We compare the gene expression over developmental time for two morphs of a marine annelid, Streblospio benedicti, which has an intraspecific developmental dimorphism. There are two distinct developmental morphs which differ in their egg size, embryological development time, larval ecology, and morphology. These are either obligately feeding planktotrophic (PP) larvae or non-feeding lecithotrophic (LL) larvae. Despite these developmental differences, as adults they are morphologically indistinguishable outside of some reproductive traits and occupy the same environmental niches. The larval traits are heritable, meaning the differences in development are genetic and not plastic (Levin and Creed, 1986). The two morphs have been characterized extensively in terms of life-history and genetic differences (Levin, 1984; Levin and Creed, 1986; Gibson et al., 2010; Zakas and Rockman, 2014; Zakas et al., 2018; Zakas, 2022). Intriguingly, crosses between the morphs are viable with no obvious fitness effects, and these F₁ offspring can have intermediate larval traits compared to the parentals (Levin and Creed, 1986; Zakas, 2022). Embryological differences between the two types (and thus egg sizes) have been briefly described (McCAIN, 2008) but here we detail the full time course of embryogenesis from the one-cell embryo through the larval phase in detail for both morphs, and present data from reciprocal F₁ crosses (PL or LP) between the morphs in both directions across the same developmental period.

Spiralian animals have a famously conserved pattern of embryological cleavage (reviewed in Lyons et al., 2012), so unsurprisingly we find the two morphs proceed through the same developmental stages despite starting from eggs of different sizes (8× volume difference). Despite the similarity in embryo morphology, there are some notable differences between the two morphs: the absolute time between each stage is shifted such that the LL embryos take longer to reach an equivalent larval stage, which is expected as larger, more yolky cells can take longer to divide (McCAIN, 2008). At the swimming larval stage there are some notable morphological and behavioral differences (reviewed in Zakas, 2022, Figure 1), notably the PP larvae are obligately feeding and have feeding structures, while the LL are not. While the embryological stages may not be morphologically different other than size, it is reasonable to expect that they could be expressing different genes or changing the relative timing of expression to produce the morphological and behavioral differences seen in the larvae.

We measured total gene expression from the six developmental stages using RNAseq (Figure 2) with at least four biological replicates per morph at each stage (Figure 2A). Using the full dataset, the first two principal component analysis (PCA) show most of the variance in total gene expression is due to developmental stage and morph (PC1; Figure 2B). As expected the LL individuals appear to transcriptionally fall behind the PP offspring at each morphologically defined, equivalent developmental stage. We expect this pattern as LL offspring develop more slowly to reach these first developmental stages but reach the juvenile stage more quickly in absolute time than PP offspring, which develop in the water column for a longer period (Figure 1). Notably, pre-gastrulation development is distinctly separated from post-gastrulation by the second principal component.

As expected for an intraspecific comparison, most genes are conserved, having the same expression levels at all stages in both morphs. Only 36.2% of all expressed genes are significantly differentially expressed (DE) between PP and LL at any stage in this dataset. We find that early in development over a third of these DE genes are significantly different between the morphs, but these differences tend to be quite small in magnitude. At gastrulation the number of significant genes decreases to less than 5% of the total DE genes, however these remaining expression differences are much larger in magnitude (Figure 2C). It appears that the two morphs are more functionally distinct during early development, likely because of the different metabolic requirements imposed on them by the differences in maternal egg provisioning (Moran and McAlister, 2009; Zakas, 2022; Harry and Zakas, 2023).

We use our RNAseq time course dataset to quantify the extent that modifications to gene expression timing and amount contribute to developmental differences. We define heteromorphic genes as homologous genes whose expression differs significantly between larval morphs, but the pattern of expression (expression profile) does not change (Figure 3). The significant difference in heteromorphic genes could be at only one or a few discrete time points. Heterochronic genes are those whose overall expression pattern and timing change between the developmental morphs, as discussed in the clustering algorithm below. We categorize genes that are only expressed in one morph over the full time course of development as morph-specific genes. These genes are specific to only one developmental type.

To differentiate heterochronic shifts in gene expression from heteromorphic ones, we clustered all gene expression patterns from PP into a representative set of expression profiles using Mfuzz (v2.60.0) (no additional clusters were found when using LL). We selected six clusters based on the criteria that the average correlation between cluster pairs increased with each additional cluster, such that the final clusters generated sufficiently represented the diversity of gene expression patterns (Figure 4, Figure 4—figure supplement 1). We then assigned each gene to a cluster in the PP and LL datasets independently. A gene’s expression is considered heterochronic when it appears in one cluster for PP and a different cluster for LL.

