Abstract
Genome-wide association studies are transformative in revealing the polygenetic basis of common diseases, with autoimmune diseases leading the charge. Although the field is just over 10 years old, advances in understanding the underlying mechanistic pathways of these conditions, which result from a dense multifactorial blend of genetic, developmental and environmental factors, have already been informative, including insights into therapeutic possibilities. Nevertheless, the challenge of identifying the actual causal genes and pathways and their biological effects on altering disease risk remains for many identified susceptibility regions. It is this fundamental knowledge that will underpin the revolution in patient stratification, the discovery of therapeutic targets and clinical trial design in the next 20 years. Here we outline recent advances in analytical and phenotyping approaches and the emergence of large cohorts with standardized gene-expression data and other phenotypic data that are fueling a bounty of discovery and improved understanding of human physiology.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Change history
15 April 2020
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
07 February 2019
In the version of this article initially published, the bibliographic information for reference 2 was incorrect in the reference list, and reference 2 was cited incorrectly at the end of the second sentence in the second paragraph (“...were identified2.”). The correct reference 2 is as follows: “Kong, A. et al. The nature of nurture: Effects of parental genotypes. Science 359, 424–428 (2018).” The reference that should be cited at the end of the aforementioned sentence, which should be numbered ‘5’ (“...were identified5.”), is as follows: “Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).” All subsequent references (5–161) should be renumbered accordingly (6–162) in the list and text. Also, several of the gene symbols in Table 2 were formatted incorrectly (without commas); the correct gene symbols are as follows: column 3 row 13, RBM17, IL2RA; column 3 row 30, DEXI, CLEC16A; column 3 row 39, UBASH3A, ICOSLG; column 4 row 15, PTEN, KLLN; column 4 row 21, CLEC7A, CLEC9A; and column 5 rows 7–9, AL391559.1, ENSG00000238747, RP11-63K6.7, RP3-512E2.2. The errors have been corrected in the HTML and PDF version of the article.
References
Wang, W. Y. S., Barratt, B. J., Clayton, D. G. & Todd, J. A. Genome-wide association studies: Theoretical and practical concerns. Nat. Rev. Genet. 6, 109–118 (2005).
Kong, A. et al. The nature of nurture: Effects of parental genotypes. Science 359, 424–428 (2018).
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
Timpson, N. J., Greenwood, C. M. T., Soranzo, N., Lawson, D. J. & Richards, J. B. Genetic architecture: The shape of the genetic contribution to human traits and disease. Nat. Rev. Genet. 19, 110–124 (2018).
Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).
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).
Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature https://doi.org/10.1038/nature25973 (2018).
Cooper, N.J. et al. Type 1 diabetes genome-wide association analysis with imputation identifies five new disease regions. bioRxiv https://doi.org/10.1101/120022 (2017).
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).
The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).
Mahajan, A. et al. Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. bioRxiv https://doi.org/10.1101/245506 (2018).
Ziegler, A. G. et al. Primary prevention of beta-cell autoimmunity and type 1 diabetes - The Global Platform for the Prevention of Autoimmune Diabetes (GPPAD) perspectives. Mol. Metab. 5, 255–262 (2016).
Thomas, N. J. et al. Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank. Lancet Diabetes Endocrinol. 6, 122–129 (2018).
Brorsson, C. A. et al. Genetic risk score modelling for disease progression in new-onset type 1 diabetes patients: increased genetic load of islet-expressed and cytokine-regulated candidate genes predicts poorer glycemic control. J. Diabetes Res. 2016, 9570424 (2016).
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, 1–10 (2015).
Tian, C. et al. Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections. Nat. Commun. 8, 599 (2017).
Ferreira, M. A. et al. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat. Genet. 49, 1752–1757 (2017).
Wijmenga, C. & Zhernakova, A. The importance of cohort studies in the post-GWAS era. Nat. Genet. https://doi.org/10.1038/s41588-018-0066-3 (2018).
Cortes, A. et al. Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank. Nat. Genet. 49, 1311–1318 (2017).
Bycroft, C. et al. Genome-wide genetic data on ~ 500, 000 UK Biobank participants. bioRxiv https://doi.org/10.1101/166298 (2017).
Zhou, X. & Stephens, M. Genome-wide efficient mixed model analysis for association studies. Nat. Genet. 44, 821–824 (2012).
