novel classification and comparison of mild and severe ...€¦ · novel classification and...

Novel Classification and Comparison of Mild and Severe Rheumatoid Arthritis

by

Reena Yaman

A thesis submitted in conformity with the requirements for the degree of Master of Science

Institute of Medical Science University of Toronto

© Copyright by Reena Yaman 2017

ii

Novel Classification and Comparison of Mild and Severe

Rheumatoid Arthritis

Reena Yaman

Master of Science

Institute of Medical Science University of Toronto

2017

Abstract

Rheumatoid arthritis (RA) presents in a highly variable fashion, with some patients not

responding to currently-available therapy and suffering from regularly active disease. Few

available markers currently exist to identify patients destined for severe disease, though there is

evidence of a genetic basis for these differences. The goal of this study is therefore to

investigate genetic RA risk loci and gene expression differences in patients presenting with mild

versus severe disease. Disease severity was defined based on number of biologic drug failures to

capture disease severity in a clinically relevant manner. Our findings suggest that these two

groups do in fact harbor genetic differences at RA risk loci, though these seem to depend on

serology as well. The Ly9-CD244 gene region presented with the most significant difference

between patient groups. These groups also demonstrated significantly different gene expression

profiles, though our findings are preliminary and require further investigation.

iii

Acknowledgments

I could not have reached the end of this rewarding journey on my own, and owe the following

individuals my deepest thanks for helping me get this far.

Firstly, I would like to thank my parents, for providing me with unwavering support, in all its

forms, and instilling in me a drive to constantly challenge myself and work hard to succeed. I

could not have accomplished this without you.

I would also like to thank my supervisor, Dr. Katherine Siminovitch, for believing in me and

providing me with numerous opportunities to expand my skills and knowledge base. I am

grateful for having had the great opportunity to study under your mentorship.

I would like to extend my sincere gratitude to my committee members, Dr. Keystone and Dr.

Branch, for their feedback, patience and support, without which this project would not have

been possible.

I would also like to thank my siblings, for always being there to help me work through the little

things and the constant motivation, support, and encouragement they continue to provide me

with. Additionally, I am deeply grateful for my friends, especially Soha and Pavit, for their

unwavering confidence in my abilities and their emotional support. I am also grateful for David,

for his constant encouragement, feedback, and guidance along the way.

I would like to acknowledge all of the members of the Siminovitch lab, for creating a

welcoming environment and always serving as valuable resources over the course of my degree.

I would like to express a warm thank you to all of the patients who took the time to participate

in the current study and for their vital contributions.

Finally, I further extend my thanks to the generous donors of the Eliot A Phillipson Department

of Medicine Studentship at Mount Sinai Hospital Eliot A Philipson and the Queen Elizabeth II

Graduate Scholarship in Science and Technology for their financial support of my graduate

work.

iv

Contributions

I would like to thank Gang Xie for developing the genotyping panel used in the current study

and his feedback on our results.

I would like to thank the Clinical Genomics Centre, specifically Swan Cot and Roger Shi, for

the genotyping and gene expression analyses, respectively.

I would also like to thank Ramanandan Prabhakaran for handling the bioinformatic analyses of

our gene expression data and creating Figures 3-3 to 3-5.

I would like to acknowledge the work of Robyn Chen Sang and Ilana Sutherland, our clinical

coordinators, for managing patient enrolment and blood draws, as well as assisting in obtaining

patient data.

I would like to express my thanks to Dr. Christopher Amos and his group, specifically David

Qian, for performing the PLINK analysis of our genotyping data.

Finally, I would like to thank Dr. Cynthia Guidos and her group for their assistance in

developing the mass cytometry panel that I hope will be utilized very soon as a future direction

for the current study.

v

Table of Contents

Acknowledgments................................................................................................................iii

Contributions........................................................................................................................iv

TableofContents..................................................................................................................v

ListofAbbreviations...........................................................................................................viii

ListofTables........................................................................................................................xiii

ListofFigures......................................................................................................................xv

ListofAppendices................................................................................................................xvi

Chapter1Introduction..........................................................................................................1

Introduction.....................................................................................................................11

1.1 RheumatoidArthritisOverview.............................................................................................1

1.2 Epidemiology........................................................................................................................1

1.3 ClinicalPresentation.............................................................................................................2

1.4 Diagnosis..............................................................................................................................2

1.5 EtiologyandPathogenesis.....................................................................................................4

1.5.1 GeneticRiskFactorsforRheumatoidArthritis......................................................................4

1.5.2 ImmuneSystemDysregulationinRheumatoidArthritis.....................................................11

1.6 TreatmentandTreatmentRecommendations.....................................................................13

1.6.1 Corticosteroids.....................................................................................................................14

1.6.2 Nonsteroidalanti-inflammatorydrugs................................................................................14

1.6.3 DiseaseModifyingAnti-RheumaticDrugs...........................................................................15

1.6.4 TreatmentRecommendations.............................................................................................20

1.7 PrognosticMarkersinRheumatoidArthritis........................................................................24

1.7.1 Biomarkers...........................................................................................................................24

1.7.2 DefiningSevereDiseaseinRheumatoidArthritis................................................................25

1.7.3 CurrentlyIdentifiedPrognosticMarkers.............................................................................27

1.8 Summary............................................................................................................................34

1.9 ResearchAimsAndHypotheses..........................................................................................35

vi

Chapter2MaterialsandMethods........................................................................................37

MaterialsandMethods..................................................................................................372

2.1 PatientRecruitmentandSampleCollection........................................................................37

2.2 ChartReviews.....................................................................................................................38

2.2.1 Self-reportedMeasuresofDiseaseActivityandDisability..................................................41

2.2.2 Physician’sMeasuresofDiseaseActivity.............................................................................41

2.2.3 SerologicalTestsforAcutePhaseReactants.......................................................................42

2.2.4 Treatment............................................................................................................................43

2.3 GroupAssignment..............................................................................................................45

2.4 Genotyping.........................................................................................................................46

2.5 RNA-sequencing..................................................................................................................48

2.6 StatisticalAnalysis...............................................................................................................49

2.6.1 Demographic&ClinicalData...............................................................................................49

2.6.2 GenotypingData..................................................................................................................49

2.6.3 RNA-seqData.......................................................................................................................49

Chapter3Results.................................................................................................................51

Results...........................................................................................................................513

3.1 ClinicalandDemographicData............................................................................................51

3.2 GenotypingData.................................................................................................................54

3.3 RNA-sequencingData..........................................................................................................61

Chapter4DiscussionandConclusions..................................................................................68

DiscussionandConclusions............................................................................................684

4.1 GeneralDiscussion..............................................................................................................68

4.1.1 ClinicalandDemographicFindings......................................................................................69

4.1.2 GeneticFindings...................................................................................................................71

4.1.3 GeneExpressionFindings....................................................................................................74

4.2 Conclusions.........................................................................................................................77

Chapter5FutureDirections.................................................................................................79

FutureDirections...........................................................................................................795

5.1 GeneralFutureDirections...................................................................................................79

vii

5.2 ImmunophenotypingbyMassCytometry............................................................................81

References...........................................................................................................................86

Appendices........................................................................................................................105

viii

List of Abbreviations

ACPA: anti-citrullinated peptide antibody

ACR: American College of Rheumatology

ADS: Assay Design Suite

ANCA: anti-neutrophil cytoplasmic antibody

APC: antigen presenting cell

Arg: arginine

AZA: azathioprine

bDMARD: biologic disease-modifying anti-rheumatic drug

CarP: carbamylated protein

CCP: circular citrullinated peptide

CCR6: C-C chemokine receptor 6

CD: cluster of differentiation

cDMARD: conventional disease-modifying antirheumatic drug

cDNA: complementary DNA

COBRA: Combinatietherapie Bij Reumatoide Artritis

COPD: chronic obstructive pulmonary disease

CRP: C-reactive protein

CTLA-4: cytotoxic T lymphocyte associated protein 4

CTX-II: Type II collagen c-telopeptide

ix

DAS28: disease activity score using the 28-joint count assessment method

ddNTP: dideoxynucleotide

DGE: differential gene expression

DMARD: disease-modifying antirheumatic drug

DNA: deoxyribonucleic acid

dNTP: deoxynucleotide

EDTA: ethylenediaminetetraacetic acid

ELISA: enzyme-linked immunosorbent assay

eQTL: expression quantitative trait loci

ESR: erythrocyte sedimentation rate

EULAR: European League Against Rheumatism

FDR: false discovery rate

FPKM: fragments per kilobase of transcript per million mapped reads

GC: glucocorticoid

GM-CSF: granulocyte macrophage colony-stimulating factor

GPA: granulomatosis with polyangiitis

GWAS: genome wide association study

HAQ: health assessment questionnaire

HAQ-DI: health assessment questionnaire disability index

HCQ: hydroxychloroquine

x

HLA: human leukocyte antigen

Ig: immunoglobulin

IL: interleukin

JAK: janus kinase

JIA: juvenile idiopathic arthritis

LD: linkage disequilibrium

LEF: leflunomide

Ly9: lymphocyte antigen 9

MALDI-TOF: matrix-assisted laser desorption/ionization time-of-flight

MD global: physician’s global assessment

MHCII: major histocompatibility complex class II

MMP: matrix metalloproteinase

MRI: magnetic resonance imaging

mRNA: messenger RNA

MTX: methotrexate

NF-κΒ: nuclear factor kappa beta

NSAID: nonsteroidal anti-inflammatory drug

OPG: osteoprotegrin

OR: odds ratio

PAD: peptidylarginine deiminase

xi

PADI4: peptidyl deaminases citrullinating enzyme 4

PCR: polymerase chain reaction

PTPN22: protein tyrosine phosphatase non-receptor type 22

qPCR: quantitative polymerase chain reaction

RA: rheumatoid arthritis

RANKL: ligand to receptor activator of nuclear factor-κB

RF: rheumatoid factor

RIN: RNA integrity number

SBE: single base extension

SE: shared epitope

SHS: Sharp/van der Heijde score

SJC: swollen joint count

SLAM: signaling lymphocytic activation molecule

SNP: single nucleotide polymorphism

SSZ: sulfasalazine

T2T: treat to target

TCR: T cell receptor

Th1: type 1 T helper



xii

TJC: tender joint count

TLR: toll-like receptor

TNF: tumor necrosis factor

TNFi: tumor necrosis factor inhibitors

TNFα: tumor necrosis factor α

Treg: regulatory T cell

Trp: tryptophan

USS: ultrasound scanning

VAS: visual analogue scale

xiii

List of Tables TABLE1-12010ACR/EULARRHEUMATOIDARTHRITISCLASSIFICATIONCRITERIA......................................................................3TABLE1-21987ACRRHEUMATOIDARTHRITISCLASSIFICATIONCRITERIA..................................................................................4TABLE1-3MOSTREPRODUCIBLERHEUMATOIDARTHRITISGENESANDGENETICRISKLOCI...............................................................8TABLE1-4RHEUMATOIDARTHRITISGENETICRISKLOCITHATHAVEBEENINVESTIGATEDFORFUNCTIONALINFLUENCEONTHEDISEASE.....9TABLE1-5MOSTCOMMONLYUSEDCONVENTIONALDMARDSINTHETREATMENTOFRHEUMATOIDARTHRITIS,THEIRMECHANISMSOF

ACTIONANDREPORTEDSIDEEFFECTS........................................................................................................................16TABLE1-6MOSTCOMMONLYUSEDBIOLOGICDMARDSINTHETREATMENTOFRHEUMATOIDARTHRITIS,THEIRMECHANISMSOFACTION

ANDREPORTEDSIDEEFFECTS...................................................................................................................................18TABLE1-7COMMONLYUSEDMEASURESFORTHECLASSIFICATIONOFSEVERERHEUMATOIDARTHRITISISPROGNOSTICSTUDIES...........26TABLE1-8BIOMARKERSIDENTIFIEDATBASELINE,DEFINEDASEARLYRA,TOASSOCIATEWITHRADIOLOGICALOUTCOMESINRHEUMATOID

ARTHRITIS............................................................................................................................................................29TABLE1-9GENETICBIOMARKERSFOUNDTOBEASSOCIATEDWITHPOORPROGNOSISINRHEUMATOIDARTHRITIS..............................32TABLE2-1CLINICALANDDEMOGRAPHICDATACOLLECTEDFORALLENROLLEDPATIENTS...............................................................39TABLE2-2COLLECTEDDRUGINFORMATIONCATEGORIESANDMOSTCOMMONLYPRESCRIBEDDRUGSINEACHGROUP.......................44TABLE2-3INCLUSIONCRITERIAFORMILDANDSEVEREGROUPS...............................................................................................45TABLE2-4RNA-SEQUENCINGCOMPARISONSPERFORMEDLISTINGGROUPSANDCOMPAREDSUBGROUPS,INCLUDINGNUMBEROF

PATIENTSINEACHGROUP.......................................................................................................................................50TABLE3-1CLINICALANDDEMOGRAPHICDATARESULTSSHOWINGAVERAGEAGEANDDISEASEDURATION,ASWELLASPERCENTOF

FEMALES,EVERSMOKERS,PATIENTSWITHFAMILYHISTORYOFRA,ANDPATIENTRF,ANTI-CCP,ANDEROSIONSTATUSINMILD

ANDSEVEREGROUPS(*P<0.05;**P<0.01)............................................................................................................53TABLE3-2ALLSIGNIFICANTSNPANALYSISRESULTSFORCOMPARISONOFTHEMILD(N=55)ANDSEVERE(N=34)GROUPS,INCLUDINGALL

ELIGIBLEPARTICIPANTS(P<0.05).............................................................................................................................55TABLE3-3ALLSIGNIFICANTSNPANALYSISRESULTSFORCOMPARISONOFMILD(N=42)ANDSEVERE(N=32)GROUPS,INCLUDINGONLY

RHEUMATOIDFACTORPOSITIVEELIGIBLEPARTICIPANTS(P<0.05)...................................................................................56TABLE3-4ALLSIGNIFICANTSNPANALYSISRESULTSFORCOMPARISONOFMILD(N=13)ANDSEVERE(N=2)GROUPS,INCLUDINGONLY

RHEUMATOIDFACTORNEGATIVEELIGIBLEPARTICIPANTS(P<0.05).................................................................................57TABLE3-5ALLSIGNIFICANTSNPANALYSISRESULTSFORCOMPARISONOFMILD(N=30)ANDSEVERE(N=24)GROUPS,INCLUDINGONLY

ANTI-CCPPOSITIVEELIGIBLEPARTICIPANTS(P<0.05)..................................................................................................58TABLE3-6ALLSIGNIFICANTSNPANALYSISRESULTSFORCOMPARISONOFMILD(N=22)ANDSEVERE(N=7)GROUPS,INCLUDINGONLY

ANTI-CCPNEGATIVEELIGIBLEPARTICIPANTS(P<0.05).................................................................................................59TABLE3-7ALLSIGNIFICANTSNPANALYSISRESULTSFORCOMPARISONOFMILD(N=37)ANDSEVERE(N=31)GROUPS,INCLUDINGONLY

ELIGIBLEPARTICIPANTSWITHEROSIVEDISEASE(P<0.05)..............................................................................................60TABLE3-8ALLSIGNIFICANTSNPANALYSISRESULTSFORCOMPARISONOFMILD(N=16)ANDSEVERE(N=2)GROUPS,INCLUDINGONLY

ELIGIBLEPARTICIPANTSWITHNON-EROSIVEDISEASE(P<0.05).......................................................................................61

xiv

TABLE3-9OVERVIEWOFFINDINGSFROMCOMPARISONOFACTIVEANDINACTIVESUBGROUPSOFMILDANDSEVEREPATIENT

POPULATIONS.......................................................................................................................................................66TABLE3-10OVERVIEWOFFINDINGSFROMCOMPARISONOFMILDANDSEVERESUBGROUPSOFACTIVEANDINACTIVEPATIENT

POPULATIONSATTIMEOFBLOODDRAW....................................................................................................................66TABLE5-1MASSCYTOMETRYPANELCONSISTINGOF35ANTIBODIES(27TARGETINGSURFACEANTIGENSAND8TARGETING

INTRACELLULARCYTOKINES)USEDFORTHEANALYSISOFDENSITYGRADIENT-SEPARATEDHUMANPBMCS..............................83TABLE5-2MASSCYTOMETRYCONSISTINGOF18ANTIBODIESTARGETINGSURFACEANTIGENS,INCLUDINGSURFACEMARKERSOF

NEUTROPHILACTIVATION,USEDFORTHEANALYSISOFLYSEDWHOLEBLOOD.....................................................................84

xv

List of Figures FIGURE1-1FLOWCHARTILLUSTRATINGTHESEQUENCEINWHICHRHEUMATOIDARTHRITISDMARDTHERAPYISESCALATEDIN

ACCORDANCEWITHTHE2015AMERICANCOLLEGEOFRHEUMATOLOGYMANAGEMENTGUIDELINES....................................22FIGURE2-1STUDYOVERVIEWOUTLININGGENERALSTUDYCOMPONENTSANDSTEPSEQUENCE.....................................................37FIGURE2-2DIAGRAMILLUSTRATINGTHESINGLEBASEEXTENSIONSTEPINSNPGENOTYPINGUSINGTHEAGENABIOSCIENCETMIPLEX®

ASSAYANDMASSARRAY®SYSTEMPCRAMPLIFICATIONPRODUCTSARECOMBINEDWITHMASS-MODIFIEDDIDEOXYNUCLEOTIDES,

WITHEACHNUCLEOTIDEHAVINGAUNIQUEANDDETECTABLEMASS.THEAMPLIFIEDGENOMICSEGMENTOFINTERESTISEXTENDED

BYASINGLEMASS-MODIFIEDDDNTPCORRESPONDINGTOTHEBASEPRESENTATTHESNPOFINTEREST.THERESULTINGSINGLE

BASEEXTENSIONPRODUCTSARETHENANALYZEDBYMALDI-TOFMASSSPECTROMETRYTODETERMINETHEALLELESCARRIEDAT

EACHSITEOFPOLYMORPHISM.................................................................................................................................47FIGURE3-1NUMBEROFPATIENTSINTHEMILDGROUPWITHLESSTHAN5,5-9,10-14,15-19,AND20ORMOREYEARSDISEASE

DURATION(ASOF2016)........................................................................................................................................52FIGURE3-2PERCENTOFPATIENTSINMILDANDSEVEREGROUPSTHATPRESENTEDWITHRFPOSITIVE,ANTI-CCPPOSITIVEANDEROSIVE

DISEASE(*P<0.05;**P<0.01)..............................................................................................................................54FIGURE3-3HEATMAPILLUSTRATING37SIGNIFICANTDIFFERENTIALLYEXPRESSEDGENESSHOWING≥3FOLDDIFFERENCEFROM

COMPARISONOFGENEEXPRESSIONDATAFROMACTIVEANDINACTIVEPATIENTSINTHEMILDGROUP(Q<0.05)......................63FIGURE3-4HEATMAPILLUSTRATING35SIGNIFICANTDIFFERENTIALLYEXPRESSEDGENESFROMCOMPARISONOFGENEEXPRESSIONDATA

FROMACTIVEANDINACTIVEPATIENTSINTHESEVEREGROUP(Q<0.05)...........................................................................64FIGURE3-5HEATMAPILLUSTRATING54SIGNIFICANTDIFFERENTIALLYEXPRESSEDGENESFROMCOMPARISONOFGENEEXPRESSIONDATA

FROMMILDANDSEVEREPATIENTSWITHINACTIVEDISEASEATTIMEOFBLOODDRAW(Q<0.05)...........................................65

xvi

List of Appendices APPENDIXTABLE1GENETICTARGETSINVESTIGATEDUSINGGENOTYPEDUSINGIPLEX®ASSAYANDMASSARRAY®SYSTEM.COLUMNS

OUTLINECHROMOSOME(CHR),SINGLENUCLEOTIDEPOLYMORPHISMOFINTEREST(SNP),WHETHERTHESNPISAPEAKSNPORA

LINKAGEDISEQUILIBRIUM(LD)SNP,R2VALUEFORLD,PREVIOUSLYIDENTIFIEDRARISKALLELE,GENEANDTHETARGETEDDNA

SEQUENCE.........................................................................................................................................................105

1

Chapter 1 Introduction

Introduction 1

1.1 Rheumatoid Arthritis Overview

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that primarily affects the

joints, but can involve other organs as well (CentresforDiseaseControlandPrevention

2016). It is a form of autoimmune polyarthritis that affects joints symmetrically (Arnettetal.

1988) and is characterized by synovial membrane and systemic inflammation, as well as

synovial hyperplasia (Scott,Wolfe&Huizinga2010,Lee,Weinblatt2001). The presence of

autoantibodies, mainly rheumatoid factor (RF) and anti-citrullinated peptide antibodies

(ACPAs), is observed in many RA patients (Bax, Huizinga & Toes 2014). Patients typically

present with joint pain, redness and swelling (CentresforDiseaseControlandPrevention

2016). RA can affect any diarthrodial joint, but most commonly involves the small joints of the

hands and feet (Imboden2009). The joint autoimmune reaction leads to damage of articular

cartilage and bone (Scott,Wolfe&Huizinga2010), which can accumulate over time if

untreated (Wolfe,Sharp1998) leading to irreversible joint destruction and disability (Scott,

Wolfe&Huizinga2010,Lee,Weinblatt2001).

RA is associated with premature death as patient lifespan can be decreased by 3 to 10 years,

based on disease severity (Brooks2006,Alamanos,Drosos2005). Moreover, RA is associated

with disability and diminished quality of life (Brooks2006).

1.2 Epidemiology

The prevalence of RA in industrialized countries is 0.5-1 % with a mean incidence rate of 20-50

per 100,000 people per year. These statistics have been found to vary by ethnicity and

geographical location with lower prevalence in Southern European (0.3-0.7%) and developing

countries (0.1-0.5%) (Alamanos,Voulgari&Drosos2006). Furthermore, RA

2

disproportionately affects females and is seen 2 to 3 times more commonly in women than in

men (Alamanos,Drosos2005,SilmanAJ2001).

1.3 Clinical Presentation

RA can present at any age, but according to a review by Alamanos and Drosos, peak age of

onset is between 40 and 50 years of age (Alamanos, Drosos 2005).

Patients with RA commonly present with comorbidities with cardiovascular disease being the

most prevalent (Symmons,Gabriel2011,Dougadosetal.2014). Infections, mental health

conditions, and malignancies are additional comorbidities posing significant risks for RA

patients. As reviewed by Dougados et al., many of these conditions can result from treatment,

shared risk factors, or the chronic inflammation characteristic of RA (Symmons,Gabriel2011,

Dougadosetal.2014).

1.4 Diagnosis

RA is currently diagnosed based on the 2010 American College of Rheumatology (ACR)/

European League Against Rheumatism (EULAR) classification criteria, which take into

consideration the number of active joints, symptom duration, as well as serological and acute-

phase reactant factors. Patients’ disease is scored on these four domains, and a cumulative score

of 6/10 or above is required for classification and diagnosis of RA. Prior to 2010 ACR/ EULAR

classification criteria, the 1987 ACR criteria were used for RA diagnosis, however they lacked

the sensitivity to identify early RA and thus early intervention to prevent permanent damage

(Aletahaetal.2010,Arnettetal.1988,Neogietal.2010). Both the 2010 and 1987

classification criteria measures are outlined in Tables 1-1 and 1-2 below for comparison.

According to the 2010 ACR/EULAR Classification Criteria, patients who have at least 1 joint

with definite swelling that cannot be attributed to another disease and score ≥6 out of 10 on the

criteria shown in Table 1-1 are classified as having RA. Each criterion is further described in

the original 1988 article (Aletahaetal.2010).

3

Table 1-1 2010 ACR/EULAR Rheumatoid Arthritis Classification Criteria

1.Jointinvolvement(0-5)

1largejoint

2-10largejoints

1-3smalljoints

4-10smalljoints

>10joints(atleast1smalljoint)

2.Serology(0-3)

ACPAnegativeandRFnegative

ACPAlow-positiveorRFlow-positive

ACPAhigh-positiveorRFhigh-positive

3.Acute-phasereactants(0-1)

NormalCRPandnormalESR

AbnormalCRPorabnormalESR

4.Durationofsymptoms(0-1)

<6weeks

≥6weeks

0

1

2

3

5

0

2

3

0

1

0

1

4

According to the 1987 RA Classification Criteria, to be classified as having rheumatoid arthritis,

a patient must present with 4 out of 7 from the criteria listed in Table 1-2. Each criterion is

further described in the original 1988 article (Arnettetal.1988).

Table 1-2 1987 ACR Rheumatoid Arthritis Classification Criteria

1.Morningstiffness*

2.Arthritisof3ormorejointareas*

3.Arthritisofhandjoints*

4.Symmetricarthritis*

5.Rheumatoidnodules

6.Serumrheumatoidfactor

7.Radiographicchanges

* Symptoms present for at least 6 weeks

1.5 Etiology and Pathogenesis

1.5.1 Genetic Risk Factors for Rheumatoid Arthritis

It is understood that RA results from genetic effects within an environmental context. The most

well-established environmental risk factor for RA, specifically for ACPA positive disease, is

smoking followed by silica exposure (Klareskogetal.2006,Hunt,Emery2014). The genetic

contribution to RA is highly significant and is estimated to be in the range of 50 to 60%

(MacGregoretal.2000,Klareskogetal.2006).

Though rare in the general population, with a prevalence of <1%, the prevalence among siblings

is 2 to 4% and further increases to 12.3 to 15.4% for an unaffected monozygotic twin with an

5

affected twin, although smaller studies have reported higher twin concordance (Seldinetal.

1999). Concordance rates are dependent on disease prevalence, however, with rates being lower

for diseases of lower prevalence, and can therefore underestimate actual genetic contribution to

the disease. To address this limitation, MacGregor et al. conducted a study aimed at identifying

RA heritability estimates (measures of genetic contribution that are independent of population

prevalence) using twin data from nationwide Finnish and United Kingdom studies. This study

reported a genetic contribution of ~60%, emphasizing the significant role of genetics in RA

(MacGregor et al. 2000).

The most strongly associated genetic factors with the development of RA (accounting for 30 to

50% of genetic risk) are located in the human leukocyte antigen (HLA) alleles (Bowes,Barton

2008,Imboden2009). The shared epitope (SE) hypothesis, which posits that a shared major

histocompatibility complex class II (MHCII) epitope contributes to RA pathogenesis, resulted

from the observation that several alleles of the HLA-DRB1 gene, which code for a specific

conserved 5 amino acid sequence around the peptide-binding groove served as RA risk alleles

(Gregersen,Silver&Winchester1987,Viatte,Plant&Raychaudhuri2013). HLA-DR

proteins are expressed on antigen presenting cells (APCs) and serve to present antigens to T

cells, which identify a portion of the HLA-DR molecule as well as the presented peptide. The

SE motif is located in the HLA-DR peptide-binding groove and therefore affects both peptide

binding and antigen presentation (Gregersen,Silver&Winchester1987,Huizingaetal.

2005). Though the SE hypothesis alone cannot explain the risk conferred by these alleles, HLA-

DRB1 is likely linked to pathogenesis through other mechanisms. As reviewed by Coenen et al.,

these associations are largely applicable to only anti-CCP positive subsets of RA patients

(Coenen,Gregersen2009).

Candidate gene studies later permitted the identification of additional susceptibility loci such as

PTPN22, PADI4, and CTLA-4 (Begovichetal.2004,Suzukietal.2003,Plengeetal.2005).

Genome wide association study (GWAS) technology, however, was crucial in identifying non-

HLA loci and novel genetic risk factors are discovered at an astounding rate (Coenen,

Gregersen2009). GWAS investigate the association of gene variants with the disease. As is

seen in other complex diseases, variant associations tend to have odds ratios (ORs) that are not

greater than 1.5, suggesting that each individual allele makes only a small contribution to overall

6

genetic disease risk (Stahletal.2010,Raychaudhurietal.2008). A single nucleotide

polymorphism (SNP) is a common variation in a single deoxyribonucleic acid (DNA) subunit,

called a nucleotide, at a specific genomic location. A GWAS compares cases (i.e. patients) to

controls (i.e. unaffected individuals) and genotypes approximately 1 million SNPs. The genome-

wide significance level α is usually set to 5 × 10-8 and rejection of the null hypothesis is

considered to indicate the presence of disease-associated variants at that genomic location

(Kochi,Suzuki&Yamamoto2014). Thus, a GWAS makes it possible to conclude that a single

nucleotide variation at a specific genomic location can increase the risk of developing a disease,

RA in this case, with a 0.000005% chance that a difference between the patient and control

groups does not in fact exist.

