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What Have We Learned from GWAS for Atopic Dermatitis?

  • Sara J. Brown
    Correspondence
    Correspondence: Sara J. Brown, Skin Research Group, Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, Scotland DD1 9SY, United Kingdom.
    Affiliations
    Skin Research Group, Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, Scotland, United Kingdom
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Open ArchivePublished:June 08, 2020DOI:https://doi.org/10.1016/j.jid.2020.05.100
      GWASs have revealed multiple loci associated with atopic dermatitis (AD). Some have confirmed pre-existing knowledge, including the role of skin barrier and type 2 inflammation in AD pathogenesis, whereas others have provided newer insights, including evidence of autoimmunity and previously unrecognized genes controlling epidermal differentiation. The majority of risk loci are in intergenic regions for which functional mechanism(s) remain unknown. These loci require detailed molecular studies carried out in cells and tissues of relevance to AD. Genomic findings to date account for ∼30% of AD heritability, therefore, considerable further work is needed to fully understand individual risk.

      Abbreviations:

      AD (atopic dermatitis), MR (mendelian randomization)

      An introduction to GWAS

      A GWAS aims to identify regions of the genome that are associated with a specific trait or disease (https://ghr.nlm.nih.gov/primer/genomicresearch/gwastudies). The technique compares the frequency of SNPs and other types of variants (e.g., deletions and insertions) between cases and controls, similar to a massive case-control study. Large numbers of variants are assessed (1–2 million may be screened directly and many more by imputation), sampling regions across the whole genome, and large numbers (thousands-tens of thousands) of cases and controls are needed to achieve sufficient statistical power. Results may be summarized in the form of a Manhattan plot (Figure 1). Complex traits result from the interactions of multiple genetic effects, many of which have small effect sizes. GWAS is a feasible approach for the study of common complex traits because a sufficient sample size can be obtained. The effect sizes detected by GWAS can range from odds ratio (OR) >2 (i.e., risk more than doubled) to very small effect sizes (OR 1.1 or lower, i.e., <10% increased risk).
      Figure thumbnail gr1
      Figure 1Summary of key features in a Manhattan plot. A Manhattan plot is the conventional method for displaying results from GWAS. Each SNP is represented by a dot on the plot, and its position is determined by genomic location and a statistical test of association with the trait of interest. AD, atopic dermatitis; chr, chromosome.

      GWAS applied to atopic dermatitis

      Atopic dermatitis (AD) is a common (affecting 0.2–24.6% of children [
      • Brown S.J.
      • Relton C.L.
      • Liao H.
      • Zhao Y.
      • Sandilands A.
      • Wilson I.J.
      • et al.
      Filaggrin null mutations and childhood atopic eczema: a population-based case-control study.
      ,
      • Odhiambo J.A.
      • Williams H.C.
      • Clayton T.O.
      • Robertson C.F.
      • Asher M.I.
      Group IPTS
      Global variations in prevalence of eczema symptoms in children from ISAAC Phase Three.
      ] and up to 10% of adults [
      • Bieber T.
      Atopic dermatitis.
      ]) and complex trait caused by the interactions of multiple genetic and environmental factors. AD is highly heritable (72–86% concordance in monozygotic twin pairs [
      • Larsen F.S.
      • Holm N.V.
      • Henningsen K.
      Atopic dermatitis. A genetic-epidemiologic study in a population-based twin sample.
      ,
      • Schultz Larsen F.
      Atopic dermatitis: a genetic-epidemiologic study in a population-based twin sample.
      ]), and this provides the rationale for genetic studies. GWAS and GWAS meta-analysis have revealed ∼31 loci associated with AD, including four with secondary independent signals (
      • Paternoster 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.
      ). Some AD risk loci have confirmed pre-existing knowledge, including the role of skin barrier and type 2 inflammation in AD pathogenesis; the epidermal differentiation complex on chromosome 1q21.3 includes FLG encoding FLG, and the cytokine cluster on chr5q31.1 includes genes encoding IL-13 and IL-4 (Figure 1). Other loci have provided newer insights, including evidence for autoimmunity (
      • Paternoster 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.
      ) and a role for Langerhans cells, indicated by variants in a locus on 2p13.3, which affect the expression levels of CD207 (langerin) in the skin (
      • Paternoster 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.
      ).
      GWAS of multiple atopic traits has shown a considerable overlap in the genetic risk profiles for AD, asthma, and allergic rhinitis (
      • Ferreira M.A.R.
      • Vonk J.M.
      • Baurecht H.
      • Marenholz I.
      • Tian C.
      • Hoffman J.D.
      • et al.
      Eleven loci with new reproducible genetic associations with allergic disease risk.
      ,
      • Ferreira M.A.
      • Vonk J.M.
      • Baurecht H.
      • Marenholz I.
      • Tian C.
      • Hoffman J.D.
      • et al.
      Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.
      ) attributed predominantly to lymphocyte-mediated immunity. Only two loci indicate AD-specific effects, and these are both within the epidermal differentiation complex on chromosome 1q21.3 attributed to FLG and HRNR-RPTN (
      • Ferreira M.A.
      • Vonk J.M.
      • Baurecht H.
      • Marenholz I.
      • Tian C.
      • Hoffman J.D.
      • et al.
      Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.
      ).
      An extension to GWAS focusing on protein-coding variants used exome genotype and skin transcriptome data (
      • Mucha S.
      • Baurecht H.
      • Novak N.
      • Rodríguez E.
      • Bej S.
      • Mayr G.
      • et al.
      Protein-coding variants contribute to the risk of atopic dermatitis and skin-specific gene expression.
      ). This study identified an additional 12% of AD heritability explained by rare protein-coding variation in genes, including IL4R, IL13, JAK1, JAK2, and TYK2, as well as novel candidate genes DOK2 and CD200R1.

