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 drivers of m6A methylation in human brain, lung, heart and muscle

Nature Genetics volume 53, pages 1156–1165 (2021)Cite this article

Subjects

Abstract

The most prevalent post-transcriptional mRNA modification, N6-methyladenosine (m6A), plays diverse RNA-regulatory roles, but its genetic control in human tissues remains uncharted. Here we report 129 transcriptome-wide m6A profiles, covering 91 individuals and 4 tissues (brain, lung, muscle and heart) from GTEx/eGTEx. We integrate these with interindividual genetic and expression variation, revealing 8,843 tissue-specific and 469 tissue-shared m6A quantitative trait loci (QTLs), which are modestly enriched in, but mostly orthogonal to, expression QTLs. We integrate m6A QTLs with disease genetics, identifying 184 GWAS-colocalized m6A QTL, including brain m6A QTLs underlying neuroticism, depression, schizophrenia and anxiety; lung m6A QTLs underlying expiratory flow and asthma; and muscle/heart m6A QTLs underlying coronary artery disease. Last, we predict novel m6A regulators that show preferential binding in m6A QTLs, protein interactions with known m6A regulators and expression correlation with the m6A levels of their targets. Our results provide important insights and resources for understanding both cis and trans regulation of epitranscriptomic modifications, their interindividual variation and their roles in human disease.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study design and m6A landscape across tissues.
Fig. 2: Genetically driven m6A sites across tissues.
Fig. 3: m6A-QTL tissue specificity.
Fig. 4: m6A QTL versus eQTL comparison.
Fig. 5: m6A-QTLs help interpret GWAS loci.
Fig. 6: Predicted m6A regulators.

Data availability

All eGTEx protected data, including m6A sequencing reads and matched RNA-seq data, are available on dbGaP with the accession number phs000424.v8.p2. Additionally, the data can be accessed via AnVIL with authentication: https://anvil.terra.bio/#workspaces/anvil-datastorage/AnVIL_GTEx_V8_hg38. As the raw sequencing data with genetic information are protected, application and authentication are needed before accessing the data. All non-protected data for m6A can be visualized via the GTEx portal (https://www.gtexportal.org) as part of eGTEx v8. The m6A QTLs identified in each tissue can be downloaded from Supplementary Tables 1–7. The eQTL datasets are from GTEx v8, which can be accessed at https://gtexportal.org/home/datasets. The LCL m6A-QTL datasets from Zhang et al.28 can be downloaded from https://doi.org/10.5281/zenodo.3870952. The previously curated m6A sites can be downloaded from RMBase (http://rna.sysu.edu.cn/rmbase/). The RNA-binding sites can be downloaded from http://lulab.life.tsinghua.edu.cn/postar/.

Code availability

Code for m6A data processing, m6A-QTL calling and relevant functional analyses and additional information can be found at http://compbio.mit.edu/m6AQTLs/ and also on Zenodo at https://doi.org/10.5281/zenodo.4764136.

References

  1. Ward, L. D. & Kellis, M. Interpreting noncoding genetic variation in complex traits and human disease. Nat. Biotechnol. 30, 1095–1106 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tak, Y. G. & Farnham, P. J. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin 8, 57 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  5. Sun, W. et al. Histone acetylome-wide association study of autism spectrum disorder. Cell 167, 1385–1397 (2016).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Huan, T. et al. Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease. Nat. Commun. 10, 4267 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  PubMed Central  Google Scholar 

  9. Wu, L. et al. Variation and genetic control of protein abundance in humans. Nature 499, 79–82 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Park, E. et al. Population and allelic variation of A-to-I RNA editing in human transcriptomes. Genome Biol. 18, 143 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Frye, M., Jaffrey, S. R., Pan, T., Rechavi, G. & Suzuki, T. RNA modifications: what have we learned and where are we headed? Nat. Rev. Genet. 17, 365–372 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Zaccara, S., Ries, R. J. & Jaffrey, S. R. Reading, writing and erasing mRNA methylation. Nat. Rev. Mol. Cell Biol. 20, 608–624 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Zhao, B. S., Roundtree, I. A. & He, C. Post-transcriptional gene regulation by mRNA modifications. Nat. Rev. Mol. Cell Biol. 18, 31–42 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Barbieri, I. & Kouzarides, T. Role of RNA modifications in cancer. Nat. Rev. Cancer https://doi.org/10.1038/s41568-020-0253-2 (2020).

  17. Yao, B. et al. Epigenetic mechanisms in neurogenesis. Nat. Rev. Neurosci. 17, 537–549 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li, X., Xiong, X. & Yi, C. Epitranscriptome sequencing technologies: decoding RNA modifications. Nat. Methods 14, 23–31 (2016).

