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One SNP at a Time: Moving beyond GWAS in Psoriasis

  • Helen Ray-Jones
    Correspondence
    Correspondence: Helen Ray-Jones, Arthritis Research UK Centre for Epidemiology, Centre for Musculoskeletal Research, Institute for Inflammation and Repair, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
    Affiliations
    Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom

    The Dermatology Centre, Salford Royal NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
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  • Stephen Eyre
    Affiliations
    Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
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  • Anne Barton
    Affiliations
    Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom

    NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
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  • Richard B. Warren
    Affiliations
    The Dermatology Centre, Salford Royal NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
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Open ArchivePublished:January 22, 2016DOI:https://doi.org/10.1016/j.jid.2015.11.025
      Although genome-wide association studies have revealed important insights into the global genetic basis of psoriasis, the findings require further investigation. At present, the known genetic risk loci are largely uncharacterized in terms of the variant or gene responsible for the association, the biological pathway involved, and the main cell type driving the pathology. This review primarily focuses on current approaches toward gaining a complete understanding of how these known genetic loci contribute to an increased disease risk in psoriasis.

      Abbreviations:

      ChIP (chromatin immunoprecipitation), eQTL (expression quantitative trait locus), GPP (generalized pustular psoriasis), GWAS (genome-wide association study), LCE (late cornified envelope), LD (linkage dysequilibrium), MHC (major histocompatibility complex), SNP (single nucleotide polymorphism)

      Introduction

      Psoriasis is thought to be dependent on a complex interplay between many genetic loci and environmental factors. The development of sophisticated methods for rapid genotyping of DNA has led to the era of high-powered genome-wide association studies (GWASs), which have revolutionized our understanding of complex trait genetics (
      • Stranger B.E.
      • Stahl E.A.
      • Raj T.
      Progress and promise of genome-wide association studies for human complex trait genetics.
      ). GWAS and more targeted candidate gene approaches [Immunochip;
      • Tsoi L.C.
      • Spain S.L.
      • Knight J.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • et al.
      Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
      ] have identified more than 40 single nucleotide polymorphisms (SNPs) associated with psoriasis at a genome-wide significance level (P < 5 × 10–8), many of which are situated near genes involved in adaptive and innate immunity pathways, which are summarized in Supplementary Table S1 (online) and reviewed by
      • Mahil S.K.
      • Capon F.
      • Barker J.N.
      Genetics of psoriasis.
      . In the “post-GWAS” era, many challenges remain before the full genetic component of disease association can be understood. One of these challenges is to better understand how the known genetic loci confer risk to disease, which is the primary focus of this review.

