UNRAVELING GENE GENE

INTERACTIONS IN RHEUMATOID

ARTHRITIS

Master Degree Project (120 credits) in Systems Biology

Second Cycle 45 credits

Spring term 2021

Student: Saad Salman Khan Lodhi

Supervisor: Lina-Marcela Diaz-Gallo, Karolinska Institute

Examiner: Andreas Tilevik, University of Skövde

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Abstract Rheumatoid arthritis (RA) is a systematic autoimmune disorder characterized by a persistent joint inflammation. A subset of HLA-DRB1 alleles known as shared epitope (SE) are the strongest genetic risk factors to develop anti-citrullinated protein antibody positive (ACPA-positive) RA. A strong enrichment of interactions exists between ACPA-positive RA-associated genetic variants and HLA-DRB1 SE alleles in disease development. Pathway analysis was performed to investigate how the interactions between risk variants (SNPs) with HLA-DRB1 from a previous study related to ACPA-positive RA. Gene-gene interactions analysis was performed between non-HLA risk variants and HLA-DRB1 SE alleles in SRQ biobank (SRQb) case-control cohort. We also evaluated whether the reported gene-gene interactions from a previous study relate to methotrexate (MTX) response for RA patients, at three and six months of follow-up in EIRA study. Interaction analysis based on an additive model was performed to understand the combined effect of two risk factors in the disease and treatment response. Two out of three genes from pathway analysis that were RXRA and NR3C1, pointed to ACPA-positive RA related important pathways including vitamin D receptor (VDR) pathway and adipocytokine signaling pathway. The replication analysis in SRQ-case-control study showed 2.627% of the evaluated SNPs insignificant additive interaction with HLA-DRB1 SE alleles. No interactions were significant in relation to the response to MTX monotherapy after 3 and 6 months follow-up. This project provides new insights into the gene-gene interactions in the study of ACPA-positive RA and suggests candidate genes for future functional studies.

Popular scientific summary

Rheumatoid arthritis (RA) is a systemic autoimmune disorder, characterized by joint inflammation. An autoimmune disease is a disorder that occurs when one’s immune system is unable to distinguish between self and non-self entities and attack healthy body cells. Anti-citrullinated protein antibody (ACPA)-positive and ACPA-negative are two different subsets of RA. These subsets differ in genetic, environmental risk factors and disease progression. In general, ACPA-positive patients follow a more severe disease course. Epidemiological studies have revealed that the global prevalence of RA is different in different genetic ancestry populations.

Both genetic and environmental risk factors are strongly associated with the development of RA. Those factors include HLA-DRB1 risk alleles, sex (Whitacre et al., 1999; Whitacre, 2001) and smoking (Klareskog et al., 2006; Padyukov et al., 2004). For instance, females (Barragán-Martínez et al.,2012; Ngo et al., 2014; Quintero et al., 2012) are more affected by RA than males with a ratio of 7:3. Apart from that, clinical factors may also differ between both sexes. Patients with RA are more susceptible to cardiovascular and lung infections than the general population. Currently, RA has no cure but remission has become an achievable target, where the immunosuppressant methotrexate (MTX) is used as the first line of treatment. More understanding of factors involved in the treatment response needs to be achieved to increase the treatment success rate in RA.

This study consisted of three different aims that could lead to a better insight into the genetics of ACPA-positive RA and its relation to MTX monotherapy response. Functional pathway analysis was done using previously detected SNPs (Single nucleotide polymorphisms) in interaction with the most important genetic risk factor to ACPA-positive RA, a subset of HLA-DRB1 gene variants. To find out if there are similar interactions as they were captured between HLA-DRB1 and non-HLA-DRB1 risk alleles in a previously published study in EIRA, a Swedish case-control study was analyzed. An independent replication analysis of gene-gene interactions was performed including ACPA-positive RA cases from the Swedish Rheumatology Quality register biobank (SRQb) and healthy controls from the same population. Finally, it was also evaluated if these genetic interactions relate or not to MTX monotherapy treatment response at 3 and 6 months of patient’s follow-up.

The pathway analysis revealed a candidate pathway with 3 genes annotated (RXRA, NR3C1 and ESRRG) from the SNPs of interest. The genes RXRA and NR3C1 pointed to a couple of pathways that relate to RA, vitamin D receptor (VDR) pathway and adipocytokine signaling pathway. Among the significant interactions detected in SRQb case control replication analysis, one pointed to the known genetic risk factors of RA, the rs2476601 SNP in the PTPN22 gene. This interaction between HLA-DRB1 ACPA-positive RA risk alleles and PTPN22 gene has been previously reported. Other significant interactions between HLA-DRB1 and non-HLA-DRB1 risk alleles were observed, they correspond to 2.463% of the SNPs tested. Finally, no significant interactions were observed when they were tested for MTX response at 3 and 6 months follow-up.

This project also contributed to updating the bioinformatics tool called GEIPAC that was used for interaction analysis in SRQb-case control study. This improved tool will be used to address both SRQb interaction and MTX response interaction studies in the future.

Table of Contents Abbreviations .......................................................................................................................................... 1

Introduction ............................................................................................................................................. 2

Rheumatoid arthritis ........................................................................................................................... 2

Treatment ............................................................................................................................................ 2

Methotrexate ...................................................................................................................................... 2

Classification criteria and DAS28 ......................................................................................................... 3

Genetics of RA ..................................................................................................................................... 3

Interactions ......................................................................................................................................... 4

Aims ......................................................................................................................................................... 5

Materials and Methods ........................................................................................................................... 5

Studied population .............................................................................................................................. 5

Data ..................................................................................................................................................... 6

Pathway analysis ................................................................................................................................. 6

Association analysis betwen ACPA-positive RA and healthy controls. ............................................... 7

Interaction analysis in SRQb and methotrexate response in EIRA ...................................................... 7

Results ..................................................................................................................................................... 8

Pathway analysis ................................................................................................................................. 8

SRQb Replication of Interaction analysis ........................................................................................... 11

Interaction analysis for MTX response in EIRA .................................................................................. 14

Discussion .............................................................................................................................................. 15

Ethical aspects, gender perspectives, and impact on the society ......................................................... 18

Future perspectives ............................................................................................................................... 18

Acknowledgements ............................................................................................................................... 19

References ............................................................................................................................................. 20

Appendix................................................................................................................................................ 26

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Abbreviations

ACPA Anti-citrullinated protein antibody

AP Attributable proportion

EIRA Epidemiological Investigation of Rheumatoid Arthritis

ESRRG Estrogen Related Receptor Gamma

HLA Human leukocyte antigen

MTX Methotrexate

MS Multiple Sclerosis

NR3C1 Nuclear Receptor Subfamily 3 Group C Member 1

OR Odds ratio

PTPN22 Protein tyrosine phosphatase, non-receptor type 22

RA Rheumatoid arthritis

RXRA Retinoid X Receptor Alpha

SE Shared epitope

SRQb Swedish Rheumatology Quality Register biobank

SNP Single nucleotide polymorphism

VDR Vitamin D receptor

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Introduction

Rheumatoid arthritis Rheumatoid arthritis (RA) is a chronic systematic inflammatory autoimmune disorder characterized by persistent joint inflammation. RA is caused due to a complex interplay between genetics and environment as discussed by Smolen et al., 2018. Based on the presence or absence of antibodies to citrullinated peptide antigens (ACPA), RA patients are grouped into two subsets, ACPA-positive and ACPA-negative. These differ in their associated genetic risk factors (Padyukov et al., 2011), disease prognosis and treatment responses (Smolen et al., 2018). Genetic and environmental risk factors have been identified to be associated with RA development. Although RA is not curable, remission has become a reachable goal. However, many patients still cannot achieve remission and more work needs to be done to provide every patient with the benefit of therapeutic success.