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huang, H., Weng, H. & Chen, J. m6A modification in coding and non-coding RNAs: roles and therapeutic implications in cancer. Cancer Cell 37, 270–288 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Livneh, I., Moshitch-Moshkovitz, S., Amariglio, N., Rechavi, G. & Dominissini, D. The m6A epitranscriptome: transcriptome plasticity in brain development and function. Nat. Rev. Neurosci. 21, 36–51 (2020).

    Article  CAS  PubMed  Google Scholar 

  22. Shi, H., Wei, J. & He, C. Where, when, and how: context-dependent functions of RNA methylation writers, readers, and erasers. Mol. Cell 74, 640–650 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Arguello, A. E., DeLiberto, A. N. & Kleiner, R. E. RNA chemical proteomics reveals the N6-methyladenosine (m6A)-regulated protein–RNA interactome. J. Am. Chem. Soc. 139, 17249–17252 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Edupuganti, R. R. et al. N6-methyladenosine (m6A) recruits and repels proteins to regulate mRNA homeostasis. Nat. Struct. Mol. Biol. 24, 870–878 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Patil, D. P., Pickering, B. F. & Jaffrey, S. R. Reading m6A in the transcriptome: m6A-binding proteins. Trends Cell Biol. 28, 113–127 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Liu, J. et al. Landscape and regulation of m6A and m6Am methylome across human and mouse tissues. Mol. Cell 77, 426–440 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Xiao, S. et al. The RNA N6-methyladenosine modification landscape of human fetal tissues. Nat. Cell Biol. 21, 651–661 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Zhang, Z. et al. Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability. Nat. Genet. 52, 939–949 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. eGTEx Project. Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease. Nat. Genet. 49, 1664–1670 (2017).

    Article  CAS  Google Scholar 

  30. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  32. Schwartz, S. et al. High-resolution mapping reveals a conserved, widespread, dynamic mRNA methylation program in yeast meiosis. Cell 155, 1409–1421 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Xuan, J.-J. et al. RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data. Nucleic Acids Res. 46, D327–D334 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lin, S., Choe, J., Du, P., Triboulet, R. & Gregory, R. I. The m6A methyltransferase METTL3 promotes translation in human cancer cells. Mol. Cell 62, 335–345 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Visvanathan, A. et al. Essential role of METTL3-mediated m6A modification in glioma stem-like cells maintenance and radioresistance. Oncogene 37, 522–533 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Stegle, O., Parts, L., Durbin, R. & Winn, J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput. Biol. 6, e1000770 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Huang, H. et al. Recognition of RNA N6-methyladenosine by IGF2BP proteins enhances mRNA stability and translation. Nat. Cell Biol. 20, 285–295 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang, X. et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).

    Article  PubMed  CAS  Google Scholar 

  41. Amaral, A. C. et al. Prenatal protein malnutrition decreases KCNJ3 and 2DG activity in rat prefrontal cortex. Neuroscience 286, 79–86 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Doré, A. S. et al. Structure of class C GPCR metabotropic glutamate receptor 5 transmembrane domain. Nature 511, 557–562 (2014).

    Article  PubMed  CAS  Google Scholar 

  43. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tindi, J. O. et al. ANKS1B gene product AIDA-1 controls hippocampal synaptic transmission by regulating GluN2B subunit localization. J. Neurosci. 35, 8986–8996 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Cheng, W., Wang, F., Feng, A., Li, X. & Yu, W. CXXC5 attenuates pulmonary fibrosis in a bleomycin-induced mouse model and MLFs by suppression of the CD40/CD40L pathway. Biomed Res. Int. 2020, 7840652 (2020).

    PubMed  PubMed Central  Google Scholar 

  46. Zielonka, M., Xia, J., Friedel, R. H., Offermanns, S. & Worzfeld, T. A systematic expression analysis implicates Plexin-B2 and its ligand Sema4C in the regulation of the vascular and endocrine system. Exp. Cell Res. 316, 2477–2486 (2010).

    Article  CAS  PubMed  Google Scholar 

  47. Kelly, E. A. B. et al. Potential contribution of IL-7 to allergen-induced eosinophilic airway inflammation in asthma. J. Immunol. 182, 1404–1410 (2009).

    Article  CAS  PubMed  Google Scholar 

  48. Cui, H. et al. Impairment of fatty acid oxidation in alveolar epithelial cells mediates acute lung injury. Am. J. Respir. Cell Mol. Biol. 60, 167–178 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Tzimas, C. et al. WIPI1 is a conserved mediator of right ventricular failure. JCI Insight 5, e122989 (2019).