      Genetics of Psoriasis: A Brief Overview

      The genetic locus conferring the greatest risk for psoriasis susceptibility in both European and Chinese populations is the major histocompatibility complex (MHC) class I, implicating the involvement of the adaptive immune system in psoriasis pathology (
      • Ellinghaus E.
      • Ellinghaus D.
      • Stuart P.E.
      • Nair R.P.
      • Debrus S.
      • Raelson J.V.
      • et al.
      Genome-wide association study identifies a psoriasis susceptibility locus at TRAF3IP2.
      ,
      • Liu Y.
      • Helms C.
      • Liao W.
      • Zaba L.C.
      • Duan S.
      • Gardner J.
      • et al.
      A genome-wide association study of psoriasis and psoriatic arthritis identifies new disease loci.
      ,
      • Nair R.P.
      • Duffin K.C.
      • Helms C.
      • Ding J.
      • Stuart P.E.
      • Goldgar D.
      • et al.
      Genome-wide scan reveals association of psoriasis with IL-23 and NF-kappa B pathways.
      ,
      • Strange A.
      • Capon F.
      • Spencer C.C.A.
      • Knight J.
      • Weale M.E.
      • Allen M.H.
      • et al.
      A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1.
      ,
      • Stuart P.E.
      • Nair R.P.
      • Ellinghaus E.
      • Ding J.
      • Tejasvi T.
      • Gudjonsson J.E.
      • et al.
      Genome-wide association analysis identifies three psoriasis susceptibility loci.
      ,
      • Tsoi L.C.
      • Spain S.L.
      • Knight J.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • et al.
      Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
      ,
      • Zhang X.J.
      • Huang W.
      • Yang S.
      • Sun L.D.
      • Zhang F.Y.
      • Zhu Q.X.
      • et al.
      Psoriasis genome-wide association study identifies susceptibility variants within late cornified envelope (LCE) gene cluster at 1q21.
      ). Within the MHC, an allele at the HLA gene, HLA-C*06:02, shows the strongest association with psoriasis. However, it is evident that independent risk associations exist across the MHC (
      • Feng B.J.
      • Sun L.D.
      • Soltani-Arabshahi R.
      • Bowcock A.M.
      • Nair R.P.
      • Stuart P.
      • et al.
      Multiple loci within the major histocompatibility complex confer risk of psoriasis.
      ), including ethnicity-specific signals (
      • Yin X.
      • Low H.Q.
      • Wang L.
      • Li Y.
      • Ellinghaus E.
      • Han J.
      • et al.
      Genome-wide meta-analysis identifies multiple novel associations and ethnic heterogeneity of psoriasis susceptibility.
      ). A recent fine mapping study confirmed the presence of independent signals at HLA-C*12:03, HLA-B, HLA-A, and HLA-DQA1 through conditional analysis (
      • Okada Y.
      • Han B.
      • Tsoi L.C.
      • Stuart P.E.
      • Ellinghaus E.
      • Tejasvi T.
      • et al.
      Fine mapping major histocompatibility complex associations in psoriasis and its clinical subtypes.
      ). Outside of the MHC, a second well-established risk locus resides at the gene for endoplasmic reticulum aminopeptidase 1 (
      • Strange A.
      • Capon F.
      • Spencer C.C.A.
      • Knight J.
      • Weale M.E.
      • Allen M.H.
      • et al.
      A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1.
      ). The endoplasmic reticulum aminopeptidase 1 protein is thought to be responsible for N-terminal trimming of peptides allowing binding to the MHC class I molecule (
      • Alvarez-Navarro C.
      • de Castro J.A.L.
      ERAP1 structure, function and pathogenetic role in ankylosing spondylitis and other MHC-associated diseases.
      ,
      • Saric T.
      • Chang S.C.
      • Hattori A.
      • York I.A.
      • Markant S.
      • Rock K.L.
      • et al.
      An IFN-gamma-induced aminopeptidase in the ER, ERAP1, trims precursors to MHC class I-presented peptides.
      ); therefore, this signal further implicates the involvement of the adaptive immune system in psoriasis.
      Many SNPs have also implicated gene candidates from innate immunity pathways in European cohorts (
      • Capon F.
      • Bijlmakers M.J.
      • Wolf N.
      • Quaranta M.
      • Huffmeier U.
      • Allen M.
      • et al.
      Identification of ZNF313/RNF114 as a novel psoriasis susceptibility gene.
      ,
      • Cargill M.
      • Schrodi S.J.
      • Chang M.
      • Garcia V.E.
      • Brandon R.
      • Callis K.P.
      • et al.
      A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes.
      ,
      • Ellinghaus D.
      • Ellinghaus E.
      • Nair R.P.
      • Stuart P.E.
      • Esko T.
      • Metspalu A.
      • et al.
      Combined analysis of genome-wide association studies for crohn disease and psoriasis identifies seven shared susceptibility loci.
      ,
      • Nair R.P.
      • Duffin K.C.
      • Helms C.
      • Ding J.
      • Stuart P.E.
      • Goldgar D.
      • et al.
      Genome-wide scan reveals association of psoriasis with IL-23 and NF-kappa B pathways.
      ,
      • Strange A.
      • Capon F.
      • Spencer C.C.A.
      • Knight J.
      • Weale M.E.
      • Allen M.H.
      • et al.
      A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1.
      ,
      • Stuart P.E.
      • Nair R.P.
      • Ellinghaus E.
      • Ding J.
      • Tejasvi T.
      • Gudjonsson J.E.
      • et al.
      Genome-wide association analysis identifies three psoriasis susceptibility loci.
      ,
      • Tsoi L.C.
      • Spain S.L.
      • Knight J.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • et al.
      Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
      ,
      • Tsoi L.C.
      • Spain S.L.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • Knight J.
      • et al.
      Enhanced meta-analysis and replication studies identify five new psoriasis susceptibility loci.
      ). These include NF-κB signaling (e.g., REL, TNIP1, NFKBIA, and CARD14), IFN signaling (e.g., IL28RA and TYK2), T-cell regulation (e.g., RUNX3, IL13, TAGAP, ETS1, and MBD2), and antiviral signaling (e.g., IFIH1, DDX58, and RNF114). Multiple loci containing genes involved in the IL-23 pathway specifically implicate a role for Th17 cells (e.g., TNFAIP3, IL23R, IL12B, TRAF3IP2, IL23A, and STAT3).
      Aside from the immune system, skin barrier regulatory genes of the late cornified envelope (LCE) within the epidermal differentiation complex are associated with psoriasis in both European and Chinese populations (
      • de Cid R.
      • Riveira-Munoz E.
      • Zeeuwen P.
      • Robarge J.
      • Liao W.
      • Dannhauser E.N.
      • et al.
      Deletion of the late cornified envelope LCE3B and LCE3C genes as a susceptibility factor for psoriasis.
      ,
      • Strange A.
      • Capon F.
      • Spencer C.C.A.
      • Knight J.
      • Weale M.E.
      • Allen M.H.
      • et al.
      A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1.
      ,
      • Tsoi L.C.
      • Spain S.L.
      • Knight J.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • et al.
      Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
      ,
      • Zhang X.J.
      • Huang W.
      • Yang S.
      • Sun L.D.
      • Zhang F.Y.
      • Zhu Q.X.
      • et al.
      Psoriasis genome-wide association study identifies susceptibility variants within late cornified envelope (LCE) gene cluster at 1q21.
      ). The variants in this region likely tag a 30-kb deletion including the genes LCE3C and LCE3B. The loss of these genes is thought to impair reparation of the skin barrier after injury (
      • Bergboer J.G.M.
      • Tjabringa G.S.
      • Kamsteeg M.
      • van Vlijmen-Willems I.
      • Rodijk-Olthuis D.
      • Jansen P.A.M.
      • et al.
      Psoriasis risk genes of the Late Cornified Envelope-3 group are distinctly expressed compared with genes of other LCE groups.
      ,
      • Bergboer J.G.M.
      • Zeeuwen P.
      • Schalkwijk J.
      Genetics of psoriasis: evidence for epistatic interaction between skin barrier abnormalities and immune deviation.
