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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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.
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).
Cooper, G. S., Bynum, M. L. K. & Somers, E. C. Recent insights in the epidemiology of autoimmune diseases: improved prevalence estimates and understanding of clustering of diseases. J. Autoimmun. 33, 197–207 (2009).
El-Gabalawy, H., Guenther, L. C. & Bernstein, C. N. Epidemiology of immune-mediated inflammatory diseases: incidence, prevalence, natural history, and comorbidities. J. Rheumatol. Suppl. 85, 2–10 (2010).
Cotsapas, C. et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 7, e1002254 (2011).
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
Ellinghaus, D. et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat. Genet. 48, 510–518 (2016).
International Genetics of Ankylosing Spondylitis Consortium et al. Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci.Nat. Genet. 45, 730–738 (2013).
Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).
Westra, H.-J. et al. Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes. Nat. Genet. 50, 1366–1374 (2018).
Asimit, J. L. et al. Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases. Nat. Commun. 10, 3216 (2019).
Spain, S. L. & Barrett, J. C. Strategies for fine-mapping complex traits. Hum. Mol. Genet. 24, R111–R119 (2015).
Hauberg, M. E. et al. Large-scale identification of common trait and disease variants affecting gene expression. Am. J. Hum. Genet. 100, 885–894 (2017).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e1324 (2016).
Hannon, E. et al. An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation. Genome Biol. 17, 176 (2016).
Hernandez, D. G. et al. Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain. Neurobiol. Dis. 47, 20–28 (2012).
Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008).
Umans, B. D., Battle, A. & Gilad, Y. Where are the disease-associated eQTLs? Trends Genet. 37, 109–124 (2021).
Chun, S. et al. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat. Genet. 49, 600–605 (2017).
Dubois, P. C. A. et al. Multiple common variants for celiac disease influencing immune gene expression. Nat. Genet. 42, 295–302 (2010).
Trynka, G. et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat. Genet. 43, 1193–1201 (2011).
Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).
de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).
Hinks, A. et al. Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis. Nat. Genet. 45, 664–669 (2013).
International Multiple Sclerosis Genetics Consortium et al. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet. 45, 1353–1360 (2013).
International Multiple Sclerosis Genetics Consortium et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).
Faraco, J. et al. ImmunoChip study implicates antigen presentation to T cells in narcolepsy. PLoS Genet. 9, e1003270 (2013).
Cordell, H. J. et al. International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways. Nat. Commun. 6, 8019 (2015).
Liu, J. Z. et al. Dense fine-mapping study identifies new susceptibility loci for primary biliary cirrhosis. Nat. Genet. 44, 1137–1141 (2012).
Tsoi, L. C. et al. Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nat. Genet. 44, 1341–1348 (2012).
Eyre, S. et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat. Genet. 44, 1336–1340 (2012).
Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).
Stahl, E. A. et al. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat. Genet. 42, 508–514 (2010).
Bentham, J. et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat. Genet. 47, 1457–1464 (2015).
Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).
Jostins, L. et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).
Anderson, C. A. et al. Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47. Nat. Genet. 43, 246–252 (2011).
International Multiple Sclerosis Genetics Consortium. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019).
Cortes, A. & Brown, M. A. Promise and pitfalls of the Immunochip. Arthritis Res. Ther. 13, 101 (2011).
Polychronakos, C. Fine points in mapping autoimmunity. Nat. Genet. 43, 1173–1174 (2011).
Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).
Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016).
GTEx Consortium et al. Genetic effects on gene expression across human tissues.Nature 550, 204–213 (2017).
Vosa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).
Watt, S. et al. Genetic perturbation of PU.1 binding and chromatin looping at neutrophil enhancers associates with autoimmune disease. Nat. Commun. 12, 2298 (2021).
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
Chen, W. et al. Fine mapping causal variants with an approximate Bayesian method using marginal test statistics. Genetics 200, 719–736 (2015).
Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).
Caron, B., Luo, Y. & Rausell, A. NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans. Genome Biol. 20, 32 (2019).
Ulirsch, J. C. et al. Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat. Genet. 51, 683–693 (2019).
Sandborn, W. J. et al. Vedolizumab as induction and maintenance therapy for Crohn’s disease. N. Engl. J. Med. 369, 711–721 (2013).
Feagan, B. G. et al. Vedolizumab as induction and maintenance therapy for ulcerative colitis. N. Engl. J. Med. 369, 699–710 (2013).
Kim-Hellmuth, S. et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. Nat. Commun. 8, 266 (2017).
Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).
Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384.e1319 (2016).
Robertson, C. C. et al. Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes. Nat. Genet. 53, 962–971 (2021).
Ji, S.-G. et al. Genome-wide association study of primary sclerosing cholangitis identifies new risk loci and quantifies the genetic relationship with inflammatory bowel disease. Nat. Genet. 49, 269–273 (2017).
Battle, A. et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14–24 (2014).
Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
Hunter, C. A. & Jones, S. A. IL-6 as a keystone cytokine in health and disease. Nat. Immunol. 16, 448–457 (2015).
Benner, C. et al. Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am. J. Hum. Genet. 101, 539–551 (2017).
Walter, M. et al. IDDM2/insulin VNTR modifies risk conferred by IDDM1/HLA for development of Type 1 diabetes and associated autoimmunity. Diabetologia 46, 712–720 (2003).
Kindt, A. S. D. et al. Allele-specific methylation of type 1 diabetes susceptibility genes. J. Autoimmun. 89, 63–74 (2018).
Ashuach, T. et al. MPRAnalyze: statistical framework for massively parallel reporter assays. Genome Biol. 20, 183 (2019).
Schofield, E. C. et al. CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets. Bioinformatics 32, 2511–2513 (2016).
Tan, A., Abecasis, G. R. & Kang, H. M. Unified representation of genetic variants. Bioinformatics 31, 2202–2204 (2015).
Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).
Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).
Danecek, P. et al. Twelve years of SAMtools and BCFtools.Gigascience 10, giab008 (2021).
Casale, F. P., Rakitsch, B., Lippert, C. & Stegle, O. Efficient set tests for the genetic analysis of correlated traits. Nat. Methods 12, 755–758 (2015).
Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).
Davis, J. R. et al. An efficient multiple-testing adjustment for eQTL studies that accounts for linkage disequilibrium between variants. Am. J. Hum. Genet. 98, 216–224 (2016).
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).
Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
Muerdter, F. et al. Resolving systematic errors in widely used enhancer activity assays in human cells. Nat. Methods 15, 141–149 (2018).
Klein, J. C. et al. A systematic evaluation of the design and context dependencies of massively parallel reporter assays. Nat. Methods 17, 1083–1091 (2020).
Lazic, S. E. Ranking, selecting, and prioritising genes with desirability functions. PeerJ 3, e1444 (2015).
Porcu, E. et al. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat. Commun. 10, 3300 (2019).
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.
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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