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Genome-wide landscape of RNA-binding protein target site dysregulation reveals a major impact on psychiatric disorder risk

Nature Genetics volume 53, pages 166–173 (2021)Cite this article

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Abstract

Despite the strong genetic basis of psychiatric disorders, the underlying molecular mechanisms are largely unmapped. RNA-binding proteins (RBPs) are responsible for most post-transcriptional regulation, from splicing to translation to localization. RBPs thus act as key gatekeepers of cellular homeostasis, especially in the brain. However, quantifying the pathogenic contribution of noncoding variants impacting RBP target sites is challenging. Here, we leverage a deep learning approach that can accurately predict the RBP target site dysregulation effects of mutations and discover that RBP dysregulation is a principal contributor to psychiatric disorder risk. RBP dysregulation explains a substantial amount of heritability not captured by large-scale molecular quantitative trait loci studies and has a stronger impact than common coding region variants. We share the genome-wide profiles of RBP dysregulation, which we use to identify DDHD2 as a candidate schizophrenia risk gene. This resource provides a new analytical framework to connect the full range of RNA regulation to complex disease.

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Fig. 1: Negative selection signatures differentiate RBPs by their regulatory function.
Fig. 2: Genome-wide RBP dysregulation is a significant source of psychiatric disorder heritability.
Fig. 3: RBP dysregulation underlies shared and distinct genetic architectures of psychiatric phenotypes.
Fig. 4: Functional RBP regulatory mapping identifies a schizophrenia risk variant in the DDHD2 3′ UTR.

Data availability

All variant predicted scores have been made available to download and as an interactive Web interface available at https://hb.flatironinstitute.org/seqweaver.

Code availability

The code used in this study is available at https://hb.flatironinstitute.org/seqweaver/about.

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Acknowledgements

We thank Z. Zhang, A. Andersen and S. Lall for their help with the manuscript. We also thank all members of the Troyanskaya and Darnell laboratory for helpful discussions. This work is supported by National Institutes of Health grant nos. R01HG005998, U54HL117798 and R01GM071966, U.S. Department of Health and Human Services grant no. HHSN272201000054C and Simons Foundation grant no. 395506. O.G.T. is a senior fellow of the Genetic Networks program of the Canadian Institute for Advanced Research. We thank the Simons Foundation Autism Research Initiative, Simons Foundation and Flatiron Institute. A substantial portion of the work in this paper was performed at the Terascale Infrastructure for Groundbreaking Research in Science and Engineering high-performance computer center at Princeton University, which is jointly supported by the Princeton Institute for Computational Science and Engineering and the Princeton University Office of Information Technology’s Research Computing department.

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Authors and Affiliations

  1. Flatiron Institute, Simons Foundation, New York, NY, USA

    Christopher Y. Park, Jian Zhou, Aaron K. Wong, Kathleen M. Chen & Olga G. Troyanskaya

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Chandra L. Theesfeld & Olga G. Troyanskaya

  3. Laboratory of Molecular Neuro-Oncology, The Rockefeller University, New York, NY, USA

    Robert B. Darnell

  4. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Robert B. Darnell

  5. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Olga G. Troyanskaya

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Contributions

C.Y.P. and O.G.T. conceived the study. C.Y.P. designed the study, developed the computational methods and performed the analyses. J.Z., C.L.T. and R.B.D. contributed ideas and insights. A.K.W. and K.M.C. developed the Web interface/software. C.Y.P. and O.G.T. wrote the manuscript.

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Correspondence to Christopher Y. Park or Olga G. Troyanskaya.

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Peer review information Nature Genetics thanks Amalio Telenti and Thomas Werge for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Population genetics revels negative selection acting on RBP target site dysregulation.

a, Across the Seqweaver profiled RBPs, we observe differential selection signatures for variants when segregated by their RBP target site dysregulation levels. Specifically, for gnomAD cohort noncoding variants (MAF bins x-axis), mean RBP dysregulation (Y-axis) shows an inverse relation with allele frequency, consistent with significant negative selection acting on high impact RBP disrupting variants. b, The top RBPs previously implicated by their autism de novo mutation risk (Zhou, Park, Theesfeld et al.), all show significant negative selection signatures, consistent with selection impeding RBP impacting variants from reaching high population prevalence. P-values from Wald test for slope and all inferred mean RBP dysregulation scores were normalized by subtracting average dysregulation predicted scores of common variants (MAF > 0.05) for comparison (95% CI).

