-associated genetic variants, distinguishing the specific variants that cause congenital defects remains a major challenge. More specifically, most variants detected in clinical sequencing occur in genes not previously associated with disease or in noncoding regions of the genome that lack predictable functional consequences. Enhancing the ability to illuminate causal variants holds the promise of improving the quality of life for patients and in some cases may provide a window for therapeutic intervention that would otherwise be missed. In this proposal we leverage our institute’s unparalleled pediatric genetic data repository to guide the development of scalable cell-based systems that, when coupled with phenotypic validation in both animal and patient-derived organoid models, will systematically identify genetic variants that are responsible for congenital defects in our undiagnosed rare disease patient population. We will (Aim 1) catalog loss-of-function variants associated with the most prevalent congenital defects in our patient population, perform genome-scale CRISPR screens in relevant organoid models to distinguish variant-harboring genes that play a role in development, and validate the phenotypic consequences of gene loss in a zebrafish model. In parallel, we will (Aim 2) catalog noncoding variants (i.e. intronic, putative cis-regulatory) associated with prevalent congenital defects, develop a suite of massively parallel genomic assays capable of profiling the regulatory impact of noncoding genetic variants at scale, and perturb the expression of candidate variant- associated genes in a zebrafish model to determine the phenotypic consequences. Finally, we will (Aim 3) generate patient-derived organoid models, utilize precision genome engineering in combination with single-cell transcriptomics in patient-derived organoids to validate the causal role of specific variants in congenital defects, and characterize the impact of variants on development using spatial transcriptomics in patient-derived organoids as a proxy. We anticipate that the work outlined in this proposal will establish an experimental framework that can be deployed to identify genetic variants that are responsible for a wide variety of congenital defects.