Though the majority of SNPs are considered to produce no functional change, mutations in

promoter, enhancer, or silencer regions can lead to altered gene transcription. Those in locus

control regions or those that affect messenger RNA (mRNA) stability can alter gene expression

(Chanock2007). Specific variants can cause disease by affecting genetic function through

various mechanisms. These are briefly described below:

• A silent mutation, also known as a synonymous mutation, is a mutation in which the

base pair change does not alter the coded amino acid and therefore does not change the

sequence of amino acids in the coded protein. These have been reported to alter mRNA

stability, however (Chanock2007).

• A nonsense mutation is one where the base pair change leads to the replacement of an

amino acid coding sequence with one coding a stop codon leading to the production of a

truncated protein.

• A frame-shift mutation results from the insertion, duplication, or deletion leading to a

shift in the reading frame resulting in a different amino acid sequence and commonly a

non-functional protein.

• A missense mutation is one where the base pair change leads to the replacement of an

amino acid with another in the protein coded by the gene.

• Alternative splicing mutations occur in regions coding for splicing patterns and can lead

to alternative splicing of the coded protein.

7

• Mutations altering the level of transcript expression occur in regions responsible for

regulating RNA expression and can alter these levels. Expression quantitative trait loci

(eQTL) are genomic regions containing variants that control levels of expression of one

or more genes. eQTLs can be located close to the genes whose expression they regulate

or at a different genomic location, referred to as local or cis and distant or trans eQTLs,

respectively.

As reviewed by Kochi et al. of the 100 known non-HLA risk loci, the majority affect splicing or

transcript expression, with only 16% being in linkage disequilibrium (LD) with missense SNPs

(Kochi,Suzuki&Yamamoto2014).

The most reproducible genetic associations with RA, as reviewed by Coenen et al. and Kochi et

al., are listed in Table 1-3 (Coenen,Gregersen2009,Kochi,Suzuki&Yamamoto2014).

After the MHCII loci, PTPN22 shows both the strongest and most reproducible association to

the disease (Coenen,Gregersen2009). SNP rs2476601 at amino acid 620 leads to the

replacement of arginine (Arg) with tryptophan (Trp)(Coenen,Gregersen2009). This risk

allele has been found to be associated with numerous other autoimmune diseases. Additionally,

the minor allele (A) is much more prevalent in European and North American populations. After

PTPN22, the TRAF1-C5 region of the genome seems to have considerable association with RA

and shows a greater association for the anti-CCP positive patient subgroup.

8

Table 1-3 Most reproducible rheumatoid arthritis genes and genetic risk loci

GeneticRegion RiskLocus/Loci

MHCII,mainlyHLA-DRB1butotherlocias

well

SEalleles

Frequencyofdifferentallelesinthisregion

differbetweenpopulations

PTPN22

OnemissenseSNP:rs2476601leadingto

substitutionofargininewithtryptophan

andalteringproteinactivity

TRAF1-C5region TwoSNPs:rs3761847andrs10818488

STAT4 RiskhaplotypecontainingSNPsinintron

regionalteringsplicingandexpression

6q23region TwoSNPs:rs10499194andrs6920220

Predominantlyassociatedwithanti-CCP

positivesubgroup

PADI4

Riskhaplotypealteringgeneexpression

Chromosome1p36.AlsoseeninAsian

populations

4q27region Regionassociationcontaining4genes:

KIAA1109,Tenr,IL2,andIL21

CCR6 Dinucleotidepolymorphismin5’flanking

regionalteringgenetranscription

9

TNFAIP3 Onemissensemutationleadingto

substitutionofphenylalaninewithcysteine

atposition127leadingtoimpairedA20

function

TTtoApolymorphismleadingtoreduced

TNFAIP3expression

AdaptedfromCoenen&Gregersen,2009andKochietal.,2014.

As reviewed by Viatte et al., the susceptibility loci investigated for mechanistic influence on the

disease are listed in Table 1-4 (Viatte, Plant & Raychaudhuri 2013).

Table 1-4 Rheumatoid arthritis genetic risk loci that have been investigated for functional

influence on the disease

GeneticRegion RiskLocus/Loci

PTPN22 SNPrs2476601;downregulatesTCR

signaling

PADI4 Haplotype;Involvedinthepost-

translationalconversion(citrullination)of

argininetocitrulline

CCR6 Polymorphismleadingtohigher

expressionofCCR6,whichencodesCCR6,a

chemokinereceptorexpressedbyTh17

cells

Adaptedfrom:Viatteetal.,2013.

10

SNP rs2476601, discussed above, is a non-synonymous mutation in PTPN22. This gene codes

protein tyrosine phosphatase non-receptor type 22 (PTPN22), which is a phosphatase that

dephosphorylates Src family kinases and thus decreases T cell receptor (TCR) signaling, which

is responsible for the identification of antigens bound to MHC molecules. The risk allele has

been shown to be a loss-of-function allele that leads to decreased protein levels and thus

increased number and activation of T cells, as well as other immune cell subsets (Viatte,Plant

&Raychaudhuri2013,Zhangetal.2011).

Peptidyl deaminases citrullinating enzyme 4 (PADI4), coded by PADI4, is an enzyme that post-

translationally converts arginine into citrulline. This locus has been found to be RA-specific. A

haplotype leading to increased stability of PADI4 mRNA transcripts has been found to be

associated with ACPA-positive RA. This results in an increase in citrullinated peptides, the

autoantigen targets of ACPA, which in turn elicit immune responses (Viatte,Plant&

Raychaudhuri2013).

C-C chemokine receptor 6 (CCR6), coded by CCR6, is a chemokine receptor expressed by Th17

cells. A CCR6 polymorphism leading to increased gene expression, as well as increased serum

IL-17 levels has been found to be associated with RA (Kochietal.2010).

As reviewed by Kochi et al., only 5.5% and 4.7% of heritable risk can be explained by known

non-MHC risk loci in European and Asian populations, respectively (this includes both genetic

and environmental risk). It is suggested that the remaining genetic risk likely results from

uncommon variants, defined as a minor allele frequency of <1%, to account for the “missing

heritability”(Kochi,Suzuki&Yamamoto2014). Low frequency variants have yet to be

identified (Viatte,Plant&Raychaudhuri2013). The authors also emphasize that single genetic

factors are insufficient in predicting disease severity or treatment response and that polygenic

approaches are more likely required as prognostic biomarkers.

Okada et al. conducted a genome-wide association study (GWAS) meta-analysis of RA, which

was published in 2014. This included genomic data from more than 100,000 subjects, comprised

of 29,880 RA cases and 73,758 healthy controls, of both European and Asian ancestry. They

evaluated approximately 10 million SNPs and identified a total of 101 risk loci, 42 of which

11

were novel associations with the disease. They further demonstrated that the genes identified are

targeted by currently approved RA treatments and that other genes may point to drugs used to

treat other diseases, such as cancer, which could potentially be repurposed for use in RA based

on these findings.

1.5.2 Immune System Dysregulation in Rheumatoid Arthritis

Though the exact mechanisms underlying the development of RA remain poorly understood,

both adaptive and innate immune responses have been implicated in disease pathogenesis

(McInnes,Leung&Liew2000,Behrensetal.2007,Edwardsetal.2004,Takemuraetal.

2001,Brennan,McInnes2008,McInnes,Schett2011). Immune cell subsets in combination

with non-immune cell subsets such as fibroblasts and endothelial cells have all been discovered

to play a role in disease etiology (Mohammed,Smookler&Khokha2003,Meyer,Franssen&

Pap2006,AngusMcQuibbanetal.2002). These different immune components and their links

to RA are briefly described below.

1.5.2.1 T cell Involvement

A shift toward a pro-inflammatory type 1 T helper (Th1) versus an anti-inflammatory type 2 T

helper (Th2) response, and the associated cytokines, has been observed in RA patients

(McInnes,Leung&Liew2000,Cañeteetal.2000,Schulze-Koops,Kalden2001).

Furthermore, Th17 cells are implicated in the production of inflammatory cytokines (IL-17 and

TNF-α) leading to the activation of other immune cell subtypes including neutrophils and

monocytes, as well as synovial fibroblasts (Weaveretal.2007,Miossec,Korn&Kuchroo

2009). Additionally, dysfunction in T regulatory cells (Tregs), which reduce inflammation, has

also been demonstrated in RA (Chabaudetal.1999,Ehrensteinetal.2004).

12

1.5.2.2 Antibody and B cell Involvement

Antibodies serve as defense molecules that target specific pathogenic antigens, neutralize

pathogens, and activate the immune response. The production of antibodies targeting self-

tissues, referred to as autoantibodies, is seen in certain autoimmune diseases and can lead to

tissue damage. The presence of autoantibodies in the serum of RA patients has been well

documented. RF, a group of autoantibodies targeting the Fc portion of human immunoglobulin

G (IgG), was the first to be described in 1957, and is observed at high serum levels in 80% of

RA patients (Franklinetal.1957,McArdleetal.2015).

ACPAs, which target epitopes resulting from the deimination of charged arginine residues to

produce neutral citrulline, were more recently discovered and show higher disease specificity.

Diagnostic tools developed to test patient serum for ACPA use a circular citrullinated peptide

(CCP)-2 enzyme-linked immunosorbent assay (ELISA), which allows for the detection of

antibodies directed against circular citrullinated peptide (anti-CCP). Both RF and anti-CCP tests

have been incorporated into clinical practice as well as current diagnostic criteria for RA(Bax,

Huizinga&Toes2014,Aletahaetal.2010). Importantly, ACPAs have been found to bind to

osteoclasts, inducing osteoclastgenesis and breakdown of bone leading to the observed joint

damage (Harreetal.2012). Furthermore, they can activate the immune system through

complement pathways and interaction with Fc-receptor expressing cells, further demonstrating

the pathogenic potential of these autoantibodies (Trouwetal.2009,Claveletal.2008,Bax,

Huizinga&Toes2014).

Several other autoantibodies with different target epitopes have since been identified. Anti-

carbamylated protein (CarP) antibodies, which target epitopes resulting from the carbamylation

of lysine residues to produce homocitrulline, have been discovered. These have also been shown

to be present in both ACPA-positive and a substantial (16-30%) proportion of ACPA-negative

patients (Shietal.2011). Additionally, anti-peptidylarginine deiminase (PAD) antibodies

targeting PAD enzymes, which are responsible for protein citrullination, have been identified

and demonstrated to activate their enzyme target as well (Darrahetal.2013).

Moreover, the pathogenic link to autoantibody production by plasmablasts and, more recently,

the observed treatment response to Rituximab, a B cell depletion therapy, further implicate B

cells in the etiology of RA (McInnes,Leung&Liew2000,Seyleretal.2005).

13

1.5.2.3 Cytokine and Other Immune Cell Involvement

Studies also demonstrate fibroblast invasion of cartilage (Müller-Ladneretal.1996) and

osteoclast activation leading to erosion of bone (Cohenetal.2008) in RA patients, suggesting a

role in disease development and progression. Toll-like receptor (TLR) responses in innate

immune cell subsets, including macrophages and dendritic cells, also appear to be involved.

Additionally, cytokine production by innate immune cell subsets leads to neovascularization,

hyperplasia and other inflammatory reactions resulting in cartilage and bone damage and

destruction (Woolley2003,Haringmanetal.2005,Cascãoetal.2010,Foell,Wittkowski&

Roth2007,Goh,Midwood2012,Nigrovic,Lee2007,Hueberetal.2010). Several cytokines

associated with disease pathogenesis have been identified over the years, some of which are

targets of currently-available treatments (discussed in more detail below). TNF, IL-1, and IL-6

and affiliated pathways form the main treatment targets for currently existing biologic drugs

approved for use in the treatment of RA. IL-12, IL-23, IL-15, GM-CSF, IL-17, and IL-18 have

also been investigated as potential targets for future therapies. A useful summary of cytokines

and their role in disease pathogenesis has been published by McInnes et al.(McInnes,Schett

2007).

RA is also thought to be a heterogeneous condition with a number of different

pathophysiologies with similar clinical presentations (vanderHelm-vanMil,Huizinga2008).

It is thus clearly evident that RA is a complex disease with multifactorial etiology and a

tremendous interplay of many components of the immune system contributing to disease

pathogenesis.

1.6 Treatment and Treatment Recommendations

RA patient outcomes have significantly improved in past years. Numerous developments have

aided in this, including an emphasis on early diagnosis and treatment, the development of

reliable assessment tools, and the understanding that treatment can serve to slow or stop disease

progression. The central role of methotrexate (MTX) in treating disease, and the advent of a new

14

class of biologic disease-modifying antirheumatic drugs (bDMARDs or biologics) have

significantly contributed to this medical breakthrough. The mainstay of current RA treatment is

therefore immunosuppression and involves the use of combinations of corticosteroids,

nonsteroidal anti-inflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs

(DMARDS) (Smolen et al. 2016, Negrei et al. 2016).

1.6.1 Corticosteroids

Corticosteroids, also referred to as glucocorticoids (GCs), are hormones that bind to GC

receptors and inhibit cytokine transcription and inflammatory responses through genomic and

non-genomic effects (Negrei et al. 2016). These lead to rapid symptom relief and, in

combination with DMARD therapy, have been shown to be effective in preventing joint damage

in early RA.

Corticosteroid use, however, has numerous side effects including weight gain, heart failure,

hypertension, diabetes, myopathy, peptic ulcers, infections, sleep and mood disturbances, as

well as osteoporosis. It is therefore recommended that corticosteroids be used sparingly and then

tapered and stopped to prevent adverse events associated with their long-term use.

1.6.2 Nonsteroidal anti-inflammatory drugs

Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce prostaglandin production and inhibit

cyclooxygenases 1 and 2. These are very commonly used for symptom relief to control pain and

inflammation. They have been shown to be ineffective in preventing joint damage, however, and

are thus used in combination with other drug categories in the treatment of RA. NSAID use is

associated with mainly gastrointestinal side effects, including peptic ulcer disease. However

their long-term use can lead to adverse cardiovascular and renal outcomes, with higher patient

morbidity and mortality (Negrei et al. 2016, Harirforoosh, Jamali 2009).

15

1.6.3 Disease Modifying Anti-Rheumatic Drugs

Disease Modifying Anti-Rheumatic Drugs (DMARDs) act on the immune system to prevent or

slow disease progression, and thus protect joints from destruction. DMARDs can be placed into

one of two categories: conventional DMARDs (cDMARDs) and biologic DMARDs

(bDMARDs) (Arthritis Research UK 2016).

1.6.3.1 Conventional DMARDs

Conventional DMARDs, cDMARDs or DMARDs as they are referred to in ACR RA Treatment

Recommendations, are synthetic molecules that target and reduce inflammation in a general

fashion (Vaz et al. 2009, Singh et al. 2016). Table 1-5 shows conventional DMARDs, their

mechanisms of action and their most common side effects.

16

Table 1-5 Most commonly used conventional DMARDs in the treatment of rheumatoid

arthritis, their mechanisms of action and reported side effects

TradeName(GenericName) Mechanismofaction Sideeffects

Methotrexate;MTX Partiallyunknown;folic

acidantagonist;inhibits

DNA,RNA,andprotein

synthesisinrapidly

dividingcells.

Gastrointestinal,hepatic,

pulmonary,hematologic,

cutaneous,neurological,

andopportunisticinfection.

MTXisalsoteratogenic.

Plaquenil

(hydroxychloroquine);HCQ

Partiallyunknown;

IncreasespHincell

vacuoles;interfereswith

TLRsignalingandantigen

processing.

Gastrointestinal,cutaneous,

and,rarely,ocular

retinopathy.

Azulfidine(sulfasalazine);SSZ Partiallyunknown;

reducessynthesisof

specificcytokines(TNF-α,

IL-1andIL-6);Decreases

NF-κΒandTcell

activation;Decreases

antibodyproductionbyB

cells.


cutaneous,neurologic,

hematologic,and,rarely,

eosinophilicpneumonia.

Arava(leflunomide);LEF Inhibitspyrimidine

synthesisandthus

tyrosinekinaseandNF-κΒ

activationthereby

affectingTcellactivation.


hematologic,neurologic,

cardiovascular,cutaneous,

andincreasedinfections.

17

Abbreviations:IL,interleukin;NF-κΒ,nuclearfactorkappabeta;TNF-α,tumornecrosisfactor

alpha.Adaptedfrom:AmericanCollegeofRheumatology,2016;Negreietal.,2016;Fox,1993;

Kyburzetal.,2006.

Of note, Minocin (minocycline), Imuran (azathioprine; AZA) and gold therapy are also related

to the cDMARD medications commonly used for RA therapy. However, they were not included

as cDMARDs in the 2015 ACR RA Treatment Recommendations due to their infrequent use in

addition to the lack of new data on these agents and their overall effectiveness in RA treatment

since 2012 (Singh et al. 2016).

1.6.3.2 Biologic DMARDs

Biologic DMARDs, bDMARDs or biologics, are genetically engineered biomolecules, such as

antibodies, that reduce inflammation by targeting specific immune components and hence

interfere with the inflammatory process and reduce inflammation. Currently used biologics are

listed in Table 1-6 along with a brief description of their mechanism of action and related side

effects. It is worth emphasizing, however, that biologics are associated with numerous side

effects, which necessitate meticulous management before, after, and during treatment. Because

of their immunosuppressive nature, biologics also increase the risk of infection, and potentially

malignancy. In addition, they may induce allergic reactions, especially infusion-related reactions

since many biologics are administered subcutaneously.

Tumor necrosis factor inhibitors (TNFi), or anti-tumor necrosis factor (anti-TNF) therapies,

comprise a large proportion of currently approved biologics for RA treatment. However, 35-

40% of RA patients do not respond to anti-TNF therapy and may require the use of alternative

biologics that operate through different mechanisms (Negreietal.2016). Examples of specific

adverse effects for TNFi include allergic reactions, enhanced immunogenicity, increased risk of

infections (most notably tuberculosis), cancer, worsening of heart failure, the development of

antibodies targeting double-stranded DNA/ lupus-like syndrome, and, rarely, demyelinating

diseases (Winthrop2006,Negreietal.2016).

18

Table 1-6 Most commonly used biologic DMARDs in the treatment of rheumatoid

arthritis, their mechanisms of action and reported side effects

TradeName(Generic

Name)

Mechanismofaction Sideeffects

Enbrel(etanercept) TNFinhibitor.Molecule

consistingof2TNF-α

receptorchainsanda

humanIgG1Fcportion.

Infections(specificconcernwith

tuberculosisinfectionand

reactivation),cancer,worsening

ofheartfailure,allergic

reaction,andimmunogenicity.

Rarely,demyelinatingdiseases.

Remicade(infliximab) TNF-αinhibitor.Chimeric

monoclonalantibodywith

humanIgG-1Fcregion

andmurineFvregion

targetingTNF-α.







Humira(adalimumab) TNF-αinhibitor.Human

monoclonalantibody

targetingTNF-α.







19

Simponi(golimumab) TNF-αinhibitor.Human

monoclonalantibody

targetingTNF-α.







Cimzia(certolizumab

pegol)

TNF-αinhibitor.

Polyethyleneglycol-

coatedFabportionofa

humanmonoclonalIgG

antibodytargetingTNF-α.







Orencia(abatacept) Recombinantreceptor

consistingofCTLA-4

extracellulardomainand

humanIgG-1Fcportion.

ThisbindstoAPCsand

blocksTcellcostimulation

andactivation.

Nausea,headaches,

exacerbationofCOPD,allergic

reaction,infusionsitereactions,

infection,reducedprotection

fromvaccines.

Rituxan(rituximab) Chimericmonoclonal

antibodyconsistingof

humanIgG-1Fcregion

andmurineFvregion

targetingCD20.

Nausea,headache,diarrhea,

musclespasm,peripheral

edema,anemia,infusionsite

reactions,infection,

reactivationofhepatitisB

(rare),progressivemultifocal

leukoencepalopathy(rare).

20

Actemra(tocilizumab) IgG-1monoclonal

antibodytargetingIL-6

receptor.

Headache,nasopharyngitis,

infusionsitereactions,

infection,hypertension,

increasedalanine

aminotransferase,diverticulitis,

dyslipidemia,hepaticenzyme

levelincrease.

Abbreviations:APC,antigenpresentingcell;CD20,clusterofdifferentiation20;COPD,chronic

obstructivepulmonarydisease;CTLA-4,cytotoxicTlymphocyteassociatedprotein4;Ig,

immunoglobulin;IL,interleukin;TNF-α,tumornecrosisfactoralpha.Adaptedfrom:American

CollegeofRheumatology,2016;Dillman,1997;Negreietal.,2016;Winthrop,2006.

Of note, Kineret (anakinra) is an IL-1 receptor antagonist related to the bDMARD treatments

listed in Table 1-6. However, since it is infrequently used for RA treatment and there was a lack

of new data on this treatment and its effectiveness in RA treatment since 2012, it was not

included as a bDMARD in the 2015 ACR RA Treatment Recommendations (Singh et al. 2016).

1.6.4 Treatment Recommendations

Despite these numerous available therapies, some patients continue to present with treatment-

resistant disease, which has fueled the search for additional treatments. Tofactinib (trade name

Xeljanz) is a synthetic small molecule Janus Kinase (JAK) inhibitor that is administered orally.

This is a newer treatment for RA and is only used after multiple bDMARD failure.

“Treat to target” (T2T) is the current recommended treatment strategy for RA, with the present

goal being achievement of remission or at least low disease activity, as defined by validated

indices, including joint assessment. This approach involves the careful monitoring of disease

activity, personalizing treatment to optimize patient benefit and reduce patient risk, and

collaboration with patients in the decision-making process. Treatment is subsequently

21

reassessed and adjusted accordingly in order to achieve treatment goals and optimize patient

outcomes (Smolenetal.2016,Smolen2016).

In order to incorporate recent knowledge advances into patient treatment and care, the ACR

published the 2015 RA management guidelines, which were preceded by the 2012 guideline,

which in turn was preceded by the 2008 guideline (Singhetal.2016,Singhetal.2012,Saaget

al.2008). These guidelines were developed as a result of systematic reviews and the

collaboration of multidisciplinary teams of experts aimed at optimizing disease management and

maximizing patient benefit. Figure 1-1 Provides a general overview of the sequence in which

treatments are escalated based on persistent moderate or high disease activity in accordance with

the 2015 ACR management guidelines.

22

Figure 1-1 Flow chart illustrating the sequence in which rheumatoid arthritis DMARD

therapy is escalated in accordance with the 2015 American College of Rheumatology

management guidelines

DMARDNaïve

DMARD

Monotherapy

Anyof:

CombinationDMARDTherapy

TNFi(±MTX)

Biologic(±MTX)

Anyofuntried:

TNFi(±MTX)

Biologic(±MTX)

Tofactinib(±MTX)

Low,Moderate,orHighDisease

Activity

ModerateorHighDiseaseActivity

ModerateorHighDiseaseActivity

23

The current treatment recommendations, based on the 2015 ACR management guidelines, are

summarized below.

• For patients naïve to DMARD therapy, cDMARD monotherapy (preferably MTX) is

recommended. Whereas, for patients who continue to experience moderate or high

disease activity despite cDMARD monotherapy, recommendations suggest any of the

following steps:

o Use of a combination of cDMARDs

o Addition of biologic or tofactinib, with or without MTX

• For those who fail to respond to treatment using a single TNFi, it is recommended they

continue onto another form of TNFi or be placed on a non-TNFi biologic, with or

without MTX

• For those who fail to respond to non-TNFi biologic treatment, it is recommended they be

placed on another non-TNFi biologic, with or without MTX

• For those who have failed TNFi and non-TNFi treatment, it is recommended they be

placed on another non-TNFi biologic or tofactinib, with or without MTX

• For those who have been placed on and failed multiple TNFi biologic treatments, it is

recommended they be switched to non-TNFi biologic therapy or tofactinib, with or

without MTX and then to another non-TNFi biologic or tofactinib, with or without

MTX, if their first switch does not lead to response to treatment

• For TNFi naïve patients who have failed multiple non-TNFi biologic therapies, it is

recommended they be placed on TNFi biologic treatment, with or without MTX.

• For those who are not TNFi naïve and have failed multiple non-TNFi biologic therapies,

it is recommended they be placed on tofactinib, with or without MTX

As described in Figure 1-1, for patients demonstrating resistance to cDMARD therapy,

biologics are usually prescribed either alone or in combination with cDMARDs in an attempt to

induce remission.

However it appears that compliance with guidelines is not universal. Garrood et al. conducted a

study in 2011 to investigate United Kingdom rheumatologists’ compliance with National

24

Institute for Health and Clinical Excellence treatment guidelines. They surveyed 258

rheumatologists and found that aggressive treatment was not used for newly diagnosed patients,

despite guideline suggestions. Rheumatologists indicated that the main reasons for not

prescribing aggressive treatment were patient acceptance, monitoring requirements, and

concerns about treatment side effects (Garrood,Shattles&Scott2011). Furthermore, a review

by Scott et al. suggests that economic, medical, and social costs need to be weighed against

effectiveness of treatment when considering treatment choices for patients with RA (Scott,

Wolfe&Huizinga2010). More importantly, there is an additional limitation to RA disease

management: the limited availability of biomarkers to enable prediction of a particular patient’s

disease course and hence the best-suited treatment strategy for their disease.

1.7 Prognostic Markers in Rheumatoid Arthritis

1.7.1 Biomarkers

Biomarkers are measurable biomolecules that can be used to indicate specific pathological

processes, such as disease activity, prognosis, and treatment response. Their role is very

important in RA, given the highly unpredictable nature of the disease itself, its variable response

to treatment, and the wide range of available treatment choices to induce remission. Optimal

treatment is of particular importance due to the high costs and potential toxicities of the

therapies used to manage the disease. More importantly, it is clearly established that early,

aggressive treatment can serve to prevent permanent joint damage, attenuate disability and

improve overall patient prognosis; hence the significance of selecting the correct agent and also

determining the appropriate timing for treatment initiation. It is therefore evident that

biomarkers can have great therapeutic value in allowing the provision of timely and

personalized treatment and care for RA patients (Eastmanetal.2012,Gibsonetal.2012).

Despite their significance, there are currently few available such biomarkers for prediction of

disease flares or the personalization of treatment. Furthermore, up to 40% of patients show

resistance to treatment and those who do respond may not experience complete reduction in

disease activity and symptoms. Finally, not all RA patients develop a severe form of the disease

that requires aggressive treatment (McArdleetal.2015,Plantetal.2011).

25

Importantly, there have been no clinically useful biomarkers identified that can predict patient

prognosis, and the nature of the disease does not lend itself to discovery of one single marker. A

panel consisting of various biomarkers therefore seems more feasible to aid in differentiating

patients destined to more aggressive disease courses with worse prognosis. The concepts of

actionable biomarkers that signify potential treatment target pathways, as well as mechanistic

biomarkers, which reflect disease pathogenesis, are interesting directions for future study (Mc

Ardleetal.2015,Eastmanetal.2012,Robinsonetal.2013,Gibsonetal.2012).

1.7.2 Defining Severe Disease in Rheumatoid Arthritis

As reviewed by Scott et al., severe RA is much more poorly defined than its counterpart, RA

remission. In their review, Scott et al. describe four criteria used to define severe RA, which

encompass radiological measures, physician assessment, and self-reported patient assessment

(Scottetal.2013).

The most commonly used measure of disease activity in RA is the Disease Activity Score using

the 28-joint count assessment method (DAS28). It is a measure of disease activity calculated

using number of swollen and tender joints (out of 28 joints), erythrocyte sedimentation rate

(ESR), and RA activity self-reported visual analogue scale (VAS) score. It is limited to

assessment at only one time point, however, and does not take into account erosions, extra-

articular manifestations, and disability. Furthermore, it requires a calculator or computer to tally

the score and assigns heavy weighting to ESR (Anderson et al. 2011).

