At least half of the genes in the yeast genome are influenced by local eQTLs that segregate between the laboratory strain BY, a close relative of the genome reference strain S288C, and RM, a vineyard isolate that is related to strains commonly used in wine making (Peter et al., 2018). Most of these eQTLs act in cis (Albert et al., 2018), making the BY and RM strains a rich reservoir for identifying causal cis-acting variants. Here, we studied DNA variants in yeast promoters, which we defined as the intergenic region upstream of the transcription start site of a given gene up to the coding region of the adjacent gene, for a maximum of 1000 bases. These promoter regions differ between BY and RM at 11,768 single-nucleotide variants (SNVs) and 2442 insertion/deletion variants (indels) in 3176 genes (Bloom et al., 2013).

To assay the effects of individual variants on gene expression, we designed an MPRA composed of two synthetic promoter libraries encoded by pooled oligonucleotides (Figure 1, Table 1, Supplementary file 1). The ‘TSS’ library assayed all variants in the 144 nucleotides immediately upstream of the transcription start site (Pelechano et al., 2013), a region that is highly enriched for transcription factor binding sites (Lin et al., 2010). When multiple variants were present within this region for a given gene, we designed one sequence that carried the BY allele at all variants, one sequence that carried the RM allele at all variants, and a set of sequences that each carried the RM allele at a single variant and the BY allele at all other variants. The ‘Upstream’ library mostly assayed variants located further upstream of the transcription start site than those in the TSS library, using a pair of sequences that represented 144 bp of genomic DNA centered on the given variant. By design, the TSS and Upstream libraries shared a subset of variants. Together, they assayed 7005 unique variants (5,758 SNVs and 1247 indels; Figure 1—figure supplement 1) in the promoters of 3076 genes, with a median of two variants per gene (Figure 1—figure supplement 2).

The synthesized oligo libraries were placed upstream of a yEGFP reporter gene (Sheff and Thorn, 2004) on a low-copy number yeast plasmid (Figure 1). Prior to adding the yEGFP gene, we added random barcodes with a length of 20 nucleotides downstream of the reporter gene (Figure 1 and Figure 1—figure supplement 3). These barcodes were expressed as part of the 3’ end of the reporter mRNA, such that barcode abundance provided a measure of gene expression driven by the given oligo. We used paired-end sequencing to map each barcode to the oligo it tagged (Figure 1—figure supplement 3). Most oligos were tagged by hundreds of barcodes (Table 1, Figure 1—figure supplement 4) to control for possible influences of the expressed barcodes on mRNA levels. The final plasmid libraries we used in our experiments contained more than 90% of the designed oligos, which assayed 5832 variants in the promoters of 2503 genes (Table 1; see Materials and methods and Figure 1—figure supplement 5 for a description of oligos lost during oligo synthesis and/or cloning). The libraries were transformed into the BY strain and grown in independent replicate cultures (Table 1). We grew each culture to late exponential phase, extracted mRNA and plasmid DNA from each culture, and sequenced barcodes (Figure 1—figure supplement 6) to a median depth of 46 million RNA reads (range 19–70 million) and 31 million DNA reads (range 14–84 million) per sample (Table 1—source data 1).

We conducted three analyses to assess the reliability of our data. First, we measured the reproducibility of oligo-driven reporter expression. We summed the RNA counts of all barcodes assigned to a given oligo (Supplementary file 2), divided them by the respective summed DNA counts to normalize for unequal library composition, and log₂-transformed the resulting ratio. Spearman’s rank correlation coefficients (rho) among pairs of replicates ranged from 0.79 to 0.90 (median = 0.83; all p<2.2e-16) in the Upstream library and from 0.55 to 0.93 (median = 0.74; all p<2.2e-16) in the TSS library (Figure 1—figure supplement 7A & D).

Second, our two libraries included a common set of 200 oligos whose sequences we had sampled from a prior study (Sharon et al., 2012), allowing us to determine reproducibility between replicates, libraries, and with prior work. Median correlations between TSS and Upstream replicates (rho = 0.76; all p<2.2e-16) were nearly as high as those between replicates within each library (TSS: 0.8, Upstream: 0.99; all p<2.2e-16; Figure 1—figure supplement 8). Further, the 200 oligos showed significant, positive correlations with their published expression levels (rho ≥ 0.4, p≤1e-6), in spite of experimental and design differences between studies (Figure 1—figure supplement 7B & E).

Third, we asked if the promoter fragments in our plasmid libraries were able to recapitulate the expression of genes in their native genomic locations. To do so, we computed the average expression driven by all oligos extracted from the promoter region of a given gene. Although each oligo contained at most 150 bp of promoter sequence on a plasmid, expression in both libraries correlated significantly (rho ≥ 0.23, p<2.2e-16) with the native mRNA levels of genes in the genome (Figure 1—figure supplement 7C & F). In sum, our assay quantified the regulatory activity of promoter fragments in a manner that was reproducible within our study and when compared to earlier work, and that reflected the activity of native promoters in the genome.

The median fold change between alleles was 1.4-fold, and only 29 causal variants (6%) altered expression by more than two-fold (Figure 2A). These magnitudes are in good agreement with known genetic effects in the BY/RM strains, in which the vast majority of local eQTLs (Albert et al., 2018) and allele-specific, cis-acting effects on expression (Albert et al., 2014a) alter mRNA abundance by less than 2-fold.

Our MPRA assayed some variants multiple times in different sequence contexts, and we used this redundancy to assess the reproducibility of variant effects. First, 359 variants that reside in the intergenic region between two divergently expressed genes were tested twice in the same library, but in opposite orientation relative to the reporter gene (Figure 2—figure supplement 1A). Variants that were significant in at least one strand orientation were likely to also be significant in the other orientation (Fisher’s exact test (FET): p=0.0003, odds ratio (OR) = 7). These significant variants also agreed well in the direction of variant effect, that is whether the RM allele drives higher or lower expression than the BY allele (FET: p=0.007, OR = 6.1). Second, 527 variants were assayed in both the TSS and the Upstream libraries, where they were embedded in oligos that had the same strand orientation but differed in the exact window of DNA surrounding each variant (Figure 2—figure supplement 1B). Among these, variants that were significant in at least one library were likely to also be significant in the other (FET p=0.007, OR = 4), with significant directional agreement (FET p=0.046, OR = 2.9). Thus, in spite of the small effects of natural sequence variants, our assay was able to reproducibly identify individual causal variants.

Our assay identified several known causal variants. For example, a variant in the promoter of the OLE1 gene affects OLE1 expression in cis and is likely to be the single causal variant in this promoter (Lutz et al., 2019). This variant was highly significant in the MPRA (Figure 2B), while a second variant in the OLE1 promoter was not (Figure 2A).

In the promoter of the SFA1 gene, the MPRA detected a single causal variant out of nine that were assayed (Figure 2A). The RM allele of the causal variant is also present in a strain used for bioethanol production (YJS329), where it has been proposed to increase SFA1 expression by creating an Msn2/4 binding site (Maurer et al., 2017; Zheng et al., 2012). In our assay, this allele increased gene expression significantly (Figure 2C). Taken together, these results show that our MPRA reproducibly detected hundreds of individual cis-acting DNA variants.

The identity of causal variants in QTL regions is a major question in the genetics of complex traits. We asked if the variants identified by our MPRA underlie the many local eQTLs that segregate between the BY and RM strains. Specifically, we turned to local eQTLs mapped in 1012 recombinant individuals obtained by crossing BY and RM (Albert et al., 2018). Because of genetic linkage in the cross, the effects of neighboring variants create one aggregated, spread-out signal of local variation at each gene. For 2884 genes, these effects were strong enough to be detected as local eQTLs at genome-wide significance (Albert et al., 2018). We asked to what extent these local eQTLs can be explained by individual causal variants identified here (Figure 3—source data 1).