Ma, C., Blackwell, T., Boehnke, M. & Scott, L. J. Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants genetic epidemiology. Genet. Epidemiol. 37, 539–550 (2013).
Rainbow, D.B. et al. A rare IL2RA haplotype identifies SNP rs61839660 as causal for autoimmunity. bioRxiv https://doi.org/10.1101/108126 (2017).
Morris Andrew. Transethnic meta-analysis of genomewide association studies. Genet. Epidemiol. 35, 809–822 (2011).
Kichaev, G. & Pasaniuc, B. Leveraging functional-annotation data in trans-ethnic fine-mapping studies. Am. J. Hum. Genet. 97, 260–271 (2015).
Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
Wallace, C. et al. Dissection of a complex disease susceptibility region using a bayesian stochastic search approach to fine mapping. PLoS Genet. 11, e1005272 (2015).
Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).
Chen, W. et al. Fine mapping causal variants with an approximate bayesian method using marginal test statistics. Genetics 200, 719–736 (2015).
Benner, C. et al. FINEMAP: Efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
Newcombe, P. J., Conti, D. V. & Richardson, S. JAM: a scalable Bayesian framework for joint analysis of marginal SNP effects. Genet. Epidemiol. 40, 188–201 (2016).
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).
Inshaw, J. R. J., Walker, N. M., Wallace, C., Bottolo, L. & Todd, J. A. The chromosome 6q22.33 region is associated with age at diagnosis of type 1 diabetes and disease risk in those diagnosed under 5 years of age. Diabetologia 61, 147–157 (2018).
Pekalski, M. L. et al. Neonatal and adult recent thymic emigrants produce IL-8 and express complement receptors CR1 and CR2. JCI Insight 2, e93739 (2017).
Davies, J. L. et al. Increased THEMIS first exon usage in CD4+ T-cells is associated with a genotype that is protective against multiple sclerosis. PLoS One 11, 1–11 (2016).
Ferreira, R. C. et al. Functional IL6R 358Ala allele impairs classical IL-6 receptor signaling and influences risk of diverse inflammatory diseases. PLoS Genet. 9, e1003444 (2013).
Al-Mossawi, H. et al. The autoimmune disease risk allele rs6897932 modulates monocyte IL7R surface and soluble receptor levels in a context-specific manner. bioRxiv https://doi.org/10.1101/262410 (2018).
Lappalainen, T. & Greally, J. M. Associating cellular epigenetic models with human phenotypes. Nat. Rev. Genet. 18, 441–451 (2017).
Mokry, L. E. et al. Vitamin D and risk of multiple sclerosis: a Mendelian randomization study. PLoS Med. 12, 1–20 (2015).
Rhead, B. et al. Mendelian randomization shows a causal effect of low vitamin D on multiple sclerosis risk. Neurol. Genet. 2, e97 (2016).
Burgess, S., Timpson, N. J., Ebrahim, S. & Smith, G. D. Mendelian randomization: Where are we now and where are we going? Int. J. Epidemiol. 44, 379–388 (2015).
Koellinger, P. D. & Harden, K. P. Using nature to understand nurture. Science 359, 657–658 (2018).
Kindt, A.S.D. et al. Allele-specific methylation of type 1 diabetes susceptibility genes. J. Autoimmun. https://doi.org/10.1016/j.jaut.2017.11.008 (2017).
Hemani, G., Tilling, K. & Davey Smith, G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 13, e1007081 (2017).
Hartwig, F. P., Davies, N. M., Hemani, G. & Smith, G. D. Counterfactual causation: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int. J. Epidemiol. 45, 1717–1726 (2016).
Inoshita, M. et al. Retraction: A significant causal association between C-reactive protein levels and schizophrenia. Sci. Rep. 8, 46947 (2018).
Feingold, E. A. et al. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science 306, 636–640 (2004).
Adams, D. et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat. Biotechnol. 30, 224–226 (2012).
Bernstein, B. E. et al. The NIH Roadmap Epigenomics Mapping Consortium complex. Nat. Biotechnol. 28, 1045–1048 (2010).
Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).
Elkon, R. & Agami, R. Characterization of noncoding regulatory DNA in the human genome. Nat. Biotechnol. 35, 732–746 (2017).
Hnisz, D., Day, D. S. & Young, R. A. Insulated neighborhoods: structural and functional units of mammalian gene control. Cell 167, 1188–1200 (2016).
Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390 (2012).
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).
