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Genetic variants regulating expression levels and isoform diversity during embryogenesis


Embryonic development is driven by tightly regulated patterns of gene expression, despite extensive genetic variation among individuals. Studies of expression quantitative trait loci1,2,3,4 (eQTL) indicate that genetic variation frequently alters gene expression in cell-culture models and differentiated tissues5,6. However, the extent and types of genetic variation impacting embryonic gene expression, and their interactions with developmental programs, remain largely unknown. Here we assessed the effect of genetic variation on transcriptional (expression levels) and post-transcriptional (3′ RNA processing) regulation across multiple stages of metazoan development, using 80 inbred Drosophila wild isolates7, identifying thousands of developmental-stage-specific and shared QTL. Given the small blocks of linkage disequilibrium in Drosophila7,8,9, we obtain near base-pair resolution, resolving causal mutations in developmental enhancers, validated transcription-factor-binding sites and RNA motifs. This fine-grain mapping uncovered extensive allelic interactions within enhancers that have opposite effects, thereby buffering their impact on enhancer activity. QTL affecting 3′ RNA processing identify new functional motifs leading to transcript isoform diversity and changes in the lengths of 3′ untranslated regions. These results highlight how developmental stage influences the effects of genetic variation and uncover multiple mechanisms that regulate and buffer expression variation during embryogenesis.

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Figure 1: Quantifying developmental and genetic variance.
Figure 2: Regulatory QTL at developmental enhancers.
Figure 3: 3′ Isoform QTL during embryonic development.
Figure 4: Developmental and genetic regulation of 3′ UTR length.

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This work was supported technically by the European Molecular Biology Laboratory (EMBL) Genomics Core facility, and financially by the European Research Council (ERC; FP/2007-2013), ERC advanced grant CisRegVar to E.E.M.F., EMBL predoctoral funds to E.E.M.F. and E.B.

Author information

Authors and Affiliations



E.E.M.F., E.B., E.C. and N.K. designed the study, explored results and prepared the manuscript, with contributions from all authors. E.C. led the experiments with help from L.C., H.E.G., R.R.V., R.M.-F. and B.Z. N.K. led the data processing and QTL calling, with help from F.P.C., J.F.D. and O.S. D.H. led the biological analysis, with input from D.G and N.K.

Corresponding authors

Correspondence to Ewan Birney or Eileen E. M. Furlong.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks S. Celniker and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 3′-Tag-seq provides an accurate measure of gene expression.

a, Tightly staged embryos at three embryonic time points were collected from 80 genetically diverse inbred lines. Stages 5–6 (2–4 h) before lineage commitment, including blastoderm stages, stages 10–11 (6–8 h) when major cell lineages within the mesoderm and ectoderm are specified, stages 13–14 (10–12 h), onset of tissue differentiation. b, Confirming the developmental stage of all samples: 3′ Tag-seq gene expression levels for all 254 samples (including replicates) were correlated to time points from a reference embryonic time-course (modENCODE). Two-hundred and forty-two samples showed their strongest, and a further five samples their second-strongest, correlation with the expected time points (indicated in red). The remaining mis-staged samples were discarded from the QTL analysis. c, 3′-Tag-seq is highly reproducible; correlation between two biological replicates (independent embryo collections, RNA-extraction and 3′-Tag-seq) from the Drosophila Genetic Reference Panel (DGRP) line RAL-375. d, Gene expression level estimates from 3′-Tag-seq are highly correlated with standard RNA-seq. Scatter plot shows 5% trimmed means of expression levels from 22 RNA samples, obtained from 10–12 h staged embryos from 22 inbred lines, sequenced by both 3′-Tag-seq and standard RNA-seq. Spearman’s rho of means = 0.9, P < 2.2 × 10−16.