Treatment Insufficient treatment of RA can lead to accumulating joint damage and irreversible physical disability. For treatment of RA and other inflammatory arthritis, DMARDs (Disease Modifying Anti-Rheumatic Drugs) and biological DMARDs are used. DMARDs are synthetic drugs whereas biological DMARDs usually include monoclonal antibodies. Synthetic DMARDs are further categorized as conventional and targeted DMARDs. The mode of action and target of conventional DMARDs is usually unknown. In contrast, targeted DMARDs are designed to target specific molecules or pathways within the cell. Biological DMARDs are specific and target cellular proteins including interleukin-6 (IL-6) receptor, tumor necrosis factor-alpha (TNF-α) or CD20 membrane protein in B-cells. Few examples include tocilizumab, rituximab and tofactinib (Singh et al., 2016).

Methotrexate Methotrexate (MTX) is considered the first line of treatment for RA. Methotrexate, a conventional synthetic DMARD, acts by inhibiting protein and nucleic acid synthesis that leads to inhibition of immune cells (Cronstein, 2005). Patients who do not respond to MTX are prescribed a second line of therapy (Singh et al., 2016). Commonly MTX is given orally but if disease activity is not reduced, a switch to subcutaneous delivery is considered as it improves bioavailability as there is no intestinal barrier in between (Pichlmeier & Heuer, 2014). Patients that are initially diagnosed with RA are given MTX in low doses and if remission is not achieved, additional medications like other DMARDs and biologics are given as combined therapy (Singh et al., 2016).

Nevertheless, effectiveness also varies from patient to patient. If ineffective, sulfasalazine (a DMARD) or biological medications (e.g. TNF inhibitors) could be added. The aim is still to control disease activity and achieve remission (Bello et al., 2017). Absorption of MTX taken orally is variable and makes it impossible to define the minimum dose. (Lucas et al., 2019; Nair et al., 2016)

Several mechanisms of action are associated with MTX including anti-inflammatory action such as inhibition of purine and pyrimidine synthesis, inhibition of transmethylation reactions, reduced T-cell proliferation. etc. MTX also promotes the release of adenosine. The activation of receptors on macrophages and neutrophils mediated by the release of adenosine decreases the production of pro-inflammatory cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α). In addition to that, adenine also increases the production of anti-inflammatory cytokines such as interleukin-10 (IL-10) that is linked to disease remission (Cronstein, 2020; Lopez‐Olivo et al., 2014).

The adverse effects associated with MTX as compiled by Smolen et al., 2018 from Recommendations of German Society of Rheumatology include stomatitis, nausea, hematological effects such as leukocytopenia, anemia and respiratory effects including pneumonitis and atypical pneumonia. Recommended guidelines from European League Against Rheumatism (EULAR) suggest administration of folic acid while ongoing therapy with MTX reduces gastro-intestinal

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side effects and liver dysfunction which avoids discontinuation of the MTX treatment. Folic acid is administered on the next day of MTX administration to avoid competition between molecules (Whittle & Hughes, 2004).

Classification criteria and DAS28 The current treatment goal for RA is remission and involves treat-to-target as adopted by American College of Rheumatology (ACR) and EULAR. This approach adopts tight monitoring of disease course and change of treatment method if disease activity is not reduced (Singh et al., 2016; Smolen et al., 2017). The ACR criteria determine response as either a positive response (improvement) or a negative response (no improvement) in lowering disease activity when compared with baseline.

Due to variability in symptoms and signs of RA, assessment of disease activity relies on various variables. Disease Activity Score 28 (DAS28) is an important criterion to measure disease activity in RA. DAS28 is a modification of DAS and includes 28 joint counts (Van, 2014). Disease activity is measured through examination of joint swelling and tenderness, X-ray imaging and measuring blood markers of inflammation including erythrocyte sedimentation rate (ESR) and C reactive protein (CRP) tests. In addition to that, the patient’s overall health assessment is also evaluated. Furthermore, DAS28 index is an absolute measure to evaluate disease course and activity. Cut points in DAS28 helps to classify patients in remission and disease activity with a magnitude of low, moderate or high. The EULAR response uses a change in the DAS levels to classify individual patients as non-, moderate, or good responders, depending on the extent of change and the level of disease activity reached (Fransen & Van, 2005; Van et al., 1996).

Genetics of RA Genome-wide association (GWA) studies have become a common approach in identifying the association of candidate genes to complex diseases such as hypertension, rheumatoid arthritis, type 1 diabetes and type 2 diabetes (WTCCC, 2007). The human leukocyte antigen (HLA) plays a very important role in many clinical disorders, such as cerebral malaria, hepatic cirrhosis immunodeficiencies and autoimmune diseases such RA (Klein & Sato, 2000). In terms of RA susceptibility, HLA is the most important genetic risk factor (Gregersen et al., 1987) and as evaluated by Diaz-Gallo, 2018; Klareskog et al., 2008; Raychaudhuri et al., 2012, Weyand et al., 1992 and many others. The HLA complex is located on the short arm of chromosome 6. Class I and II HLA genes contribute to the immune response and are both functionally and structurally diverse from one another. The α polypeptide chain of the class I molecule is encoded by class I genes whereas the β chain of the class I molecule is encoded by beta2-microglobulin gene located on chromosome 15. The class I molecule consists of an α chain consisting of five domains and one beta2-microglobulin light chain encoded by beta2-microglobulin gene. In contrast, both α and β chains of class II molecule are encoded by class II genes. Both class II α and β chains consist of four domains. HLA-DRB1 is a class II gene. The letter D indicates the class, R represents the family and B represents the chain, in this case β chain (Jan, 2000).

A subset of HLA-DRB1 gene variants, commonly known as shared epitope (SE) alleles, is the most important genetic contributor to develop ACPA-positive RA, the biggest subgroup of RA which represents 60% of the patients. HLA-DRB1*01 and HLA-DRB1*04 are found to be significantly associated to RA development (Gregersen et al., 1987). HLA-DR4 has shown a relative risk of 4.2 for RA in different populations (Klein & Sato, 2000). HLA contributes to 11-37% of RA heritability (Kurkó et al., 2013). The SE alleles linked to RA susceptibility include HLA-DRB1*01,*04 and *10. There are other signals of association with ACPA-positive RA susceptibility from the extended MHC region which contains class I and II HLA genetic regions (Kurkó et al., 2013, Raychaudhuri et al., 2012).