Vahedi, G. et al. Super-enhancers delineate disease-associated regulatory nodes in T cells. Nature 520, 558–562 (2015).
Kichaev, G. et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 10, e1004722 (2014).
Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).
Yang, J., Fritsche, L. G., Zhou, X. & Abecasis, G. R. A scalable Bayesian method for integrating functional information in genome-wide association studies. Am. J. Hum. Genet. 101, 404–416 (2017).
Marson, A., Housley, W. J. & Hafler, D. A. Genetic basis of autoimmunity. J. Clin. Invest. 125, 2234–2241 (2015).
Kasela, S. et al. Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells. PLoS Genet. 13, e1006643 (2017).
De Jager, P. L. et al. ImmVar project: Insights and design considerations for future studies of ‘healthy’ immune variation. Semin. Immunol. 27, 51–57 (2015).
Todd, J. A. Evidence that UBASH3 is a causal gene for type 1 diabetes. Eur. J. Hum. Genet. https://doi.org/10.1038/s41431-018-0142-2 (2018).
Ongen, H. et al. Estimating the causal tissues for complex traits and diseases. Nat. Genet. 49, 1676–1683 (2017).
Reinius, B. et al. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq. Nat. Genet. 48, 1430–1435 (2016).
van der Wijst, M.G.P., Brugge, H., de Vries, D.H. & Franke, L.H. Single-cell RNA sequencing reveals cell-type specific cis-eQTLs in peripheral blood mononuclear cells. bioRxiv https://doi.org/10.1101/177568 (2017).
Sun, B.B. et al. Genomic atlas of the human plasma proteome. Nature (in the press).
Keshishian, H. et al. Multiplexed, quantitative workflow for sensitive biomarker discovery in plasma yields novel candidates for early myocardial injury. Mol. Cell. Proteomics 14, 2375–2393 (2015).
Dendrou, C. A. et al. Cell-specific protein phenotypes for the autoimmune locus IL2RA using a genotype-selectable human bioresource. Nat. Genet. 41, 1011–1015 (2009).
Corbin, L. J. et al. Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference. Nat. Commun. 9, 711 (2018).
Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1312 (2002).
Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).
Davison, L. J. et al. Long-range DNA looping and gene expression analyses identify DEXI as an autoimmune disease candidate gene. Hum. Mol. Genet. 21, 322–333 (2012).
Lieberman-aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–294 (2009).
Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572 (2017).
Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 (2016).
Burren, O. S. et al. Chromosome contacts in activated T cells identify autoimmune disease candidate genes. Genome Biol. 18, 165 (2017).
Hughes, J. R. et al. Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nat. Genet. 46, 205–212 (2014).
Davies, J. O. J. et al. Multiplexed analysis of chromosome conformation at vastly improved sensitivity. Nat. Methods 13, 74–80 (2016).
Li, G. et al. ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biol. 11, 1–13 (2010).
Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).
Simeonov, D. R. et al. Discovery of stimulation-responsive immune enhancers with CRISPR activation. Nature 549, 111–115 (2017).
Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424–431 (2018).
Kumasaka, N., Knights, A. & Gaffney, D. High resolution genetic mapping of causal regulatory interactions in the human genome. bioRxiv https://doi.org/10.1101/227389 (2017).
Ercolini, A. M. & Miller, S. D. The role of infections in autoimmune disease. Clin. Exp. Immunol. 155, 1–15 (2008).
Matzaraki, V., Kumar, V., Wijmenga, C. & Zhernakova, A. The MHC locus and genetic susceptibility to autoimmune and infectious diseases. Genome Biol. 18, 76 (2017).
Rodriguez-calvo, T., Sabouri, S., Anquetil, F. & Von Herrath, M. G. The viral paradigm in type 1 diabetes : Who are the main suspects? Autoimmun. Rev. 15, 964–969 (2016).
Ferreira, R. C. et al. A type 1 interferon transcriptional signature precedes autoimmunity in children genetically at risk for type 1 diabetes. Diabetes 63, 2538–2550 (2014).
Beyerlein, A., Donnachie, E., Jergens, S. & Ziegler, A. Infections in early life and development of type 1 diabetes. J. Am. Med. Assoc. 315, 1899–1901 (2016).
Trowsdale, J. & Knight, J. C. Major histocompatibility complex genomics and human disease. Annu. Rev. Genomics Hum. Genet. 14, 301–323 (2013).