Extended Data Fig. 2 Regions with peak childhood stage expression shows the largest enrichment association with RBP dysregulation.

We test the overlap between prefrontal cortex brain differential expressed regions and RBP dysregulation SNPs (the top 0.5%) associated with each disorder in comparison to the genome-wide rate. We also plot the enrichment overlap for the subset of regions in which the expression was highest during childhood stage. All ORs have an enrichment p-value of P < 2.2 × 10−16. Error bars are 95% CI.

Extended Data Fig. 3 Cross-ancestry replication – RBP dysregulation effects replicate in an independent cohort.

Replication of estimated schizophrenia RBP dysregulation effect sizes (τ*, European Psychiatric Genomics Consortium (PGC)) when compared to estimates from an East Asian cohort (Lam et al). P-value computed using spearman rank test of RBP effect sizes.

Extended Data Fig. 4 RBP dysregulation effects for cross-disorder risk replicate in iPSYCH cohort.

Replication of estimated cross-disorder RBP dysregulation effect sizes (τ*, Psychiatric Genomics Consortium cohort) when compared to estimates from the iPSYCH cohort. P-value computed using spearman rank sum test of RBP effect sizes.

Extended Data Fig. 5 RBP dysregulation is a major contributor to human phenotypic variation.

The per-SNP heritability effect sizes (τ*) for each RBP dysregulation is plotted across a collection of psychiatric traits, brain-associated anthropomorphic traits and representative non-brain related phenotypes previously examined by the Brainstorm Consortium study. The dashed line indicates RBP models below FDR 0.05 threshold after multiple hypothesis correction (block jackknife-based one-sided p-values; Benjamini-Hochberg correction).

Extended Data Fig. 6 Heatmap showing patterns of correlated GWAS effect sizes between psychiatric disorders and behavioral-cognitive phenotypes for variants affecting RBP dysregulation.

For each pair of disorder and phenotype (x,y), we extracted the top RBP dysregulation set of variants that influence disorder x and their GWAS effect sizes on both x and y. We then calculated correlation between the GWAS effect sizes on x and the GWAS effect sizes on y, and tested whether this correlation was significantly different from zero. Stars represent statistical significance *** P < 0.001, ** P < 0.01, * P < 0.05.

Extended Data Fig. 7 Heatmap showing patterns of correlated GWAS effect sizes between psychiatric disorders for variants affecting RBP dysregulation.

For each pair of disorders (x,y), we extracted the top RBP dysregulation set of variants that influence disorder x and their GWAS effect sizes on both x and y. We then calculated correlation between the GWAS effect sizes on x and the GWAS effect sizes on y, and tested whether this correlation was significantly different from zero. Stars represent statistical significance ***P < 0.001, **P < 0.01, *P < 0.05.

Extended Data Fig. 8 Heritability enrichment for the collective RBP dysregulation effects in comparison to QTL and genomic functional annotations for schizophrenia.

The top 0.1%, 0.5%, 1% SNPs with the largest overall RBP dysregulation effects were compared to known molecular QTLs and gene/promoter annotations for their enrichment of heritability using PGC schizophrenia GWAS.

Extended Data Fig. 9 Estimated RBP dysregulation effects are robust after conditioning on conserved genomic elements.

The per-SNP heritability effect sizes (τ*) for each RBP dysregulation is plotted across the five major psychiatric disorders after inclusion of vertebrate, mammal and primate conserved phastCons elements to the conditioning baseline annotation set (including QTL annotations). The dashed line indicates RBP models below FDR 0.05 threshold after multiple hypothesis correction (jackknife one-sided p-values; Benjamini-Hochberg correction).

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Park, C.Y., Zhou, J., Wong, A.K. et al. Genome-wide landscape of RNA-binding protein target site dysregulation reveals a major impact on psychiatric disorder risk. Nat Genet 53, 166–173 (2021). https://doi.org/10.1038/s41588-020-00761-3

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