Whole-genome sequencing projects are providing unprecedented information about human genetic variation. Polymorphisms abound in the human genome, in both coding and non-coding regions, but it remains a major challenge to associate genome variation with a functional consequence. There is growing awareness that genetic interactions, involving combinations of polymorphic alleles, must play a major role in determining phenotype. Yet, we have a limited understanding of how genetic variation translates into genetic interactions that affect an individual. One of the keys to solving this challenging problem will most certainly be an understanding of the general rules governing genetic networks, and how they are rewired in response to environmental or genetic perturbation. The budding yeast Saccharomyces cerevisiae has served as the pioneer model organism for virtually all genome-scale methods, and offers a unique format for exploring genetic networks. Our group developed the Synthetic Genetic Array (SGA) method, which automates yeast genetics and enables systematic analysis of genetic interactions. In the last grant period, we used the SGA method to complete a reference genetic interaction map for yeast, in standard growth conditions. The global network is rich in functional information, mapping a cellular wiring diagram of pleiotropy. Our analysis also revealed that a portion of the network was not mappable, with ~35% of query gene mutants exhibiting weak digenic genetic interaction profiles. These observations emphasize the need to survey genetic interactions in a condition-specific manner, to understand how genetic networks respond to genetic and other insults that may lead to disease states.
AIM 1 : Mapping condition-specific genetic networks on a genome-wide scale. We will use the SGA method to generate unbiased, genome-scale maps of genetic interactions across diverse conditions. Our systematic approach will generate the largest dynamic biological network of its kind, and will provide a resource to quantify environmental influences on genetic network structure.
AIM 2 : Global mapping of higher-order genetic interaction networks. We will map a network comprised of complex genetic interactions involving more than two genes. We will focus on hub genes, which are highly connected in the genetic network, and may act as general genetic modifiers. Modeling complex genetic interactions involving more than two genes will allow us to derive general rules governing genetic robustness and the relationship between genotype and phenotype.
AIM 3 : Quantification and analysis of condition-specific and higher-order genetic interactions. We will develop a computational framework for measuring genetic interactions across environments and genetic backgrounds, which will provide the basis for addressing several fundamental questions regarding the plasticity of genetic networks and the ability of higher-order genetic interactions t modulate complex phenotypes.

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 #
5R01HG005853-06
Application #
9115915
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Feingold, Elise A
Project Start
2010-09-26
Project End
2017-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
6
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Toronto
Department
Type
DUNS #
259999779
City
Toronto
State
ON
Country
Canada
Zip Code
M5 1S8
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
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
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:

Showing the most recent 10 out of 50 publications