Making Lemonade: Putting the Wisdom of the Genome to Work in Atopic Dermatitis
2021; Elsevier BV; Volume: 141; Issue: 11 Linguagem: Inglês
10.1016/j.jid.2021.05.027
ISSN1523-1747
AutoresZhaolin Zhang, James T. Elder,
Tópico(s)Immune Cell Function and Interaction
ResumoGetting from a GWAS hit to an actionable gene remains a challenge in complex disease genetics. In a new article of the Journal of Investigative Dermatology, Sobczyk et al., 2021Sobczyk M.K. Richardson T.G. Zuber V. Min J.L. Gaunt T.R. Paternoster L. Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci.J Invest Dermatol. 2021; 141: 2620-2629Google Scholar use a wide variety of genomic data to generate a prioritization algorithm to tackle this problem in atopic dermatitis, calling on the wisdom of the genome to generate promising results. Getting from a GWAS hit to an actionable gene remains a challenge in complex disease genetics. In a new article of the Journal of Investigative Dermatology, Sobczyk et al., 2021Sobczyk M.K. Richardson T.G. Zuber V. Min J.L. Gaunt T.R. Paternoster L. Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci.J Invest Dermatol. 2021; 141: 2620-2629Google Scholar use a wide variety of genomic data to generate a prioritization algorithm to tackle this problem in atopic dermatitis, calling on the wisdom of the genome to generate promising results. The past decade has yielded great progress in the genetic analysis of complex genetic disorders, in which multiple genes and environmental factors interact to determine risk. Prominent among these are the immune-mediated inflammatory diseases (IMIDs), several of which involve the skin, including atopic dermatitis (AD), acne, alopecia areata, psoriasis, lupus, and vitiligo. While providing new insights into disease pathogenesis, GWASs of AD, psoriasis, and other IMIDs have uncovered several challenges. In addition to the modest ORs associated with most IMID susceptibility loci, most of these genetic signals occur in putative regulatory regions (Farh et al., 2015Farh K.K. Marson A. Zhu J. Kleinewietfeld M. Housley W.J. Beik S. et al.Genetic and epigenetic fine mapping of causal autoimmune disease variants.Nature. 2015; 518: 337-343Crossref PubMed Scopus (1026) Google Scholar). Although the discovery that many genetic variants exert their effects via gene regulation is very exciting, the most elegant feature of genetic analysis—the ability to work with genomic DNA from blood or other sources—is replaced by a more complex scenario that requires analysis of multiple molecular readouts (e.g., mRNA, protein, DNA methylation) in disease-relevant cell types, preferably studied in their normal physiologic contexts. Besides uncertainty as to which cell types are disease relevant, these signals may emanate from only a small fraction of the cell types present in diseased tissue, and the pathogenic cell types are often hard to access experimentally in humans. Moreover, the existence of linkage disequilibrium (LD)—the nonrandom segregation of closely spaced genetic markers because of our common evolutionary history—complicates the identification of the best genetic markers and candidate genes within a genetic signal generated by GWAS. Adding further complexity, each disease-associated region can contain multiple genetic signals independent of LD (Mahajan et al., 2018Mahajan A. Taliun D. Thurner M. Robertson N.R. Torres J.M. Rayner N.W. et al.Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.Nat Genet. 2018; 50: 1505-1513Crossref PubMed Scopus (522) Google Scholar), and a given genetic signal may influence the expression of multiple coordinately regulated genes by both cis- and trans- mechanisms (Võsa et al., 2018 1Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. bioRxiv 2018.1Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. bioRxiv 2018.). Clearly, we cannot naively interpret localization of interesting genes to the vicinities of association signals as proof that these genes exert causal roles. Indeed, overcoming this critical barrier to progress is the key to unlocking the treasure chest of genetic signals revealed by GWAS, requiring strategic retooling of available resources.Clinical Implications•Most genetic signals identified by GWAS are regulatory in nature and often reside in regions of high linkage disequilibrium, making the identification of causal genes a challenge.•To meet this challenge, Sobczyk et al., 2021Sobczyk M.K. Richardson T.G. Zuber V. Min J.L. Gaunt T.R. Paternoster L. Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci.J Invest Dermatol. 2021; 141: 2620-2629Google Scholar compiled over 100 molecular resources relevant to atopic dermatitis (AD) and developed a scoring system for prioritization of candidate genes across 25 AD-associated genetic regions, yielding clear top candidates for multiple AD loci but also several other regions in which genes with similarly high scores were closely spaced and functionally related.•Indirect validation using functional enrichment and interaction tools revealed strong enrichment for cytokine-mediated signaling pathways and Jak–signal transducer and activator of transcription signaling.