The Health Assessment Questionnaire Disability Index (HAQ-DI) is another commonly used

measure. It is a self-reported measure of disability calculated based on functional assessment.

The main limitation of this scale is that it is an indirect measure of disease severity, which relies

on subjective patient reporting.

Radiological measures, on the other hand, focus on joint damage. The Sharp/van der Heijde

Score (SHS) is one such measure that evaluates erosions in 44 joints and joint-space narrowing

in 42 joints. The Scott Modification of the Larsen Method is another measure of radiological

damage that examines erosions and joint destruction in hands, wrists, and feet. Limitations of

such methods are their inability to consider factors other than joint damage and the reliance on

26

experts to score each measurement. These measures are summarized and described in Table 1-

7.

Table 1-7 Commonly used measures for the classification of severe rheumatoid arthritis is

prognostic studies

Scale Description

DiseaseActivityScore28• Activitymeasurement

• Usesprovider,patient,andlabmeasures

• Score<2.6:remission

Score≥2.6and<3.2:lowactivity

Score≥3.2and≤5.1:moderateactivity

Score>5.1:highactivity

HealthAssessment

QuestionnaireDisability

Index

• Self-reporteddisabilityassessment

• Scoreof1-2:moderatetoseveredisability

Scoreof2-3:severetoveryseveredisability

Sharp/vanderHeijdeScore• Measureofradiologicaldamage

• Scorebetween0and448

• Higherscoreisworse

ScottmodificationofLarsen

Method

• Measureofradiologicaldamage

• Scorebetween0and250

• Higherscoreisworse

Adaptedfrom:Scott,Lewis,Cope,&Steer,2013;Andersonetal.,2011;Smolenetal.,

2016.

Serological, genetic, environmental and epidemiological, biochemical, radiological, and gene

expression factors have been reported as predictive markers for severe RA, characterized by

worse scores on the above assessment tools. These are described in the following sections.

27

1.7.3 Currently Identified Prognostic Markers

1.7.3.1 Serological

Historically, RA patients could be differentiated based on presence of RF in their serum. Higher

rates of extra-articular manifestations and joint damage are associated with RF positive disease.

Furthermore the IgA isotype has been associated with worse outcomes than IgM and IgG RF

(Scottetal.2013).

However, the predictive power of RF for disease development is limited by its lack of

specificity to RA. Furthermore, the usefulness of RF as a prognostic marker is negatively

associated with disease progression (Syversenetal.2008,Nelletal.2005).

More recently, ACPA, a more specific autoantibody to RA, has been used to subcategorize RA

patients with those testing positive for anti-CCP having more aggressive disease (Robinsonet

al.2013). ACPA presence has been found to be a predictor of joint damage (Lindqvistetal.

2005) appears prior to disease onset, is stable over time, and has demonstrated prognostic value

(Forslindetal.2004,Kastbometal.2004,Rönnelidetal.2005).

1.7.3.2 Environmental and Epidemiological

In addition to being a risk factor for seropositive RA, smoking has been found to be associated

with worse prognosis, based on swollen joint count (SJC), nodules, and Sharp and HAQ scores.

As reviewed by Scott et al., in addition to smoking, social deprivation, female gender, and

periodontitis have also been found to be associated with poorer prognosis while alcohol

consumption and oral contraceptive use seem to provide protective effects and less severe

disease course (Scottetal.2013). Unfortunately, currently known environmental and

epidemiological risk factors for severe RA do not provide opportunity for intervention.

28

1.7.3.3 Imaging

Radiological imaging assessments using both magnetic resonance imaging (MRI) and

ultrasound scanning (USS) in early RA patients have been found to be predictive of radiological

outcomes. Power Doppler assessment of synovial inflammation using USS technology has also

been found to predict radiographic progression in both early RA and established RA patients

(Freestonetal.2010). The presence of bone marrow edema, detected through MRI at disease

onset (baseline) has been found to be associated with subsequent joint damage years into the

disease course (Hetlandetal.2009,Palosaarietal.2006,Haavardsholmetal.2008).

1.7.3.4 Biochemical

ESR and C-reactive protein (CRP) are currently used biochemical prognostic markers. These are

general markers of inflammation, however, and do not offer disease-specific value. As a result,

these do not present actionable biomarkers with therapeutic potential or pathophysiological

value. Molecules related to bone turnover and immune function have therefore been investigated

with the aim of finding a marker, which exhibits an association with RA disease severity and

prognosis. Some of these molecules are described below.

Matrix metalloproteinases (MMPs) are enzymes involved in the proteolysis of extracellular

proteins including immune signaling molecules such as cytokines (Scottetal.2013). They are

also involved in the breakdown of collagen (McArdleetal.2015). Elevated levels of both

MMP-1 and MMP-3 have been found to correlate with radiographic progression, yet these

findings have not been consistently replicated (McArdleetal.2015). Type II collagen c-

telopeptide (CTX-II) is a cross-linked peptide reflecting turnover and remodeling of bone.

Currently-available diagnostics exist aimed at targeting CTX-II in urine. It has been found that

urine CTX-II levels correlate with radiological progression at 4 years in early RA patients

(Garneroetal.2002). Ligand to receptor activator of nuclear factor-κB (RANKL) is a cytokine

necessary for osteoclastogenesis, the development of osteoclasts, which serve to break down

bone. Osteoprotegrin (OPG) is a soluble mediator of bone turnover and exerts its role by binding

to and inhibiting RANKL. This inhibits the binding of RANKL to its receptor and thus prevents

its function (Scottetal.2013). The Combinatietherapie Bij Reumatoide Artritis (COBRA)

29

study found that the ratio of RANKL to OPG predicted radiological damage in RA over the

course of 11 years (VanTuyletal.2010).

Mc Ardle et al. provide a review of prospective studies evaluating biomarkers identified at

baseline, defined as early RA, in predicting radiological progression as an outcome measure (Mc

Ardleetal.2015). The biomarkers discussed are summarized in Table 1-8 below.

Table 1-8 Biomarkers identified at baseline, defined as early RA, to associate with

radiological outcomes in rheumatoid arthritis

Biomarker Category Identif ied Proteins

Autoantibodies RF

Anti-CCP

Anti-Carp

Acute Phase Reactants ESR

CRP

A-SAA

Cytokines and Chemokines IL-6

IL-13

IL-16

IL-22

IL-33

CXCL13

30

Adipokines Adiponectin

Visfatin

Angiogenesis Markers VEGF

Angiopotietin-1

Enzyme Mediators of Destruction MMP-1

MMP-3

Collagen Degradation Products CTX-I

CTX-II

Collagen type II degradation product C1,2C

Collagen type II degradation product C2C

Abbreviations: A-SAA, acute-phase serum amyloid A; anti-Carp, anti-carbamylated protein

antibodies; CCL, chemokine ligand; anti-CCP, anti-cyclic citrullinated peptide antibodies; CRP, C-

reactive protein; CTX, C-terminal telopeptide of collagen; CXCL, chemokine (C-X-C) motif ligand;

ESR, erythrocyte sedimentation rate; IL, interleukin; MMP, matrix metalloproteinase; RF,

rheumatoid factor; VEGF, vascular endothelial growth factor. Adapted from Mc Ardle et al., 2015.

1.7.3.5 Genetic

In addition to accounting for approximately 36% of disease heritability, HLA-DRB1 alleles have

been found to have a reproducible association with worse disease outcome for RA patients

(Gonzalez-Gay,Garcia-Porrua&Hajeer2002). Other genetic associations with poor prognosis

have been observed, although these have not been replicated and are thus not as well

established. Current findings are discussed below and summarized in Table 1-9.

31

Marinou et al. conducted a cross-sectional study on a population of 964 RA patients. Their

outcome measure of disease severity was based on x-ray damage, assessed using Modified

Larsen scores. They found that the PTPN22 minor allele was associated with higher levels of

damage (Marinouetal.2007). This finding was of borderline significance, however, and other

studies have failed to replicate this finding (Karlsonetal.2008,VanNiesetal.2010).

Marinou et al. also reported that the IL6 promoter SNP rs1800795 was found to be associated

with higher levels of radiological damage in seropositive RA (Marinouetal.2007).

Additionally, the IL10 SNP rs1800872 was found to associate with erosive damage in ACPA-

negative RA. Another study by Huizinga et al. identified a second IL10 locus (1082)

polymorphism with the GG genotype associated with higher rates of disease progression, as

measured by radiological damage, versus the AA genotype (Huizingaetal.2000). Though

promising, other studies have failed to replicate these findings (Paradowska-Goryckaetal.

2010,Pawliketal.2005b,Nemecetal.2009).

Cantagrel et al. investigated the association of two polymorphisms in IL1B and one in IL1RN

with erosive damage in 108 early RA patients. They found that, in the presence of SE alleles, the

IL1B exon 5 allele 2 was associated with increased risk of erosive disease at two years

(Cantagreletal.1999). Buchs et al. also investigated the IL1 locus and found that the rare IL1B

(+3952) allele 2 was associated with erosive disease (Buchsetal.2001). Furthermore, it has

been demonstrated that the exon 5 (+3952) allele 2 is associated with higher disease activity,

characterized by higher ESR levels and DAS28 scores (Pawliketal.2005a). Despite this, other

studies have failed to replicate these findings (Harrisonetal.2008,Johnsenetal.2008).

A meta-analysis analyzing data on 1418 RA patients identified four SNPs at the IL15 locus that

had associations with disease severity, as measured by radiological damage. One was protective,

rs6821171, while the other three were associated with increased radiological damage rs7667746,

rs7665842, and rs4371699 (Kneveletal.2012b). This finding seems to support the preliminary

evidence that anti-IL-15 monoclonal antibody therapy may be effective in treating RA (Baslund

etal.2005).

Kurreeman et al. identified two SNPs in the TRAF1/C5 locus, rs2900180 and rs1070130, were

found to be associated with erosions at 5 years regardless of ACPA status (Kurreemanetal.

2007), yet a meta-analysis conducted in 2012 failed to replicate this (Kneveletal.2012a).

32

Van Der Linden et al. identified that a SNP in the CD40 gene region, rs4810485, was associated

with a higher rate of joint destruction as measured by Sharp score in anti-CCP positive patients.

The risk variant of this SNP was associated with a greater increase in Sharp score in the Leiden

Early Arthritis Clinic cohort and this finding was subsequently replicated in the North American

Rheumatoid Arthritis Consortium cohort (Van Der Linden et al. 2009).

Table 1-9 Genetic biomarkers found to be associated with poor prognosis in rheumatoid

arthritis

Gene Function

HLA-DRB1 EncodesMHCIImoleculesinvolvedinantigen

presentationofimmunogenicpeptidestoTcells.

PTPN22 Encodesproteintyrosinephosphatasenon-receptortype

22dephosphorylatesSrcfamilykinases,whichdecreases

TCRsignaling.

IL1B&ILRN EncodesIL-1ΒandIL1-RN.IL-1isapro-inflammatory

cytokinethatactivatesTcells,promoteschemotaxisof

immunecellsandfacilitatespannusformation.

IL6 EncodesIL-6,apro-inflammatorycytokineinvolvedinB

cellmaturation,promotionofproductionofotherpro-

inflammatorycytokinesandneutrophilchemotaxis,

involvedinacuteandchronicinflammatoryresponses.

IL10 EncodesIL-10,ananti-inflammatorycytokinethatinhibits

macrophages,Th1,andNKcells.

33

IL15 EncodesIL-15,aninnatecytokine,whichactivates

neutrophils,naturalkiller,andendothelialcellsand

preventsapoptosisinfibroblasts.

TRAF1/C5 TRAF1encodesaTNFreceptor-associatedfactorfamily

adaptorproteinthatmediatesTNFreceptorfamily

membersdownstreamsignaling,whichareresponsible

fornumerouscellularfunctionsincludingbone

remodelingandcytokineactivationorinhibition.

C5encodescomplementcomponent5.

CD40 EncodesCD40,acostimulatoryAPCcellsurfacemolecule,

which,uponbindingtoCD40ligandonThcells,induces

theiractivation.

Abbreviations:APC,antigenpresentingcell;CD40,clusterofdifferentiation40;IL,interleukin;

MHCII,majorhistocompatibilitycomplexII;NK,naturalkiller;TCR,Tcellreceptor;Th1,type1T

helper;TNF,tumornecrosisfactor.AdaptedfromScottetal.,2013&Coenenetal.,2009.

1.7.3.6 Gene Expression

Gene expression analyses assess the expression i.e. activity of genes, providing information on

biological processes occurring in tissues and cells of interest. Gene expression analysis can

serve to shed light on functional and time-specific changes in gene activation and transcription

that go beyond the mere analysis of presence or absence of a particular gene variant. This can

provide a glimpse of the biological processes occurring and can therefore provide information

that cannot be seen using genotyping approaches. Gene expression information can thus

complement genetic findings and enhance our understanding of underlying mechanistic

dysregulation.

34

Burska et al. produced a thorough review of the current information on the use of gene

expression analysis in RA diagnosis, prognosis, and treatment response. They discuss the merit

of these approaches in the personalization of cancer and transplant management, and

demonstrate the great potential for these approaches to be used for the management of RA as

well. Gene expression studies investigating disease pathogenesis show promise in their ability to

differentiate patients with RA from the patterns seen in patients with osteoarthritis or healthy

controls. Differential gene expression (DGE) signatures that differentiate early and established

RA have also been identified. Furthermore, numerous studies investigating response to specific

drugs have identified gene expression differences between responders and non-responders

(Burskaetal.2014).

Reynolds et al. investigated gene expression patterns correlating with severe RA, defined by

radiographic measures. They analyzed RNA obtained from peripheral blood mononuclear cells

(PBMCs) and its relation to total number of hand and foot erosions. They found that patients

with highly erosive disease (>10 erosions) could clearly be differentiated from those with mild

disease, characterized by no erosions, through the investigation of DGE (Reynoldsetal.2013).

Tang et al. further investigated these findings and observed a significant correlation between the

interferon-g receptor gene IFNGR2 and radiographic progression. Moreover, differences in

levels of expression of this gene were observed between patients and controls, as well as among

patients with erosions versus those without erosive disease (Tangetal.2015).

1.8 Summary

Despite these extensive research findings, presently available prognostic indicators have limited

value, many need replication, and very few have clinical application. However, the importance

of biomarkers is evident, not only for determining the disease course and prognosis but also for

personalizing treatment delivery, reducing morbidity, and minimizing treatment-associated

adverse events. Therefore, the discovery of useful biomarkers is needed to help determine

disease severity and prognosis in RA. It appears that there is a significant role of genetics in

both RA susceptibility as well as risk for severe disease. Given the significance of genetics in

disease susceptibility and progression, in addition to the vast research efforts in this field, this

35

study seeks to further explore gene and gene expression characteristics in patients with severe

RA, defined by our group in a novel, clinically-relevant manner.

1.9 Research Aims And Hypotheses

The goal of this study is to identify potential gene and expression biomarkers, which can help to

distinguish patients with severe, progressive disease who will require aggressive therapy from

those who will respond well to conventional treatment. A unique aspect of our study is our

characterization of clinically-relevant severe RA based on number of drug failures.

Hypothesis: Clinical differences between patients in the mild and severe groups will correlate

with genetic factors and gene expression differences in their peripheral blood.

This hypothesis will be addressed through the following specific aims.

Aim 1: To determine whether the proportion of patients with erosions differs between the mild

and severe groups.

This study utilizes number of drug failures as a marker of disease severity. It is known that

uncontrolled disease, and therefore continued inflammation, causes erosion of cartilage and

bone. Those unresponsive to drugs, by definition, have uncontrolled disease (characterized by

high DAS or other markers of disease activity). This, over time, will make the severe group

more likely to develop erosions over the course of their disease. Confirmation of Aim 1 supports

the premise that number of drug failures can be used as a marker of disease severity.

Aim 2: To determine whether RA risk alleles differentially associate with the mild and severe

groups.

Firstly, it is known that the disease has, at least in part, a genetic origin. Furthermore, thus far,

several of the observed genetic associations have been linked to disease pathogenesis.

Additionally, drugs used to treat RA, though all aiming to suppress the immune system and

reduce inflammation, have different targets and operate through different mechanisms. It is

therefore likely that patients presenting at opposite ends of the disease spectrum, and having

markedly different response to currently-available treatment, will have different genetic

36

predispositions underlying their disease. If this is the case, these genetic differences could

potentially be used (as biomarkers) to differentiate patients with severe, treatment–resistant

disease from those with mild, treatment-responsive disease.

Aim 3: To determine whether gene expression profiles differ between active and inactive

disease states within the mild and severe groups.

As gene expression profiles vary from one time point to another and reflect gene transcription at

each time point, it is likely that patients with active disease, who are mounting an inflammatory

response against their autoantigens, have different expression profiles from those who are in

remission. Though the differences may not be the same for each group, it is expected that these

variations will be observed within each group.

Aim 4: To determine whether gene regulation patterns differ between the mild and severe

groups.

Based on the assumption that varying mechanisms underlie different RA disease courses, we

predict that gene expression among patients with the same activity state within the same group

will show similar gene expression profiles.

37

Chapter 2 Materials and Methods

Materials and Methods 2

2.1 Patient Recruitment and Sample Collection

This project involved the enrollment of patients during their routine clinical visit with their

rheumatologist. Those who fulfilled the enrollment criteria and consented to participating in the

study underwent a single blood draw and their clinical information was provided to the

appropriate study investigators. Blood samples were used for genotyping and gene expression

analysis. The patient charts and online test results were used to collect pertinent clinical

information for the study. This process is outlined in Figure 2-1 and described further in the

following sections.

Figure 2-1 Study overview outlining general study components and step sequence

38

This study was approved by the Mount Sinai Hospital Research Ethics Board (MSH REB) MSH

REB number: 04-0184-E. The MSH REB functions in accordance with PartC,Division5ofthe

FoodandDrugRegulationsofHealthCanada, the Tri-Council Policy Statement, and the

International Conference on Harmonization/ Good Clinical Practice Guidelines.

Patients fulfilling the enrolment criteria were asked if they would be willing to participate in the

current study during one of their visits with their rheumatologist. Informed consent was obtained

from eligible patients and whole blood samples were collected from each participant. A single

venous blood draw was conducted to obtain a total of four 10mL tubes of peripheral blood,

including one sodium heparin tube, one ethylenediaminetetraacetic acid (EDTA) tube, and one

PAXgene Blood RNA tube. Blood was drawn by certified phlebotomists, either through the

Study Coordinator or at a blood-testing lab via requisition form. The samples were immediately

transported to our facility where they were registered and stored. EDTA tubes for DNA

processing were stored at 4 oC and PAXgene Blood RNA tubes were stored at -20oC until

processing.

Enrolment criteria were as follows:

• At time of consent, participants must be at least 18 years of age, have a confirmed

diagnosis of RA (based on ACR 1987 or 2010 criteria), be willing to sign release of

information to allow access to medical records, and provide informed consent.

• Patients younger than 18 years or older than 80 years of age were excluded from the

current study. Patients who were unable to comprehend the study process and provide

informed consent were also excluded.

2.2 Chart Reviews

Thorough chart reviews were performed on each patient chart to extract study-relevant

information and characterize each patient’s disease course. Gender, date of birth, year of

diagnosis, smoking status during first visit, family history of RA, juvenile idiopathic arthritis

(JIA), RF, anti-CCP, and erosion status were also recorded.

39

All visits from January 2014 to March 2016 in addition to a baseline visit were additionally

tracked for each participant. The baseline visit was defined as the first visit at current clinic

and/or visit at which a patient was started on MTX for mild patients and started on a different or

new biologic for severe patients. Additionally, visits after which patients in the severe group

switched biologics were also recorded in order to ensure patients were switching due to lack of

drug efficacy and high disease activity.

Chart reviews were performed to collect demographic and disease-characterizing data as defined

in Table 2-1 below.

Table 2-1 Clinical and demographic data collected for all enrolled patients

Variable Values

Gender Maleorfemale

Age Calculatedas2016minusbirthyear

YearofDiagnosis Ifknown;yearofsymptomonsetif

unknown

Asnotedbyrheumatologistinchartorin

referral.

Smokingstatusatfirstvisit Smoker,non-smoker,ex-smoker

Basedonself-reportorasreportedby

accompanyingfamilymember,spouse,

etc.Thosewithnorecordedsmoking

historywereconsiderednon-smokers.

40

FamilyhistoryofRA Positiveornegative

Atfirstvisitbasedonself-reportoras

reportedbyaccompanyingfamily

member,spouse,etc.Thosewithno

recordedfamilyhistoryofRAwere

consideredtohavenegativefamilyhistory.

RFstatus Positive,negative,orunknown

Markedaspositiveifeverfoundtobe

positive(evenifresultsdifferedbetween

tests)andunknownifnevertested.

Anti-CCPstatus Positive,negative,orunknown


positive(evenifresultsvariedbetween

tests)andunknownifnevertested.

Erosionstatus Positive,negative,orunknown


positiveandunknownifnevertested.

JIAstatus Positiveornegative

Markedaspositiveifnotedtobe

previouslydiagnosedasJIAinchartorif

diagnosedbeforetheageof18.

41

In addition to the general information, time-specific data was also recorded in order to describe

patients’ disease courses. This included self-reported measures of disease activity and disability

using three validated scales, physician measures of disease activity based on 28 joint counts,

serological test results for acute phase reactants ESR and CRP, and the medications being taken

at each visit.

2.2.1 Self-reported Measures of Disease Activity and Disability

At each recorded visit, patients were required to complete self-assessment questionnaires to

assess pain, overall quality of life, and functional status as part of their visit routine. The Health

Assessment Questionnaire and Visual Analogue Scales for pain and global assessment results

were collected for the current study.

RA self-assessment questionnaires:

• Health Assessment Questionnaire (HAQ): patient score the ease with which they can

perform daily tasks from 0 (without difficulty) to 3 (unable to perform). These tasks are

divided into eight sections: arising, walking, dressing, eating, hygiene, other activities,

reach, and grip.

• Visual Analogue Scale (VAS) for pain: self-reported pain assessment tool to measure

pain severity; patients are required to place a mark indicating pain level on a 10cm line

marked 0 at one end (no pain) and 100 at the other (worst pain experienced).

• VAS for patient global assessment: self-report assessment tool to measure the overall

effect of the disease over a specific time period; patients indicate the current effect of

their disease by placing a mark on a 10 cm line marked 0 at one end (lowest score) and

100 at the other (highest score) signifying severe functional limitation.

2.2.2 Physician’s Measures of Disease Activity

Routine clinic appointments involve the assessment of patients’ conditions through the

examination of joint swelling and their overall symptoms. Tender and Swollen Joint Counts, in

addition to the Physician’s Global Assessment scores were recorded for each included visit.

42

• Tender joint count (TJC): presence of joint pain at 28-joint count joints upon exertion of

pressure by examiner.

• Swollen joint count (SJC): presence of swelling at 28-joint count joints.

• Physician’s Global Assessment (MD global): physician’s score of overall disease

activity on a visual analogue scale (VAS). The higher the score, the higher the disease

activity.

TJC and SJC were assessed on the 28-joint count, which assesses the knee,

metacarpophalangeal,proximalinterphalangeal, wrist, elbow, and shoulder joints (Scott,

Houssien1996).

2.2.3 Serological Tests for Acute Phase Reactants

Patients are routinely tested for ESR and C-reactive protein to assess general levels of

inflammation. These were also recorded to provide information on disease activity and for the

calculation of DAS28 scores.

• Erythrocyte Sedimentation Rate (ESR): a non-specific measure of inflammation

determined through identifying the rate at which erythrocytes sediment and reported in

millimeters of plasma at the top of the tube after 1 hour (mm/hr). Higherlevelsindicate

inflammation.

• C-Reactive Protein (CRP): a non-specific measure of inflammation determined through

measuring blood levels of CRP. Normal levels of CRP are <10 milligrams per liter

(mg/L). Higher levels indicate inflammation.

The DAS28 score was calculated using the following formula:

DAS28 = (0.56×(TJC28)½) + (0.28×(SJC28)½) + (0.70×ln(ESR)) + (0.014×MDglobal)

43

2.2.4 Treatment

Treatment at each recorded visit was documented. Due to the wide array of medications with

which RA patients are treated, these are subdivided into five categories: cDMARDs, NSAIDs,

corticosteroids, biologics, and other drugs and supplements. These are listed in Table 2-2 along

with the most commonly used drugs in each category that are prescribed to RA patients.

44

Table 2-2 Collected drug information categories and most commonly prescribed drugs in

each group

Drug Category Drugs Included

Conventional DMARDs Methotrexate, Leflunomide (Arava),

Hydroxychloroquine (Plaquenil),

Sulfasalazine (Azulfidine), Azathioprine

(Imuran), Gold

NSAIDS Naprosyn, Celebrex, Meloxicam (Mobicox),

Ibuprofen (Advil, Motrin), Dicoflenac

(Arthrotec, Voltaren), Enteric-coated

acetylsalicylic acid (Entrophen, ECASA),

Vioxx, Orudis, Ketoprofen, Nabumetone

(Relafen), Indomethacin (Indocid, Indocin)

Corticosteroids Methylprednisolone (Depo-Medrol)

injections, oral prednisone, oral

methylprednisolone

Biologics Etanercept (Enbrel), Adalimumab (Humira),

Certolizumab pegol (Cimzia), Infliximab

(Remicade), Golimumab (Simponi),

Anakinra (Kineret), Abatacept (Orencia),

Rituximab (Rituxan), Tocilizumab (Actemra)

Other drugs and supplements Statins, Vitamin D, Folic Acid, Calcium

Additional medications that did not fall into these categories, including small molecule

inhibitors such as Xeljanz, as well as notes included by the rheumatologist were also recorded

for reference purposes.

45

2.3 Group Assignment

Patients were then assigned to one of two groups, mild or severe, based on the review of their

medical record data. Good response to, and thus maintenance on, cDMARD therapy was used as

an indicator of mild RA. Number of biologic failures, excluding those resulting from allergic

reactions, was used as a measure of disease severity. JIA cases were excluded from each group.

The inclusion criteria for the mild and severe groups are as follows:

• Criteria for inclusion in the mild group were: patients must have had diagnosed RA for 3

or more years and never been on biologic therapy.

• Criteria for inclusion in the severe group were: patients must have had diagnosed RA for

3 or more years and failed 3 or more biologics for efficacy, not as a result of allergies.

Patients who did not fulfill these criteria were excluded from the study.

The inclusion criteria for each group are summarized in Table 2-3.

Table 2-3 Inclusion criteria for mild and severe groups

Mild Criteria Severe Criteria

• Diagnosed ≥3 years ago

• Never been on biologics

• Never diagnosed with JIA

• Failed≥3biologics

• NeverdiagnosedwithJIA

A total of 89 patients qualified for the current study: 55 with mild and 34 with severe disease,

based on the described criteria.

Of the eligible patients, a total of 11 patients were selected for RNA-sequencing analysis,

matched for age, gender, and disease duration. The selected patients were further categorized

based on disease activity at time of blood draw. Patients with a greater than or equal to 5

46

swollen joints (out of a 28 joint count) at time of blood draw were considered to have active

disease. Patients with less than 5 swollen joints at time of blood draw were considered to have

inactive disease.

2.4 Genotyping

All eligible patients were genotyped using a panel containing 206 SNPs, including 104 leading

SNPs and 102 LD SNPs. These spanned all 101 RA risk loci identified by Okada et al. (2014),

as well as 7 additional HLA-DRB1 SE loci. Appendix Table 1 lists and describes the targeted

SNPs. All 206 SNPs of interest were accommodated in a total of 7 reaction panels.

The Agena BioscienceTM iPLEX® Assay and MassARRAY® System was used for the genetic

analysis of whole blood samples obtained from patients. Patient samples were analyzed based

on the protocol described by Gabriel et al. A general overview of the technique follows.

This technology works by combining single base extension (SBE) technology with matrix-

assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. It

specifically targets the region of interest as a result of locus-specific polymerase chain reaction

(PCR) and extension reactions. PCR and iPLEX extension primers were designed using the

Assay Design Suite (ADS) software.

