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Genetic variants affecting RNA stability influence complex traits and disease risk

Nature Genetics volume 57, pages 2578–2588 (2025)Cite this article

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Abstract

Gene expression is modulated jointly by transcriptional regulation and messenger RNA stability, yet the latter is often overlooked in studies on genetic variants. Here, leveraging metabolic labeling data (Bru/BruChase-seq) and a new computational pipeline, RNAtracker, we categorize genes as allele-specific RNA stability (asRS) or allele-specific RNA transcription events. We identify more than 5,000 asRS variants among 665 genes across a panel of 11 human cell lines. These variants directly overlap conserved microRNA target regions and allele-specific RNA-binding protein sites, illuminating mechanisms through which stability is mediated. Furthermore, we identified causal asRS variants using a massively parallel screen (MapUTR) for variants that affect post-transcriptional mRNA abundance, as well as through CRISPR prime editing approaches. Notably, asRS genes were enriched significantly among a multitude of immune-related pathways and contribute to the risk of several immune system diseases. This work highlights RNA stability as a critical, yet understudied mechanism linking genetic variation and disease.

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Fig. 1: RNAtracker categorizes genes by the underlying mechanisms of allelic imbalance.
Fig. 2: asRS and asRT events overlapping GTEx eQTL and their target genes.
Fig. 3: asRS variants are enriched within functional regions.
Fig. 4: Experimental support for asRS variants.
Fig. 5: asRS genes are enriched in immune-related processes.
Fig. 6: asRS events illuminate mechanisms underlying significant GWAS hits for various diseases.

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Data availability

Bru-seq/BruChase-seq from 16 human cell lines (GM12878, HCT116, HepG2, IMR-90, K562, MCF-7, PC-3, Panc1, PC-9, A673, MCF10A, Calu3, Caco-2, OCI-LY7, endothelial cell of umbilical vein (HUVEC) and mammary epithelial cell (HMEC)) were downloaded from the ENCODE data portal (https://www.encodeproject.org/). Accession IDs can be found in Supplementary Table 1b. The GRCh38 reference genome and gene annotation can be found at https://www.gencodegenes.org/human/release_36.html (filenames: GRCh38.primary_assembly.genome.fa.gz; gencode.v36.primary_assembly.annotation.gtf.gz). Significant GTEx cis-eQTLs were downloaded from the GTex portal (v.8 release) at https://www.gtexportal.org/home/datasets (GTEx_Analysis_v8_eQTL.tar). ABSOLUTE CNVs from CCLE can be obtained from https://depmap.org/portal/data_page/?release=CCLE+2019&file=CCLE_ABSOLUTE_combined_20181227.xlsx&tab=allData. Allele-specific binding sites were obtained from our previous work (Supplementary Data 2 from ref. 22). SNPs overlapping miRNA seed regions that create or disrupt miRNA binding sites were downloaded from miRNASNPv3 (ref. 25). eCLIP data for reproducible peaks (as determined from the irreproducible discovery rate approach50) were downloaded from the ENCODE portal. ActD RNA-seq data can be accessed on GEO (Series record GSE276016). MapUTR sequencing data can be accessed on GEO (Series record GSE298114). CRISPR editing results can be accessed on GEO (Series record GSE298112). All GWAS summary statistics used in this paper can be downloaded from the GWAS catalog (https://www.ebi.ac.uk/gwas/; accession codes in Supplementary Table 8a). GRCh38 genotype reference files from the 1000 Genomes project can be found at https://www.internationalgenome.org/data-portal/data-collection/grch38. Source data are provided with this paper.

Code availability

Code for reproducing the RNAtracker gene categorization results and other data analysis scripts is available via GitHub at https://github.com/gxiaolab/RNAtracker and via Zenodo at https://doi.org/10.5281/zenodo.15528784 (ref. 55). We used bbduk from the BBmap package (v.38.91) (https://sourceforge.net/projects/bbmap/) for read adapter trimming, STAR47 (v.2.7.8a) for read mapping, Picard Tools (https://broadinstitute.github.io/picard/) (v.1.94) to remove PCR duplicates and extract uniquely mapped reads, NeoloopFinder43 (v.0.3.0) for CNV predictions, rrvgo52 (v.1.6.0) for GO enrichment analysis, PLINK56 (v.1.9) to obtain tag SNPs and bedtools (v.2.30.0)57 to overlap genomic regions. Perbase (v.0.10.0) (https://github.com/sstadick/perbase) was used to obtain variant allelic counts in the CRISPR prime editing sequencing data. MPRAnalyze48 was used to identify functional variants in the MapUTR data. S-LDSC54 was used to estimate disease heritability. FUSION.compute_weights.R from http://gusevlab.org/projects/fusion/ was used to build gene prediction models. For analyzing whole-genome sequencing data, we used bwa mem58 (v.0.7.17) for read mapping and CNVpytor41 (v.1.3.1) for identifying CNV regions. The VGAM59 (v.1.1) R package was used to compute probability density values and simulate allelic counts. Genome coordinate conversions were performed using liftOver (https://www.bioconductor.org/packages/release/workflows/html/liftOver.html). Other R packages used for plotting include ComplexUpSet60, ComplexHeatmap61 and AllelicImbalance62.