Initially, we considered all 2300 genes with available data from both the cross and the MPRA, irrespective of whether their local effect had reached significance in the cross. Likewise, we summed the MPRA effects of all assayed variants for a given gene, irrespective of their significance. There was a significant correlation between local eQTL effects and summed MPRA effects (rho = 0.1, p=1e-6). This overall correlation is likely degraded by noise in both datasets, in particular for small, non-significant effects. Therefore, we first restricted our analyses to variants with significant (5% FDR) MPRA effects. The summed effects of these significant variants showed a stronger correlation with eQTL effects than did those of all variants (rho = 0.24, p=5e-6). Next, to also avoid noise in the eQTL effect estimates, we further restricted the comparison to the 238 genes with strong local eQTLs—those with a LOD score of at least 50. This filter again improved the correlation with summed significant MPRA effects (rho = 0.47, p=2e-5). Allele-specific expression in diploid hybrids provides an independent estimate of the aggregated effect of all cis-acting variants that affect a given gene (Emerson et al., 2010; Ronald et al., 2005; Wittkopp et al., 2004). Genes with significant allele-specific expression in published BY/RM hybrid data (Albert et al., 2014a; Albert et al., 2018) were more likely to have a causal variant in our MPRA (FET: OR = 3.2, p<2.2e-16). These agreements show that, in aggregate, our MPRA successfully captured effects of variants that cause local, cis-acting eQTLs.

We asked whether single MPRA variants could account for local eQTLs. At each gene, we extracted the most significant variant among those with an FDR of ≤5%. The effects of these single variants correlated with strong local eQTLs (rho = 0.41, p=3e-4) and showed significant agreement between the direction of the effects of the variants and the eQTLs (FET: OR = 6.9, p=9e-5; Figure 3A). For example, the MPRA effect for the single causal variant in the OLE1 promoter (Lutz et al., 2019) recapitulated the magnitude of the OLE1 local eQTL almost perfectly (Figure 3A & C). Thus, we identified individual causal variants that underlie local eQTLs.

QTL regions for organismal traits can harbor multiple causal variants (Mackay et al., 2009), but it is less clear whether QTLs for cellular traits such as gene expression have a similarly complex molecular basis. To test whether local eQTLs contain multiple causal variants, we considered, where available, the second-most significant MPRA variant for each gene, if that variant was still significant at an FDR of 5%. The effects of these second variants correlated with eQTL effects (rho = 0.3, p=0.03; Figure 3B). Further, there were significant correlations between the number of nominally significant (p<0.05) MPRA variants identified for a gene and that gene’s local eQTL LOD score (rho = 0.12, p=0.003) and its absolute eQTL effect size (rho = 0.14, p=3e-5). Thus, some local eQTLs are due to multiple causal promoter variants.

Some genes were affected by more than two causal variants. For example, we detected four causal variants at the CWP1 gene (Figure 2A). At each of these variants, the RM allele increased expression, and their summed effects approximated that of the CWP1 local eQTL (Figure 3C).

Some genes had two significant variants with opposite direction of effect. For example, we detected two variants located 47 bases apart in the promoter of the UFO1 gene that had effects of similar magnitude but in opposite direction (Figure 2A). UFO1 does not have a significant local eQTL (Figure 3C), suggesting that this gene is influenced by two variants whose effects cancel each other out.

Together, these observations suggest that the molecular basis of local eQTLs spans a range of scenarios. Some local eQTLs are due to single causal variants, while others arise from joint effects of multiple causal variants. When multiple variants have effects in the same direction, they sum to create a stronger eQTL. When their effects are in the opposite directions, they may be invisible in a cross.

Our plasmid-based MPRA tested variants in a molecular context that differs from the native genomic context in two respects: in the DNA sequence beyond that present on the oligos and, potentially, in the chromatin state in which each variant is embedded. Specifically, nucleosomes influence gene expression (Han and Grunstein, 1988), and may mask or otherwise alter the effects of variants they bind (Rando and Winston, 2012; Zhu et al., 2018). We asked whether the effects of variants located in nucleosome-free regions in the genome (Brogaard et al., 2012) were better captured by our MPRA than those of nucleosome-bound variants. Indeed, the summed effects of significant variants in nucleosome-free regions correlated better with strong local eQTLs than those of nucleosome-bound variants, which showed no significant correlation (Figure 3D). A similar difference was seen for the single most significant MPRA variant (Figure 3D). This better agreement between the genome and the MPRA for the effects of variants in nucleosome-free regions would be expected if these variants are also not bound by nucleosomes on the plasmids, and are thus exposed to a molecular environment that resembles that in the genome. More broadly, this result suggests that nucleosome occupancy and chromatin state may alter the regulatory effects of genomic variants, consistent with MPRAs in human cells (Inoue et al., 2019; Maricque et al., 2019) and with the fact that many human local eQTLs vary among tissues and cell types (GTEx Consortium et al., 2017; Yao et al., 2020).

The presence of multiple causal variants in single promoters raises the question of whether these variants have independent, additive effects or if the effects of some variants are modulated by the presence of the other variant(s) in a non-additive, epistatic interaction. To address this question, we made use of the fact that the TSS library included 342 variant pairs for which there were oligos representing all four combinations of the two alleles at the two variants. For each such variant pair, we tested whether the joint effect of both variants differed from the sum of their individual effects (Figure 4—source data 1).

There were five variant pairs that showed evidence for non-additive effects at an FDR of ≤ 20%. While the π₁ statistic (Storey and Tibshirani, 2003) suggested that at least 34% of variant pairs have non-additive effects, the majority of these cases could not be detected at individual significance, presumably due to limited statistical power.

The five significant pairs showed a range of epistatic patterns (Figure 4 and Figure 4—figure supplement 1). For example, the RM alleles at two variants upstream of DAD2 did not have significant effects individually (p>0.4 compared to the oligo with two BY alleles), but when combined, the two RM alleles drove significantly higher expression (p=2e-6, FDR = 0.01%; Figure 4). RM carries a derived allele at each of these variants, indicated by the fact that the BY alleles are present in a strain from the Taiwanese clade of yeast isolates, which split early from other isolates (including BY and RM) during S. cerevisiae evolution (Peter et al., 2018). Both derived alleles are found at high and nearly identical frequencies in the yeast population (~74%), and the two variants, which are separated by only two nucleotides, show very high linkage disequilibrium (D’=0.999, r² = 0.99). Thus, these two epistatic variants form a tightly linked haplotype that increases gene expression only when both RM alleles are present, perhaps because both variants are needed to create a binding site for a transcriptional activator.

The RM alleles at two SNVs upstream of RTT101 each reduced expression compared to the BY background (FDR < 0.1%), but an oligo carrying both RM alleles reduced expression to a similar degree as either variant alone (Figure 4B). Both RM alleles are derived, and the two variants have very little recombination (D’=0.998). However, the two RM alleles do not form a common haplotype (r² = 0.08), likely because while the RM ‘A’ allele at 352,322 bp on chromosome X is present in 42% of yeast isolates, the RM ‘G’ allele at 352,304 bp is much rarer, with a frequency of 6%. These results suggest that a common promoter variant reduces the expression of RTT101. A second, much rarer variant has a similar effect in isolation, but its effect is obscured in the presence of the more common variant, perhaps because both variants abrogate binding of the same transcriptional activator.

Together, these results show that cis-acting promoter variants can influence gene expression in a non-additive fashion. Such epistatic effects may be widespread, but the resultant deviations from additive expectations are often small, making their systematic detection challenging.