Genomic DNA from the patient whole blood samples was isolated and quantified. PCR was

then used to amplify the genomic regions of interest through thermal cycling. PCR products

were then mixed with shrimp alkaline phosphatase to dephosphorylate unincorporated

deoxynucleotides (dNTPs). The iPLEX reaction involves combining the PCR products, SBE

primers, and mass-modified dideoxynucleotides (ddNTPs). SBE primers are then used to target

loci of interest. These are oligonucleotides i.e. short nucleotide sequences that anneal directly

upstream of the site of interest, permitting the genotyping of the polymorphic region on the

amplified genomic segments. An extension enzyme then adds complementary bases according

to the genetic sequence at each location. Each primer is thus extended by a single mass-modified

base, which corresponds to the polymorphism at the site of interest. The samples are then

desalted and dispensed to a SpectroCHIP® Array. They are then placed into the MassARRAY®

47

Analyzer, which identifies individual’s alleles for each SNP by identifying the mass-modified

ddNTPs using MALDI-TOF technology. The mass measured corresponds to the sequence and

thus the alleles at the site of polymorphism of interest (Gabriel, Ziaugra & Tabbaa 2009). The

SBE extension step is outlined in Figure 2-2.

Figure 2-2 Diagram illustrating the single base extension step in SNP genotyping using the

Agena BioscienceTM iPLEX® Assay and MassARRAY® System

PCR amplification products are combined with mass-modified dideoxynucleotides, with each

nucleotide having a unique and detectable mass. The amplified genomic segment of interest is

extended by a single mass-modified ddNTP corresponding to the base present at the SNP of

interest. The resulting single base extension products are then analyzed by MALDI-TOF mass

spectrometry to determine the alleles carried at each site of polymorphism.

48

2.5 RNA-sequencing

RNA-sequencing, or RNA-seq, is an analytical tool that permits the analysis of gene expression

across the transcriptome and the detection of both known and novel transcripts. RNA-seq was

used to identify DGE between the mild and severe patient groups. A total of 11 patient samples

were analyzed using this technology: four mild and seven severe patient samples. The chosen

samples were matched for gender, age, and disease duration. RNA-seq analysis consists of three

general steps: library preparation, sequencing, and data analysis.

RNA quality was tested using the Agilent® Bioanalyzer® and an RNA Integrity Number (RIN)

of >7 was considered to be good quality. The Illumina® Globin-Zero Gold rRNA Removal Kit,

designed specifically for use on human whole blood, was used to remove rRNA and globin

mRNA from the patient samples. A complementary DNA (cDNA) library was then created

through a reverse transcription reaction. The Illumina® TruSeq Stranded Total RNA Library

Preparation Kit was used for the library preparation step. The Illumina® HiSeq 2500 RNA

sequencer was used to perform RNA-sequencing. Sample preparation and sequencing were

performed as outlined in the provider’s manuals.

Following is a brief description of how this technology operates. Forward and reverse strands

are replicated using clonal amplification and then sequenced using sequencing primers and

fluorescently tagged nucleotides. The clusters on the flow cell are excited by a light source to

determine the color emitted representing the nucleotide that was added to each strand, thus

providing the sequence of the template. Identical strands are read at the same time. The emission

wavelength and signal intensity determine the base call. The more sequencing cycles performed,

the longer the length of the read.

Indices are used to separate reads with similar sequences. Similar reads cluster together and

forward and reverse strands are paired to produce contiguous sequences. Contiguous sequences

are then aligned with the reference genome in order to identify the genes being expressed and

identify DGE. The data analysis step is described in the Statistical Analysis section that follows.

49

2.6 Statistical Analysis

2.6.1 Demographic & Clinical Data

Comparisons of mean age and disease duration between the mild and severe groups were

undertaken using an unpaired two-tailed Student’s t-test with level of significance set at 0.05

indicating a statistically significant difference between groups. All other clinical and

demographic variable proportions were compared between the mild and severe groups using the

Chi-square test with level of significance set to 0.05. Missing data (e.g. unknown

erosion/RF/anti-CCP status) was excluded when performing the statistical comparisons.

2.6.2 Genotyping Data

To compare allele frequencies of the analyzed SNPs between the two groups, a case/control

association analysis was performed on PLINK (v1.07). Members of the severe group were

assigned as cases and those in the mild group were assigned as controls. From the resulting data,

genetic associations with a p-value of <0.05 were considered to be significant. The overall

groups were compared, followed by a comparison of the RF positive, RF negative, anti-CCP

positive, anti-CCP negative, erosive, and non-erosive subgroups.

2.6.3 RNA-seq Data

The first RNA-seq data analysis step consisted of a quality control step using FASTQC v.0.11.0

in order to determine overall read quality. The FASTQC reports provide basic statistics, as well

as graphical representations of quality metrics. RSeQC (v2.6.4), an RNA-seq specific module,

was used in order to perform RNA-seq quality control analysis. This program analyzes aligned

files and provides statistics and plots for each sample (Wang, Wang & Li 2012).

The BOWTIE2 (v2.2.5) – TOPHAT (2.1.0) pipeline was then used to align the raw sequencing

data, in the form of FASTQ files, to the human genome (hg38, iGenome GTF definition file)

(Langmead, Salzberg 2012, Kim et al. 2013). SAMTOOLS (v0.1.19) and TRIMMOMATIC

(v0.36) were also used in the alignment step as accessory programs (Li et al. 2009, Bolger,

50

Lohse & Usadel 2014). Finally, transcript assembly, abundance estimation, and identification of

differential regulation were done on CUFFLINKS (v2.2.1). CUFFDIFF with quartile

normalization was used to identify DGE between compared groups (Trapnell et al. 2012).

Results of differential expression testing were uploaded into the cummeRbund software (v3.2.1)

for data presentation and graphing (Trapnell et al. 2012). A false discovery rate (FDR) threshold

of q<0.05 was set after differential testing in order to obtain the final results. A total of five

DGE comparisons were performed, including the comparison of patients with different activity

levels within each of the mild and severe groups, patients with the same activity level across

groups and the comparison of all eligible patients in both groups. These analyses, including

number of patients in each comparison group, are outlined in Table 2-4. Heatmaps were

constructed in Microsoft Excel using scaled fragments per kilobase of transcript per million

mapped reads (FPKM) values.

Table 2-4 RNA-sequencing comparisons performed listing groups and compared

subgroups, including number of patients in each group

Analysis Group Compared Subgroups

Mild (n=4) Active (n=1) vs. Inactive (n=3)

Severe (n=7) Active (n=4) vs. Inactive (n=3)

Active (n=5) Mild (n=1) vs. Severe (n=4)

Inactive (n=6) Mild (n=3) vs. Severe (n=3)

All eligible patients (n=11) Mild (n=4) vs. Severe (n=7)

51

Chapter 3 Results

Results 3

3.1 Clinical and Demographic Data

A total of 89 RA patients were eligible for the current study, 55 presenting with mild disease

and 34 presenting with severe disease, based on the developed criteria. As shown in Table 3-1,

the mean age of patients with mild disease was significantly higher at 64.2 ± 12.7 years

compared to the severe group with mean age of 57.7 ± 10.7 years (p<0.05). Disease duration

was 21.9 ± 12.1 years for the mild group and 22.5 ± 11.3 years for the severe group. There was

no significant difference (p>0.05) for this measure. In terms of the group classification criteria,

patients in the severe group failed an average of 5.4 ± 1.5 biologics by the time of the 2016 chart

reviews while those in the comparison group had been on none. DAS28 information was

available for one or more pre-biologic drug switches for 32 out of the 34 patients in the severe

group. Available scores were pooled and averaged for each patient, providing an average

DAS28 score prior to biologic drug switch over the course of their disease. The mean pre-drug

switch DAS28 score for patients in the biologic group was 4.2 ± 1.0 and the median was found

to be 4.1. Average biologic treatment duration was found to be 17.4 ± 13.9 months using a

random sample of 10 patients in the severe group with available treatment duration for at least 2

biologic therapies.

All patients in the mild group had disease durations of 5 or more years, with 28 out of 55

patients having disease durations of 20 or more years. This data is shown in Figure 3-1 below.

52

Figure 3-1 Number of patients in the mild group with less than 5, 5-9, 10-14, 15-19, and 20

or more years disease duration (as of 2016)

Chi-squared comparisons yielded no differences in proportions of females, patients who had

ever smoked, patients with reported family history, and anti-CCP positive patients between the

two groups. There was a 16% difference in the proportion of anti-CCP positive patients between

the two groups, with a larger percentage of anti-CCP positive patients in the severe group (71%)

as compared to the mild group (55%). However, this difference did not achieve statistical

significance. Similarly, there was also an observable difference between groups in the presence

of positive family history of RA, which was seen in 27% of the mild group and 38% of the

severe group. This difference did not reach statistical significance. There was a predominance of

RF positive patients in the severe group (94%) as compared to the mild group (76%) (p<0.05).

Additionally, a significantly larger proportion of patients in the severe group (31 out of 33

patients) had erosions as compared to the mild group (37 out of 53 patients) (p<0.01). These

findings are summarized in Table 3-1. The percent of patients in each group presenting with RF

positive, anti-CCP positive, and erosive disease are shown in the bar chart in Figure 3-2.

0

5

10

15

20

25

30

<5years 5-9years 10-14years 15-19years ≥20years

Num

bero

fPaa

entsinM

ildGroup

DiseaseDuraaon(2016)

53

Table 3-1 Clinical and demographic data results showing average age and disease

duration, as well as percent of females, ever smokers, patients with family history of RA,

and patient RF, anti-CCP, and erosion status in mild and severe groups (* p<0.05; **

p<0.01)

Mild (n=55) Severe (n=34)

Female, n (%) 42 (76%) 28 (82%)

Age, mean ± SD years * 64.2 ± 12.7 57.7 ± 10.7

Disease duration, mean ± SD years 21.9 ± 12.1 22.5 ± 11.3

Ever smokers, n (%) 15 (27%) 9 (26%)

Family history of RA, n (%) 15 (27%) 13 (38%)

RF positive, n (%) * 42 (76%) 32 (94%)

Anti-CCP positive, n (%) 30 (55%) 24 (71%)

Positive for erosions, n (%) ** 37 (67%) 31 (91%)

Erosion data was missing for a total of 3 patients (2 in the mild group and 1 in the severe group),

while anti-CCP data was missing for a total of 6 patients (3 in each group). Missing data for the

non-test measures, smoking and family history, were considered to be negative if not otherwise

mentioned and there were therefore no missing values for any other demographic or clinical

variables.

54

Figure 3-2 Percent of patients in mild and severe groups that presented with RF positive,

anti-CCP positive and erosive disease (* p<0.05; **p<0.01)

3.2 Genotyping Data

168 SNPs were successfully genotyped while 38 SNPs failed genotyping, including 15 LD

SNPs and 23 leading SNPs. Thus, 91 out of the 101 identified RA risk SNPs were genotyped,

either through leading or LD SNPs targeting these loci. We were unable to obtain genetic

information on the following genes containing risk SNPs: CDK2, CLNK, CXCR5, FCGR2A,

ILF3, IRF4, MIR4328, PRKCH, TNFRSF14, and TXNDC11. The significant genetic findings

(p<0.05) are illustrated in Tables 3-2 to 3-8.

A total of 89 eligible RA patients were included in the current study, 55 and 34 presenting with

mild and severe disease, respectively. All eligible patients were genotyped using the developed

panel and SNP associations were compared between the two groups.

Three genomic regions with differentially associated variants between the mild and severe

groups emerged including the Ly9-CD244, PPIL4, and DNASE1L3-ABHD6-PXK regions. SNPs

0

20

40

60

80

100

RheumatoidFactorposisve

Ans-CCPposisve Erosive

Percen

tofP

aaen

ts(%

)

ClinicalFactor

Mild

Severe

***

55

rs4656942, rs9498368, and rs73081554 in these regions showed significant differential allelic

association between the mild and severe groups (p<0.05). The odds ratios were 0.3, 0.4, and 0.2,

respectively. Risk alleles and allele frequencies are further described in Table 3-2 below.

Table 3-2 All significant SNP analysis results for comparison of the mild (n=55) and severe

(n=34) groups, including all eligible participants (p<0.05)

SNP CHR BP A1 F_A F_U A2 CHISQ P OR Gene SNP

status

Risk

Allele

rs4656942 1 160831048 A 0.1029 0.2636 G 6.726 0.009503 0.3205 Ly9-CD244 Leading G

rs9498368 6 149835078 A 0.1324 0.2818 G 5.388 0.02027 0.3887 PPIL4 LD G

rs73081554 3 58302935 T 0.02941 0.1182 C 4.291 0.03831 0.2261 DNASE1L3-

ABHD6-PXK

Leading C

A1,minorallele;A2,majorallele;BP,basepair(physicalposition);CHISQ,Chi-squarebasicalleleteststatistic;CHR,chromosome;F_A,A1

frequencyinseveregroup;F_U,A1frequencyinmildgroup;LD,linkagedisequilibrium;OR,oddsratioforA1;P,asymptoticp-value;SNP,

singlenucleotidepolymorphism.

All enrolled patients were then separated based on RF positivity into RF positive and RF

negative groups. Patients with mild and severe disease were then compared within each of these

groups.

The RF positive group included a total of 74 patients, 42 with mild disease and 32 with severe

disease. Eight SNPs differed between the RF positive mild and severe groups. These include,

once again, Ly9-CD244 SNP rs4656942 and PPIL4 SNP rs9498368. New genomic regions that

showed differential allelic association include C5orf30 SNP rs1991797, IRF8 SNPs rs9927316

and rs13330176, CCL19-CCL21 SNP rs10972201, and FADS2 SNPs rs968567 and rs61897793

(p<0.05). Risk alleles and allele frequencies are further described in Table 3-3 below.

56

Table 3-3 All significant SNP analysis results for comparison of mild (n=42) and severe

(n=32) groups, including only rheumatoid factor positive eligible participants (p<0.05)

SNP CHR BasePair

Location

A1 F_A F_U A2 CHISQ P OR Gene SNP

status

Risk

Allele


rs9498368 6 149835078 A 0.1406 0.2976 G 5.059 0.02449 0.386 PPIL4 LD G

rs1991797 5 102622453 T 0.4062 0.25 G 4.092 0.04309 2.053 C5orf30 LD T

rs9927316 16 86016401 G 0.1562 0.2976 C 4.021 0.04495 0.437 IRF8 LD C

rs13330176 16 86019087 A 0.1562 0.2976 T 4.021 0.04495 0.437 IRF8 Leading T

rs10972201 9 34707373 A 0.3906 0.2381 G 3.994 0.04567 2.051 CCL19-CCL21 LD A

rs968567 11 61595564 T 0.1406 0.04762 C 3.922 0.04767 3.273 FADS2 Leading T

rs61897793 11 61599347 A 0.1406 0.04762 G 3.922 0.04767 3.273 FADS2 LD A




The RF negative group included a total of 15 patients, 13 with mild disease and 2 with severe

disease. Eight SNPs showed differential association between RF negative patients in the severe

and mild groups. These associations were characterized by high odds ratios (>=10) or odds

ratios of 0. SNP rs13385025 in the B3GNT2 gene region showed the most significant

differential allelic association between the two groups (p<0.005). Other observed associations

spanned the MANEAL, CD28, ARAP1, and CDK4 gene regions. Risk alleles and allele

frequencies are further described in Table 3-4 below.

57


(n=2) groups, including only rheumatoid factor negative eligible participants (p<0.05)

SNP CHR BasePair

Location


status

Risk

Allele

rs13385025 2 62461120 A 0.5000 0.03846 G 8.205 0.004177 25.000 B3GNT2 Leading A

rs28411352 1 38278579 T 0.5000 0.07692 C 5.370 0.02049 12.000 MANEAL Leading T

rs67164465 1 38281858 A 0.5000 0.07692 G 5.370 0.02049 12.000 MANEAL LD A

rs1980421 2 204610004 A 0.7500 0.2308 G 4.451 0.0349 10.000 CD28 LD A

rs1980422 2 204610396 C 0.7500 0.2308 T 4.451 0.0349 10.000 CD28 Leading C

rs11605042 11 72411664 G 0 0.5385 A 4.038 0.0445 0 ARAP1 Leading A

rs3765105 11 72414189 G 0 0.5385 A 4.038 0.0445 0 ARAP1 LD A

rs701006 12 58106836 A 0 0.5385 G 4.038 0.0445 0 CDK4 LD G




Similarly, all enrolled patients were assigned to anti-CCP positive and anti-CCP negative groups

based on available serological data and patients with mild and severe disease were compared

within each of these groups.

A total of 54 patients were anti-CCP positive, 30 with mild disease and 24 with severe disease.

rs9498368 in the PPIL4 region was also identified as a risk allele for severe RA in anti-CCP

positive patients (p<0.01). IRF5 SNP rs3778752, HLA-DRB1*0404 tag SNP rs3130626, RTN2

rs67630314, and VDJC rs11089637 also showed differential association between the two

groups. The intron variants rs3130070 and rs2736157 also showed differential allelic association

with an OR of 4.2 (p<0.05). The ETS1 leading SNP demonstrated an inverse association

between the minor allele and the severe group with an OR of 0.43. Risk alleles and allele

frequencies are further described in Table 3-5 below.

58


(n=24) groups, including only anti-CCP positive eligible participants (p<0.05)

SNP CHR BasePair

Location


status

Risk

Allele

rs9498368 6 149835078 A 0.1042 0.3167 G 6.967 0.008303 0.251 PPIL4 LD G

rs3778752 7 128580047 T 0.6042 0.3833 G 5.209 0.02247 2.455 IRF5 LD T

rs3130626 6 31598489 G 0.0625 0.2167 A 5.022 0.02503 0.241 HLA-

DRB1*0404

LD A

rs7105899 11 128494441 G 0.375 0.5833 A 4.631 0.0314 0.429 ETS1 LD A

rs67630314 10 64041772 AATAA 0.1667 0.35 G 4.563 0.03267 0.371 RTN2 LD G

rs11089637 22 21979096 C 0.1042 0.2667 T 4.496 0.03398 0.320 VDJC Leading T

rs3130070 6 31591808 G 0.0625 0.2 A 4.215 0.04006 0.267 HLA-

DRB1*0404

LD A

rs2736157 6 31600820 G 0.0625 0.2 A 4.215 0.04006 0.267 HLA-

DRB1*0404

Leading A

rs73013527 11 128496952 T 0.5625 0.3667 C 4.126 0.04223 2.221 ETS1 Leading T




The anti-CCP negative subgroup was comprised of a total of 29 patients, 22 with mild disease

and 7 with severe disease. A total of 9 SNPs showed differential association between these two

groups.

The CD28 and IL6R SNPs, along with their LD SNPs, were observed to have differential variant

association between the anti-CCP negative subgroups. Ly9-CD244 SNP rs4656942 was

observed again in this group with the minor allele absent from any of the members of the severe

group. STAT4 SNP rs12612769 and ANXA3 SNP rs2867461 yielded ORs of 3.96 and 3.48,

59

respectively. HLA-DRB1*0404 tag SNP rs3115572 and its LD SNP rs3096700 provided an OR

of 3.61 (p<0.05). Risk alleles and allele frequencies are further described in Table 3-6 below.


(n=7) groups, including only anti-CCP negative eligible participants (p<0.05)


status

Risk

Allele

rs1980421 2 204610004 A 0.5714 0.2273 G 5.877 0.01534 4.533 CD28 LD A

rs1980422 2 204610396 C 0.5714 0.2273 T 5.877 0.01534 4.533 CD28 Leading C

rs4656942 1 160831048 A 0 0.2727 G 4.814 0.02823 0 Ly9-CD244 Leading G

rs12612769 2 191953998 C 0.4286 0.1591 A 4.435 0.03521 3.964 STAT4 LD C

rs2867461 4 79513215 A 0.6429 0.3409 G 3.992 0.04572 3.480 ANXA3 LD A

rs4129267 1 154426264 T 0.7143 0.4091 C 3.962 0.04655 3.611 IL6R LD T

rs2228145 1 154426970 C 0.7143 0.4091 A 3.962 0.04655 3.611 IL6R Leading C

rs3115572 6 32220484 C 0.7143 0.4091 G 3.962 0.04655 3.611 HLA-

DRB1*0404

Leading C

rs3096700 6 32221782 A 0.7143 0.4091 C 3.962 0.04655 3.611 HLA-

DRB1*0404

LD A




A total of 37 and 31 mild and severe patients, respectively, presented with erosive disease. The

comparison of these groups yielded a total of 9 differential SNP associations. The previously

observed associations in the Ly9-CD244, PPIL4, CD28, and RTN2 were also identified in the

erosive subgroup comparison of the mild and severe groups. Risk alleles and allele frequencies

are further described in Table 3-7 below.

60


(n=31) groups, including only eligible participants with erosive disease (p<0.05)


status

Risk

Allele


rs9498368 6 149835078 A 0.1129 0.2703 G 5.25 0.02194 0.344 PPIL4 LD G

rs1980421 2 204610004 A 0.3065 0.1622 G 3.991 0.04574 2.283 CD28 LD A

rs1980422 2 204610396 C 0.3065 0.1622 T 3.991 0.04574 2.283 CD28 Leading C

rs67630314 10 64041772 AATAA 0.2097 0.3649 G 3.913 0.04791 0.462 RTN2 LD G




A total of 18 patients presented with non-erosive disease, consisting of 16 mild cases and 2

severe cases. The comparison of the non-erosive subgroups of the mild and severe groups

yielded allelic associations in four SNPs spanning three genes. These differed from those

identified in other subgroups. rs947474 and its LD SNP rs10796035 in the PRKCQ gene region

yielded an OR of 21 (p<0.01). rs4452313 in PLCL2 presented an OR of 9.7 (p<0.05).

rs67164465 in MTFINPP5B presented an OR of 9 (p<0.05). Risk alleles and allele frequencies

are further described in Table 3-8 below.

61


(n=2) groups, including only eligible participants with non-erosive disease (p<0.05)


status

Risk

Allele

rs947474 10 6390450 G 0.75 0.125 A 8.867 0.002904 21.000 PRKCQ Leading G

rs10796035 10 6396623 G 0.75 0.125 A 8.867 0.002904 21.000 PRKCQ LD G

rs4452313 3 17047032 T 0.5 0.09375 A 4.906 0.02676 9.667 PLCL2 Leading T

rs67164465 1 38281858 A 0.75 0.25 G 4.189 0.04068 9.000 MTFINPP5B LD A




3.3 RNA-sequencing Data

RNA-seq was used to compare gene expression profiles between patients with different activity

levels within each group, as well as across activity levels between patients in the mild and

severe groups. Gene expression patterns were also compared between the mild and severe

groups, containing all eligible patients.

The comparison of samples obtained from patients in the mild group with active disease to those

with inactive disease at time of blood draw yielded 2,342 significant differentially expressed

genes (q<0.05). Of these, 37 genes showed a ≥3 fold difference in expression levels between the

active and inactive groups. The heatmap in Figure 3-3 below shows these genes.

The comparison of samples obtained from patients in the severe group with active disease to

those with inactive disease at time of blood draw yielded 35 significant differentially expressed

genes (q<0.05). The results of this comparison are illustrated in the heatmap in Figure 3-4

below.

62

The comparison of samples obtained from patients with active disease at time of blood draw in

the mild and severe groups yielded no significant differentially expressed genes (q>0.05).

The comparison of samples obtained from patients with inactive disease at time of blood draw

in the mild and severe groups yielded 54 significant differentially expressed genes (q<0.05).

These are illustrated in the heatmap in Figure 3-5 below. The mild and severe groups could thus

be differentiated across the inactive disease state, defined as less than 5 out of 28 swollen joints.

The differentially expressed gene patterns included numerous genes known to be involved in

immune function. Numerous major histocompatibility genes, including the RA risk factor and

disease severity risk locus HLA-DRB1 were among the genes that showed significant differential

regulation.

63

Figure 3-3 Heatmap illustrating 37 significant differentially expressed genes showing ≥3

fold difference from comparison of gene expression data from active and inactive patients

in the mild group (q<0.05)

G1, Mild; G2, Severe; A, Active; I, Inactive

64

Figure 3-4 Heatmap illustrating 35 significant differentially expressed genes from

comparison of gene expression data from active and inactive patients in the severe group

(q<0.05)


65

Figure 3-5 Heatmap illustrating 54 significant differentially expressed genes from

comparison of gene expression data from mild and severe patients with inactive disease at

time of blood draw (q<0.05)


66

The results of the comparison of the active and inactive subgroups of the mild and severe patient

populations are summarized in Table 3-9 below.

Table 3-9 Overview of findings from comparison of active and inactive subgroups of mild

and severe patient populations

Group Comparison Differential Gene Expression (DGE) Results

Mild Active vs. Inactive Significant differentially expressed genes

Severe Active vs. Inactive Significant differentially expressed genes

The results of the comparison of the mild and severe subgroups of the populations of patients

with active and inactive disease at time of blood draw are summarized in Table 3-10 below.

Table 3-10 Overview of findings from comparison of mild and severe subgroups of active

and inactive patient populations at time of blood draw

Group Comparison Differential Gene Expression (DGE) Results

Active Mild vs. Severe No significant differentially expressed genes

Inactive Mild vs. Severe Significant differentially expressed genes

The comparison of gene expression patterns of the mild and severe patient groups, including all

activity states at time of blood draw, showed no significant differentially expressed genes

(q>0.05). The mild and severe groups could thus not be differentiated without taking disease

activity at time of blood draw into consideration.

67

It can be seen in each of the heatmaps in Figures 3-2 to 3-4 that there remains individual

variability within each group.

Fewer gene expression differences were observed between patients with active and inactive

disease in the severe group as compared to the mild group, with 35 and 2,342 differentially

expressed genes, respectively.

68

Chapter 4 Discussion and Conclusions

Discussion and Conclusions 4

4.1 General Discussion

The current study aimed to define two groups of RA patients presenting at either extreme of

disease manifestation, and to compare clinical, demographic, genetic, and gene expression

markers between these groups, with the goal of identifying potential biomarkers for severe

disease course in RA. Based on the described classification scheme, which focuses on

differentiating patients using number and type of drug failure as a marker for disease severity,

we were able to identify clinical, serological, genetic, and gene expression differences between

the mild and severe groups. These are further discussed in the sections below.

The benefits of conducting a retrospective study for our purposes include no loss to follow-up

and simpler implementation as compared to a prospective study. It is therefore useful in

identifying potential risk factors and generating hypotheses to be tested later on larger samples

and in prospective studies.

The limitations of this study include the small sample size of the population studied.

Furthermore, due to the large number of risk SNPs assessed, there is a potential concern that

false positive findings were identified as a result of multiple comparisons. The study was also

restricted to a specific patient population (a group of patients seen by a single physician at a

single location), which can detract from the generalizability of these findings. This further poses

the issue that the sampled population may have been skewed as participants were recruited

based on prior knowledge of their disease courses, which may have excluded other potentially

eligible patients. Ethnicity information was not collected, although this information may be

useful based on the evidence that ethnic origin can affect genetic predisposition for RA. Due to

the retrospective nature of the study, it is not possible to establish causation. Additionally, the

retrospective nature of the study makes it possible for patients in the mild group to potentially

commence biologic therapy. The possibility of unidentified confounding variables affecting the

results is another drawback of the study design.

69

Increasing sample size, replicating findings, and diversifying the patient population, as well as

considering its ethnic composition, can serve to eliminate these limitations. Additionally,

conducting a prospective study will aid in eliminating many of the other limitations discussed.

Although this study did not deconstruct biologics based on mechanism of action, we may learn

that this ultimately plays a role in the starkly contrasting disease courses of the patients in the

two groups. Further investigation may guide treatment recommendations, offering a more

precise sequence in which each drug is administered, or even point to novel pathways that can

be targeted for patients with treatment-resistant disease.