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Around 100 non-HLA genetic loci have been identified to be associated with susceptibility to RA in different populations (Okada et al., 2014). Among them, genetic variants in the PTPN22 and IL23R genes have been found through target genetic association studies (Varade et al., 2009). The genetic polymorphism associated with the susceptibility to RA are enriched in enhancers and regulatory regions related to the immune system function, mainly but not exclusively to T cells (Trynka et al., 2013). Several gene-gene interaction studies in RA have shown that the HLA-DRB1 significantly interacts with several single nucleotide polymorphisms including variants in PTPN22, HTR2A and MAP2K4 genes (Källberg et al., 2007; Lenz et al., 2015; Seddighzadeh et al., 2010; Shchetynsky et al., 2015; Diaz-Gallo et al., 2018).

Interactions To understand the genetic and environmental background of complex diseases, such as RA, it is necessary to study beyond dichotomies and univariate analysis. In such regard, comprehensive interaction assessments between and among risk factors are an important tool. The effect of two risk factors on one another and disease risk is measured by interaction analysis. In general, interaction is declared when the combined effect’s magnitude of two or more factors is significantly different from the combined effect’s magnitude predicted by the model being tested (Rothman, 1976; VanderWeele, 2009; Wang et al., 2010; Diaz-Gallo et al., 2018; Diaz-Gallo et al., 2021). Both additive and multiplicative models have been used to study interactions.

The additive model is based on work by Rothman (1976) of the sufficient cause model, where there are sufficient causes (risk factors) for a disease to occur. This model assumes and addresses the impact of a risk factor on a disease is the same in the presence or absence of another risk factor. However, the additive model has been criticized for always giving positive results but still is a favored model as compared to the multiplicative model based on previous literature (Kendler & Gardner, 2010; Rothman, Greeenland & Lash, 2008). In the multiplicative model, the scale of measurement is ratio scale and there is only interaction present if the scale is not followed by the exposed group. The null hypothesis for the additive model is OR11 = OR10 + OR01 – 1 and for the multiplicative model is OR11 = OR10 × OR01.

The attributable proportion (AP) represents the reduction in disease if the exposure is removed or was never there. AP can be illustrated as the amount of disease in the exposed group attributable to the exposure. This measure is only appropriate when one risk factor is considered the cause for a disease (Dicker et al., 2006). Odds ratio (OR) or relative risks (RR) are used to quantify the association between a genetic variant and a disease. Interaction studies between two risk factors result in three ORs which include OR11, OR10 and OR01. OR11 carries both risk factors while OR10 and OR01 carry one out of two factors. Both of them contain only that factor that is not present in the other one. The following equation (Diaz-Gallo et al., 2018) represents the departure from additivity with AP as an estimate of interaction, OR is used as a measure of association (Rothman et al., 2008):

AP = (ORSE1SNP1 − ORSE1SNP0 − ORSE0SNP1 + 1)/ORSE1SNP1

where the presence and absence of risk allele is represented by 1 and 0 respectively.

The dominion hypothesis (Diaz-Gallo et al., 2018) suggests that the HLA-DRB1-risk alleles function as a genetic hub (figure 1). It is involved in multiple interactions with non-HLA genetic variants that by themselves have a modest effect size in ACPA-positive RA. A set of 1492 SNPs that represent 270 independent genetic regions were identified to be in statistical interaction with HLA-DRB1 alleles in ACPA-positive RA, which together with the previous associated genetic variants may explain 54% of the narrow heritability of RA in the Swedish population as suggested in the same study (Diaz-Gallo et al., 2018). Figure 1 introduces the concept of additive interactions that is based on the sufficient cause model (Rothman, 1976).

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Figure 1. Describes the additive model of sufficient cause model. The model assumes that there should be sufficient causes for a disease to occur.

Based on the findings by Diaz-Gallo et al., 2018, follow-up studies should evaluate different aspects such as testing the described interactions in independent ACPA-positive RA case-controls cohorts, to estimate whether these genetic interactions are associated with disease prognosis and/or treatment response and to explore how these statistical interactions reflect ACPA-positive RA’s pathogenic mechanisms at molecular, cellular, tissue and systemic levels.

Aims The aim of this project is to contribute with the follow-up study of gene-gene interactions in ACPA-positive RA. The specific aims were:

To propose possible mechanism of actions (at molecular, cellular or tissue level) of the detected statistical interactions, using available data bases and bioinformatic tools for pathway analysis.

To perform an independent replication study of the previously identified gene-gene interactions. The cases are from the Swedish Rheumatology Quality Register biobank (SRQb) and the healthy controls from a multiple sclerosis study.

To address, for the first time, whether the previously detected gene-gene interactions relate to MTX monotherapy treatment response at 3 months and 6 months patients follow-up.

The project aims to contribute towards personalized medicine by searching for genetic markers that may contribute to treatment response in ACPA-positive RA. Both pathway and interaction analysis can contribute equally to find genetic markers that may be a risk factor in the disease. In addition to that, a better understanding of those genetic factors that work together in disease development and progression could lead to early diagnosis of the disease and potentially better treatments in the future. Additionally, such studies could lead to the development of better methods/tools that could help in understanding not only ACPA-positive RA but also other complex disorders such as Multiple Sclerosis that has a very high prevalence (188.9/100000) in Sweden.

Materials and Methods

Studied population In total, 4674 ACPA-positive patients and 5758 healthy controls from Sweden were included in this project (Table 1). Epidemiological Investigation of Rheumatoid Arthritis (EIRA) is a case-control study that investigates risk factors of rheumatoid arthritis (RA). A case is defined as an individual who is diagnosed with RA based on the 1987 American College of Rheumatology criteria (Arnet et al., 1987). From EIRA, 1509 ACPA-positive RA cases were included in this project for MTX response. The case-control replication of interaction analysis included 3165 ACPA-positive RA cases from SRQb. SRQb is a collection of blood samples from RA patients linked to the registry, which is used to study the incidence, course and response to treatment (Eriksson et al.,

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2014). 5758 healthy controls from Sweden were included from a case-control study in multiple sclerosis

Table 1. Displays the number and percentage of men and women in this project.

Cohort and percentage

Total Females Males

SRQb-Cases (%)

3165 2275 (71.9)

890 (28.1)

Healthy Controls (%)

5758 4332 (75.2)

1426 (24.8)

EIRA Cases (%)

1509 1083 (71.8)

426 (28.2)

Data The 1492 SNPs that were previously found to be in interaction with HLA-DRB1 SE alleles (Diaz-Gallo et al., 2018) were used to address all the aims. They were selected based on having the same direction of AP in the study cohorts and an AP p<0.05 (Diaz-Gallo et al., 2018).

The interactions between these 1492 SNPs and HLA-DRB1 SE alleles were evaluated for the MTX monotherapy response at 3 and 6 months of follow-up. In total 681 SNPs (45.6%) out of 1492 were available for this analysis in EIRA selected cases. Based on the EULAR response criteria, the ACPA-positive RA cases were assigned as non-responders and responders. There was no data available for 259 cases for 3 months follow-up and 343 for 6 months follow-up. Table 2 shows the number of cases classified accordingly to the EULAR response for MTX monotherapy status at both follow-up points.

Table 2. ACPA-positive RA Cases at 3 and 6 months EULAR response

EIRA Cases 3 months Percentage 6 months Percentage Responders 601 39.8% 549 36.3% Non-responders 651 43.1% 619 41.0% No data 257 17.1% 341 22.7% Total 1509 100% 1509 100%

All data representation was performed using R studio (Team, 2020) in R (Team, 2013). Results for SRQ-case and controls were visualized by a manhattan plot using R’s qqman package (Turner, 2014). Results for MTX analysis in EIRA for 3 and 6 months follow-up study were visualized using visualization package ggplot2 (Wickham, 2011) in R. In addition to that, R was also used for basic data manipulation and summary statistics for all cohorts.