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Acknowledgements

We thank members of the Xiao laboratory for helpful discussions and comments on this work. This work was supported in part by grants from the National Institutes of Health (U01HG009417 and R01AG075206 to X.X.). E.H. was supported by the Graduate Research Fellowship of the NSF under Grant No. DGE-2034835. T.F. was supported by the UCLA Hyde Fellowship and Dissertation Year Fellowship. K.A. was supported by the University of California-Historically Black Colleges and Universities (UC-HBCU) Fellowship. S.T. was supported by the NIH T32GM145388. M.L. was supported by NHGRI grant UM1 HG009382. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. Guanao Yan

    Present address: Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA

Authors and Affiliations

  1. Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA

    Elaine Huang, Ryo Yamamoto, Kofi Amoah, Jonatan Hervoso, Jingyi Jessica Li & Xinshu Xiao

  2. Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA

    Ting Fu, Ling Zhang, Thuy Linh Nguyen, Carlos Gonzalez-Figueroa, Jae Hoon Bahn, Rajagopal Varada & Xinshu Xiao

  3. Department of Statistics and Data Science, University of California, Los Angeles, CA, USA

    Guanao Yan & Jingyi Jessica Li

  4. Molecular Biology Interdepartmental Program, University of California, Los Angeles, CA, USA

    Sari Terrazas & Xinshu Xiao

  5. Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, CA, USA

    Armen Khanbabaei

  6. Center for RNA Biomedicine and Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA

    Michelle T. Paulsen & Mats Ljungman

  7. Departments of Radiation Oncology and Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA

    Michelle T. Paulsen & Mats Ljungman

  8. Department of Pathology, University of Michigan, Ann Arbor, MI, USA

    Brian Magnuson

  9. Department of Human Genetics, University of California, Los Angeles, CA, USA

    Jingyi Jessica Li

  10. Department of Computational Medicine, University of California, Los Angeles, CA, USA

    Jingyi Jessica Li

  11. Department of Biostatistics, University of California, Los Angeles, CA, USA

    Jingyi Jessica Li

  12. Molecular Biology Institute, University of California, Los Angeles, CA, USA

    Xinshu Xiao

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Contributions

E.H., L.Z. and X.X. designed the study with inputs from all other authors. E.H., L.Z., K.A., R.Y. and J.H. conducted the bioinformatics works. E.H., G.Y. and J.J.L. worked on the statistical modeling. T.F., S.T., T.L.N., C.G.-F., A.K., J.H.B. and R.V. conducted the molecular, cellular and biochemical experiments. M.T.P. and B.M. generated the Bru-seq/BruChase-seq data. X.X., J.J.L. and M.L. provided supervisory inputs. All authors contributed to the writing of the paper. All authors approved the final paper.

Corresponding author

Correspondence to Xinshu Xiao.

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Extended data

Extended Data Fig. 1 RNAtracker facilities the classification of ASE genes.

a, Transcriptomic comparison of Bru-labeled vs. unlabeled K562 cells based on two-sided Pearson’s correlation test (p < 2.2 e-16). b, Allelic ratio distribution after copy-number variant (CNV) removal for the 11 cell lines with >100 genes eligible for classification. Allelic ratio (AR) is calculated by dividing the number of reference allelic counts by total counts per variant. c, Number of genes eligible for classification by RNAtracker in each cell line. d, Number of genes identified as asRS, asRT, or mixed.

Source data

Extended Data Fig. 2 Read coverage, gene half-life, and alternative-splicing contribute minimally to RNAtracker performance.

a, Coverage of asRS, asRT, and mixed genes. For each gene, we take the average coverage across all genic regions. b, Estimated half-lives of asRS, asRT, and mixed genes. In boxplots, minima/maxima represent least/greatest proportion values, bounds show 25th and 75th percentiles, and whiskers indicate values within 1.5 * the interquartile range. c, Number of genes with and without gene classification changes after removing SNVs in alternatively spliced regions.

Source data

Extended Data Fig. 3 RNAtracker exhibits high Precision and Recall in simulations.

a,b,c 1,000 allelic count datasets were simulated by sampling reads from beta-binomial models using hyperparameters representing 3 different allelic imbalance conditions to assess Precision (a), Recall (b), and Recall among genes passing classification thresholds only (c). In boxplots, minima/maxima represent least/greatest proportion values, bounds show 25th and 75th percentiles, and whiskers indicate values within 1.5 * the interquartile range.

