Genome sequencing has provided an unprecedented view into the extent of human genetic variation. Yet, our ability to link
specific genetic variants to phenotypes remains limited. Moreover, genetic interactions between complex combinations of
variants likely contribute to the challenge. To discover rules governing genetic interaction networks, we previously
constructed all possible ~18 million yeast double mutants to generate a global yeast genetic interaction map, which reveals
a functional ‘wiring diagram’ of a eukaryotic cell. In the context of the last funding cycle, we systematically analyzed how
the global yeast genetic interaction network responds to different conditions, and we discovered that it is remarkably robust
to environmental perturbation. On the other hand, our systematic analysis of trigenic interactions associated with triple
mutants and genetic interactions involving natural variants revealed the prevalence of complex genetic interactions and their
immense potential to modify phenotype. To explore gene function and genetic networks in human cells, we also established
an efficient genome-wide CRSPR-Cas9 platform for mapping genetic interactions, and we constructed a ‘scaffold’ genetic
network for a reference human cell line. Like the yeast genetic network, the topology of the human network is informative
of gene function and suggests that general properties of genetic networks are highly conserved.
Here, we propose continued systematic analysis of complex genetic interaction networks and phenotypes in yeast, and the
application of the results for the cogent design of experiments to continue mapping genetic networks in human cells.
Aim 1: Conditional phenotypes and genetic networks dynamics in the context of diverse genetic backgrounds. We
will perform systematic phenotypic and genetic analyses in wild, genetically diverse yeast strains to identify genetic
modifiers underlying background-specific gene essentiality. We will also map genetic interactions in wild yeast isolates to
quantify the effect of genetic background on genetic networks and more generally, the genotype-phenotype relationship.
Aim 2: Quantitative single cell read-outs for assaying the phenotypic consequences of genetic variation. We will
produce quantitative cell biological phenotypic profiles associated with gene perturbation and explore the influence of cell
state on the effects of genetic perturbation, using proteome dynamics as a phenotypic read-out. These projects will map
genetic determinants of subcellular morphology, reveal connections between conserved compartments, and establish
methods to use the proteome as a read-out for genotype-phenotype analysis.
Aim 3: Mapping a global genetic interaction network for a human cell line. Based on our current human genetic
interaction dataset, we will select and screen an informative set of query gene mutants, with an emphasis on essential genes,
to expand our scaffold genetic network and efficiently map networks underlying a set of functionally representative protein
complexes. This network will provide a powerful resource for annotating human gene function and identify conserved
network properties that can be used to discover disease gene modifiers, including those underlying cancer cell genetic