Pathway analysis In order to explore possible mechanistic relation of statistical interactions in the context of ACPA-positive RA, various tools and programs were used. Gene annotation for the 1492 interacting SNPs was obtained using g:SNPense, which maps SNPs to genes based on coordinates and predicts the effect of SNPs on genes. g:SNPense is a tool of g:Profiler that characterizes genes as well as performs enrichment analysis (Raudvere et al., 2019). Additionally, Ensembl’s variant effect predictor tool (McLaren et al., 2016) was also used to locate the genes on which the SNPs have their effects. Gene lists from the results of both tools were used for pathway analysis.

Reactome is an open source and manually curated pathway database (Fabregat et al., 2018). Any event that results in changing the state of a biological molecule is considered as a reaction in reactome. The database links the proteins from input genes to their molecular functions. In other

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words, it organizes molecules to their biological pathways. A pathway consists of a network of participants such as proteins, nucleic acids and small molecules in interaction. The tool cross references hundreds of online bioinformatic resources such as NCBI, Ensembl, UniProt, UCSC Genome Brower and PubMed and all pathways are backed by scientific literature. Reactome provides pathways and p-values of enrichment test as well as false discovery rate (FDR) using Benjamini-Hochberg method. The overrepresentation analysis uses a statistical hypergeometric distribution test that determines the enriched pathways from the input data. Five percent of FDR was considered significant. HLA-DRB1 was included in the gene annotated list from SNPs of interest. In the tool’s setting, the analysis was projected to humans which means all non-human identifiers were converted to their human equivalents. No other parameters were changed.

The workflow used to find significant pathways in Reactome from the SNPs of interest is represented in figure 2.

Figure 2. Workflow for pathway analysis with Reactome to point out top significant pathways from gene list of interest. The list of SNPs from previous interaction studies (Diaz-Gallo 2018) was used to annotate genes and the annotated genes together with HLA-DRB1 were added in the tool to find pathways with over-represented genes.

Association analysis between ACPA-positive RA and healthy controls. Association analysis using logistic regression, between ACPA-positive RA cases from SRQb and healthy controls (Table 1), was implemented using Plink (v1.9) (Chang et al., 2015). The analysis was performed to evaluate the univariate association of HLA-DRB1 SE alleles and the studied SNPs in that case-control group. The SNPs from this association analysis were compared to SNPs from the previous study (Diaz-Gallo et al., 2018).

Interaction analysis in SRQb and methotrexate response in EIRA The additive model of interaction was implemented as previously described by Diaz-Gallo et al., 2018. In order to run the interaction analysis, a minimum of five individuals are required per group of comparison. Interactions between HLA-DRB1 SE alleles and SNPs from EIRA data were tested using the additive model.

AP = (ORSE1SNP1 − ORSE1SNP0 − ORSE0SNP1 + 1)/ORSE1SNP1

The additive model of interaction was implemented using GEIPAC (v0.2.0; https://github.com/menzzana) to address whether there are any significant interactions between the 1492 SNPs and HLA-DRB1 SE alleles in the development of ACPA-positive RA in an independent case-control cohort. Logistic regression was implemented using GEIPAC to access AP, p-values and CI (95% CI). The replication interaction analysis included ACPA-positive RA cases from SRQb and healthy controls from Swedish population. GEIPAC is a tool written in C++ to address additive and multiplicative interactions in large data sets. GEIPAC is based on a

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previously published gene-environment interaction analysis tool (Ding et al., 2011). Sex was used as a covariate, since the implementation of continuous covariates in GEIPAC was not available by the time of the analysis.

The additive model of interaction was also implemented, to address whether there is significant interaction between the 1492 SNPs and HLA-DRB1 SE alleles in the response to MTX monotherapy at 3 and 6 months of follow-up. GEISA (version 0.1.3; https://github.com/menzzana/geisa) software was used for that purpose. GEISA is an update of JEIRA that is a Java implementation of GEIRA, a gene-environment interaction analysis tool (Ding et al., 2011). ACPA-positive cases from EIRA who were under MTX monotherapy treatment and had follow-up information of response at 3 and 6 months were included in this analysis (Table 1). The attributable proportion (AP), its respective p-value and 95% confidence intervals (95% CI) were calculated using logistic regression implemented in GEISA. The interaction analysis was adjusted by genetic ancestry differences, by including the first five principal components calculated using the genotypes of each individual. The principal components (PCs) were calculated as described previously in Diaz-Gallo et al 2018, using the EIGENSOFT (v6.1.1) program (https://www.hsph.harvard.edu/alkes-price/software/; Price et al., 2006). Sex, age at the start of MTX treatment, smoking status of individuals and the first five PCs were included in the interaction analysis as covariates. Results from both GEIPAC and GEISA calculate additive and multiplicative models of interactions. Both tools provide in their output, the ORs necessary to calculate interaction in both models, the double exposure OR11 and both of the single exposure ORs, namely OR10 and OR01.

Results

Pathway analysis Reactome results showed that only 88 out of 258 input genes along HLA-DRB1 passed the hypergeometric distribution test and pointed to 562 pathways. At least one gene out of 88 was present in at least one of the resulting pathways that were 562 in total. The no. of significant pathways based on p-values < 0.05 were 25 and after FDR correction (FDR < 0.05) only 9 were left (1.6%). Most of the listed pathways were part of the immune system. Table 3 includes the most significant pathways that passed the FDR correction.

Table 3. Top nine significant pathways from Reactome analysis. In total 258 no. of genes were input in Reactome, and annotated from the 1492 SNPs previously reported no significant interaction with HLA-DRB1 SE alleles in risk to develop ACPA-positive RA. The gene symbol, p-values and FDR adjusted p-values are displayed for each pathway.

Pathway Gene p-value FDR Translocation of ZAP-70 to Immunological synapse

PTPN22, HLA-DRB1 7.90e-13 4.90e-10

Phosphorylation of CD3 and TCR zeta chains

PTPN22,HLA-DRB1 1.96e-12 6.08e-10

PD-1 signaling HLA-DRB1 6.56e-11 1.35e-08 Generation of second messenger molecules

HLA-DRB1 7.79e-10 1.21e-07

Costimulation by the CD28 family

HLA-DRB1 3.43e-07 4.25e-05

Nuclear Receptor transcription pathway

NR3C1 RXRA ESRRG

3.20e-06 3.14e-04

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Downstream TCR signaling

HLA-DRB1 3.57e-06 3.14e-04

TCR signaling PTPN22, HLA-DRB1 4.50e-06 3.30e-04 MHC class II antigen presentation

HLA-DRB1 4.85e-06 3.30e-04

Most of the top pathways include the PTPN22 gene while all of them had HLA-DRB1, except for the Nuclear Receptor transcription pathway. The Nuclear Receptor proteins are DNA-binding transcription factors belonging to the family of multi-functional proteins that bind to hormones, vitamins and signaling molecules. The Nuclear Receptor transcription pathway had three genes that were annotated from the 1492 SNPs in interaction with HLA-DRB1. The three genes were protein-coding genes and included NR3C1(Nuclear Receptor Subfamily 3 Group C Member 1), RXRA (retinoid X receptor alpha) and ESRRG (Estrogen Related Receptor Gamma). The hierarchical arrangement of genome-wide overview of pathway analysis is described in figure 3. The overrepresented pathways are coded in yellow color and most of them point to the immune system.