Extended Data Figure 2 Variance decomposition analysis.

a, Out-of-sample prediction to assess the importance of the genotype by developmental component in the variance component analysis (shown in Fig. 1b). Left, comparison of the out-of-sample Pearson correlation coefficient without accounting for GxD interactions (x axes) versus the full model, including a trans GxD component (y axes). Right, corresponding analysis for the cis component. Prediction performance was assessed using tenfold cross-validation. Genes above the diagonal (red) indicate improved prediction by accounting for GxD. b, c, Biological and molecular function gene ontology (GO) enrichments are shown for all significant terms (Fisher’s exact test, P value < 0.05). Clustergrams reflect the fraction of genes shared by any two categories, with 0 indicating nested categories and 1 reflecting orthogonality. b, Enrichment for trans variation. Genes that function in DNA binding (for example, transcription factor activity) are almost exclusively enriched. c, Enrichment for cis variation. Genes that function in metabolic processes, RNA maturation and binding are enriched. d, F1 embryos from genetic crosses between three genotypes (RAL-517 × RAL-765 and RAL-362 × RAL-517), were collected at three stages of embryogenesis. Mean relative expression of maternal allele as a fraction of total expression is shown for each developmental stage. Point of balanced maternal/paternal expression is indicated by red dotted line.

Extended Data Figure 3 Getting to the causal genetic variant of embryonic eQTL.

a, Overview of eQTL and 3iQTL, with numbers of genes with eQTL in different functional categories. b, The relationship between stage-specific eQTL (single-stage) and gene expression. Box plot, median expression levels of genes with stage-specific eQTL at each developmental stage. Genes are grouped by eQTL stage specificity. Genes do not appear more highly expressed at the stage at which the stage-specific eQTL was called compared to other stages. c, Gene ontology enrichment of common eQTL. y Axis shows −log10 P value (Fisher’s exact test) after FDR correction of selected biological process gene ontology terms enriched (positive) or depleted (negative). eQTL are enriched in genes involved in metabolic and enzyme-driven catalytic processes (catalytic activity, FDR-adjusted P value = 0.0193), and depleted in essential developmental genes. Although true globally, there are a surprising number of essential developmental regulators with expression variation during embryogenesis, including 103 transcription factors, 27 of which have relatively large effect sizes (a, bottom table). d, Global enrichment of eQTL in gene features calculated using multivariate logistic regression. Bars represent 95% confidence interval of odds ratios, showing increased/decreased likelihood for a genetic variant to be a QTL. e, Distribution of lead eQTL variants in a meta gene plot showing the gene body and 1 kb upstream/downstream. f, Representative example of QTL with multiple significant associated variants. Top, Manhattan plot showing all tested variants in region (strongly associated variants in red, neighbouring genes with unadjusted P values in grey). Middle top, median 3′-Tag-seq coverage for Major (dark red) and Minor (blue) genotypes. For comparison, the Major signal is shown in light red and the Minor signal in light blue. Middle, library-size-adjusted coverage for each line with Major (red) and Minor (blue) genotypes. Bottom, median signal of standard RNA-seq on a subset of lines. Box plot (right), normalized expression levels across time points for both genotypes. ecd has multiple associated variants within the exon of the gene, the lead variant is associated with an increase in the overall levels of the gene’s expression in the Minor, compared with the Major, genotype.

Extended Data Figure 4 eQTL are highly concordant with three independent data sets.

a–d, Overlap between 3′ Tag-seq eQTL effects and different orthogonal data sets, showing genes with concordant (blue) and discordant (red) direction of effects. Black line shows linear fit through all data points. a, Comparison between common eQTL and expression level differences observed from standard RNA-seq of 22 lines at the 10–12 h stage. Only eQTL with at least two RNA-seq data points for both alleles and a predicted effect size of >0.25 at 10–12 h are shown. b, Comparison between common eQTL and ASE in embryos from two F1 crosses. Only genes with significant ASE (binomial test, P < 0.1) and consistent direction in all three developmental stages are shown. Circles, genes classified as maternal32; triangles, non-maternal genes (zygotic). c, Comparison between embryonic common eQTL and adult eQTL (microarray-based expression) from the same population9. Only genes for which the lead variant was tested in both studies are shown. d, Comparison between embryonic-stage-specific eQTL and adult eQTL from the same population9. Only genes for which the lead variant was tested in both studies are shown.

