F2 yeast segregants datasets.

A) Reference panel and experiments from the barcoded bulk sequencing previously described in (29). The 99,995 F2 yeast segregants in the reference panel were derived from an F1 cross between a laboratory strain of yeast (BY) and a natural vineyard strain (RM). Thus, they only have 2 possible alleles at each of the 41,594 polymorphic sites. The lineage barcodes enabled fitness estimation from competition assays in 18 environments recapitulating the adaptation to temperature gradients, the ability to process different sources of carbon and the resistance to antifungal compounds. B) Pooled scRNA-seq dataset from a single batch. We performed scRNA-seq of the first batch of F2 segregants (n=4,489) to obtain genotypes that are similar to the reference panel and cell’s expression profiles. The F2 segregant barcodes are typically not expressed so single cells have their own cell barcode which is included in all amplified reads of the same droplet. Non-covered sites, sequencing errors and the presence of reads in the wrong library (index swapping) are corrected for using the HMM described in Figure S1.

scRNA-seq dataset features

. The doublets were defined as scRNA-seq barcodes with high coverage (>=third quartile) without significant genotype association to any F2 segregants and for which the 2 most similar F2 segregants do not share genotypic similarity (R2 < 0.1). The breadth of coverage is defined as the proportion of BY/RM polymorphic sites covered in a single cell.

Single-cell RNA-seq data recapitulate bulk DNA and RNA assays results.

A) Effect of the HMM on the relatedness between single-cell genotypes and their closest reference lineage.

Variance partitioning of the 30C phenotype from scRNA-seq data.

The percentages represent the proportion of fitness variance (whole rectangle area) explained by the components and their overlaps. The ellipse area represents the phenotype variance explained by genotype or expression variation. The black area of the rectangle represents the residual of the model while the other colored areas represent the shared and exclusive components explaining fitness variation. The size of the shared component area (purple) is obtained by subtracting the variance explained by the model in Equation 4 to the one in Equation 2 or in Equation 3 as it is a shared component of trait variation explained by genotype and expression variation. The size of the red genotype-exclusive component is obtained by subtracting the size of the shared component from the variance explained by the model in Equation 2. The size of the blue expression-exclusive component is obtained by subtracting the size of the shared component from the variance explained by the model in Equation 3. Finally, the size of the residuals is obtained by subtracting 1 by the sum of the size of all the defined components (red + blue + purple).

eQTL features underlying trait variation across the BY/RM segregants.

A) Mapping of the 30C QTL in the eQTL hotspots. We represent the hotspots of expression regulation as genomic windows (25 kb) to acknowledge the uncertainty around the real position of the eQTL due to linkage disequilibrium. We annotated the 5 top eQTL hotspots and the eQTL hotspots in which the top additive QTL identified by the BB-QTL mapping of the 30C phenotype are located. In these regions, we represented the most affected trans-regulated genes in red, the most affect cis-regulated gene in blue and the genes of the top QTL in black. The double quotation characters represent the absence of such genes in the associated region. We also represented the rank of the QTL in the set of 159 QTL of the 30C phenotype. B) Partitioning of the expression heritability or explained variance (R2) among cis- and trans-eQTL. Each pair of points connected by a line represents a gene. Green lines represent the genes that are only have trans-eQTL and orange lines represent the genes that have both trans- and cis-eQTL. C) Comparison of the mean effect size between cis- and trans-eQTL. Each pair of points connected by a line represents a gene. The ratio of the average effect size between cis- and trans-eQTL is represented by the line color. The sample size of each eQTL category is represented in the x-axis. This is the number of trans-eQTL and cis-eQTL used for calculating the average effect sizes per gene not the number of points per distribution.