The identification of hundreds of causal variants enabled us to ask whether causal variants tend to share molecular or population genetic characteristics. To address this question, we assembled a set of 2998 features describing each variant. These features comprised sequence characteristics of the alleles, evolutionary properties of the variant, and features that describe the gene that is regulated by the promoter in which the variant resides (Supplementary file 3 and Supplementary file 4). Most features (2,967) described transcription factor (TF) binding (see below).

We divided variants into a ‘causal’ group (defined as variants with an FDR of ≤5%; n = 459) and a non-causal group (variants with a raw p-value>0.2; n = 3,774). For variants located in divergent promoters that were assayed in both orientations, the results from the two orientations were considered separately. For this reason, the number of causal variants in these analyses was slightly higher than the 451 unique variants reported above. We used logistic regression to test the association of each feature with variant causality (Figure 5—source data 1). While these analyses cannot isolate the individual contributions of features that are correlated with each other, they provide an overview of the characteristics of causal variants.

Overall, causal variants were more likely to be SNVs than indels (Figure 5A). At the same time, longer indels were more likely to be causal than shorter indels (Figure 5A). Causal variants were less likely to be bound by a nucleosome in the genome (FDR = 4%). Nucleosomes may shield such variants from DNA-binding factors even on the plasmid reporter if the local DNA sequence around the tested variants can direct nucleosome formation at least partially.

Causal variants were enriched at nucleotides with higher PhastCons scores (FDR = 1%, Figure 5A), suggesting that natural variants that occur at nucleotides that are more conserved across yeast species are more likely to affect gene expression than those at less conserved nucleotides. Variants in the promoters of essential genes were less likely to be causal (FDR = 6%). Causal variants were also less likely to occur in promoters of genes with many synthetic genetic interactions (Figure 5A), which tends to be a property of essential genes (Costanzo et al., 2016). Essential genes are required for yeast viability, and are known to have less genetic (Albert et al., 2018), environmental (Eng et al., 2010), and stochastic (Newman et al., 2006) variation in gene expression. The observation that natural variants in promoters of essential genes are less likely to perturb gene expression is consistent with negative selection acting to purge causal variants from essential gene promoters. In further support of this hypothesis, causal variants had lower derived allele frequencies than non-causal variants (FDR = 10%), as expected if negative selection prevents variants that affect gene expression from rising to higher frequency (Kita et al., 2017; Ronald and Akey, 2007).

Gene expression is shaped by chromatin state as well as by the binding of TFs to cis-regulatory elements (Rando and Winston, 2012). Previous work suggested that while cis-eQTLs can perturb multiple aspects of chromatin, these molecular changes tend to ultimately be caused by sequence-directed differences in TF binding (Degner et al., 2012; Kasowski et al., 2013; Kilpinen et al., 2013; McVicker et al., 2013). To explore the influence of single causal variants on TF binding, we computed the expected propensity of each of 196 TFs to bind to the two alleles at each variant. Because some TFs have different affinities for the two DNA strands at their binding sites (de Boer et al., 2020), we separately considered binding to the sense and the antisense strand, as well as in a strand-agnostic manner. TFs bind to individual ‘strong’ sites that closely match their motif, as well as to weaker sites with imperfect motif matches (de Boer et al., 2020). To capture this distinction, we computed separate feature sets that considered only strong or only weak binding sites. These analyses cannot disambiguate sharing of similar binding motifs by different TFs but probe the overall role of perturbed TF binding in variant causality. Finally, we also aggregated the 196 TF-specific feature sets into summary features that reported maximum and average allelic differences at each variant across the 196 TFs (Materials and methods).

Overall, TF binding was strongly and broadly associated with variant causality (Figure 5B). Across the 2,940 TF-specific features, 481 features for 139 distinct TFs showed significant associations at an FDR of 5% (Figure 5—source data 1). All of the 27 summary features were significant at this threshold. For example, causal variants were more likely to result in gains or losses of strong binding sites (62%; n = 286) than non-causal variants (49%; n = 1,846; FET p=6e-8). These associations were found on both DNA strands (Figure 5B).

Causality was associated with differences in strong as well as weak TF binding (Figure 5B; Figure 5—figure supplement 1). While yeast promoters harbor relatively few strong binding sites, they contain many binding sites that are individually weak but that in combination greatly influence promoter activity (de Boer et al., 2020). In our logistic regression analyses of the 196 individual TFs, differences in the various weak TF-binding metrics showed stronger associations with causality than those in the strong TF-binding metrics (Figure 5C). Because of the relatively low density of strong binding sites, many natural variants change no or only a small number of these sites. In contrast, the high density of weak binding sites presents a large mutational target, such that essentially every variant perturbs one or more weak binding sites. Evidently, these subtle differences can result in detectable expression changes. Overall, these analyses provided clear evidence that variants predicted to perturb TF binding are more likely to alter gene expression than variants not predicted to do so.

The prediction of the effects of individual variants in a given genome is a major challenge for modern genetics and genomics. We tested whether the hundreds of causal variants we identified might enable us to predict the effects of individual cis-acting variants from their annotated features. We built 112 multiple logistic regression models for predicting variant causality from various feature subsets (Figure 6—source data 1). We trained these models using repeated 10-fold cross-validation on a random subset comprising 90% of the causal and non-causal variants and tested model performance on the remaining 10% of the variants. The best model achieved an area under the receiver operating curve (AUC) of 0.71 (Figure 6A; see inset for details on the model). Cross-validated elastic-net regularization, which is less prone to potential overfitting, did not improve model performance (best model AUC = 0.71).

We also built a series of multiple linear regression models that used the same 112 feature combinations to predict the absolute log-fold change caused by a given variant. When fit to the entire dataset, nearly all models fit the data better than expected by chance, with r² values of up to 0.33 at a p-value of 3e-50 for a model including non-TF features as well as all TF features (Figure 6A). A model considering only the TF features fit the data almost as well (r² = 0.32, p=7e-49). The median r² across the 112 models was 0.09. Next, we used 90% of the variants to train these linear regression models using repeated 10-fold cross-validation and predicted absolute log-fold change in the remaining 10% of the variants. The best correlation between predicted and observed fold-changes was rho = 0.15 (p=0.0004). Several other models performed nearly as well (Figure 6A; Figure 6—source data 1). The median model prediction performance was rho = 0.07. Models fit with cross-validated elastic-net regularization performed no better than the corresponding unregularized linear models (best rho = 0.12, p=0.02; median rho = 0.005). Overall, prediction models built from various feature sets were able to predict the identity and effect size of causal variants better than chance, but with modest accuracy.

Recently, de Boer et al. reported a model for predicting reporter gene expression driven by millions of random DNA fragments (de Boer et al., 2020). We tested whether this model was capable of predicting individual variant effects in our data. Using the DNA sequence centered on each variant in our library as input, we predicted reporter gene expression driven by each oligo in our libraries. These predicted expression values showed a significant correlation with measured expression in our MPRA (Figure 6B). We then computed the predicted fold change of each variant as the difference between the predicted expression for the respective BY and RM oligos. The predicted and measured fold-changes of all variants correlated significantly (Figure 6C). This correlation increased for variants that were causal in our data (Figure 6C), although this subset would be unknown during variant effect prediction from genome sequence alone. When we ignored the direction of the fold-changes, prediction performance was reduced for all variants (rho = 0.09, p=1e-8), and degraded completely for causal variants (rho = 0, p=0.9). This suggests that much of the ability of this independent model to predict variant effects in our data derives from the model’s ability to correctly capture whether altered binding of individual transcription factors to a given allele increases or decreases expression.