      Outstanding questions

      The most highly significant peak on chromosome 1q21.3 includes the well-known FLG AD risk (
      • Irvine A.D.
      • McLean W.H.
      • Leung D.Y.
      Filaggrin mutations associated with skin and allergic diseases.
      ), but loss-of-function mutations and copy-number variation within FLG (
      • Brown S.J.
      • Kroboth K.
      • Sandilands A.
      • Campbell L.E.
      • Pohler E.
      • Kezic S.
      • et al.
      Intragenic copy number variation within filaggrin contributes to the risk of atopic dermatitis with a dose-dependent effect.
      ) do not fully explain this strong effect. It is therefore likely that the epidermal differentiation complex, a dense cluster of 63 genes (
      • de Guzman Strong C.
      • Conlan S.
      • Deming C.B.
      • Cheng J.
      • Sears K.E.
      • Segre J.A.
      A milieu of regulatory elements in the epidermal differentiation complex syntenic block: implications for atopic dermatitis and psoriasis.
      ), contains additional risk mechanisms (
      • Paternoster 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.
      ,
      • Paternoster L.
      • Standl M.
      • Chen C.M.
      • Ramasamy A.
      • Bønnelykke K.
      • Duijts L.
      • et al.
      Meta-analysis of genome-wide association studies identifies three new risk loci for atopic dermatitis.
      ). Variants in FLG2 may contribute to AD persistence (
      • Margolis D.J.
      • Gupta J.
      • Apter A.J.
      • Ganguly T.
      • Hoffstad O.
      • Papadopoulos M.
      • et al.
      Filaggrin-2 variation is associated with more persistent atopic dermatitis in African American subjects.
      ), and an in-frame deletion in SPRR3 has been associated with AD (
      • Marenholz I.
      • Rivera V.A.
      • Esparza-Gordillo J.
      • Bauerfeind A.
      • Lee-Kirsch M.A.
      • Ciechanowicz A.
      • et al.
      Association screening in the Epidermal Differentiation Complex (EDC) identifies an SPRR3 repeat number variant as a risk factor for eczema.
      ), but additional genetic and epigenetic mechanisms in this highly repetitive and therefore challenging region remain to be defined.
      The majority of loci identified by GWAS are in intergenic regions for which functional mechanism(s) remain unknown; these loci require detailed molecular studies carried out in cells and tissues of relevance to AD. One locus for which functional studies have been conducted is on chromosome 11q13.5 (
      • Esparza-Gordillo J.
      • Weidinger S.
      • Fölster-Holst R.
      • Bauerfeind A.
      • Ruschendorf F.
      • Patone G.
      • et al.
      A common variant on chromosome 11q13 is associated with atopic dermatitis.
      ,
      • O’Regan G.M.
      • Campbell L.E.
      • Cordell H.J.
      • Irvine A.D.
      • McLean W.H.
      • Brown S.J.
      Chromosome 11q13.5 variant associated with childhood eczema: an effect supplementary to filaggrin mutations.
      ). The risk SNPs lie in a long intergenic region between EMSY and LRRC32; both are strong candidate genes for AD risk. EMSY encodes a transcriptional regulator previously uncharacterized in the skin. We have shown that EMSY acts as a transcriptional repressor in keratinocytes, controlling multiple aspects of skin barrier formation (
      • Elias M.S.
      • Wright S.C.
      • Remenyi J.
      • Abbott J.C.
      • Bray S.E.
      • Cole C.
      • et al.
      EMSY expression affects multiple components of the skin barrier with relevance to atopic dermatitis.
      ). LRRC32 encodes a transmembrane receptor on activated T-regulatory cells that modulates TGF-β activity. There is evidence of a functional variant in LRRC32, which may play a role in AD (
      • Manz J.
      • Rodríguez E.
      • ElSharawy A.
      • Oesau E.M.
      • Petersen B.S.
      • Baurecht H.
      • et al.
      Targeted resequencing and functional testing identifies low-frequency missense variants in the gene encoding GARP as significant contributors to atopic dermatitis risk.
      ). Both skin and blood are likely to be tissues with direct relevance to the pathophysiology of AD. Differential methylation has shown that skin tissue shows greater epigenetic dysregulation than blood from patients with AD (
      • Rodríguez E.
      • Baurecht H.
      • Wahn A.F.
      • Kretschmer A.
      • Hotze M.
      • Zeilinger S.
      • et al.
      An integrated epigenetic and transcriptomic analysis reveals distinct tissue-specific patterns of DNA methylation associated with atopic dermatitis.
      ), but the specific cell types implicated in GWAS risk mechanisms remain a question of importance.
      As of now, GWAS findings account for <20% of AD heritability (
      • Paternoster 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.
      ), and even with the additional risk attributed to protein-coding variants, ∼70% of heritability remains to be explained (
      • Mucha S.
      • Baurecht H.
      • Novak N.
      • Rodríguez E.
      • Bej S.
      • Mayr G.
      • et al.
      Protein-coding variants contribute to the risk of atopic dermatitis and skin-specific gene expression.
      ). Considerable further work is therefore needed to fully understand individual risk.

