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Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases

Nature Genetics volume 54, pages 251–262 (2022)Cite this article

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Abstract

The resolution of causal genetic variants informs understanding of disease biology. We used regulatory quantitative trait loci (QTLs) from the BLUEPRINT, GTEx and eQTLGen projects to fine-map putative causal variants for 12 immune-mediated diseases. We identify 340 unique loci that colocalize with high posterior probability (≥98%) with regulatory QTLs and apply Bayesian frameworks to fine-map associations at each locus. We show that fine-mapping credible sets derived from regulatory QTLs are smaller compared to disease summary statistics. Further, they are enriched for more functionally interpretable candidate causal variants and for putatively causal insertion/deletion (INDEL) polymorphisms. Finally, we use massively parallel reporter assays to evaluate candidate causal variants at the ITGA4 locus associated with inflammatory bowel disease. Overall, our findings suggest that fine-mapping applied to disease-colocalizing regulatory QTLs can enhance the discovery of putative causal disease variants and enhance insights into the underlying causal genes and molecular mechanisms.

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Fig. 1: Summary diagram of colocalization and fine-mapping results.
Fig. 2: Summary of colocalization results.
Fig. 3: Fine-mapping of IMD and QTL loci.
Fig. 4: Fine-mapping of the ITGA4 locus in monocytes.
Fig. 5: Fine-mapping of the BACH2 locus in CD4+ T cells.

Data availability

All the IMD summary statistics were obtained from the GWAS catalog (https://www.ebi.ac.uk/gwas/), Immunobase (https://genetics.opentargets.org/immunobase) and IBD genetics (https://www.ibdgenetics.org/). The BLUEPRINT phase 2 Genotype data (VCFs) have been deposited in the EGA (https://ega-archive.org/datasets/) under accession EGAD00001005192. All QTL summary statistics are available under accession codes EGAD00001005199 and EGAD00001005200. All data are freely available but managed by the BLUEPRINT Data Access Committee. The eQTL data from eQTLGen and GTEx consortium (v7) were obtained from https://www.eqtlgen.org/ and https://www.gtexportal.org, respectively. The independent LD blocks for human genome were obtained from https://bitbucket.org/nygcresearch/ldetect-data. All analysis results are available in the main text or supplementary information. All sequencing reads were mapped to the GRCh37/hg19 (https://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20130301/homo_sapiens/reference/) human reference genome. Source data are provided with this paper.

Code availability

We performed our analyses using the following publicly available software: GATK (v3.4; https://gatk.broadinstitute.org) was used for performing VQSR, BEAGLE (v4.1; https://faculty.washington.edu/browning/beagle/beagle.html) was used for imputation and phasing, VT (v0.5; https://genome.sph.umich.edu/wiki/Vt) was used for variant normalization, LIMIX (v1.0; https://github.com/limix/limix-legacy) was used for QTL analyses, gwas-pw (v0.21; https://github.com/joepickrell/gwas-pw) was used for colocalization, FINEMAP (v1.1; http://www.christianbenner.com/) and CAVIARBF (v0.1.4.1; https://bitbucket.org/Wenan/caviarbf) were used for fine-mapping; GCTA (v1.26.0; https://yanglab.westlake.edu.cn/software/gcta) was used for conditional analysis and TWMR (https://github.com/eleporcu/TWMR/commit/62994ec) was used for Mendelian randomization. All the codes for this study are publicly available at GitHub (https://github.com/teamsoranzo/QTL_IMD_Finemap). PLINK (v1.9; https://www.cog-genomics.org/plink/1.9/) and BCFTools (v1.4; https://samtools.github.io/bcftools) were used for other statistical and data analyses. All codes for this study are publicly available at GitHub (https://github.com/teamsoranzo/QTL_IMD_Finemap).

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Acknowledgements

K.K. is supported by the National Institute for Health Research (NIHR) BRC (Biomedical Research Centre, Cardiovascular Theme). This study was conducted using the BLUEPRINT (http://www.blueprint-epigenome.eu/) data funded by EU FP7 High Impact Project BLUEPRINT (HEALTH-F5-2011-282510) and the Canadian Institutes of Health Research (CIHR EP1-120608). N.S. is supported by the Wellcome Trust, the British Heart Foundation, the National Institute for Health Research (NIHR) BRC (Biomedical Research Centre, Cardiovascular Theme) and the Italian Ministry of Finance (to Fondazione Human Technopole). We thank L. Chen and V. Iotchkova for the initial technical discussion on analysis strategy and K. M. de Lange for helping with IBD GWAS data. We thank V. Sankaran and E. Bao for sharing ATAC-seq data. We also thank Q. Lin for releasing the new BLUEPRINT phase 2 data through EGA, European Molecular Biology Laboratory–European Bioinformatics Institute and acknowledge support from the Cambridge National Institute for Health Research Biomedical Research Centre and the International Multiple Sclerosis Genetics Consortium. We also gratefully acknowledge W.H. Ouwehand and K. Downes as part of the National Health Service Blood and Transplant for their contribution to volunteer recruitment and blood collections.

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Authors and Affiliations

  1. Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    Kousik Kundu, Manuel Tardaguila, Alice L. Mann, Stephen Watt, Hannes Ponstingl, Louella Vasquez, Dominique Von Schiller, Carl A. Anderson, Klaudia Walter & Nicole Soranzo

  2. Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK

    Kousik Kundu & Nicole Soranzo

  3. Division of Respiratory Medicine, Department of Medicine, University of Cambridge School of Clinical Medicine, Addenbrooke’s and Papworth Hospitals, Cambridge, UK

    Nicholas W. Morrell

  4. Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Oliver Stegle

  5. Division of Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany

    Oliver Stegle

  6. Cellular Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    Oliver Stegle

  7. Genomic Medicine Center, Children’s Mercy Kansas City and Children’s Mercy Research Institute, Kansas City, MO, USA

    Tomi Pastinen

  8. Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK

    Stephen J. Sawcer

  9. British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK

    Nicole Soranzo

  10. National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK

    Nicole Soranzo

  11. Genomics Research Centre, Human Technopole, Milan, Italy

    Nicole Soranzo

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  1. Kousik Kundu
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Contributions

K.K. and N.S. designed the study. K.K. and A.L.M. acquired the data. K.K. performed the analysis. M.T. and D.V.S. performed experimental validation. H.P., L.V., N.W.M., O.S., T.P. and S.J.S. provided substantial support on all analyses. K.K., M.T., A.L.M., S.W., C.A.A., K.W. and N.S. interpreted the results. K.K., M.T., A.L.M. and N.S. wrote the manuscript. All authors read and approved the final version of the manuscript.

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Correspondence to Nicole Soranzo.

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C.A.A. is a paid consultant for Genomics plc and BridgeBio. All other authors declare no competing interests.

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Kundu, K., Tardaguila, M., Mann, A.L. et al. Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases. Nat Genet 54, 251–262 (2022). https://doi.org/10.1038/s41588-022-01025-y

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