•Clustering of functionally related genes likely reflects the higher-order structure of the genome in addition to gene duplication events. •Most genetic signals identified by GWAS are regulatory in nature and often reside in regions of high linkage disequilibrium, making the identification of causal genes a challenge.•To meet this challenge, Sobczyk et al., 2021Sobczyk M.K. Richardson T.G. Zuber V. Min J.L. Gaunt T.R. Paternoster L. Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci.J Invest Dermatol. 2021; 141: 2620-2629Google Scholar compiled over 100 molecular resources relevant to atopic dermatitis (AD) and developed a scoring system for prioritization of candidate genes across 25 AD-associated genetic regions, yielding clear top candidates for multiple AD loci but also several other regions in which genes with similarly high scores were closely spaced and functionally related.•Indirect validation using functional enrichment and interaction tools revealed strong enrichment for cytokine-mediated signaling pathways and Jak–signal transducer and activator of transcription signaling.•Clustering of functionally related genes likely reflects the higher-order structure of the genome in addition to gene duplication events. In a new article of the Journal of Investigative Dermatology, Sobczyk et al., 2021Sobczyk M.K. Richardson T.G. Zuber V. Min J.L. Gaunt T.R. Paternoster L. Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci.J Invest Dermatol. 2021; 141: 2620-2629Google Scholar have made an important step toward this goal. They developed a bioinformatics pipeline to systematically prioritize candidate causal genes at 25 AD loci that emerged from their earlier multiancestry GWAS of AD (Paternoster et al., 2015Paternoster L. Standl M. Waage J. Baurecht H. Hotze M. Strachan D.P. et al.Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis.Nat Genet. 2015; 47: 1449-1456Crossref PubMed Scopus (315) Google Scholar). Their pipeline (Figure 1) utilizes over 100 molecular resources relevant to AD, including RNA, protein, and DNA methylation quantitative trait locus (QTL) datasets derived from skin or other immune-relevant tissues, as well as other, less skin-specific datasets for regulatory variant prediction, including promoter-enhancer interactions, expression studies, and variant fine mapping. The authors weighted the prioritization algorithm to emphasize the most robust datasets and de-emphasize those in which there was a high a priori likelihood of false positive results (such as individual expression QTL signals, which are abundant throughout the genome). Although the choice of weighting schemes reflects the judgment of the authors, their rationale is based in well-founded assumptions that are not specific to AD, including the use of statistical methods such as transcriptome-wide association studies and colocalization studies that formally compare the association patterns in QTL studies and GWASs. Their results are reinforced by the striking differences in aggregate scores between individual genes and their near neighbors (for example, 1q21.3-IL6R, 10q21.2-ADO, 11p13-PRR5L, 5p13.2-IL7R, 11q24.3-ETS1, 2q37.1-INPP5D, 12q15-MDM1, and 14q32.32-TRAF3). Indirect validation studies using functional enrichment and interaction tools revealed strong enrichment for cytokine-mediated signaling pathways and Jak–signal transducer and activator of transcription signaling, consistent with the clinical efficacy of biologicals targeting IL-4 receptor-α, blocking IL-4 and IL-13 intracellular signaling. Despite these successes, some ambiguities remain. The pipeline of Sobczyk et al., 2021Sobczyk M.K. Richardson T.G. Zuber V. Min J.L. Gaunt T.R. Paternoster L. Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci.J Invest Dermatol. 2021; 141: 2620-2629Google Scholar produced examples of similarly high-scoring genes that are closely spaced and functionally related, including IL18R1, IL18RAP, and IL1R1 in the IL-1 like gene cluster on chr 2q12.1 and IL2RA versus IL15RA on chr 10p15.1. Recently, IL2RA has been independently implicated by CRISPR-Cas mutagenesis experiments demonstrating an effect of the T-allele at rs61839660 on IL2RA gene expression (Simeonov et al., 2017Simeonov D.R. Gowen B.G. Boontanrart M. Roth T.L. Gagnon J.D. Mumbach M.R. et al.Discovery of stimulation-responsive immune enhancers with CRISPR activation [published correction appears in Nature 2018;559:E13].Nature. 2017; 549: 111-115Crossref PubMed Scopus (132) Google Scholar). In other cases, the structural and functional relatedness of closely spaced, high-scoring gene candidates was less clear, including LRRC32 and EMSY on Chr 11q13.5; KIF3A, PDLIM4, SLC22A4, and IRF1 in the 5q31.1 cytokine gene cluster; and STMN3, LIME1, and ARFRP1 on 20q13.33. Supporting the candidacy of KIF3A, the derived (nonancestral) alleles at two implicated SNPs near KIF3A are CpG dinucleotides that can become methylated, reducing KIF3A expression when present, decreasing barrier function, and increasing risk for allergic skin responses (Stevens et al., 2020Stevens M.L. Zhang Z. Johansson E. Ray S. Jagpal A. Ruff B.P. et al.Disease-associated KIF3A variants alter gene methylation and expression impacting skin barrier and atopic dermatitis risk.Nat Commun. 