      ). Alternatively, the loss of an epidermal-specific enhancer element within the deleted region could be causing aberrant global transcription of epidermal differentiation complex 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.
      ).
      Several recent studies have demonstrated that Chinese populations display a number of unique genetic associations with psoriasis, such as NFKB1, PTTG1, MTHFR, and CCDC129 (
      • Sun L.D.
      • Cheng H.
      • Wang Z.X.
      • Zhang A.P.
      • Wang P.G.
      • Xu J.H.
      • et al.
      Association analyses identify six new psoriasis susceptibility loci in the Chinese population.
      ,
      • Zuo X.
      • Sun L.
      • Yin X.
      • Gao J.
      • Sheng Y.
      • Xu J.
      • et al.
      Whole-exome SNP array identifies 15 new susceptibility loci for psoriasis.
      ), and share some loci with European populations (
      • Cheng H.
      • Li Y.
      • Zuo X.B.
      • Tang H.Y.
      • Tang X.F.
      • Gao J.P.
      • et al.
      Identification of a missense variant in LNPEP that confers psoriasis risk.
      ,
      • Li Y.
      • Cheng H.
      • Zuo X.B.
      • Sheng Y.J.
      • Zhou F.S.
      • Tang X.F.
      • et al.
      Association analyses identifying two common susceptibility loci shared by psoriasis and systemic lupus erythematosus in the Chinese Han population.
      ,
      • Sheng Y.
      • Jin X.
      • Xu J.
      • Gao J.
      • Du X.
      • Duan D.
      • et al.
      Sequencing-based approach identified three new susceptibility loci for psoriasis.
      ,
      • Tang H.
      • Jin X.
      • Li Y.
      • Jiang H.
      • Tang X.
      • Yang X.
      • et al.
      A large-scale screen for coding variants predisposing to psoriasis.
      ,
      • Zhang X.J.
      • Huang W.
      • Yang S.
      • Sun L.D.
      • Zhang F.Y.
      • Zhu Q.X.
      • et al.
      Psoriasis genome-wide association study identifies susceptibility variants within late cornified envelope (LCE) gene cluster at 1q21.
      ). Recently a transethnic psoriasis GWAS, including both Chinese and European cohorts, identified four novel loci in European patients (LOC144817, COG6, RUNX1, and TP63) and population-specific effects at several loci (
      • Yin X.
      • Low H.Q.
      • Wang L.
      • Li Y.
      • Ellinghaus E.
      • Han J.
      • et al.
      Genome-wide meta-analysis identifies multiple novel associations and ethnic heterogeneity of psoriasis susceptibility.
      ). Further transethnic GWAS studies that compare allele frequencies, odds ratios, and disease pathways between different populations will advance the current understanding of global psoriasis pathogenesis.
      The genetics of late-onset psoriasis, in which disease occurs after 40 years of age, substantially overlaps with that of early-onset psoriasis. In a GWAS, the known type I psoriasis risk loci IL12B and HLA-C reached genome-wide significance, and six more known loci reached study-wide significance (IL23R, TRAF3IP2, IL23A, IFIH1, RNF114, and HLA-A) (
      • Hebert H.L.
      • Bowes J.
      • Smith R.L.
      • Flynn E.
      • Parslew R.
      • Alsharqi A.
      • et al.
      Identification of loci associated with late-onset psoriasis using dense genotyping of immune-related regions.
      ). However, late-onset psoriasis may also have unique risk loci at IL1B and IL1R1 (
      • Hebert H.L.
      • Bowes J.
      • Smith R.L.
      • McHugh N.J.
      • Barker J.
      • Griffiths C.E.M.
      • et al.
      Polymorphisms in IL-1B distinguish between psoriasis of early and late onset.
      ,
      • Hebert H.L.
      • Bowes J.
      • Smith R.L.
      • Flynn E.
      • Parslew R.
      • Alsharqi A.
      • et al.
      Identification of loci associated with late-onset psoriasis using dense genotyping of immune-related regions.
      ,
      • Reich K.
      • Mossner R.
      • Konig I.R.
      • Westphal G.
      • Ziegler A.
      • Neumann C.
      Promoter polymorphisms of the genes encoding tumor necrosis factor-alpha and interleukin-1 beta are associated with different subtypes of psoriasis characterized by early and late disease onset.
      ). Subsequent well-powered studies will be required to determine how an increasing age of onset affects the strength of these genetic associations.
      The genetic architecture of psoriasis subtypes are gradually being defined; for example, generalized pustular psoriasis (GPP) is associated with protein-coding mutations in CARD14 (
      • Jordan C.T.
      • Cao L.
      • Roberson E.D.O.
      • Pierson K.C.
      • Yang C.F.
      • Joyce C.E.
      • et al.
      PSORS2 is due to mutations in CARD14.
      ,
      • Qin P.P.
      • Zhang Q.L.
      • Chen M.F.
      • Fu X.
      • Wang C.
      • Wang Z.Z.
      • et al.
      Variant analysis of CARD14 in a Chinese Han population with psoriasis vulgaris and generalized pustular psoriasis.
      ,
      • Sugiura K.
      • Muto M.
      • Akiyama M.
      CARD14 c.526G > C (p.Asp176His) is a significant risk factor for generalized pustular psoriasis with psoriasis vulgaris in the Japanese cohort.
      ) and IL36RN (
      • Hayashi M.
      • Nakayama T.
      • Hirota T.
      • Saeki H.
      • Nobeyama Y.
      • Ito T.
      • et al.
      Novel IL36RN gene mutation revealed by analysis of 8 Japanese patients with generalized pustular psoriasis.
      ,
      • Korber A.
      • Mossner R.
      • Renner R.
      • Sticht H.
      • Wilsmann-Theis D.
      • Schulz P.
      • et al.
      Mutations in IL36RN in patients with generalized pustular psoriasis.
      ,
      • Li M.
      • Han J.W.
      • Lu Z.Y.
      • Li H.G.
      • Zhu K.J.
      • Cheng R.H.
      • et al.
      Prevalent and rare mutations in IL-36RN gene in chinese patients with generalized pustular psoriasis and psoriasis vulgaris.
      ,
      • Sugiura K.
      • Takemoto A.
      • Yamaguchi M.
      • Takahashi H.
      • Shoda Y.
      • Mitsuma T.
      • et al.
      The majority of generalized pustular psoriasis without psoriasis vulgaris is caused by deficiency of interleukin-36 receptor antagonist.
      ). In CARD14, the de novo mutation p.Glu138Ala was found in a child with GPP (
      • Jordan C.T.
      • Cao L.
      • Roberson E.D.O.
      • Pierson K.C.
      • Yang C.F.
      • Joyce C.E.
      • et al.
      PSORS2 is due to mutations in CARD14.
      ), and the rare variant p.Asp176His was shown to predispose to GPP with plaque psoriasis in Japanese patients (
      • Sugiura K.
      • Muto M.
      • Akiyama M.
      CARD14 c.526G > C (p.Asp176His) is a significant risk factor for generalized pustular psoriasis with psoriasis vulgaris in the Japanese cohort.
      ). In IL36RN, protein modeling and biochemical analyses showed that the GPP-associated mutation p.L27P reduced the stability of IL36RN protein and decreased its expression and potency, leading to increased proinflammatory signaling (
      • Marrakchi S.
      • Guigue P.
      • Renshaw B.R.
      • Puel A.
      • Pei X.Y.
      • Fraitag S.
      • et al.
      Interleukin-36-receptor antagonist deficiency and generalized pustular psoriasis.
      ). Rare IL36RN mutations are thought to be uniquely associated with GPP (
      • Capon F.
      IL36RN mutations in Generalized Pustular Psoriasis: just the tip of the iceberg?.
      ,
      • Sugiura K.
      • Takemoto A.
      • Yamaguchi M.
      • Takahashi H.
      • Shoda Y.
      • Mitsuma T.
      • et al.
      The majority of generalized pustular psoriasis without psoriasis vulgaris is caused by deficiency of interleukin-36 receptor antagonist.
      ) and are linked with a more severe disease phenotype and earlier age of onset (
      • Hussain S.
      • Berki D.M.
      • Choon S.E.
      • Burden A.D.
      • Allen M.H.
      • Arostegui J.I.
      • et al.
      IL36RN mutations define a severe autoinflammatory phenotype of generalized pustular psoriasis.
      ).