Todd, J. A., Bell, J. I. & McDevitt, H. O. HLA-DQB gene contributes to susceptibility and resistance to insulin-dependent diabetes mellitus. Nature 327, 599–604 (1987).
Howson, J. M. M., Walker, N. M., Clayton, D. & Todd, J. A. Confirmation of HLA class II independent type 1 diabetes associations in the major histocompatibility complex including HLA-B and HLA-A. Diabetes Obes. Metab. 11, 31–45 (2009).
Eriksson, N. et al. Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet. 6, e1000993 (2010).
Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome-wide association studies. Nat. Rev. Genet. 17, 129–145 (2016).
Kelly, R. J., Rouquier, S., Giorgi, D., Lennon, G. G. & Lowe, J. B. Sequence and expression of a candidate for the human secretor blood group alpha(1,2)fucosyltransferase gene (FUT2). Homozygosity for an enzyme- inactivating nonsense mutation commonly correlates with the non-secretor phenotype. J. Biol. Chem. 270, 4640–4649 (1995).
Lindesmith, L. et al. Human susceptibility and resistance to Norwalk virus infection. Nat. Med. 9, 548–553 (2003).
Boren, T., Falk, P., Roth, K., Larson, G. & Normark, S. Attachment of Helicobacter pylori to human gastric epithelium mediated by blood group antigens. Science 262, 1892–1895 (1993).
Rausch, P. et al. Colonic mucosa-associated microbiota is influenced by an interaction of Crohn disease and FUT2 (Secretor) genotype. Proc. Natl. Acad. Sci. USA 108, 19030–19035 (2011).
Wacklin, P. et al. Faecal microbiota composition in adults is associated with the FUT2 gene determining the secretor status. PLoS One 9, e94863 (2014).
Wacklin, P. et al. Secretor genotype (FUT2 gene) is strongly associated with the composition of bifidobacteria in the human intestine. PLoS One 6, e20113 (2011).
Tong, M. et al. Reprograming of gut microbiome energy metabolism by the FUT2 Crohn’s disease risk polymorphism. ISME J. 8, 2193–2206 (2014).
Jacobs, J. P. & Braun, J. Immune and genetic gardening of the intestinal microbiome. FEBS Lett. 588, 4102–4111 (2014).
Turpin, W. et al. FUT2 genotype and secretory status are not associated with fecal microbial composition and inferred function in healthy subjects. Gut Microbes https://doi.org/10.1080/19490976.2018.1445956 (2018).
Smyth, D. J. et al. FUT2 nonsecretor status links type 1 diabetes susceptibility and resistance to infection. Diabetes 60, 3081–3084 (2011).
Mcgovern, D. P. B. et al. Fucosyltransferase 2 (FUT2) non-secretor status is associated with Crohn’s disease. Hum. Mol. Genet 19, 3468–3476 (2010).
Hall, A. B., Tolonen, A. C. & Xavier, R. J. Human genetic variation and the gut microbiome in disease. Nat. Rev. Genet. 18, 690–699 (2017).
Thorven, M. et al. A homozygous nonsense mutation (428G→A) in the human secretor (FUT2) gene provides resistance to symptomatic norovirus (GGII) infections. J. Virol. 79, 15351–15355 (2005).
Dodd, D. et al. A gut bacterial pathway metabolizes aromatic amino acids into nine circulating metabolites. Nature 551, 648–652 (2017).
Kamada, N., Seo, S. U., Chen, G. Y. & Núñez, G. Role of the gut microbiota in immunity and inflammatory disease. Nat. Rev. Immunol. 13, 321–335 (2013).
Mclean, J. S. Advancements toward a systems level understanding of the human oral microbiome. Front. Cell. Infect. Microbiol. 4, 98 (2014).
Köhling, H. L., Plummer, S. F., Marchesi, J. R., Davidge, K. S. & Ludgate, M. The microbiota and autoimmunity: Their role in thyroid autoimmune diseases. Clin. Immunol. 183, 63–74 (2017).
Yurkovetskiy, L. A., Pickard, J. M. & Chervonsky, A. V. Microbiota and autoimmunity: exploring new avenues. Cell Host Microbe 17, 548–552 (2015).
Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes. PLoS One 6, 1–9 (2011).
de Goffau, M. C. et al. Fecal microbiota composition differs between children with β-cell autoimmunity and those without. Diabetes 62, 1238–1244 (2013).
Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1. Cell Host Microbe 17, 260–273 (2015).
Needell, J. C. & Zipris, D. The role of the intestinal microbiome in type 1 diabetes pathogenesis. Curr. Diab. Rep. 16, 89 (2016).
Paun, A., Yau, C. & Danska, J. S. The influence of the microbiome on type 1 diabetes. J. Immunol. 198, 590–595 (2017).
Dunne, J. L. et al. The intestinal microbiome in type 1 diabetes. Clin. Exp. Immunol. 177, 30–37 (2014).
Mullaney, J. A. et al. Type 1 diabetes susceptibility alleles are associated with distinct alterations in the gut microbiota. Microbiome 6, 35 (2018).
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
Mosca, A., Leclerc, M. & Hugot, J. P. Gut microbiota diversity and human diseases: should we reintroduce key predators in our ecosystem? Front. Microbiol. 7, 455 (2016).
Santin, I., Dos Santos, R. S. & Eizirik, D. L. Pancreatic beta cell survival and signaling pathways: effects of type 1 diabetes-associated genetic variants. Methods Mol. Biol. 1433, 21–54 (2016).
Marroqui, L. et al. TYK2, a candidate gene for type 1 diabetes, modulates apoptosis and the innate immune response in human pancreatic β-cells. Diabetes 64, 3808–3817 (2015).
Dendrou, C. A. et al. Resolving TYK2 locus genotype-to-phenotype differences in autoimmunity. Sci. Transl. Med. 8, 363ra149 (2016).
Dooley, J. et al. Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes. Nat. Genet. 48, 519–527 (2016).
Nogueira, T. C. et al. GLIS3, a susceptibility gene for type 1 and type 2 diabetes, modulates pancreatic beta cell apoptosis via regulation of a splice variant of the BH3-only protein Bim. PLoS Genet. 9, e1003532 (2013).
Graham, K. L. et al. Pathogenic mechanisms in type 1 diabetes : the islet is both target and driver of disease. Rev. Diabet. Stud. 9, 148–168 (2012).
Liu, J. Z. et al. Dense fine-mapping study identifies new susceptibility loci for primary biliary cirrhosis. Nat. Genet. 44, 1137–1141 (2012).
Boettger, L. M., Handsaker, R. E., Zody, M. C. & McCarroll, S. A. Structural haplotypes and recent evolution of the human 17q21.31 region. Nat. Genet. 44, 881–885 (2012).
Bekpen, C., Tastekin, I., Siswara, P., Akdis, C. A. & Eichler, E. E. Primate segmental duplication creates novel promoters for the LRRC37 gene family within the 17q21.31 inversion polymorphism region. Genome Res. 22, 1050–1058 (2012).
Zody, M. C. et al. Evolutionary toggling of the MAPT 17q21.31 inversion region. Nat. Genet. 40, 1076–1083 (2008).
Zhang, C.-C. et al. Meta-analysis of the association between variants in MAPT and neurodegenerative diseases. Oncotarget 8, 44994–45007 (2017).
Lai, M. C. et al. Haplotype-specific MAPT exon 3 expression regulated by common intronic polymorphisms associated with Parkinsonian disorders. Mol. Neurodegener. 12, 79 (2017).
Van De Bunt, M. et al. Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycemic traits to their downstream effectors. PLoS Genet. 11, e1005694 (2015).
Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc. Natl. Acad. Sci. USA 114, 2301–2306 (2017).
Huising, M. O. et al. CRFR1 is expressed on pancreatic β cells, promotes β cell proliferation, and potentiates insulin secretion in a glucose-dependent manner. Proc. Natl. Acad. Sci. USA 107, 912–917 (2010).
Blaabjerg, L. et al. CRFR1 activation protects against cytokine-induced beta cell death. J. Mol. Endocrinol. 53, 417–427 (2015).
Schmid, J. et al. Modulation of pancreatic islets-stress axis by hypothalamic releasing hormones and 11β-hydroxysteroid dehydrogenase. Proc. Natl. Acad. Sci. USA 108, 13722–13727 (2011).
Miklossy, J. et al. Beta amyloid and hyperphosphorylated tau deposits in the pancreas in type 2 diabetes. Neurobiol. Aging 31, 1503–1515 (2010).
Maj, M. et al. Expression of TAU in insulin-secreting cells and its interaction with the calcium-binding protein secretagogin. J. Endocrinol. 205, 25–36 (2010).