2020; 11: 4092Crossref PubMed Scopus (6) Google Scholar). Taking a longer view, all of these findings seem to relate to the wisdom of the genome, which favors spatial colocalization of functionally related genes. Prime examples of this wisdom include the major histocompatibility complex on chr 6p21.3, the epidermal differentiation complex on chr 1q21.3, the α- and β-globin gene clusters, and the 2q12.1 (IL-1-like) and 5q31.1 (T helper type 2–related) cytokine gene clusters. It is attractive to speculate that the evolutionary reasons for such clustering extend beyond simple gene duplication to encompass important features of three-dimensional chromatin organization. These include the formation of topologically associating domains (Delaneau et al., 2019Delaneau O. Zazhytska M. Borel C. Giannuzzi G. Rey G. Howald C. et al.Chromatin three-dimensional interactions mediate genetic effects on gene expression.Science. 2019; 364: eaat8266Crossref PubMed Scopus (72) Google Scholar) and even higher orders of chromatin structure, including A and B compartments of active versus inactive chromatin and chromosome territories within the nucleus (Szabo et al., 2019Szabo Q. Bantignies F. Cavalli G. Principles of genome folding into topologically associating domains.Sci Adv. 2019; 5eaaw1668Crossref PubMed Scopus (176) Google Scholar). These higher-order levels of genomic organization do not appear to be broadly conserved across phyla and likely depend on physical properties of chromatin that are not yet fully understood (Szabo et al., 2019Szabo Q. Bantignies F. Cavalli G. Principles of genome folding into topologically associating domains.Sci Adv. 2019; 5eaaw1668Crossref PubMed Scopus (176) Google Scholar). In mammals, these features of higher-order chromatin organization function to bring together promoters and enhancers in different combinations, which in turn depend on the cellular context via the coordinated expression of transcription factors, chromatin remodeling proteins, and chromatin loop anchors, notably the CTCF/cohesion complex. Indeed, DNA methylation has been shown to influence the anchoring function of CTCF across various cell lineages, via two specific positions in the CTCF binding site (Wang et al., 2012Wang H. Maurano M.T. Qu H. Varley K.E. Gertz J. Pauli F. et al.Widespread plasticity in CTCF occupancy linked to DNA methylation.Genome Res. 2012; 22: 1680-1688Crossref PubMed Scopus (342) Google Scholar), and grand canyons with markedly reduced DNA methylation are found in stem cells and early progenitor cells (Zhang et al., 2020Zhang X. Jeong M. Huang X. Wang X.Q. Wang X. Zhou W. et al.Large DNA methylation nadirs anchor chromatin loops maintaining hematopoietic stem cell identity.Mol Cell. 2020; 78 (506–21.e6)Abstract Full Text Full Text PDF Scopus (19) Google Scholar). Thus, inclusion of DNA methylation datasets appears to have been a wise choice for prioritizing AD loci. The spatially colocated gene sets nominated by the prioritization algorithm of Sobczyk et al., 2021Sobczyk M.K. Richardson T.G. Zuber V. Min J.L. Gaunt T.R. Paternoster L. Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci.J Invest Dermatol. 2021; 141: 2620-2629Google Scholar clearly occupy a smaller fraction of genomic space than do the large gene clusters exemplified above. Nevertheless, based on the wisdom of the genome principle, we can expect to find more and more examples of disease-associated genetic signals in which genes not created by simple duplication may prove to work together in particular functional contexts. If, as we now appreciate, most disease-associated variants are regulatory, it will not be surprising to learn that more than one structurally unrelated gene may be responsible for mediating the effect of certain genetic signals, not only in AD but in many other IMIDs as well. With the rapid recent advances in gene expression, DNA methylation, and epigenetic profiling, including the use of single cells and spatially defined sequencing from tissue sections, we can expect that the months and years to come will see extensive use of all of these tools in taking the lemons of genetic complexity identified by GWAS to create a tasty lemonade of functional genomic insights for AD and other IMIDs. Zhaolin Zhang: http://orcid.org/0000-0003-1978-8160 James T. Elder: http://orcid.org/0000-0003-4215-3294 The authors state no conflict of interest. Triangulating Molecular Evidence to Prioritize Candidate Causal Genes at Established Atopic Dermatitis LociJournal of Investigative DermatologyVol. 141Issue 11PreviewGWASs for atopic dermatitis have identified 25 reproducible loci. We attempt to prioritize the candidate causal genes at these loci using extensive molecular resources compiled into a bioinformatics pipeline. We identified a list of 103 molecular resources for atopic dermatitis etiology, including expression, protein, and DNA methylation quantitative trait loci datasets in the skin or immune-relevant tissues, which were tested for overlap with GWAS signals. This was combined with functional annotation using regulatory variant prediction and features such as promoter‒enhancer interactions, expression studies, and variant fine mapping. Full-Text PDF Open Access
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