We identified 354 genes from our set of DE genes (45.9%) where both the PP and LL expression pattern matched the same cluster. (These are heteromorphic genes and are significantly DE in at least one developmental stage, but do not have different profiles of expression between PP and LL.) Approximately half of these are assigned to clusters 2 and 5. Cluster 2, which shows a pattern of maternal transcript degradation with no subsequent zygotic expression, is likely to contain genes associated with embryogenesis that are shared by both morphs. Cluster 5 shows a pattern of largely post-gastrulation zygotic gene expression. Based on this pattern, these genes are likely to be associated with shared larval features. Although there are some late-appearing heteromorphic genes that are associated with discrete larval differences: ‘chitin catabolic processes’, for example, are overexpressed in PP at the swimming stage (when they grow swimming chaetae) and are lowly expressed in LL which do not make swimming chaetae (see Supplementary file 1).

Next, we identified heterochronies, which we define as a gene that switches clusters – and therefore expression profiles – between PP and LL. There are 224 heterochronic genes, which is 29% of DE genes. As we reduced the complexity of the entire gene expression dataset into six clusters, some genes with similar profiles in both PP and LL were ultimately assigned to different clusters, generating a false positive. To ensure against these false positives, we filtered genes by Pearson correlation between sample means in PP and LL. Genes with a correlation r>0.85 were not counted as heterochronic.

Heterochronies are a change in the gene expression profile depending on developmental morph, but how different are the patterns of heterochronic changes? Parsimoniously, we would expect most heterochronies to have a similar overall shape that is simply shifted by a stage or two (like a switch from cluster 1 to cluster 2; Figure 4). Examining the six clusters above, it is obvious that some expression profiles have similar trajectories (clusters 5 and 6, for example, both show increasing expression post blastula), while others are essentially inverted expression patterns (clusters 1 and 4 for example). Clusters are ordered in a correlation heatmap (Figure 5A) from early gene expression to late gene expression. We find that most genes that switch expression profiles between PP and LL do so between the most similar clusters, and thus are near the diagonal. But where we see opposite profiles, the trend is early expression in PP genes compared to LL (top left corner). This is expected given the delay in embryogenesis for LL compared to PP. That said, many genes are expressed earlier in LL compared to PP (bottom right orange triangle in Figure 5A) but most of these are relatively small shifts.

We quantify the magnitude of change in gene expression between PP and LL independently of the cluster assignment by estimating the Pearson correlation directly. This shows that most heterochronies are minor changes in the timing of gene expression. There is a left-tailed distribution indicating that a few genes have a large difference in expression patterns (like a profile inversion) between PP and LL (Figure 5B).

Gene ontology (GO) enrichment tests show heterochronic genes that are expressed earlier in PP are functionally enriched for mesoderm specification, cell fate specification pathways, and several organogenesis pathways (others include BMP signaling, mesoderm specification, intestinal epithelial cell differentiation, and gene silencing (see Supplementary file 1)). Notably, heterochronic genes which are shifted earlier in LL are enriched for many metabolic functions. The shift in expression of metabolic genes is consistent with the life-history and feeding differences between the larvae, and it may be that most of what is differentiating a PP and LL larvae is the onset of feeding and gut-related developmental programs.

Morph-specific genes, by definition, have no expression in one morph and cannot be assigned to a cluster. To quantify morph-specific genes we use a data-adaptive flag method (DAFS) to calculate expression thresholds for each sample, below which genes are not considered as expressed. Using this approach, we identify 195 genes (25.3% of DE genes) which are only expressed in one morph (Figure 6). We find considerably more P-specific genes (150 genes in PP vs 45 genes in LL), which tend to be expressed in later developmental stages. These include genes which code for proteins such as Fibropellin, Forkhead-box, Crumbs-like, Notch-2, and many zinc-finger proteins. While the function of these genes during development remains to be determined, it is possible that they maintain PP-specific larval traits.

