Jonathan Heydari (Newcastle University): Bayesian hierarchical modelling for inferring genetic interactions

Identifying genetic interactions for a given micro-organism such as yeast is difficult. Quantitative Fitness Analysis (QFA) is a high-throughput robotic genetic methodology for quantifying the fitness of microbial cultures. A QFA comparison can be used to compare fitness observations for different genotypes and thereby infer genetic interaction strengths.
A QFA comparison consists of a control screen of ~4000 strains and query screen that differs by some query mutation. For each strain ~8 independent cultures are inoculated and photographed over ~10 time points (~1,000,000 overall measurements). Current frequentist statistical approaches to QFA do not model between-genotype variation or difference in genotype variation under different conditions.
In this poster, a Bayesian approach is introduced to evaluate hierarchical models that better reflect the structure of QFA experiments. Our new hierarchical models make the novel use of variable selection to determine where there is evidence of genetic interaction. First, a two-stage approach is presented: a hierarchical logistic model is fitted to microbial culture growth curves and then a hierarchical genetic interaction model. Next, a one-stage Bayesian approach is presented: a joint hierarchical model which models growth and genetic interaction simultaneously. Linear noise approximations of a stochastic logistic growth model are introduced later for improving model fit. Using improved modelling we find new evidence for genes which interact with telomeres in yeast.
Keywords: Big data; Epistasis; Hierarchical; Genetic interaction; Variable selection;
References: J Heydari, C Lawless, D Lydall and D J Wilkinson. Bayesian hierarchical modelling for inferring genetic interactions in yeast, in submission.
J Heydari, C Lawless, D Lydall and D J Wilkinson. Fast Bayesian parameter estimation for stochastic logistic growth models, arXiv:1310.5524.