Abstract
A major challenge in human genetics is to identify the molecular mechanisms of trait-associated and disease-associated variants. To achieve this, quantitative trait locus (QTL) mapping of genetic variants with intermediate molecular phenotypes such as gene expression and splicing have been widely adopted1,2. However, despite successes, the molecular basis for a considerable fraction of trait-associated and disease-associated variants remains unclear3,4. Here we show that ADAR-mediated adenosine-to-inosine RNA editing, a post-transcriptional event vital for suppressing cellular double-stranded RNA (dsRNA)-mediated innate immune interferon responses5,6,7,8,9,10,11, is an important potential mechanism underlying genetic variants associated with common inflammatory diseases. We identified and characterized 30,319 cis-RNA editing QTLs (edQTLs) across 49 human tissues. These edQTLs were significantly enriched in genome-wide association study signals for autoimmune and immune-mediated diseases. Colocalization analysis of edQTLs with disease risk loci further pinpointed key, putatively immunogenic dsRNAs formed by expected inverted repeat Alu elements as well as unexpected, highly over-represented cis-natural antisense transcripts. Furthermore, inflammatory disease risk variants, in aggregate, were associated with reduced editing of nearby dsRNAs and induced interferon responses in inflammatory diseases. This unique directional effect agrees with the established mechanism that lack of RNA editing by ADAR1 leads to the specific activation of the dsRNA sensor MDA5 and subsequent interferon responses and inflammation7,8,9. Our findings implicate cellular dsRNA editing and sensing as a previously underappreciated mechanism of common inflammatory diseases.
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Data availability
GTEx V8 release editing-level data and edQTL call sets are available on the GTEx Portal: https://gtexportal.org. Details of the GWAS summary statistics used for colocalization are provided in Supplementary Table 1. icSHAPE data were obtained from the RASP database: http://rasp.zhanglab.net/. CLIP data were obtained from the POSTAR3 database: http://postar.ncrnalab.org/.
Code availability
A full pipeline to preprocess the GWAS and edQTL data, prioritize relevant loci, run colocalization tests and generate the associated plots is publicly available at https://github.com/mikegloudemans/rna-editing-coloc and https://github.com/vargasliqin/GTEx_edQTL.
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Acknowledgements
We acknowledge the GTEx consortium for making the data available for this study; members of the Li and Montgomery laboratories for insightful discussions and suggestions; C. Walkley, J. Engreitz, A. Marderstein and O. de Geode for critical reading of the manuscript; and X. Liu, W. Tsui and V. Sochat for technical support. This work was funded by US National Institutes of Health grants R01 GM102484, R01 GM124215, R01 MH115080, R35 GM144100, R01 AG066490, R01 MH125244, U01 HG009431 and U01 HG007593. Q.L. was partially funded by the American Heart Association Postdoctoral Fellowship (17IFUNP33820059). M.J.G. was funded by NLM training grant T15 LM 007033 and a Stanford Graduate Fellowship.
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Q.L., M.J.G., S.B.M. and J.B.L. conceptualized the study. Q.L., M.J.G., J.M.G., B.F., G.R., Y.I.L., J.-B.M., S.B.M. and J.B.L. performed the study. Q.L., M.J.G., J.M.G., B.F. and F.A. undertook the formal analysis. Q.L. and J.B.L. wrote the original draft of the manuscript. Q.L., M.J.G., J.M.G., T.S., G.R., Y.I.L., J.K.P., S.B.M. and J.B.L. reviewed and edited the manuscript. J.B.L. acquired funding. S.B.M. and J.B.L. supervised the study.
