Refining the resolution of the yeast genotype-phenotype map using single-cell RNA-sequencing

Area of Science:

• Genetics
• Molecular Biology
• Systems Biology

Background:

• Genotype-phenotype mapping (GPM) traditionally struggles with dataset scale and transcriptome data integration.
• Existing methods offer limited understanding of how selection modulates transcriptomes and the roles of cis- vs. trans-regulatory elements.

Purpose of the Study:

• To overcome limitations in traditional GPM by leveraging single-cell RNA sequencing (scRNA-seq) for large-scale analysis.
• To identify single-cell expression quantitative trait loci (eQTL) and map transcriptome variation to fitness variation in yeast.
• To elucidate the relative importance of cis- and trans-regulation in trait variation.

Main Methods:

• Collected scRNA-seq data from 18,233 yeast cells across 4489 F2 segregants.
• Performed eQTL mapping on scRNA-seq data to identify single-cell eQTL.
• Inferred fitness variation from segregant bulk fitness assays and associated it with transcriptome variation.

Main Results:

• Successfully recapitulated established GPM findings from yeast bulk assays at an unprecedented scale.
• Revealed novel associations between phenotypic and transcriptomic variations, identifying new regulatory hotspots.
• Demonstrated a larger aggregate effect of trans-regulation compared to cis-regulation on trait variation.
• Identified new gene functions with high expression heritability.

Conclusions:

• Integrating large-scale scRNA-seq data significantly enhances GPM by providing a high-resolution view of transcriptomic regulation.
• The study underscores the substantial contribution of trans-regulatory elements to phenotypic variation.
• Findings pave the way for deeper understanding of the genetic basis of complex traits.