4.1.1 Clinical and Demographic Findings

The developed group classification criteria have shown clinical relevance, despite the exclusion

of previously-validated classification methods or measures. Our cutoff point of three years was

based on the understanding that patients are assessed every three to six months, therefore

providing sufficient time for any requisite biologic initiation, as well as assessment of treatment

failure. The average disease duration for the mild group was 21.9 ± 12.1 years with no required

treatment escalation for this group. Disease duration for all patients in this group was found to

be 5 or more years, further demonstrating that all patients in this group did in fact have well-

established mild RA. Similarly, the cutoff point of failure of three or more biologics to qualify

for the severe group was based on the recommended escalation of biologic treatment. The

average number of biologics that patients in the severe group had been treated with was 5.4 ±

1.5. A random sample of patients in the severe group showed average biologic treatment

duration to be 17.4 ± 13.9 months, confirming that biologic switches were only being prescribed

after a reasonable trial of each therapy. In designing these criteria, we attempted to capture

disease severity from a physician’s clinical standpoint, distinguishing cases that are difficult to

manage from those that are responsive to currently-available therapies and treatment guidelines.

The results of the current study present several interesting trends. No significant differences

between the two groups were identified for gender, disease duration, smoking status, family

history, and anti-CCP status. Differences were observed, however, in presence of erosions, RF

status, and average age between the two groups.

70

In the studied population of RA patients, the severe group was significantly younger, on

average, than the patients in the mild group. Patients in the mild group had an average age of

64.2 ± 12.7 years while the severe group had an average age of 57.7 ±10.7years. The groups

demonstrated comparable mean disease duration: 20.9 ± 12.1 years for the mild group and 21.5

± 11.3 years for the severe group. In a way, this increases the apparent severity of the severe

group’s disease. It creates the profile of an RA patient who is not only nonresponsive to

currently-available treatment regimens but also more likely to have joint damage at a younger

age than other patients in the RA population.

RF positive patients comprised 76% of the mild group as compared to 94% of the severe group,

yielding a significant difference (p<0.05). This once again corroborates well-established studies

that demonstrate that RF positivity is correlated with a worse prognosis (Scott et al. 2013).

Furthermore, 67% of the mild group was identified to be positive for erosions as compared to

91% of the severe group, a difference reaching statistical significance (p<0.05). This also

confirms our premise that number of drug failures can be used to classify patients’ disease

severity, of which one important hallmark is accumulation of more radiological damage over

time.

Both groups were consistent with RA gender trends, with 3.2 and 4.6 times more females than

males in the mild and severe groups, respectively. Furthermore, females comprised a greater

percentage of the severe group (95%) than the mild group (74%). Interestingly, contrary to the

findings of other studies investigating prognostic markers (Scott et al. 2013), a significantly

larger proportion of females affected by severe, treatment-resistant disease was not observed.

A similar proportion of patients in each group had smoked at one point in their lives (27% and

26% in the mild and severe groups, respectively). This suggests that, contrary to other studies on

disease severity (Manfredsdottir et al. 2006, Másdóttir et al. 2000), although smoking may be an

important risk factor for the development of RA, it may not necessarily have an impact on the

severity and treatment-resistance of the disease. These discrepancies may be attributed to inter-

study differences in defining disease severity and suggests that perhaps our definition pertains to

the classification of a patient group with a larger contribution from genetic, as opposed to

environmental, factors.

71

All other tested clinical and demographic variables showed no significant differences, however

there were several interesting trends. Of note, a larger proportion (16% more) of patients testing

positive for anti-CCP was seen in the severe group, as compared to the mild group, though this

finding was not found to be statistically significant (p>0.05). Similar to RF positivity, anti-CCP

positivity has been associated with worse disease outcomes in RA patients. Though the observed

trend in our data lends support to previous findings, the difference did not reach statistical

significance. This difference could be attributed to the small sample size, missing data, or the

limited data set.

Missing clinical data (i.e. unknown erosion, RF, anti-CCP status) was excluded when

performing the statistical comparisons. This exclusion of missing data assumes that samples

with known data are representative of the entire population, which may not necessarily be true.

Patients with no mention of personal and family history measures were considered to be

negative for these variables. Importantly, the results for these measures may be different from

those found in the current study should this assumption prove to be false.

Our approach differs from other studies investigating disease severity as the majority of these

studies use joint damage and/or disability to assess severity. Our results show, however, that a

larger proportion of patients in the severe group have erosions on radiologic imaging. This

suggests that our chosen disease severity categorization scheme overlaps with other schemes

based on joint damage. Furthermore, RF is one of the earliest biomarkers used to predict disease

severity in RA and positive testing for RF correlates with worse prognosis (Scott et al. 2013).

Our finding that the severe group contains a larger proportion of RF positive patients further

supports this correlation. Together, these findings lend crucial support to our premise that the

number of drug failures and the classification of patients based on our designed criteria yield

clinically relevant prognostic information.

4.1.2 Genetic Findings

The association of previously identified RA risk SNPs with each group was subsequently

investigated and revealed several interesting results. The mild and severe groups were

compared, including all eligible patients, followed by the comparison of RF, anti-CCP, and

72

erosion positive and negative subgroups, all demonstrating statistically significant differential

allelic associations. The comparison of both groups, prior to subgroup analysis, yielded

differential allelic association at three distinct genetic loci. The association of the Ly9-CD244

rs4656942 G allele with the severe group was by far the most significant (p<0.01). The OR for

this association was 0.32 for the minor non-risk allele, which translates to a 3.12 OR for the risk

allele. Patients in the severe group were therefore 3.12 times more likely to carry the G allele

than the A allele for this SNP. The Ly9-CD244 is part of the chromosome 1 region coding for

numerous stimulatory lymphocytic activation molecule (SLAM) family members. These

molecules are involved in both the innate and adaptive immune responses. Both Ly9 and CD244

are protein-coding genes, which code for lymphocytic antigen 9 and CD244, respectively

(Suzuki et al. 2008). The risk SNP in the Ly9-CD244 gene region was found when comparing

all members of both groups as well as in the RF positive and anti-CCP negative patient

populations. The odds ratio was even higher (3.86) for the comparison of mild versus severe RF

positive patients (p<0.05). This could indicate that using RF status in conjunction with

genotyping data could better differentiate mild from severe disease courses in RA patients.

The G allele of the PPIL4 SNP rs9498368 as well as the C allele of the DNASE1L3-ABHD6PXK

SNP rs73081554 also demonstrated significant association with the severe group. The risk SNP

association in the PPIL4 gene was seen for the entire population investigated but when the

population was further subdivided, the association only held true for the seropositive (RF and

anti-CCP) and erosive subgroups. The association of the G risk allele became especially

significant in the comparison of mild and severe anti-CCP positive patients and yielded a

slightly higher OR of 3.99 (p<0.01). PPIL4 encodes peptidylprolyl isomerase like 4, a member

of the cyclophilin family. Cyclophilins are enzymes that catalyze the isomerization of prolines

and are the targets of certain immunosuppressive therapies such as the use of cyclosporin for the

prevention of transplant rejection (Davis et al. 2010).

The comparison of the anti-CCP negative subgroup yielded differential allelic associations in

SNPs spanning the CD28, Ly9-CD244, IL6R, and HLA-DRB1 loci. The findings that IL6R and

HLA-DRB1 SNPs were found to offer discriminatory power between mild and severe patients

also relate to previous discoveries. Previous literature on RA disease severity has implicated

IL6R and HLA-DRB1 genes and pathways in identifying patients with severe disease (Gonzalez-

Gay, Garcia-Porrua & Hajeer 2002, Marinou et al. 2007). It is also interesting to note that

73

interference with the IL-6 pathway is the mechanism of action for tocilizumab (Actemra), a

currently-available RA treatment.

Similar to the anti-CCP negative subgroup comparison, the comparison of the RF negative

subgroup yielded differential allelic association in the CD28 gene region. However, due to the

small sizes of the compared groups, these results must be interpreted with caution.

These are promising findings as a smaller percentage of RA patients present with seronegative

disease and many studies do not include this group of patients. It lends support to previous

findings that seropositive and seronegative disease have different genetic bases. It also supports

the observed overlap between RF and anti-CCP positivity in RA patients. Further investigation

of these trends could serve to illuminate pathways underlying the development of seronegative

disease. These groups tend to be smaller, however, encompassing only 13 out of 55 (23.6%) and

2 out of 34 (5.9%) mild and severe RF negative patients, respectively. For anti-CCP negative

disease there were 22 out of 55 (40.0%) and 7 out of 34 (20.6%) mild and severe patients,

respectively.

The C5orf30 locus rs1991797 T allele showed association with the severe group in RF positive

patients. C5orf30 denotes the chromosome 5 open reading frame 30 locus. A separate variant,

rs26232, at this locus was recently described to both confer risk of RA development and play a

role in tissue damage severity (Muthanaetal.2015). The function of this gene is

undetermined, yet the study suggests that it serves as a negative regulator of tissue damage in

the disease.

The comparison of the non-erosive populations presented a new group of genes that were not

identified in the analyses of any other subgroups. PRKCQ was found to have an especially large

OR of 21 for the G risk allele for both the leading and LD SNPs (p<0.01). Significant allele

association differences in the PLCL2 and MTFINPP5B genes were also observed with high ORs

(p<0.05). As this analysis involved the comparison of 16 mild and 2 severe patients, these

results are likely false positives due to the small sample size, especially for the severe group.

We were thus able to demonstrate that patients in the two groups seem to carry different alleles

at risk loci. Moreover, it is worth noting that the alleles that appear to be associated with severe

disease are not necessarily the alleles that predispose individuals to developing RA.

74

The investigation of subgroups based on erosion status and seropositivity showed that allele

associations were different for the RF positive and RF negative groups, and for the anti-CCP

positive and anti-CCP negative groups. This could indicate, as has been previously reported, that

there may be different underlying mechanisms for seropositive and seronegative disease

(Padyukov et al. 2011). The subgroup comparison findings must be interpreted cautiously

because some of the groups are extremely small perhaps thus yielding false positive findings.

The risk alleles and loci were also found to differ based on subgroup membership, including RF

status, anti-CCP status, and erosion status. Analysis of the subgroups of the entire population

investigated (RF positive, RF negative, anti-CCP positive, anti-CCP negative, erosive, and non-

erosive populations) revealed more genetic differences identified between the mild and severe

groups. This could indicate real genetic differences between the groups, which, in addition to

other serological biomarkers such as RF and anti-CCP positivity, can provide better predictive

value. It is important to remain cautious, however, as subgrouping can lead to even smaller

sample populations, which are prone to more error and thus false positive results. Furthermore, a

p-value of less than 0.05 was considered to be statistically significant and no corrections for

multiple testing, such as the Bonferroni correction, were performed. This was due to the limited

power of the current study as a result of the small sample size, and the intention to replicate any

findings in an independent cohort. It is important, however, that future replication is performed

on a sample of adequate size and statistical analysis is appropriately corrected for multiple

testing.

Similar to other studies, we were unable to confirm previously reported genetic differences

between mild and severe RA patients. However, several findings possess some links to

previously identified risk genes and pathways. The observed odds ratios are much higher than

those observed for RA risk yet significance levels are low, likely owing to the small sample size

of the studied population.

4.1.3 Gene Expression Findings

The comparison of gene expression patterns within a subset of the two groups consisting of a

total of 11 patients also yielded numerous significant differences.

75

4.1.3.1 Comparison of Active versus Inactive Subgroups

The comparison of gene expression profiles of active mild patients to those of inactive mild

patients produced a multitude of differentially expressed genes, suggesting RNA-sequencing

data can be reflective of clinical status. Gene expression patterns of four mild patients were

compared, only one of which had active disease. Unfortunately, the small active comparison

group (a single sample) increases the risk of identifying false positive DGE patterns.

The comparison of mild patients with active disease to those with inactive disease yielded

considerable overlap in gene expression within the inactive group and differences in expression

patterns between the two groups. As the active mild group consisted of only one person, this

profile is likely not representative of other patients in the same group. The correlation of

individual gene expression patterns in the inactive mild group, however, suggests that real

differences do in fact exist. Further replication will be necessary.

Fewer significant DGE differences were observed for the comparison of active with inactive

disease states in the severe group, as compared to the mild group, which yielded 35 and 2,342

genes, respectively. However, the small sample size and observed heterogeneity within the

severe group, combined with the fact that the active mild comparison group consisted of only

one sample, make further investigation necessary in order to adequately interpret this finding.

Using validated measures of disease activity, instead of SJC exclusively, would give a more

comprehensive picture of disease activity, and therefore more clearly classify patients’ disease.

4.1.3.2 Comparison of Mild versus Severe Subgroups

No significant DGE was observed in the comparison of mild and severe patients with active

disease at time of blood draw. This demonstrates that disease activity contributes significantly to

gene expression patterns.

We further found that significant differences in gene regulation exist between the groups of

patients with mild and severe disease across the inactive disease state. This finding suggests that

a pattern of upregulation or downregulation in the expression of the identified genes can be used

76

to discriminate individuals with mild disease versus those with severe disease, even if they

present with inactive disease at the time of blood draw. This could indicate that their diseases

operate through different pathways. It is important to consider medication, especially those

exerting their effects by acting on biological pathways, in interpreting these findings. This was

not controlled for, due to both ethical constraints and the retrospective nature of the current

study. Prospective studies can therefore serve to shed light on these findings.

Gene expression differences between the two groups were only observed when comparing

inactive disease states. These groups therefore had similar gene expression profiles when

experiencing flares of active disease. This could perhaps illustrate the severe group’s elevated

baseline activity level, yet similar gene expression pattern during flares.

4.1.3.3 Gene Expression Limitations

Inconsistent gene expression patterns within groups are clearly visible. As the comparison of

gene expression is essentially the comparison of the average of normalized expression levels of

each gene between groups, individual profiles can thus skew group averages. This can therefore

create differences that do not in fact exist and may not be representative of the expression trends

of the entire group.

The heterogeneity within groups can contribute to the observed inconsistencies in gene

expression patterns in these groups. Therefore, further classification and categorization related

to these different disease states is necessary. Treatment effects can increase the group

heterogeneity, especially given the underlying pathways upon which disease-modifying drugs

act. Additionally, cell population effects may have played a role, as these were not controlled for

in the current study since RNA-sequencing was performed on the heterogeneous immune cell

populations that constitute whole blood.

Limitations of the gene expression data include an inability to discern whether the gene

expression pattern reflects general inflammation or an RA-specific trend. Furthermore, while

RNA-sequencing showed some value in differentiating the mild and severe groups, this

technique is highly sensitive and is affected by numerous factors that were neither accounted nor

controlled for during sample collection. These include medications, for which adequate control

77

measures are limited by ethical and design constraints. Additionally, the RNA sample originated

from whole blood, which consists of a heterogeneous cell population. Access to patient groups

is another important consideration, as it is difficult to find mild patients with active disease and

severe patients with inactive disease based on both disease nature and our categorization

scheme. Furthermore, no biological or technical replicates were included, which could help

control for additional noise. More data is therefore required in order to completely support or

refute the assumption that mild and severe patients can be differentiated based on gene

regulation profiles. Additionally, performing gene expression analyses on specific cell types,

instead of a heterogeneous whole blood population, preferably on treatment-naïve patients at a

controlled date and time of blood draw, could assist in eliminating confounding variables and

irrelevant background information. Due to these limitations, and the understanding that any

findings would require replication using optimized and targeted experimental design, DGE

results were not validated. Future findings will therefore require further validation by

quantitative PCR (qPCR).

4.2 Conclusions

This study approached the differentiation of mild and severe RA patients in a novel way. We

chose to classify patients with severe disease based on the number of biologic drug failures

experienced, and to classify mild patients as those who have been maintained on conventional

DMARD therapy for three or more years. Our findings support the premise that this

categorization scheme does differentiate patients in both a clinically relevant manner and one

that correlates with radiological damage. Furthermore, we were able to identify genetic

differences between these two defined groups. The differential allelic association of SNP

rs4656942 in the Ly9-CD244 genomic region was the most significant among these. Based on

our findings, we propose that specific genetic RA risk factors can serve as potential prognostic

biomarkers to aid in distinguishing RA patients with treatment-resistant disease. Through our

preliminary data, we were also able to demonstrate that gene expression across inactive, but not

active, disease activity states differed between the mild and severe patient groups.

We have identified and classified two groups of RA patients with markedly different disease

courses and prognoses, which can be distinguished genetically. This could explain the lack of

78

success in achieving remission in patients with severe disease using currently-available

therapies that act on specific biological mechanisms. Links to relevant underlying mechanistic

pathways may provide insight into the pathophysiology of the disease and help to explain the

observed variable disease courses and best-suited treatment options. Thus, the replication of

these findings, in addition to further investigation of the genetic, environmental, and functional

differences between these groups, can serve to further personalize treatment for RA patients and

even potentially develop or repurpose drugs that can aid a specific subgroup of patients.

79

Chapter 5 Future Directions

Future Directions 5

5.1 General Future Directions

The current study presents many interesting findings, which introduce numerous future

directions to further investigate the characteristics of the defined groups of RA patients.

An important first step would be the replication of the current findings. Preferably, this analysis

would also be conducted on a sample of larger size. Additionally, as joint damage is commonly

used to assess disease severity, future studies should more precisely address joint damage

differences in these patients. This could involve determining onset time and rate of joint damage

and quantifying erosions, as our study only focused on presence versus absence of erosions.

Furthermore, this study limited genetic investigation to known RA risk loci. Investigating non-

risk loci may hold greater prognostic value, and could lead to the discovery of novel disease-

associated variants.

Investigating and identifying other differences, in the form of genetic, cellular, and even

lifestyle factors, between these groups can serve to increase understanding of RA and its

subsets. This can also be used in conjunction with other identified markers to predict patient

disease course and prognosis. Replication and validation of the observed trends is needed to

develop the use of these factors for the prediction of RA disease course and prognosis.

Determination of the proportion of RA patients that have severe disease, as defined by our

study, is another important future consideration. The current study recruited patients whose

disease severity was characterized in either of our “extreme” categories. Therefore this study

design did not enable the comparison of these groups to the RA population as a whole nor to

other groups whose disease severity fell in between these two categories.

RNA-sequencing provides a large amount of information from any analyzed sample, and as a

result of this high throughput nature, further analysis of the raw data can be conducted. This

includes the identification of novel splice variants and variant association differences between

the mild and severe groups. It may also be possible to perform a DGE analysis comparing fewer

80

patients to control for medication at time of blood draw, or to match patients on other variables

to reduce confounding factors. It may also be useful to characterize disease activity at time of

blood draw using standardized measures, such as the DAS28, and to further subdivide disease

activity level into low, moderate, and high. Moreover, investigation of gene expression in a

specific cell subtype, such as T cells, as well as novel technologies, which have enabled

interrogation of the RNA expression profile within individual cells, could offer interesting

findings. These approaches can provide important mechanistic information, as well as identify

DGE that could be masked as a result of the heterogeneous origin of the RNA sample. Finally,

should these findings be replicated, it would still be important to determine if the observed

differential gene regulation corresponds to protein expression. Pathway analyses could elucidate

mechanistic links between these differential expression profiles and disease pathogenesis in

these two groups of patients. It may also be useful to compare seropositive and erosive groups

using RNA-sequencing.

Future experiments should also aim to address the limitations described in the discussion

section. Ideally, this would involve more stringent grouping, optimized matching on

demographic and clinical variables such as age and RF status, as well as treatment conditions;

patients with minimal or no treatment would be ideal for this comparison. Controlled blood

draw conditions, including time of blood draw, should also be as closely matched as possible, as

seasonal effects on human gene expression patterns have recently been observed (Dopico et al.

2015). Finally, as our study has demonstrated the significant effect of disease activity in

determining gene expression patterns, it would be necessary to use more comprehensive

validated disease activity scores, such as the DAS28, in order to better characterize disease

activity and its effect on gene expression patterns, as well as eliminate it as a confounding

variable if desired.

In addition to confirming these results and better characterizing these two populations,

mechanistic studies will enable a better understanding of the significance of these findings and

the identification of functional roles played by these genes in disease pathogenesis. This can be

achieved through analysis of both gene and protein expression, as well as other functional

studies.

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5.2 Immunophenotyping by Mass Cytometry

One area of research that is already being explored by our group is the use of mass cytometry in

immunophenotyping peripheral blood immune cell populations. We have developed a mass

cytometry panel for the investigation of granulomatosis with polyangiitis (GPA) relapse and

remission, in order to provide information on protein- and cellular-level differences in these

patients’ disease. Immunophenotyping by mass cytometry can therefore, in a similar fashion,

serve to provide important mechanistic information on RA pathogenesis.

GPA, previously referred to as Wegener’s granulomatosis, is an autoimmune disorder

characterized by necrotizing granulomatosis of the small blood vessels leading to vessel

blockages and ischemia (Csernok, Gross 2013). Patients also present with the presence of anti-

neutrophil cytoplasmic antibodies (ANCAs), primarily targeting proteinase 3 (PR3), in 80-90%

of cases (McKinney et al. 2014, Csernok, Gross 2013). This autoimmune reaction can lead to

organ damage, most notably pulmonary and kidney damage, and is associated with high

morbidity and mortality (Jennette, Falk & Gasim 2011, Jayne 2009, Gómez-Puerta, Bosch

2009). Both genetic and environmental risk factors have been identified, however, similar to

RA, the pathogenesis of GPA remains unknown (Heckmann et al. 2008, Wieczorek, Holle &

Epplen 2010, Mahr et al. 2010, Csernok, Gross 2013, Xie et al. 2013). Additionally, studies

have demonstrated aberrancies in numerous immune cell subsets in GPA patients. These include

T cells, B cells, and dendritic cells, as well as neutrophils, which express the autoantigen for the

disease (Gómez-Puerta, Bosch 2009, Hewins et al. 2004, Wilde et al. 2009, Csernok et al.

2006).

Patient disease courses vary, with many demonstrating resistance to treatment, relapse, and

disease-related morbidity. Relapse rates have been estimated to be as high as 60% in 7 years,

emphasizing the great benefit molecular assays permitting the personalization of treatment can

provide for GPA patients (Guillevin et al. 2011, Kallenberg 2011). There is therefore a great

need for an assay to predict relapse and remission, as well as response to treatment for GPA

patients, making personalized treatment possible.

Our GPA project therefore aims to identify biomarkers for the prediction of relapse and

remission in GPA patients, minimizing overtreatment as well as undertreatment and thereby

reducing side effects and disease-associated damage, respectively. We have opted to utilize

82

mass cytometry, a single-cell resolution technology that allows the analysis of up to 40

parameters. Cells are stained with antibodies conjugated to rare earth metals and are analyzed by

time-of-flight mass spectrometry (Nair et al. 2015). This permits immunophenotyping, as well

as biomarker discovery and mechanistic investigation. The use of this technology, and the

developed panel, would therefore provide an interesting and feasible future direction for the

current study. In such a way, immunophenotyping can aid in illuminating underlying cellular

and mechanistic differences between these two groups of RA patients presenting with variable

disease courses.

Two panels were designed, one for the analysis of T cell, B cell, monocyte, and dendritic cell

populations on PBMCs, the other specifically designed to investigate neutrophils and their

activation status on leukocytes isolated from whole blood. The designed panels are shown in

Tables 4-1 and 4-2 below.

83

Table 5-1 Mass cytometry panel consisting of 35 antibodies (27 targeting surface antigens

and 8 targeting intracellular cytokines) used for the analysis of density gradient-separated

human PBMCs

Tag Antigen Clone Cell population/subpopulation 141Pr HLA-DR L243 Dendritic cells & T cell activation 142Nd CD8a RPA-T8 CD8a positive T cells 143Nd CD5 UCHT2 Regulatory B cells 144Nd CD38 HIT2 Plasmablasts & T cell activation 145Nd IFNγ B27 Activation marker 146Nd IgD IA6-2 IgD positive B cells 147Sm CD4 SK3 CD4 positive T cells 148Nd CD21 Bu32 Marginal zone B cells 149Sm TNFα MAb11 Activation marker 150Nd CD45RA HI100 Non-memory T cells 151Eu CD123 6H6 Plasmacytoid dendritic cells 152Sm CD11c Bu15 Dendritic cells 153Eu CD3 UCHT1 T cells 154Sm CD24 ML5 Transitional B cells 155Gd CD1c L161 Myeloid dendritic cells 156Gd CD14 M5E2 Monocytes 158Gd CD27 O323 B cell subgroups 159Tb MIP1b D21-1351 Activation marker 160Gd CD25 2A3 Regulatory T cells 161Dy CXCR5 RF8B2 Follicular helper T cells 162Dy IL-2 MQ1-17H12 Activation marker 163Dy CD20 2H7 B cells 164Dy CD154 24-31 T cell activation 165Ho CD16 3G8 Non-classical (vs. classical) monocytes 166Er IL-17A BL168 Activation marker 167Er IL-6 MQ2-13A5 Activation marker 168Er CD69 FN50 T cell & Natural killer cell activation 169Tm TCRgd 5A6.E9 Gamma delta T cells 170Er CD56 NCAM16.2 Natural killer & Natural killer T cells 171Yb IL-10 JES3-19F1 Activation marker 172Yb CD127 eBioRDR5 Regulatory T cells 173Yb CD45RO UCHL1 Memory regulatory T cells 174Yb CD19 HIB19 B cells 175Lu IL-8 E8N1 Activation marker 176Yb CCR7 G043H7 Non-effector T cells 191Ir DNA N/A DNA 193Ir DNA N/A DNA 194Pt N/A N/A Viability Columns represent the rare earth metals tag, the targeted antigen, clone information, and the cell populations that express the marker.

84

Table 5-2 Mass cytometry consisting of 18 antibodies targeting surface antigens, including

surface markers of neutrophil activation, used for the analysis of lysed whole blood

Tag Antigen Clone Cell population/subpopulation 89Y CD45 H130 Hematopoietic cells

(Neutrophils are CD45 low or negative) 141Pr HLA-DR L243 Dendritic cells & T cell activation 143Nd CD15

(SSEA-1) W6D3 Neutrophils (CD15+)

145Nd CD35 E11 Neutrophil activation 147Sm CD62L DREG-56 Neutrophil activation 149Sm CD18 TS1/18 Neutrophil activation 153Eu CD3 UCHT1 T cells 155Gd CD1c L161 Dendritic cells 156Gd CD14 M5E2 Monocytes 159Tb CD66 CD66a-B1.1 Neutrophils (CD66+) 161Dy CD177 MEM-166 Neutrophil activation (mediates PR3

expression) 163Dy CD63 H5C6 Neutrophil activation 165Ho CD16 3G8 Neutrophils (CD16+) 168Er CD69 FN50 T cell and Neutrophil activation 170Er PR3 MCPR3-3 GPA autoantigen 172Yb CD49d 9F10 Neutrophils (CD49-) 174Yb CD19 HIB19 B cells 176Yb CD11b

(activated) CBRM1/5 Neutrophil activation

191Ir DNA N/A DNA 193Ir DNA N/A DNA 194Pt N/A N/A Viability Columns represent the rare earth metals tag, the targeted antigen, clone information, and the cell populations that express the marker.

As the panel has been developed for use on GPA patient samples, and therefore contains

disease-related targets, minor changes to the panel could optimize it for use on RA samples. The

incorporation of CD146, a marker of Th17-like effector memory T cells which have been found

to be elevated in the peripheral blood of patients with autoimmune diseases, will aid in tailoring

the panel to RA studies (Dagur et al. 2011). A decrease in angiogenic T cells, characterized as a

CD3+ CD31+CXCR4+ population, has previously been observed in the peripheral blood of RA

patients as compared to healthy controls (Rodríguez-Carrio et al. 2014). The addition of CD31

and CXCR4 to the mass cytometry panel could therefore serve to replicate these findings, and to

further characterize this population and related immunological responses. Incorporating an

antibody targeting IL-4, in order to hone in on T cell responses, as well as IL-1, which is the

target of anakinra therapy, can serve to further illuminate the underlying pathogenesis of the

disease.

85

Kerkman et al. utilized biotinylated CCP-2, which is used to identify ACPA, in conjunction with

fluorophore-tagged streptavidin in order to identify autoantibody-producing B cells in RA

patients using flow cytometry (Kerkman et al. 2016). A similar technique using combinatorial

peptides was utilized by Newell et al. to identify T cells targeting specific epitopes by mass

cytometry. This was accomplished through the use of mutated streptavidin to enable metal

tagging (Newell et al. 2013). Alternatively, the use of a metal-tagged anti-biotin antibody

(Fluidigm catalog number 3150008B) in order to identify the biotin-tagged autoantigen and

visualization of it using mass cytometry can achieve similar results. Incorporating such an assay

will aid in the identification of autoantibody-producing B cells and in the comparison of this cell

population between mild and severe patient groups.

