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High-throughput identification of noncoding functional SNPs via type IIS enzyme restriction

Nature Genetics volume 50, pages 1180–1188 (2018)Cite this article

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Abstract

Genome-wide association studies (GWAS) have identified many disease-associated noncoding variants, but cannot distinguish functional single-nucleotide polymorphisms (fSNPs) from others that reside incidentally within risk loci. To address this challenge, we developed an unbiased high-throughput screen that employs type IIS enzymatic restriction to identify fSNPs that allelically modulate the binding of regulatory proteins. We coupled this approach, termed SNP-seq, with flanking restriction enhanced pulldown (FREP) to identify regulation of CD40 by three disease-associated fSNPs via four regulatory proteins, RBPJ, RSRC2 and FUBP-1/TRAP150. Applying this approach across 27 loci associated with juvenile idiopathic arthritis, we identified 148 candidate fSNPs, including two that regulate STAT4 via the regulatory proteins SATB2 and H1.2. Together, these findings establish the utility of tandem SNP-seq/FREP to bridge the gap between GWAS and disease mechanism.

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Fig. 1: Diagram of tandem SNP-seq and FREP.
Fig. 2: Screening of fSNPs within the CD40 locus.
Fig. 3: Validation of fSNPs rs4810485, rs6032664, rs6065926, rs1883832 and rs6074022 as CD40 fSNPs.
Fig. 4: Expression of CD40 in RNAi knockdown experiments with human B cells and human synovial fibroblasts.
Fig. 5: Demonstration of the binding of RBPJ to rs4810485 and TRAP150 to rs6065926.
Fig. 6: SNP-seq high-throughput screening of 608 JIA-associated SNPs.
Fig. 7: Characterization of fSNPs at the STAT4 locus.

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Acknowledgements

We thank P. Y. Lee, I-C. Ho, P. Libby and R. M. Plenge for scientific discussions. This work was supported by grants from the Arthritis National Research Foundation, National Multiple Sclerosis Society, NIH R21 NS096443 and NIH R21 AR070378 (G.L.), and from the Rheumatology Research Foundation, NIH R01 AR065538, NIH P30 AR070253 and the Fundación Bechara (P.A.N.).

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  1. Gang Li & Yu Yang

    Present address: Division of Cardiology and The Aging Institute, University of Pittsburgh, Pittsburgh, PA, USA

Authors and Affiliations

  1. Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Gang Li, Marta Martínez-Bonet, Yu Yang, Jing Cui, Hung N. Nguyen, Pierre Cunin, Anaïs Levescot, Ming Bai, Michael B. Brenner, Soumya Raychaudhuri & Peter A. Nigrovic

  2. Department of Periodontology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Di Wu

  3. Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Harm-Jan Westra, Soumya Raychaudhuri & Richard L. Maas

  4. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan

    Yukinori Okada

  5. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan

    Yukinori Okada

  6. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Soumya Raychaudhuri

  7. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Soumya Raychaudhuri

  8. School of Biological Sciences, University of Manchester, Manchester, UK

    Soumya Raychaudhuri

  9. Biochemistry, Molecular Biology and Biophysics Department, University of Minnesota Medical School, Minneapolis, MN, USA

    Eric A. Hendrickson

  10. Division of Immunology, Boston Children’s Hospital, Boston, MA, USA

    Peter A. Nigrovic

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Contributions

G.L. developed SNP-seq and FREP, designed the study, performed all the experiments and analyzed the data in the laboratory of P.A.N. G.L. and P.A.N. drafted the manuscript. M.M.-B. performed experiments and analysis and revised the manuscript. D.W. performed data analysis and revised the manuscript. J.C. performed sequencing data analyses. Y.Y. assisted with experiments. P.C. and A.L. assisted with analysis of CD40 expression by FACS. M.B. assisted with the 3 C assay. H.N.N. performed siRNA experiments on human synovial fibroblasts and assisted with the ChIP assays in the laboratory of M.B.B. E.A.H. assisted with the CRISPR–Cas9 experiments. Y.O., M.M.-B. and S.R. assisted with the fine-mapping analysis at the CD40 and STAT4 loci. H.-J.W. assisted with figure formatting and data analysis. S.R., E.A.H. and R.L.M. assisted with data analysis and revised the manuscript.

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Correspondence to Gang Li or Peter A. Nigrovic.

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Integrated supplementary information

Supplementary Figure 1 Allele-specific gel shifting at CD40 variant rs1883832 but not rs6074022.

EMSA revealing lack of allele-specific gel shifting at rs6074022 (T, non-risk/minor allele; C, risk/major allele) but confirming allele-specific gel shifting for rs1883832 (T, non-risk/minor allele; C, risk/major allele). The data reflect five replicate experiments with similar results

Supplementary Figure 2 EMSA with gel supershift showing the enhanced binding of protein to rs6032664 and rs6065926 by anti-RSRC2 (left) and anti-FUBP1 (right) antibodies.

The data represent three biological replicates with similar results. Red brackets indicate enhanced intensity, rather than shift, upon binding of specific antibody. Ab, antibody

Supplementary Figure 3 Algorithm for identification of candidate fSNPs by allele-specific enrichment at cycle 10.

C1 and C2, sequence counts for control (no nuclear extract) at alleles 1 and 2 for each SNP. S1.1 and S1.2, sequence counts for test (with nuclear extract) replicates 1 and 2 for allele 1 at each SNP; S2.1 and S2.2, same for allele 2

Supplementary Figure 4 Algorithm for identification of candidate fSNPs by progressive enrichment across cycles 4, 7 and 10 as numbers 1, 2 and 3.

C1 and C2, sequence counts for control (no nuclear extract) at alleles 1 and 2 for each SNP. S1.1 and S1.2, sequence counts for test (with nuclear extract) replicates 1 and 2 for allele 1 at each SNP; S2.1 and S2.2, same for allele 2

Supplementary Figure 5 EMSA showing allele-imbalanced gel shifting on 14 randomly selected candidate fSNPs from 9 JIA-associated loci using nuclear extract from PBMCs.

The number in parentheses is the number of candidate fSNPs divided by the total number of SNPs within LD r2 > 0.8 of the lead SNP at the locus. The data represent two repeats with similar results

Supplementary Figure 6 EMSA for assessment of allele-imbalanced gel shifting as per SNP-seq.

a,b, EMSAs are shown for the seven negative SNPs at STAT4 (a) and the six negative SNPs at FAS (b). The FAS SNPs rs2182408 and rs2148287 show clear allele-specific shifting, while rs7574865 at STAT4 and rs1926194 at FAS are borderline. The data represent three replicates with similar results

Supplementary Figure 7 Expression of CD40 in Notch1- and PUF60-knockdown human synovial fibroblasts.

a, CD40 expression (lower panel) is not affected by downregulation of Notch1 measured by qPCR with either actin or 18S as a control (upper panel). b, CD40 expression is downregulated (lower panel) after knockdown of Puf60 (upper panel). Expression is shown compared with two controls, actin and 18S. The data points represent two biological repeats; statistical testing not performed

Supplementary Figure 8 3C assay at the CD40 locus with rs4810485, rs6032664 and rs6065926.

a, PCR showing bands with predicted sizes for ligation between fSNPs in five samples. Data reflect three biological replicates. b, Sequence traces showing the ligation of five different sequences from rs4810485, rs6032664 and rs6065926 with an interposed BamHI site

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Li, G., Martínez-Bonet, M., Wu, D. et al. High-throughput identification of noncoding functional SNPs via type IIS enzyme restriction. Nat Genet 50, 1180–1188 (2018). https://doi.org/10.1038/s41588-018-0159-z

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