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Highly Combinatorial Genetic Interaction Analysis Reveals a Multi-Drug Transporter Influence Network
Albi Celaj1, Marinella Gebbia2, Louai Musa2
1Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada.
Cell Systems
|November 1, 2019
Summary
This study introduces X-gene genetic analysis (XGA) to study complex traits by testing many gene combinations in yeast. XGA successfully identified high-order genetic interactions, advancing complex trait research.
Area of Science:
- Genetics
- Systems Biology
- Molecular Biology
Background:
- Complex traits often arise from non-additive interactions between multiple genetic variants.
- Dissecting these high-order genetic interactions is challenging, even in model organisms like yeast (Saccharomyces cerevisiae).
Purpose of the Study:
- To develop and demonstrate a strategy called X-gene genetic analysis (XGA) for engineering and profiling highly combinatorial gene perturbations.
- To apply XGA to yeast ABC transporters to uncover complex genetic interactions influencing compound resistance.
Main Methods:
- Engineered 5,353 yeast strains with random subsets of 16 ABC transporter deletions.
- Profiled each strain's resistance to 16 compounds, generating 85,648 genotype-to-resistance observations.
- Utilized neural networks to model functional relationships and guide further analysis.
Main Results:
- Identified high-order genetic interactions for 13 out of 16 yeast ABC transporters studied.
- Discovered that fluconazole resistance was non-additively influenced by five genes.
- Demonstrated the power of highly combinatorial genetic perturbation in dissecting complex traits.
Conclusions:
- X-gene genetic analysis (XGA) is an effective strategy for dissecting complex traits driven by high-order genetic interactions.
- The approach is scalable and applicable to other model systems, including human cells.
- This work provides a foundation for understanding complex genetic architectures in various biological contexts.
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