N'Guessan Arnaud, Tong Wen Yuan, Heydari Hamed, Nguyen Ba Alex N
Department of Cell and Systems Biology, University of Toronto, Ramsay Wright Laboratories, Toronto, Canada.
Department of Biology, University of Toronto at Mississauga, Mississauga, Canada.
Elife. 2025 Jul 28;13:RP93906. doi: 10.7554/eLife.93906.
Genotype-phenotype mapping (GPM), or the association of trait variation to genetic variation, has been a long-lasting problem in biology. The existing approaches to this problem allowed researchers to partially understand within- and between-species variation as well as the emergence or evolution of phenotypes. However, traditional GPM methods typically ignore the transcriptome or have low statistical power due to challenges related to dataset scale. Thus, it is not clear to what extent selection modulates transcriptomes and whether cis- or trans-regulatory elements are more important. To overcome these challenges, we leveraged the cost efficiency and scalability of single-cell RNA sequencing (scRNA-seq) by collecting data from 18,233 yeast cells from 4489 F2 segregants derived from an F1 cross between the laboratory strain BY4741 and the vineyard strain RM11-1a. More precisely, we performed expression quantitative trait loci (eQTL) mapping with the scRNA-seq data to identify single-cell eQTL and transcriptome variation patterns associated with fitness variation inferred from the segregant bulk fitness assay. Due to the larger scale of our dataset and its multidimensionality, we could recapitulate results from decades of work in GPM from yeast bulk assays while revealing new associations between phenotypic and transcriptomic variations at a broad scale. We evaluated the strength of the association between phenotype variation and expression variation, revealed new hotspots of gene expression regulation associated with trait variation, revealed new gene functions with high expression heritability, and highlighted the larger aggregate effect of trans-regulation compared to cis-regulation. Altogether, these results suggest that integrating large-scale scRNA-seq data into GPM improves our understanding of trait variation in the context of transcriptomic regulation.
基因型-表型映射（GPM），即性状变异与基因变异的关联，一直是生物学中的一个长期问题。针对这个问题的现有方法使研究人员能够部分理解物种内部和物种之间的变异以及表型的出现或进化。然而，传统的GPM方法通常忽略转录组，或者由于与数据集规模相关的挑战而统计能力较低。因此，尚不清楚选择在多大程度上调节转录组，以及顺式或反式调控元件哪个更重要。为了克服这些挑战，我们利用单细胞RNA测序（scRNA-seq）的成本效益和可扩展性，从实验室菌株BY4741和葡萄园菌株RM11-1a的F1杂交产生的4489个F2分离株的18233个酵母细胞中收集数据。更确切地说，我们使用scRNA-seq数据进行表达定量性状位点（eQTL）映射，以识别与从分离群体适应性测定推断出的适应性变异相关的单细胞eQTL和转录组变异模式。由于我们的数据集规模更大且具有多维性，我们可以概括酵母大量测定中数十年GPM工作的结果，同时在广泛范围内揭示表型和转录组变异之间的新关联。我们评估了表型变异与表达变异之间关联的强度，揭示了与性状变异相关的基因表达调控新热点，揭示了具有高表达遗传力的新基因功能，并强调了反式调控与顺式调控相比具有更大的总体效应。总之，这些结果表明，将大规模scRNA-seq数据整合到GPM中可以提高我们在转录组调控背景下对性状变异的理解。
