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

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

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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.

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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.

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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.

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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.

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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

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  1. Xushen Xiong
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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.

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Correspondence to Manolis Kellis.

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The authors declare no competing interests.

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

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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.

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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

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