Predicting Synthetic Lethality
Sharyl L. Wong1, Lan O. Zhang2, Amy H. Tong, Debra S.
Goldberg, Oliver D. King, Guillaume Lesage, Marc Vidal, Brenda Andrews, Howard
Bussey, Charles Boone, Frederick P. Roth
1sharyl_wong@student.hms.harvard.edu, Harvard Medical School; 2lan_zhang@student.hms.harvard.edu, Harvard Medical School
Synthetic lethality strongly supports the existence of genetic buffering. It
arises when mutations to a set of genes cause cell death, while mutations to
any subset of these genes do not. Currently comprehensive identification of
synthetic lethal relationships in yeast and particularly in higher organisms
is largely infeasible. Therefore, predicting synthetic lethality may expedite
identification of redundant genes and pathways that buffer an organism from
the phenotypic consequences of genetic mutation. Integrating multiple data
types including co-localization, correlated mRNA expression, physical
interaction, protein function, and sequence homology, we constructed a
probabilistic decision tree with which we successfully predicted synthetic
lethal gene pairs in Saccharomyces cerevisiae. Furthermore, the gene pair
characteristics important in generating predictions may better our
understanding of genetic robustness.