    Google Scholar 

  50. Webster, D. R. & Bratcher, J. M. Developmental regulation of cardiac MAP4 protein expression. Cell Motil. Cytoskeleton 63, 512–522 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. Mohamed, B. A. et al. Targeted disruption of Hspa4 gene leads to cardiac hypertrophy and fibrosis. J. Mol. Cell. Cardiol. 53, 459–468 (2012).

    Article  CAS  PubMed  Google Scholar 

  52. Hernandez-Zimbron, L. F. et al. Molecular age-related changes in the anterior segment of the eye. J. Ophthalmol. 2017, 1295132 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Laing, N. G. et al. Mutations and polymorphisms of the skeletal muscle α-actin gene (ACTA1). Hum. Mutat. 30, 1267–1277 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Gamazon, E. R. et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat. Genet. 50, 956–967 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  CAS  Google Scholar 

  58. Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ardain, A., Marakalala, M. J. & Leslie, A. Tissue-resident innate immunity in the lung. Immunology 159, 245–256 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Whitsett, J. A. & Alenghat, T. Respiratory epithelial cells orchestrate pulmonary innate immunity. Nat. Immunol. 16, 27–35 (2015).

    Article  CAS  PubMed  Google Scholar 

  62. Chiu, C. & Openshaw, P. J. Antiviral B cell and T cell immunity in the lungs. Nat. Immunol. 16, 18–26 (2015).

    Article  CAS  PubMed  Google Scholar 

  63. Zheng, N., Su, J., Hu, H., Wang, J. & Chen, X. Research progress of N6-methyladenosine in the cardiovascular system. Med. Sci. Monit. 26, e921742 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Zhu, Y. et al. POSTAR2: deciphering the post-transcriptional regulatory logics. Nucleic Acids Res. 47, D203–D211 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Yang, Y., Hsu, P. J., Chen, Y.-S. & Yang, Y.-G. Dynamic transcriptomic m6A decoration: writers, erasers, readers and functions in RNA metabolism. Cell Res. 28, 616–624 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Shah, A. et al. The DEAD-box RNA helicase DDX3 interacts with m6A RNA demethylase ALKBH5. Stem Cells Int. 2017, 8596135 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Yang, E.-W. et al. Allele-specific binding of RNA-binding proteins reveals functional genetic variants in the RNA. Nat. Commun. 10, 1338 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Wang, X. et al. Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures. eLife 5, e10557 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Zhou, K. I. et al. Regulation of co-transcriptional pre-mRNA splicing by m6A through the low-complexity protein hnRNPG. Mol. Cell 76, 70–81 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Garcia-Campos, M. A. et al. Deciphering the ‘m6A code’ via antibody-independent quantitative profiling. Cell 178, 731–747 (2019).

    Article  CAS  PubMed  Google Scholar 

  73. Wang, Y., Xiao, Y., Dong, S., Yu, Q. & Jia, G. Antibody-free enzyme-assisted chemical approach for detection of N6-methyladenosine. Nat. Chem. Biol. https://doi.org/10.1038/s41589-020-0525-x (2020).

  74. Zhang, Z. et al. Single-base mapping of m6A by an antibody-independent method. Sci. Adv. 5, eaax0250 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Shu, X. et al. A metabolic labeling method detects m6A transcriptome-wide at single base resolution. Nat. Chem. Biol. https://doi.org/10.1038/s41589-020-0526-9 (2020).

  76. Liu, H. et al. Accurate detection of m6A RNA modifications in native RNA sequences. Nat. Commun. 10, 4079 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Carithers, L. J. et al. A novel approach to high-quality postmortem tissue procurement: the GTEx Project. Biopreserv. Biobank. 13, 311–319 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Batista, P. J. et al. m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 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).

    Article  CAS  PubMed  Google Scholar 

  82. Huang, Q. Q., Ritchie, S. C., Brozynska, M. & Inouye, M. Power, false discovery rate and Winner’s Curse in eQTL studies. Nucleic Acids Res. 46, e133 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Iotchkova, V. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat. Genet. 51, 343–353 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank C. Boix for helping with figure visualization, analysis suggestion and discussion, and K. Kang and other Kellis laboratory members for discussions and suggestions. We thank the GTEx Consortium for providing the samples for m6A profiling. We thank K. G. Ardlie and F. Aguet for helping with GTEx data deposition and providing helpful suggestions. This work was supported by the NIH grants NIH HG007610, HG008155, HG009446, MH109978, AG054012, AG058002, AG062377, NS110453, NS115064, AG067151, AG062335 and MH119509 to M.K., and CA232115 and CA233671 to R.I.G.

