Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability

Abstract

N6-methyladenosine (m6A) plays important roles in regulating messenger RNA processing. Despite rapid progress in this field, little is known about the genetic determinants of m6A modification and their role in common diseases. In this study, we mapped the quantitative trait loci (QTLs) of m6A peaks in 60 Yoruba (YRI) lymphoblastoid cell lines. We found that m6A QTLs are largely independent of expression and splicing QTLs and are enriched with binding sites of RNA-binding proteins, RNA structure-changing variants and transcriptional features. Joint analysis of the QTLs of m6A and related molecular traits suggests that the downstream effects of m6A are heterogeneous and context dependent. We identified proteins that mediate m6A effects on translation. Through integration with data from genome-wide association studies, we show that m6A QTLs contribute to the heritability of various immune and blood-related traits at levels comparable to splicing QTLs and roughly half of expression QTLs. By leveraging m6A QTLs in a transcriptome-wide association study framework, we identified putative risk genes of these traits.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Mapping common genetic variants associated with m6A.
Fig. 2: Functional features enriched in m6A QTLs.
Fig. 3: m6A installation is coupled with transcriptional processes.
Fig. 4: Joint analysis of m6A QTLs and other molecular QTLs.
Fig. 5: Integrated analysis of m6A QTLs and GWAS data of human complex traits.
Fig. 6: m6A TWAS and colocalization analysis.

Data availability

The m6A profiles of the 60 YRI samples generated in this study have been deposited with the Gene Expression Omnibus repository under accession no. GSE125377. The summary statistics data and imputed genotype data are available at https://doi.org/10.5281/zenodo.3870952. The source data for Fig. 3e can be found in the Supplementary Information.

Code availability

The code used for m6A QTL data processing and analysis are available at https://scottzijiezhang.github.io/m6AQTL_reproducibleDocument/index.html. Our method for joint peak calling is implemented as the R package MeRIPtools and is freely available at https://github.com/scottzijiezhang/MeRIPtools.

References

  1. 1.

    Fu, Y., Dominissini, D., Rechavi, G. & He, C. Gene expression regulation mediated through reversible m6A RNA methylation. Nat. Rev. Genet. 15, 293–306 (2014).

    CAS  PubMed  Google Scholar 

  2. 2.

    Roundtree, I. A., Evans, M. E., Pan, T. & He, C. Dynamic RNA modifications in gene expression regulation. Cell 169, 1187–1200 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Xiao, W. et al. Nuclear m6A reader YTHDC1 regulates mRNA splicing. Mol. Cell 61, 507–519 (2016).

    CAS  PubMed  Google Scholar 

  4. 4.

    Kasowitz, S. D. et al. Nuclear m6A reader YTHDC1 regulates alternative polyadenylation and splicing during mouse oocyte development. PLoS Genet. 14, e1007412 (2018).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Louloupi, A., Ntini, E., Conrad, T. & Ørom, U. A. V. Transient N-6-methyladenosine transcriptome sequencing reveals a regulatory role of m6A in splicing efficiency. Cell Rep. 23, 3429–3437 (2018).

    CAS  PubMed  Google Scholar 

  6. 6.

    Roundtree, I. A. et al. YTHDC1 mediates nuclear export of N 6-methyladenosine methylated mRNAs. eLife 6, e31311 (2017).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Wang, X. et al. N 6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Wang, X. et al. N 6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Zhou, J. et al. Dynamic m6A mRNA methylation directs translational control of heat shock response. Nature 526, 591–594 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Shi, H. et al. m6A facilitates hippocampus-dependent learning and memory through YTHDF1. Nature 563, 249–253 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Li, A. et al. Cytoplasmic m6A reader YTHDF3 promotes mRNA translation. Cell Res. 27, 444–447 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Shi, H. et al. YTHDF3 facilitates translation and decay of N 6-methyladenosine-modified RNA. Cell Res. 27, 315–328 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Liu, J. et al. A METTL3–METTL14 complex mediates mammalian nuclear RNA N 6-adenosine methylation. Nat. Chem. Biol. 10, 93–95 (2013).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Wang, P., Doxtader, K. A. & Nam, Y. Structural basis for cooperative function of Mettl3 and Mettl14 methyltransferases. Mol. Cell 63, 306–317 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Zheng, G. et al. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol. Cell 49, 18–29 (2013).