Expression of functionally distinct categories of genes may evolve by different mechanisms. Morph-specific genes may be involved in very different functional processes than heterochronic genes in this system. GO enrichment tests for morph-specific genes expressed in PP found a few functions related to cell fate specification and signaling pathways (GO enrichment tests for biological process, p<0.05; no significant functional enrichments for LL-specific genes), but a distinct lack of genes involved in metabolic processes. This is because metabolic pathways genes shifted earlier in the development of LL offspring are still required in both offspring morphs. But the morph-specific genes are not required for development and may be modified by different evolutionary mechanisms. These findings also imply that PP embryos require a few specific cell types to produce a PP larval form that are reduced or lost in LL.

Overall, we find that heteromorphic changes in expression accounts for most gene expression differences, which is expected as this is the smallest change in gene expression of the three categories. Gene expression need only be significantly different at one stage to fit this category, and such differences may not necessarily translate into biological differences in development. Morph-specific and heterochronic changes make up very similar proportions of the remaining differences (Figure 7). This demonstrates that while true heterochronies are common, they are not the main driver of gene expression differences in development.

Because we are using an intraspecific comparison, we can extend our analysis to understand the regulatory architecture behind these expression differences. We included RNAseq time course data for offspring from reciprocal crosses between the two morphs, meaning PP and LL parents were crossed in both directions alternating the role of the mother. Cross of the two developmental morphs to produce F₁ offspring, which can have a range of intermediate traits, but typically closely resemble their mother’s phenotype (Zakas and Rockman, 2014). This is particularly useful to disentangle maternal effects, as both F₁s (PL and LP; mother’s genotype is listed first) are heterozygotes, but they originate from different egg sizes and mothers with different genetic backgrounds. F₁s allow us to identify the regulatory architecture underlying DE, and to assess the impact of maternal background on gene expression.

F₁s have a general pattern of intermediate expression values but have more variability within replicates and clear cases of outliers (Figure 8A, Figure 8—figure supplement 1). They have considerably more variability across replicates, and some genes are misexpressed – where the F₁ expression value is outside the range of the parental difference (Figure 8A). Misexpression is reported in hybridization studies and is thought to be the result of epistatic interactions between divergent genomes that reduce the fitness of offspring contributing to sympatric speciation (Norrström et al., 2011; Moran et al., 2021). Whether misexpression affects the fitness of the embryos in S. benedicti is unclear, although F₁s typically develop normally in the lab and no systematic reduction in viability has been observed. In previous studies of gene expression in eggs of the two morphs, we saw a similar pattern where F₁ eggs typically have intermediate expression values compared to parental genotypes, but high levels of misexpression (Harry and Zakas, 2023). For genes that are DE between PP and LL samples, we find that F₁ gene expression patterns are split between those matching the maternal and paternal gene expression pattern, with slightly more genes matching the paternal expression at each stage (Figure 8B). We expect most transcripts in the first and second stages of development to be maternally derived, so the high proportion of genes matching paternal expression patterns is somewhat unexpected.

We find that most genes (>8000 genes on average, out of 14,327 genes expressed) have extremely conserved expression patterns among PP, LL, and F₁ embryos at each developmental stage. The high variability and misexpression of F₁s negatively impacts our statistical power by introducing variation and results in fewer genes being confidently assigned gene regulatory mechanisms. As a result, almost no genes can be identified as having a dominant or additive inheritance pattern, while 150 genes are identified as overdominant over the course of development. Notably, very few morph-specific genes are expressed at a significant level in either F₁s (between 0 [PP] and 3 [LL] genes), strongly suggesting negative regulation of expression that acts in trans for these genes.

We leverage the F₁ offspring to dissect the regulatory architecture underlying developmental gene expression differences; we use allele-specific expression patterns in F₁ offspring – tracking the expression of the maternal or paternal allele – to assign gene regulatory differences as either cis- or trans-acting modifications (or both; Davidson and Peter, 2015). Typically this approach is used in hybrids (Wittkopp et al., 2004; Tirosh et al., 2009; McManus et al., 2010; Coolon et al., 2014; Wang et al., 2020), but we have adapted it to intraspecific F₁ offspring to test whether early divergence is consistent with predictions in the literature (Harry and Zakas, 2023).

