Whole genome sequencing has provided unprecedented information about human genetic variation. There is growing awareness that interactions between variants play a major role in determining phenotype. Yet, we lack an understanding of how genetic variation translates into genetic interactions that affect an individual. A key to solving this problem requires an understanding of the rules governing genetic networks. During our current funding period, we used the Synthetic Genetic Array method, which we developed to automate yeast genetics, to complete a reference genetic interaction map for yeast. This network provides a global view of the functional organization of a cell and reveals a hierarchical model of cell function. The reference network enabled our efforts to explore biological network dynamics in response to environmental and genetic perturbations, including genetic suppression and triple mutant interactions. Our work underscores the potential of genetic interactions to impact the inference of phenotype from genome sequence information. Although gene editing approaches offer the promise to accelerate similar studies in other systems, many types of important genetic interactions remain relatively unexplored, and can only be mapped on a genome-scale in yeast. Thus, we propose continued analysis of complex genetic interaction networks in yeast, which we will use as a model for designing informative experiments to explore genetic interactions in human cells.
Aim 1 : An iterative computational-experimental approach to map a condition-specific genetic network. We will test specific gene-condition combinations expected to yield many genetic interactions. Based on preliminary analyses, we estimate that a systematic analysis of condition-specific interactions will expand the global genetic interaction network by ~3-fold, providing a resource to explore the influence of environment on genetic network wiring.
Aim 2 : Large-scale mapping of suppressor genetic networks in yeast. We will map extragenic and dosage suppression genetic interaction networks for yeast essential genes. This analysis will uncover novel relationships between genes and provide a template for similar studies in more complex systems.
Aim 3 : Elucidating the landscape and principles of complex genetic interactions. We will map Complex HaploInsufficiency (CHI) interactions between heterozygous alleles of essential genes in a diploid strain and also test the potential of genetic interactions involving naturally occurring genetic variants to modify phenotype. These studies will offer insights into how ploidy and natural variation shape the penetrance of mutant phenotypes and complex traits.
Aim 4 : Translating insights from the global yeast genetic interaction network to human cells. Applying our knowledge of the yeast reference network, we will select and screen an informative set of query gene mutants to efficiently map a scaffold genetic network for a human cell. This network will identify genetic network properties that are generally conserved and provide a resource for annotating human gene function.

Public Health Relevance

The heritability of complex traits, including disease, is governed by the complex interplay of many genetic variants. This project will produce unique datasets and tools that will reveal how groups of genes interact in normal and diseased cells and how gene networks are rewired in response to environmental and genetic insults. The dynamic genetic interaction maps produced by the project will provide insights into gene function, and provide a template for understanding drug action and the link between genotype and phenotype, including the genetic basis of human disease.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG005853-08
Application #
9613843
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Feingold, Elise A
Project Start
2010-09-26
Project End
2021-11-30
Budget Start
2018-12-01
Budget End
2019-11-30
Support Year
8
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Toronto
Department
Type
DUNS #
259999779
City
Toronto
State
ON
Country
Canada
Zip Code
M5 1S8
Kuzmin, Elena; VanderSluis, Benjamin; Wang, Wen et al. (2018) Systematic analysis of complex genetic interactions. Science 360:
Nelson, Justin; Simpkins, Scott W; Safizadeh, Hamid et al. (2018) MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action. Bioinformatics 34:1251-1252
Ciftci-Yilmaz, Sultan; Au, Wei-Chun; Mishra, Prashant K et al. (2018) A Genome-Wide Screen Reveals a Role for the HIR Histone Chaperone Complex in Preventing Mislocalization of Budding Yeast CENP-A. Genetics 210:203-218
Sing, Tina L; Hung, Minnie P; Ohnuki, Shinsuke et al. (2018) The budding yeast RSC complex maintains ploidy by promoting spindle pole body insertion. J Cell Biol 217:2445-2462
Hou, Jing; van Leeuwen, Jolanda; Andrews, Brenda J et al. (2018) Genetic Network Complexity Shapes Background-Dependent Phenotypic Expression. Trends Genet 34:578-586
Bottoms, Scott; Dickinson, Quinn; McGee, Mick et al. (2018) Chemical genomic guided engineering of gamma-valerolactone tolerant yeast. Microb Cell Fact 17:5
VanderSluis, Benjamin; Costanzo, Michael; Billmann, Maximilian et al. (2018) Integrating genetic and protein-protein interaction networks maps a functional wiring diagram of a cell. Curr Opin Microbiol 45:170-179
Kayatekin, Can; Amasino, Audra; Gaglia, Giorgio et al. (2018) Translocon Declogger Ste24 Protects against IAPP Oligomer-Induced Proteotoxicity. Cell 173:62-73.e9
van Leeuwen, Jolanda; Boone, Charles; Andrews, Brenda J (2017) Mapping a diversity of genetic interactions in yeast. Curr Opin Syst Biol 6:14-21
van Leeuwen, Jolanda; Pons, Carles; Boone, Charles et al. (2017) Mechanisms of suppression: The wiring of genetic resilience. Bioessays 39:

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