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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.

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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.

Keywords:
ABC transporterscombinatorial genetic analysisdrug effluxdrug resistanceepistasisgenetic modelinggenotype-to-phenotype modelinghigh-order genetic interactionsinterpretable neural networkvisible neural network

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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.