    CAS  PubMed  Google Scholar 

  16. 16.

    Jia, G. et al. N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat. Chem. Biol. 7, 885–887 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Wei, J. et al. Differential m6A, m6Am, and m1A demethylation mediated by FTO in the cell nucleus and cytoplasm. Mol. Cell 71, 973–985.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Frye, M., Harada, B. T., Behm, M. & He, C. RNA modifications modulate gene expression during development. Science 361, 1346–1349 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Huang, H. et al. Recognition of RNA N 6-methyladenosine by IGF2BP proteins enhances mRNA stability and translation. Nat. Cell Biol. 20, 285–295 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Edupuganti, R. R. et al. N 6-methyladenosine (m6A) recruits and repels proteins to regulate mRNA homeostasis. Nat. Struct. Mol. Biol. 24, 870–878 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Liu, J. et al. m6A mRNA methylation regulates AKT activity to promote the proliferation and tumorigenicity of endometrial cancer. Nat. Cell Biol. 20, 1074–1083 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Deng, X. et al. RNA N 6-methyladenosine modification in cancers: current status and perspectives. Cell Res. 28, 507–517 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Barbieri, I. et al. Promoter-bound METTL3 maintains myeloid leukaemia by m6A-dependent translation control. Nature 552, 126–131 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Vu, L. P. et al. The N 6-methyladenosine (m6A)-forming enzyme METTL3 controls myeloid differentiation of normal hematopoietic and leukemia cells. Nat. Med. 23, 1369–1376 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Li, Z. et al. FTO plays an oncogenic role in acute myeloid leukemia as a N 6-methyladenosine RNA demethylase. Cancer Cell 31, 127–141 (2017).

    PubMed  Google Scholar 

  26. 26.

    Su, R. et al. R-2HG exhibits anti-tumor activity by targeting FTO/m6A/MYC/CEBPA signaling. Cell 172, 90–105.e23 (2018).

    CAS  PubMed  Google Scholar 

  27. 27.

    Zhao, S. et al. Detailed modeling of positive selection improves detection of cancer driver genes. Nat. Commun. 10, 3399 (2019).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Banovich, N. E. et al. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet. 10, e1004663 (2014).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Battle, A. et al. Impact of regulatory variation from RNA to protein. Science 347, 664–667 (2015).

    CAS  PubMed  Google Scholar 

  30. 30.

    Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Gate, R. E. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat. Genet. 50, 1140–1150 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Grubert, F. et al. Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell 162, 1051–1065 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Pai, A. A. et al. The contribution of RNA decay quantitative trait loci to inter-individual variation in steady-state gene expression levels. PLoS Genet. 8, e1003000 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e24 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Pickrell, J. K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Hormozdiari, F. et al. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat. Genet. 50, 1041–1047 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Lee, M. N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Wen, X., Pique-Regi, R. & Luca, F. Integrating molecular QTL data into genome-wide genetic association analysis: probabilistic assessment of enrichment and colocalization. PLoS Genet. 13, e1006646 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600–604 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Chun, S. et al. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat. Genet. 49, 600–605 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Yao, D.W., O’Connor, L.J., Price, A.L. & Gusev, A. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat. Genet. 52, 626–633 (2020).

    CAS  PubMed  Google Scholar 

  43. 43.

    Takata, A., Matsumoto, N. & Kato, T. Genome-wide identification of splicing QTLs in the human brain and their enrichment among schizophrenia-associated loci. Nat. Commun. 8, 14519 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).

    CAS  PubMed  Google Scholar 

  45. 45.

    Meyer, K. D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).

    CAS  PubMed  Google Scholar 

  47. 47.

    Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  PubMed  Google Scholar 

  48. 48.

    Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine-mapping. Preprint at https://www.biorxiv.org/content/10.1101/501114v1(2018).