      Why Hasn’t GWAS Provided all the Answers?

      Missing heritability

      To date GWAS has only revealed a small proportion of the genetic component of psoriasis. In Europeans, the proportion of psoriasis heritability explained by GWAS variants was most recently estimated at 22% (
      • Tsoi L.C.
      • Spain S.L.
      • Knight J.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • et al.
      Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
      ), whereas in Chinese it is reportedly 45.7% (
      • Jiang L.
      • Liu L.
      • Cheng Y.
      • Lin Y.
      • Shen C.
      • Zhu C.
      • et al.
      More heritability probably captured by psoriasis genome-wide association study in Han Chinese.
      ). Several reasons have been proposed for the apparent missing heritability in complex disease, including gene-gene and gene-environment interactions and the existence of highly deleterious rare variants, although the latter may not greatly impact on psoriasis heritability (
      • Hunt K.A.
      • Mistry V.
      • Bockett N.A.
      • Ahmad T.
      • Ban M.
      • Barker J.N.
      • et al.
      Negligible impact of rare autoimmune-locus coding-region variants on missing heritability.
      ,
      • Tang H.
      • Jin X.
      • Li Y.
      • Jiang H.
      • Tang X.
      • Yang X.
      • et al.
      A large-scale screen for coding variants predisposing to psoriasis.
      ). Ultimately, it is likely that more genetic signals will be discovered along with the increased use of next-generation sequencing technology that encompasses whole genomes or exomes. Additionally, increased study power through large sample sets and refined statistical methods (fine mapping, genotype calling, and imputation) can identify common novel loci, strengthen known signals, and find independent effects at known loci (
      • Tsoi L.C.
      • Spain S.L.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • Knight J.
      • et al.
      Enhanced meta-analysis and replication studies identify five new psoriasis susceptibility loci.
      ,
      • Yin X.
      • Low H.Q.
      • Wang L.
      • Li Y.
      • Ellinghaus E.
      • Han J.
      • et al.
      Genome-wide meta-analysis identifies multiple novel associations and ethnic heterogeneity of psoriasis susceptibility.
      ). To detect rare variants, however, novel statistical analysis techniques such as burden testing may be required.

      Interpreting association signals

      GWAS-associated variants usually require further extensive interrogation for a full interpretation of the data. In part, this is because of the number of highly correlated genetic variants in linkage dysequilibrium (LD) that may be causal. Research has shown that only 5% of lead GWAS SNPs are likely to be causal and tend to lie an average distance of 14 kb from the probable causal SNP (
      • Farh 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.
      ). Thus, the first task after GWAS is to perform dense genotyping, resequencing or imputation, to test the association of all variants in LD with the lead variant and gain a detailed picture of potentially causal variants. The remainder of this review addresses the question of how to best utilize the current gains made by GWAS by identifying the function of putative causal variants, particularly in noncoding regions (Figure 1).
      Figure 1
      Figure 1Workflow for identification of putative causal variants and the genes they affect. Genome-wide association study (GWAS) is a hypothesis-free method for identifying single nucleotide polymorphisms (SNPs) correlating with disease risk. Dense, targeted genotyping arrays such as Immunochip can be used for both replication of GWAS loci and genetic fine-mapping of all variants in disease-associated loci, further narrowing down the association signal. Bioinformatics can then be used to both locate and functionally annotate SNPs in linkage dysequilibrium (LD) with the lead SNPs. In noncoding regions, SNPs coinciding with regulatory features such as histone modifications or transcription factor binding sites are most likely to have a functional effect. Appropriate functional experimental techniques can then be used to investigate genotype-specific protein interactions (ChIP), DNA conformation (3C), and gene expression (eQTL and reporter gene assays) in disease-relevant cell types. SNPs associated with disease that coincide with coding regions of genes require bespoke experimental confirmation dependent on both position within gene (e.g., binding domain) and function of protein (e.g., enzymatic activity). Once a causal variant affecting gene function has been identified, its effect on relevant biochemical pathways and resultant disease phenotype can be investigated.