Figure 3. The genome-wide arrangement of the over-represented pathways from Reactome analysis.

NR3C1 is a transcription factor by binding to glucocorticoid response elements in glucocorticoid response genes to active transcription (Lu & Cidlowski, 2005). Besides this, it also regulates other transcription factors. RXRA is also a transcription factor and plays an important role in nuclear import of VDR (Vitamin D receptor) (Prufer & Barsony, 2002) as well as initiation of transcription in VDRE (Vitamin D response elements). RXRA connects an important pathway, vitamin D receptor pathway to HLA-DRB1. One of the target genes of VDRE is HLA-DRB1 based on the presence of VDRE in the promotor region of HLA-DRB1 alleles (Cocco et al., 2012). Figure 4 displays the target genes in VDR pathway as curated in WikiPathways (Slenter et al., 2018).

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Figure 4. The activation of VDRE (Vitamin D response element) and its target genes are displayed. (https://www.wikipathways.org/index.php/Pathway:WP2877). The RXRA, VDR and VDRE complex results in the transcription initiation of VDRE. One of the target genes HLA-DRB1 is highlighted and the presence of VDRE in HLA-DRB1 alleles is observed.

Both RXRA and NR3C1 participate in adipogenesis. Adipogenesis is the development of fat cells from preadipocytes (Rosen & Spiegelman, 2000) and is linked to metabolic disorders such as obesity (Al-Sulaiti et al., 2019). NR3C1 is an important component in transcription factor regulation in adipogenesis and it binds to glucocorticoid receptor (GR) which is the starting point for adipogenesis (John et al., 2016). The secretory products of adipogenesis, which include leptin and AdipoQ (Adiponectin, C1Q and collagen domain containing), are associated with RA (Qian et al., 2018) and other autoimmune disorders due to their regulatory role in the immune system. Leptin is also a modulator of neuroendocrine function (Park & Ahima, 2015) and essential in immune response (Pucino et al., 2014) and associated with cardiovascular health (Jamar et al., 2017; Nalini et al., 2015). The complete pathway and its secretory products are displayed in figure 5.

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Figure 5. Displays the transcription factors involved in adipogenesis that is the differentiation of

preadipocytes in adipocytes. The mechanism of action of NR3C1 in the pathway is displayed(Transcription

factor regulation in adipogenesis (Homo sapiens) - WikiPathways). NR3C1 is the transcription factor for

PPAR γ (peroxisome proliferator-activated receptor gamma) that upregulates the levels of adipocytokines

such as leptin and ADIPOQ (adiponectin).

SRQb Replication of Interaction analysis The enrichment of possible interactions between HLA-DRB1 risk alleles and non-HLA SNPs in ACPA- positive RA was tested using interaction analysis. For both SRQb cases and healthy controls, 609 SNPs out of 1492 were available for the interaction analysis with HLA-DRB1. The top hits were present mainly in chromosome 1. HLA-DRB1 was used as a marker for interaction analysis. The complete results of interaction analysis in the case-control cohort of SRQb cases and healthy controls are presented in table S1 of the supplementary material (Appendix link). Table 4 shows the top hits after FDR correction.

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Table 4. The 16 SNPs that are insignificant interactions with HLA-DRB1 SE alleles, from the replication analysis. The analysis was done using GEIPAC. The full table of the results is disclosed in table S1 in a file online (Appendix)

Chr SNP AP p-value

FDR BH

AP(95% CI)

OR11 (95% CI)

OR01 (95% CI)

OR10 (95% CI)

1 rs1935838

4.19e-06 0.0004

0.22 (0.13- 0.32)

4.57 (3.95-5.30)

1.38 (1.16-1.64)

3.17 (2.74- 3.67)

1 rs12565589 4.20e-06 0.0004

0.22 (0.13- 0.32)

4.62 (3.98-5.36)

1.36 (1.14- 1.63)

3.22 (2.78- 3.73)

1 rs10858021 4.29e-06 0.0004

0.22 (0.13- 0.32)

4.57 (3.94-5.29)

1.39 (1.17- 1.66)

3.16 (2.73- 3.65)

1 rs10858019 4.80e-06 0.0004

0.22 (0.13- 0.32)

4.58 (3.95-5.30)

1.39 (1.16- 1.65)

3.18 (2.75- 3.67)

1 rs6679677 4.85e-06 0.0004

0.25 (0.14- 0.36)

5.06 (4.35-5.89)

1.57 (1.29- 1.91)

3.21 (2.84- 3.62)

1 rs10858018 5.03e-06 0.0004

0.22 (0.13- 0.32)

4.57 (3.95-5.30)

1.39 (1.16- 1.65)

3.17 (2.74- 3.67)

1 rs12044534 5.21-06 0.0004

0.22 (0.13- 0.32)

4.58 (3.95-5.30)

1.39 (1.16- 1.65)

3.18 (2.75- 3.68)

1 rs2797412 6.32e-06 0.0004

0.22 (0.12- 0.32)

4.57 (3.94-5.30)

1.39 (1.16- 1.65)

3.18 (2.75- 3.68)

1 rs10858017 7.26e-06 0.0004

0.22 (0.12- 0.31)

4.58 (3.96-5.31)

1.40 (1.18- 1.67)

3.18 (2.75- 3.68)

1 rs1230678 7.59e-06 0.0004

0.22 (0.12- 0.31)

4.56 (3.94-5.29)

1.39 (1.16- 1.65)

3.18 (2.75- 3.68)

1 rs1230679 7.589e-06 0.0004

0.22 (0.12-0.31)

4.56 (3.94-5.29)

1.39 (1.16- 1.65)

3.1 8(2.75- 3.68)

1 rs2476601 8.64e-06 0.0004

0.25 (0.14- 0.36)

4.95 (4.25-5.75)

1.52 (1.25- 1.85)

3.20 (2.83- 3.61)

1 rs2797409 9.22e-06 0.0004

0.22 (0.12- 0.31)

4.523 (3.91-5.24)

1.38 (1.16- 1.64)

3.16 (2.74- 3.66)

1 rs2797408 1.14e-05 0.0005

0.21 (0.12- 0.31)

4.53 (3.91-5.25)

1.38 (1.16- 1.65)

3.17 (2.74- 3.67)

18 rs949022 0.000796 0.03

0.39 (0.16- 0.62)

5.49 (3.89-7.76)

1.14 (0.69- 1.87)

3.19 (2.88- 3.55)

3 rs4686213 0.000964 0.04

0.27 (0.11- 0.43)

3.84 (2.81-5.26)

1.16 (0.84- 1.60)

2.64( 1.84- 3.79)

Chromosome number: chr, attributable proportion: AP, odds ratio double exposure: OR11, odds ratio for exposure to risk alle from the SNP but not HLA-DRB1 SE alleles: OR01, odds ratio for exposure to t HLA-DRB1 SE alleles but not risk allele of SNP: OR10

All resulting 609 SNPs from SRQb case and controls analysis were distributed genome wide from chromosome 1 to 18. Out of the most significant 16 SNPs (Table 4) that passed FDR <0.05, 14 were present in chromosome 1 while one SNP was found in chromosome 3 and another in chromosome 18. The genome-wide distribution of all the SNPs from SRQb interaction analysis is represented in a Manhattan plot in Figure 6. The dots above the trend line at 3 represents the 16 SNPs that passed the FDR threshold at 0.05 genome-wide.