In summary, models based on individual features trained on our variant effects, as well as a completely independent model (de Boer et al., 2020), showed similar ability to predict variant effects from DNA sequence. Although prediction performance was modest in both cases, the fact that a model trained on an independent experiment can capture some effects in our data demonstrates that the prediction of individual variant effects from DNA sequence alone is becoming possible.

Our design was based on 45,543 variants previously detected from short-read sequencing data of the BY and RM strains (Bloom et al., 2013). Because the BY strain is nearly identical to the genome reference strain (Engel et al., 2014), RM alleles are by definition ‘alternative’ alleles. Variants close to the telomeres were excluded as described in Albert et al., 2014b. Variants included SNVs and short indels (Supplementary file 6). When multiple alternative alleles were reported for a variant, we used the alternative allele with the highest likelihood. We obtained gene annotations for the sacCer3 yeast reference genome build from SGD (https://www.yeastgenome.org) (Cherry et al., 2012). Coding genes, as well as tRNAs, snoRNAs, snRNAs, and other non-coding RNAs were included in the design. Noncoding genes that overlapped a coding gene were removed, along with genes on the mitochondrial and 2µ plasmid genomes.

We defined transcription start sites based on full-length yeast transcript isoforms obtained by capturing and sequencing both the 5’ cap and 3’ polyadenylation site of yeast RNAs (Pelechano et al., 2013). Specifically, we considered transcript isoforms reported in Supplementary Data S2 from Pelechano et al., 2013 that completely contained their annotated gene, avoiding isoforms that initiated transcription inside of annotated gene models or that terminated transcription prematurely. For each gene, we summed the number of reported counts for all isoforms that started at the same 5’ base in YPD medium. Based on these summed counts, we selected the 5’ position with the most read counts to represent the transcription start site for the given gene.

For the TSS library, we included DNA variants between BY and RM located within the 144 bp upstream of the transcription start site for each gene. We designed ‘oligo blocks’, sets of oligos carrying allelic versions of these 144 bp sequences. We extracted the reference genome sequence for these regions to form oligos carrying BY alleles at all variants. For each variant in the 144 bp, we created one additional oligo carrying the RM allele at this variant. We also generated one oligo per block that carried RM alleles at all variants. The TSS library comprised such oligo blocks for all genes that harbored variants in the design space of this library.

For the Upstream library, we considered all variants located between 72 bp upstream of the start codon of each gene and the coding sequence of the next upstream gene, up to a maximum distance of 1 kb. For each such variant, we created an oligo block consisting of one oligo that carried the BY allele at the variant flanked by the reference genome sequence centered on the variant and one oligo that carried the same flanking sequence but with the RM allele at the variant. At any other variants within the sequence covered by the oligo block, both oligos carried the BY allele such that each oligo block assayed a single variant. We subsampled 4547 variants from this design. Specifically, we included all variants located in promoters whose genes had at least some evidence (nominal p<0.05) of allele-specific expression (ASE) in either of two ASE datasets (Albert et al., 2018; Albert et al., 2014a). We also included variants from 1000 randomly selected genes without evidence of ASE (p>0.2), as well as 2062 additional variants selected at random from the remaining variants.

Both designs were strand-specific, such that for genes located on the plus strand we designed oligos based on the reference genome sequence, while for genes located on the minus strand, we used the reverse-complement of the reference sequence. Thus, in the finished reporter constructs, all oligos were correctly oriented with respect to the fluorescent reporter gene.

We removed oligo blocks in which at least one oligo happened to contain a restriction site that would be recognized by the restriction enzymes we used during cloning. We also removed oligo blocks in which RM insertion variants increased the length of at least one oligo to more than the synthesis limit of 200 bp.

In both libraries, we included an identical set of 200 oligos based on promoter fragments studied by Sharon et al., 2012. Because the promoter fragments in that study were shorter (103 bp) than ours (144 bp), we added a random DNA sequence (TATAGAACGGAATCACCTCTGACAAGTAGCGTCAAATCGGT) between the AscI cloning site and the p2 priming site such that this random sequence was removed during cloning (Figure 1—figure supplement 3). This extra sequence safeguarded against preferential PCR amplification of what would otherwise have been shorter oligo molecules.

To each designed oligo sequence, we added restriction sites and library-specific priming sites as shown in Figure 1—figure supplement 3. The resulting oligos had a median length of 195 bp. RM insertion variants increased the length of some oligos, such that the maximum oligo length was 200 bp. Because oligo synthesis is challenging for sequences rich in adenine (which are enriched in promoter sequences), we reverse-complemented the designed sequences of oligos that carried more adenines than thymines. Because the single-stranded oligo libraries are amplified by PCR prior to use, these reverse-complement oligos behaved equivalently equivalent to the designed oligos in downstream experiments. Oligos were purchased as one pool from Agilent Technologies.

Inspired by the design of prior work (Sharon et al., 2012), we placed the promoter fragment library upstream of a fluorescent yEGFP gene on a plasmid (Figure 1—figure supplement 3). Similar to Kosuri et al., 2013, we measured reporter gene expression by quantifying yEGFP mRNA instead of fluorescence signal. Plasmids carried a CEN/ARS origin of replication and an ADH1 terminator downstream of the reporter constructs. We included random 20 bp barcodes downstream of the yEGFP gene, such that the barcodes were transcribed as part of the yEGFP mRNA. Prior to insertion of the yEGFP gene into plasmids, we used paired-end sequencing to generate a dictionary linking each random barcode to the promoter fragment it tagged. We amplified the barcode from plasmid DNA or yEGFP cDNA to generate Illumina sequencing libraries. Reporter expression was quantified by high-throughput sequencing and counting of barcode reads. Details on these procedures are given below.

For the TSS library, the designed promoter fragments were inserted immediately upstream of the yEGFP ORF. For the Upstream library, an invariant HIS3 minimal promoter was inserted between the oligo sequences and the yEGFP ORF (Sharon et al., 2012).

The TSS and Upstream libraries were selectively amplified from the common oligo pool (Agilent). Per library, each of eight parallel reactions contained 0.2 µL of oligo pool diluted 1:10 in TE buffer for ~10 ng template DNA, 5 µL of 2x Kapa library amplification mix (Roche, KK2702), 0.2 µL forward primer (10 µM; TSS: orc281, Upstream: orc279), 0.2 µL reverse primer (10 µM; TSS: orc285; Upstream: orc283), and 4.4 µL water. Cycling conditions were: 98 °C for 2 min, 5 cycles of 98 °C for 3 s, 50 °C for 20 s, 60 °C for 10 s, 10 cycles of 98 °C for 3 s, 60 °C for 30 s, and a final extension at 60 °C for 2 min followed by a hold at 10 °C. The eight parallel reactions were pooled, cleaned up using QiaQuick PCR purification kits (Qiagen #28106), eluted in 30 µL EB buffer, and quantified with Qubit HS DNA kits (Thermo Fisher #Q32854).

To generate barcoded libraries, we performed PCR in eight parallel reactions. Each contained 10 ng of amplified library, 10 µL 5x Phusion buffer, 1 µL dNTP mix (10 mM, Invitrogen #18427013), 1 µL forward primer (10 µM; TSS: orc291, Upstream: orc287), 1 µL reverse primer (10 µM; TSS: orc292, Upstream: orc288), 0.5 µL Phusion HS II DNA Polymerase (ThermoFisher #F549L), and water to 50 µL. Cycling was performed as follows: 98 °C for 2 min, 14 cycles (TSS) or 12 cycles (Upstream) of 98 °C for 3 s, 60 °C for 30 s, 72 °C for 30 s, a final extension at 72 °C for 2 min, and a hold at 10 °C. The eight parallel reactions per library were combined, cleaned up using QiaQuick PCR purification kits, eluted in 30 µL EB buffer, and quantified with Qubit HS DNA kits.