Author information

Author notes

  1. These authors contributed equally: Xushen Xiong, Lei Hou.

Authors and Affiliations

  1. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Xushen Xiong, Lei Hou, Yongjin P. Park & Manolis Kellis

  2. The Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Xushen Xiong, Lei Hou, Yongjin P. Park, Benoit Molinie, Kristin G. Ardlie, François Aguet & Manolis Kellis

  3. Stem Cell Program, Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA

    Xushen Xiong & Richard I. Gregory

Authors
  1. Xushen Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongjin P. Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Benoit Molinie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Richard I. Gregory
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Manolis Kellis
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

GTEx Consortium

  • Kristin G. Ardlie
  •  & François Aguet

Contributions

This study was designed by X.X., L.H. and M.K., and directed and coordinated by M.K. B.M. performed the m6A profiling; X.X. and L.H. performed the bioinformatic analysis with help from Y.P. and R.I.G. and under the supervision of M.K. All authors participated in the discussion of the project. X.X., L.H. and M.K. wrote the manuscript.

Corresponding author

Correspondence to Manolis Kellis.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 m6A landscape across tissues.

a. Positional enrichment for aggregated meRIP-seq signal surrounding the known m6A motif (GGACH) vs. three control sequences (colors). b. m6A peak positional density (y-axis) along the gene structure (x-axis) in brain, lung, heart and muscle. c. Cumulative distribution (y-axis) of GGACH motif enrichment (-log10q-value, x-axis) across samples (teal) vs. shuffled controls (salmon). d. Peak Count per sample. e. Peaks shared by fewer individuals (left) are more likely previously-undetected (red). f. Similar to b for for only previously-unreported m6A peaks found in > =2 individuals in our study. g. Pearson correlation (heatmap) and hierarchical clustering (tree) of mRNA-seq profiles across the 129 GTEx samples (rows/columns) that match the individuals profiled here shows tissue-specific clustering (colors) and co-clustering of heart and muscle, as shown for m6A profiles in Fig. 1b. h. KEGG pathway enrichments for the genes harboring tissue-specific m6A.

Extended Data Fig. 2 Identification of genetically-driven m6A.

a. Pearson correlation (color) for PEER factors shows saturation (correlated factors) after first 2 factors (red box) in lung (top) and after first 5 factors (red box) in muscle/heart (bottom). b. Pearson correlation between PEER factors (columns) and donor phenotypic measurements (rows) helps interpret factors in lung and muscle/heart. c,d. Manhattan plot of m6A-QTLs in lung (c) and muscle/heart (d), and m6A-QTL examples. Boxes=25%–75% percentile; line=median; whiskers=1.5 IQR; p-values=FastQTL linear regressions.

Extended Data Fig. 3 Tissue specificity of m6A-QTL.

a. Summary of shared/specific g-m6As and m6A across tissues. b. KEGG pathway enrichments for tissue-specific gmGenes. c. Tissue-intersections of the eQTLs identified from the same samples for m6A-QTL calling. d,e. Correlation between p-value (x-axis) and eQTL effect (y-axis) between tissues, with positive- and negative-effect eQTLs separated for full GTEx-V8 (d)), and subsampled to individuals used here (e;). f. Nominal p-values (y-axis) of simulated m6A-QTLs (Positive), and simulated NULL m6A-QTLs controls without QTL effects (Control). Boxes=25%–75% percentile; line=median; whiskers=1.5 IQR. g. m6A-QTL overlaps between tissues in simulated data show much higher tissue-sharing (teal curve) than in observed real data (peach curve). h. Effect sizes directionality between m6A-QTL from real tissue data and simulated data are almost 100% consistent. i. Effect directionality consistency when m6A-QTLs were identified with 50, 38 and 30 samples. j. Gene expression distribution of the tissue-specific vs. tissue-shared m6A-QTLs in each tissue. k. Gene expression in tissues for tissue-specific gmGenes in each tissue. Statistical test was carried out by two-sided paired Wilcoxon test. l. Correlation (adjusted R2) of eQTLs between GTEx primary tissues and YRI LCL cells. Boxes=25%–75% percentile; line=median; whiskers=1.5 IQR. m. Comparison of m6A-QTL effects size between this study (eGTEx tissues, x-axis) and the other m6A-QTL study (YRI LCLs, y-axis). Green dots represent the m6A-QTLs shared by the two studies. Directionality consistency and corresponding p-value (vs. the 50% expected by chance) calculated using one-sided Fisher exact test (inset box). n. Correlation of m6A-QTLs between eGTEx primary tissues and YRI LCLs. o. Correlation between p-value and m6A-QTL effect in LCL cell lines for m6A-QTLs identified in eGTEx tissues, with positive- and negative-effect eGTEx m6A-QTLs separated. Directionality consistency and corresponding p-value (vs. the 50% expected by chance) calculated using one-sided Fisher exact test.

