  Quantitative Fitness Analysis (QFA) is a high-throughput experimental and
computational methodology for measuring the growth of microbial populations.
QFA screens can be used to compare the health of cell populations with and
without a mutation in a query gene in order to infer genetic interaction
strengths genome-wide, examining thousands of separate genotypes. We introduce
Bayesian, hierarchical models of population growth rates and genetic
interactions that better reflect QFA experimental design than current
approaches. Our new approach models population dynamics and genetic interaction
simultaneously, thereby avoiding passing information between models via a
univariate fitness summary. Matching experimental structure more closely,
Bayesian hierarchical approaches use data more efficiently and find new
evidence for genes which interact with yeast telomeres within a published
dataset.
