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An atlas of alternative polyadenylation quantitative trait loci contributing to complex trait and disease heritability

Nature Genetics volume 53, pages 994–1005 (2021)Cite this article

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Abstract

Genome-wide association studies have identified thousands of noncoding variants associated with human traits and diseases. However, the functional interpretation of these variants is a major challenge. Here, we constructed a multi-tissue atlas of human 3′UTR alternative polyadenylation (APA) quantitative trait loci (3′aQTLs), containing approximately 0.4 million common genetic variants associated with the APA of target genes, identified in 46 tissues isolated from 467 individuals (Genotype-Tissue Expression Project). Mechanistically, 3′aQTLs can alter poly(A) motifs, RNA secondary structure and RNA-binding protein–binding sites, leading to thousands of APA changes. Our CRISPR-based experiments indicate that such 3′aQTLs can alter APA regulation. Furthermore, we demonstrate that mapping 3′aQTLs can identify APA regulators, such as La-related protein 4. Finally, 3′aQTLs are colocalized with approximately 16.1% of trait-associated variants and are largely distinct from other QTLs, such as expression QTLs. Together, our findings show that 3′aQTLs contribute substantially to the molecular mechanisms underlying human complex traits and diseases.

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Fig. 1: Atlas of genetic variations associated with 3′UTR usage across 46 human tissues.
Fig. 2: Tissue-specific 3′aQTLs.
Fig. 3: 3′aQTL represent a new type of molecular QTL.
Fig. 4: 3′aQTLs can alter PAS and uridylate-rich motifs in human tissues.
Fig. 5: LARP4 is an APA regulator.
Fig. 6: Association between 3′aQTLs and human GWAS diseases/traits.
Fig. 7: Colocalization of 3′aQTLs with complex trait-associated loci.

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Data availability

Raw GTEx RNA-seq and genotype files are available to authorized users through dbGaP release, under accession no. phs000424.v7.p2. A list of 3′aQTLs, lead 3′aQTLs and their associated APA genes, isoform usage-controlled 3′aQTLs and expression-controlled 3′aQTLs are freely available at Synapse (accession no. syn22236281; https://doi.org/10.7303/syn22236281). Raw and processed PAC-seq data for the LARP4-depletion experiment have been deposited with the Gene Expression Omnibus under accession no. GSE139548. The proteomics data have been deposited with the MassIVE database under accession no. MSV000087000. A website portal dedicated to trait- and disease-associated 3′aQTLs can be accessed at https://wlcb.oit.uci.edu/3aQTL/index.php. Source data are provided with this paper.

Code availability

The open-source DaPars v.2.0 program is freely available at https://github.com/3UTR/DaPars2.

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Acknowledgements

We thank L. Hou, Z. Cui and members of the Li laboratory for helpful discussions. This work was supported by a Computational Cancer Biology Training Program fellowship Cancer Prevention Research Institute of Texas (CPRIT) grant no. RP170593 (K.-L.H.), National Institutes of Health grants to W.L. (nos. R01HG007538, R01CA193466, R01CA228140), P.J. (no. R03CA223893), E.J.W. (no. R01GM134539) and a University of California, Irvine (UCI) Cancer Center support grant (no. P30CA062203). E.J.W. acknowledges support from University of Texas Medical Branch (UTMB) Department of Biochemistry and Molecular Biology startup funds and the Welch Foundation (no. H-1889). The UTMB Mass Spectrometry Facility is supported in part by CPRIT grant no. RP190682 (W.K.R.). We also thank I. Toufique and P. Papadopoulos at the UCI Research Cyber Infrastructure Center for high-performance computing support.

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  1. These authors contributed equally: Lei Li, Kai-Lieh Huang.

Authors and Affiliations

  1. Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, USA

    Lei Li, Ya Cui, Yumei Li & Wei Li

  2. Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, USA

    Kai-Lieh Huang, Nathan D. Elrod, Ping Ji, William K. Russell & Eric J. Wagner

  3. Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA

    Yipeng Gao

  4. The Gertrude H. Sergievsky Center and Department of Neurology, Columbia University, New York, NY, USA

    Gao Wang

  5. Department of Statistics, University of California, Los Angeles, CA, USA

    Yiling Elaine Chen

  6. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA

    Fanglue Peng

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Contributions

L.L. and W.L. conceived and supervised the project. L.L., Y.G., Y.C., Y.E.C. and G.W. performed the data analyses. K.-L.H., N.D.E., W.K.R. and P.J. performed the experiments. L.L., Y.L., Y.C., F.P., E.J.W. and W.L. interpreted the data and wrote the manuscript.

