Bayesian hierarchical modelling for inferring genetic interactions in yeast

TL;DR

This paper introduces Bayesian hierarchical models for analyzing high-throughput yeast genetic interaction data, improving inference accuracy by modeling population dynamics and interactions simultaneously.

Contribution

The authors develop Bayesian hierarchical models that better reflect experimental design, allowing more efficient data use and revealing new genetic interactions in yeast.

Findings

Improved detection of genetic interactions in yeast.

More efficient data utilization through hierarchical modeling.

Discovery of new gene interactions with yeast telomeres.

Abstract

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.