Wijesekara, N. et al. Amyloid-β and islet amyloid pathologies link Alzheimer disease and type 2 diabetes in a transgenic model. FASEB J. 31, 5409–5418 (2017).
Eberhard, D. Neuron and beta-cell evolution: Learning about neurons is learning about beta-cells. BioEssays 35, 584 (2013).
Calderari, S. et al. Molecular genetics of the transcription factor GLIS3 identifies its dual function in beta cells and neurons. Genomics 110, 98–111 (2018).
Marroqui, L. et al. Interferon-alpha mediates human beta cell HLA class I overexpression, endoplasmic reticulum stress and apoptosis, three hallmarks of early human type 1 diabetes. Diabetologia. 60, 656–667 (2017).
Perri, E. R., Thomas, C. J., Parakh, S., Spencer, D. M. & Atkin, J. D. The unfolded protein response and the role of protein disulfide isomerase in neurodegeneration. Front. Cell Dev. Biol. 3, 80 (2015).
Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).
Sanseau, P. et al. Use of genome-wide association studies for drug repositioning. Nat. Biotechnol. 30, 317–320 (2012).
Koreth, J. et al. Interleukin-2 and regulatory T cells in graft-versus-host disease. N. Engl. J. Med. 365, 2055–2066 (2017).
He, J. et al. Low-dose interleukin-2 treatment selectively modulates CD4+ T cell subsets in patients with systemic lupus erythematosus. Nat. Med. 22, 991–993 (2016).
Saadoun, D. et al. Regulatory T-cell responses to low-dose interleukin-2 in HCV-induced vasculitis. N. Engl. J. Med. 365, 2067–2077 (2017).
Todd, J. A. et al. Regulatory T cell responses in participants with type 1 diabetes after a single dose of interleukin-2: a non-randomised, open label, adaptive dose-finding trial. PLoS Med. 13, 27727279 (2016).
Vodovotz, Y. et al. Solving immunology? Trends Immunol. 38, 116–127 (2017).
Pappalardo, J. L. & Hafler, D. A. The Human Functional Genomics Project: understanding generation of diversity. Cell 167, 894–896 (2017).
Segerstolpe, A. et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24, 593–607 (2016).
Ivison, S., Des Rosiers, C., Lesage, S., Rioux, J. D. & Levings, M. K. Biomarker-guided stratification of autoimmune patients for biologic therapy. Curr. Opin. Immunol. 49, 56–63 (2017).
West, N. R. et al. Oncostatin M drives intestinal inflammation and predicts response to tumor necrosis factor-neutralizing therapy in patients with inflammatory bowel disease. Nat. Med. 23, 579–589 (2017).
Flicek, P. et al. Ensembl 2014. Nucleic Acids Res. 42, D749–D755 (2014).
Huang, Q.Q., Ritchie, S.C., Brozynska, M. & Inouye, M. Power, false discovery rate and Winner’s Curse in eQTL studies. bioRxiv https://doi.org/10.1101/209171 (2017).
Lotta, L. A. et al. Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a Mendelian randomisation analysis. PLoS Med. 13, e1002179 (2016).
Tibshirani, R. A simple method for assessing sample sizes in microarray experiments. BMC Bioinformatics 7, 106 (2006).
Acknowledgements
We thank the JDRF (grant codes 9-2011-253 and 5-SRA-2015-130-A-N) and Wellcome (grant codes 091157 and 107212). O.S.B. is funded by Wellcome (grant code WT107881).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’ note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Inshaw, J.R.J., Cutler, A.J., Burren, O.S. et al. Approaches and advances in the genetic causes of autoimmune disease and their implications. Nat Immunol 19, 674–684 (2018). https://doi.org/10.1038/s41590-018-0129-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41590-018-0129-8
This article is cited by
-
An autoimmune pleiotropic SNP modulates IRF5 alternative promoter usage through ZBTB3-mediated chromatin looping
Nature Communications (2023)
-
Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections
Science China Life Sciences (2023)
-
Thymocyte regulatory variant alters transcription factor binding and protects from type 1 diabetes in infants
Scientific Reports (2022)
-
New insights into pathogenesis of IgA nephropathy
International Urology and Nephrology (2022)
-
Human immunology and immunotherapy: main achievements and challenges
Cellular & Molecular Immunology (2021)