  49. 49.

    Wen, X. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. Ann. Appl. Stat. 10, 1619–1638 (2016).

    Google Scholar 

  50. 50.

    Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Wan, Y. et al. Landscape and variation of RNA secondary structure across the human transcriptome. Nature 505, 706–709 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Agarwal, V., Bell, G. W., Nam, J.-W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, e05005 (2015).

    PubMed Central  Google Scholar 

  53. 53.

    Pers, T. H., Timshel, P. & Hirschhorn, J. N. SNPsnap: a Web-based tool for identification and annotation of matched SNPs. Bioinformatics 31, 418–420 (2015).

    CAS  PubMed  Google Scholar 

  54. 54.

    Chen, T. et al. m6A RNA methylation is regulated by microRNAs and promotes reprogramming to pluripotency. Cell Stem Cell 16, 289–301 (2015).

    CAS  PubMed  Google Scholar 

  55. 55.

    Das, S. & Krainer, A. R. Emerging functions of SRSF1, splicing factor and oncoprotein, in RNA metabolism and cancer. Mol. Cancer Res. 12, 1195–1204 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Bertero, A. et al. The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency. Nature 555, 256–259 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Slobodin, B. et al. Transcription impacts the efficiency of mRNA translation via co-transcriptional N6-adenosine methylation. Cell 169, 326–337.e12 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Aguilo, F. et al. Coordination of m6A mRNA methylation and gene transcription by ZFP217 regulates pluripotency and reprogramming. Cell Stem Cell 17, 689–704 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    CAS  Google Scholar 

  60. 60.

    Huang, H. et al. Histone H3 trimethylation at lysine 36 guides m6A RNA modification co-transcriptionally. Nature 567, 414–419 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Lee, J. et al. Effective breast cancer combination therapy targeting BACH1 and mitochondrial metabolism. Nature 568, 254–258 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Wiel, C. et al. BACH1 stabilization by antioxidants stimulates lung cancer metastasis. Cell 178, 330–345.e22 (2019).

    CAS  PubMed  Google Scholar 

  63. 63.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Hansson, G. K. Inflammation, atherosclerosis, and coronary artery disease. N. Engl. J. Med. 352, 1685–1695 (2005).

    CAS  PubMed  Google Scholar 

  65. 65.

    Nath, A. P. et al. Multivariate genome-wide association analysis of a cytokine network reveals variants with widespread immune, haematological, and cardiometabolic pleiotropy. Am. J. Hum. Genet. 105, 1076–1090 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Stolk, L. et al. Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways. Nat. Genet. 44, 260–268 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Li, H.-B. et al. m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature 548, 338–342 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Zheng, Q., Hou, J., Zhou, Y., Li, Z. & Cao, X. The RNA helicase DDX46 inhibits innate immunity by entrapping m6A-demethylated antiviral transcripts in the nucleus. Nat. Immunol. 18, 1094–1103 (2017).

    CAS  PubMed  Google Scholar 

  69. 69.

    Lichinchi, G. et al. Dynamics of the human and viral m6A RNA methylomes during HIV-1 infection of T cells. Nat. Microbiol. 1, 16011 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Han, D. et al. Anti-tumour immunity controlled through mRNA m6A methylation and YTHDF1 in dendritic cells. Nature 566, 270–274 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51, 592–599 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Nakao, K. et al. Fusion of the nucleoporin gene, NUP98, and the putative RNA helicase gene, DZXX10, by inversion 11 (p15q22) chromosome translocation in a patient with etoposide-related myelodysplastic syndrome. Intern. Med. 39, 412–415 (2000).

    CAS  PubMed  Google Scholar 

  75. 75.

    Snyder, E. et al. Compound heterozygosity for Y box proteins causes sterility due to loss of translational repression. PLoS Genet. 11, e1005690 (2015).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Roy, R. et al. hnRNPA1 couples nuclear export and translation of specific mRNAs downstream of FGF-2/S6K2 signalling. Nucleic Acids Res. 42, 12483–12497 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Liu, N. et al. N 6-methyladenosine-dependent RNA structural switches regulate RNA–protein interactions. Nature 518, 560–564 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Manning, K. S. & Cooper, T. A. The roles of RNA processing in translating genotype to phenotype. Nat. Rev. Mol. Cell Biol. 18, 102–114 (2017).