Extended Data Fig. 4 Comparison between eQTL and m6A-QTL.

a-b. m6A-QTLs show a modest but significant enrichment for eQTLs in the matching tissues, with m6A-QTLs separated into exonic (b) and intronic (c). NAnnot=21811, 17687, 40078, 24317, 39687 for Brain Cortex, Brain Frontal Cortex (BA9), Lung, Heart Left Ventricle, Skeletal Muscle (see Methods) for exonic m6A-QTLs. NAnnot=23091, 18724, 41807, 25927, 42425 for Brain Cortex, Brain Frontal Cortex (BA9), Lung, Heart Left Ventricle, Skeletal Muscle (see Methods) for intronic m6A-QTLs. Error bars denote the upper bound and the lower bound for the 95% CI of effect size. P-values are calculated by Garfield using a logistic regression model with ‘feature matching’. c. Number of eQTLs identified (y-axis) for increasing number of PEER factors removed (x-axis) shows inflection-point for each tissue (colors). d. Genomic region distribution for g-m6As mediating stabilization vs. degradation (p-values: Fisher exact test). e. Overlap between gmGenes and eGenes identified from the matching GTEx individuals. f. Effect size comparison between m6A-QTL and GTEx eQTL for the m6A-QTLs identified in this study. g. Effect size comparison between m6A-QTL and GTEx eQTL for the eQTLs identified by GTEx V8.

Extended Data Fig. 5 Overlap between m6A-QTL and disease GWAS hits.

a-b. Overlaid Manhattan plots showing genomic position (y-axis) and m6A-QTL P-value (x-axis) for lead SNPs (points) across traits (colors) that show colocalization between GWAS variants and m6A-QTLs in lung (a) and muscle/heart (b). c. Illustrative example showing a brain intronic m6A-QTL that is overlapped with a ClinVar-curated variant related to Congenital cataract. d. Illustrative example showing a muscle/heart 5′-UTR m6A-QTL that is overlapped with a ClinVar-curated variant related to Nemaline myopathy. Boxes = 25%–75% percentile; line=median; whiskers=1.5 IQR. e. Same plot as Fig. 5d, but shown using S-LDSC Baseline v1.2, which corrects for coding region, UTR, intron, promoter, enhancer, multiple histone marks, and eQTLs, shows robustness of results to this correction.

Extended Data Fig. 6 Identification of m6A regulator candidates.

a. Enrichment (y axis, log) and corresponding p-value (x-axis, Bonferroni-corrected) between m6A-QTL SNPs and RNA binding protein (RBP) binding sites, for each tissue (color), highlighting 10 most enriched RBPs in each tissue (labels). b. Quantile-Quantile plot showing the p-value distribution observed in the correlation test between RBP expression vs. m6A levels (y-axis), compared to the non-target (circle) or permutation controls (triangle) (x-axis). Significant RBPs (FDR < 0.1) are shown in red. P-values are calculated by two-sided Pearson correlation tests. c,d. Correlation of predicted m6A-regulator RBP mRNA expression level (x-axis) vs. methylation level of its m6A targets (y-axis) for RBM15 and FIP1L1 in brain, respectively. Grey shadow denotes the 95% confidence region for the regression fit.

Supplementary information

Reporting Summary

Supplementary Tables 1–7

Supplementary Table 1: Information on the samples used in this study. Supplementary Table 2: m6A QTLs (empirical P < 0.005) identified in brain, lung and muscle/heart. Supplementary Table 3: m6A QTLs (two-step multiple-testing correction FDR < 0.2) identified in brain. Supplementary Table 4: GWAS-colocalized m6A QTLs in brain, lung and muscle/heart; the colocalized loci include a similar proportion of genome-wide significant (P < 5 × 10−8) and subthreshold (5 × 10−8 < P < 10−4) loci. Supplementary Table 5: ClinVar overlapped m6A QTLs in brain, lung and muscle/heart. Supplementary Table 6: Potential regulator proteins involved in m6A regulation. Supplementary Table 7: Allele-specific RBP binding overlapped our m6A QTLs.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiong, X., Hou, L., Park, Y.P. et al. Genetic drivers of m6A methylation in human brain, lung, heart and muscle. Nat Genet 53, 1156–1165 (2021). https://doi.org/10.1038/s41588-021-00890-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-021-00890-3

This article is cited by

Search

Advanced 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