      Complementary approaches

      Other approaches have used GWAS data to leverage additional understanding of the molecular mechanisms underpinning AD. A genome-wide comparative analysis of AD versus psoriasis showed that opposing mechanisms appear to be more prominent than shared effects for these patterns of skin inflammation (
      • Baurecht H.
      • Hotze M.
      • Brand S.
      • Büning C.
      • Cormican P.
      • Corvin A.
      • et al.
      Genome-wide comparative analysis of atopic dermatitis and psoriasis gives insight into opposing genetic mechanisms.
      ). Opposing loci include the T helper type 2 locus control region (chromosome 5q31.1), epidermal differentiation complex (overlying a long noncoding RNA, FLG-AS1), and the major histocompatibility complex (chromosome 6p21-22). Previously unreported pleiotropic alleles with opposing effects on AD and psoriasis risk were identified in PRKRA and ANXA6/TNIP1.
      Mendelian randomization (MR) is a statistical analysis technique that uses genetic risk to define phenotypes; this circumvents some of the limitations in conventional epidemiology, including confounding and reverse causation (
      • Budu-Aggrey A.
      • Paternoster L.
      Research techniques made simple: using genetic variants for randomization.
      ). SNPs from GWAS are used in MR as a proxy for AD and other phenotypes, and this approach can be used to distinguish causation from association. MR studies in AD have investigated causal links with prenatal alcohol exposure (
      • Shaheen S.O.
      • Rutterford C.
      • Zuccolo L.
      • Ring S.M.
      • Davey Smith G.
      • Holloway J.W.
      • et al.
      Prenatal alcohol exposure and childhood atopic disease: a Mendelian randomization approach.
      ) and vitamin D levels (
      • Manousaki D.
      • Paternoster L.
      • Standl M.
      • Moffatt M.F.
      • Farrall M.
      • Bouzigon E.
      • et al.
      Vitamin D levels and susceptibility to asthma, elevated immunoglobulin E levels, and atopic dermatitis: a Mendelian randomization study.
      ), each has no causal effect on AD. Another approach has combined MR and multiple-trait colocalization to define cell-specific inflammatory drivers of autoimmune and atopic disease (
      • McGowan L.M.
      • Davey Smith G.
      • Gaunt T.R.
      • Richardson T.G.
      Integrating Mendelian randomization and multiple-trait colocalization to uncover cell-specific inflammatory drivers of autoimmune and atopic disease.
      ).
      Longitudinal latent class analysis uses phenotypic data at multiple timepoints to define subgroups within the heterogeneous patient population by mathematical modeling. Large population cohort studies from the UK and the Netherlands showed five distinct subgroups of AD with remarkable replication (
      • Paternoster L.
      • Savenije O.E.M.
      • Heron J.
      • Evans D.M.
      • Vonk J.M.
      • Brunekreef B.
      • et al.
      Identification of atopic dermatitis subgroups in children from 2 longitudinal birth cohorts.
      ). GWAS SNPs mapped to these subgroups revealed the strongest association with the most persistent disease. However, further work is required to gain a sufficiently powerful genetic risk score to prospectively predict an individual’s trajectory for AD.