    CAS  PubMed  Google Scholar 

  79. 79.

    Gurdasani, D., Barroso, I., Zeggini, E. & Sandhu, M. S. Genomics of disease risk in globally diverse populations. Nat. Rev. Genet. 20, 520–535 (2019).

    CAS  PubMed  Google Scholar 

  80. 80.

    Shi, H. Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data. Am. J. Hum. Genet. 106, 805–817 (2020).

    CAS  PubMed  Google Scholar 

  81. 81.

    Mogil, L. S. et al. Genetic architecture of gene expression traits across diverse populations. PLoS Genet. 14, e1007586 (2018).

    PubMed  PubMed Central  Google Scholar 

  82. 82.

    Ndungu, A., Payne, A., Torres, J. M., van de Bunt, M. & McCarthy, M. I. A multi-tissue transcriptome analysis of human metabolites guides interpretability of associations based on multi-SNP models for gene expression. Am. J. Hum. Genet. 106, 188–201 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Schmiedel, B. J. et al. Impact of genetic polymorphisms on human immune cell gene expression. Cell 175, 1701–1715.e16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Calderon, D. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat. Genet. 51, 1494–1505 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Zhao, B. S. et al. m6A-dependent maternal mRNA clearance facilitates zebrafish maternal-to-zygotic transition. Nature 542, 475–478 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Liu, J. et al. N 6-methyladenosine of chromosome-associated regulatory RNA regulates chromatin state and transcription. Science 367, 580–586 (2020).

    CAS  PubMed  Google Scholar 

  87. 87.

    Gazal, S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49, 1421–1427 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J. K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Cui, X. et al. Guitar: an R/Bioconductor package for gene annotation guided transcriptomic analysis of RNA-related genomic features. BioMed Res. Int. 2016, 8367534 (2016).

    PubMed  PubMed Central  Google Scholar 

  92. 92.

    Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    PubMed  Google Scholar 

  93. 93.

    Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 (Bethesda) 1, 457–470 (2011).

    Google Scholar 

  95. 95.

    Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    CAS  PubMed  Google Scholar 

  96. 96.

    Coetzee, S. G., Coetzee, G. A. & Hazelett, D. J. motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites. Bioinformatics 31, 3847–3849 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Dey, K. K., Xie, D. & Stephens, M. A new sequence logo plot to highlight enrichment and depletion. BMC Bioinformatics 19, 473 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Xia, Z. et al. Dynamic analyses of alternative polyadenylation from RNA-seq reveal a 3′-UTR landscape across seven tumour types. Nat. Commun. 5, 5274 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    PubMed  PubMed Central  Google Scholar 

  101. 101.

    Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Y. Gilad, Y.I. Li, M. Chen and L. Barreiro for helpful discussions, and X. Wen for advice on computational analysis. The data on coronary artery disease have been contributed by CARDIoGRAMplusC4D investigators and have been downloaded from http://www.cardiogramplusc4d.org/. C.H. acknowledges support from National Institutes of Health (NIH) grant no. RM1HG008935. X.H. acknowledges support from NIH grant no. R01MH110531. M.S. acknowledges support from NIH grant no. HG002585.

Author information

Affiliations

Authors

Contributions

Z.Zhang, K.L., M.S., X.H. and C.H. designed the study. Z. Zhang, Z. Zou, M. Qiu, J.T., L.S., H.S., A.C.Z. and C.H. conducted and supervised the experiments. Z. Zhang, K.L., Y.Z., G.W., M. Qiao, Z.L., M.S. and X.H. conducted and supervised the analyses. Z. Zhang, K.L., L.S., J.M., M.S., X.H. and C.H. wrote the paper.

Corresponding authors

Correspondence to Matthew Stephens or Xin He or Chuan He.