We determine the regulatory architecture underlying DE by calculating P and L allele-specific expression in reads from F₁ samples and assigned primary regulatory modes according to established criteria (Wittkopp et al., 2004; Graze et al., 2009; Coolon et al., 2014; Wang et al., 2020). As this is an intraspecific comparison with little fixed genomic differences between the morphs, not all genes have alleles that can be assigned parentage; however, a set of ~2000 DE genes have morph-differentiating SNPs enabling the parental assignment of F₁ transcript reads. The regulatory modes of these DE genes are assigned as ‘cis’, ‘trans’, ‘cis+trans’, ‘cis × trans’, or ‘compensatory’ (see Supplementary file 2 for formal criteria). Of this set, misexpression in F₁ offspring causes many genes to receive ambiguous assignments. As a result, only 143 genes have informative regulatory mode assignments. While this is a small number, we expect that the genes we can classify are proportionally representative of the remaining regulatory architecture.

The primary mode of regulatory change throughout development is trans-acting (Figure 9). No differences are caused by purely cis-acting regulatory modifications, though we did find some cis+trans and cis-×-trans interactions. We found that the number of genes with compensatory regulatory modifications increases sharply after gastrulation, indicating that gene expression may be more tightly controlled past that point. Studies across Drosophila species, for example, have indicated that maternal effects are regulated differently than zygotic effects, but in this case there are larger trans-acting factors in early (maternally controlled) developmental stages (Cartwright and Lott, 2020). We do not see such a clear effect; trans-acting factors make up most of the regulatory architecture throughout development. Interestingly, this result is different from previous studies of egg mRNA expression of this species, where cis- and trans-regulatory modifications were found at similar rates for maternal gene expression (Harry and Zakas, 2023). Comparison across the egg (maternal) and embryo (maternal and zygotic) regulatory landscape shows greater cis-acting regulation in the maternally expressed genes. These results demonstrate the possibility that maternal and zygotic gene regulatory architecture evolve through distinct mechanisms and on separate timescales.

We tested the possibility that the reciprocal F₁s generated in the study (for the three stages that have both LP and PL samples) might have different regulatory mode changes due to parental effects. Splitting our analyses into separate PL and LP parental arrangements did not yield significantly different results, but treating them separately meant fewer genes could be assigned a mode.

We also investigated parent-of-origin effects on gene expression using F₁s. These occur when an allele’s expression is at least partially dependent on which parent contributed that allele. For instance, maternal effects may involve polymorphisms that affect the development of an offspring only when contributed as part of the maternal genome (and which impose no developmental variation when contributed by the paternal genome). These effects are a form of epigenetic inheritance which can allow for complex, adaptive maternal-zygote interactions such as certain metabolic genes being specifically activated in the offspring to match the nutrient content of its mother’s egg. Due to animal constraints where LL mothers produce far fewer offspring per clutch (10–40 on average), we were only able to collect samples for F₁ offspring with L mothers (LP) for three developmental stages (16-cell, gastrula, and swimming). All six time points were sampled for F₁s with PP mothers (PL). To find parental effects (maternal or paternal), we conducted a differential expression analysis contrasting the PL samples to the LP samples. Tests for differential expression show that many significant differences in gene expression between the F₁ morphs arise at early stages (ostensibly due to the presence of maternal transcripts inherited through the egg) and then drop sharply after gastrulation (Figure 10; Figure 8—figure supplement 2). This indicates that while a few parental effects on gene expression may persist throughout development, most parental effects are limited to early embryogenesis, which is consistent with expectations for maternal transcription degradation.

We use lab-reared male and female S. benedicti originally sampled from Newark Bay Bayonne, New Jersey (PP), and Long Beach, California (LL). All experimental procedures and growth incubations are carried out at 20°C unless otherwise noted. To produce offspring for sampling, we crossed virgin females with one male for each test cross (Cross ID in Figure 2A). PP offspring were fed small quantities of our lab’s standard feeding algae after the swimming stage had been reached to avoid starvation. As clutch sizes can be quite small, we took advantage of the multiple broods that can be produced by a single mating event. We used three to five consecutive broods per female where all offspring were full sibs. This is necessary as embryo number is limited (10–40 embryos per clutch for LL and 100–400 for PP) and 10–40 pooled embryos are required to produce sufficient input mRNA. In this study brood number and developmental stage are confounded within sample groups (Cross IDs).

To build a timeline of development, we removed embryos from the maternal brood pouch and observed development in a Petri dish in artificial seawater in an incubator at 20°C (previous work indicates development is normal outside the brood pouch). We selected six distinct time points that we confirmed with cell counts (nuclei) using Hoechst: 16 cells, blastula (64 cells), and gastrula (124 cells). Time to each development stage was averaged over at least four clutch observations. Later embryo and larval stages were identified by morphological differences (appearance of ciliated band trochophores and eye development). Six stages capture a broad scope of developmentally critical periods while having enough timing separation to be distinct (each embryonic time point was ~12 hr apart). Images were captured using a ×40 objective on a Zeiss Axio inverted microscope with an indicator for scale which was subsequently used to scale images to the same relative size in Figure 1.