Because the barcodes are part of the reporter mRNA molecules, their sequence potentially influences mRNA abundance in cis, for example by affecting mRNA stability. Indeed, pilot experiments suggested that barcodes with a high number of guanines had lower expression, in line with known effects of guanines in the 3’UTR (Shalem et al., 2015). Barcodes starting with two guanines had particularly low expression, and an adenine at the first position increased expression variance. To increase overall library expression while maintaining high barcode complexity, all experiments reported here used random barcodes in which (1) the first base was either cytosine or thymine, (2) the next three positions and every remaining even position contained no guanines but each of the other three DNA bases at equal frequency, and (3) the remaining odd positions carried each of the four bases at equal frequency.

The base plasmid ‘RCP83’ was generated synthetically (Gen9/Ginkgo Bioworks) with a bacterial ampicillin marker and a yeast kanMX resistance cassette (Supplementary file 7 contains the map of this plasmid after completed library construction; the RCP83 backbone is available at Addgene under ID #163466). The barcoded library was directionally ligated into the plasmid at an SfiI site. Upstream of this site, the plasmid contained the ‘PS1’ priming site used in barcode annotation (see below). Downstream of the SfiI site, the plasmid carried an ADH1 terminator, ensuring efficient termination of the reporter constructs. We isolated RCP83 from E. coli cultures by maxi-prep using Qiagen Plasmid Plus Purification kits (Qiagen #12963, #12965) and quantified plasmid DNA using Qubit dsDNA HS Assay Kit (Thermo Fisher #Q32854). We digested 10 µg of RCP83 using 10 µL SfiI enzyme (NEB, #R0123S) in 50 µL 10x NEB CutSmart buffer and water to 500 µL. This mixture was incubated at 50 °C for 4 hr and cooled to room temperature. To prevent religation of the backbone, we added 10 µL of Shrimp Alkaline Phosphatase (NEB #M0371L) and incubated at 37 °C for 1 hr. We purified the digested vector from a 0.8% agarose gel (1.2 g agarose Fisher #21-255-00GM) in 150 mL 1x TAE (Fisher #FERB49) using a QiaQuick Gel Extraction Kit (Qiagen #28706) and quantified using a Qubit dsDNA HS Assay Kit.

We digested 1 µg of barcoded library using 5 µL SfiI enzyme, 10 µL 10x NEB CutSmart buffer, and water to 100 µL at 50 °C for 2 hr. Digested insert was purified using QiaQuick PCR purification and eluted in 30 µL EB.

Ligation of the digested library into the digested backbone was performed by mixing 1 µg of vector, 100 ng of library, 80 µL of 10x T4 DNA ligase buffer, 4 µL of T4 DNA ligase (NEB #M0202M), and water to 800 µL. The mixture was incubated at 16 °C for 18 hr, 65 °C for 20 min, and held at 12 °C. The reaction was cleaned up with a DNA Clean and Concentrator kit (Zymo Research, #D4013) and eluted in 25 µL water.

Transformation into E. coli was performed by electroporation into E. cloni 10G SUPREME Electrocompetent Cells (Lucigen, #60080–2), in 11 parallel transformations (1 mL each) on a Bio-Rad MicroPulser (Bio-Rad, #165–2100). The parallel transformations were combined and mixed with a total of 9.5 mL of recovery medium. To estimate the number of transformants obtained, we plated a dilution series (10-fold dilution steps from 1:100 to 1:10⁶) on LB carbenicillin (100 µg / mL) plates, grew the plates overnight at 30 °C and counted colonies after 24 hr. We obtained an approximate of 25 million and 230 million transformants for the TSS and Upstream libraries, respectively. Negative controls, for which we had performed the ligation and transformation with digested backbone but no insert, yielded negligible numbers of colonies. The transformed cells were plated on 15 cm LB + carbenicillin plates (500 µL per plate), grown overnight at 30 °C, and scraped into a total of 25 mL LB medium. The optical density (OD₆₀₀) of these cells suspensions was determined, and 2 OD₆₀₀ units (~1 billion cells) were grown overnight at 30 °C in 400 mL LB + carbenicillin. After 24 hr, plasmids were isolated from 200 mL of these cultures using Qiagen Plasmid Plus Purification kits (Qiagen #12963, #12965). The remaining 200 mL of the overnight cultures were spun down, resuspended in 6 mL 20% glycerol, and frozen at −80 °C in 1 mL ultra-concentrated aliquots.

We annotated designed promoters to random barcodes by high-throughput sequencing. To generate the sequencing libraries, we performed four parallel PCR reactions each for the TSS and the Upstream library. In each reaction, we combined 800 ng of plasmid library with 25 µL 2x Kapa library amplification mix (Roche, #KK2702), 1 µL forward primer (10 µM; orc295), 1 µL reverse primer (10 µM; orc296), and water to 50 µL. PCR was performed as follows: 98 °C for 2 min, 10 cycles of 98 °C for 3 s, 72 °C for 30 s, and a final extension at 72 °C for 2 min with a hold at 12 °C. The four reactions were combined, and clean-up performed using 360 µL AmPure XP beads for 200 µL combined reactions. Beads were incubated with the library at room temperature for 5 min, separated on a magnetic rack, washed twice with 500 µL 80% EtOH, and eluted from the beads with 50 µL elution (TE) buffer for 5 min. Sequencing libraries were quantified using qPCR Library Quantification kits (Roche, #KK4824).

We sequenced these annotation libraries in paired-end configuration with 250 bp read length on two lanes of an Illumina HiSeq 2500 instrument. At this read length, both of the paired reads covered the barcode and the associated oligo entirely. This configuration aided in distinguishing sequencing errors, which are present on only one of the paired reads, from synthesis and PCR errors in the sequenced molecule and are therefore present in both paired reads. Paired reads were merged using PEAR (Zhang et al., 2014) using parameters -v 75 m 250 j 8 -y 2G -c 40 -b 64. Between 75 and 85% of read pairs were merged successfully.

We retained merged reads that contained two invariant sequences expected to be present in well-formed sequencing reads: in between the oligo and the barcode (TSS: CCTGCAGGGGTTTAGCCGGCGTG; Upstream: CCTGCAGGGTTCCGCAGCCACAC; these correspond to the AscI, p2, and SbfI sequences; Figure 1—figure supplement 3, Supplementary file 5) and upstream of the oligo (GGCCGTAATGGCC in both libraries; corresponding to the SfiI-A site included in the synthetic oligo sequences; Figure 1—figure supplement 3, Supplementary file 5). These invariant sequences were used to locate the positions of the barcode and oligo in each read. This filter retained ~ 170 million reads in the TSS library and ~125 million reads in the Upstream library, representing 24 million (TSS) and 46 million (Upstream) unique barcodes. Most of these unique barcodes were rare (Figure 1—figure supplement 4), as indicated by median read counts per barcode of two (TSS) and one (Upstream). We did not group barcodes with similar sequences at this stage.

We examined barcodes that appeared to tag multiple oligos and concluded that the overwhelming majority of barcodes tagged a single oligo. Specifically, for the overwhelming majority of cases apparent tagging of multiple oligos, a single ‘primary’ oligo dominated the barcode, usually making up at least 90% of oligo reads for the barcode. Examination of the ‘secondary’ oligos showed that these usually differed from the primary oligo by a single nucleotide, most often involving a skipped base. Because our paired-end sequencing strategy reduced sequencing errors, we deemed it likely that these secondary oligos arose as PCR errors during barcoding or Illumina sequencing library generation. In downstream analyses, we only considered the primary oligo as the oligo tagged by the given barcode.