Ethics declarations

Competing interests

C.H. is a founder and scientific advisory board member of Accent Therapeutics and a shareholder of Epican Genentech.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Joint m6A peak calling and QTL mapping.

a, Distribution of merged m6A peak length. Dash line marks the mean peak width. b, Distribution of all m6A peaks vs. ePeaks on a meta-gene. c, Proportion of all m6A peaks vs. ePeaks in each genomic annotation. d, m6A motif learned by Homer2, and visualized using EDlogo package. e, Spatial distribution of m6A-QTLs illustrated by density plot of SNP to peak distances of m6A-QTL with nominal P-value < 1×10-4 in a 2 Mb window surrounding m6A peaks. We also showed the significance by the -log10 P-value of the association tests in the blue dots. f, Volcano plot of overall statistics of m6A-QTLs with peak-level FDR < 10% (ePeaks). g, Distribution of the number of causal effects of ePeaks (FDR < 10%) by SuSiE fine-mapping with uniform prior. We set SuSiE parameters L = 3 (assuming at most three causal effects) and coverage = 0.95 (95% coverage for credible sets).

Extended Data Fig. 2 Heritability of m6A peaks and independence of m6A-QTLs, eQTL and sQTLs.

a, Distribution of estimated heritability of the 19,130 peaks included in the TWAS analysis, in which 918 peaks had estimated heritability significantly greater than 0 (minimum heritability P-value of 0.01). b, Distribution of estimated heritability of ePeaks (n = 822 peaks). c, Enrichment (log2 odds ratio) of m6A-QTLs in gene annotations. d, Distribution of the LD between the lead ePeak SNP and the eGene SNP in genes that have both ePeak and eGene mapped. e, Overlap between ePeak-harboring genes and eSplicing-harboring (splicing event with at least one significant sQTL) gene (both at FDR < 10%) mapped in YRI LCL samples. f, Distribution of the distance between the lead ePeak SNP and the eSplicing SNP in genes that have both ePeak and eSplicing mapped. g, Distribution of the LD between the lead ePeak SNP and eSplicing SNP in genes that have both ePeak and eSpicing mapped.

Extended Data Fig. 3 Contribution of RNA features and transcriptional features to m6A variation.

a, Enrichment of m6A-QTLs in RNA related features by Torus. Error bars represent the 95% confidence intervals. b, Enrichment of m6A-QTLs in the binding sites of RNA polymerase2 subunit A (POLR2A), and phosphorylated POLR2A at two residues (S2 and S5) by Torus joint analysis of all annotations (upper panel), and enrichment of m6A-QTLs in histone modifications from Torus joint analysis. Error bars indicate the 95% confidence intervals. c, Proportion of putative causal m6A-QTNs in RNA features and transcription factor binding site annotations (see Methods). d-e, To confirm that transcription rate affects mRNA and protein level, we ascertained transcription rate QTLs (Txn-QTLs) and assessed the correlation between transcription rate (Txn)-QTL effect sizes (30 min and 60 min 4sU labelling, respectively) and eQTL effect size (panel d, n = 425 and 1,387 SNP-gene pairs), and protein-QTL effect sizes (panel e, n = 425 and 408 SNP-gene pairs). Correlation is computed using linear regression. Fitted lines and 95% confidence intervals are shown in blue lines and shades.

Extended Data Fig. 4 Downstream effects of m6A are context dependent.

a, The number and fraction of m6A-QTLs in chromatin related genomic regions (using the union of promoter and enhancer regions annotated by ChromHMM in GM12878 cell line), and in chromatin related eQTLs (eQTLs with nominal P-value < 0.05 and also in promoter and enhancer regions). b, High correlations of effect sizes between molecular QTLs along the cascade from transcription to translation. Correlation is computed using linear regression, in which fitted lines and 95% confidence intervals are shown in blue lines and shades. c, Log2 fold change of translation efficiency of m6A methylated transcripts in METTL3 knockdown vs. controls. d, Log2 fold changes of translation efficiency upon YTHDF1 (m6A reader protein) knockdown. Transcripts harboring YTHDF1-bound m6A peaks are labeled in yellow and non-targets in blue.