Extended Data Figure 5 Identifying the underlying cause of regulatory eQTL.

a, Global enrichment of QTL in DNaseI (DHS), TF-bound regions (ChIP) or occupied TF-binding sites (TFBS bound), compared with randomly selected regions of the genome using multivariate logistic regression. Bars represent 95% confidence interval of odds ratios. b, Heatmap showing distal QTL (>1 kb from TSS) overlap with putative developmental enhancers based on the presence of DHS, occupancy by two or more transcription factors (TFBS) or the histone modifications H3K4me1 and H3K27ac at one or more stages of embryogenesis. c, Frequency (left axis) and cumulative distribution (right axis) of eQTL >1 kb away from the TSS. d, Obtaining a high confidence set of bound TF motifs. Assessment of Drosophila TF ChIP data sets during embryogenesis with associated position weight matrices (PWMs). Only PWM-ChIP pairs with high area under the curve in receiver-operating characteristic curves and enrichment over shuffled motifs were included. e, Broken TFBS (in the Minor genotype) in regions bound by the TF (dark red) or DHS (light red) during embryogenesis. f, Created TFBS (in the Minor genotype) for the indicated TF in DHS regions during embryogenesis. g, Luciferase assays of regulatory QTL associated with the genes indicated. The activity of each Minor allele and Majormin (where the Minor allele variant was placed in the Major allele) is compared to that of the Major genotype (set to one). All values are expressed as mean ± s.d. for three biological replicates, normalized to values of the Major allele. For the regulatory region associated with CG9601, the lead variant is causal and has the same effect in the Minor or Major background. CG1113, CG5039 and CG31922 confirm the significant difference between the Major and Minor genotypes, but the lead variant tested is not causal. Student’s t-test, *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Figure 6 QTL in occupied transcription factor binding sites

a, Top, Manhattan plot showing all tested variants around CG17343 locus (lead variant in red, neighbouring genes with unadjusted P values in grey). Middle, ChIP-chip signal for the GATA transcription factor Pannier (Pnr) at 4–6 h (stages 8/9) and 6–8 h (stages 10–11, matching the middle QTL time point) of development. Bottom, Pnr PWM and changes in its motif in Minor allele (red). Box plot (right), normalized expression levels across time points for both genotypes. b, Luciferase assay for the promoter-proximal element, showing a 0.85-fold decrease in the Minor genotype over the Major genotype. When the Minor allele variant is placed in the Major genetic background (Majormin) expression is decreased to 0.36-fold. c, As in a for the Eogt locus. Note that the QTL is stage-specific, significantly affecting gene expression at the middle time point (6–8 h; indicated as filled boxes in boxplot), matching the occupancy of Pnr at this stage. d, Luciferase assay for the Eogt 3′ element. Although the GATA site mutation has little effect within the Minor genotype, it causes a significant reduction in expression when placed in the Major background (Major min), reducing reporter gene expression to 0.47-fold. b, d, All values are expressed as mean ± s.d. for three biological replicates, normalized to values of the Major allele. Student’s t-test, **P < 0.01, ***P < 0.001.

Extended Data Figure 7 Global scans for allelic heterogeneity and epistasis.

a, b, A simple linear model was used to test for allelic heterogeneity (marginal effects of additional loci; a) and interactions/epistasis (b) for all variants within 500 bp (approximate size of regulatory elements) of the lead variant for each common eQTL. This process was iterated to remove all significant marginal effects and the residuals used to test for epistatic interactions with the lead variant. Resulting P values for both marginal and epistatic effects were corrected for multiple testing (Bonferroni) within each eQTL. The minimum P value for each eQTL was then corrected across all eQTL and used to control the FDR for epistatic and marginal effects. Distributions of these FDRs (a, b) are shown. Out of 1,164 gene eQTL tested, 9.3% have evidence for one or more marginal effects and 1.8% for an epistatic effect (FDR < 10%), despite limited power due to strong linkage and relatively modest sample sizes. c, Manhattan plots show Bonferroni-corrected P values for both marginal (top) and epistatic (middle top) effects, around the eQTL associated with the gene CG8564 (located ~35 kb downstream). Genetic variants are colour-coded based on their degree of linkage to the lead SNP (r2). d, Table showing summary statistics for marginal and epistatic effects.