Embryos were collected from a single female, and 10–40 were processed for total mRNA extraction at each time point. If the number of embryos was insufficient for all time points, we used multiple broods. We used a minimum of 20 LL embryos for stages 16-cell, blastula, gastrula, and 10 LL embryos for stages trochophore, swimming, 1 week. PP females have large clutches, so embryos were divided equally (~40 embryos/clutch) among the six stages. With this approach we could not collect all time points for the same females, but there are at least two complete sets and at least four replicates per stage for each morph. The same sampling stages were used when collecting F₁ samples in this experiment (Figure 2A).

Embryo RNA extraction used the Arcturus PicoPure kit including the DNAse step. We used a Qubit RNA kit to measure RNA yields for pilot data, but once established we bypassed this step to maximize RNA yields. Libraries were constructed with the NEB UltraII Stranded RNA library prep kit (cat# E7760S) for Illumina. Libraries were sequenced on two lanes of 150 bp on the Illumina NovaSeq.

We used TrimGalore (cutadapt) (Martin, 2011) and FastP (Chen et al., 2018) for quality assessment and trimming. Reads were mapped to a reference of all transcript sequences (transcripts extracted with GFFread, Pertea and Pertea, 2020) that are annotated in the S. benedicti reference genome (Zakas et al., 2022) using Salmon 1.10 (Patro et al., 2017) using the default scoring parameters. The reference genome for S. benedicti is a chromosome-level assembly with >99% of genes occurring in the first 11 chromosomal scaffolds (Zakas et al., 2022). Individual sample transcript expression quantification estimates were summarized to the gene-level using the Tximport R package (Soneson et al., 2015).

As the S. benedicti genome is from P individuals (Zakas et al., 2022), we checked the mapping rate of P and L samples (as well as both F₁s) at each developmental stage to ensure similar mapping rates (Figure 2—figure supplement 1). We calculated mapping by dividing the sum of expression estimates for all genes by the sequencing depth for each sample individually, and then averaging samples from each developmental stage by morph to produce an average mapping rate (Figure 2—figure supplement 1). We find LL and PP mapping rates do not differ significantly (two-sided t-test; p=0.1118), nor do the F₁ samples’ mapping rates differ from any other group (Figure 2—figure supplement 2). While this does not eliminate the possibility of mapping bias, these results indicate that missing data would not substantially change our results.

Samples which received fewer than 2 million total reads were excluded from further analyses since our expected sequencing depth was between 20 and 40 million reads (six samples total). Samples were normalized using the DESeq2 R package (Love et al., 2014) and expression estimates were transformed using the variance stabilizing transformation function to perform PCA using the function prcomp (R Development Core Team, 2020), which is plotted for the first two principal components with ggplot2 (Wickham, 2016; Figure 2B).

We used DESeq2 to determine significance in expression differences (Love et al., 2014) using the variable grouping method recommended by the DESeq2 manual. We used two factors for characterization: developmental stage and genotype (P, L, PL, LP) which generate a factor level for each stageHead2/genotype combination (i.e. ‘16-cell_Lecithotroph’) which we called ‘multiFactor’ for DESeq2 as ‘design = ~multiFactor’. The Benjamini-Hochberg false discovery rate (FDR) algorithm was used with p-values between factor levels of ‘multiFactor’ to reduce the incidence of false positives for differential expression. Throughout this study, the threshold for significant gene expression differences is an FDR-adjusted p-value of 0.05 or less, and an expression fold-change greater than twofold.

To summarize expression clusters we used the Mfuzz 2.58 R package (Futschik and Carlisle, 2005; Kumar and E Futschik, 2007). Clusters are based on mean expression estimates from P samples. Normalization was (DESeq2, Love et al., 2014), log2-transformed, and then filtered for genes with low variability/expression and standardized using the Mfuzz functions ‘filter.std’ and ‘standardize’, respectively. Mfuzz requires a priori cluster numbers. To capture the representative sample of expression patterns, we found the highest number of clusters for which Pearson correlations of pairs of cluster centroids (the single most cluster-representative gene for each cluster) did not exceed 0.85 (r<0.85) for any pair of clusters. This resulted in six clusters. The fuzzifier coefficient (m) required by Mfuzz was estimated to be 1.71 by the function ‘mestimate’ (Futschik and Carlisle, 2005; Kumar and E Futschik, 2007). Following cluster generation with the P expression data, the L expression data was mapped onto the clusters using the Mfuzz function ‘membership’ so both morphs could be compared. Clusters were plotted using the function ‘mfuzz.plot2’ (Figure 4, Figure 4—figure supplement 2).