We next mapped these primary oligos to our designed sequences. Synthesis and PCR errors result in oligos that do not match the designed sequences perfectly. Because our interest was in the effects of individual sequence variants that usually alter just a single nucleotide, errors in the oligo molecules run the risk of dominating the modest effects exerted by natural regulatory variants. Therefore, we retained only barcodes for which the primary oligo perfectly matched a designed oligo. In the TSS library, we retained 9.2 million barcodes (38% of unique barcodes), while in the Upstream library we retained 20 million barcodes (43%).

In the TSS library, these barcodes tagged 6565 oligos, which was 91% of all designed TSS oligos. In the Upstream library, the barcodes tagged 9646 oligos, representing 98% of the design. Examination of designed oligos that were not present in the cloned libraries revealed that oligos starting with a guanine had lower representation in the library (Figure 1—figure supplement 5), especially for oligos that started with two guanines. This technical bias was more apparent in the TSS library, in which a higher fraction of oligos started with disfavored nucleotides than in the Upstream library.

Raw data and barcode assignments to oligos are available under GEO accession GSE155944 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE155944).

After plasmid library generation, the fluorescent reporter gene was subcloned in between the promoter fragments and the barcodes of the annotated plasmid libraries by restriction digestion and ligation. Specifically, we used a codon-optimized yEGFP based on sequence from plasmid pKT0127, a gift from Kurt Thorn (Addgene plasmid #8728; https://www.addgene.org/8728/). In the Upstream library, the 100 bases upstream of the HIS3 gene (Supplementary file 5) were included as an invariant minimal promoter between the oligo library and yEGFP, as in Sharon et al., 2012. yEGFP and the HIS3 minimal promoter sequences were synthesized by Genewiz and were amplified using primers orc362_yeGFP (TSS) or orc362_HIS3 (Upstream), and orc363. These sequences were subcloned into the base plasmid (RCP83) to generate the final reporter plasmids.

Reporter gene inserts and the barcode-mapped plasmid library were digested as follows. We mixed 20 µg of insert or 15 µg plasmid library with 5 µL AscI (NEB R0558L), 5 µL SbfI-HF (NEB R3642L), 50 µL 10x CutSmart NEB buffer, and water to 500 µL. These mixtures were incubated at room temperature for 2 hr (insert) or 4 hr (plasmid library). The library plasmids were cooled to room temperature. We added 10 µL rSAP (NEB #M0371L) and incubated at 37 °C for 1 hr to prevent plasmid re-ligation. Digests were gel purified on 2% (insert) agarose or 0.8% (plasmid library), and bands at 700 bp (insert) or 6 kb (plasmid library) cut and purified using QiaQuick Gel Extraction Kit (Qiagen #28706) with elution into 30 µL EB.

Ligation of the digested reporter gene into the digested library plasmids was performed by mixing 2 µg (TSS) or 1 µg (Upstream) of vector, 700 ng (TSS) or 233 ng (Upstream) of insert, 80 µL of 10x T4 DNA ligase buffer, 8 µL of T4 DNA ligase (NEB #M0202M), and water to 800 µL. The mixture was incubated at 16 °C for 18 hr, 65 °C for 20 min, and held at 12 °C. The reaction was cleaned up and eluted in 25 µL water using the Zymo DNA clean and concentrator kit.

The ligated reactions were transformed into E. coli as described above for first step cloning, with 11 parallel transformations per library. Yields were 33 million (TSS) and 1.9 million (Upstream) cells per mL. Per library, 10 mL transformation mix was plated on twenty 15 cm LB + carbenicillin plates, grown overnight at 30 °C, and harvested by scraping in 5 mL LB per plate. From the resulting dense cell mix, we grew 2 billion cells in 100 mL LB + carbenicillin medium at 30 °C for ~ 20 hr for plasmid isolation. We condensed 45 mL of cells into 4 mL aliquots of 30% glycerol.

For the TSS library, we grew one aliquot overnight in 400 mL LB + carbenicillin medium at 30 °C and performed three maxi preps and two mega preps (200 mL each) using Qiagen Plasmid Plus Purification kits (Qiagen #12963, #12965, #12981). For the Upstream library, we sent glycerol stocks for large-scale plasmid prep (Genewiz) yielding 491 µg of plasmid library.

Library integrity was confirmed by restriction analysis with AscI and SbfI-HF. We also created next-generation sequencing libraries targeting the barcodes as described below and sequenced them on an Illumina MiSeq instrument to confirm that the libraries had retained high barcode complexity after subcloning.

We used a prototrophic yeast laboratory strain BY (MATa) without resistance cassettes (YLK1879) (Bloom et al., 2013).

YNB (MSG) + glucose + G418 medium was prepared as follows: dissolve 6.7 g YNB without amino acids and without NH₄SO₄ and 1 g monosodium glutamate in 900 mL H₂O, autoclave, let cool to room temperature, add 100 mL sterile glucose (20%), add 1 mL G418 (1,000X).

YPD medium was made by combining 5 g yeast extract (Fisher #212720), 10 g peptone (Fisher #211820), and 435 mL milliQ water, autoclaving, and adding 50 mL 20% glucose (Fisher #901521).

The libraries were transformed into using the LiAc method (Gietz and Schiestl, 2007). For the TSS library, we transformed 1.6 L of yeast growing in YPD at an OD₆₀₀ of 1.25 with 248 µg plasmid DNA. We split the 1.6 L culture in half, harvested by centrifugation at 3,000 g for 5 min at 20 °C, washed twice with water (once with half and once with 1/5 of the culture volume), and pelleted by centrifugation. Each of the two cell pellets was mixed with transformation mix (28.8 mL PEG 50% w/v, 4.32 mL LiAc 1M, 6 mL single-stranded carrier DNA at 2 mg / mL, water to 43.2 mL, as well as half the plasmid DNA) in a 50 mL conical tube. Prior to making the transformation mix, single-stranded carrier DNA was denatured for 5 min in a boiling water bath and chilled on watery ice. Transformation mix was kept on ice until use. Cells mixed with transformation mix were heat shocked at 42 °C for 60 min in a shaking water bath. To recover the cells, we poured the transformation mixture into 1 L YPD medium pre-warmed to 30 °C and shook them at 30 °C for 4 hr. Recovered cells were spun at 5,000 g for 5 min, the liquid decanted, and the pellet resuspended in 8 mL PBS buffer. We plated the mixture on fourteen 150 × 15 mm plates (Fisher #08-757-14; 500 µL YPD + G418 per plate) and grew the cells at 30 °C for 2 d. A dilution series plated in parallel indicated 1.16 million cells per 500 µL plated cells, for a total of 16.2 million yeast transformants.

Plates were scraped with 5 mL PBS per plate, the harvested cells combined, spun (3,000 rpm for 5 min), and resuspended in 30 mL PBS. This cell suspension had an OD₆₀₀ of 128, as estimated by measuring a dilution series. We inoculated 400 million cells (104 µL) in 250 mL YNB (MSG) + glucose + G418 medium, grew them at 30 °C for 26 hr, measured OD₆₀₀, and stored aliquots (750 µL culture in 750 µL 40% glycerol) at −80 °C. Each aliquot contained ~ 60 million cells.

The Upstream library was processed similarly, with the following differences. Transformation was performed using 320 µg of plasmid DNA and resulted in a total of 67 million transformants. About 100 OD units were harvested from the plates, and 1 billion cells (133 µL) grown overnight. Stored aliquots from this culture contained 250 million cells in 500 µL.

