Bayesian hierarchical modelling for inferring genetic interactions in yeast

Abstract

Identifying genetic interactions for a given microorganism, such as yeast, is difficult. Quantitative Fitness Analysis (QFA) is a high-throughput experimental and computa tional methodology for quantifying the fitness of microbial cultures. QFA can be used to compare between fitness observations for different genotypes and thereby infer genetic interaction strengths. Current “naive” frequentist statistical approaches used in QFA do not model between-genotype variation or difference in genotype variation under differ ent conditions. In this thesis, a Bayesian approach is introduced to evaluate hierarchical models that better reflect the structure or design of QFA experiments. First, a two-stage approach is presented: a hierarchical logistic model is fitted to microbial culture growth curves and then a hierarchical interaction model is fitted to fitness summaries inferred for each genotype. Next, a one-stage Bayesian approach is presented: a joint hierarchi cal model which simultaneously models fitness and genetic interaction, thereby avoiding passing information between models via a univariate fitness summary. The new hierarchical approaches are then compared using a dataset examining the effect of telomere defects on yeast. By better describing the experimental structure, new evidence is found for genes and complexes which interact with the telomere cap. Various extensions of these models, including models for data transformation, batch effects and intrinsically stochastic growth models are also considered.