      Considerations for Functional Annotation of GWAS Variants

      In a minority of psoriasis susceptibility loci, the GWAS signal intersects with coding regions of genes (e.g., IL23R and CARD14). In these cases, the function of the variants can be readily assessed (
      • di Meglio P.
      • Villanova F.
      • Napolitano L.
      • Tosi I.
      • Barberio M.T.
      • Mak R.K.
      • et al.
      The IL23R A/GIn381 allele promotes IL-23 unresponsiveness in human memory T-helper 17 cells and impairs Th17 responses in psoriasis patients.
      ,
      • Jordan C.T.
      • Cao L.
      • Roberson E.D.O.
      • Pierson K.C.
      • Yang C.F.
      • Joyce C.E.
      • et al.
      PSORS2 is due to mutations in CARD14.
      ,
      • Sarin R.
      • Wu X.X.
      • Abraham C.
      Inflammatory disease protective R381Q IL23 receptor polymorphism results in decreased primary CD4+and CD8+human T-cell functional responses.
      ). The genetic association of psoriasis with CARD14 was initially discovered through linkage mapping (
      • Tomfohrde J.
      • Silverman A.
      • Barnes R.
      • Fernandezvina M.A.
      • Young M.
      • Lory D.
      • et al.
      Gene for familial psoriasis susceptibility mapped to the distal end of human-chromosome 17q.
      ,) followed by positional cloning using next-generation sequencing that identified an excess of rare missense variants in families affected by the disease and in psoriasis cohorts (
      • Jordan C.T.
      • Cao L.
      • Roberson E.D.O.
      • Duan S.H.
      • Helms C.A.
      • Nair R.P.
      • et al.
      Rare and common variants in CARD14, encoding an epidermal regulator of NF-kappaB, in psoriasis.
      ,
      • Jordan C.T.
      • Cao L.
      • Roberson E.D.O.
      • Pierson K.C.
      • Yang C.F.
      • Joyce C.E.
      • et al.
      PSORS2 is due to mutations in CARD14.
      ). After these studies, a psoriasis-associated common missense variant discovered by
      • Jordan C.T.
      • Cao L.
      • Roberson E.D.O.
      • Duan S.H.
      • Helms C.A.
      • Nair R.P.
      • et al.
      Rare and common variants in CARD14, encoding an epidermal regulator of NF-kappaB, in psoriasis.
      achieved genome-wide significance in cohorts of European and Chinese ancestry (
      • Tang H.
      • Jin X.
      • Li Y.
      • Jiang H.
      • Tang X.
      • Yang X.
      • et al.
      A large-scale screen for coding variants predisposing to psoriasis.
      ,
      • Tsoi L.C.
      • Spain S.L.
      • Knight J.
      • Ellinghaus E.
      • Stuart P.E.
      • Capon F.
      • et al.
      Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity.
      ). CARD14 is a scaffolding protein that has a role in NF-κB activation. In functional experiments, some of the associated rare variants were found to affect CARD14 splicing, leading to increased downstream NF-κB expression in keratinocytes (
      • Jordan C.T.
      • Cao L.
      • Roberson E.D.O.
      • Duan S.H.
      • Helms C.A.
      • Nair R.P.
      • et al.
      Rare and common variants in CARD14, encoding an epidermal regulator of NF-kappaB, in psoriasis.
      ,
      • Jordan C.T.
      • Cao L.
      • Roberson E.D.O.
      • Pierson K.C.
      • Yang C.F.
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      PSORS2 is due to mutations in CARD14.
      ).
      The majority of psoriasis-associated GWAS loci are located outside of traditional gene coding regions, often in regulatory enhancer regions characterized by open areas of accessible chromatin that are sensitive to DNAse I and contain modified histone marks (
      • Ernst J.
      • Kheradpour P.
      • Mikkelsen T.S.
      • Shoresh N.
      • Ward L.D.
      • Epstein C.B.
      • et al.
      Mapping and analysis of chromatin state dynamics in nine human cell types.
      ). Here, identification of the causal SNP becomes more challenging; bioinformatic evidence is first used to form a hypothesis about which SNPs are likely to be causal. The hypothesis should then be tested directly with functional experiments to show the mechanism by which the putative causal SNP affects gene expression or function.
      Bioinformatic and experimental approaches must take into account relevant cell types and stimulatory factors that may affect regulatory mechanisms. Transcriptome studies in psoriasis have demonstrated that gene expression is often tissue or cell type specific (
      • Filkor K.
      • Hegedus Z.
      • Szasz A.
      • Tubak V.
      • Kemeny L.
      • Kondorosi E.
      • et al.
      Genome wide transcriptome analysis of dendritic cells identifies genes with altered expression in psoriasis.
      ,
      • Jabbari A.
      • Suarez-Farinas M.
      • Dewell S.
      • Krueger J.G.
      Transcriptional profiling of psoriasis using RNA-seq reveals previously unidentified differentially expressed genes.
      ,
      • Li B.S.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-Seq provides insights into disease mechanisms.
      ,
      • Suarez-Farinas M.
      • Li K.
      • Fuentes-Duculan J.
      • Hayden K.
      • Brodmerkel C.
      • Krueger J.G.
      Expanding the psoriasis disease profile: interrogation of the skin and serum of patients with moderate-to-severe psoriasis.
      ,
      • Tian S.Y.
      • Krueger J.G.
      • Li K.
      • Jabbari A.
      • Brodmerkel C.
      • Lowes M.A.
      • et al.
      Meta-Analysis Derived (MAD) transcriptome of psoriasis defines the “core” pathogenesis of disease.
      ). As well as protein coding genes, long noncoding RNAs have recently been shown to have substantially different expression between skin and other tissues (
      • Tsoi L.C.
      • Iyer M.K.
      • Stuart P.E.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • et al.
      Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin.
      ). With respect to the selection of relevant cell types in psoriasis, dysregulated gene transcripts in psoriatic skin are often derived from keratinocytes, fibroblasts, and immune cells, whereas GWAS candidate gene expression often derives from multiple immune cell types, particularly neutrophils (
      • Swindell W.R.
      • Stuart P.E.
      • Sarkar M.K.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Cellular dissection of psoriasis for transcriptome analyses and the post-GWAS era.
      ). Additionally, interaction analysis of psoriasis GWAS hits with cell-specific epigenetic marks of gene activity revealed T helper cells (Th1, Th2, and Th17) to be likely key cells in driving susceptibility to psoriasis (
      • Farh 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.
      ). Research has also shown that endothelial cells, which highly express the candidate gene CARD14, are likely to be important (
      • Harden J.L.
      • Lewis S.M.
      • Pierson K.C.
      • Suarez-Farinas M.
      • Lentini T.
      • Ortenzio F.S.
      • et al.
      CARD14 expression in dermal endothelial cells in psoriasis.
      ). Stimulation is also likely to be an important factor, especially because psoriatic lesions are thought to be subjected to a range of cytokines, such as tumor necrosis factor-α, OSM IL-22, IL-17A, and IL-1α (
      • Bernard F.X.
      • Morel F.
      • Camus M.
      • Pedretti N.
      • Barrault C.
      • Garnier J.
      • et al.
      Keratinocytes under fire of proinflammatory cytokines: bona fide innate immune cells involved in the physiopathology of chronic atopic dermatitis and psoriasis.
      ,
      • Guilloteau K.
      • Paris I.
      • Pedretti N.
      • Boniface K.
      • Juchaux F.
      • Huguier V.
      • et al.
      Skin inflammation induced by the synergistic action of IL-17A, IL-22, oncostatin M, IL-1 alpha, and TNF-alpha recapitulates some features of psoriasis.
      ,
      • Rabeony H.
      • Petit-Paris I.
      • Garnier J.
      • Barrault C.
      • Pedretti N.
      • Guilloteau K.
      • et al.
      Inhibition of keratinocyte differentiation by the synergistic effect of IL-17A.
      ). An inflammatory milieu may be required for pathogenic mechanisms to occur.