The inclusion of these markers would tailor the current panel to the investigation of RA

samples, based on currently known disease mechanisms, and potentially elucidate additional cell

subsets and mechanisms that contribute to severe disease.

The findings of the current study remain preliminary and much replication and optimization is

required for the development of clinically useful biomarkers. Despite these limitations, this

study and its findings show much promise and there are numerous exciting future investigations

to better define and further characterize the investigated RA populations.

86

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Appendices

Appendix Table 1 Genetic targets investigated using genotyped using IPLEX® Assay and

MassARRAY® System. Columns outline chromosome (chr), single nucleotide

polymorphism of interest (SNP), whether the SNP is a peak SNP or a linkage

disequilibrium (LD) SNP, r2 value for LD, previously identified RA risk allele, gene and

the targeted DNA sequence.

SNP not genotyped; SNP failed genotyping; HLA-DRB1 SE targets

Chr. SNP rs_SNP Peak SNP SNP in LD

with the

peak SNP

LD (r2) Strongest SNP-

Risk Allele

Gene DNA Sequence

2q13 rs6732565 rs6732565 Yes rs1533299 0.9 - 1.0 rs6732565-A ACOXL

GGCTTAGAGAAAACTTTCTTCAG

ACACAGAAAGTATGAACTGTCA

AGGAAAAAGTTGGCA[A/G]AAA

TGGATTTTATCAAAATTAAAAAC

AGCTGCTCTTTGAAAAACACTGC

AAAGAAATAAA

2q11.2 rs9653442 rs9653442 Yes rs1160542 0.9 - 1.0 rs9653442-C AFF3

CACACAGTCCTGGCCCATCGGG

CTCTCTGGAGGCCCTTCTTCCTT

GGCTGTCACCTATTT[C/T]TAAAC

TGATATGTAATAGTTGTACATAT

TTATGGACTATATGTAATATTTT

GATACATGC

5q11.2 rs7731626 rs7731626 Yes Not Found

rs7731626-G ANKRD5

5

GCTGGGTGCTTGGTTTGTTCCCC

GTCTTGGTTGGCGGTTCGGGGNG

GGGTGGTGGAGGGG[A/G]AGGG

TTAAGAATTATAGCAGGTGTCTG

NTGGCTGAGAGGTCAGTAAACA

ATTCCAGATGC

106


with the

peak SNP


Risk Allele

Gene DNA Sequence

4q21 rs10028001 rs10028001 Yes rs2867461 0.9 - 1.0 rs10028001-T ANXA3

CCTTCCTCTTGGAGCTCTTTGGG

AGTTACTGAGAGGCAAGACACC

GAGGAAGAGAGCACC[A/T]CCCA

CTGAAGCTGGGCNTGAATTTATA

AGGGTTCCTCTTCCTCTTCCCTT

ACCCCAATCT

11q13 rs11605042 rs11605042 Yes rs3765105 0.9 - 1.0 rs11605042-G ARAP1

TGCCAAGTGCCCACTGTGTGCTA

GCCACTGTTCTAGGATCTGGGGG

CTACAGGCAGTGGT[A/G]GCCAA

AAGGAGTTTATGTCCCANTGGG

GTACAGAGAATCTATAGATAAG

TACATTAATGG

10q21.2 rs71508903 rs71508903 Yes rs35892992 0.8 - 0.9 rs71508903-T ARID5B

CCTAACTTTCTGGCGAGGAGTCT

TGAGTAGAAGGGAGGTGGAAAC

AAAAAAGGATGGGAA[C/T]ATGT

TTGAGTTTCCACAAAGCTACACT

TCCAAGCAAACTTTGAATTAATA

TGTCACTCCC

6q21 rs9372120 rs9372120 Yes rs9372121 0.9 - 1.0 rs9372120-G ATG5

GGGTATGCATAGGTTATATGCA

AATACTACACCATTTTATATCAG

ACTCTCAAACATCAG[G/T]AGAA

TTTGGTAACCCAGGGAGGTCCTG

GAACTAATCACCCAGAGGTATC

GACAGATGGCT

11q22 chr11:107967

350

rs13819388

7

Yes rs73000527 0.9 - 1.0 chr11:10796735

0-A

ATM

ACTTTTTTTTTTTTTTTTTTTTTTT

TTTTGAGACACTCTTGTCGCTCA

GGCTGGAGTGCA[A/G]TGGCGCA

ATCTCAGCTCACTGCAACCTCCG

CCTCCAGGGTTTAAGCGATTCTC

CTACCTC

107


with the

peak SNP


Risk Allele

Gene DNA Sequence

2p15 rs13385025 rs13385025 Yes rs6707337 0.9 - 1.0 rs13385025-A B3GNT2

ATGGCTCACTGCACCCTTGACTT

CCTGGGCTTGTGTGATCCNACAG

GTGTATACCACCAC[A/G]CCCAG

CAAAGATNTCCACTTTCTGTTCC

CAGATGCTATTTAAGGCCCCACA

TGGCATTTG

8p23.1 rs2736337 rs2736337 Yes rs13277113 0.9 - 1.0 rs2736337-C BLK

AGGCTGCCATAACAAAATGCCA

CAAGACTGGGTGGCTTAAACAA

TAGAAGNTTATTGTCT[C/T]ACN

GTTCTGGAGGCTGGAANTCCAA

GATCAAAGTGCCTGCTGGTTTGG

TTTCTTCTGAGT

4p15.2 rs11933540 rs11933540 Yes rs36020664 0.9 - 1.0 rs11933540-C C4orf52

AGAATCTCTTTGGTTAAAAGAA

AAGTTCATCCTACAGCATTAATC

ATTCACCAGGTGAGG[C/T]ATGG

GTTATCAGTCCATCCNTGGTTTG

GTTATTCTCCAGCCAACTGTGGC

CAAACAGGTG

5q21.1 rs2561477 rs2561477 Yes rs1991797 0.9 - 1.0 rs2561477-G C5orf30

ATAGTGAATGTTTTGTCTACCTC

AGTAAAATATGGTAGATTTCATA

CACCAGACTGCCTG[A/G]TTGAA

ATTTCCAGAGCTCTTAAACCACA

TCTGTATCTTCTACTTAACAACC

TAGGTTAAA

9p13.3 rs11574914 rs11574914 Yes rs10972201 0.9 - 1.0 rs11574914-A CCL19

GTGAGGAGACAGTCATGGTGTT

CCAGGGGAGAGGCAGTGAGGCC

TGCATGAAAGCAGTAG[C/T]TGG

GAATAGAAGGAAGGCTCAGGCC

CAGACATATTCAACTAATGAGG

ACATAAAATTTGG

108


with the

peak SNP


Risk Allele

Gene DNA Sequence

6q27 rs1571878 rs1571878 Yes rs10946216 0.9 - 1.0 rs1571878-C CCR6

TTCTGTGAGTGAGAAGGTTTGGG

AAATAGTGAGTTATTCCAGCAG

GGCTCTNAAGGGCCA[C/T]TGAT

AAACAANCTCTAATANGAATAA

ACTAGGGAAGGATGTAGTTAGC

ATCTTATTAATG

1p13.1 rs624988 rs624988 Yes rs771587 0.9 - 1.0 rs624988-T CD2

TTGTTTGCTGTCTTTCTCCCTCCA

CTAGAATGTAAGTTCCATGAAG

GCTGCCATCTGGTC[A/G]GGGTG

TTTCCACATGCCCCCACCACTTA

GTATGGTGGCTGTCACAGTGTGG

GGGTGCCAT

18q22.2 rs2469434 rs2469434 Yes rs4891376 0.9 - 1.0 rs2469434-C CD226

AAAAAAAAAAAAAAAGTTCTAG

AGGCCTGGACTTGCAATTGGTGT

CTGAANGGCAGGGTT[A/G]GCAA

TGGAGGAGNGGGTGAGATGTCT

TTGGGACTGAGCCCCCAGCCTGT

GGGATCTGATA


GAATGACTATCTTTCATTTGATA

AATATCCGCAAGCTATTTTGGTT

TTTGACAAATTAGA[C/T]GAAAC

AGGTATTATGAAAAGACTTGGG

AAAATTGAGGACAAATTAGTTA

ACTAGATACTA


GGAGCTAGAATAGAGTTAATGC

CTCTCAAAGGCTTGCTAATCCTT

CTTTTAAAACAAAAA[C/T]CAAG

AGCAGNCCTGGNAGGGCCTTCA

ACAAGCAAACAACCAGCTGGGT

TTTAATAACCTT

109


with the

peak SNP


Risk Allele

Gene DNA Sequence

11q12.2 rs508970 rs508970 Yes rs10792304 0.9 - 1.0 rs508970-A CD5

AGCTCCTTGATCTCCCTGGGGAG

GGGGTGGGCTGAGCNNCAGGAA

TGTGCTCTCAGAGAT[C/T]TCTGC

CTCTCCTNGAGCCTCCCGGTGGA

GTGGTATTATCGCAGGAAAACT

CCAGTTTGTT

6p23 chr6:1410321

2

rs74984480 Yes rs115686522 0.9 - 1.0 chr6:14103212-

T

CD83

TAAAGGCTATCGAAATCAGCTT

GAGGTCTTGGGGACTGAGTCTG

ACCCAGGCAGAGANCC[C/T]GTN

GGAAGTCCTCTGGCTCAGCTGG

GTGGTCTTGGTCCTGCCTGACTC

TGGCCTAGTTGC

12q13.2 rs773125 rs773125

Yes rs705700 0.8 - 0.9 rs773125-A CDK2

TAATCTCTCTTTTTTTTTTTTTTTT

TTTTTTTTGAGATGGAGTCTCTC

TCTGTTGCCCAG[A/G]CTGGAGA

GCAGTGGCGTGATCTCAGCTCAC

TGCAACCTCCACCTCCCAGGTTC

AGGTGAT

12q14.1 rs1633360 rs1633360 Yes rs701006 0.9 - 1.0 rs1633360-T CDK4

TTGTTTGTTTGTATTGAGACAGA

GTTTCACTCTTGTTGCCCAGGCT

GGAGTGCANTGGTG[C/T]GATCT

CGGCTCACCGCAACCTTGGCCTC

CCAGGTTCAAGCAATTCTCCTGC

CTCAGCCTC

7q21.2 rs4272 rs4272 Yes rs42031 0.9 - 1.0 rs4272-G CDK6

ATAATGATAAAACACCTAGATA

CCCAAAATACTACATCTATATAT

TCAAATCTACTAATC[A/G]TGTT

ACAAATGCATGCAGCTTATTTGG

GGGCTTAGTCTAATTTTTATTTT

CTTAGGTCCA

110


with the

peak SNP


Risk Allele

Gene DNA Sequence

2q33.1 rs6715284 rs6715284 Yes rs13398075 0.9 - 1.0 rs6715284-G CFLAR

CCAGGGCAGAGCTATATGGCAG

AGCTGGATGGAGACCTGGGTTN

TTCCCCCAACCCCCAT[C/G]CCC

ACTCCAGTAGGGACTCACCTCTT

TCTGGAGTTCCAACTGGGTATAG

TAGAGGTTTGT

4p16.1 rs13142500 rs13142500 Yes Not Found

rs13142500-C CLNK

GAAACACCTTCTCTACTAAAAA

AGTACAAAAAATTAGCCGGGCG

NGNTGGCGGGCGCCTG[C/T]AGT

CCCAGCTACTCNGGAGGCTGAG

GCAGGAGAATGGCGTGAACCCG

GGAGGCGGACCTT

13q14.11 rs9603616 rs9603616 Yes rs12872801 0.9 - 1.0 rs9603616-C COG6

TTGTGTGTGTATGTGATGAGTTA

CTGAAACATTATATCTCTTTAAC

TANACTCNGTATTT[C/T]NCTTTC

TAATCTAATTGATGGNTCATTTC

CTGCAAAGGTGGCTTCTCTCTGT

GATTCCTC

2q33.2 rs3087243 rs3087243 Yes rs11571316 0.9 - 1.0 rs3087243-G CTLA4

TTCTTGGAAGGTATCCATCCTCT

TTCCTTTTGATTTNTTCACCACTA

TTTGGGATATAAC[A/G]TGGGTT

AACACAGACATANCAGTCCTTT

ATAAATCAATTGGCATGCTGTTT

AACACAGGT

11q23.3 rs10790268 rs10790268

Yes Not Found rs10790268-G CXCR5

AGGCGGGGTTTCACCATGTTAGC

CAGGATGGTNTCGATCTCCTGAC

CTCGTGATCAGCCC[A/G]CCACG

GGCTCCCAAAGTGCTGGGATTA

CAGGCGTGAGCCACTGCACCTG

GCCTAAACCCA

111


with the

peak SNP


Risk Allele

Gene DNA Sequence

3p14.3 rs73081554 rs73081554 Yes rs73077957 0.9 - 1.0 rs73081554-T DNASE1

L3

TGAGACAGAGCACTTAGATTTTA

AAATCTATGGGAGATGTTTTGGG

ATAAAACTTGCAAC[C/T]CTGTG

GTTAAGGCTTACTTTTTTTATTTA

ACACTTAATGCCTTGTGTGTTAT

ATATTGGT

3p24.1 rs3806624 rs3806624 Yes rs34269949 0.9 - 1.0 rs3806624-G EOMES

TGTGCTTTGAAGTTACAGCTTCC

GACGAGGAAAGAGACTTCCTTG

GGCCTGTCTCCAACT[C/T]GCCCC

AGTTTCCCCAGCCTCCGGGACGG

GCGCTTCCCTGCAAGCTATCAGC

TTGAAGAGT

11q24.3 rs73013527 rs73013527 Yes rs7105899 0.9 - 1.0 rs73013527-C ETS1

AGCCCCAGAATCACTAAAAACT

AAATCCTAGGTTGGGTTCCCTGG

CCTAGGCGATTCCTC[C/T]GCCTC

TGTGCAAGAGTCACCATGAACC

CTATCTCTGCCTTCCAGCTGCCG

TCCAATCCAA

6p21 rs2234067 rs2234067 Yes rs1885205 0.9 - 1.0 rs2234067-C ETV7

CGGCTGGAGGCTGTGTGCAGGA

CCCACGCCTCCCAGGCNAGCGA

GCGGGCAGCGCCCCGG[G/T]GCT

GCGCTCCCACGGGGCCGGCCCC

TGCCCTGCCCTGCCCTGCTCCTA

GCCCGCAGCGGC

11q12.2 rs968567 rs968567 Yes rs61897793 0.9 - 1.0 rs968567-C FADS1

TGGAACCCGAGGCGGGGGGAGC

CGGAGGGGCGGGCAGAGGAGGT

GTCGAGGCCCTGAGCT[A/G]CCG

GGGAGTTTTTACTGGAGGCAAA

AGTCCATAGCGGGAGGGCTGAG

GGAGGGGCGGAGG

112


with the

peak SNP


Risk Allele

Gene DNA Sequence

11q21 rs4409785 rs4409785 Yes rs11021232 0.9 - 1.0 rs4409785-C FAM76B

TGAAAAGTATAAGATCAAGTTG

CAGCAGGTTTGATGTCTGGTGAG

GGTGACACTCTTCAC[C/T]TCAA

GATGGCGTCTTGTCACTGCATCC

TCACATGGTGAGGGACAGAAGG

ACAAAAAGGGA

17q12 rs1877030 rs1877030 Yes rs1054488 0.9 - 1.0 rs1877030-C FBXL20

ATACCCAGAGGAAGCTAGGTTC

CCAGAGTATTCCACCAAAAAGG

AGGAAATGACCAACTC[C/T]TGC

CCCAGTAGAGAGAAGACAGAGA

ATGCTTGGCACCAGGTGGGTCTC

ATCGCCTGCCCC

1q23.3 rs72717009 rs72717009 Yes rs56383975 0.9 - 1.0 rs72717009-T FCGR2A

ATGGGTCAGAAAGCACCCAGTT

CATGATAGGTAGNTTAGGTCGC

ATGGTGACTTGACCCA[C/T]ACT

CAAACGTTCAGTTTCCACCAAAG

CCCAGTAACAGGCCAAGAGCTG

TCTCTCAAAAGG

1q23 chr1:1616442

58

rs75409195 Yes Not Found

rs75409195-C FCGR2B

AGTACAGAGGAGGCTCCATAGC

CCAACAAGGNCTGAGGCAGTTG

GAGAAGNTCTNGGGGA[C/G]AG

CAGTGTAGTGTAGTGATGGANT

GCACATAGTGGGGCCAGACTGC

CTGAGTTCAAATCT

1q23.1 rs2317230 rs2317230 Yes rs7528684 0.9 - 1.0 rs2317230-T FCRL3

AAAGAAAGACACAAAATGTTAT

TTTAACATAGGTTCCTTCAATGN

CTAGTTTATTGAGAG[G/T]TTTTA

ACATGAAGAGATGTTGAATTTTA

TTGAAGGCCTTTTCTGCATTTGA

GATAATCAT

113


with the

peak SNP


Risk Allele

Gene DNA Sequence

10p14 rs3824660 rs3824660 Yes rs371668 &

rs386680

0.9 - 1.0 rs3824660-C GATA3

AGCCAGGACTCCCCTTCCTTGCA

GGAACAGGAGGCTTAACTCAAG

TTGGTCCCCCAGAGA[A/G]GGGA

GGCCCAGAGGAGGACCCCGGAG

GGTAGGTAGGGAAGAAAAATGA

GCTCTGAAGACT

8q22.3 rs678347 rs678347 Yes rs507201 0.9 - 1.0 rs678347-G GRHL2

TAGCACTAATCACAAATGTCATG

ACATCATTACTATTGTCNCAATC

TATTTCATGTTTAC[C/T]TTGCTC

ACTANATGACAAACTCCAGGAG

GGTGTGGGCCTGGTCTCTGCTGT

TTGCCGTTA

6p21.32 rs9268839 rs9268839 Yes Not Found rs9268839-G HLA-

DRB1

AGGGAACAATTAAAATCATTGT

CATGTTAGGATTTCGATTTATAC

TAAATGTAATGGGAA[A/G]CAGT

TGAAGAGTCCATGACCCCAACA

CAGGTCCACAAACTTTTTTTTTT

GGACTTTCTAA

6p21.3 rs4947332 rs4947332 SE Tag

SNP

rs2227955 &

rs9469064

0.9 - 1.0 rs4947332 HLA-

DRB1*01

01

GGCACTCACTCAGCATTCCCATT

CCAGAGCAGCCTCTGCAACGTCT

ACCAAAACCCTTTC[C/T]GGCAA

ATTGAACAGGCTGGGTATTTGAT

GATATTAAGGAATTATTGTTAAT

TTTGTGAGA

6p21.3 rs6457614 rs6457614 SE Tag

SNP

rs17427445

& rs7755224

0.9 - 1.0 rs6457614 HLA-

DRB1*01

01

TGTGGTGTTTTGTTATAGCAACA

CAAACAGTCAAAGACAACATCC

TAGAGGGCNNTGTCT[G/T]CCAG

CCAGCACCATTCAATTANNCNTN

TGGACTGACTACATCTAGGTAAT

GGGTATATAT

114


with the

peak SNP


Risk Allele

Gene DNA Sequence

6p21.3 rs3817964 rs3817964 SE Tag

SNP

rs9268500 &

rs3763305

0.9 - 1.0 rs3817964-T HLA-

DRB1*04

01

AAACACAACCCAATCTCTACCA

GAGTCTACTATGNCACCTTNAAC

CCCATTAGGCCATAA[A/T]CATA

AAGGGAAGGGTCCCTGGAAGGN

NCAAAGTAACTCCACAATCTGA

GGAGAGACACAC

6p21.3 rs660895 rs660895 SE Tag

SNP

rs3997872 &

rs510205

0.9 - 1.0 rs660895-G HLA-

DRB1*04

01

CCTTCAGGAATGGAAGGGGATG

CACAGAGTNAAGCCACCCAACA

AAAACAAGACTTGTAT[A/G]GCT

ANANATGGAAGGGANATCAACC

AGGAAATTATTTTGGAAATCCCA

GTGTAGTTACAA

6p21.3 rs6910071

rs6910071 SE Tag

SNP

rs9268145 0.9 - 1.0 rs6910071-G HLA-

DRB1*04

01

CCTTGACAGGAACTTTGGGTTTT

AATATTAATGTGATTTAATTTCA

GGATGAGGAATCTC[A/G]GCTGA

TATTGGGTTTGCTTAAATCATTT

GTAACTGAGATATGAGAACCAG

ATTTGCATTT

6p21.3 rs2395533 rs2395533 SE Tag

SNP

rs3828796 0.8 - 0.9 rs2395533 HLA-

DRB1*04

04

AATTCCACAGTTATTCAAATGAC

TCAGATTATGGAGTTTCCAGCAT

CTCATACACCACCATG[C/T]ACC

TACTTGCAAGTTCCATTGGTTAC

TGTTAGTCAAATGTCCCCAACCT

AACAGCAGAAA

6p21.3 rs2736157 rs2736157 SE Tag

SNP

rs3130626 &

rs3130070

0.9 - 1.0 rs2736157-C HLA-

DRB1*04

04

CCATGGCCCCTCAGGCCACAATC

TCCACTCCACCCAAACTTATCTT

TCCCTCAGGCTTTT[C/T]CCCCCT

CCTCCAATTTTTAAACCACAATA

AATTTGTTTGTTCCTACCCACCT

TCGGTTCT

115


with the

peak SNP


Risk Allele

Gene DNA Sequence

6p21.3 rs3115572 rs3115572 SE Tag

SNP

rs3096700 &

rs3130316

0.9 - 1.0 rs3115572 HLA-

DRB1*04

04

GAAACTTTCTAGCCTAAACCATG

ATCAGTCAATTCAGTTGCACCCA

CAATTCAAACATCN[C/G]CTTAC

TAGATCAAATTAACTCTGCCCTC

TCAGTTGTCAGAAGATTAAAAA

GTCTGCATGT

21q22 rs2236668 rs2236668 Yes Not Found

rs2236668-C ICOSLG

TCTCCGGACTCACAGCCCAGGC

ACCTGGAGGACAAACCAAAATG

GTNAGGGCAGCAGCGA[C/G/T]G

GGGGGTGTCCCTAAAGCCCCTG

GTGTTCCTGAGCTCTCAGCAGCC

CCCTAAACACCCTA

21q22.11 rs73194058 rs73194058 Yes rs11702844 0.9 - 1.0 rs73194058-C IFNGR2

GATCTCAGCTCGCTGCAACCTCC

GCCTCCTGGATTCAATNGATTCT

CCTGNCCCAGCCTA[A/C]CGAGT

AGCTGGGATTACAGGTGCCNAC

CACCATGCCTGGCTAGTTTTTGT

ATTTTTAGTA

17q12-

q21

chr17:380318

57

rs59716545

Yes rs8076131 0.8 - 0.9 chr17:38031857

-G

IKZF3

AAAAATATATATATATTTAGCTA

AAAGGAAGTAAAAATTCAAAGC

CCCAANTTTTTTTTN[G/T]TTTGT

TTNTTTTGAGATAGAGTCTCACT

CTTTCACCCATGCTGGAGTGCAG

TGGCGTCAT

4q27 rs45475795 rs45475795 Yes rs10023971 0.9 - 1.0 rs45475795-G IL2

AGACTTTTTAAGTAAAAAGAGG

TTATAAGAGACAGGAAAAGGTC

ATCATATAANGATGTG[C/T]GTC

AATTCATCAAAAGCTTATAATAA

TTATAAACATATATATATACACA

CACACACACAC

116


with the

peak SNP


Risk Allele

Gene DNA Sequence

3q22.3 rs9826828 rs9826828

Yes rs73230016

&

rs10212476

0.9 - 1.0 rs9826828-A IL20RB

AAACAAGAGTTCTAGAGATTGG

TTGCACAACAATGGGAATATATT

TAAATATTACCAATT[A/G]TATA

TTTAAAAANAANTAAGATAGTA

AATTTTATGTGTATTTCAACACA

ATTTTTTTTTT

10p15.1 rs706778 rs706778 Yes rs7072793 0.9 - 1.0 rs706778-T IL2RA

AGAAAGCTTCATGAGGGTGACA

TGGAAAGGGAGCCCTGAGGGAC

TGGTAAATTTCCATCA[A/G]TGA

CATTCCAGGGAGAGAGGCCCCA

AGACACAGGGGCAGCAGGTGGT

CCACTGTGCTCCT

22q12.3 rs3218251 rs3218251 Yes rs3218253 0.9 - 1.0 rs3218251-A IL2RB

CAACCCGGAGAGGTGGAGAAGG

GATGGGTGGATAGCCTGGNTGC

CCGGAGAGGGTACGGG[A/T]TAT

GGGGTGGGACAGGCGGNAGCCC

CCTCCTGACCTCTGTGGAGTGGG

GCCCATATACCG

5q31.1 rs657075 rs657075 Yes rs17165633 0.9 - 1.0 rs657075-A IL3

GAAGTCAGAAAATACCAATCCT

GGAGGAAGCAGGTTGGTGTGGG

GAAGTGACATTTGTTG[A/G]CAG

GGGCCTCNGGGCAGGCCTGTGG

AGGATAGGGAAATGGAACAGTC

CTGGCAGTTCAGG

1q21.3 rs2228145 rs2228145 Yes rs4129267 0.9 - 1.0 rs2228145-A IL6R

TTTTCTCCATATTCTCCTCTTCCT

CCTCTATCTTCAANTTTTTTTTTA

ACCTAGNGCAAG[A/C/T]TTCTTC

TTCAGTACCACTGCCCACNTTCC

TGGTTGCTGGAGGGAGCCTGGC

CTTCGGAAC

117


with the

peak SNP


Risk Allele

Gene DNA Sequence

19p13 chr19:107719

41

rs14762211

3

Yes Not Found

rs147622113-C ILF3

ATCCCGTCTGTACTAAAAATACA

AAAATTAGCCAGGAATGGTGGT

CTGTGGCTGNAATCC[C/T]AGNT

ACTCAGNAGAGTGAGGCNGNAG

AANNGCTTGAACCCACGAGGCG

GAAGTTGCAGTT

Xq28 rs5987194 rs5987194 Yes rs3027933 0.9 - 1.0 rs5987194-C IRAK1

ATTGACAATATACCACAGAGAA

AAGATTTATTTCAAATGACTTTT

GACTACAGTGCTCTG[C/G]AAAC

CTGTAGTGGGACATATTNTAAG

GCCAACAATAACACTCCAGGGG

CAGTAAACACAC

6p25.3 rs9378815 rs9378815 Yes rs6930468 0.9 - 1.0 rs9378815-C IRF4

TTCTCCACGTCCCCACTAGACTC

AGGAGCCCAGCTGGCTTCACCC

AGTGGNTCCNGCACC[C/G/T]GG

GCNGCAGGCNGAGCTGCTTGCC

AATCCCGCGCTGTGAGCCGGCA

CTCCTCAGCCCCTG

7q32 chr7:1285800

42

rs3778753 Yes rs3778752 0.9 - 1.0 chr7:128580042

-G

IRF5

CAAGGCTTTTGCCTGCAGCTAGG

TCCACACGAGCTCTAACCCGAA

CAGCATCCANNCTCC[C/T]AGAA

GCTCCCTTCTGCCCAGAAAAGG

GCTAGGTCTAATTCAGACCACCC

CAATCAAGGCC

16q24.1 rs13330176 rs13330176 Yes rs9927316 0.9 - 1.0 rs13330176-A IRF8

GTGTCCGCCCAGGAAATACCCT

GAGAAAATTAGACAACTAAATG

ATATTGACTGTTCTCA[A/T]GAAT

TACTCCAATAATTTGTTTTTTCCT

TGACCTGCAGGTTAAAGTCAGC

ATTCATTGAT

118


with the

peak SNP


Risk Allele

Gene DNA Sequence

7p15.1 rs67250450 rs67250450 Yes rs10951192 0.9 - 1.0 rs67250450-T JAZF1

TACCACAAACATTAACTAAAAG

GCAGATATGNGACAGAGCTTTC

ACCACGTAGCACTAAA[C/T]ATA

TTGTTTAATATGTAAGGGTTGCT

ACATGTTACTAGTTTTACAATTG

TTCTTATGTCA

2p23.1 rs10175798 rs10175798 Yes rs10173253 0.9 - 1.0 rs10175798-A LBH

