Fine mapping of HLA effects in Oral and non-Oral lichen planus

Fine mapping of HLA effects in Oral and non-Oral lichen planus | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Fine mapping of HLA effects in Oral and non-Oral lichen planus View ORCID Profile Jarmo Ritari , Mary Reeve , FinnGen , Maria Siponen , Mari Vehviläinen , Tuula Salo , Kazutoyo Osoegawa , Marcelo Fernandez Viña , Benjamin Goudey , View ORCID Profile Jukka Partanen , Emmanuel JM Mignot doi: https://doi.org/10.1101/2025.11.17.25340396 Jarmo Ritari * Research and Development, Finnish Red Cross Blood Service , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jarmo Ritari Mary Reeve † Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki , Helsinki, Finland ‡ Broad Institute of MIT and Harvard , Cambridge, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Siponen § Institute of Dentistry, Faculty of Health Sciences, University of Eastern Finland , Kuopio, Finland ¶ Odontology Education Unit, and Department of Oral and Maxillofacial Diseases, Kuopio University Hospital , Kuopio, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mari Vehviläinen || Department of Oral and Maxillofacial Diseases, Faculty of Medicine, University of Helsinki , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tuula Salo || Department of Oral and Maxillofacial Diseases, Faculty of Medicine, University of Helsinki , Helsinki, Finland # Translational Immunology Research Program, Faculty of Medicine, University of Helsinki , Helsinki, Finland ** iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki , Helsinki, Finland †† Department of Pathology, HUS Helsinki University Hospital , Helsinki, Finland ‡‡ Oral Pathology, Research Unit of Population Health, Faculty of Medicine, University of Oulu , Oulu, Finland §§ Medical Research Center, Oulu University Hospital , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kazutoyo Osoegawa ¶¶ Stanford Blood Centre, Department of Pathology , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marcelo Fernandez Viña ¶¶ Stanford Blood Centre, Department of Pathology , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benjamin Goudey || || Australian BioCommons, The University of Melbourne , North Melbourne 3051, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jukka Partanen * Research and Development, Finnish Red Cross Blood Service , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jukka Partanen For correspondence: mignot{at}stanford.edu Jukka.Partanen{at}veripalvelu.fi Emmanuel JM Mignot ## Department of Psychiatry, Stanford School of Medicine , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: mignot{at}stanford.edu Jukka.Partanen{at}veripalvelu.fi Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Lichen planus (LP) is an inflammatory disease affecting squamous epithelia, typically manifesting in a cutaneous (non-OLP) and an oral mucosa (OLP) form, the latter conferring elevated risk of oral squamous cell carcinoma. Despite presence of CD4+ and CD8+ T-cell infiltrates in LP lesions, specific autoantibodies or target T-cell antigens have not been identified. A recent genome wide association study (GWAS) uncovered 27 genome-wide independent associations, with the strongest signal within HLA class II, particularly involving DQB1*05:01 . This association showed stronger effects in non-OLP (OR=2.09) versus OLP (OR=1.36). Here, we performed a high-resolution HLA fine-mapping analysis in FinnGen to dissect this strong class II signal and clarify its relationship to LP subtypes. We find that most DQA1*01∼DQB1*05:01 haplotypes confer increased susceptibility, with the highest risk conferred by DQA1*01:05∼DQB1*05:01 followed by DQA1*01:01∼DQB1*05:01 . In subjects not carrying DQB1*05:01 , DRB1*15:01∼DQA1*01:02∼DQB1*06:02 had a strong protective effect, more pronounced in non-OLP than OLP. Further associations were found in DRB1*09:01 and DQB1*02:02 as well as independent HLA class I associations with A*03:01 , B*08:01 and B*13:02 , all stronger in OLP versus non-OLP. Conditioning SNP associations for these effects eliminated the HLA GWAS signal. These effects highlight that while the DQB1*05:01 association remains largely invariant to DQA1 polymorphisms across LP subtypes, the variable strength of HLA associations in non-OLP and OLP forms suggests distinct immunopathological mechanisms. The presence of trans-heterodimer effects in this disease illustrates the need to analyze HLA-DQ -associated diseases with methods beyond simple conditioning. The invariance to DQA1 polymorphisms may facilitate the identification of potential pathological epitopes. Introduction Lichen planus (LP) is a chronic inflammatory mucocutaneus disease of unknown origin that causes lesions and irritation on the skin (cutaneous LP), mucous membranes (penile, vulvar, oral, or esophageal LP), scalp (lichen planopilaris), or nails (lichen unguis), with a lifelong prevalence of ∼1% of the population( 1 , 2 ). Most common forms of LP are oral (OLP) and cutaneous LP (non-OLP). OLP is considered more significant, as it tends to be chronic and is associated with an increased risk of squamous cell carcinoma( 3 ). Non-OLP frequently leaves hyperpigmentation marks upon resolution and may relapse. As LP lesions demonstrate CD4+ and CD8+ T cell infiltrates( 4 ), and no infectious agent has been systematically found (although an association with hepatitis C virus (HCV) has been documented( 5 – 7 ), it is considered a T-cell mediated autoinflammatory disease. Until recently, limited genetic studies had been conducted in LP. This changed with the study of Reeve et al .( 8 ) who conducted genome-wide association analyses (GWAS) in FinnGen on OLP (n = 3,323) and non-OLP (n = 4,356) cases with replication in the UK Biobank (UKBB), where subtype information was unavailable and thus combined for replication. These authors identified 27 independent associations at 25 distinct genomic locations, many of which were shared between OLP and non-OLP, with seven associations unique to one subgroup. Most of these associations overlapped with other autoimmune diseases, most notably hypothyroidism. Beyond the autoimmune disease overlap, a notable finding was the association of nine SNPs with increased eosinophil counts, potentially indicating a previously underappreciated role of these cells in LP pathogenesis( 8 ). The most significant association noted was in the HLA complex, with imputation suggesting a primary association with DQB1*05:01 (p=7.5×10 −211 ) , an effect that was stronger in non-OLP vs OLP (OR=2.09 vs 1.36)( 8 ). The analysis of HLA is complicated by the potential role of DQ trans-heterozygote effects, which have been identified in other strongly HLA-DQ associated diseases such as celiac disease and narcolepsy ( 9 – 12 ). In some cases, novel heterodimers formed between α and β chains encoded on different chromosomes may increase disease susceptibility( 12 ). In other cases, trans-encoded heterodimers may be functionally inactive and reduce susceptibility by competing for heterodimerization with the primary susceptibility heterodimer encoded in cis( 10 , 11 ). Indeed, trans DQ heterodimers can also influence disease predisposition or in transplantation outcomes including rejection, GvHD or disease relapse( 13 , 14 ), although these trans-complementations can only occur within the DQ1 ( DQA1*01 bearing haplotypes) and within non-DQ1 ( DQA1*02 , 03 or 04 bearing haplotypes) families because of molecular constrains for subunit pairing. In our current study, we performed a fine-mapping HLA analysis of the strong class II signal detected in the previous GWAS to clarify the varying strength patterns of allele association in LP subtypes. We specifically analyzed possible trans-heterodimer effects in the DQ locus. Our results demonstrate that the previous DQB1*05:01 association represents the collective influence of multiple HLA-DQ and Class I effects, and that DQB1*06:02 confers and independent protective effect. With a few exceptions, we confirmed that the HLA signal is highly similar between non-OLP and OLP, differing primarily in magnitude, suggesting etiological overlap in non-OLP vs OLP, as suggested by the GWAS study. Interestingly, although most HLA associations were stronger in non-OLP, several associations were unique to OLP, suggesting distinct HLA factors may be involved in OLP pathogenesis. Methods Study cohort The FinnGen study( 15 ) ( www.finngen.fi ), is a large-scale genomics initiative that has analyzed over 500,000 Finnish biobank samples and correlated genetic variation with health data to understand disease mechanisms and predispositions. The project is a collaboration between research organisations and biobanks within Finland and international industry partners (Supplementary Table 1). The FinnGen includes biobank participants with acute and chronic diseases, healthy blood donors, and population collections. FinnGen data freeze 12 (DF12) consists of 520,210 individuals. We used the deep electronic health registry data in FinnGen DF12 and records of clinic of diagnosis( 8 ) to create OLP(n=3,651) and non-OLP (n=4,808) subgroupings. 