Samples were processed using two different sets of protocols. Briefly, the initial six TSS replicates were processed at smaller scale and with two-step PCR reactions. Below, this protocol is called ‘Protocol 1’. The remaining TSS samples and all Upstream samples were processed at larger scale and using one-step PCR reactions (‘Protocol 2’). See Table 1—source data 1 for further details on each sample.

All PCR products from a given batch, in particular those made from DNA and cDNA from the same sample, were quantified (using Qubit or qPCR, see above) and pooled to equal molarity. Pooled libraries were gel extracted using QiaQuick Gel Extraction kits from 2% gels with 1% EtBr, run alongside a 50 bp DNA ladder. The libraries were visible in sharp bands narrowly centered on 198 bp. Excised pools were eluted in 30 µL EB buffer. Pooled libraries were quantified using qPCR Library Quantification (KAPA, #KK4824).

Sequencing was performed on Illumina HiSeq 2500 instruments in ‘rapid mode’ using 15–20% phiX spike-in, reading 50 bp single ends and the i7 index.

Sequences were demultiplexed using a custom php script that allowed up to one mismatch between the expected and the observed index. Indexes for each sample are available in Table 1 – Source Data one and Supplementary file 5. In each sample, we counted the number of reads that contained a unique barcode. Only barcodes that had been previously observed in the annotation runs were retained. Barcode reads that did not perfectly match the annotation were excluded. While this also excluded reads with sequence errors that could in principle be added to the analyses, the resulting data loss was minor: across all DNA and RNA samples, a median of 85% (a range of 75–90%) of reads mapped to known barcodes. Barcode counts for technical replicates, in which multiple indexed libraries had been created from the same growth culture, were summed. As a metric for the expression driven by each promoter oligo, we considered the log₂ ratio of summed RNA counts of all barcodes assigned to a given oligo, divided by the summed DNA counts. We used these ratios for visualization and comparison to other data, but not in significance testing (see below).

For two out of twelve TSS samples and two out of six Upstream samples, DNA counts were unavailable. We replaced these DNA counts with those from one other sample from the same batch (Table 1 – Source Data 1). In the Upstream samples, we tested three additional strategies for addressing missing DNA values: replacement with the sum of all Upstream DNA samples, replacement with the counts from the Upstream annotation run, or sample exclusion. These different treatments did influence how many variants reached statistical significance, but only very slightly altered the estimated variant effect sizes (fold-changes). Eliminating the two Upstream samples with missing DNA would have resulted in fewer significant variants than we chose to report here, presumably due to reduced power (Myint et al., 2019). Among the options that retained all samples with RNA counts, our choice of using DNA counts from another sample from the same batch resulted in the fewest significant variant effects. Thus, our treatment of missing DNA was conservative, while still allowing us to use RNA data from all available samples.

To compare the expression driven by promoter fragments to native gene expression in the genome, we computed the average expression driven by all fragments (irrespective of which allele they carried) designed from the promoter of the given gene.

Raw data and barcode counts are available under GEO accession GSE155944 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE155944).

Tests for differential allele activity were performed in the ‘mpra’ R package (Myint et al., 2019). At each variant, we performed pairwise comparisons of the expression driven by the RM allele versus that driven by the BY allele. When there were multiple variants within a promoter fragment assayed by the TSS library, we computed one test per variant such that the oligo carrying the RM allele for one particular variant was compared to the oligo carrying BY alleles at all variants.

We analyzed only those oligos that had summed barcode counts larger than zero in every replicate in both DNA and RNA data. After this filter, 2427 unique variants in 1429 promoters remained in the TSS library. In the Upstream library, 4467 variants in 1824 promoters remained.

We used the ‘sum’ barcode aggregation option in mpra, as well as mpra’s normalization for library size. When a variant was assayed multiple times on a given strand in the two libraries, we used the smallest p-value and its associated fold change for downstream analyses. FDR was calculated by the mpra package (Myint et al., 2019).

To estimate the fraction of variants that have effects irrespective of their individual significance, we computed the π₁ statistic (Storey and Tibshirani, 2003). To do so, we used the qvalue package in R (Storey et al., 2020) to obtain the π₀ statistic (the proportion of statistical tests that are truly null) from the distribution of p-values of individual variants and then calculated π₁ = 1 – π₀.

Local eQTL effects were obtained from Albert et al., 2018. Effect sizes were expressed as log-fold changes of the RM allele relative to the BY allele. Because eQTLs can span wide genomic intervals, effect sizes and LOD scores of local eQTLs can differ slightly depending on whether they are determined at the peak marker of the given local eQTL or at the location of the gene itself. To use a metric that can be applied to all genes with MPRA data irrespective of whether the gene had a significant local eQTL, we used effects and LOD scores at the gene location. Confidence intervals for Spearman’s rank correlation were computed using the DescTools package (Signorell et al., 2020).

Data on allele-specific mRNA expression in the BY/RM hybrid were obtained from Source Data seven in Albert et al., 2018, which comprised allele-specific expression data from two datasets (Albert et al., 2018; Albert et al., 2014a). We performed a Fisher’s exact test on whether genes with at least one causal MPRA variant were more likely to show genome-wide significant allele-specific expression in at least one of the two datasets in Albert et al., 2018.

We tested for non-additive interactions among pairs of variants assayed in the TSS library. Specifically, we assayed 342 variant pairs in promoters for which exactly two variants were present in the promoter regions assayed by the TSS library. For these variant pairs, our design included four oligos that represented all possible combinations of the two alleles at the two given variants. We retained only promoters in which all four oligos had summed barcode counts larger than zero in DNA and RNA in every replicate.

To test for interactions, we used the mpra package (Myint et al., 2019) to fit the following model:y=β0+β1x1+β2x2+β3x1x2+ϵ

In the model, y is the expression driven by an oligo, x₁ and x₂ are indicator variables relating an observation to the given genotype, β₀ is the intercept, β₁ and β₂ are the additive effects of the two variants, β₃ is the interaction effect that we sought to test, and ε is the residual error. We contrasted this model to a model without the β₃x₁x₂ term.

Pairwise linkage disequilibrium was calculated based on genotype data from a worldwide panel of yeast isolates (Peter et al., 2018). We computed the D’ and r² linkage disequilibrium statistics as implemented in the ‘genetics’ R package (Warnes, 2019), using the two most frequent alleles at each marker.

We gathered 31 non-TF features to describe each variant (Supplementary file 3). Gene annotations were obtained from Albert et al., 2018. Nucleosome scores of a given variant in the genome were based on nucleosome occupancies reported in Supplementary Table 2 of Brogaard et al., 2012, by extending the positions of the reported nucleosome center by 72 nucleotides in both directions. Nucleosome-free regions were defined as those with no reported occupancy. The number of TATA box motifs were counted for each variant allele and its flanking sequence using the consensus TATA(A/T)A(A/T)(A/G) (Basehoar et al., 2004). Similarly, start codons were defined as occurrences of ‘ATG’. Ancestral alleles were defined as those present in the Taiwanese soil strain EN14S01 (‘standardized name’: ‘AMH’) from the highly diverged clade 17, using the genotype data from Peter et al., 2018. We did not assign ancestral status for variants at which EN14S01 was heterozygous. Allele frequencies were obtained from genotypes in Peter et al., 2018. PhastCons scores, which quantify nucleotide conservation across seven Saccharomyces species (Siepel et al., 2005), were obtained for the positions corresponding to our variants from the UCSC genome browser (https://genome.ucsc.edu/index.html) (Haeussler et al., 2019).

