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.
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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.
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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.
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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.
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C.H. is a founder and scientific advisory board member of Accent Therapeutics and a shareholder of Epican Genentech.
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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.
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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
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DOI: https://doi.org/10.1038/s41588-020-0644-z
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