      Bioinformatic Approaches toward Functional Annotation of GWAS Variants

      Before expensive, hypothesis-driven laboratory experiments are undertaken, bioinformatics may be used to annotate disease-associated SNPs. Publically available data can be interrogated in order to (i) define the set of associated variants that may be causal, (ii) determine which of these variants is correlated with the expression of genes, and (iii) annotate the associated variants with epigenetic features that indicate which variants are present in potential gene regulatory regions.
      Freely available databases such as 1000 Genomes (
      • Altshuler D.M.
      • Durbin R.M.
      • Abecasis G.R.
      • Bentley D.R.
      • Chakravarti A.
      • Clark A.G.
      • et al.
      An integrated map of genetic variation from 1,092 human genomes.
      ) can be interrogated to identify SNPs in LD with the index GWAS SNP. Statistical packages may then be used to prioritize potential causative SNPs. For example, the Probabilistic Identification of Causal SNPs (PICS) algorithm combines the underlying haplotype structure and the strength of the genetic evidence in a bayesian analysis to assign probability scores for the likelihood of each SNP in LD being causal (
      • Farh 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.
      ). The Probabilistic Annotation INTegratOR (PAINTOR) combines the genetic association data with functional annotation data to score SNPs (
      • Kichaev G.
      • Yang W.Y.
      • Lindstrom S.
      • Hormozdiari F.
      • Eskin E.
      • Price A.L.
      • et al.
      Integrating functional data to prioritize causal variants in statistical fine-mapping studies.
      ), whereas the Combined Annotation Dependent Depletion (CADD) algorithm gathers evidence from multiple resources to assign a score as to the likelihood of any variant being deleterious (
      • Kircher M.
      • Witten D.M.
      • Jain P.
      • O'Roak B.J.
      • Cooper G.M.
      • Shendure J.
      A general framework for estimating the relative pathogenicity of human genetic variants.
      ).
      If the associated genetic variants are involved in differential regulation of gene expression, there should be a correlation between genotype and gene expression (
      • Nicolae D.L.
      • Gamazon E.
      • Zhang W.
      • Duan S.W.
      • Dolan M.E.
      • Cox N.J.
      Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS.
      ). Expression quantitative trait loci (eQTLs) can be identified in databases such as GenVAR (
      • Yang T.P.
      • Beazley C.
      • Montgomery S.B.
      • Dimas A.S.
      • Gutierrez-Arcelus M.
      • Stranger B.E.
      • et al.
      Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies.
      ), GTex (
      • Lonsdale J.
      • Thomas J.
      • Salvatore M.
      • Phillips R.
      • Lo E.
      • Shad S.
      • et al.
      The Genotype-Tissue Expression (GTEx) project.
      ), and RegulomeDB (
      • Boyle A.P.
      • Hong E.L.
      • Hariharan M.
      • Cheng Y.
      • Schaub M.A.
      • Kasowski M.
      • et al.
      Annotation of functional variation in personal genomes using RegulomeDB.
      ). Importantly, the lead SNP associated with disease risk must be the lead SNP correlating with expression (lead eQTL)—and not merely in strong LD—for evidence of altered gene expression to be fully informative. Ideally, the colocalization of both signals from the same SNP needs to be statistically proven (
      • Guo H.
      • Fortune M.D.
      • Burren O.S.
      • Schofield E.
      • Todd J.A.
      • Wallace C.
      Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases.
      ). To date it has been unusual for GWAS association signals to coincide with eQTL signals; this may due in part to eQTLs acting in a cell- and stimulation-specific manner. For example, a recent analysis identified cell-specific cis-eQTL effects in monocytes and CD4+ T cells for several traits, including psoriasis (
      • Raj T.
      • Rothamel K.
      • Mostafavi S.
      • Ye C.
      • Lee M.N.
      • Replogle J.M.
      • et al.
      Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes.
      ).
      A wealth of bioinformatic data has been generated by international efforts such as ENCODE (
      • Dunham I.
      • Kundaje A.
      • Aldred S.F.
      • Collins P.J.
      • Davis C.
      • Doyle F.
      • et al.
      An integrated encyclopedia of DNA elements in the human genome.
      ) and NIH Roadmap Epigenomics (
      • Bernstein B.E.
      • Stamatoyannopoulos J.A.
      • Costello J.F.
      • Ren B.
      • Milosavljevic A.
      • Meissner A.
      • et al.
      The NIH Roadmap Epigenomics Mapping Consortium.
      ), which annotate different cell types with epigenetic markers of genome activity. Online tools use these data to apply functional scores to SNPs based on information about their regulatory features. For example, the Ensembl Variant Effect Predictor (VEP) indicates noncoding SNP consequences and assigns scores to exonic SNPs through predictors of protein function: PolyPhen (
      • Ramensky V.
      • Bork P.
      • Sunyaev S.
      Human non-synonymous SNPs: server and survey.
      ) and SIFT (
      • Kumar P.
      • Henikoff S.
      • Ng P.C.
      Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.
      ). Another useful tool, PrediXcan, combines information from large-scale transcriptome datasets with genotype data to enable the identification of disease-associated genes in a GWAS locus (
      • Gamazon E.R.
      • Wheeler H.E.
      • Shah K.P.
      • Mozaffari S.V.
      • Aquino-Michaels K.
      • Carroll R.J.
      • et al.
      A gene-based association method for mapping traits using reference transcriptome data.
      ).