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S.B.M. is a consultant for BioMarin, MyOme and Tenaya Therapeutics. J.B.L. is a co-founder of AIRNA Bio and a consultant for Risen Pharma. J.B.L. and Q.L. are named inventors of a provisional patent filed by Stanford University (serial no. 63/473,678), describing a method related to this work. The other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Quantification of RNA editing levels in GTEx data.
a, Number of editing sites used for cis-edQTL mapping in each tissue. Editing sites mapped uniquely by ≥20 reads in ≥60 samples of each tissue type were considered. Both GTEx V6p and V8 results are plotted here for comparison. b, Comparison of RNA editing levels quantified using exact read count versus estimated read count for cis-NAT editing sites (Methods). To maximize the number of tested cis-NAT editing sites, we collectively analyzed strand-specific RNA-seq data of 5 human tissues (spleen, lung, thyroid, colon and skin) by pooling the raw reads together. In total, n = 806 editing sites of 47 cis-NAT dsRNAs were used for comparison (≥20 reads coverage). c, Percentage of editing variance across individuals explained by top 10 principal components (PCs) of editing level. d, Association between ADAR1 expression level with PC 1 of editing level in n = 175 Brain – cerebellar hemisphere samples. e, Density plot of editing level measurements and editing level variance between individuals (n = 175; normalized standard deviation) of 123,707 sites in Brain – cerebellar hemisphere tissue. Editing sites detected in ≥60 individuals were used for this analysis.
Extended Data Fig.2 Identification of RNA editing QTLs across GTEx tissues.
a, Fraction of editing sites (left) and edited genes (right) found with edQTLs across n = 49 GTEx tissues. The LCLs data was obtained from previous edQTL study33. b, Fractions of edQTLs shared between multiple editing sites. c, Fractions of edGenes having multiple independent edQTLs. Each of the tissues (n = 49) was tested and plotted individually in b and c. d, Exemplary locus of TRMT9B showing two independent edQTLs each regulating multiple editing sites. The two edQTLs are represented by their lead SNPs rs13268982 and rs34995506, respectively. Genome browser view of the TRMT9B 3’ UTR that consists of four IRAlus (shown in the 1st track from top), with editing sites (2nd track from top), SNP location (black bars) and variant-editing association effect sizes for each editing site (3rd and 4th tracks) shown from top to bottom. Effect sizes are colored by direction of effects: red for positive effect and blue for negative effect. e, Comparison of the number of edSites identified in this study and in Park et al., 2021. For each tissue type, edSites are compared between two studies by evaluating (1) whether they are tested for genetic association, and (2) whether the associations are significant under different p-value cut-offs (p < 1e-3 on the left, p < 1e-5 on the right). f, Sharing of edQTLs across tissues by sign (same direction of effects). Euclidean distance matrix across tissues was calculated for hierarchical clustering using Ward's method. g, Fraction of edQTLs shared by the number of tissues according to sign. g’, Fraction of edQTLs shared by the number of tissues according to magnitude. Effects with more than 2-fold change of sizes from one another are considered as of different magnitudes. h, Number of tissue-specific edQTLs effects found in 20 representative tissues. Representative tissues were picked by two criteria: 1) large sample size (≥150); 2) least number of shared editing sites with other tissue. For example, a high number of editing sites are shared between three arteries tissues so we choose Artery-Coronary tissue as the representative one since it has the largest sample size.
Extended Data Fig. 3 Comparison between edQTLs, eQTLs and sQTLs in GTEx tissues.
a, Sharing of edQTLs with eQTLs and sQTLs. We estimated the fraction of shared QTLs according to Storey’s π1 (Methods). SNPs with matching numbers and allele frequencies with the edQTLs were randomly sampled genome-wide in each tissue to be used as the control set. b, Distance between the best edQTL and best eQTL for genes with both types of QTL, using 1,478 genes in whole blood tissue as an example. c, Enrichment of functional elements underlying edQTLs, eQTLs and sQTLs (left to right). We used a Bayesian hierarchical model to identify putatively causal variants driving a QTL from a set of variants associated with the locus, and quantified the enrichment of strongly associated variants in functional elements. For edQTLs, we additionally identified the functional elements underlying edQTL SNPs that are >800 bp away from the edSites (grey dots). We used chromHMM annotations and gene-level annotations (e.g. intronic variant, splice region variants, UTR variants, etc.) from snpEff. Splice region variants include variants located within the region of splice site (1-3 bases of the exon side or 3-8 bases of the intron side) and branch point. Structured RNA are defined as regions with icSHAPE score ≥0.7 in RNA structure mapping data in vivo from multiple cell lines obtained from http://rasp.zhanglab.net/. ADAR1 and other RBP binding sites are defined using CLIP peaks obtain from http://postar.ncrnalab.org/. Error bars represent 95% confidence intervals. edQTLs n = 30,319; eQTLs n = 24,740; sQTLs n = 14,424.