490,211 individuals without an LP ICD diagnosis were used as controls. Phenotypic definitions Individuals with LP were identified using ICD codes: ICD10 L43 (with extensions 0, 1, 3, 9) and ICD8/9 6970 (with Finnish extensions A, B, C). Due to the lack of body location specificity in these codes, patients were categorized into subgroups of oral LP (OLP) and non-oral LP (non-OLP) based on the clinic type where the diagnosis was made. Specifically, diagnoses made at dental or orthodontic clinics were classified as OLP, while all others were classified as non-OLP, following the methodology of Reeve and co-workers( 8 ). HLA data and association analysis Array-based genotype data in FinnGen were called and subjected to variant and sample level quality control (QC) followed by phasing and imputation as described in the FinnGen flagship paper( 15 ). HLA haplotypes are denoted by alleles separated by “∼”. HLA heterodimers are denoted by alleles separated by “/”. Alleles of classical HLA genes ( A, B, C, DRB1, DQA1, DQB1, and DPB1 ) were imputed as part of the FinnGen analysis pipeline using a Finnish population-specific reference panel( 16 ) and the HIBAG algorithm( 17 ). Posterior probability (PP) of imputation was retained in the HLA allele dosage information within the GP field of VCF data that were produced by the imputation pipeline ( https://github.com/FRCBS/HLA-imputation/tree/master/src ) using plink2 command --vcf dosage=GP. We performed conditional association analyses by selecting a specific HLA allele dosage as an additional analysis covariate. To select specific combinations of DQ alleles for analysis of DQ haplotypes, we handled the imputed HLA alleles as integer dosage values in R. Thresholds for converting imputed dosages to integer values were as follows: <0.5 (0), ≥0.5 & <1.5 ( 1 ), and ≥1.5 ( 2 ). We then converted the allele data to plink genotype format (.bed) by first transforming the dosages into ped/map text files using R. We applied the plink command --make-bed for this conversion. DQA1∼DQB1 haplotype homozygotes were defined as individuals with an imputed allele dosage of ≥1.5 in both genes simultaneously. We coded the resulting allele combinations as a binary variable, depending on the homozygosity status of the alleles. These genotypes were then converted to PLINK format as described above. Rationale for analyzing complex HLA effects To properly dissect the complex effects of multiple HLA alleles in lichen planus, multiple parallel analyses were necessary. Initially, effects in class I and class II regions were analyzed independently and conditioned on each other to identify linkage disequilibrium (LD) effects. Relative predisposition effects, i.e., displacement of alleles other than the main risk allele, are identified by conditioning or sequentially removing selected genotypes( 18 ) and analyzing residual effects, especially in cases of trans-heterodimer effects in DQ-associated diseases. Simple conditioning may be inadequate, requiring the removal of specific genotypes. We employed also a homozygous only analysis where all other samples than DQ homozygotes are excluded. This allows observing the main DQ effects without the complication of trans-heterodimer effects. These complex effects necessitate understanding allele homology and amino acid polymorphisms, particularly those in exon 3 (encoding the HLA α2/β2 domain) or the signal peptide (Supplementary Figure 1), which may not affect peptide binding and presentation, unlike those in exon 1 and 2. The analyses involve iterative processes and evaluation of the results in the light of the DQ effects described above. Supplementary Tables 2 and 3 and Supplementary Figure 2 detail the designs and analyses used, while Supplementary Figure 3 reports on LD between associated alleles. Supplementary Table 4 shows what are likely possible DQα, DQβ allele pairings based on their presence in known, well documented haplotypes. Data analysis Data management and visualization was conducted using R software version 4.3.2 or later ( https://www.r-project.org/ ) with the packages tidyverse( 19 ) version 1.3.0 and data.table( 20 ) version 1.15.4 in RStudio. For association analyses, we used Regenie( 21 ) version 3.0.1 or 3.6 with default settings, taking plink-formatted HLA genotypes as input. Genotype data for the full genome null model in Regenie step 1 were prepared using FinnGen HapMap3 variants in plink format. To select a set of independent common SNPs for the null model, we applied additional filters using plink( 22 ) commands: --maf 0.1, --mac 100, --geno 0.1, --hwe 1e-15, --mind 0.01, and --thin-count 500000. Our analysis included the basic covariates: age at death or end of follow-up, sex, sampling year, region of birth, FinnGen2 chip, batch, and the first ten genetic principal components. Conditional analyses incorporated HLA allele dosages as additional covariates, depending on the conditioning step. To account for multiple testing, we corrected association p-values across tested HLA genotypes and the three LP phenotypes (OLP, non-OLP, All) using the Benjamini-Yekutieli method( 23 ). A false discovery rate (FDR) threshold of <0.05 was considered significant. Detailed analysis results presented in the figures are reported as supplementary data. Ethics Statement Study subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, separate research cohorts, collected prior the Finnish Biobank Act came into effect (in September 2013) and start of FinnGen (August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Fimea (Finnish Medicines Agency), the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) statement number for the FinnGen study is Nr HUS/990/2017. The FinnGen study is approved by Finnish Institute for Health and Welfare (permit numbers: THL/2031/6.02.00/2017, THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/2222/6.02.00/2018, THL/283/6.02.00/2019, THL/1721/5.05.00/2019 and THL/1524/5.05.00/2020), Digital and population data service agency (permit numbers: VRK43431/2017-3, VRK/6909/2018-3, VRK/4415/2019-3), the Social Insurance Institution (permit numbers: KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019, KELA 98/522/2019, KELA 134/522/2019, KELA 138/522/2019, KELA 2/522/2020, KELA 16/522/2020), Findata permit numbers THL/2364/14.02/2020, THL/4055/14.06.00/2020, THL/3433/14.06.00/2020, THL/4432/14.06/2020, THL/5189/14.06/2020, THL/5894/14.06.00/2020, THL/6619/14.06.00/2020, THL/209/14.06.00/2021, THL/688/14.06.00/2021, THL/1284/14.06.00/2021, THL/1965/14.06.00/2021, THL/5546/14.02.00/2020, THL/2658/14.06.00/2021, THL/4235/14.06.00/2021, Statistics Finland (permit numbers: TK-53-1041-17 and TK/143/07.03.00/2020 (earlier TK-53-90-20) TK/1735/07.03.00/2021, TK/3112/07.03.00/2021) and Finnish Registry for Kidney Diseases permission/extract from the meeting minutes on 4 th July 2019. The Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data Freeze 11 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26, BB2020_1, BB2021_65, Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, HUS/248/2020, HUS/430/2021 §28, §29, HUS/150/2022 §12, §13, §14, §15, §16, §17, §18, §23, §58, §59, HUS/128/2023 §18, Auria Biobank AB17-5154 and amendment #1 (August 17 2020) and amendments BB_2021-0140, BB_2021-0156 (August 26 2021, Feb 2 2022), BB_2021-0169, BB_2021-0179, BB_2021-0161, AB20-5926 and amendment #1 (April 23 2020) and it’s modifications (Sep 22 2021), BB_2022-0262, BB_2022-0256, Biobank Borealis of Northern Finland_2017_1013, 2021_5010, 2021_5010 Amendment, 2021_5018, 2021_5018 Amendment, 2021_5015, 2021_5015 Amendment, 2021_5015 Amendment_2, 2021_5023, 2021_5023 Amendment, 2021_5023 Amendment_2, 2021_5017, 2021_5017 Amendment, 2022_6001, 2022_6001 Amendment, 2022_6006 Amendment, 2022_6006 Amendment, 2022_6006 Amendment_2, BB22-0067, 2022_0262, 2022_0262 Amendment, Biobank of Eastern Finland 1186/2018 and amendment 22§/2020, 53§/2021, 13§/2022, 14§/2022, 15§/2022, 27§/2022, 28§/2022, 29§/2022, 33§/2022, 35§/2022, 36§/2022, 37§/2022, 39§/2022, 7§/2023, 32§/2023, 33§/2023, 34§/2023, 35§/2023, 36§/2023, 37§/2023, 38§/2023, 39§/2023, 40§/2023, 41§/2023, Finnish Clinical Biobank Tampere MH0004 and amendments (21.02.2020 & 06.10.2020), BB2021-0140 8§/2021, 9§/2021, §9/2022, §10/2022, §12/2022, 13§/2022, §20/2022, §21/2022, §22/2022, §23/2022, 28§/2022, 29§/2022, 30§/2022, 31§/2022, 32§/2022, 38§/2022, 40§/2022, 42§/2022, 1§/2023, Central Finland Biobank 1-2017, BB_2021-0161, BB_2021-0169, BB_2021-0179, BB_2021-0170, BB_2022-0256, BB_2022-0262, BB22-0067, Decision allowing to continue data processing until 31 st Aug 2024 for projects: BB_2021-0179, BB22-0067,BB_2022-0262, BB_2021-0170, BB_2021-0164, BB_2021-0161, and BB_2021-0169, and Terveystalo Biobank STB 2018001 and amendment 25 th Aug 2020, Finnish Hematological Registry and Clinical Biobank decision 18 th June 2021, Arctic biobank P0844: ARC_2021_1001. Results Non-adjusted HLA association analysis reconfirms DQB1*05:01 as key driver of LP susceptibility A recent GWAS found a highly significant association peak in the HLA class II region extending into class I( 8 ). To understand which HLA allele was primarily associated with lichen planus, we analyzed the association of all HLA alleles within HLA-DR, DQ, DP, A, B and C with LP overall (non-OLP+OLP), non-OLP only and OLP only ( Table 1 and 2 ) View this table: View inline View popup Table 1. HLA allele associations without additional covariates. View this table: View inline View popup Table 2. Case–control comparison of HLA allele frequencies. For haplotypes, the reported frequency represents an estimate derived from all constituent alleles and may differ from the frequencies of individual alleles. Consistent with Reeve et al .( 8 ), the strongest association overall was with DQB1*05:01 and alleles DQA1*01:01 and DRB1*01:01 , known to form the most common HLA class II haplotype in Finland. HLA class I A*03:01 , B*35:01 , C*04:01 were also significant ( Table 1 ). These above-mentioned HLA class I and II alleles form the most common HLA haplotype in Finland. Following this, significant DRB1*15:01∼ DQA1*01:02∼DQB1*06:02 associations highlight a likely independent, protective signal( 24 ) ( Figure 1 ). DRB1*10:01∼DQA1*01:05∼DQB1*05:01 was also significantly associated with a high odds ratio ( Table 1 and 2 ). This is expected given it is similar in sequence to DRB1*01:01∼DQA1*01:01∼DQB1*05:01 in the DQ region but rarer ( DQA1*01:01 and DQA1*01:05 only differ by two amino acids, see below). Download figure Open in new tab Figure 1. LP association analysis limited to DQA1 ∼ DQB1 homozygotic samples. a) Odds ratios (OR, x-axis) of top alleles from a model adjusted for basic covariates. b) As in (a), but with DQA1*01:05 and DQB1*05:01 allele dosages included as additional covariates. c) As in (a), but with DQA1*01:05, DQB1*05:01 and DQB1*06:02 allele dosages included as additional covariates. d) DQA1 ∼ DQB1 homozygous haplotypes (y-axis) and their corresponding numbers of cases available for analysis. Error bars in (a-c) represent 95% confidence intervals. HLA-DQB1*05:01 effect increases susceptibility in combination with multiple DQA1*01 alleles Of note, the DQB1*05:01 association was stronger than the DRB1*01:01 association, suggesting a primarily DQ effect. Indeed, DQB1*05:01 is found in multiple extended haplotypes, including the common DRB1*01:01∼DQA1*01:01∼DQB1*05:01 haplotype but also other less common haplotypes DRB1*01:02 or DRB1*01:03 . Based on known, strong HLA haplotypes in the Finnish population, a primary DQA1*01:01/DQB1*05:01 heterodimer association explains that DQB1*05:01 was more strongly associated than DRB1*01:01 ( Table 1 ). Beyond DQB1*05:01 , both DRB1*10:01 (OR=2.24) and DQA1*01:05 (OR=2.31) showed a slightly larger effect size than DRB1*01:01 and DQA1*01:01 (OR=1.68) but a lower p-value, reflecting the fact the DRB1*10:01 haplotype is less common in the Finnish (and other European ancestry) population ( Table 1 ). Further support for DQ as the primary locus of susceptibility is given by the sequence similarity of DQA1*01:01∼DQB1*05:01 and DQA1*01:05∼DQB1*05:01 , which only differs by two amino acid replacements, one of them in the signal peptide (-7, V to M), one another at position 2 (D to G)). These findings establish both DQA1*01:05/DQB1*05:01 and DQA1*01:01/DQB1*05:01 as primary susceptibility heterodimers in this disease, with stronger associations observed in non-OLP than OLP patients, likely because non-OLP represents a more pathogenetically homogeneous group ( Table 1 ). DRB1*15:01∼ DQA1*01:02∼DQB1*06:02 protective effect A strong protective effect was observed at DRB1*15:01∼DQA1*01:02∼DQB1*06:02 ( Table 1 ). As this combination is almost exclusively associated with DRB1*15:01∼DQA1*01:02∼DQB1*06:02 in Finns (and other European ancestries), it is impossible to differentiate a DR or a DQ effect; additional studies in African, Middle Eastern or South Chinese populations would be needed to differentiate DR and DQ effects, as LD of DQA1*01:02∼DQB1*06:02 with DRB1*15:01 is not absolute in these ethnic groups( 11 , 25 ). As will be shown later, however, the DRB1*15:01∼ DQA1*01:02∼DQB1*06:02 protective effect is only present in DQB1*05:01 negative patients (in DQB1*05:01 positive patients, DQA1*01:02∼DQB1*06:02 increases risk, see below), albeit the risk differs between DQA1*01:01 vs DQA1*01:05 ( Figure 2 ). Download figure Open in new tab Figure 2. Selected DQA1 ∼ DQB1 diploid haplotypes and their LP association odds ratios (OR, x-axis), adjusted for Class I (A*02:01, A*03:01, B*08:01, B*13:02, B*07:02). a) Haplotypes containing the DQB1*05:01 allele. b) Haplotypes containing the DQB1*06:02 allele. Error bars represent 95% confidence intervals. Other HLA class II effects were noted including an association with DPB1*04:02 (increased susceptibility) and DPB1*05:01 (protection) as well as weaker protective effects of DRB1*13:01∼DQA1*01:03∼DQB1*06:03 and DRB1*11:01∼DQA1*05:05∼DQB1*03:01 . These effects where no longer detected after controlling for the main effects as discussed later. Homozygous-only analysis of DQA1∼DQB1 haplotypes confirms DQB1*05:01 as the main risk factor and DQB1*06:02 independently protective A limitation in the above analysis is the potential role of DQ trans-heterozygote effects, which have been identified in other strongly HLA-DQ associated diseases such as celiac disease and narcolepsy( 9 – 12 ). In some cases, novel heterodimers formed between α and β chains encoded on different chromosomes may increase disease susceptibility( 12 ). In other cases, these trans-encoded heterodimers may be functionally inactive and reduce susceptibility by competing for heterodimerization with the primary susceptibility heterodimer encoded in cis( 10 , 11 ). To test this, we first conducted an HLA-DQ homozygous analysis only ( Figure 1 ), an approach where DQ homozygous individuals are compared with each other while controlling for population stratification using principal component analysis (PCA). Here, the most significant DQ heterodimers are removed sequentially until nothing significant is left, an analysis called a relative predisposition effect analysis( 26 ). One important disadvantage is that the homozygote only analysis greatly reduces power, thus can only be conducted when large sample sizes are available, and for frequent haplotypes. For example, DQA1*01:05∼DQB1*05:01 homozygotes are only expected at a frequency of 7 for 100,000 controls in this sample and perhaps ∼5 times more in patients, thus homozygosity effects cannot be observed even in our large sample. This analysis ( Figure 1 , Supplementary Table 5) confirmed increased disease susceptibility in individuals homozygous for DQA1*01:01∼DQB1*05:01 . Additionally, after conditioning on DQB1*05:01 ( Figure 1b ), we observed an independent protective effect in DQA1*01:02∼DQB1*06:02 homozygotes that persisted even after controlling for the primary susceptibility factors, indicating this protection operates independently of DQA1*01∼DQB1*05:01 . Notably, this protective effect reached statistical significance only in the OLP subgroup and in the combined analysis of all cases, but not in the non-OLP subgroup where the effect was weaker. Further analysis on associated haplotypes as heterozygous shows trans-heterodimer effects Having identified HLA-DQA1*01:05/DQB1*05:01 and DQA1*01:01/DQB1*05:01 heterodimers as the main susceptibility factors, and DQA1*01:02∼DQB1*0602 as an independent protective haplotype, we next studied LP predisposition in heterozygous individuals with the DQ1 haplotypes ( Figure 2 ). We examined only combinations involving frequent haplotypes in the population and those associated with LP. Since the predisposing effect of DQB1*05:01 and the protective effect of DQB1*06:02 were found to be independent of each other, we analyzed the effects of DQA1*01:01/5∼DQB1*05:01 and DQA1*01:02∼DQB1*06:02 combinations separately. In all subsequent figures, we present data from OLP, non-OLP, and combined LP samples, noting that non-OLP generally shows stronger HLA associations. To eliminate potential confounding effects from class I alleles, these analyses were conducted both with and without controlling for HLA-A*03:01 , B*13:02 , and B*08:01 —independent HLA Class I effects that we identified in subsequent analysis (detailed below). As can be seen in Figure 2 , a striking pattern of susceptibility was evident when various DQ1 heterozygous are present in trans of DQB1*05:01 (panel a). For example, the DQA1*01:03∼DQB1*06:03/DQA1*01:05∼DQB1*05:01 or DQA1*01:02∼DQB1*06:02/DQA1*01:05∼DQB1*05:01 heterozygote combinations carried as much susceptibility as DQA1*01:01∼DQB1*05:01/ DQA1*01:05∼DQB1*05:01 (containing two high susceptibility haplotypes). This indicates that trans DQA1*01:02 or 03/DQB1*05:01 heterodimers carry as much susceptibility as DQA1*01:01/DQB1*05:01 in predisposing to disease ( DQA1*01:03∼DQB1*06:03 or DQA1*01:02∼DQB1*06:04 homozygotes have no effects or insignificant weak protective effects in the homozygous only analysis). This is also confirmed by the fact DQA1*01:03∼DQB1*06:03/DQA1*01:01∼DQB1*05:01 (panel a) and DQA1*01:02∼DQB1*06:02/DQA1*01:01∼DQB1*05:01 (panel b) carries similar or slightly lower risk as DQA1*01:01∼DQB1*05:01/DQA1*01:01∼DQB1*05:01 homozygotes. Similarly, the genotype DQA1*01:04∼DQB1*05:03, DQA1*01:01∼DQB1*05:01 has a similar risk as DQA1*01:01∼DQB1*05:01 homozygotes. Overall, results suggest that DQA1*01:02, DQA1*01:03/DQB1*05:01 heterodimers carry as high a susceptibility risk as DQA1*01:01/DQB1*05:01 , but significantly lower risk than DQA1*01:05/DQB1*05:01 . Of note, DQB1 is also likely to have some influence, as DQA1*01:02∼DQB1*06:04 and DQA1*01:02∼DQB1*05:02 in trans of DQA1*01:01∼DQB1*05:01 reduces somewhat susceptibility. Further, DQA1*01:01∼DQB1*05:01 with DQA1*01:04∼DQB1*05:03 also has a somewhat lower risk, and as DQA1*01:04 and DQA1*01:05 are molecularly equivalent, being only distinct in exon 3 at position 199 where DQA1*01:04 carries a T instead of an A in all other DQA1 subtypes (Supplementary Figure 1) Independent of this, DRB1*15:01∼DQA1*01:02∼DQB1*06:02 was found to protective in all other heterozygous combinations, but this protective effect was only found in DQB1*05:01 negative patients (panel b), suggesting heterogeneity of effects in DQB1*05:01 positive vs negative patients. Conditional HLA allele associations reveal non-OLP vs OLP differences Following the findings described above, analyses were repeated after controlling for the main HLA class II effects, DQA1*01:05 , DQB1*05:01 (and DQA1*01:01 ), followed by DQB1*06:02 , revealing a disappearance of the B*35:01 and C*04:01 effects, indicating that these HLA class I effects were secondary to LD with HLA-DR and DQ . All these findings were made overall and in both non-OLP and OLP subtypes, with stronger effects in non-OLP ( Figure 3 ). Download figure Open in new tab Figure 3. Results of LP association analysis for top HLA alleles. a-h) Odds ratios (OR, x-axis) of a conditioning chain starting from basic covariates and progressively adding HLA alleles as additional covariates to the logistic regression model. The alleles included as additional covariates are given on top of each panel. Error bars represent 95% confidence intervals. As shown n Figure 3 , it can be noted that controlling for DQA1*01:05 , DQB1*05:01 , and DQB1*06:02 eliminates most HLA class II effects, except for weak predisposing effects of DRB1*09:01 and DQB1*02:02 that was more preeminent in OLP, the subtype least associated with DQB1*05:01 and DQB1*06:02 . DRB1*09:01 , a frequent Asian subtype, is present in the context of the DRB1*09:01∼DQA1*03:02∼DQB1*03:03 haplotype; further analysis would be needed to establish whether the effect is DR or DQ mediated. The DQB1*02:02 effect, predominant in OLP cases, was likely mediated by DQA1*02:01/DQB1*02:02 heterodimers rather than by DRB1*07:01 . Indeed, DRB1*07:01 is commonly associated with DQA1*02:01∼DQB1*02:02 and DQA1*02:01∼DQB1*03:03 in the general population. As DQB1*02:02 is more strongly associated than DRB1*07:01 or DQA1*02:01 (OR 1.35 vs. 1.24; Supplementary Data, basic allele analysis), this effect is more likely mediated by DQA1*02:01/DQB1*02:02 than by DRB1 . Further, DQB1*02:01 , a subtype differing only in exon 3 and functionally equivalent to DQB1*02:02 , present in the DRB1*03:01∼DQA1*05:01∼DQB1*02:01 was not associated. Studies in African populations in which a haplotype bearing DRB1*09:01-DQB1*02:02 is common would be particularly informative to confirm these possible additional susceptibility factors. These results confirm that LP is primarily associated with DQA1*01:05/DQB1*05:01 , DQA1*01:01/DQB1*05:01 (strongest in non-OLP), and very weakly with DQA1*02:01/DQB1*02:02 (stronger in OLP) and DRB1*09:01∼DQA1*03:02∼DQB1*03:03 (only in OLP). It also displays a strong protective association with DRB1*15:01∼DQA1*01:02∼DQB1*06:02 (strongest in non-OLP). Following controlling for these HLA-class II effects, a predisposing effect at A*03:01 (both OLP and non OLP), B*13:02 (stronger in non-OLP) and B*08:01 (stronger in OLP) remained ( Figure 3 ). In contrast, effects at B*35:01 and C*04:01 found in the uncorrected sample association ( Table 1 ) disappeared, all explained by LD (Supplementary Figure 3). Conditioning MHC SNP effects on DQA1*01:05, DQA1*01:01, DQB1*05:01 and DQB*06:02 shows that the association signal is explained by HLA alleles We next explored how the GWAS SNP signals observed in the HLA region changed following conditioning on DQA1*01:05, DQB1*05:01 and DQB*06:02 , which represent most of the HLA class II signal in LP. As can be seen in Figure 4 , although a weak signal may remain in the HLA class II region, additional signals were found in HLA Class I as found in other DQ associated diseases such as Type 1 diabetes( 27 , 28 ), celiac disease( 29 ) or narcolepsy( 9 ). Unlike in these other diseases, no effect is observed in the HLA-DP region( 9 , 27 , 28 , 30 ). The HLA class I signal disappeared when controlling for A*03:01, HLA-B*13:02 and B*08:01 , indicating that our analysis considered most effects present within the HLA region. Download figure Open in new tab Figure 4. Manhattan plots of LP association p-values of MHC region SNPs on chromosome 6. a-h) Analyses adjusted for basic covariates and HLA alleles as indicated on top of each panel. Class I: A*02:01 , A*03:01 , B*08:01 , B*13:02 , B*07:02 ; Class II: DQA1*01:05 , DQB1*02:02 , DQB1*03:01 , DQB1*05:01 , DQB1*06:02 , DRB1*09:01 ; A: A*02:01 , A*03:01 ; B: B*08:01 , B*13:02 , B*35:01 , B*07:02 . The red horizontal line indicates the genome-wide significance threshold (p < 5 × 10 −8 ) Discussion This study furthers our understanding of HLA effects in LP and illustrates the complexity of HLA-DQ associated diseases. As previously reported, we found that the strongest HLA effects in LP are a DQB1*05:01 predisposing effect and an independent DQB1*06:02 protective effect, and that both effects are stronger in non-OLP versus OLP. This suggests that non-OLP (typically cutaneous), a disease affecting 0.2% to 1% worldwide, is autoimmune-like and has a more homogeneous etiology( 1 , 2 ). Nonetheless, it is worth noting that even for non-OLP cases, DQB1*05:01 frequency was only 31% vs 19% in controls ( Table 2 ), suggesting complexity and heterogeneity. Further, only in DQB1*05:01 negative patients was DQB1*06:02 protective, thus at least three separate subtypes of disease are most likely present: DQB1*05:01 positive, both DQB1*05:01 and DQB1*06:02 negative and DQB1*05:01 negative but DQB1*06:02 positive. A clinical comparison of these subtypes could reveal clinical and prognostic differences of interest to the dermatologic communities. Oral lichen planus (prevalence 1%)( 31 ), in contrast, although sharing the same pathophysiology in a significant number of cases (approximately a third considering differences in odds ratios), is more heterogeneous. Considering the overlapping HLA association in OLP and non-OLP, it is likely that OLP includes at least 70% of cases with a pathophysiology that does not involve the same autoimmune process as non-OLP. Some of these cases have a weak HLA association with DQA1*02:01/DQB1*02:02 and DRB1*09:01 . DRB1*09:01 predominates in Asia and thus could be best replicated in non-European ancestries. The association with DRB1*09:01∼DQA1*03:02∼DQB1*03:03 is also present in rare DQB1*06:02 positive OLP cases ( Figure 2 ), suggesting a real subtype more frequently oral but also possibly cutaneous. In contrast, the DQB1*02:02 associated allele was not increased in DQB1*06:02 positive non-OLP cases ( Figure 2 ), suggesting those cases to be more non-oral. The weakly HLA-associated OLP cases may either share a distinct autoimmune basis or have infectious or other causes. Indeed, many diseases of diverse pathophysiology, including infections( 32 , 33 ), cancer( 34 , 35 ) and even neurodegenerative diseases( 36 ) have weak HLA associations. Here too, additional clinical correlation and studying potential triggers stratified by HLA subtypes may be informative. Lichen planus (more cutaneous than oral), has been suggested to be associated with and triggered by hepatitis C( 5 – 7 ), a virus whose clearance is modulated by HLA-DR and DQ , notably DRB1 ∗ 01:01 , DQA1 ∗ 03:03/DQB1 ∗ 03:01 , DQA1 ∗ 05:05/DQB1 ∗ 03:01 for clearance and DQA1 ∗ 01:02/DQB1 ∗ 06:02 and DQA1 ∗ 05:01/HLA-DQB1 ∗ 02:01 for persistence( 37 ). Small studies have also suggested differences in HLA association in some LP subgroups, such as DQB1*02:02 in the vulvovaginal gingival form( 38 ). Of additional interest, OLP carries a higher documented cancer risk( 39 ), which would be interesting to explore in cases with DQB1*05:01, DQB1*06:02 , DQB1*02:02 and DRB1*09:01 . More detailed analysis offered additional information on the molecular nature of the DQB1*05:01 association. Because non-OLP is more strongly HLA-associated, the signal was easiest to dissect in this subgroup. Strikingly, DQB1*05:01 , the dominant effect, not only exhibited an approximate doubling of predisposing effects for DQA1*01:01∼DQB1*05:01 homozygous vs heterozygous, as reported in other HLA associated diseases( 40 – 44 ), but also showed a unique pattern of increased heterozygosity with any other DQA1*01 alleles in trans. Two effects were noted. First, as expected, increased susceptibility of DQA1*01:05∼DQB1*05:01 ( DRB1*10 ) was noted in trans of any haplotype versus the DQA1*01:01∼DQB1*05:01 ( DRB1*01 ) version. As an example, DQA1*01:01∼DQB1*05:01 / DQA1*01:05∼DQB1*05:01 carried higher susceptibility than DQA1*01:01∼DQB1*05:01 homozygotes in non-OLP ( DQA1*01:05∼DQB1*05:01 homozygotes are too rare to be tested) ( Figure 2 ). It can be speculated that DQA1*01:05 vs. DQA1*01:01 could contribute distinct physiological effects to explain the observed higher risk for DQA1*01:05/DQB1*05:01 in non-OLP, for example better binding of a common epitope in the presence of DQA1*01:05 . A similar finding exists in another DQB1*05:01 associated disease, anti inglon-5, where DQA1*01:05∼DQB1*05:01 is much more strongly associated than DQA1*01:01∼DQB1*05:01 , and where the risks had the following order: DQA1*01:05∼DQB1*05:01 > DQA1*01:01∼DQB1*05:01 > DQA1*01:04∼DQB1*05:03 ( 24 ). As demonstrated by our results, analysis of homozygous and heterozygous DQ1 carriers revealed that any DQA1*01 allele ( DQA1*01:01, DQA1*01:02, DQA1*01:03, DQA1*01:04 , or DQA1*01:05 ) in combination with DQB1*05:01 increases disease susceptibility. This suggests all DQA1*01/DQB1*05:01 heterodimers predispose individuals to LP, although with varying degrees of effect—notably, DQA1*01:05/DQB1*05:01 demonstrating a stronger association than DQA1*01:01/DQB1*05:01 . Second and more surprisingly, the presence of DQA1*01:03 and DQA1*01:02 in trans of DQA1*01:05∼DQB1*05:01 increased susceptibility almost as much as DQA1*01:01∼DQB1*05:01 homozygous ( Figure 2 ). Intriguingly, in trans with DQB1*01:05∼DQB1*05:01 , the effect of DQA1*01:03 and DQA1*01:04 was equivalent or stronger than DQA1*01:01∼DQB1*05:01 homozygous, while DQA1*01:03 and DQA1*01:04 in the presence of DQA1*01:01∼DQB1*05:01 , had slightly lower predisposition ( Figure 2 ). These findings demonstrate in LP, all DQA1*01/DQB1*05:01 combinations independent of DQA1*01 diversity are predisposing. DQA1*01:05/DQB1*05:01 has the highest effect, followed by DQA1*01:01/DQB1*05:01 and then DQA1*01:03/DQB1*05:01 , DQA1*01:04/DQB1*05:01 , and DQA1*01:02/DQB1*05:01 . Of note, the exact ranking of predisposition for these DQ5 heterodimers was difficult to determine without a full understanding of the epitope(s) involved and its/their binding properties to HLA-DQ . Indeed, it is likely that the “other” DQB1*05 or DQB1*06 alleles of these haplotypes are not entirely neutral, and as these can also pair with DQA1*01 alleles, DQB1 allele competition could occur. This could reduce DQA1*01/DQB1*05:01 abundance and susceptibility, a phenomenon observed in narcolepsy, although primarily with DQA1*01 alleles( 9 – 11 ). In favor of this, DQA1*01:04 is likely molecularly equivalent to DQA1*01:05 , as it differs in exon 3 at position 199 (A->T), and area of DQA1 that is not known to change peptide binding. Despite this however, DQA1*01:04∼DQB1*05:03 does not rank high in predisposition in trans to DQA1*01:01∼DQB1*05:01 , suggesting that DQB1*05:03 could reduce the effect of DQA1*01:04∼DQB1*05:01 , a competing effect. Nonetheless, although DQB1*05:01 differs from DQB1*06 at multiple positions, it only differs at position β57 from DQB1*05:02 (V->S) and DQB1*05:03 (V->D), two alleles that are not associated with LP (Supplementary Figure 1). The β57 position is critical to peptide binding at P9, the most C terminal portion of the presented peptide. Similarly, DQA1*01 alleles differ from each other at positions α2, α25, α34 and α41, positions that mostly interact with P1 and less so with P4( 45 ). This suggests that an important peptide presented in LP must involve conserved binding through P6 and P9, while being more tolerant at N terminal positions P1 and P4. This prediction will only be verifiable once the autoantigen is identified. The independent protective effects of DRB1*15:01∼DQA1*01:02∼DQB1*06:02 , which is only visible in DQB1*05:01 negative individuals, is more difficult to explain; protective effects such as that of this haplotype in Type 1 diabetes are still largely unexplained( 46 ). It may suggest the existence of a separate subtype of the disease or be also present in DQB1*05:01 positive effect but superseded by the stronger risk effect of DQA1*01:02/DQB1*05:01 on the disease. Studying this and similar effects in subjects with African ancestry, where DRB1*12:01∼ DQA1*01:05∼DQB1*0501 and DRB1*11:01∼DQA1*01:02∼DQB1*06:02 haplotypes are common, could help map this effect to DQ versus DR, although large numbers will be required as the protective association in lichen planus is not extremely high. Besides HLA Class II effects, clear independent HLA class I effects were also noted with A*03:01 , B*13:02 and B*08:01 , present in both non-OLP and OLP, as well as protective effects of B*07:02 in OLP only. Associations of HLA class I alleles independent of class II allele associations is common to most autoimmune diseases such as Type 1 diabetes( 47 ), multiple sclerosis( 48 ), and narcolepsy( 9 ). The role of HLA class I in LP is in line with the well-known observation that lesions in this disease involve both CD4 and CD8 T cells( 4 , 49 ). Most likely, although the antigen(s) is (are) unknown in this disease, CD4+ T cell help and involvement of CD8+ T cells is crucial. This is also revealed by the involvement of other immune loci, although interestingly these point to a role of eosinophils( 8 ), cells that can also be HLA-Class II positive. Unlike other autoimmune diseases however, autoantibodies have not been described, although to our knowledge, no systematic search has been initiated. The pattern of HLA associations identified in this study provides valuable clues for epitope discovery in LP. The observation that all DQA1*01/DQB1*05:01 heterodimers confer susceptibility—with varying degrees of risk—suggests that specific structural features of these molecules are critical for presenting pathogenic peptides. Particularly informative is our finding that DQB1*05:01 confers risk while the closely related DQB1*05:02 and DQB1*05:03 alleles do not, differing only at position β57 (V→S/D), which influences peptide binding at P9. Similarly, the variable risk among different DQA1*01 subtypes (with DQA1*01:05 conferring the highest risk) points to the involvement of polymorphic residues at positions α2, α25, α34, and α41, which primarily affect binding at P1 and P4. These findings suggest that putative LP-associated epitopes likely maintain conserved anchoring at P6 and P9 positions while exhibiting greater tolerance for variation at N-terminal positions. This molecular fingerprint could significantly narrow the search for viral or autoantigens in LP, enabling targeted approaches that focus on peptides with these specific binding characteristics. Future studies employing in silico peptide binding predictions, followed by experimental validation with T cell assays from patients stratified by HLA risk genotypes, may successfully identify the elusive triggering antigen(s) in this complex disorder. In summary, building upon the study by Reeve et al.( 8 ), we have fine-mapped and characterized HLA association effects in LP. As observed in many other diseases primarily associated with HLA class II alleles, we identified complex effects within the HLA class II region along with additional, weaker effects in HLA Class I. Most of these associations can be attributed to amino acid polymorphisms that alter peptide binding rather than regulatory polymorphisms. A particularly notable feature of this disease is that the predisposing effect of DQB1*05:01 remains largely invariant regardless of DQA1*01 subtype. In this respect, LP joins other HLA-DQ associated diseases (narcolepsy, celiac disease, and anti-IgLON5 encephalitis) where strong trans DQα/β heterodimerization effects have been demonstrated to play a crucial role, necessitating specialized statistical modeling approaches beyond conventional genetic analysis. Data Availability FinnGen summary statistics data are available at https://r12.finngen.fi/. All data produced in the present study are available upon reasonable request to the authors. https://r12.finngen.fi/ Data and code availability Analysis scripts are available in GitHub at https://github.com/FRCBS/LP_HLA . HLA allele association analysis summary statistics are available as Supplementary Data file. MHC region SNP association analysis summary statistics are available at https://drive.google.com/drive/folders/1k1ygQIfrXgD8CkO-djw4ykaFB_NgwRAV . FinnGen summary statistics data are available at https://r12.finngen.fi/ . Declaration of interests The authors declare no competing interests. Funding The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie, AstraZeneca UK, Biogen, Bristol Myers Squibb (and Celgene Corporation & Celgene International II), Genentech, Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, Pfizer, GlaxoSmithKline Intellectual Property Development, Sanofi US Services, Maze Therapeutics, Janssen Biotech, Novartis, and Boehringer Ingelheim. Acknowledgements We want to acknowledge the participants and investigators of the FinnGen study. The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie Inc., AstraZeneca UK Ltd, Biogen MA Inc., Bristol Myers Squibb Inc. (and Celgene Corporation & Celgene International II Sàrl), Genentech Inc., Merck Sharp & Dohme LCC, Pfizer Inc., GlaxoSmithKline Intellectual Property Development Ltd., Sanofi US Services Inc., Maze Therapeutics Inc., Johnson&Johnson Innovative Medicine Inc., Novartis AG, Boehringer Ingelheim International GmbH and Bayer AG. Following biobanks are acknowledged for delivering biobank samples to FinnGen: Auria Biobank ( www.auria.fi/biopankki ), THL Biobank ( www.thl.fi/biobank ), Helsinki Biobank ( www.helsinginbiopankki.fi ), Biobank Borealis of Northern Finland ( https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki/Pages/Biobank-Borealis-briefly-in-English.aspx ), Finnish Clinical Biobank Tampere ( www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere ), Biobank of Eastern Finland ( www.ita-suomenbiopankki.fi/en ), Central Finland Biobank ( www.ksshp.fi/fi-FI/Potilaalle/Biopankki ), Finnish Red Cross Blood Service Biobank ( www.veripalvelu.fi/verenluovutus/biopankkitoiminta ), Terveystalo Biobank ( www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/ ) and Arctic Biobank ( https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/northern-finland-birth-cohorts-and-arctic-biobank ). All Finnish Biobanks are members of BBMRI.fi infrastructure ( https://www.bbmri-eric.eu/national-nodes/finland/ ). Finnish Biobank Cooperative -FINBB ( https://finbb.fi/ ) is the coordinator of BBMRI-ERIC operations in Finland. The Finnish biobank data can be accessed through the Fingenious® services ( https://site.fingenious.fi/en/ ) managed by FINBB. Footnotes Participants of the FinnGen are listed in Supplementary Table 1 References 1. ↵ Anttonen , V. , E. Pöykkö , E. Kiviniemi , J. Jokelainen , L. Huilaja , and S.-P. Sinikumpu . 2023 . Characteristics, comorbidities, and treatment practices of lichen planus in Northern Finland: A register-based study among 619 subjects . Health Sci Rep 6 : e1327 . OpenUrl 2. ↵ Gorouhi , F. , P. Davari , and N. Fazel . 2014 . Cutaneous and mucosal lichen planus: a comprehensive review of clinical subtypes, risk factors, diagnosis, and prognosis . ScientificWorldJournal 2014 : 742826 . OpenUrl CrossRef PubMed 3. ↵ Louisy , A. , E. Humbert , and M. Samimi . 2024 . Oral Lichen Planus: An Update on Diagnosis and Management . Am J Clin Dermatol 25 : 35 – 53 . OpenUrl PubMed 4. ↵ Schinner , J. , T. Cunha , J. U. Mayer , S. Hörster , P. Kind , D. Didona , C. Keber , M. Hertl , T. Worzfeld , and H. A. Juratli . 2023 . Skin-infiltrating T cells display distinct inflammatory signatures in lichen planus, bullous pemphigoid and pemphigus vulgaris . Front Immunol 14 : 1203776 . OpenUrl PubMed 5. ↵ Georgescu , S. R. , M. Tampa , M. I. Mitran , C. I. Mitran , M. I. Sarbu , I. Nicolae , C. Matei , C. Caruntu , M. Neagu , and M. I. Popa . 2019 . Potential pathogenic mechanisms involved in the association between lichen planus and hepatitis C virus infection . Exp Ther Med 17 : 1045 – 1051 . OpenUrl PubMed 6. Garg , B. , A. Arbabi , and P. A. Kirkland . 2024 . Extrahepatic Manifestations of Chronic Hepatitis C Virus (HCV) Infection . Cureus 16 : e57343 . OpenUrl 7. ↵ García-Pola , M. , L. Rodríguez-Fonseca , C. Suárez-Fernández , R. Sanjuán-Pardavila , J. Seoane-Romero , and S. Rodríguez-López . 2023 . Bidirectional Association between Lichen Planus and Hepatitis C-An Update Systematic Review and Meta-Analysis . J Clin Med 12 : 5777 . OpenUrl PubMed 8. ↵ Reeve , M. P. , M. Vehviläinen , S. Luo , J. Ritari , J. Karjalainen , J. Gracia-Tabuenca , J. Mehtonen , S. S. Padmanabhuni , N. Kolosov , M. Artomov , H. Siirtola , H. M. Olilla , FinnGen , D. Graham , J. Partanen , R. J. Xavier , M. J. Daly , S. Ripatti , T. Salo , and M. Siponen . 2024 . Oral and non-oral lichen planus show genetic heterogeneity and differential risk for autoimmune disease and oral cancer . Am J Hum Genet 111 : 1047 – 1060 . OpenUrl PubMed 9. ↵ Ollila , H. M. , J.-M. Ravel , F. Han , J. Faraco , L. Lin , X. Zheng , G. Plazzi , Y. Dauvilliers , F. Pizza , S.-C. Hong , P. Jennum , S. Knudsen , B. R. Kornum , X. S. Dong , H. Yan , H. Hong , C. Coquillard , J. Mahlios , O. Jolanki , M. Einen , I. Arnulf , B. Högl , B. Frauscher , C. Crowe , M. Partinen , Y. S. Huang , P. Bourgin , O. Vaarala , A. Désautels , J. Montplaisir , S. J. Mack , M. Mindrinos , M. Fernandez-Vina , and E. Mignot . 2015 . HLA-DPB1 and HLA class I confer risk of and protection from narcolepsy . Am J Hum Genet 96 : 136 – 146 . OpenUrl CrossRef PubMed 10. ↵ Ollila , H. M. , M. Fernandez-Vina , and E. Mignot . 2015 . HLA-DQ allele competition in narcolepsy: a comment on Tafti et al. DQB1 locus alone explains most of the risk and protection in narcolepsy with cataplexy in Europe . Sleep 38 : 147 – 151 . OpenUrl CrossRef PubMed 11. ↵ Han , F. , L. Lin , J. Li , S. X. Dong , P. An , L. Zhao , N. Y. Liu , Q. Y. Li , H. Yan , Z. C. Gao , J. Faraco , K. P. Strohl , X. Liu , H. Miyadera , and E. Mignot . 2012 . HLA-DQ association and allele competition in Chinese narcolepsy . Tissue Antigens 80 : 328 – 335 . OpenUrl CrossRef PubMed Web of Science 12. ↵ Sollid , L. M. , G. Markussen , J. Ek , H. Gjerde , F. Vartdal , and E. Thorsby . 1989 . Evidence for a primary association of celiac disease to a particular HLA-DQ alpha/beta heterodimer . J Exp Med 169 : 345 – 350 . OpenUrl Abstract / FREE Full Text 13. ↵ Kleid , L. , J. Walter , P. Moehnle , C. Wichmann , J. Kovács , A. Humpe , C. Schneider , S. Michel , N. Kneidinger , M. Irlbeck , J. Fertmann , A. Dick , and T. Kauke . 2024 . High-Risk HLA-DQ Mismatches Are Associated With Adverse Outcomes After Lung Transplantation . Transpl Int 37 : 13010 . OpenUrl PubMed 14. ↵ Petersdorf , E. W. , M. Bengtsson , M. Horowitz , C. McKallor , S. R. Spellman , E. Spierings , T. A. Gooley , P. Stevenson , and International Histocompatibility Working Group in Hematopoietic Cell Transplantation . 2022 . HLA-DQ heterodimers in hematopoietic cell transplantation . Blood 139 : 3009 – 3017 . OpenUrl CrossRef PubMed 15. ↵ Kurki , M. I. , J. Karjalainen , P. Palta , T. P. Sipilä , K. Kristiansson , K. M. Donner , M. P. Reeve , H. Laivuori , M. Aavikko , M. A. Kaunisto , A. Loukola , E. Lahtela , H. Mattsson , P. Laiho , P. Della Briotta Parolo , A. A. Lehisto , M. Kanai , N. Mars , J. Rämö , T. Kiiskinen , H. O. Heyne , K. Veerapen , S. Rüeger , S. Lemmelä , W. Zhou , S. Ruotsalainen , K. Pärn , T. Hiekkalinna , S. Koskelainen , T. Paajanen , V. Llorens , J. Gracia-Tabuenca , H. Siirtola , K. Reis , A. G. Elnahas , B. Sun , C. N. Foley , K. Aalto-Setälä , K. Alasoo , M. Arvas , K. Auro , S. Biswas , A. Bizaki-Vallaskangas , O. Carpen , C.-Y. Chen , O. A. Dada , Z. Ding , M. G. Ehm , K. Eklund , M. Färkkilä , H. Finucane , A. Ganna , A. Ghazal , R. R. Graham , E. M. Green , A. Hakanen , M. Hautalahti , Å. K. Hedman , M. Hiltunen , R. Hinttala , I. Hovatta , X. Hu , A. Huertas-Vazquez , L. Huilaja , J. Hunkapiller , H. Jacob , J.-N. Jensen , H. Joensuu , S. John , V. Julkunen , M. Jung , J. Junttila , K. Kaarniranta , M. Kähönen , R. Kajanne , L. Kallio , R. Kälviäinen , J. Kaprio , FinnGen , N. Kerimov , J. Kettunen , E. Kilpeläinen , T. Kilpi , K. Klinger , V.-M. Kosma , T. Kuopio , V. Kurra , T. Laisk , J. Laukkanen , N. Lawless , A. Liu , S. Longerich , R. Mägi , J. Mäkelä , A. Mäkitie , A. Malarstig , A. Mannermaa , J. Maranville , A. Matakidou , T. Meretoja , S. V. Mozaffari , M. E. K. Niemi , M. Niemi , T. Niiranen , C. J. O Donnell , M. E. Obeidat , G. Okafo , H. M. Ollila , A. Palomäki , T. Palotie , J. Partanen , D. S. Paul , M. Pelkonen , R. K. Pendergrass , S. Petrovski , A. Pitkäranta , A. Platt , D. Pulford , E. Punkka , P. Pussinen , N. Raghavan , F. Rahimov , D. Rajpal , N. A. Renaud , B. Riley-Gillis , R. Rodosthenous , E. Saarentaus , A. Salminen , E. Salminen , V. Salomaa , J. Schleutker , R. Serpi , H.-Y. Shen , R. Siegel , K. Silander , S. Siltanen , S. Soini , H. Soininen , J. H. Sul , I. Tachmazidou , K. Tasanen , P. Tienari , S. Toppila-Salmi , T. Tukiainen , T. Tuomi , J. A. Turunen , J. C. Ulirsch , F. Vaura , P. Virolainen , J. Waring , D. Waterworth , R. Yang , M. Nelis , A. Reigo , A. Metspalu , L. Milani , T. Esko , C. Fox , A. S. Havulinna , M. Perola , S. Ripatti , A. Jalanko , T. Laitinen , T. P. Mäkelä , R. Plenge , M. McCarthy , H. Runz , M. J. Daly , and A. Palotie . 2023 . FinnGen provides genetic insights from a well-phenotyped isolated population . Nature 613 : 508 – 518 . OpenUrl CrossRef PubMed 16. ↵ Ritari , J. , K. Hyvärinen , J. Clancy , FinnGen , J. Partanen , and S. Koskela . 2020 . Increasing accuracy of HLA imputation by a population-specific reference panel in a FinnGen biobank cohort . NAR Genom Bioinform 2 : lqaa030 . OpenUrl 17. ↵ Zheng , X. , J. Shen , C. Cox , J. C. Wakefield , M. G. Ehm , M. R. Nelson , and B. S. Weir . 2014 . HIBAG--HLA genotype imputation with attribute bagging . Pharmacogenomics J 14 : 192 – 200 . OpenUrl CrossRef PubMed 18. ↵ Hollenbach , J. A. , S. J. Mack , G. Thomson , and P.-A. Gourraud . 2012 . Analytical methods for disease association studies with immunogenetic data . Methods Mol Biol 882 : 245 – 266 . OpenUrl CrossRef PubMed 19. ↵ Wickham , H. , M. Averick , J. Bryan , W. Chang , L. McGowan , R. François , G. Grolemund , A. Hayes , L. Henry , J. Hester , M. Kuhn , T. Pedersen , E. Miller , S. Bache , K. Müller , J. Ooms , D. Robinson , D. Seidel , V. Spinu , K. Takahashi , D. Vaughan , C. Wilke , K. Woo , and H. Yutani . 2019 . Welcome to the Tidyverse . JOSS 4 : 1686 . OpenUrl CrossRef 20. ↵ Barrett , T. , M. Dowle , A. Srinivasan , J. Gorecki , M. Chirico , T. Hocking , B. Schwendinger , and I. Krylov . data.table: Extension of “data.frame.” . 21. ↵ Mbatchou , J. , L. Barnard , J. Backman , A. Marcketta , J. A. Kosmicki , A. Ziyatdinov , C. Benner , C. O’Dushlaine , M. Barber , B. Boutkov , L. Habegger , M. Ferreira , A. Baras , J. Reid , G. Abecasis , E. Maxwell , and J. Marchini . 2021 . Computationally efficient whole-genome regression for quantitative and binary traits . Nat Genet 53 : 1097 – 1103 . OpenUrl CrossRef PubMed 22. ↵ Chang , C. C. , C. C. Chow , L. C. Tellier , S. Vattikuti , S. M. Purcell , and J. J. Lee . 2015 . Second-generation PLINK: rising to the challenge of larger and richer datasets . Gigascience 4 : 7 . OpenUrl CrossRef PubMed 23. ↵ Benjamini , Y. , and D. Yekutieli . 2001 . The control of the false discovery rate in multiple testing under dependency . Ann. Statist . 29 . 24. ↵ Yogeshwar , S. M. , S. Muñiz-Castrillo , L. Sabater , V. Peris-Sempere , V. Mallajosyula , G. Luo , H. Yan , E. Yu , J. Zhang , L. Lin , F. Fagundes Bueno , X. Ji , G. Picard , V. Rogemond , A. L. Pinto , A. Heidbreder , R. Höftberger , F. Graus , J. Dalmau , J. Santamaria , A. Iranzo , B. Schreiner , M. P. Giannoccaro , R. Liguori , T. Shimohata , A. Kimura , Y. Ono , S. Binks , S. Mariotto , A. Dinoto , M. Bonello , C. J. Hartmann , N. Tambasco , P. Nigro , H. Prüss , A. McKeon , M. M. Davis , S. R. Irani , J. Honnorat , C. Gaig , C. Finke , and E. Mignot . 2024 . HLA-DQB1*05 subtypes and not DRB1*10:01 mediates risk in anti-IgLON5 disease . Brain 147 : 2579 – 2592 . OpenUrl CrossRef PubMed 25. ↵ Mignot , E. , L. Lin , W. Rogers , Y. Honda , X. Qiu , X. Lin , M. Okun , H. Hohjoh , T. Miki , S. Hsu , M. Leffell , F. Grumet , M. Fernandez-Vina , M. Honda , and N. Risch . 2001 . Complex HLA-DR and -DQ interactions confer risk of narcolepsy-cataplexy in three ethnic groups . Am J Hum Genet 68 : 686 – 699 . OpenUrl CrossRef PubMed Web of Science 26. ↵ Payami , H. , S. Joe , N. R. Farid , V. Stenszky , S. H. Chan , P. P. Yeo , J. S. Cheah , and G. Thomson . 1989 . Relative predispositional effects (RPEs) of marker alleles with disease: HLA-DR alleles and Graves disease . Am J Hum Genet 45 : 541 – 546 . OpenUrl PubMed Web of Science 27. ↵ Michalek , D. A. , C. Tern , W. Zhou , C. C. Robertson , E. Farber , P. Campolieto , W.-M. Chen , S. Onengut-Gumuscu , and S. S. Rich . 2024 . A multi-ancestry genome-wide association study in type 1 diabetes . Hum Mol Genet 33 : 958 – 968 . OpenUrl CrossRef PubMed 28. ↵ Hu , X. , A. J. Deutsch , T. L. Lenz , S. Onengut-Gumuscu , B. Han , W.-M. Chen , J. M. M. Howson , J. A. Todd , P. I. W. de Bakker , S. S. Rich , and S. Raychaudhuri . 2015 . Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk . Nat Genet 47 : 898 – 905 . OpenUrl CrossRef PubMed 29. ↵ Gutierrez-Achury , J. , A. Zhernakova , S. L. Pulit , G. Trynka , K. A. Hunt , J. Romanos , S. Raychaudhuri , D. A. van Heel , C. Wijmenga , and P. I. W. de Bakker . 2015 . Fine mapping in the MHC region accounts for 18% additional genetic risk for celiac disease . Nat Genet 47 : 577 – 578 . OpenUrl CrossRef PubMed 30. ↵ Hadley , D. , W. Hagopian , E. Liu , J.-X. She , O. Simell , B. Akolkar , A.-G. Ziegler , M. Rewers , J. P. Krischer , W.-M. Chen , S. Onengut-Gumuscu , T. L. Bugawan , S. S. Rich , H. Erlich , D. Agardh , and TEDDY Study Group . 2015 . HLA-DPB1*04:01 Protects Genetically Susceptible Children from Celiac Disease Autoimmunity in the TEDDY Study . Am J Gastroenterol 110 : 915 – 920 . OpenUrl CrossRef PubMed 31. ↵ McCartan , B. E. , and C. M. Healy . 2008 . The reported prevalence of oral lichen planus: a review and critique . J Oral Pathol Med 37 : 447 – 453 . OpenUrl PubMed 32. ↵ Sanchez-Mazas , A . 2020 . A review of HLA allele and SNP associations with highly prevalent infectious diseases in human populations . Swiss Med Wkly 150 : w20214 . OpenUrl CrossRef PubMed 33. ↵ Blackwell , J. M. , S. E. Jamieson , and D. Burgner . 2009 . HLA and infectious diseases . Clin Microbiol Rev 22 : 370 – 385 , Table of Contents. OpenUrl Abstract / FREE Full Text 34. ↵ Wang , Q.-L. , T.-M. Wang , C.-M. Deng , W.-L. Zhang , Y.-Q. He , W.-Q. Xue , Y. Liao , D.-W. Yang , M.-Q. Zheng , and W.-H. Jia . 2023 . Association of HLA diversity with the risk of 25 cancers in the UK Biobank . EBioMedicine 92 : 104588 . OpenUrl PubMed 35. ↵ Wang , W. , H. M. Ollila , A. S. Whittemore , S. Demehri , N. M. Ioannidis , E. Jorgenson , E. Mignot , and M. M. Asgari . 2018 . Genetic variants in the HLA class II region associated with risk of cutaneous squamous cell carcinoma . Cancer Immunol Immunother 67 : 1123 – 1133 . OpenUrl PubMed 36. ↵ Le Guen , Y. , G. Luo , A. Ambati , V. Damotte , I. Jansen , E. Yu , A. Nicolas , I. de Rojas , T. Peixoto Leal , A. Miyashita , C. Bellenguez , M. M. Lian , K. Parveen , T. Morizono , H. Park , B. Grenier-Boley , T. Naito , F. Küçükali , S. D. Talyansky , S. M. Yogeshwar , V. Sempere , W. Satake , V. Alvarez , B. Arosio , M. E. Belloy , L. Benussi , A. Boland , B. Borroni , M. J. Bullido , P. Caffarra , J. Clarimon , A. Daniele , D. Darling , S. Debette , J.-F. Deleuze , M. Dichgans , C. Dufouil , E. During , E. Düzel , D. Galimberti , G. Garcia-Ribas , J. M. García-Alberca , P. García-González , V. Giedraitis , O. Goldhardt , C. Graff , E. Grünblatt , O. Hanon , L. Hausner , S. Heilmann-Heimbach , H. Holstege , J. Hort , Y. J. Jung , D. Jürgen , S. Kern , T. Kuulasmaa , K. H. Lee , L. Lin , C. Masullo , P. Mecocci , S. Mehrabian , A. de Mendonça , M. Boada , P. Mir , S. Moebus , F. Moreno , B. Nacmias , G. Nicolas , S. Niida , B. G. Nordestgaard , G. Papenberg , J. Papma , L. Parnetti , F. Pasquier , P. Pastor , O. Peters , Y. A. L. Pijnenburg , G. Piñol-Ripoll , J. Popp , L. M. Porcel , R. Puerta , J. Pérez-Tur , I. Rainero , I. Ramakers , L. M. Real , S. Riedel-Heller , E. Rodriguez-Rodriguez , O. A. Ross , L. J. Royo , D. Rujescu , N. Scarmeas , P. Scheltens , N. Scherbaum , A. Schneider , D. Seripa , I. Skoog , V. Solfrizzi , G. Spalletta , A. Squassina , J. van Swieten , R. Sánchez-Valle , E.-K. Tan , T. Tegos , C. Teunissen , J. Q. Thomassen , L. Tremolizzo , M. Vyhnalek , F. Verhey , M. Waern , J. Wiltfang , J. Zhang , EADB, GR@ACE study group, DEGESCO consortium, DemGene, EADI, GERAD, Asian Parkinson’s Disease Genetics consortium , H. Zetterberg , K. Blennow , Z. He , J. Williams , P. Amouyel , F. Jessen , P. G. Kehoe , O. A. Andreassen , C. Van Duin , M. Tsolaki , P. Sánchez-Juan , R. Frikke-Schmidt , K. Sleegers , T. Toda , A. Zettergren , M. Ingelsson , Y. Okada , G. Rossi , M. Hiltunen , J. Gim , K. Ozaki , R. Sims , J. N. Foo , W. van der Flier , T. Ikeuchi , A. Ramirez , I. Mata , A. Ruiz , Z. Gan-Or , J.-C. Lambert , M. D. Greicius , and E. Mignot . 2023 . Multiancestry analysis of the HLA locus in Alzheimer’s and Parkinson’s diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes . Proc Natl Acad Sci U S A 120 : e2302720120 . OpenUrl CrossRef PubMed 37. ↵ Valencia , A. , C. Vergara , C. L. Thio , N. Vince , V. Douillard , A. Grifoni , A. L. Cox , E. O. Johnson , A. H. Kral , J. J. Goedert , A. Mangia , V. Piazzolla , S. H. Mehta , G. D. Kirk , A. Y. Kim , G. M. Lauer , R. T. Chung , J. C. Price , S. I. Khakoo , L. Alric , M. E. Cramp , S. M. Donfield , B. R. Edlin , M. P. Busch , G. Alexander , H. R. Rosen , E. L. Murphy , G. L. Wojcik , M. Carrington , P.-A. Gourraud , A. Sette , D. L. Thomas , and P. Duggal . 2022 . Trans-ancestral fine-mapping of MHC reveals key amino acids associated with spontaneous clearance of hepatitis C in HLA-DQβ1 . Am J Hum Genet 109 : 299 – 310 . OpenUrl CrossRef PubMed 38. ↵ Setterfield , J. F. , S. Neill , P. J. Shirlaw , J. Theron , R. Vaughan , M. Escudier , S. J. Challacombe , and M. M. Black . 2006 . The vulvovaginal gingival syndrome: a severe subgroup of lichen planus with characteristic clinical features and a novel association with the class II HLA DQB1*0201 allele . J Am Acad Dermatol 55 : 98 – 113 . OpenUrl CrossRef PubMed Web of Science 39. ↵ Warnakulasuriya , S. , O. Kujan , J. M. Aguirre-Urizar , J. V. Bagan , M. Á. González-Moles , A. R. Kerr , G. Lodi , F. W. Mello , L. Monteiro , G. R. Ogden , P. Sloan , and N. W. Johnson . 2021 . Oral potentially malignant disorders: A consensus report from an international seminar on nomenclature and classification, convened by the WHO Collaborating Centre for Oral Cancer . Oral Dis 27 : 1862 – 1880 . OpenUrl CrossRef PubMed 40. ↵ Barcellos , L. F. , S. Sawcer , P. P. Ramsay , S. E. Baranzini , G. Thomson , F. Briggs , B. C. A. Cree , A. B. Begovich , P. Villoslada , X. Montalban , A. Uccelli , G. Savettieri , R. R. Lincoln , C. DeLoa , J. L. Haines , M. A. Pericak-Vance , A. Compston , S. L. Hauser , and J. R. Oksenberg . 2006 . Heterogeneity at the HLA-DRB1 locus and risk for multiple sclerosis . Hum Mol Genet 15 : 2813 – 2824 . OpenUrl CrossRef PubMed Web of Science 41. Kawabata , Y. , H. Ikegami , T. Awata , A. Imagawa , T. Maruyama , E. Kawasaki , S. Tanaka , A. Shimada , H. Osawa , T. Kobayashi , T. Hanafusa , K. Tokunaga , H. Makino , and Committee on Type 1 Diabetes, Japan Diabetes Society . 2009 . Differential association of HLA with three subtypes of type 1 diabetes: fulminant, slowly progressive and acute-onset . Diabetologia 52 : 2513 – 2521 . OpenUrl PubMed 42. Pelin , Z. , C. Guilleminault , N. Risch , F. C. Grumet , and E. Mignot . 1998 . HLA-DQB1*0602 homozygosity increases relative risk for narcolepsy but not disease severity in two ethnic groups. US Modafinil in Narcolepsy Multicenter Study Group . Tissue Antigens 51 : 96 – 100 . OpenUrl PubMed Web of Science 43. Peris Sempere , V. , S. Muñiz-Castrillo , A. Ambati , S. Binks , A.-L. Pinto , V. Rogemond , S. J. Pittock , D. Dubey , M. D. Geschwind , J. M. Gelfand , S. Dilwali , S.-T. Lee , J. Knight , K. S. Elliott , S. Irani , J. Honnorat , and E. Mignot . 2022 . Human Leukocyte Antigen Association Study Reveals DRB1*04:02 Effects Additional to DRB1*07:01 in Anti-LGI1 Encephalitis . Neurol Neuroimmunol Neuroinflamm 9 : e1140 . OpenUrl Abstract / FREE Full Text 44. ↵ Rasmussen , H. B. , M. A. Kelly , and J. Clausen . 2001 . Additive effect of the HLA-DR15 haplotype on susceptibility to multiple sclerosis . Mult Scler 7 : 91 – 93 . OpenUrl CrossRef PubMed Web of Science 45. ↵ Sarri , C. A. , G. E. Papadopoulos , A. Papa , A. Tsakris , D. Pervanidou , A. Baka , C. Politis , C. Billinis , C. Hadjichristodoulou , Z. Mamuris , and MALWEST project . 2018 . Amino acid signatures in the HLA class II peptide-binding region associated with protection/susceptibility to the severe West Nile Virus disease . PLoS One 13 : e0205557 . OpenUrl PubMed 46. ↵ Simmons , K. M. , A. M. Mitchell , A. A. Alkanani , K. A. McDaniel , E. E. Baschal , T. Armstrong , L. Pyle , L. Yu , and A. W. Michels . 2020 . Failed Genetic Protection: Type 1 Diabetes in the Presence of HLA-DQB1*06:02 . Diabetes 69 : 1763 – 1769 . OpenUrl CrossRef PubMed 47. ↵ Tosur , M. , S. Onengut-Gumuscu , and M. J. Redondo . 2025 . Type 1 Diabetes Genetic Risk Scores: History, Application and Future Directions . Curr Diab Rep 25 : 22 . OpenUrl PubMed 48. ↵ International Multiple Sclerosis Genetics Consortium . 2019 . Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility . Science 365 : eaav7188 . OpenUrl Abstract / FREE Full Text 49. ↵ Kersh , A. E. , S. Sati , J. Huang , C. Murphy , O. Ahart , and T. H. Leung . 2024 . CXCL9, CXCL10, and CCL19 synergistically recruit T lymphocytes to skin in lichen planus . JCI Insight 9 : e179899 . OpenUrl View the discussion thread. Back to top Previous Next Posted November 19, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Fine mapping of HLA effects in Oral and non-Oral lichen planus Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Fine mapping of HLA effects in Oral and non-Oral lichen planus Jarmo Ritari , Mary Reeve , FinnGen , Maria Siponen , Mari Vehviläinen , Tuula Salo , Kazutoyo Osoegawa , Marcelo Fernandez Viña , Benjamin Goudey , Jukka Partanen , Emmanuel JM Mignot medRxiv 2025.11.17.25340396; doi: https://doi.org/10.1101/2025.11.17.25340396 Share This Article: Copy Citation Tools Fine mapping of HLA effects in Oral and non-Oral lichen planus Jarmo Ritari , Mary Reeve , FinnGen , Maria Siponen , Mari Vehviläinen , Tuula Salo , Kazutoyo Osoegawa , Marcelo Fernandez Viña , Benjamin Goudey , Jukka Partanen , Emmanuel JM Mignot medRxiv 2025.11.17.25340396; doi: https://doi.org/10.1101/2025.11.17.25340396 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15228) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6598) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9230) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a004f51bfe121b23',t:'MTc3OTU0ODcxMg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();