Bayesian hierarchical modelling for inferring genetic interactions in yeast

TL;DR

This paper introduces Bayesian hierarchical models to improve the inference of genetic interactions in yeast, addressing limitations of previous methods by better modeling experimental variability and structure.

Contribution

The paper develops and compares hierarchical Bayesian models for genetic interaction analysis in yeast, enhancing accuracy over traditional frequentist approaches.

Findings

New evidence for gene interactions with telomere defects

Hierarchical models outperform naive methods in capturing variability

Extensions handle batch effects and stochastic growth

Abstract

Identifying genetic interactions for a given microorganism such as yeast is difficult. Quantitative Fitness Analysis (QFA) is a high-throughput experimental and computational 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 different 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 hierarchical model which does not require a univariate summary of fitness, used to pass information between models. 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.