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Figure 6. Manhattan plot displaying the p-values from the interaction tests between 609 SNPs and HLA-DRB1 SE alleles in the risk to ACPA-positive RA compared to healthy controls. Top 16 interacting SNPs (Table 5) are present in chromosomes 1, 3 and 18 and are highlighted green. A cutoff line above 3.0 -log (AP p-value) represents the most significant 16 SNPs that passed a FDR of 0.05.

The association analysis was performed to compare the allele risk frequencies between ACPA-positive cases and healthy controls for HLA-DRB1 alleles and non-HLA risk variants. The analysis was based on logistic regression and resulted in OR with 95% CI values. The association analysis was controlled by sex. The top SNPs from the SRQb results were compared with the important SNPs from the previous study (Diaz Gallo et al., 2018). Results for association analysis were also added for the tagged SNPs based on linkage disequilibrium (LD) that could represent the SNPs not present in SRQb study (online supplementary material table S7 from Diaz Gallo et al., 2018). The results for association analysis (Plink1.9) represent the top SNPs from the interaction analysis in SRQb-cases controls study and are represented in table 5.

Table 5. Association analysis results of comparing the allele risk frequencies between ACPA-positive RA cases and healthy-controls, of the HLA-DRB1 SE alleles, the PTPN22 and chromosome 9 variants previously reported in interaction with SE alleles.

Chromosome Genetic variant

MAF OR(U-L) p-value FDR BH

6 HLA-DRB1 SE 0.38 2.54 (2.37-2.72) 3.00e-53 1.86e-150 1 rs2476601 0.13 1.51 (1.38-1.66) 1.21e-18 2.49e-16 9 rs10760127 0.47 1.08 (1.01-1.15) 0.02 0.28 1 rs1935838 0.26 1.37 (1.28-1.48) 8.78e-18 3.39e16 1 rs1256558 0.26 1.36 (1.26-1.46) 3.80e-16 1.57e-14

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Interaction analysis for MTX response in EIRA For the three months follow-up of MTX response, 475 SNPs from the 1492 SNPs of interest were available for interaction analyses with HLA-DRB1 SE alleles. For the six months follow-up of MTX response, 488 SNPs out of 1492 SNPs of interest were available and passed the threshold criteria for the interaction analysis. To be called a responder or non-responder, EULAR response measurement was considered as the outcome of the interaction analysis. The full results are represented in figure 7 and fully disclosed in the Appendix. Suggestive interactions with SNPs and HLA-DRB1 SE alleles in the decreased risk for responder and non-responder at 3 and 6 months are presented in Tables 6 and 7, respectively. Nevertheless, these interactions did not remain significant after FDR correction (FDR<0.05).

Table 6. Top five suggestive SNPs in interaction with HLA-DRB1 SE alleles for MTX response in ACPA-positive RA cases from EIRA at 3 months follow-up. The analysis was done using GEISA. The full table of the results is disclosed in table S2 in a file online (Appendix)

Chr SNP AP p-value

FDR BH

AP (95% CI)

OR11 (95% CI)

OR01 (95% CI)

OR10 (95% CI)

12 rs2705165 0.046 0.995 -0.97 (-1.93 - -0.02)

3.10 (0.80– 12.12)

3.59 (0.89 -14.93)

3.53 (0.84-14.88)

6 rs465483 0.060 0.995 -1.03 (-2.10 -0.04)

1.77 (0.61-5.20)

2.29 (0.72 – 7.35)

2.30 (0.76-7.01)

11 rs7951950 0.089 0.995 -0.84 (-1.81 -0.13)

1.94 (0.88-4.28)

2.64 (1.04-6.69)

1.94 (0.85-4.44)

11 rs7933475 0.089 0.995 -0.84 (-1.81 -0.13)

1.94 (0.88-4.28)

2.64 (1.04-6.69)

1.94 (0.85-4.44)

12 rs17760877

0.089 0.995 -0.79 (-1.71- 0.12)

2.43 (0.79 – 7.47)

2.87 (0.86-9.52)

2.50 (0.76-8.19)

Chromosome number: chr, attributable proportion: AP, odds ratio double exposure: OR11, odds ratio for exposure to risk allele from the SNP but not HLA-DRB1 SE alleles: OR01, odds ratio for exposure to HLA-DRB1 SE alleles but not risk allele of SNP: OR10

Table 7. Top five suggestive SNPs in interaction with HLA-DRB1 SE alleles for MTX response in ACPA-positive RA cases from EIRA at 6 months follow-up. The analysis was done using GEISA. The full table of the results is disclosed in table S3 in a file online (Appendix)

Chromosome number: chr, attributable proportion: AP, odds ratio double exposure: OR11, odds ratio for exposure to risk allele from the SNP but not HLA-DRB1 SE alleles: OR01, odds ratio for exposure to HLA-DRB1 SE alleles but not risk allele of SNP: OR10.

One SNP from 3 months analysis had a p-value less than 0.05 (table 6) and three SNPs from 6 months had a p-value lower than 0.05. No SNPs passed the FDR<0.05 correction. Genome-wide

Chr SNP AP p-value

FDR BH

AP (95% CI)

OR11 (95% CI)

OR01 (95% CI)

OR10 (95% CI)

12 rs372076 0.02 0.98 -1.20 (-2.23- -0.18)

2.52 (1.10-5.76)

3.54 (1.36-9.22)

3.01 (1.277.12)

12 rs17760877

0.03 0.98 -1.04 (-1.97- -0.12)

4.23 (1.34-13.37)

5.26 (1.53-18.11)

4.40 (1.29-14.89)

3 rs2063836 0.04 0.98 -2.03 (-3.95- -0.10)

1.10 (0.59-2.05)

2.43 (1.00-5.99)

1.91 (1.05-3.47)

3 rs6805814 0.06 0.98 -1.15 (-2.32- 0.03)

1.77 (0.62-5.04)

1.96 (0.63-6.07)

2.84 (0.92-8.81)

5 rs459400 0.06 0.98 -0.90 (-1.83- 0.04)

2.34 (1.01-5.43)

3.00 (1.15-7.89)

2.45 (1.01-5.91)

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distribution of the results from the additive model of interaction between 609 SNPs and HLA-DRB1 alleles to MTX response at 3 and 6 months are presented in table 6 and 7 and figure 7.

Figure 7. Results of the additive model of interaction, between SNPs of interest and HLA-DRB1 SE alleles in the outcome to respond to MTX monotherapy at 3 (panel A) or 6 (panels B) months of follow-up. Both panels A and B represent the negative log of AP p-values from the interaction analysis. Dots above the line at 1.3 on the y-axis represent the SNPs that had a p-value less than 0.05.