      Experimental Approaches toward Functional Annotation of GWAS Variants

      Experimental approaches can be used to characterize the effect of putative causal variants on gene expression, deduce the mechanism by which this occurs, and link this to the disease phenotype. As an example, a noncoding SNP associated with myocardial infarction was recently shown to alter SORT1 cell-specific (liver) expression via creation of a transcription factor binding site, ultimately leading to altered levels of low-density lipoprotein cholesterol (
      • Musunuru K.
      • Strong A.
      • Frank-Kamenetsky M.
      • Lee N.E.
      • Ahfeldt T.
      • Sachs K.V.
      • et al.
      From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus.
      ). Causal genetic mechanisms such as this can be deduced using targeted experimental techniques; several of which are described below.

      DNA interactions

      Noncoding regulatory elements have been shown to interact with distant genes through DNA looping in a cell-type specific manner (
      • Dryden N.H.
      • Broome L.R.
      • Dudbridge F.
      • Johnson N.
      • Orr N.
      • Schoenfelder S.
      • et al.
      Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C.
      ,
      • Mifsud B.
      • Tavares-Cadete F.
      • Young A.N.
      • Sugar R.
      • Schoenfelder S.
      • Ferreira L.
      • et al.
      Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C.
      ,
      • Tolhuis B.
      • Palstra R.J.
      • Splinter E.
      • Grosveld F.
      • de Laat W.
      Looping and interaction between hypersensitive sites in the active beta-globin locus.
      ). To test if a specific DNA interaction exists, chromosome conformation capture (3C) can be utilized (
      • Dekker J.
      • Rippe K.
      • Dekker M.
      • Kleckner N.
      Capturing chromosome conformation.
      ). 3C is a powerful hypothesis-driven method that works best over relatively small regions of DNA (10 kb to 1 Mb) (
      • Naumova N.
      • Smith E.M.
      • Zhan Y.
      • Dekker J.
      Analysis of long-range chromatin interactions using chromosome conformation capture.
      ). In order to capture the interactions, the DNA is first cross-linked within the cell environment, followed by digestion with a restriction enzyme creating small fragments. These fragments undergo intramolecular ligation, followed by reversal of the original cross-links. The product containing the interacting DNA is detected using quantitative PCR. The method has recently been developed into hypothesis-free Hi-C, which utilizes ligation of labeled nucleotides coupled with high-throughput sequencing to identify all genomic interactions at relatively low resolution (
      • Belton J.M.
      • McCord R.P.
      • Gibcus J.H.
      • Naumova N.
      • Zhan Y.
      • Dekker J.
      Hi-C: a comprehensive technique to capture the conformation of genomes.
      ,
      • Lieberman-Aiden E.
      • van Berkum N.L.
      • Williams L.
      • Imakaev M.
      • Ragoczy T.
      • Telling A.
      • et al.
      Comprehensive mapping of long-range interactions reveals folding principles of the human genome.
      ). A further derivative of Hi-C, so-called capture Hi-C, gains resolution by enriching target loci with RNA baits (
      • Dryden N.H.
      • Broome L.R.
      • Dudbridge F.
      • Johnson N.
      • Orr N.
      • Schoenfelder S.
      • et al.
      Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C.
      ,
      • Jager R.
      • Migliorini G.
      • Henrion M.
      • Kandaswamy R.
      • Speedy H.E.
      • Heindl A.
      • et al.
      Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci.
      ,
      • Mifsud B.
      • Tavares-Cadete F.
      • Young A.N.
      • Sugar R.
      • Schoenfelder S.
      • Ferreira L.
      • et al.
      Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C.
      ) and is an ideal technique for interrogating target genes at psoriasis-associated loci.