ACTTCAACCCACACACATTCTTG

CCTTTTAGATTTCTTATGTACNC

AAATGAGTTTCACC[A/G]AAAAA

TTGGCTAGAAACTTCCCTTCTCC

TACTCACTGTCTTTTTTTAACCCC

AAGCCTTT

6q23 rs17264332 rs17264332 Yes rs6920220 0.9 - 1.0 rs17264332-G LOC1001

30476

ATAATCTCATATTCCTCCTACTG

TTATTTTATTAAGTACCTNATTTT

ATTTTTATTTTCT[A/G]CATGGTT

CAGCCTAGTTGTTTCTATTAAAG

CCAAGATAACTTCAATTGCTCAA

CAACAAG

1q25.1 rs2105325 rs2105325 Yes rs1557121 0.9 - 1.0 rs2105325-C LOC1005

06023

AAAATCATCATCTCCTCCCTGCT

TAACAAACAGTCCAGGTTTTGTA

ATGGCAAACATACT[A/C]CCTGC

ATGACCATTCACCCCTGGGACAC

CCTGCAGCAGCGTCCCCACCACT

AGTGAATGC

21q22.12 rs8133843 rs8133843 Yes rs9979383 0.8 - 0.9 rs8133843-A LOC1005

06403

ACCACTCTTAACACAGTTAAATG

GACATCTTTACAATATATAACTA

TAATTATCCAAAAG[A/G]ATATT

AATATCTATAGTATTTACTGTAT

ANAAAAACAGTTTTCATTATTAT

TAACAATTC

119


with the

peak SNP


Risk Allele

Gene DNA Sequence

15q23 rs8026898 rs8026898 Yes rs919053 0.9 - 1.0 rs8026898-A LOC1458

37

GGAGGGACACTGATGAAATTTG

TCTTTCAGAACCGCTTCTGGAGT

CTTCCTCCCAAGAGC[A/G]AGGC

ACTGGTGCTGCTCTGTCAAACTG

TTCAAGTGCCTTCATGTAACTGC

TTGCTGTCTC

1p34.3 rs12140275 rs12140275 Yes rs12131057 0.8 - 0.9 rs12140275-A LOC3394

42

CACTGCACTCCAGCCTGGGCGA

CAGAGCGAGACTCTGTCTCAAA

AAATAAATAAATAAAT[A/T]AAT

NAATTAAAGCAAATTACTTTAA

ATAAAAAGCAGGATAAAGTAAG

TACTCTAAGAGTT

1q23 rs4656942 rs4656942 Yes Not Found

rs4656942-G LY9

ACTAGGCCCCCCAACCCACCCA

CAAGAGCTGCAGGCTCCACTCCT

CCCTCCCTGCTCCCT[A/G]TTCCT

CATCATGCTCNGCCCTAGGCTGC

CTGCATCCTTACAAATGTTGAGT

CCACCTGAG

1p34.3 rs28411352 rs28411352 Yes rs67164465 0.9 - 1.0 rs28411352-T MANEAL

ACAGCCAAGGACAACTCATGCT

CAGCAGTTGGTTTTCCAGGAGG

ACTGGAAACCTCCTGC[C/T]TTAT

CAACTTCTGNTGAGGTCAATGGT

CAACAGAAGCCACAATCCTTTTG

GGGAAGGGAG

8q24.21 rs1516971 rs1516971 Yes rs10098765 0.9 - 1.0 rs1516971-T MIR1208

GAATAAGTAGAACATTGGGCAG

ATAACTGCACCTCTGCATCATCA

ATGGGCAAATGACTG[C/T]ACCC

ACGTTCCTGCACCTGTATTCTGA

GAAGCTTAGATTAAATGATTTCT

GAAAGTCCTT

120


with the

peak SNP


Risk Allele

Gene DNA Sequence

Xq21 chrX:784646

16

rs20140874

2

Yes Not Found

chrX:78464616-

A

MIR4328

CTGAGAGACAGTTTGTTATAATT

TCTGTTCTTTTACATTTGCTGAG

GAGAGCTTCCAACT[A/C]TGTGG

TCAATTTTGGAATAGGTGTGGTG

TGGTGCTGAAAAAATGTATATTC

TGTTGATTT

6p21.1 rs2233424 rs2233424 Yes rs28362856 0.9 - 1.0 rs2233424-T NFKBIE

GAAGCAGTGCTTCCCCATCCCAT

CGCCTACATGCCCTCCCCCAAAG

ATGCCAGCTTGTAC[A/G]GACAA

TTAATAGATATTTCTTTTAAAAC

AAATGAATGATCCGGGACCGGT

GGCTCACAGC

1p36.13 rs2301888 rs2301888 Yes rs2240335 0.9 - 1.0 rs2301888-G PADI4

GCCTCCCGGCCCAAGGATCTCTC

AGGTTCCTATCTCCCTGTCCATC

TAAGGAAGAGTGGC[C/T]NGTGA

GGCACCAGGCTGAACCCCAGGA

GCCCGGGTCCCCCAAGTGTCTCT

TGGCCCTCAG

3p24.3 rs4452313 rs4452313 Yes rs7653834 0.9 - 1.0 rs4452313-T PLCL2

CTTCTATTAAATAAAGTAGGCAG

TTATGTTAGAAAGATGNATGTTT

TTCCATAGGACTCA[A/T]TATGG

CTATTTATGTTTTTAATNCTAAA

TATGAGTCGTATAAACATTTCTA

TTTTTCCTG

14q32.33 rs2582532 rs2582532 Yes rs2819464 0.9 - 1.0 rs2582532-C PLD4

TTGTGGGGTGACCACTCTGAGG

AGATAAGGCGAGTCCCTGCAGC

AGAGGACAACCCCCTC[C/T]GCC

AGCAGTAGGGGTCCCACTCTGC

CGCCATCTCTGCCCTCTCCACGC

CTAGCCTCTCCA

121


with the

peak SNP


Risk Allele

Gene DNA Sequence

6q25.1 rs9373594 rs9373594 Yes rs9498368 0.9 - 1.0 rs9373594-T PPIL4

TCAGTCTCAAAAAAAAAAAAAA

AAAAAAAAAAAANATATATATA

TATATANATATATANA[C/T]ACA

TATAAAAAATATGCATAATAGT

ATTACTTTTAGTAGCAAAGTAGG

AACAACAAAATA

14q23.1 rs3783782 rs3783782 Yes Not Found rs3783782-A PRKCH

AGATTTTTGATTTTTTTTTTTTTT

AAGACAGAGTCTNGCTCTGTTGC

CCAGGCTGGAGTG[C/T]AATGGC

GTGATCTTGGTTCACTGCAACCT

TTGCCTCCCGGGTGCAAGCGATT

CTCCCTGC

10p15.1 rs947474 rs947474 Yes rs10796035 0.8 - 0.9 rs947474-A PRKCQ

TGAAGGGTGACAAAGAATTCAA

AACACTCACAGGACAATTTTCCT

AACCCTTGGTCTCTC[A/G]GAAT

GCTNTTTTTTAGGCTAATTTGTTT

TGATGAGAAAACTATGCCTGCT

AGGCCTATTT

18p11.21 rs8083786 rs8083786 Yes rs7241016 0.9 - 1.0 rs8083786-G PTPN2

TGGTGTGTGCCTGTAATTTCAAC

TACGCAGGAGGCTGAGGTAAGT

GAATCACTTGAACCC[A/G]GGAG

GCAGAGNTTGCAGTGAGCTGAC

GTCGCACCACTGCACTCCAGCCT

GGGCGACAGAG

1p13.2 rs2476601 rs2476601 Yes rs6679677 0.9 - 1.0 rs2476601-A PTPN22

TTATACTTACTGAACTGTACTCA

CCAGCTTCCTCAACCANAATAA

ATGATTCANGTGTCN[A/G]TACA

GGAAGTGGAGGGGGGATTTCAT

CATCTATCCTTGGAGCAGTTGCT

ATCCAAAATGT

122


with the

peak SNP


Risk Allele

Gene DNA Sequence

1q31 rs17668708 rs17668708 Yes rs4915314 0.9 - 1.0 rs17668708-C PTPRC

TATTATACTTCACCTCTCAGAGG

CATTTAACGCTATTGACAACTTC

CTTCTTTTCAAAAN[C/T]CTGTCC

TCCCCTAATTTTCATGAAACATG

AAACTACCTTCTAGTTCTCTTAT

TTATCTCA

14q24.1 rs1950897 rs1950897 Yes rs10131490 0.9 - 1.0 rs1950897-T RAD51B

CAGATTATGAAGGGTTGAAATC

ATTCACTTTATCTAGTGGTGTTN

TGAAAACCAGTCTAC[A/G]CAAA

GGGAGGAAATAAAAAAACAAGC

CACCTGATTTTCAGCATTTGCTG

ATTTCCATGGT

15q14 rs8032939 rs8032939 Yes rs8035957 0.9 - 1.0 rs8032939-C RASGRP

1

GCAGATATAATAAGCACCCTGT

CCCAAGCCTGAAGAAGCTACAG

GCAATACTACAGTACA[C/T]GGT

ACAGTCAGGGCCTAGAGGGAGG

TAAACACGGGCAGTTCAGAGGA

AGGGGAAAGTCAG

21q22 chr21:359282

40

rs14786809

1

Yes rs2834532 0.9 - 1.0 chr21:35928240

-C

RCAN1

TGAAACCCTGTCTCTACGAAAA

ATACAAAAAATTAGCCGGGCAT

GGTGGCGGGTGCCTGT[C/T]GTC

TCAGCTACTCGGGAGGCTGAGG

CAGGAGAATGGTGTGAACCCGG

GAGGCAGAGCTTG

2p16.1 rs34695944 rs34695944 Yes rs12466919 0.9 - 1.0 rs34695944-C REL

GAGATTCTTGGTTCCTTCTGGTG

GGGAATGGTAGTAAGTCTGNTT

GCCTTCTCAANATAT[C/T]CCCTT

AAATGTCTCAAAAGTACCTTAA

GTAGCGGGGCACAGTGGCCCAT

GCCTGTAGTTA

123


with the

peak SNP


Risk Allele

Gene DNA Sequence

22q13.1 rs909685 rs909685 Yes rs2069235 0.9 - 1.0 rs909685-A RPL3

GTCTGCCCACCCCTGGGCCTCTG

GCCTGGAAGGGCGAAGCCACTG

GCTTTGTGAGGGGGC[A/T]TGTC

TGCTTGGGTCATTTCTGCCTCTN

ATGCCTTCATTTAGCAAAGCTTT

ATTGAATCTG

10q21.2 rs6479800 rs6479800 Yes rs67630314 0.9 - 1.0 rs6479800-C RTKN2

TCTACTAAAAATACAAAAAATT

AGCCAGGTGTGGTGGTGTGCAC

CTGTAATCCCAGTTAC[C/G]CAG

GAGGCTGAGGCAGGAGGATGGC

ATGAACCCGGGAGGCGGAGGTT

GCAGTGAGCTGAG

10q22.3 rs726288 rs726288 Yes rs10887226 0.9 - 1.0 rs726288-T SFTPD

CAGCACCTGCTCGAGGAATGGC

AAATTGCCAGGCAAATGTGCAC

CACACTCCCAGCCTGC[A/G]CTC

AGGAAGGACATTACTAAGNACT

CAGGACTCTGGCTGCATTTTCAG

ACCAGCACTATC

12q24.12 rs10774624 rs10774624 Yes rs7137828 0.8 - 0.9 rs10774624-G SH2B3

AGCTAACATTTTTGTTTGTTTAG

AGATGGGGGTCTCACTATGTTGC

CCAGCTTGGCCTCC[A/G]ACTCC

TGAGCTNAAGTGATCCTCCCACT

TCAGCCTCCCAAAGTGCAGGGA

TTACAGGCAT

2p14 rs1858037 rs1858037 Yes rs11126034 0.9 - 1.0 rs1858037-T SPRED2

AAAATGAGGGCCAACCCAGAAA

AATGAAGAACGTGCCTCAAAAT

NAGGTCCCAAGACATA[A/T]CCC

CGCATCCAATCATACTGAATATT

TTTTTTGGCAGGAAAACTTGTTT

ATTTGGGGGAA

124


with the

peak SNP


Risk Allele

Gene DNA Sequence

2q32.3 rs11889341 rs11889341 Yes rs12612769 0.9 - 1.0 rs11889341-T STAT4

TTCTTTCATTTTTTTCCACATGTC

TACCAAATTCCAATAACATTTAC

TGAACATCTTATT[C/T]TTTTACN

ACTGCTCTGCTGGGCCAGCTCTG

TCATAAATCAAGTGTTCCTTTGT

GGGTGGG

6q25.3 rs2451258 rs2451258 Yes rs1994564 0.9 - 1.0 rs2451258-T TAGAP

AAGCTCTTATGCAGGACAGCTC

GACAGGGACCAGGCGAAAACTC

ATGAAGGCCAGGAAAG[A/G]CA

CATTAGGGTCAAACATGGATGG

AAAAAAGACCAGAGGAAGGAG

CATGCTAGGATCAGC

4p11 rs2664035 rs2664035 Yes Not Found rs2664035-A TEC

TGACCCAGGAGAGCCTATACAA

ATGAGAGATTTCCCAGGGTGTTT

GAGACTCATGTCATN[A/G]TACT

TGTAACTGATTGCCTGCCCCCAC

CANCATGTTTGTTAACCTAGGTA

AAATATTAAA

6q23.3 rs7752903 rs7752903 Yes rs111883038 0.9 - 1.0 rs7752903-G TNFAIP3

AAGAGAAATGTGTAGACTGCTC

AAACTGGTTCTAATAAAGTTTTC

CCTCATTTTGAATGT[G/T]TCTGT

TCAGTAACGAAAAACTGTAAAT

AAATAATACAAAAAGCATATTT

ATAACCTAAGT

1p36 chr1:2523811 rs18778617

4

Yes Not Found chr1:2523811-G TNFRSF1

4

CTCCCCTGCAGCGCTGATGCCCC

CCCTCCCCTGCCATGCTGACGCC

CCCTCCCCTGNTGN[A/G]CTGGC

ANCCCCTCCCCTGCCGCGCTGAN

GCCCCCTCCCCTGATGCACTGGC

GCCCCCTCC

125


with the

peak SNP


Risk Allele

Gene DNA Sequence

1p36.23 rs227163 rs227163 Yes rs227161 0.9 - 1.0 rs227163-C TNFRSF9

TACGTTTTTTCTAGGGAATTGGT

CATTTTGTCTGAGTTTTCAAATG

NATTGGCAAAAACC[C/T]GTTCA

TAGTAGTCTGTTATCGNNTGGTA

AGCTTTCATTCTACATAGATTAA

GTAATGGAG

8q21.13 rs998731 rs998731 Yes Not Found rs998731-T TPD52

GCCAATCATATATTCTAGAACAT

TCCTGGGCTTTTCNACCTGTATC

ATAAATCATCCATA[C/T]GAATC

AGAATAGAACTAAGGAGTGTCA

ACCAGGAAGTTAAAGACTAAAC

CTACCTTCCAG

9q33.2 rs10985070 rs10985070 Yes rs10760119 0.9 - 1.0 rs10985070-C TRAF1

GATGACTCATGTCCTAAGTACCT

TCCTAAGTCAATATACAACCAG

ATTTGATCATCATCA[A/C]AGGT

GGGCTTGGGGTTCATGGTCAAG

GGCAGATGCCAGGAGTAAGAGA

TGGAAGGACAGA

11p12 rs331463 rs331463 Yes rs11033650 0.9 - 1.0 rs331463-T TRAF6

ACTACATTTTAGAAGTGAAAAA

AAGGGCAGAAAATACCAGAAAG

CATAGAATGTGGTAAA[A/T]GGA

GGTGTTTCATAAACCTTTTTGTT

TCAGCTGAGTCAGTATGCATATT

AATTTTCTTTC

16p13.13 rs4780401 rs4780401 Yes rs11075012 0.9 - 1.0 rs4780401-T TXNDC1

1

ATATGTGTCATGTGCTGTGACAG

TGGTCCCAGAAAATTATGATACT

GTTTTTATTGTACT[G/T]TTTCTTT

TCTTTGACAGAGTCTTGCCCTGT

CACCCAGGCTGGAGTGCAGTGG

CACCATCT

126


with the

peak SNP


Risk Allele

Gene DNA Sequence

19p13.2 rs34536443 rs34536443 Yes rs74956615 0.9 - 1.0 rs34536443-G TYK2

CTTTCTAATTGCTCTAGCAAACT

CCCGGTGGGGCTGCGGGCCTGG

CTCTCACNNTNGGGG[C/G]GCTC

TGGCTNGAGTCACAGTGCGTCA

GCAGCTCATACAGGGTCACCCC

GAAGGACCAGAC

21q22.3 rs1893592 rs1893592 Yes Not Found

rs1893592-A UBASH3

A

GACTTTGAAAACGATCCCCCATT

ATCATCGTGTGGCATTTTCCAGT

CCAGANTTGCAGGT[A/C]TGTTT

GAGGACTGTCTAGTAGGAAAGG

TAACAATAACAGCAACACTGAT

TATGGCTAGCA

10q11.23 rs2671692 rs2671692 Yes rs2663038 0.8 - 0.9 rs2671692-A WDFY4

TTGTCATGGGTCAGGTTCTGCAC

GGAGAAAGTTGATTGGGAATGA

GGATTGCAGGGCTGG[A/G]CCNG

NGGAGAAGTGCAACCACAATGT

AGTCTCAGCAAAGACTTCAGCT

GATTCCAGAGCA

22q11.21 rs11089637 rs11089637 Yes rs5994638 0.8 - 0.9 rs11089637-C YDJC

CCAAGACCCCTGGCCTGTTTTTG

TTCACATTCTGACTGCAGAGCCT

GTTGCATTCCACCA[C/T]TTGGCA

TATTTCTTCCCACAGGGTCCCTC

TGAGCCCAGAAACAGCCAGTTA

CCACCCTCC

10p11.22 rs793108 rs793108 Yes rs1250317 0.9 - 1.0 rs793108-T ZNF438

ATGAATCCACAAATTCTCATAAA

GTTTCTGACCATGTGTCAAAACT

CTAAGGAATGTTTG[C/T]ATCTA

CTTACCTCTGTGAAAGTGCATTA

TCTTTCACGAAATGGCTAAAATC

CAAACTGAG

127


with the

peak SNP


Risk Allele

Gene DNA Sequence

17p13.2 rs72634030 rs72634030 Yes rs60963557 0.8 - 0.9 rs72634030-A ZNF594

AGTGTTATGAGCAAGTGCCGGC

TAGAGCCTTTGCAGATAGTCCCC

AGGCTTGGGCTCCCT[A/C]CTCA

CTAGTCTGGCTGCATTTGCTTTG

TTTTTCCTTTCTCAAAGTTGGAG

CCTACATCTG

4q27 LD-

rs10023971

rs10023971 YES

ATAAATATAGTCTTACATATTTT

AGATTAATTGTAGCTANCTTTTG

AGCNTTTACTTTGT[A/G]TCAAAT

GTTGGGCCAAGCAATTTTCTGGC

TTCTATTATCTTCTATTATGTTTA

ATCCTTA

8q24.21 LD-

rs10098765

rs10098765 YES

ATATCTTTGAATACCTTGAAATT

CAAAGCATCTGTATGACAANTG

CACTAATATCAAAGC[A/G]GAAA

GACACAGACCAATAAAAATTAT

TTGNAAAAAAAAAAAAAAAAAA

ATAGAGAAAAAA

14q24.1 LD-

rs10131490

rs10131490 YES

TTTCTGAGATATTCAGCTTTTAT

TTCAACTATGCTTCCAGTATCTA

CAGGGGAGTCTGTN[A/G]GTCAC

ATGTTGAGCCAGGTTTTCTATTT

CAGAAGAGGAAGGAAAAGAAT

GTGTAGGGGAA

2p23.1 LD-

rs10173253

rs10173253 YES

GAAGCGTTGGGGTCTGTGGAAG

ACATGAAATGTACCACCNAGAT

CCGCCTTCTANGGAGG[A/G]CCT

TCTGCCCAGCTGCGGGTNTGGA

ACCAGCAGCCTGCCTCCAGCTGT

TTGTTCCTTCAA

128


with the

peak SNP


Risk Allele

Gene DNA Sequence

17q12 LD-

rs1054488

rs1054488 YES

GTTTCAGTATTGAGATGGCTCAG

GAGAGGCTCTTTGATTTTTAAAG

TTTTGGGGTGGGGG[A/G]TTGTG

TGTGGTTTCTTTCTTTTGAATTTT

AATTTAGGTGTTTTGGGTTTTTTT

CCTTTAA

11q12.2 LD-

rs10792304

rs10792304 YES

TCATCTAACCCCTTTGTGCCTCA

GTTTCTCAATCTGTAAAACAGAG

CGGCGTGCGGAACA[C/T]AGTAA

GTACCACTAAGCATTTGCTGCNA

NGAGGACCCTGGCAACCATCTT

AGACAGTGGG

10p15.1 LD-

rs10796035

rs10796035 YES

AGTCCAGGGCCTGTACATACACT

GGTTCCTACAAGGGTGGTGATG

NGAAGTGAATGCTTG[A/G]GAAC

TGGGACTGGGATGTAAATTTNC

NGTTTGCCAGGGGCCAGCTCTG

GGACCTCAGGTG

10q22.3 LD-

rs10887226

rs10887226 YES

AACCTCTTTTTATTTCAACCTCG

AGAACTTCCTTTAGCACTTCTTG

CAAGNTGGACCTAG[C/T]GGTGA

CAAACTCCCTCAGTATATGTTTG

TNTGGGATAGTTTTTATCTCTCC

TTCACTAAT

6q27 LD-

rs10946216

rs10946216 YES

TTGGCCAAAGGGATGATCAATG

GCAGTCGGTCTGTTGCTTGGTCC

CCCCAACCCCTGCTC[C/T]TCTTG

GGNAGCCCCCCACCCACAGCTC

TCACCGGCCTTCCCTCTTTGCTA

CCACCACAGA

129


with the

peak SNP


Risk Allele

Gene DNA Sequence

7p15.1 LD-

rs10951192

rs10951192 YES

TAAAAGGATCTACAAGCGCAAA

GTAAGAATAATCATATGAGACG

TTCAATTTGAAACAGG[C/G]TGG

TAGAGTCCAGGAATCATCTATA

AAGCTGTGAGATGTAGCATGGT

ATATCCTGCTATA

9p13.3 LD-

rs10972201

rs10972201 YES

AGGATGGTGTAGGAGGCAGGTA

GGAGGCCTCCCTTGGGCCTTGAG

GTTTCTGTGACCTTT[A/G]CNTAA

CCCTTGGCAGGTGGGATCCCAC

AAGAGTGCCTGATGTTGAATTTC

TCATCAGCAC

11q21 LD-

rs11021232

rs11021232 YES

GAAGAATGTCTGGCATTAGTATT

TAATGCCCTAGTGTTACCAGAAG

TCAATCTATTATGC[C/T]TTCTCT

TTTTCTGGTATTTGCATTTAAGT

CTTATCTCCAGTAAGATCCTAAA

GGCTCTAT

11p12 LD-

rs11033650

rs11033650 YES

TCAGAGTAGATGGAGTTAGCTG

AAGTTTAACATTGTGAGGCCTGG

GATGGGCTGGAATCC[A/G]ACAA

TCCATTTTCATAAACTTTAATTT

GGATGTGGTAGGTGATCTCAGG

TGTTCATCCAC

2p14 LD-

rs11126034

rs11126034 YES

CCTAAAAGGTGTGTGTAGGAGG

TGGCGGTGGTAGTCTTTGCTTTA

CAATGACTGNGGATA[C/T]GACC

TTCATCCAGGAACCAATTAAGCT

AAAAGCCAGGGGTCCTGCTTAT

GTGCAGGAGCC

130


with the

peak SNP


Risk Allele

Gene DNA Sequence

6q23.3 LD-

rs111883038

rs11188303

8

YES

AGGACAGTAAGCATGGATGAGG

AAGTCACTGTGACTCATGGCAG

GGGAGGGTGGAGGGCA[A/C]CCT

CTGAGGAAGATGGANATATATN

CTGAAAGCCCTTCCTGCGGTCTA

TGAGGCCGGGTG

6p23 LD-

rs115686522

rs11568652

2

YES

ACATAAGTTGTCCCCTAGGTGAA

GGCCGTCTGCCACACTGACTGTN

TAATCTGAACTNGG[A/G]GCCAG

ANGTTCTGAGTCTTAGCGCCNCN

CTTGCTATTATCTTCCAGCATGA

TTTTTGGGC

2q33.2 LD-

rs11571316

rs11571316 YES

GATGCACAGAAGCCTTTTCTGAC

CTGCCTGTTTTCTATACACTGCT

ACACATTATAGAAA[C/T]CTTCN

AGCCCCAGCTCAAGNGCCAACA

AGCAATAACAACCTAATGGGCA

CTTCCTAATGC

2q11.2 LD-

rs1160542

rs1160542 YES

CTGTTTTCAGGTCACGTTCCTCT

AGCGCAGCAGATCCCACCTGGC

TACACGCTGGAATAA[A/G]CAAG

AGACTTCCAGAAACACAAATGC

NTGGGCTGTACCCCAGAGAGAT

TCCATCAGAGTC

21q22.11 LD-

rs11702844

rs11702844 YES

CCCAGGTAGTTTATGACATCGCT

GGGTTGTTGGNCTCTCCAAGCCC

AAAGTGACACTAGC[A/G]TTTCT

CTAGGCCTTGAACTACCTGGGG

NGTCAGAGCCACCAGCATTGAA

TGGGCATCAGT

131


with the

peak SNP


Risk Allele

Gene DNA Sequence

1p34.3 LD-

rs12131057

rs12131057 YES

ATCATTAACTTTTTATGGAGAAA

GGAGTGGTCTGACATGANTATA

TANATAAAGTTTTGG[A/G]CAAT

GGCGAATTGCTGGGCTAATTGG

NCAGGGGCCTGGAAGAAGCAAG

ATGAAATGATCA

2p16.1 LD-

rs12466919

rs12466919 YES

AAGCCTCTTTCCTTTATAAATTA

CCCAGTCTCAGTTATGTCTTTAG

AAGCAGTATGAGAA[C/T]GGACT

AACACACCCANAAATCCTAAAA

TATTTTCTTTTTTAAAAAATAGA

GACAGAGTCT

10p11.22 LD-

rs1250317

rs1250317 YES

CAAGCTATTACTTGAAGCTTCTC

CCTTTTATTTCAGAAACTTCTTG

CATTTATTNGATTT[C/T]GTTCTA

AAGANNTGAAGTGTTATGGAAT

AAATTTGCTTACGGTGATGGATG

GGCATTTTC

2q32.3 LD-

rs12612769

rs12612769 YES

CAAGAAAACAAACTCAATTTAA

AAATGGGCCAAATACCATAATA

GATGTCTCACCANAGA[A/C]GAT

ATATAGATGANAAATAGGCATA

TGAAAAGATGCTCCACATCATA

CGTGGTCAGGGAA

13q14.11 LD-

rs12872801

rs12872801 YES

TAGAATTTTAACCCATGCAGGAC

TTAAAGAAAATAATTGCTATCTG

CTCCAGTCAAAGTC[C/T]ACAGT

GACATTCCAAGANCATGTGTCA

GGAAATTGACTGAGAAAAGAGT

TTTGAATTTTT

132


with the

peak SNP


Risk Allele

Gene DNA Sequence

8p23.1 LD-

rs13277113

rs13277113 YES

GTAGACTCATGGCCATTCTGATG

CAGGCCATTTTTAAGATTAAACA

CTTATCAGATCATT[A/G]TCTGCT

TTTGGTTTTTCTAGTNCCCAGAA

ACAAACATTTTCTAGTACCCAGA

AATTGTTG

2q33.1 LD-

rs13398075

rs13398075 YES

ATCCAAGGTCTTTTTACTGTATC

ATACGAGTTCTCTTACTACCACC

AAACNCTACCTTTC[A/T]TTGCTA

AGTAAAATTAATTTCCCCATCTC

TCCTCACCTTTTTTTCATCCTGAC

TTTTGGG

2q13 LD-

rs1533299

rs1533299 YES

TGATGTCACCTGCAGGTGTGTGA

GCCTGTGGTAGCAGTCTGCTGCC

TGACATTNGCNTCA[A/G]AAGGC

GTTGCTAGCCACATCTGTCCTCA

GCTGTGAGAGTGTCTGTATGCAC

CAGCAGGCT

1q25.1 LD-

rs1557121

rs1557121 YES

CAAATTTTTAATATACAACTTAC

AGAAATCTTGCCATATTCTTTTT

TTCAAATGTTCCAN[C/T]AAGGT

GCATATACACTATAAGGGCNGC

ACATGTCATGTCACTGCTACTAA

CAATCAATCA

5q31.1 LD-

rs17165633

rs17165633 YES

AGCCCCAAACCCAGCACTCCCT

AGGGAACACTTCTTCTGTGGCTT

GCCTGGCTCCAGATC[A/T]TCCTT

TAGGTCATAGCTGTCCTTTCCTC

CTGTCTTCTCTGATGGTGCAGGC

TGGATGAGG

133


with the

peak SNP


Risk Allele

Gene DNA Sequence

6p21.3 LD-

rs17427445

rs17427445 YES

CATATTAAAGCCCAAACATCTCA

TATAAACACTTTATTATNTGAAA

TTCTTGGCCATGGAATATC[A/G]