Extended Data Fig. 5 YBX3 mediates translation efficiency of m6A modified transcripts.

a, Sucrose gradient A260 absorbance profile from YBX3 knockdown and control Hela cells. The arrows indicate the fraction sampled for subsequent qPCR analysis of YBX3 target transcripts. This experiment is repeated 2 times. b, Translation efficiency of YBX3 targets in comparison with YTHDF1 targets. We accounted for mRNA level variation by dividing polysome-bound fraction by the non-polysome-bound fraction. Transcript levels are quantified using RT-qPCR. Three polysome-bound fractions, as indicated in panel a, are sampled from sucrose gradient fractionation. 2 technical replicates were measured to obtain the data. The lower and upper hinges correspond to the first and third quartiles. Horizontal line indicates median value, and whiskers correspond to the value no further than 1.5x inter-quartile range.

Extended Data Fig. 6 Enrichment of GWAS signal in m6A-QTLs.

a, Quantile-quantile (QQ) plots of P-values from GWAS of selected traits. m6A-QTLs, eQTLs and sQTLs are shown in comparison with genome wide SNPs. GWAS SNPs are binary annotated using m6A-QTLs, eQTLs and sQTLs with P-value < 1×10-4. b, Enrichment of GWAS trait heritability assessed by stratified LD-score regression (S-LDSC). Shown are the results of GWAS traits not reported in Fig. 5b. Posterior inclusion probability (PIPs) in this analysis are derived from SuSiE with default (uniform) priors. Error bars represent the 95% confidence intervals.

Extended Data Fig. 7 Enrichment of complex trait heritability in m6A-QTNs using RNA-features-informed priors.

a, Enrichment of selected immune and blood GWAS trait heritability assessed by stratified LD-score regression (S-LDSC). PIPs of m6A-QTLs are derived from SuSiE using RNA-features-informed priors. PIPs of eQTL and sQTL are based on uniform prior. Error bars represent 95% confidence intervals. b, Enrichment parameters of annotations that are used to derive RNA-features-informed priors (by Torus) for SuSiE fine-mapping. Error bars represent the 95% confidence intervals. c, Summary of GWAS traits heritability enrichment analysis using m6A-QTL PIP (using RNA-feature informed priors) as annotation. The -log10 P-value is plotted against the enrichment of heritability. The dots are colored by disease category. The red dashed line shows FDR 5% threshold.

Extended Data Fig. 8 Partitioning complex trait heritability by m6A-QTLs, eQTLs and sQTLs.

Heritability is assessed by S-LDSC in which QTLs are binary annotated with varying SNP-level FDR thresholds of 5%, 10%, and 20%. Error bars represent standard errors.

Extended Data Fig. 9 m6A-TWAS identifies putative risk genes in human complex traits.

a, Number of significant m6A-TWAS genes in all 45 GWAS traits. Significance is defined by the Bonferroni corrected P-value 0.05. b, LocusCompare plot showing the scatter plot and aligned Manhattan plots of leukocyte count GWAS and m6A-QTL association signal at the DDX55 locus. c, Manhattan plot of GWAS association signals before and after conditioning on the TWAS-predicted m6A level (gray and blue dots, respectively) for the leukocyte count at the DDX55 locus.

Extended Data Fig. 10 m6A modification mediates the impact of genetic variation on human complex traits.

Genetic variation exerts its impact on complex traits through varies mechanisms. As one of these mechanisms, we propose that variation of m6A modification may lead to variation of mRNA processing, including mRNA decay, splicing, APA, export and translation efficiency. These variations in turn may change protein levels and functions, and lead to phenotypic variations.

Supplementary information

Supplementary Information

Supplementary Notes

Reporting Summary

Supplementary Tables

Supplementary Tables 1–6

Source data

Source Data Fig. 3

Raw western blots.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Luo, K., Zou, Z. et al. Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability. Nat Genet 52, 939–949 (2020). https://doi.org/10.1038/s41588-020-0644-z

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing