Posted on Authorea 1 Jun 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.159103361.19001792 — This a preprint and has not been peer reviewed. Data may be preliminary. eia nvriy&Sadn cdm fMdclSine,Tin hnogPoic,Cia E-mail: China, Province, Shandong words: First Taian, Key Shandong Sciences, health, Shandong public Medical & of University of School Medical Academy , [email protected] First Shandong Li Dong Shandong & Province, health, University Sciences, Shandong public Medical Taian, Medical Sciences, of of Medical School of Academy * Ji, Academy Shandong Li Long *,Dong & author: Ji University Long *Corresponding Medical , First Lv China Shandong Jian Province, , health, Shandong Tang public Taian, Zhaoyang of , Zhang School ,Wenran a Tan Tan , Wang Xiaojun Wang, Xiaohui rheumatoid and study. Sui case-control methylation Xinxia A polymorphism,DNA Chinese: gene Han related in arthritis IKZF1 between Association CpG Higher RA. of diagnosis the RA. for of marker risk a the as used increase be would can levels IKZF1 Conclusion: RA. with ciated Wang Xiaohui li dong A Chinese: study. Han case-control in arthritis rheumatoid DNA and polymorphism, methylation gene related IKZF1 between Association psta hwdsic ehlto ieecsbtenptet ihR n oto ru.TeMlil oitcregression logistic Multiple The of group. set control RA. restricted and and a RA validated with CpGs comparison and patients CpG between Through identified between showed have association differences models(P¡0.05). results we methylation the different counterparts distinct interroga- in studied healthy show RA genome-wide equivalent also that with their systematic we CpGs correlated in the performed was and determined locus studied and group levels rs1456893 We data methylation control the (GWAS) with and Chinese. that study showed group Han results association in RA The the genome-wide controls between investigate Results: (SNP) of healthy methylation to polymorphisms analysis 421 DNA determined nucleotide and detailed we of single patients a this, tion one For RA on selected 410 we based China. in Methods: in rs1456893, genotyping RA. RA locus, and of IKZF1 locus risk the IKZF1 tochro- the in the related were and The between factor there IKZF1 However, association transcription between diseases. a of predisposition. autoimmune association possibility encodes genetic some the 7, with of chromosome reported associated at evidence studies is located and few with differentiation, is disease lymphocyte gene regulates (Ikaros)) autoimmune remodeling, 1 systematic matin finger a zinc family is (IKAROS (RA) IKZF1 arthritis Rheumatoid Background: Abstract 2020 1, June 2 1 Ji Long and hnogFrtMdclUiest hnogAaeyo eia Sciences Medical of Academy Shandong & University Medical First Shandong hnogFrtMdclUiest hnogAaeyo eia Sciences Medical of Academy Shandong & University Medical First Shandong 1 ixaSui Xinxia , # igh Hou Qingzhi , KF,DAmtyain huaodarthritis Rheumatoid methylation, DNA IKZF1, 1 1 O=.79%I10-.0P001 n CpG and (OR=3.17,95%CI=1.05-9.60,P=0.041) 3 iou Wang Xiaojun , 1 igh Hou Qingzhi , # i Feng Xia , 1 a Tan Tan , # ar hn uy hn,Yei Li, Yuejin , Chen Xueyu Chen, Yanru , 1 i Feng Xia , 2 eRnZhang WenRan , 1 1 ar Chen Yanru , 31(R49,5C=.71.6P002wr asso- 13.14(OR=4.96,95%CI=1.77-13.86,P=0.002)were 1 2 hoagTang Zhaoyang , uy Chen Xueyu , n CpG and 3 1 ujnLi Yuejin , 1 inLv Jian , 31 methylation 13.14 1 1 , , Posted on Authorea 1 Jun 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.159103361.19001792 — This a preprint and has not been peer reviewed. Data may be preliminary. oyopim,SP)na h KF ou n Ars na needn a hns sample. Chinese of Han risk independent the an in nucleotide and (single risk methylation variants methods RA common DNA and between and IKZF1 Materials association locus the of IKZF1 explore associations the to near was the study SNPs) about present polymorphisms, our report of no aim an The are RA. plays there epige- methylation knowledge, relatively of DNA of our RA[17,18]. variety that To because of suggested a pathogenesis factor have Among the evidences studied in Growing frequently role diseases. have most [16]. important redefining factors technology the detection is and Epigenetic mature involve methylation explaining described, more methylation. DNA not in been mechanisms, therefore, DNA do elements have regulatory and stimuli; that potential mechanisms netic external modification, function epigenetic as to histone gene main emerged sensitive species, in recently Three are RNA changes mechanisms interactions. non-coding reversible Epigenetic gene-environment including and in Epi- heritable sequence[15]. mediate expression. of DNA gene they study of the the programming in variation cellular as risk alterations (DMPs)[4,11- the disease defined and in positions development is role methylated disease central differentially genetics influence a can putative play modifications ten epigenetic modifications and and jointly[14].Epigenetic genetic loci RA, risk In genetic tagged 13]. have hundred RA one still of studies are than (EWAS) RA association more epigenome-wide of and (GWAS) pathogenesis association the genome-wide to Recent about contributing factors reports genetic no The been significantly ,systemic RA. have Leukemia and unclear. IKZF1is there IKZF1 Lymphoblastic that syndrome[8-10].However, between Acute lym- found receptor association Sjogren’s Childhood regulates the B-cell also Primary as that of (SLE), studies such gene erythematosus regulation Previous diseases, lupus IKZF1 the autoimmune 7p12.2. the through several subband by with self-tolerance chromosome associated encoded as in is well disease[6]. located as IKZF1) the signaling, proliferation termed in involved and (also likely differentiation IKAROS explain are protein phocyte only factors can other finger discoveries that protein zinc these suggests citrullinated The variance that the disease for fact of risk the confer 20% However, that than associa- variants RA[4-7]. less genome-wide genetic of of 40 subtype meta-analyses 2010[2]. to (ACPA) Several close to antibody-associated identified disease[3]. protein 1990 have anti-citrullinated autoimmune (GWAS) from and an studies factor change is tion rheumatoid discernible RA as that such no suggesting autoantibodies ,with have antibodies, patients 0.24% RA was of synovial RA 70-80% articular Between of chronic characteristic prevalence with disease global autoimmune inflammation[1].The unexplained a is (RA) arthritis Rheumatoid CpG Introduction Higher RA. of RA. diagnosis of the risk for the marker CpG showed a as results patients used regression between be logistic differences have methylation Multiple CpG we distinct The and models( show counterparts group. 9.60,P=0.041) different that healthy control CpGs in equivalent and of RA RA their set CpGs with with restricted in between correlated a determined association was validated the levels locus and studied identified methylation rs1456893 of also the with interrogation we that comparison genome-wide and showed systematic Through group results the RA control The IKZF1 studied 410 and RA. We in the group and genotyping between Chinese. RA based performed Han between association rs1456893, and in methylation locus, data of controls (GWAS) DNA IKZF1 possibility healthy study the 421 association the in genome-wide and (SNP) investigate of patients polymorphisms to analysis nucleotide determined risk detailed single the a one we transcription some and on selected this, a IKZF1 we with For between RA. encodes associated association and 7, the China. locus is chromosome reported in and studies at RA few differentiation, located were of lymphocyte there is However, regulates gene The diseases. remodeling, (Ikaros)) autoimmune predisposition. tochromatin 1 genetic of finger related evidence zinc factor with disease family autoimmune (IKAROS systematic IKZF1 a is (RA) arthritis Rheumatoid Summary 13.14(OR=4.96,95%CI=1.77-13.86, 2 n CpG and 3 P 002wr soitdwt A KF can IKZF1 RA. with associated =0.002)were 31 ehlto eeswudincrease would levels methylation 13.14 (OR=3.17,95%CI=1.05- 3 P ¡0.05). Posted on Authorea 1 Jun 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.159103361.19001792 — This a preprint and has not been peer reviewed. Data may be preliminary. h eut hwdta s469 ou eeas soitdwt h iko Ai h dominant(OR=1.237, the model( in recessive RA of risk the with model(OR=1.433,95%CI=1.055-1.948, associated also were locus 95%CI=0.683-2.239, rs1456893 that showed results model( recessive the the in correlation statistically risk no the with 95%CI=1.103-1.924, associated were dominant(OR=1.457, locus 1.614, the rs1456893 in that showed RA models of genetic different using analysis RA. Correlation and rs1456893 between Hardy-Weinberg association with The line in groups( Weinberg, two Hardy during ( by rs7089424 group control tested and was case group during control demographic The their of controlLaw( status. terms and smoking in group differences , (RA significant income groups statistically two were 49.27 the There information( of age 1. clinical Table characteristics and (mean in clinical showing and subjects were demographic ) healthy 53.73 The group age 421 male. 157 (mean included and cases group female RA control 410 The included males. participants. study study present the The of characteristics of results The presented Results were results the (CIs). And intervals BMI. containing confidence RA, and 95% of age and and risk sex, (ORs) the were and ratios PLINK factors CpGs odds variables. adjusted with of as the relation polymorphism categorical the models, each analysis adjusted compare for to and HWE performed used to unadjusted were were The with models groups used regression two distributions. assessed Logistic were the skewed was v1.07[20]. between tests with frequencies controls variables allele square of continuous and Chi compare distributions genotype to genotypic Normal used for test. test. were test Shapiro-Wilk t tests the sum by using tested rank analyzed was Wilcoxon Normality was 26.0. data SPSS with distribution performed were analysis data All recruitment. This at participants subjects. analyses research all Statistical appropriate the from University,and of Medical obtained First level was Shandong education consent of and Committee informed Review sex, written Internal age, ( the as by controls approved we such and was questionnaire, control, study cases the between quality ( difference through For controls rate collected for call was (Sequenom). (HWE) and Then, platform equilibrium (¿0.05) Hardy-Weinberg Kit. ARRAY frequency (¿95%), Extraction allele Mass rate DNA a call iPLEX Whole-Blood with SNP a the samples the using checked using genomic individual blood extracted performed each venous first from was DNA we peripheral samples for genotyping procedure, separation blood elbow genotyping centrifugal peripheral the of after For from degrees mL DNA -20 detection. 5 methylation at stored and collected were genotyping was samples extraction, Blood and the morning tube. anticoagulation the to vacuum Immunosuppressive in according hormones, of fasted rheumatologists use two Subjects prolonged at diseases, least diseases above major autoimmune at of other population excluded. history any by were family with checkup RA with drugs diagnosed People health not with were time. diagnosed Shan- contemporaneous They same in were the RA[19]. from hospitals for cases selected criteria three the RA were of ACR/EULAR2010 All outpatients controls and hospitals. the inpatients three rheumatology and from province, recruited dong were patients RA The samples Study P P P 00 a osdrdhvn ttsial significant. statistically having considered was ¡0.05 00,al ) nytegntp n leefeuniso s469 a ttsial difference statistically had rs1456893 of frequencies allele and genotype the Only 2). ¿0.05,table 007 n vrdmnn oe(R148 95%CI=1.089-1.898, model(OR=1.438, dominant over and =0.027) P ¡0.05). P 043,ooiat(R135 95%CI=1.064-1.752, (OR=1.365, =0.483),codominant P 00) uha g e lc frsdne,BI,mrtlsau dcto level, education , status marital , BMI , residence of place , sex , age as such ¡0.05), P P 00)Tbe3). ¿0.05)(Table 00) hr een ttsial ieecso s100,r4104 and rs4917014, rs1110701, of differences statistically no were There ¡0.05). P 001.Hwvr hr a lon ttsial orlto nthe in correlation statistically no also was there However, =0.021). P 00)Fgr1.Atrajsigfrae edradBMI, and gender age, for adjusting After ¡0.05)(Figure1). 3 P P vle nalteascaintsswr two-sided, were tests association the all in -values 008,cdmnn(R129 95%CI=1.030- codominant(OR=1.289, =0.008), ± 16yas ossigo 4 eae n 62 and females 348 of consisting 11.67years) χ 2 etfrgons ffi.Cmaiosof Comparisons fit. of goodness for test ± P P 20 er) ossigo 264 of consisting years), 12.02 P vle¿E5.Tebscdata basic The -values¿1E-5). 000.Hwvr hr was there However, =0.010). 004 n vrdominant over and =0.014) P vle¿E3,minor -values¿1E-3), Posted on Authorea 1 Jun 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.159103361.19001792 — This a preprint and has not been peer reviewed. Data may be preliminary. ewe h oe n h mlctmdl CpG model. implicit the and model the explicit between model, the implicit analyze model, CpG to (Additive that performed models. showed was different regression results under Linear site The variable. rs1456893 independent and between of model). the difference site level significant as CpGs methylation with used (rs1456893) the gene was site software, IKZF1 SNP RA v1.07 the and PLINK and gene variable, IKZF1 gene Using dependent between IKZF1 the mQTL models. as of different used difference analysis was significant under Correlation site no site CpG was 4). rs1456893 (Table There and sites. group. site control remaining of the CpGs that of level than methylation greater the was in group RA of level methylation The ehlm oictosadrs fR[1.nodrt ute eemn h eainhpbtenIKZF1 between between association relationship the robust determine a further found to dataset order this RA[11].In can- with of RA[11], study risk including previous diseases, and and diabetes[31].A modifications patients[28]. methylation methylome 1 older DNA type in between allele especially and association RA, AA showing cer[29,30], of gene-environment rs2240340 reports risk a PADI4 some the indicating increase the ACPA-positive, are risk may showed are There allele the who research AA increase patients rs2240340 A reached may in PADI4 the allele particularly rs1456893 research. And RA, AA Harney’s SNPs, of interaction. rs1456893 with these risk IKZF1 consistent the Among the is increase RA. that rs1456893, may result SNP, found with the We one associated RA, identified significance. significantly of study genome-wide were of our that level subjects, locus the Chinese IKZF1 Han the in interferonnear meta-analyses regulating and STAT4 and by the pathogenesis replication as disease Through such in RA, then role in RA, involved a with pathways play pathways. signaling associated to GWAS some (IFN) T IKZF1is demonstrated previous of human that further in activities finding in be cell confirmed STAT4 our may immune-related been of STAT4 confirms inflammatory have and regulation evidence of RA IRFs experimental the with IKZF1, induction further in STAT4 If involved the and is IRF- RA[26,27]. IRF-5 regulating Ikaros of of and thereby Additionally, associations (e.g.IRF-5 Interestingly, Ikaros[23,24], interferons. (IRFs) IKZF1and cells[25]. 1 factors type of regulatory RA. and expression interferon as cytokines such the that diseases, regulate shown autoimmune essential have 8) of is occurrence studies paralysis were Ikaros to the Thus, previous lineages lead influences Furthermore, lacking. myeloid may further mice were Ikaros and and precursors mutant Abnormal system erythroid their differentiation. homozygous immune and and changed, the in cells development of of was lymphoid (NK) Moreover, occurrence, stages killer IKZF1gene of lymphocyte early natural Ikaros[22]. development normal the and the for the of of B in of regulation T, of domain required phases but development the DNA-binding is complete, different the on Ikaros the and in dependent that which development, and role are found fetal in lymphocytes important was adults during blood an cells[21].It in specification plays peripheral B lineages cell Ikaros and spleen, B that T thymus, and protein reported as DNA-binding T the the have such in of studies lymphocytes, member factor a several Previous transcription is IKZF1, a nodes. by as lymph encoded functions product protein that the family 1, finger zinc family Ikaros Discussion 2). RA(Figure with associated were =0.002) CpG that showed RA(OR=5.18,95%CI=1.91-14.07, CpG that showed (OR=3.79,95%CI=1.28-10.90, results regression CpG logistic sites Multiple other The The 5). RA (Table model. models. dominant three the the and model additive CpG the under correlated statistically CpG CpG and model, model. additive recessive the and model, explicit model, additive RA. CpG and of levels CpGs methylation of The levels methylation between association The 11,CpG 11.12, 6 CpG 16, O=.79%I10-.0 =.4)adCpG and P=0.041) (OR=3.17,95%CI=1.05-9.60, 3 9 CpG 19, n CpG and 9 ,CpG 3, P P 0 CpG 20, 001.Atrajsigfrae edradBIterslsalso results BMI,the and gender age, for adjusting After =0.001). 006 n CpG and =0.016) 31,CpG 13.14, n CpG and 3 31 n CpG and 13.14 3 CpG 23, 5adCpG and 15 7 CpG 17, 4 eesaitclycreae ihIZ1udrthe under IKZF1 with correlated statistically were 6 4adCpG and 24 93 r diie hr ssaitclcorrelation statistical is There additive. are 29.30 n CpG and 8 ,CpG 6, 31 a infiatyascae with associated significantly was 13.14 7adCpG and 17 6wr o ttsial infiatunder significant statistically not were 26 0 n CpG and 20, 2wr nysaitclycreae in correlated statistically only were 22 13.14(OR=4.96,95%CI=1.77-13.86, 5 CpG 25, 2wr ieet( different were 22 a soitdwith associated was 3 7adCpG and 27 ,CpG 4, P 8were 28 ¡0.05). 5, P Posted on Authorea 1 Jun 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.159103361.19001792 — This a preprint and has not been peer reviewed. Data may be preliminary. emvnMlA,vndrHrtBunm E enlt E isnA,WlikG,Wrsot P, Worthington Wordsworth KA, der GJ, Siminovitch Morgan van AB, Wolbink P, W, Begovich AG, Martin N, Thomson Vries PP, Wilson Y, de Tak Li ME, RE, S, Toes Weinblatt P, X, EW, Steer Harrison IE, Liu Karlson NA, E, D, AT, Horst-Bruinsma Shadick Altshuler Flynn Lee der MF, P, Y, FA, van Consortium Seldin Emery Kurreeman B, M, AH, X, Ding Seielstad Mil PL, Ke DM, Helm-van Coblyn Jager M, Reid DL, De Chang L, Kastner NP, J, Padyukov EA, Burtt TW, AW, Cui Stahl J, Bowes Huizinga G, JB, A, Xie Crusius LJ, Barton JJ, LA, B, Hocking Catanese Criswell Consortium C, KG, KH, Guiducci Ardlie Costenbader A, CI, Hinks J, Amos S, L, Eyre Alfredsson Dis L, EF, R, Remmers Rheum Chen Klareskog BP, Thomson Ann L, S, Raychaudhuri Alfredsson 5 suggests arthritis. M, study rheumatoid Seddighzadeh association ACPA-negative J, genome-wide versus Ronnelid A IB, ACPA-positive 2011;70:259-265. B, McInnes g: in Ding study A, associations RT, Arthritis Kavanaugh contrasting Rheumatoid 2018;4:18001. Ong GS, Primers of M, Firestein Investigation Dis N, P, Seielstad Rev Epidemiological Johns Emery Nat L, M, GR, arthritis. Padyukov global Rheumatoid Lassere Burmester the 4 K: S, A, from Yamamoto Gabriel V, Barton estimates Strand B, D, DH, arthritis: Williams Aletaha Solomon rheumatoid 2014;73:1316-1322. T, JS, of Dis Vos Smolen burden Rheum F, 3 Ann global Wolfe study. The L, 2010 L: disease Carmona Rev March of Nat D, burden A, Hoy arthritis. Woolf E, rheumatoid R, in Smith Buchbinder targets M, cytokine Cross and 2 cell Emerging T: Dorner 2014;10:77-88. E, Rheumatol Feist GR, Burmester CpG 1 Higher RA. of diagnosis References the RA. for of marker risk a the as increase would used be analyses. we our can Finally, restricted IKZF1 which population. treatment, Chinese of Han outcomes the the in relationship Conclusion: about RA the information explore and detailed further locus Methylation. to obtain for IKZF1gene need not IKZF1locus the we the did Therefore, at at SNP polymorphisms exist. one may other comprehensive only RA between selected a with we offer Second, associated Third, variant(s) not population. causal may IKZF1. so general Other of considerations, study, variability the functional case-control genetic of their hospital-based the representative of on a fully view based was not investigated, this were we size. First, polymorphisms subjects sample addressed. the and small be unavoidable the to was need bias to CpG study selection specific due our ethnic methylated study, of provide my differential limitations may patients. in Several shared RA populations RA of 9 ethnic of changes different were methylation pathogenesis from There of the profiles information in methylation loci health limited. methylated DNA important and differential genome-wide very be SjS candidate More be would the between might RA Therefore, + which of number positive. CD4 loci large pathogenesis false still in health are be the there loci and would papers, underlying SLE methylation these loci between in differential methylated conducted + differential been 753 CD4 has of normal identified correction in and test loci multiple RA Although al. methylation between control[34]. T-cells differential et control, + 341 and Altorok CD4 identified RA in between al. control[33]. regions separation methylation et significant different Jeffries PCA no many is not regions. individual[32]. there methylation be revealed differential would also few there dataset only indicating methylated have methylation differential to our thousand seems to contains diseases analysis usually rheumatic which autoimmune cancer systemic of diagnosis the change loci, for methylation marker DNA a with as differences Compared used methylation be distinct can show IKZF1 that that from CpGs indicating RA. group, of populations control of coun- set in and healthy restricted RA equivalent methylation a with their validated patients DNA in between and of determined identified levels interrogation have methylation we genome-wide with terparts comparison systematic Through a RA. report with patients now we RA, and 5 n CpG and 3 31 ehlto levels methylation 13.14 Posted on Authorea 1 Jun 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.159103361.19001792 — This a preprint and has not been peer reviewed. Data may be preliminary. ihcnrsiggntcacietr nadvriypnlo az nrdlns M eois2012;13:452. Genomics BMC traits lines. for inbred maize models of prediction panel whole-genome diversity of a in Comparison architecture AE: 2014;506:376-381. genetic Melchinger RM: contrasting Nature Plenge F, with discovery. Technow K, drug Yamamoto C, and F, Riedelsheimer Mimori biology Matsuda 13 H, S, to Yamanaka Momohara contributes MA, Brown KA, arthritis Karlson Siminovitch PM, rheumatoid LA, TW, Visscher Gregersen of Criswell L, Behrens L, LW, Genetics Franke Klareskog H, Moreland L, PL, Xu Padyukov M, Jager N, A, J, Takahashi Worthington Vries Lathrop De SC, T, MA, BE, de P, Bae T, Stranger Gonzalez-Gay A, JS, Galan S, Kawaguchi Liu J, Barton Raychaudhuri Y, M, K, J, Martin PK, Kubo Kamatani Bowes Myouzen R, HS, Yamada HK, S, MH, Lee A, SL, Taniguchi Eyre Choi Guo Metspalu Bridges SY, EW, G, X, L, T, K, Bang consortium Mariette Esko Arlestig Liao P, C, R, HJ, Dieude S, J, Miceli-Richard consortium Westra TW, PP, Rantapaa-Dahlqvist Cui E, Huizinga Tak Kremer HJ, L, D, Keystone RE, Guchelaar JD, G, Diogo Rodriguez-Rodriguez MA, Toes Xie Greenberg EA, Laar A, J, de Stahl AE, Zhernakova van Yang D, Jr., Eyler PL, DF, Graham Riel Mirel RJ, Su S, van Yoshida N, L, MJ, Carroll A, Ye Gupta Coenen Suzuki JC, J, X, K, Denny Yin Ohmura Zhou Y, T, L, A, Kochi Jiang Bhangale K, in DA, Ikari W, Pappas C, risk Ortmann Terao JM, T, genetic A, Raj Taub of Manoharan G, Feinberg Trynka N, intermediary RR, TJ, D, Acevedo Wu an Ekstrom Y, L, L, as Okada Reinius 12 Klareskog methylation A, 2013;31:142-147. L, DNA Biotechnol Runarsson Alfredsson Nat implicate J, E, arthritis. data Kere IKZF1 Hesselberg rheumatoid near A, association MD, variants Scheynius Fallin Common Epigenome-wide K, J: L, AP: Shchetynsky Wang Padyukov K, 2017;12:e0177320. M, MJ, Zhang One Ronninger F, PLoS Aryee M, Zhang Chinese. Y, Y, Han Liu Li in K, 11 syndrome Fang R: Sjogren’s L, Dis Guo primary Chopra Rheum S, with M, im- Chang Ann associated Palmisano Y, are Aiolos: Du M, erythematosus. S, Thomas and lupus Qu L, Ikaros 10 systemic Liu factors to Z, transcription relevance Yang the and G, volunteers of 2018;77:1516-1523. Ringheim healthy degradation J, in Kosek induces munomodulation L, iberdomide Cell Wu WE, Cancer modulator Y, Evans Ye Cereblon M, Leukemia. PH, Stanulla Lymphoblastic Schafer Germline R, R, Acute 9 CG: Fulton Baskin Mullighan Childhood E, JJ, P, Yang to Tedrick Mardis Pruett-Miller KE, Predisposition SN, Nichols N, T, e938. 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GJ, Begovich Genome-wide risk Wolbink N, RM: arthritis AG, Vries Plenge rheumatoid Wilson de L, DM, new ME, RE, Klareskog Reid Weinblatt Toes PK, Horst- TR, EW, PL, der Gregersen van Radstake Kastner Karlson Riel KA, AH, MD, TW, C, van Mil Posthumus Helm-van Huizinga Wijmenga CE, der L, LJ, Y, van Schoot Padyukov W, Hocking Consortium der AW, Thomson P, PP, van Tak Morgan J, Harrison Cui S, P, IE, Bowes E, Steer JB, Martin Bruinsma NA, Flynn A, Crusius Shadick P, X, Barton MF, LA, Emery Liu Seldin B, Criswell AT, M, B, Consortium KH, Seielstad Ding Lee Costenbader KG, PL, X, MJ, Ardlie Jager Zhernakova Ke Coenen FA, CI, De DL, J, Kurreeman PI, Amos Y, Coblyn Bakker L, Li JJ, de Alfredsson BP, Catanese J, R, Thomson NP, S, Chen Burtt Eyre C, E, CD2/CD58 G, Brouwer Guiducci and Xie A, EF, PRDM1 Hinks Remmers CD28, A, S, at 2009;41:1313-1318. Raychaudhuri variants Genet EA, Genetic Nat Stahl 6 RM: risk. Plenge arthritis MJ, rheumatoid with Daly associated PK, are Gregersen L, Klareskog J, 6 Posted on Authorea 1 Jun 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.159103361.19001792 — This a preprint and has not been peer reviewed. Data may be preliminary. ,Bc ,Jcb J ishede :A pgntcsgauei eihrlbodpeit cieovarian active predicts blood peripheral Lechner in A, signature Jones epigenetic S, An Apostolidou M: Widschwendter SA, Gayther IJ, SJ, Jacobs Ramus S, Genet A, Beck Mol Gentry-Maharaj M, R, Hum U, Menon Brown smoking. AE, MG, Teschendorff into with 30 Investigation Belvisi associated Prospective MA, loci European Birrell genetic the F, novel in identifies Ricceri 2013;22:843-851. (EPIC-Turin) study C, Nutrition association Sacerdote and Epigenome-wide K, Cancer Veldhoven JM: van Flanagan 2012;39:1159-1165. 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FY, XW, mononuclear Deng Zhu NJ, blood H, Yi Tang peripheral YZ, X, in Liu Lu YH, XB, Zhang Mo X, Lin LF, Rheumatol Wu Rev H, Nat Zhu analysis. 17 methylome DNA from pathogenesis RA Med into BMC Insights 2015;11:386-388. Genetics: 2007;447:396-398. E: arthritis. Nature Ballestar rheumatoid 16 epigenetics. of of pathogenesis Perceptions A: the Bird in 15 Epigenetics TA: Rauch K, Mikecz 2014;12:35. TT, Glant 14 7 Posted on Authorea 1 Jun 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.159103361.19001792 — This a preprint and has not been peer reviewed. Data may be preliminary. arthritis-in-han-chinese-a-case-control-study association-between-ikzf1-related-gene-polymorphism-dna-methylation-and-rheumatoid- figures.docx and KL, tables Sivils RH, with Scofield patients L, from file cells Radfar T Hosted 2014;66:731-739. 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