TATACTTNCATTTAATNTCTTCA

CCANCTCTATCCAAAAACAAAA

AAAACCCTTCAATCCCCATA

6p21 LD-

rs1885205

rs1885205 YES

GCGTCCCCCCATAGCTCCTGACT

CACTCTGGCCTTGGGCANACCCT

CATGGCCATGGACC[A/G]GGGAT

GGGGAGGTGNNAATCCTCTTTTG

TTCCCCATTTCCATACAGGGAAG

CATAGTTGT

2q33.2 LD-

rs1980421

rs1980421 YES

TCACATTTCTAAGTCTTACAGAT

ATAAATATTTTATGTTTGAGNTT

TGTCAACCAGGATT[A/G]TTTGA

ATAAATGTATAATTCTCTACAAC

NGTTAAGTTTACTAATTTATCAA

AATTGTTTC

5q21.1 LD-

rs1991797

rs1991797 YES

AATAGGGGCATTGTTGACAACT

CAAGAAAATTTCAGCAGAAATA

ATTGAAAATATCCCCC[A/C]TGG

GTTCTCTATTTACCCTAGGCAAA

AACAAAAAAATACACACTAGAA

ACCAGATCAGGC

22q13.1 LD-

rs2069235

rs2069235 YES

TTATTGAATCTGCTGGGCCCCAA

GGGAAGGCAGCGTGACTCAGAA

GCAGCCCTTGTTCTC[A/G]GGAA

GCCACAGTCCCACTGGGGAGTG

AGCCAGGTGCCCCCAGGCAGAG

GCCGCAAGAGCC

134


with the

peak SNP


Risk Allele

Gene DNA Sequence

6p21.3 LD-

rs2227955

rs2227955 YES

GAAAATTGCTGCAAAGAATGCC

TTAGAATCCTATGNTTTTANCAT

GAAGAGTGTTGTGAGTGATG[A/

C]AGGTTTGAAGGGCAAGATTAG

TGNGTCTGATAAAAATAAAATA

TTGGATAAATGCAACGAGCTCC

1p36.23 LD-rs227161 rs227161 YES

ACTCAGGAGGCTGAGGTGAGAG

GATCACCCGAGCCTGGGAGGTG

GAAGCTGCAGTGAGCC[A/T]TGA

TTGTACCACCACACTTGGCCTNG

CGACAGAGCCAGAACTTGTCTC

CAAAAAAAGAAA

10q11.23 LD-

rs2663038

rs2663038 YES

TTGGTCTATTTGGGTACAGTCAG

CCCTTATTGTGCAATTCTCCTGN

GTTTATGAAGTAAG[A/G]CCATT

CAAAATNAAATTGACTGANCTTT

TGCCAAAAGTCCAGTTGTCAAGT

GTATGTTTT

14q32.33 LD-

rs2819464

rs2819464 YES

GCTGCCCGTCTGCCTGGAGCATT

TAAGGGAGCAAAGGTCCTNTCT

GGTGAGGGCAGGGAG[G/T]GTG

GGGAGCAGCTGTCCAAGAACAG

CCCTGGCACCTCGTTGCCCAGCC

ACAGCCCAGGAT

21q22 LD-

rs2834532

rs2834532 YES

AGGATGCAAAGGGGATAAGAAG

CAGCAACTACACCCTGGCTCTGT

AAGACNGCATGTCAT[C/T]ACAC

CTTACTCAAAGGGACTCCTCAAT

ATTTAATCAGCGATTTTTTACAT

TATTACCTAA

135


with the

peak SNP


Risk Allele

Gene DNA Sequence

6p21.1 LD-

rs28362856

rs28362856 YES

CTACACTCATCCACCTTCCTTTC

TCCAATGCTGGAGATGGAAAAG

CTACATGTTCCCTTG[C/G]GGAT

AACCCTCTAAGGGCATTGCTGG

NNTTAGGCTGATAGCACTTGGA

ATAGTGGGGAGG

4q21 LD-

rs2867461

rs2867461 YES

TTTGTTAGCTTCTAAATTAAATA

GGAAGCATGATACAGAAGAATG

ATCTTTAGAGATGGG[A/G]ATTA

AATTGAGTATCAGTCTCAGGATT

NAAAAAAAGAAAATGATTGCTG

CAATTTCAGCC

Xq28 LD-

rs3027933

rs3027933 YES

GGGAAGAGCAAGGAAAAAGGT

GTGGTCCAGGCAGAGCCATACA

TCCCCTTCCTGCNNGTG[C/G]AG

AAGAGCACCCTACCTATTCTAGT

GTGGGAGGCTTCCCCGTTCTTTT

CTGGTGACCGAA

6p21.3 LD-

rs3096700

rs3096700 YES

CATGAAATGTAAAAATCATTCTT

AGCTCATGGGCTATACAAAAAA

AAAAAACAAGTNGCAGACTG[G/

T]ATTNGGCCCATTGGGTGGTAG

TTTGCTCATCCCTGGCTTAAAGA

AAAAAACAATTAATAAACCAG

6p21.3 LD-

rs3130070

rs3130070 YES

TGGGGCTTGGTTTAGTCCAGCCA

CGTCTGAGCCNGAGACGAAGAG

GTCCCTTTCTTACCT[A/G]TTGCA

GGTTCCTTGTTAAATGACTAAGG

AATGGTACTAAACTTTAGCTTTT

TGTCTTGGA

136


with the

peak SNP


Risk Allele

Gene DNA Sequence

6p21.3 LD-

rs3130316

rs3130316 YES

TGTTGACAGTTTTATGTCTTCCTT

TCCAATCTTTTGGTTTCTTTTTCT

TATCTCATTATG[C/T]TGGTAATG

ACCAAAATACAATGTTGAATAA

AAGTGATGATAGTAGTCTCCTTG

TCTTCAT

6p21.3 LD-

rs3130626

rs3130626 YES

CGTCATGCACCTGCTATGTTACG

GGAACGGGGCACTCCACNNGNN

GATCCAAAGTTGGCCTGGGT[A/

G]GGAGATGTCTTCNNNGCCACA

CCNNCTGAACCCCGCCCACTTAC

CTCACCTCTGCGCCAGGCTGC

22q12.3 LD-

rs3218253

rs3218253 YES

GCAGCAGGGGCCCCGAACTGCA

CCTGACCAGGTTCAAATCCCAGC

TCTATGCCCTTCTCG[C/T]CTGGG

TGAGAGGTTGACCCGTTTCTTAG

TCTCCTCATTTGGCAAAAAGGAG

GCATACTAG

3p24.1 LD-

rs34269949

rs34269949 YES

TAAGAAACAGTGAGAGGGAAGG

AGAAAGGAAGGACAGACAACA

GGATAGAAAACCGTCGC[A/C]NT

CATTCCTTAAAGGTCTCCCTTTC

CAGACTTCTAAGAGAAAACAGA

ACCTTGTATCAGA

10q21.2 LD-

rs35892992

rs35892992 YES

CATGTTCTGGAATATATGTAGCA

TTTGAATATGAAAGAGGAAGTT

GGCATAATTGAAAAC[C/T]TAAA

TTCATCTTCTATAAATATTACTC

TGTCAGAGGAATGGGTTTCTGA

AATTAGAGAAT

137


with the

peak SNP


Risk Allele

Gene DNA Sequence

4p15.2 LD-

rs36020664

rs36020664 YES

ATTTTTGTAACACTATGTCTTCT

GACCTCTTTATGTCCTTTAATTTC

TACTGTTGCTTAG[G/T]TTCATTC

ATATGTTACACCTTGANGGAAG

GTCAAAATAAGCTCACAATTTTT

CTCATCTC

10p14 LD-rs371668 rs371668 YES

GCTGCCCCAGAAGGGGCTGACC

CAGCAGCCACCAGNGACACNAA

AANGCANGCCTGCGGCCTGGC[C

/T]TGGGCTGCAGCCGGTTACTCG

CCCACTCACCCTTCACTCTTTCC

TTCCAGCCTCCCCCACATCCA

6p21.3 LD-

rs3763305

rs3763305 YES

TAATGAACATAGGACCTGGTAA

AATCGTGCCTCAGTTTNTCCTCT

GGGTCACATGGTCTC[A/G]TGGT

AGCTCCCCTCCCTCTGCTGGGGA

GNGCAGAGGCTCCCTCCACAGG

TGTGTGCCAGC

11q13 LD-

rs3765105

rs3765105 YES

GAAGAGGCAGGGGAGGGTCAGC

CCAAGAAGGAGTAGGGGGCCCA

CAAGGAAAGGGGCTAC[A/G]GG

GGAGGAGAGGAAAGAAAAGGA

AGGAGTCACCCAGGCCTGGAGC

AGTCCATCCTCAGAC

7q32 LD-

rs3778752

rs3778752 YES

AGCCTCAAGGCTTTTGCCTGCAG

CTAGGTCCACACGAGCTCTAACC

CGAACAGCATCCAN[A/C]CTCCN

AGAAGCTCCCTTCTGCCCAGAA

AAGGGCTAGGTCTAATTCAGAC

CACCCCAATCA

138


with the

peak SNP


Risk Allele

Gene DNA Sequence

10p14 LD-rs386680 rs386680 YES

CAGTCCTAGCTGGGACAGGGCC

AGTGCTGCCCCAGAAGGGGCTG

ACCCAGCAGCNACCAG[C/G]GAC

ACNAAAANGCANGCCTGCGGCC

TGGCNTGGGCTGCAGCCGGTTA

CTCGCCCACTCAC

6p21.3 LD-

rs3997872

rs3997872 YES

AGCATCAGGAGGCATTTTACGC

ACATACTTGCAACACTCNGACTN

ACCACAGCAGCAACAGAAGG[A/

T]GGCTATGAAGTTATNACAGNA

ANGTCCTATGTATTATAGTTATT

CAATGCAGTTGTGACTCAATA

1q21.3 LD-

rs4129267

rs4129267 YES

AGGTGGAAATGGAGAAATACTG

GGAGGGGCACTTGCTCAGCTTG

GAGTGGGGTCAATTCT[C/T]AAA

GGAAATGACATCACCTCATCTG

AGATCCAAAGGCCAAGTAGGGA

CTAGACCACAAAA

7q21.2 LD-rs42031 rs42031 YES

CATAAAACTTAAGATTAGAAAC

TTTCAAGTAAGGGTACAANGGG

TATCTTCAGAACTTTA[A/T]CCTA

AGCACATCAACGGAATTTTTCTA

GGNACCACAGAGAGTTAGAAAT

TAAATAATTGT

18q22.2 LD-

rs4891376

rs4891376 YES

AAAGTGTTGGGATTACAAGCGT

GAGCCACCATGACCAGCCAGAT

GTTAGTGTTTTTAAGA[A/G]GAA

AGCAAAATGAAAATTCAAGTGC

CATGATTACTTATAGAGGTTATC

TTTTAAAAAAGA

139


with the

peak SNP


Risk Allele

Gene DNA Sequence

8q22.3 LD-rs507201 rs507201 YES

ACGGCTGAGCTTCTATATTCCTG

CACTGATCAGTGATTGAATATGA

GCTTCCCTAAGAAG[A/G]GACAT

GACCTTGGACAAGGAGGTTGTTT

CCAGTGGAGGCAATCCTGGATG

ATGACAGCAC

6p21.3 LD-rs510205 rs510205 YES

TGCTGGTTTTGGGTAATTGCCTT

TTAAACTCTTTAANGGGGGAAA

NACNTGCGGTGGGAA[C/G]ATGT

TACCAGAGTGAGAAACAAAGGC

AGTAAATTATTTTGTTCCATGTC

TTAGATCTTGA

17p13.2 LD-

rs60963557

rs60963557 YES

ATGGACTTGGGAAAGCAATGAT

AGTTTTAAGGGGTCACTCTNTGT

GTTTGTTTCATTTAG[A/C]TGGTA

AAAGTAAAATAGTATTTAAAGN

ACTCTGTTTAATTGGATGGAAGC

CTTTTTATAG

11q12.2 LD-

rs61897793

rs61897793 YES

CTACCTCTGATCTCTTCCCTTTG

GTACTGCNGTGCTTCTCTCNCCT

TTTCCTTGCTGTGG[A/G]CAAAA

GNAACCCTCACCCNCGCCCTAA

GAGCGTGTCTGCTCTTTTCACAG

CAGTCCCACC

1p13.2 LD-

rs6679677

rs6679677 YES GGAAACTATTCAGTGCTTCCTGC

GGCTACCAGNGAACAAGGTCTG

AATCCTTGCTCCCAA[A/C]CAAT

AATCTGTGATCTTAAGCAATTTA

TTCAACTAACAAGCCTGTTTTCT

CACCTGTATT

140


with the

peak SNP


Risk Allele

Gene DNA Sequence

1p34.3 LD-

rs67164465

rs67164465 YES

AGGAGACTCTGAGCTCCTTTAGG

ACATCTTACTCATCTCTCTTTCTC

TCATGTGTTGTAC[A/G]AACACT

TGCTCCATATTCAGCCAGGCTTT

GTGTGTCTTGACAGAGCTAAAG

ACCAATGGG

10q21.2 LD-

rs67630314

rs67630314 YES

TCACTTAGAAAACGCAATCATTC

ATTTCTGAAATACCCANTAAATT

CTTTAGTTGAAATA[AATAA/G]A

GTGTAAAATAGCTTATACTATNA

ACAAAGAGTTTTTTGCCAGCCGT

TACCCAAATAATG

6q23 LD-

rs6920220

rs6920220 YES

CCATTGATAAATTATATTTTATC

TGCTTCCATCTGTTAGCAGGTAA

CNTCTCCACTAAAA[A/G]GATAT

GGTTCTGTAGAACAATGGCATAT

GCAGACAGTGATCTGTTATTCCA

CTATTCTCT

12q14.1 LD-rs701006 rs701006 YES

CCATAGTTTAGTCCAGTTAATTG

TGGGCCCCTTTGTCGGGCTAGTT

TTTGAGGGCAATAT[A/G]TAGGT

TTGTTCTGAGTCCAGGACGGCTC

TCAGTTGCCCTTTCCCTGGTTCT

CTCTGTTAA

11q24.3 LD-

rs7105899

rs7105899 YES

CAACCAGGCAGGCCGCGTGCCT

AGGAGAGCGGGCAGCGGGGTTG

GGGGGTNGGGTGGGGG[A/G]AT

GCCAATGCAGGGGCGGGGCTCC

CGGGCCTGAGAGAGAGGTGACA

GGCACCGGTGAACT

141


with the

peak SNP


Risk Allele

Gene DNA Sequence

12q24.12 LD-

rs7137828

rs7137828 YES

TCCTCACTAATCAGTAACAACAC

TGAATGTAAATCGACTAAACTCT

CCAATCAAAAGATA[C/T]AGAGT

GGCTAAATGGATTNAAAAAAAA

GGCTGGGTGCGGTGGCTCACGC

CTGTAATCCCA

18p11.21 LD-

rs7241016

rs7241016 YES

AGGCAGTTTAAGCAAGAAAGGA

AAGAGAAGGGGGGAAAGTTGTG

CCAAGGCAAANAGAAC[A/G]AC

ATTTACAAACGCCCTAAGGGGA

GAATGGACACACACACTTCCAA

GAATACAACACAGT

11q22 LD-

rs73000527

rs73000527 YES

TGAACCAGGAGTTCAGTTTAATG

TAAGCTCGTTAGTTGATCTAAAA

TAAAAACACCTTGT[A/T]TGAAT

TTTAGCACTTTGTTTTTTGCAAA

ATAATTGTTTTCCTTACATTATA

TTTAAAACA

3p14.3 LD-

rs73077957

rs73077957 YES

AAATATAAAACTCTGAGGGAAA

AAAACCCTCGGGATATAGAAAT

TCCAGGCCCTTGGAGA[A/C]GCC

CAGGGTCACAGGCTGTGAGTTA

AATAAAAAACCCACCAAACTGA

TGAACTACCCATT

3q22.3 LD-

rs73230016

rs73230016 YES

CATGCTCTATTCTAGGTTAAATG

GTAAGTTTCATTGACTTCAGCCC

CAACTGCCTCTGCTCATAC[C/T]A

CTCTCCATCTCCATNTNACCTCC

ATCACATCACTCTTCCTCTTCTTC

CATACCTCTAGATCTCA

142


with the

peak SNP


Risk Allele

Gene DNA Sequence

19p13.2 LD-

rs74956615

rs74956615 YES

TGGGGGCTGTCCGGGGTGGTTTG

AAAAATAAAACTTAGAAAAGGA

AACAGAAGTCAGTTG[A/T]NAAA

GTTAAAAAAAAAGGAGACAGTC

TCTGTATCTTCACGGGAGGTCAG

GGAAACCTCCA

1q23.1 LD-

rs7528684

rs7528684 YES

ACTTTCACATAAGTTGTTATGAG

GCTTCTGAACAGGAAAATAATA

CAAATGTACAGATCA[A/G]GGAC

TTCCCGTAATCTCACCCAGATCT

GCAAATTAGGAGATAAATCAGC

AAGCGTACCAC

3p24.3 LD-

rs7653834

rs7653834 YES

GTTGAGGGTTCAGAACTCAAAA

AGGTTCGCTCCAACTCTAGAATT

TATCNNAGGTACTTT[C/T]TACTG

GATGCTGACATGCAGAGCCTAA

GGTGGGAGCCATCTAAGAAGGA

TTCTGAGAAAG

1p13.1 LD-rs771587 rs771587 YES

TTCATTCTAATTTCCGTTTTTTGA

GAAAGCTCCATACTGTTTTCCAT

AATGGCTGTACTA[A/T]TTTACAT

TTCCACGGTCNGTGTACAAGGNT

TCCTTTTTTTCCACATCCTCACCA

ACATTT

6p21.3 LD-

rs7755224

rs7755224 YES

ATGCTATTAAGAAGATAATCCTT

GTTGACTGTAGTCCTGGTTGTTG

AGNCCATGCATATT[A/G/T]CTTT

TCCCTTCATTAGAGNAGGACTAN

AGTTGATAGCCATTATTACGAGA

GATGCCTCAG

143


with the

peak SNP


Risk Allele

Gene DNA Sequence

15q14 LD-

rs8035957

rs8035957 YES

ATGACACCATCCTGGTAATTTTT

TTGGCATAGCNATTATAACCATC

TTGTATCTCCATGA[C/T]GTGATT

TTGGATCCTCTGGCAAGCTGCAT

TATTAACCTGAAAAAGCCATAC

ACTAACACT

17q12-

q21

LD-

rs8076131

rs8076131 YES

TCTCACTCAGCACAACCCCTGCT

GGACTGTGTCTCACTGAAGGCCT

AATGTGGGACATCT[A/G]CTCTT

AAGCTCTTCCCATCAGGAGGGA

AAGGAAGTGAGGCCTGTGGGTA

CAGGCAGCAGT

15q23 LD-rs919053 rs919053 YES

GCCGAGGAGAGAGAATCCCCTT

CTGGACAGCAGNATTCAATGAC

CACTTACTAAGNTGTG[C/T]GGA

CNAGGAGGAGGTGAGCAGAGCA

CAATCAAAAACAAGGAAGGAAG

GGAGAGAAGGAGA

6p21.3 LD-

rs9268145

rs9268145 YES

AATGACTAATTGAATTGACTTCT

ATTCCATTCTTGTGAACAGGACA

NTTGACATTTGTATTAGTT[G/T]A

TTTGCAAAACAAAATTTACTATT

GATTTCTTAAGGTAAGTTTCACA

TTCCTACTTTTATATTGC

6p21.3 LD-

rs9268500

rs9268500 YES

GTTTCCCCCTTTCCAAGAAACTA

CTGCTAGTAAGATTTTATGGGAC

AGGGGTAAAAATCAANATT[C/T]

TGAAATGGAGATTTAACAATCA

AANGAGTCAGGAGATTGTTGTT

ACACAAATATTGCCCATTTGC

144


with the

peak SNP


Risk Allele

Gene DNA Sequence

6q21 LD-

rs9372121

rs9372121 YES

CTTTGGCTCCTCCTTCTCTGATA

CCCTGCCTAGCAAGTTCCAACCA

CTTCAGCAGCCCAG[A/G]ACTCC

AATTTGTGCATCCTCAGCTCAAC

TGAACTTCTTTGCTCTGCTTGAG

CTCCACTCT

6q25.1 LD-

rs9498368

rs9498368 YES

TCTCAACCTCAAAGGTGGATTTT

GAGGAAGACTGACATGGGCTGG

AATGGTGGTTTTCCA[A/G]AGAA

AGGTTATGAGGAAGCCCAACAG

GCAGAGCTATGCTAATTACATGT

GGGACCACAGT

16q24.1 LD-

rs9927316

rs9927316 YES

GCGCGGTCCAGGCGTTGAGCAA

TAGGGCTGCNCCNGGTGTCNTG

CCCGCCGCCCACCCCN[C/G]CCT

GCCTGCTCCACTTACACGGAGAC

GAGAGGCAGCTGCTGGTGTGGC

CGCGTGTGCATG

9q33.2 LD-

rs10760119

rs10760119 YES

CACTCCAGCAGCCTGGGTGACA

GAACAAGACACTGTCTCCAAAA

AAAAAAAAAAAAANAA[C/T]AG

ANGCTCCTGGTCCAGAGCTAGG

GGGNGGGGCTAGGGGGACAGAC

AGATTGATAGATAA

1q23.3 LD-

rs56383975

rs56383975 YES

GCCACTTTGTTCGTGGGCCCATT

GGGCGATCACAGGGGTGGCTGG

GGAAGGAGGCTGAGT[G/T]GNGT

CCATAGATCGGGTCATCCTATCC

ACTTGATTATTAAAATCCTCCTC

TGCTGAGGTC

145


with the

peak SNP


Risk Allele

Gene DNA Sequence

16p13.13 LD-

rs11075012

rs11075012 YES

AAACATGGAAACCTAAAACGCG

ATGAGATTAACCCCAAAGAAAG

TAAAGGAGACCAGGCN[C/T]NGT

GACTCAGCCTGTAATTCCAGCAC

TTTGGGAGGCCGAGGCAGGCAA

ATCTCCTGAGGT

20q13.12 LD-

rs4810485

rs4810485 YES

ACCTGGCTCCTTCATCCCAGCCC

CTCTGGCCTCCCCCTNCTTTAGA

GGGCTGTAGATTCC[G/T]GCCTG

AAGCCTGGGCAGGAATGACCCA

TGGTATCAAGGAAAGCAAGGGA

AGCAGCAAGGG

2p15 LD-

rs6707337

rs6707337 YES

TAATCCCAGCACTTTGGGAGGCT

GAGGTGGGCGAATCATGAGTTC

AGGGGTTGAAGACCN[G/T]CCTG

GCCAACATGGTGAAACCCTGTG

TCTACTAAAAATACAAATATTAG

CCGGGCATGGT

12q13.2 LD-rs705700 rs705700 YES

GGTTGAAAAACATCACTGAGTT

AGTTTTCTTGTAGCTTCCACCTC

AACGGGAAAATTTCC[A/G]CTGG

ATCTGCTCTTGACTCCTAGTGTA

CTTCAAACCCTTCAGTCCACCAC

AGTCTAAAGG

1p36.13 LD-

rs2240335

rs2240335 YES

CTCAAGACAAGAGGGTTTCGGC

AGCTGTGCCCCCTCCCCANCCCA

TGCAGNTACCATCAC[G/T]CNTT

TGATGGGAAACTCCTTCAGGCCT

CTGTTCCTTGGAGAGTCGAAGAC

CACGGGCAGC

146


with the

peak SNP


Risk Allele

Gene DNA Sequence

22q11.21 LD-

rs5994638

rs5994638 YES

ACCTCCTGGGTTCAAGCAATCTT

TTGCCTCAGCCTCCCTGGCAGCT

GGGGCTACAGGCAT[A/G]CACCA

CCACGCNCGGCTAATTCTTTTGT

ATTTTTAGTAGACACAGGGTTTC

GCCATGTTG

10p15.1 LD-

rs7072793

rs7072793 YES

GTCATTGAACTGGCTTTTCTGGT

TCACCTTACNGATGAACGCTCTG

TCGTCNTTTGCTGA[C/T]GCTGAG

GTTTCCAGTTGATGCTGCATGGC

ACGATGCAGGGATGTGAGAAGA

AGTTGGGGG

6q25.3 LD-

rs1994564

rs1994564 YES

ACCAAAACACCACACAAAGTAA

AGAGTCAATCANGATTCCNTCCC

TTTGNCCNTGAAGGC[C/T]GGCA

GATTTTTTAAAAANNAATTAATT

AATTAATTTTTTTTTTGAGACGG

AGTCTCGCTC

21q22.12 LD-

rs9979383

rs9979383 YES

AATCTTTATGCCAAAGTGGCATA

GTTTAGAGTGGCATCTTCTGATC

CCCATCANAATAAA[C/T]AGGTG

TAATACTGATACGACTANGAGA

CAGGCATAATTGTTGCGTCTTTT

CTTAGGTGGG

1q31 LD-

rs4915314

rs4915314 YES

TGGAGTACAGTGGCACCATCAC

ATCTCACTACAGCCTCAACACAC

ACTCATGGGCGTAGC[C/T]TCCC

AAGTAGCTGGGACTATAGGTGT

GCACCACCCTACCCGGATAATTT

TTGTATTTTAA

147


with the

peak SNP


Risk Allele

Gene DNA Sequence

3q22.3 LD-

rs10212476

rs10212476 YES

TTAAGAAAATTAAAAAACCTGG

CCAGGCGCAGTGGCTCACGCCT

GTAATCCCAGCACTTT[C/G]GGA

GGCCAAGGCAGGCGGATCACAA

GGTNAGGAGTTCGAGACCAGCC

TGGCCAACACAGT

6p21.3 LD-

rs3828796

rs3828796 YES

AATTCTAGAGCACCTGAGACTG

GGAAAGTTGCCACTGGGCNTCC

ANCAGCAGTGGTNTACTCAGG[A

/G]TCAGGGTAAACCCAGNCTAA

GGANGGTCTCCACTGCTGTGATG

GACGCATAAAGGAGGAACCAAA

6p21.3 LD-

rs9469064

rs9469064 YES GCTAGTAGTTCCAGCTGCTCAGG

AGGCTGAGGTGGGAAGATTGCT

TGAGCCTGGGGGATG[C/G]AGGT

TGCAGTGAGCTGAGATNGCACT

GCTGCACTCCAGCCTGGGCAAC

AGAGCAAGACCC

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