We obtained position weight matrices (PWMs) for 196 TFs from the ScerTF database (Spivak and Stormo, 2012). For each variant and TF, we computed how well sequences containing the variant matched the TF’s PWM. Higher ‘TFBS scores’ represent better matches between the sequence and the PWM, increasing the probability that the TF binds the given sequence.

For a given variant, we calculated TFBS scores in sliding windows of width equal to the length of the PWM, which we moved over the variant in one-base-pair increments. To compute the TFBS scores, we summed the position weights from the PWM for the bases dictated by the sequence of the given window. We computed these windowed TFBS scores for the BY and the RM allele. For each allele, we retained the best (i.e. the highest) and the mean TFBS score across windows. In the text, we report results based on the absolute difference between the best TFBS scores of the two alleles. Differences between the mean TFBS scores of the two alleles yielded similar results (Figure 5—source data 1).

For each TF, the ScerTF database provides sequence score cutoffs, above which the given sequence is considered to be a ‘strong’ match to the PWM. We deemed TFBS scores below these thresholds to be ‘weak’ TFBSs. We computed TFBS scores separately for strong and weak matches. For strong TFBSs, we considered only sequence windows in which the TFBS score exceeded the sequence score cutoff. We also computed the number of strong binding sites for each TF by counting the number of windows that exceeded the cutoff. For weak TFBSs, we only considered TFBS scores below the cutoffs for each TF.

Together, these analyses yielded five metrics for how a variant is predicted to perturb the binding of a given TF: the allelic difference in (1) the best and (2) the mean TFBS score across windows for strong TFBSs, (3) the number of strong binding sites, (4) the best and (5) the mean TFBS score across windows for weak TFBSs.

To consider strand-specificity in variant effects on TF-binding, these five allelic TFBS metrics were computed across three strand contexts, for a total of 15 features per TF: (1) the sense (or ‘plus’) strand, that is the 5’ to 3’ sequence of nucleotides upstream of the reporter gene; (2) the antisense (or ‘minus’) strand, by analyzing the reverse complement of the plus strand; (3) ‘strand-agnostic’, in which we computed the difference between the best and mean scores across windows irrespective of which strand these scores came from. Across the 196 TFs, these features comprised a total set of 2940 features.

To obtain the aggregated TF features that summarized the 196 TF-specific sets of features, we computed the following 27 features for each variant. In each of the three strand contexts, we counted the total number of strong TFBSs changed (three features). Separately for strong and weak binding, we also computed the maximum difference in best or mean TFBS score across all TFs (12 features), and the average difference in best or mean TFBS score across all TFs (12 features).

To annotate individual TFBSs in Figure 5—figure supplement 1, we extracted the genome reference sequence flanking the two variants shown in the figure, up to a number of bases corresponding to the longest TF motif annotated in the Yeastract database (Monteiro et al., 2020), and then uploaded these sequences with either the BY or the RM allele to the Yeastract web server (http://www.yeastract.com).

We used logistic regression to test if each feature influenced the classification of a variant as causal. Causal variants were defined as those variants detected at an FDR of 5% or better. We contrasted this set to a set of non-causal variants with an unadjusted p-value larger than 0.2. Variants in between these two categories were excluded from the analyses.

Features were transformed to Z-scores using the formula:Z=(Xi−μi)/σi

Here, X_i is the value of the feature for variant i, and µ_i and σ_I refer to the mean and standard deviation of the feature values across all variants, respectively.

We performed logistic regression for each feature using the model:Si=β0+βlibraryxlibrary+βexpressionxexpression+βfeaturexfeature+ϵ

Here, the response variable S_i is a binary vector indicating whether variant i is significant. We included the library a variant was measured in and the average normalized baseline expression driven by the oligos used to measure the variant as covariates in the models. β_library and β_expression are the effects of these two possible confounders, while x_library and x_expression are indicator variables relating an observation to the given covariate. β₀ is the overall intercept, and ε is the residual error. β_feature is the effect of the given feature on the probability that a variant is significant, and x_feature is an indicator variable relating the feature values to the variants.

We used ANOVA to contrast this model to one without the β_featurex_feature term. We corrected for multiple testing by computing FDR as q-values using the ‘qvalue’ package (Storey and Tibshirani, 2003).

To build predictors of variant causality (‘causal’ vs ‘non-causal’), we first divided our set of features into 112 partially redundant feature subsets. These 112 models differed in whether they considered the non-TF features, whether they included TF features from the plus, minus, and/or the strand-agnostic set, whether they considered strong and/or weak TF metrics, whether they included the aggregated TF summary features, and whether they included only features that had been significant in the single regression analyses. Details on each model are listed in Figure 6—source data 1.

For each of the 112 feature subsets, we built a logistic regression model to predict variant causality. For building these models, we first divided the variants also used in single-feature analyses into a training set comprising 90% of the data, and a test set comprising 10% of the data. Training was performed using repeated 10-fold cross validation as follows. The training set was split ten times, each time considering one of ten possible non-overlapping fractions of 10% of the training set as a validation set. In each of these ten splits, we used the remaining 90% of the training set to fit the model and computed Cohen’s Kappa on the given validation set. This process was repeated five times, on five different 10-fold splits of the training data. The models were trained to maximize Cohen’s Kappa on the validation sets. After training, each of the 112 models was applied to the 10% test set that had been held out from training. Model accuracy for the logistic regression models was measured by the area under the receiver operator characteristic curve (AUC). Elastic-net logistic regression was performed using the ‘caret’ package (Kuhn et al., 2020). We excluded 35 features with an ‘NA’ value (27 aggregated TFBS features, expression level of the associated gene, two measures of dNdS, PPI, three measures of synthetic genetic interactions, nucleosome score).

Linear regression models to predict variant effects (expressed as absolute log-fold changes) were performed using the same repeated 10-fold cross-validation scheme and trained to minimize the root-mean squared error (RMSE). Prediction accuracy was measured using Spearman correlation coefficients between predicted and actual data on the test set. All model training was done using the ‘caret’ package (Kuhn et al., 2020).

In the case of multiple linear regressions, we also calculated model fit as the R² on the entire set of variants used in the single-feature regressions. These linear models were fit using the ‘lm’ function in R.

Elastic-net regression models predicting variant effects followed the same splitting and training strategy as for the 112 non-regularized models. We tuned the model across 10 by 10 combinations of the mixing parameter (alpha) and the regularization parameter (lambda) to select the model with the lowest RMSE in cross-validation. The best model was used to predict on the 10% test set, and model accuracy was measured as above. Elastic nets were trained using the ‘caret’ package (Kuhn et al., 2020).

To predict gene expression corresponding to our variants, we used the non-positional YPD model with pTpA embedding from de Boer et al., 2020. Among the models trained in that study, this model is least sensitive to the surrounding sequence context, which differs between our sequences and those used by de Boer et al. We used the model trained in YPD medium; the same medium we used in our experiments. The model uses 110 bp of promoter sequence as input to predict gene expression. Because our oligos were 144 bp long, we could not use the complete oligo sequence for prediction. Instead, we used 110 bp of DNA centered on each variant. For each variant, we used two 110 bp sequences, one containing the BY allele and the other containing the RM allele. The sequences were one-hot encoded using code from de Boer et al. and then fed into the model.

The predicted expression values for the BY and RM alleles of each variant were subtracted to predict fold change between alleles. Predicted expression and variant effects were compared to those observed in our experiments. As observed oligo expression values, we used the ‘AveExpr’ value in Supplementary file 3 as the expression of the BY allele, and the sum of the ‘AveExpr’ and ‘logFC’ as the expression of the RM allele. The ‘logFC’ was used as the observed variant effect.