Detecting genetic interactions using parallel evolution in experimental populations

Abstract

Eukaryotic genomes contain thousands of genes organized into complex and interconnected genetic interaction networks. Most of our understanding of how genetic variation affects these networks comes from quantitative-trait loci mapping and from the systematic analysis of double-deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae. Evolve and re-sequence experiments are an alternative approach for identifying novel functional variants and genetic interactions, particularly between non-loss-of-function mutations. These experiments leverage natural selection to obtain genotypes with functionally important variants and positive genetic interactions. However, no systematic methods for detecting genetic interactions in these data are yet available. Here, we introduce a computational method based on the idea that variants in genes that interact will co-occur in evolved genotypes more often than expected by chance. We apply this method to a previously published yeast experimental evolution dataset. We find that genetic targets of selection are distributed non-uniformly among evolved genotypes, indicating that genetic interactions had a significant effect on evolutionary trajectories. We identify individual gene pairs with a statistically significant genetic interaction score. The strongest interaction is between genes TRK1 and PHO84, genes that have not been reported to interact in previous systematic studies. Our work demonstrates that leveraging parallelism in experimental evolution is useful for identifying genetic interactions that have escaped detection by other methods. This article is part of the theme issue 'Convergent evolution in the genomics era: new insights and directions'.

Keywords: experimental evolution; genetic interactions; mutual information; parallel evolution.

Figure 1.

Histogram showing the null distribution of the aggregated MI_tot statistic based on 100 000 simulations (see Methods). Observed MI_tot is indicated by the black triangle.

Figure 2.

Fitness advantage of the single TRK1 and PHO84 mutations and of the double mutant. Replicate measurements are plotted as grey circles. Mean estimates are plotted as bold circles ± standard error. The red square indicates the additive expectation for the double mutant.

Figure 3.

Network of all genes identified in significant gene pairs. Edges are scaled by MI_ij and connect all genes that co-occur in the same background at least once. Bolded lines represent significant pairwise MI_ij. Colours correspond to interconnected significant pairs. White circles indicate isolated gene pairs. Modules are labelled by size.

Figure 4.

Hierarchical clustering of genes by pairwise mutual information captures the most significant pairs and networks among significant pairs. Subclusters shown were identified by trimming row and column dendrograms to five groups and identifying the four subclusters containing less than 20 genes.