Discussion In order to better understand the role of statistical interactions in ACPA-positive RA, pathway analysis was performed. It helped in understanding the possible mechanism of action (at molecular, cellular or tissue level) of the previously identified statistical interactions in ACPA-positive RA (Diaz-Gallo et al., 2018). Reactome was used to identify the candidate pathways that could be connected to ACPA-positive RA’s pathogenic mechanisms. A total of 25 pathways were reported based on Reactome analysis out of which 9 were significant. One unique pathway called Nuclear Receptor transcription pathway (FDR 3.14e-04) was present in the most significant pathways. The three genes involved in this pathway and annotated from the SNPs of interest, were Nuclear Receptor Subfamily 3 Group C Member 1 (NR3C1), retinoid X receptor alpha (RXRA) and Estrogen Related Receptor Gamma (ESRRG). All these genes are protein coding genes. NR3C1 encodes glucocorticoid receptor that acts as a transcription factor as well as a regulator of transcription factor (Lu & Cidlowski, 2005). RXRA is a nuclear receptor that is involved in retinoic acid-mediated gene activation (Mangelsdorf et al., 1990). ESRRG encodes for a protein in the estrogen receptor-related receptor family that belongs to the nuclear hormone receptor superfamily (Eudy et al., 1998).

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Both RXRA and NR3C1 are linked directly or indirectly to multiple pathways such as NFR2 pathway, transcription factor regulation in adipogenesis, Vitamin D receptor pathway, adipocytokine signaling pathway (Prufer & Barsony, 2002). Pathway such as transcription factor regulation in adipogenesis has both RXRA and NR3C1 acting as transcription factor regulators. One of the end products of this pathway is leptin. Leptin is a hormone derived from adipose tissues and is known to regulate energy balance as well as neuroendocrine function (Park & Ahima, 2015). Many studies have reported that leptin may be linked with maintenance of certain autoimmune diseases (Pucino et al., 2014). In addition to that, leptin has also been related to the development of cardiovascular diseases (Ku et al., 2011; Nalini et al., 2015) in obese individuals (Jamar et al., 2017). Impaired function of leptin due to genetic defects or its deficiency is associated to a decrease in circulating lymphocytes (Faggioni et al., 2000).

Children with a deficiency in leptin reportedly have low T-cell count and are more susceptible to infections (Ozata et al., 1999). Studies of human autoimmune diseases in mouse models have shown evidence of leptin’s role in the development of autoimmune disorders such as rheumatoid arthritis (RA), multiple sclerosis (MS) and type 1 diabetes (Peelman et al., 2004). Additionally, RA patients have an increased production of leptin that may point to its role in pathogenesis of RA (Bokarewa et al., 2003). The other adipocytokine, adiponectin (adipoQ) has a proinflammatory role in the joints and may also lead to matrix degradation (Ozgen et al., 2010). Adiponectin also showed high expression in synovial fluid and synovium in patients with RA (Tan et al., 2009). Adiponectin also promotes the production of osteopontin in synovial tissues of RA patients which recruits osteoclasts and leads to elevation of bone erosion (Qian et al., 2018).

RXRA acts as a transcription factor in the vitamin D receptor pathway (Long et al., 2015). One of the targeted genes of vitamin D receptor (VDR) is HLA-DRB1. The interactions between HLA alleles and VDR in type 1 diabetes patients have been confirmed by promoter sequence analysis. It has been found that the interactions are mediated by vitamin D response element (VDRE) that is present in the promoter region of HLA-DRB1*03 allele(Israni et al., 2009). Vitamin D receptor response element (VDRRE) was identified and characterized in HLA-DBR1 (HLA-DRB1*1501) in a multiple sclerosis (MS) study (Ramagopalan et al., 2009). This suggests a potential role of the VDR pathway in the pathogenesis of RA, via the interaction of HLA-DRB1 with genetic variants linked to NR3C1 and RXRA mainly. The role of RXRA (Prufer & Barsony, 2002) is well evident from the vitamin D receptor pathway (figure 4) in the transcription initiation of VDRE that results when the RXRA, VDR and VDRE complex forms.

The replication interaction analysis was performed to find out if the significant interactions to ACPA-positive RA, observed previously in EIRA (Diaz Gallo et al., 2018) are also present in an independent set of case-controls from the Sweden population. The replication interaction analysis included 609 SNPs out of which 2.63% were significant after correction and mainly located in chromosome 1 (Table 5). One of the top interacting SNPs were rs2476601 that is a non-synonymous variant in exon 14 of the PTPN22 (protein tyrosine phosphatase, non-receptor type 22) gene and is a known genetic risk variant for many autoimmune disorders including RA, (Zhernakova et al., 2005). PTPN22 encodes for lymphoid tyrosine phosphatase (LYP) that has a critical role in regulating signaling in T and B lymphocytes and negatively regulates T-cell response (Menard et al., 2011; Zhang et al., 2011).

The statistical interaction analysis (based on additive interactions) may correspond to the biological complexity and the pathogenesis of ACPA-positive RA. For instance, interactions between rs2676601 SNPs and HLA-DRB1 SE alleles, have been reported previously (Källberg et al., 2007; Diaz-Gallo et al., 2018) concerning the risk of developing ACPA-positive RA. The OR for double exposure, risk allele of rs2476601 and SE carrier, was 4.95 while the OR for only the risk allele of SNP but not SE carriers, was 1.52 and 3.20 for HLA-DRB1 SE carriers but not risk allele of rs476601 SNP. This corresponds to an AP of 0.25 (Table 4) which means that 25% of the disease, in that given sufficient cause, can be attributed to this genetic interaction. Capture Hi-C studies also revealed that rs2476601 is in physical contact with HIPK1 (Homodomain Interacting Protein

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Kinase 1) and CSDE1 (cold shock domain-containing E1) genes in both CD4+ and CD8+ cells (Javierre et al., 2016; Martin et al., 2015; Schofield et al., 2016) that are of great importance in RA (Trynka et al., 2013). The top 16 SNPs (Table 4) leads to a conclusion that they exhibited significant interaction with HLA-DRB1 SE alleles in the development of ACPA-positive RA in the replication analyses.

Only one SNP in methotrexate response to EIRA at 3 months (Table 6) had an AP p-value less than 0.05 and three SNPs passed (Table 7) the minimum p-value in 6 months (figure 7). The SNP for 3 months was rs2705165 and those identified in 6 months were rs372076, rs17760877 and rs2063836. The model designed for responders and non-responders was that if the odds ratio for double exposure is less or more than individual odds ratios. The odds ratio for double exposure (HLA-DRB1 and SNPs together) in both 3 and 6 months was less than the odds ratio for HLA-DRB1 and SNPs individually. This indicated that there is a decreased risk in response if the odds ratio for double exposure decrease. For instance, rs2705165 from 3 months has a double exposure odds ratio of 3.10 while the odds ratio for the SNP is 3.59 and HLA-DRB1 is 3.53. Similarly, rs372076 from 6 months response study has a double exposure odds ratio of 2.52. The odds ratio for the SNP only is 3.54 and for HLA-DRB1 is 3.01. Additionally, after FDR correction, none of the SNPs pass the FDR p-value <0.05. The statistical correction showed that there were no significant interactions among the studied SNPs and HLA-DRB1 outcome in response to MTX monotherapy at both follow-up periods. This non-significant observation may be due to a modest sample size that was not enough to address the interactions as they were addressed in SRQb-case control study where 16 SNPs were found to be insignificant interaction with HLA-DRB1 because the sample size was enough to address the interactions. A larger data set from the same population could better analyze the possible enrichments (if present) related to exposure to ACPA-positive RA.