Extended Data Fig. 4 Characterization of edQTLs’ effects on RNA sequences and secondary structures.
a, Q-Q plot of edQTLs (n = 30,319) annotated with distance from the associated editing sites. b, Summarized edQTL effect sizes of alternative alleles by 4 types of RNA ribonucleotides at each position from –50 to +50 nt relative to the editing site. Stronger effects were observed for ±2 and ±1 positions and illustrated to show the “AUAGG” motif centered at the edited “A”. c, Further breakdown of the effects of 12 different nucleotide changes (reference allele -> alternative allele) caused by SNPs located at ±2 and ±1 positions. Reference alleles (y-axis) and alternative alleles (x-axis) were accounted for by their transcribed ribonucleotides on the RNA. d, An exemplary edSite association showing negative effects of C-to-U change at +2 position of editing site. Editing site at chr1: 184761188 and SNP at chr1: 184761186 are presented here (hg38 coordinates). Predicted RNA secondary structures for reference allele (G on DNA and C on RNA) and alternative allele (A on DNA and U on RNA) are shown on the left, with editing level measurements of different alternative allele dosages on the right. Biologically independent sample size n = 138. e, Schematic diagram showing RNA secondary structural features defined by bpRNA66. f, Frequency of edQTL SNPs found in different structural features (n = 142) compared to non-QTL SNPs (n = 4,080) in the same predicted structure. Statistical significance was calculated using two-sided Mann–Whitney U test. Box plots show interquartile ranges and median, with whiskers extending to minima and maxima.
Extended Data Fig. 5 RNA editing QTLs are enriched in immune-related diseases and immune traits.
a, QQ-plots of IBD, MS, RA and CAD GWAS with QTL annotations. The expression levels of eGenes and sGenes were matched to the levels of edGenes within ±15% of deviation (see Methods). b, Enrichment of heritability for 42 human traits and diseases mediated by edQTLs, sQTLs and eQTLs. Autoimmune diseases are shown in bold. Enrichment is measured as the regression coefficient of MESC (Methods). Error bars represent standard deviation with mean as center. Meta-analysis of RNA editing QTL mediated heritability is shown in c and heritability enrichment in c’, for 4 exemplary autoimmune and immune-related diseases. Tissue groups are defined the same way as in Fig. 2c. Tissues and tissues groups shown in c’ are the same as in c. Error bars represent standard deviation with mean as center. Sample sizes of studies are in Supplementary Table 1.
Extended Data Fig.6 Fraction of SNP heritability (h2g) mediated by edQTL in immune-related trait GWAS.
We performed MESC analysis on 33 GWAS data (n = 9,138 individuals) obtained from Sayaman et al.42. in the same way as in Fig. 2C. We filtered out 11 immune traits with low total SNP heritability (h2g < 0.01) from the final result. The remaining 22 traits are categorized and colored by immune function annotations according to Sayaman et al.42. Dashed line indicates 0.1 of mediated SNP heritability (h2g)42.
Extended Data Fig. 7 Formation of cis-NAT dsRNAs in vitro with MDA5 proteins and in human cells.
For (a) TNFRSF14:TNFRSF14-AS1, (b) CTSA:PLTP and (c) HBP1:COG cis-NAT dsRNAs, negative stain electron microscopy (EM) images are shown for MDA5 proteins incubated with cis-NAT dsRNAs (left), sense strand ssRNA control (middle) and antisense strand ssRNA control (right), respectively. All filamentation experiments were repeated independently for 3 times for each cis-NAT pair with similar results. d, In vitro editing status of dsRNAs formed by TNFRSF14:TNFRSF14-AS1 cis-NAT. Editing sites in both sense (TNFRSF14, top) and antisense (TNFRSF14-AS1, bottom) strands are shown as red bars separately for the two strands. Editing information was determined by Sanger sequencing. e, Genome browser snapshot of the CTSA:PLTP cis-NAT locus. Annotations of gene structure, Alu repeats, exonic editing sites, RNA structure mapping signal (icSHAPE scores in + and – strands) and ADAR1 CLIP signal are shown from top to bottom. f, Correlation of icSHAPE scores between sense and antisense strands for cis-NAT dsRNAs (n = 38). icSHAPE data was obtained from the RASP database: http://rasp.zhanglab.net/. ADAR1 CLIP data of human U87 cell lines was obtained from the POSTAR3 database: http://postar.ncrnalab.org/.