      Protein interactions

      Variants in regulatory regions such as enhancers and gene promoters are likely to interfere with transcription factor or histone binding (
      • McVicker G.
      • van de Geijn B.
      • Degner J.F.
      • Cain C.E.
      • Banovich N.E.
      • Raj A.
      • et al.
      Identification of genetic variants that affect histone modifications in human cells.
      ), with a subsequent effect on gene expression. Therefore, a complementary approach to studying DNA-DNA interactions is to study DNA-protein interactions at GWAS risk loci, using an in vivo technique known as chromatin immunoprecipitation (ChIP) (
      • Christova R.
      Detecting DNA-protein interactions in living cells-ChIP approach.
      ). ChIP involves formaldehyde cross-linking of DNA and its bound proteins in a living cell, followed by chromatin fragmentation, immunoprecipitation with an antibody specific for the protein of interest, reversal of cross-links, and identification of the DNA by quantitative PCR (ChIP-qPCR) or sequencing (ChIP-Seq). When the method is used in cells of different genetic backgrounds, experimental evidence can be gained as to whether a putative causal risk allele at a particular SNP affects the level of protein binding to DNA and is, therefore, functional.

      Gene expression

      Within appropriate cell types, the effect of regulatory regions on subsequent gene expression can be examined using reporter gene assays. In this technique, the regulatory region containing the disease-associated variant is cloned into a vector containing a reporter gene such as luciferase. In a relevant cell type, expression of the luciferase gene can be inferred from the amount of luciferase enzyme activity. In the near future, it is likely that such techniques will be combined with targeted genome editing in order to identify how individual SNP alleles affect gene expression. DNA currently can be altered using novel CRISPR/Cas9 genome-editing systems (
      • Cong L.
      • Ran F.A.
      • Cox D.
      • Lin S.L.
      • Barretto R.
      • Habib N.
      • et al.
      Multiplex genome engineering using CRISPR/Cas systems.
      ), as was recently demonstrated in human primary T cells (
      • Schumann K.
      • Lin S.
      • Boyer E.
      • Simeonov D.R.
      • Subramaniam M.
      • Gate R.E.
      • et al.
      Generation of knock-in primary human T cells using Cas9 ribonucleoproteins.
      ). CRISPR/Cas9 systems are likely to become standard tools for evaluating the effect of altering single SNPs on gene expression, as they can better reflect the in vivo changes that confer disease risk. From experiments such as this, novel disease pathways can be predicted by referral to RNA-seq databases, thereby identifying genes and noncoding RNAs that are coexpressed with the gene in question.
      An example of an autoimmune disease locus where the causal mechanism has been successfully identified is at TNFAIP3 in systemic lupus erythematosus (
      • Wang S.F.
      • Wen F.
      • Wiley G.B.
      • Kinter M.T.
      • Gaffney P.M.
      An Enhancer element harboring variants associated with systemic lupus erythematosus engages the TNFAIP3 promoter to influence A20 expression.
      ). Independent genetic variants in and around TNFAIP3 are associated with multiple traits, including psoriasis (
      • Nair R.P.
      • Duffin K.C.
      • Helms C.
      • Ding J.
      • Stuart P.E.
      • Goldgar D.
      • et al.
      Genome-wide scan reveals association of psoriasis with IL-23 and NF-kappa B pathways.
      ), rheumatoid arthritis (
      • Thomson W.
      • Barton A.
      • Ke X.
      • Eyre S.
      • Hinks A.
      • Bowes J.
      • et al.
      Rheumatoid arthritis association at 6q23.
      ) and systemic lupus erythematosus (
      • Han J.W.
      • Zheng H.F.
      • Cui Y.
      • Sun L.D.
      • Ye D.Q.
      • Hu Z.
      • et al.
      Genome-wide association study in a Chinese Han population identifies nine new susceptibility loci for systemic lupus erythematosus.
      ). In systemic lupus erythematosus, the causal variant was localized to a pair of tandem polymorphic dinucleotides (TT>A) in an enhancer region 42 kb downstream of the TNFAIP3 promoter (
      • Adrianto I.
      • Wen F.
      • Templeton A.
      • Wiley G.
      • King J.B.
      • Lessard C.J.
      • et al.
      Association of a functional variant downstream of TNFAIP3 with systemic lupus erythematosus.
      ). A functional study using several techniques including luciferase reporter assays, 3C, and ChIP showed that TT>A interacts with TNFAIP3 through DNA looping, hence bringing the transcription factor NF-κB into close proximity with the TNFAIP3 promoter (
      • Wang S.F.
      • Wen F.
      • Wiley G.B.
      • Kinter M.T.
      • Gaffney P.M.
      An Enhancer element harboring variants associated with systemic lupus erythematosus engages the TNFAIP3 promoter to influence A20 expression.
      ). The systemic lupus erythematosus risk variant of TT>A was found to have reduced ability to bind NF-κB, which led to aberrant expression of TNFAIP3. A similar process could be used to elucidate the casual variant in psoriasis.

      Concluding Remarks

      To fully exploit the robust GWAS data already generated and to better understand the genetic susceptibility to psoriasis, one of the post-GWAS challenges is the identification and functional annotation of causal variants in known risk loci and the genes they regulate. Incorporating GWAS data and functional experiments can describe biological pathways that lead to disease, providing targets for novel therapy development; diagnostic or prognostic biomarkers, or biomarkers to target the right treatments to the right patients.

      Conflict of Interest

      The authors state no conflict of interest.

      Acknowledgments

      HRJ is supported by The Sir Jules Thorn Charitable Trust PhD Scholarship. This work was carried out in Manchester, United Kingdom.

      Supplementary Material

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