To identify genes as heterochronic we first identify genes that have different expression patterns between the morphs and then filter those genes based on their Pearson correlation coefficients. Genes for which the PP and LL expression are on different clusters are categorized as heterochronic. Some of these genes fall onto different clusters but still have relatively similar expression profiles. Therefore, we also filter by a measure of the Pearson correlation coefficient. The threshold is based on the distribution of correlation coefficients of all PP to LL genes’ expression in the dataset (Figure 5B). Based on the distribution, a threshold of r<0.85 was selected to filter genes which would be classified as switching clusters but are similar enough in their expression between the two morphs to be removed. This filter disqualified 38 genes.

To categorize morph-specific expression we use a gene expression thresholding approach. Spurious gene expression resulting in low levels of estimated expression (few RNAseq reads) is categorically and functionally distinct from robust gene expression (Hebenstreit et al., 2011). We establish meaningful expression thresholds based on the assumption that genes with robust expression will have values which are normally distributed. Individual sample thresholds were calculated using a DAFS (George and Chang, 2014). Thresholds were used to identify genes which are expressed at significant levels in only PP or only LL offspring. For example, if a gene is expressed above its threshold in three or more (out of five) P samples at one or more developmental stages and in no more than one LL sample at any developmental stage, then that gene was categorized as morph-specific (Table 1).

We concurrently sequenced RNA libraries from F₁ offspring (Figure 2A). We generated F₁ offspring from reciprocal crosses P×L and L×P (both mother-father directions) using the same sample collection method as stated above. However, we only sequenced samples from three of the developmental stages for the F₁s in the LP direction. All analyses were performed as above.

Using F₁ data, we classified the mode of inheritance for each gene according to established criteria and differential expression tests from DESeq2 (Coolon et al., 2014; Harry and Zakas, 2023; Wang et al., 2020). Genes which were significantly DE between reciprocal F₁ offspring (PL vs LP) at any developmental stage were not considered for this analysis. The remaining genes were classified as either (1) conserved, (2) additive, (3) dominant for one genotype, or (4) misexpressed (over/under-dominant).

DE genes between PL and LP offspring are different due to parental effects. DE was detected with DESeq2 with the contrasts ‘c(“multiFactor”, “PL_sixteencell”, “LP_sixteencell”)’, ‘c(“multiFactor”, “PL_gastrula”, “LP_gastrula”)’, and ‘c(“multiFactor”, “PL_swimming”, “LP_swimming”)’ similar to PP and LL samples. We evaluate these at three of the six developmental stages. The direction of the parental effect was determined by matching the expression change with the direction of each identified gene’s expression in PP and LL samples (where those genes were DE in PP and LL samples).

To measure allele-specific expression, we assigned sequencing reads from F₁ samples to either P or L parentage by identifying fixed SNPs within the transcript sequences of the parental types. We used HyLiTE (Duchemin et al., 2015) to identify SNPs and assign reads as a P or L allele. We then categorized genes’ regulatory mode according to established empirical methods (Wittkopp et al., 2004; Graze et al., 2009; Coolon et al., 2014; Harry and Zakas, 2023; Wang et al., 2020) for each developmental stage independently. This required three comparisons of each gene’s expression which we performed using DESeq2: (1) the contrast of PP to LL samples, (2) the contrast of P alleles to L alleles within F₁ samples, and (3) a ratio of the differential expression of PP:LL to the differential expression of P:L alleles. For (3) we use a special contrast in DESeq2, applying the design (~Geno × Ori) where Geno identifies reads as either a P or L allele and Ori identifies the reads as originating from the parentals or F₁ samples. DE genes were categorized as either in ‘cis’, ‘trans’, ‘cis+trans’, or ‘cis × trans’ (Wittkopp et al., 2004; Graze et al., 2009; Coolon et al., 2014; Harry and Zakas, 2023; Wang et al., 2020).