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Correspondence to Eric J. Wagner or Wei Li.

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The authors declare no competing interests.

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Peer review information Nature Genetics thanks Stephen Montgomery, Bin Tian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Known technical covariates associated with inferred PEER factors in each tissue.

The R2 value in each cell represents the percentage of variance explained for each tissue/covariates pair. Only the most relevant sample-specific covariates were used. Gray color represents insufficient data to predict correlations. Each color code below indicates a tissue of origin.

Extended Data Fig. 2 Known donor covariates associated with inferred PEER factors in each tissue.

The R2 value in each cell represents the percentage of variance explained for each tissue/covariate pair. Only the most relevant donor-specific covariates were used. Gray color represents insufficient data to predict correlations. Each color code below indicates a tissue of origin.

Extended Data Fig. 3 PEER factors for gene expression associated with PEER factors for PDUI in each tissue.

The R2 value in each cell represents the correlation between the top PEER factors for gene expression (rows) and the most relevant PEER factors for PDUI for each tissue (columns). Each color code below indicates a tissue of origin.

Extended Data Fig. 4 Enrichment of 3′aQTL in different categories of mutagenesis variants annotations.

The enrichment score represents the log odd ratio and accessed by the program Torus. The x-axis represents three categories of variants with different effects in predicting APA isoform log fold change due to the variant. Each color code indicates a tissue of origin. The saturation mutagenesis data with log isoform fold change < 0.15 are not available from Bogard et al.

Extended Data Fig. 5 The sharing magnitude of 3′aQTLs using different FDRs at 0.01, 0.005, 0.001.

Histograms showing the estimated proportion of tissues that share lead 3′aQTLs /eQTLs, by magnitude, with other tissues, among all 46 examined tissues, among non-brain tissues only, and among brain tissues only.

Extended Data Fig. 6 sQTL have a distinct genomic distribution and functional enrichment compared with 3′aQTL.

a, Relative position distance between sQTL and their associated genes. TSS represents the transcription start site; TES represents the transcription end site. Red line represents randomly selected positions within the +/− 1Mb window for each gene. b, 3′aQTL and sQTL enrichment in functional annotations. The enrichment is shown as mean with SD across tissues. The proportion of variants was also included for 3′aQTL and sQTL. Data are presented as mean value +/− Standard deviation. n = 46 tissues examined.

Extended Data Fig. 7 3′aQTLs are validated by saturation mutagenesis data.

a, Saturation mutagenesis of the ADI1 PAS. Shown above is the measured wild-type (black) and variant cleavage distribution (red) for the SNP rs1130319. The heatmap below shows the measured isoform fold changes as a result of each SNP. The red box color indicates the SNP rs1130319.

Extended Data Fig. 8 Trans-regulator APA prediction.

a, Scatterplot of the percentage of distal polyA site usage index (PDUI) in CSTF2 over-expressed and low-expressed samples where mRNA significantly shortened (blue) or lengthened (red) are colored. b, Scatterplot of PDUI changes for LARP4 over-expressed and low-expressed samples were shown.

Extended Data Fig. 9 Representative genome browser images of the SLC9A3R2 gene.

SLC9A3R2 APA is regulated by LARP4 and binds LARP4, as assessed by LARP4 CLIP-seq.

Extended Data Fig. 10 A partitioned heritability plot for the percentage of phenotypic variance can be explained, for 35 traits, by 3′aQTLs, eQTLs, and sQTLs in aggregate.

The trait/tissue pairs with heritability not significantly greater than 0 are removed. Centre horizontal lines show median values, boxes span from the 25th percentile to the 75th percentile. Whiskers extend to 1.5 × IQR (bottom), where IQR is the interquartile range. n = 46 tissues examined.

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Li, L., Huang, KL., Gao, Y. et al. An atlas of alternative polyadenylation quantitative trait loci contributing to complex trait and disease heritability. Nat Genet 53, 994–1005 (2021). https://doi.org/10.1038/s41588-021-00864-5

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