      Future perspectives

      Findings from multiple GWAS studies have re-emphasized the importance of genetic risk mechanisms controlling both skin barrier and immune responses in AD. However, important questions remain (Figure 2). The threshold for statistical significance is necessarily stringent in GWAS because of the extreme multiple testing that occurs (Figure 1). Larger GWAS studies, including hundreds of thousands of cases and controls, could reveal additional risk loci, but each new effect size is likely to be small. Gene–gene interaction analysis is also statistically challenging because of the issues of multiple testing, and similarly, whereas gene–environment interactions are likely to be of importance in AD, they are challenging to detect on a genome-wide level. These mechanisms therefore require alternative, more targeted functional assessment (Figure 2).
      Figure thumbnail gr2
      Figure 2Future work needed to build on GWAS for the benefit of patients with AD. Additional GWAS are likely to increase understanding, but extensive follow-up work is required to test and validate functional effects at a molecular level before progress can be made in personalized medicine and rational drug design. AD, atopic dermatitis.
      The majority of GWASs performed to date have used white European and selected Asian populations. The lack of ethnic diversity in genetic research has been highlighted as a critical weakness in the field, not least in terms of equity in access to medical and scientific knowledge but also as a missed opportunity for genetic discovery (
      • Hindorff L.A.
      • Bonham V.L.
      • Brody L.C.
      • Ginoza M.E.C.
      • Hutter C.M.
      • Manolio T.A.
      • et al.
      Prioritizing diversity in human genomics research.
      ). The GWAS meta-analysis performed in 2015 was a multiancestry study (
      • Paternoster 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.
      ), but only ∼2% of cases and <1% of controls were of African–American ancestry. AD GWAS studies in more diverse ethnicities, including African populations, are ongoing.
      It is known that drugs targeting molecules or pathways informed by human genetic studies have an above-average chance of clinical success (
      • Kamb A.
      • Harper S.
      • Stefansson K.
      Human genetics as a foundation for innovative drug development.
      ). The genome-wide approach (described above) to define variants in protein-coding regions identified multiple proteins in the IL-13 pathway, and all have been successfully targeted in novel AD treatments (
      • Mucha S.
      • Baurecht H.
      • Novak N.
      • Rodríguez E.
      • Bej S.
      • Mayr G.
      • et al.
      Protein-coding variants contribute to the risk of atopic dermatitis and skin-specific gene expression.
      ). Translational genomics, drug development, and personalized medicine will progress in tandem (Figure 2) (
      • Dugger S.A.
      • Platt A.
      • Goldstein D.B.
      Drug development in the era of precision medicine.
      ,
      • Zeggini E.
      • Gloyn A.L.
      • Barton A.C.
      • Wain L.V.
      Translational genomics and precision medicine: moving from the lab to the clinic.
      ), and dermatological research is poised to be at the forefront of these exciting developments in clinical care.

      Conflict of Interest

      SJB holds research grant funding from the Wellcome Trust (reference 106865/Z/15/Z), the British Skin Foundation, Tayside Dermatological Research Charity, Pfizer Investigator-Initiated Research, and a European Union Innovative Medicines Initiative award Biomarkers in Atopic Dermatitis and Psoriasis.
      The funders had no influence in the preparation, writing, or review of this manuscript.

      Acknowledgments

      This work was supported by The Wellcome Trust (ref 106865/Z/15/Z), the British Skin Foundation , Tayside Dermatological Research Charity , Pfizer Investigator-Initiated Research and an EU IMI award ‘BIOMAP’ (Biomarkers in Atopic Dermatitis and Psoriasis).

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