Source data

Extended Data Fig. 4 Bias against asRT identification varies across cell lines.

a, Number of expressed genes with and without heterozygous genic single-nucleotide variants (SNVs). Genes without heterozygous SNVs are further categorized into whether they have intronic heterozygous SNVs or 0 heterozygous SNVs (even when introns are considered). To be considered expressed, a gene must have average base coverage ≥ 10 across all genic regions, across all 6 timepoint samples. b, Prevalence (top panel) and number (bottom panel) of intron-based asRT genes. Prevalence is calculated using the total number of genes that are testable based on having SNVs in intronic regions. c, Comparison of the prevalence of stability-regulated genes versus transcriptionally regulated genes (including and excluding intron-based asRT genes). Stability regulated genes include asRS and mixed genes. Transcriptionally regulated genes include asRT, intronic asRT, and mixed genes. To calculate prevalence, the number of genes falling under each of these categories is summed and divided by the total number of genes that were tested using RNAtracker in the cell line.

Source data

Extended Data Fig. 5 Low proportion of asRS sharing across cell lines can be attributed to their unique genetic backgrounds.

a, UpSet plot of asRS genes that are unique to or shared across cell lines. b,c UpSet plot of heterozygous single-nucleotide variants (SNVs) within genes classified by RNAtracker (b), as well as all (including intronic) heterozygous SNVs (c) that are unique to or shared across cell lines. Note that Calu3 is not shown because only the top 30 largest intersections are plotted. d, UpSet plot of all genes categorized by RNAtracker across cell lines. e, UpSet plot of asRS variants that are unique to or shared across cell lines. f, Pairs of cell lines that exhibited a significant difference between the expected and actual proportion of overlapping asRS variants. All 24 comparisons had significantly greater actual proportion than expected. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 (two-sided binomial test).

Source data

Extended Data Fig. 6 Low proportion of asRT sharing across cell lines can be attributed to their unique genetic backgrounds.

a, UpSet plot of asRT genes shared across cell lines. b, UpSet plot of asRT variants shared across cell lines. c, Pairs of cell lines that exhibited a significant difference between the expected and actual proportion of overlapping asRT variants. 33 out of 33 of these comparisons had significantly greater actual proportion than expected. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 (two-sided binomial test).

Source data

Extended Data Fig. 7 asRS and asRT events are both important contributors to gene expression.

a, Proportion of asRS and asRT variants in each cell line (n = 11) that overlapped expression quantitative trait loci (eQTLs) (p = 3.71e-20). P value was calculated via a two-sided Wilcoxon’s signed rank-test. In boxplots, minima/maxima represent least/greatest proportion values, bounds show 25th and 75th percentiles, and whiskers indicate values within 1.5 * the interquartile range. b, Enrichment (that is Fisher’s exact test odds ratio) of asRS genes that overlap eGenes (compared to asRT genes).*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001. None of the comparisons in which the asRT overlap proportion was higher than the asRS overlap proportion were significant.

Source data

Extended Data Fig. 8 asRS variants may function by disrupting interactions with trans-regulatory factors.

a, RNA binding proteins (RBPs) with allele-specific binding (ASB) sites that overlap asRS variants. X axis shows percentage of each RBP’s ASB sites that overlap asRS variants. Number of ASB sites that overlap asRS variants is shown to the right of each bar. Purple: RBPs involved with RNA stability and decay according to previous manual literature curation. b, Proportion of miRNA target gain/loss SNPs that overlap asRS or control variants in each cell line (n = 11). In boxplots, minima/maxima represent least/greatest proportion values, bounds show 25th and 75th percentiles, and whiskers indicate values within 1.5 * the interquartile range.

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Extended Data Fig. 9 Prime editing supports the causality of asRS variants.

a-c, Genomic DNA sequencing supports the successful genome editing of chr2:173364016:T > C (a), chr11:838672:C > T (b), and chr11:834745:G > T (c). d, Comparison of SNV normalized counts in Bru/BruChase-seq data (2 biological replicates per timepoint) for chr2:173364016:T > C in CDCA7. In boxplots, minima/maxima represent least/greatest proportion values, bounds show 25th and 75th percentiles, and whiskers indicate values within 1.5 * the interquartile range.

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Extended Data Fig. 10 asRS and asRT genes are involved in various pathways.

a, Top 20 enriched Gene Ontology (GO) terms for asRT genes. P values were derived from an empirical Gaussian distribution of number of control genes containing each GO term (Methods).

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Supplementary information

Supplementary Information

Supplementary Notes 1–7.

Reporting Summary

Supplementary Tables 1–9

Excel file housing Tables 1–9. Tabs are colored by Table number.

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Huang, E., Fu, T., Zhang, L. et al. Genetic variants affecting RNA stability influence complex traits and disease risk. Nat Genet 57, 2578–2588 (2025). https://doi.org/10.1038/s41588-025-02326-8

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