The SRQ interaction analysis had 3165 cases and 5758 controls (Table 1) to look for interactions while there were only 1511 total cases and 681 SNPs included in MTX reponse analysis for both 3 and 6 months case (responders)-case (non-responders) study. A larger sample size may capture more SNPs across non-HLA genetic loci associated to ACPA-positive RA. In addition to that, the genetic risk factors for disease susceptibility may not be related to disease progression as well as response to a particular medication. Instead, there may be other genetic risk factors such as those that were not addressed here. Such genetic factors may include those SNPs that are in close proximity to or within the genes that target MTX metabolism. Successful improvement in results could provide therapeutic success benefit to patients and also point the non-responders to a second line of therapy soon if they do not respond to MTX monotherapy.

HLA-DRB1 alleles are significantly associated with different autoimmune disorders such as rheumatoid arthritis (RA), multiple sclerosis (MS), systemic lupus erythematosus (SLE) and Sjögren’s syndrome, etc (Cruz-Tapias et al., 2013). Gene-gene interactions between HLA-DRB1 and non-HLA risk genes such as PTPN22 are strongly associated to the risk of developing ACPA-positive RA. Such interactions also bring significant changes in biological processes (eg. pathways). The results from SRQb-case control interactions also reflected to the concept of the dominion hypothesis (Diaz-Gallo et al., 2018) that suggested that HLA-DRB1 SE alleles function as a genetic hub that is in interaction with non-HLA risk variants which alone do not have a strong effect on the risk of developing ACPA-positive RA. The results retained the dominion hypothesis as discussed in the previous interaction study.

Gene expression analysis of immune cells such as CD4+ T cells, CD8+ T cells, CD14+ monocytes as well as PBMCs (peripheral blood mononuclear cells) could provide better insight into their transcriptomic profiles in healthy controls as well as ACPA-positive patients. Such investigation could point to the identification of differential expression of HLA-DRB genes in various immune cells in both ACPA-positive RA patients and healthy individuals. A better insight into disease progression and the interactions between HLA-DRB1 alleles and non-HLA risk alleles could be provided by functional analysis such as eQtL analysis in immune cells of ACPA positive RA patients

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and controls. Such analysis could point to those SNPs that affect gene expression and are SE-eQtLs. SE-eQtLs may identify those genes that contribute to the development of ACPA-positive RA. It is also essential to repeat all the interaction studies with a large sample size to address more interactions in the future. Further studies are also needed to understand the mechanisms of action of adipocytokines and how they contribute to proinflammatory state in RA.

Ethical aspects, gender perspectives, and impact on the society For the design of this study, sex bias was taken into consideration. The analyses were controlled by sex. The project included the use of data from EIRA (Epidemiological investigation of rheumatoid arthritis) and SRQb (Swedish Rheumatology Quality Register biobank) and healthy controls from a case-control MS study in Swedish population. Those studies have the respective ethical considerations and approvals by the respective ethical committees have been given. All the participants were informed, and they signed a written consent for data and biological sample collection.

The list of granted and awaiting permissions included the following bodies:

96-174 The Swedish epidemiological investigation of rheumatoid arthritis - EIRA - granted

2006/476-31/4 The Swedish epidemiological investigation of rheumatoid arthritis - EIRA – granted.

2010/935-31/1 Study of molecular mechanisms behind gene-gene and gene-environment interactions -granted.

The respective ethical considerations and ethical permissions have been granted for the SRQ - SRQ biobank data. The request of the genotypic and phenotypic data is being processed and access to the ethical review’s numbers is available too.

All the access data was handled in accordance with the EU Data Protection Regulation (GDPR). The society could benefit from future studies where the results have pointed to and are necessary to be done such as running interaction analysis with large data-sets and better tools. Important results may point to precision medicine research in the future that will benefit all patients. Also, the collaborations and discussions that were initiated during the project will not only benefit ACPA-positive RA patients but will also provide a better understanding of other rheumatic and autoimmune diseases.

Future perspectives The analysis has pointed to important results that need further evaluation and could direct to more functional study designs in understanding complexity in the systems biology of ACPA-positive RA. The resulting pathway analysis highlighted some genes that suggested the role of VDR, adipognesis secretory products such as leptin. Yet more research is needed to understand their exact roles. SRQb interaction analysis validated (Diaz-Gallo et al., 2018) the interactions between HLA-DRB1 and PTPN22 in risk of developing ACPA-positive RA. These statistical interactions need more functional interpretation as both genes are major players in autoimmune response and a better understanding of the genetic architecture of these genes is also needed. This study also contributed to the development of an upgrade of GEIPAC to be able to read continuous variables as covariates in the future.

The current study design in analyzing the interactions for MTX responders and non-responder may provide significant hits in interaction with HLA-DRB1 if the sample size is increased. The follow-up to this study is to combine the data for the responders and non-responders in EIRA 3 and 6 months’ timeline with responders and non-responders in SRQ 3 and 6 months follow-up of MTX treatment. This will probably reduce low sample size power problem for analysis. This analysis will be accompanied by using the list of SNPs in or close to the genes that participate in

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MTX metabolism to evaluate the interactions among the genes and with HLA-DRB1. This project contributed to updating in GEIPAC (Ding et al., 2011; Hössjer, 2016; Uvehag, 2013). All these interaction studies will in future be implemented in GEIPAC’s new update and these analyses will also include continuous variables which were not added as covariates in the current version of GEIPAC(0.1.3). The previous interaction analysis (Diaz-Gallo et al., 2018) will also be added to future studies as the sample size is today bigger in EIRA than they were back in 2018. Furthermore, the new tool’s availability would also benefit other research groups that study complex disorders such as Multiple Sclerosis. Another future study could be to investigate three factor interactions of the genetic pairs (between HLA-DRB1 and non-HLA risk alleles) with smoking status (Källberg et al., 2007, Källberg et al., 2011, Padyukov et al., 2004). This time both EIRA and SRQb cohorts will be used to investigate the risk of such interactions in ACPA-positive rheumatoid arthritis.

Acknowledgements I would like to thank and express my gratitude towards my supervisor Assistant Professor Lina Diaz for sharing her knowledge, her support during the project and investing her time in this project, my group leader Leonid Padyukov for sharing his knowledge and help with the data availability, the Börje Dahlins fond that supported me during my project. I would also like to thank Professor Ingrid Kockum, Assistant Professor Helga Westerlind for providing me with data for analysis. I will also like to thank Henric Zaazi, author of the tools GEISA/ GEIPAC and Prof. Ingrid for their really important and fruitful discussions regarding future design of interaction analysis tools which was made possible by Lina. My respects and appreciation for my examiner and teacher Andreas Tilevik. My deepest appreciation and gratefulness towards my parents, my brother and Javeria. Dedicated to my friend Johan Bergholm for his ultimate support and faith throughout this journey.

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Appendix

https://kise-my.sharepoint.com/:x:/g/personal/saad_lodhi_ki_se/EadD62CqPtJJhzYxjKxjBRgBQfuR2_p98hWhtq-i7j1oKQ?e=EE6B1V