Extended Data Figure 8 De novo motif discovery and enrichment of pA-associated RNA motifs.

a, Global enrichment of 3iQTL in gene features compared with randomly selected regions of the genome using multivariate logistic regression. Bars represent 95% profile confidence interval of odds ratios. Common 3iQTL are enriched for variants in both the 3′ and 5′ UTRs, while stage-specific 3iQTL are predominantly associated with 5′ regions, in keeping with their enrichment (both stage-specific eQTL and 3iQTL) for regulatory variants within enhancers and promoters. b, Schematic diagram of the alternative cleavage and polyadenylation machinery. CPSF and CSTF move from the RNA polymerase II C-terminal tail to the growing nascent mRNA strand. Scissors indicates the site of RNA cleavage (transcript end), which is then polyadenylated. c, De novo motif discovery identified known pA associated motifs at pA sites (E value is shown underneath motif logo). Two variations of the PAS motif (bound by CPSF) are the top two most enriched motifs. Right, the relative position of each site, centred on the transcriptional end site (TES). d, Two de novo discovered motifs (Supplementary Table 12) are highly enriched at pA sites and localized around the point of cleavage. e, Number of 3iQTL either breaking (top) or creating (bottom) motifs for known polyadenylation cleavage motifs (red), discovered positioned (orange), discovered unpositioned (dark green) and cisBP motifs (light green).

Extended Data Figure 9 3iQTL and utrQTL affecting known motifs.

a, b, Two representative examples for 3iQTL or utrQTL associated with disruption of a characterized RNA motif. Top, Manhattan plot showing tested variants around the CR42254 and CG8004 loci (lead variant in red, neighbouring genes with unadjusted P values in grey). Middle, median 3′-Tag-seq coverage between all tested lines for the Major (dark red) and Minor (blue) genotypes. For comparison, the Major and Minor genotype signals are shadowed. The sequences of RNA motifs broken and created by the QTL are shown in the middle right panel (disrupted base in red). Bottom, heatmap of 3′-Tag-seq coverage for each line.

Extended Data Figure 10 3iQTL can have phenotypes in vivo.

a, UTR-QTL breaking a PAS motif. Top, Manhattan plot of YL-1 locus (lead variant in red, neighbouring genes and unadjusted P values in grey). Middle, median 3′-Tag-seq coverage between all tested lines for the Major (dark red) and Minor (blue) genotypes. For comparison, the Major and Minor genotype signals are shadowed. Sequence of broken PAS motif (variant in red). Bottom, heatmap of 3′-Tag-seq coverage for each line. b, Luciferase assay for the YL-1 proximal pA region overlapping the lead SNP, with significantly higher activity in the Major compared to the Minor allele. When the Minor variant is placed in the Major background (Majormin), activity is decreased to the Minor level. Values are mean ± s.d. for three biological replicates, normalized to values of SV40 polyA control. Student’s t-test, **P < 0.01, ***P < 0.001. c, 3iQTL as hypomorphic alleles. Top, Manhattan plot showing tested variants around the vls locus (lead variant in red, neighbouring genes with unadjusted P values in grey). Middle, median 3′-Tag-seq coverage between all tested lines for the Major (dark red) and Minor (blue) genotypes. Bottom, heatmap of 3′-Tag-seq coverage for each line. d, Genetic epistasis experiment placing a loss-of-function vls2 allele (maintained over a CyO marked balancer chromosome) in trans to two Major or Minor 3iQTL alleles in the vls locus (shown in b: vls2/CyO × DGRP/DGRP). Each cross was performed in both directions, table shows the average viability index for each line (brackets show RAL identifiers of DGRP lines used). Viability index is the ratio of flies with the genotype DGRP/vls2 over DGRP/CyO: an index of 1 indicates no genetic interaction between the DGRP line and the vls2 allele. On average ~80 flies were counted for each of the eight crosses. Student’s t-test, *P < 0.05.

Supplementary information

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Cannavò, E., Koelling, N., Harnett, D. et al. Genetic variants regulating expression levels and isoform diversity during embryogenesis. Nature 541, 402–406 (2017).

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