Extended DataFig. 8 Overexpression of CTSA:PLTP cis-NAT in human cells.
a, Schematic diagram showing plasmid construction for bidirectional transcription of CTSA:PLTP cis-NAT. Expression is driven by two opposing EF-1alpha promoters. Sense strand (CTSA 3’UTR) and antisense strand (PLTP 3’UTR) of the cis-NAT are downstream of mClover3 and mRuby3, respectively, with 207 bp overlapping sequences to mimic cis-NAT dsRNA formation. b, Cartoon showing vector design expressing cis-NAT, its scrambled formation and ssRNA control. c, RT-PCR data for validating the transcription of sense (CTSA) and antisense (PLTP) strands of CTSA:PLTP cis-NAT under different treatments. Amplicons (spanning mClover3-CTSA) are expected only when CTSA is expressed, and amplicons (spanning mRuby3-PLTP) are expected only when PLTP is expressed. Experiments were repeated independently for 2 times with similar results. d, Real-time PCR measurement of overexpression of CTSA:PLTP cis-NAT relative to mClover3 ssRNA control. Overexpression of sense (CTSA) and antisense (PLTP) strands was measured by the co-expressed mClover3 and mRuby3, respectively. n = 1 independent experiment. e, Editing level of CTSA:PLTP cis-NAT in HEK293 cells with WT ADAR1. Editing level was measured by Sanger sequencing.
Extended Data Fig. 9 Directional effects of risk variants associated with complex traits and diseases on RNA editing levels.
a, Traits and diseases with overall negative effects on RNA editing (increased risk associated with reduced editing level). b, Traits and diseases with overall positive or unclear direction of effects on RNA editing. c, Directional effect analysis of four exemplary diseases for edQTLs (top row), eQTLs (middle row) and control edQTLs with randomly flipped signs (bottom row). In all panels, we plotted the genome-wide −log10(P) against estimated effect size in GTEx tissues (n = 49), all estimated using SLDP regression. Significant tissues are colored in red (Bonferroni corrected multiple test p-value < 1x10−3). Larger circles denote lower p-values.
Extended Data Fig. 10 Risk genetic variants are associated with reduced RNA editing levels and induced IFN score in inflammatory diseases.
RNA-seq data from patient-derived samples of three diseases (multiple sclerosis, lupus and CAD) were used to calculate the overall editing levels of dsRNAs associated with risk vs protective alleles and the IFN scores. We used allele-specific editing analysis to determine editing levels associated with risk vs protective alleles (a, b and c). The IFN scores were calculated by summing up the expression of representative ISGs for each disease (Methods), and grouped in four different bins. Samples are then grouped by their editing level differences (protective alleles – risk alleles, divided by quartiles) to show IFN scores plotted for each group and compared between groups of the same disease type (a’, b’ and c’ with matching sample size as a, b and c, respectively). ***, P < 0.001; **, P < 0.01; one-way ANOVA test. Box plots in a’, b’ and c’ show interquartile ranges and median, with whiskers extending to minima and maxima.
Supplementary information
Supplementary Table 1
Information of GWAS summary statistics used in this study.
Supplementary Table 2
Estimated heritability mediated by RNA editing in complex traits and diseases.
Supplementary Table 3
Overlap between edQTLs and GWAS catalog and colocalization results of edQTLs in GWAS of 24 traits and diseases.
Supplementary Table 4
Information of putatively immunogenic dsRNA loci formed by cis-NATs.
Supplementary Table 5
Allele-specific editing (ASED) levels measured in immune-related disease samples.
Supplementary Table 6
List of primer sequences used in this work.
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Li, Q., Gloudemans, M.J., Geisinger, J.M. et al. RNA editing underlies genetic risk of common inflammatory diseases. Nature 608, 569–577 (2022). https://doi.org/10.1038/s41586-022-05052-x
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DOI: https://doi.org/10.1038/s41586-022-05052-x
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