Refining the resolution of the yeast genotype-phenotype map using single-cell RNA-sequencing
Genotype-phenotype mapping (GPM) or the association of trait variation to genetic variation has been a long-lasting problem in biology. The existing solutions to this problem allowed researchers to understand within- and between-species variation as well as the emergence or evolution of phenotypes. However, traditional GPM methods typically ignore the transcriptome layer or have low statistical power due to challenges related to dataset scale. It is also not clear to what extent selection contributes to transcriptomic changes and which components of the transcriptomic regulation are more important between cis- and trans-regulatory elements. To overcome these challenges, we leveraged the cost efficiency and scalability of single-cell RNA-sequencing (scRNA-seq) to reveal unreported components of the yeast GPM. We reconciled data from a bulk fitness assay with scRNA-seq results from 18,233 yeast cells from 4,489 segregants of a cross between the laboratory strain BY4741 and the vineyard strain RM11-1a. The multidimensionality of this dataset allowed us to measure phenotype and expression heritability and partition the variance of cell fitness into genotype and expression components to highlight selective pressure at both levels. Due to the larger scale of our dataset, we were able to recapitulate results from decades of work in GPM from yeast bulk assays while revealing new loci explaining phenotypic and transcriptomic variations. Altogether the results suggest that integrating large-scale scRNA-seq data into GPM improves our understanding of trait variation while being able to recapitulate previous findings from bulk assays.