Although genomic technology enables efficient sequencing of genomes, it is still not possible to accurately make predictions of a cells characteristics (phenotype) from knowledge of its genome alone. Understanding the relationship between genome and phenotype has implications not just for understanding biology, but for biomanufacturing, and human health. A key contributor to this gap in knowledge is the lack of understanding of how multiple genetic mutations interact to cause changes in phenotypes. In this project the investigators will develop and validate a hybrid computational-experimental strategy to efficiently map genetic interaction networks for two types of human cells . The project will result in a new computational approach for selecting which genes to mutate in specific cell types to best understand genetic interactions, and will deploy a genome-wide gene editing pipeline to quantify genetic interactions in the human cell lines. The resulting data will be useful for understanding the basic functions of human genes, many of which still remain uncharacterized. The educational broader impacts of the project will address a key challenge in STEM disciplines, namely the underrepresentation of women in science and technology careers by involving middle and high-school students in solving computational biology problems related to the research through their local Girls Who Code chapter.
New advances in the understanding of the relationship between genotype and phenotype demand consideration of genetic interactions. Discovering how combinations of variants in the genome interact to influence cell function and phenotype is a daunting challenge given the vast number of possible gene combinations in the human genome as well as complex cell type- and tissue-specific regulatory mechanisms governing gene function. In this project the investigators will build on their successful collaboration , during which they developed a genetic interaction map for the model eukaryote, yeast, to perform large-scale genetic interaction studies in human cells. Investigators will develop a new computational approach for optimal selection of query mutants for genetic interaction screens in specific cell types using initial measurements of quantitative single mutant phenotypes from three human cell lines, HAP1 (data previously collected), HEK293T and hTERT RPE-1. A genome-scale CRISPR-Cas9 gene editing pipeline will be used to perform genome-wide screens for the selected mutants in the HEK293T and hTERT RPE-1 cell types, which are from distinct cell lineages. The resulting data will be comparatively analyzed to map core conserved and cell type-specific functional modules. The results of the study will help reduce knowledge gap and increase our understanding of how genomic changes correspond to changes in phenotype.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
