Genetic susceptibility to rheumatoid arthritis and its implications for novel drug

discovery.

Abstract

Introduction

Over 100 susceptibility loci have now been identified for rheumatoid arthritis (RA), several

of which are already the targets of approved RA therapies providing proof of concept for the

use of genetics in novel drug development for RA. Determining how these loci contribute to

disease will be key to elucidating the mechanisms driving disease development and has the

potential for major impact on therapeutic development.

Areas covered

Here we review the use of genetics in drug discovery, including the use of ‘omics’ data to

prioritise potential drug targets at susceptibility loci using RA as an exemplar. We discuss

the current state of RA genetics its impact on stratified medicine, and how the findings from

RA genetics studies can be used to inform drug discovery.

Expert opinion

It is anticipated that functional characterisation of disease variants will provide biological

validation of a gene as a drug target, providing safer targets, with an increased likelihood of

efficacy. In the future techniques such as genome editing may represent a plausible option

for RA therapy. Technologies such as genome wide chromatin conformation capture Hi-C

and CRISPR will be crucial to inform our understanding of how diseases develop and in

developing new treatments.

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Key words

Genetics

Rheumatoid Arthritis

Drug Discovery

Functional genomics

Therapeutic target

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1.0 Introduction

Rheumatoid arthritis (RA) is a complex common autoimmune disease which manifests in the

inflammation and destruction of synovial joints, affecting approximately 1% of the

population worldwide. RA can progress to disability, systemic complications and early death

all of which contribute to socioeconomic costs. Currently both conventional and biological

disease modifying anti-rheumatic drugs (DMARDs) are used in the treatment of RA. The

first line therapy for RA is a conventional DMARD such as methotrexate, sulfasalazine,

leflunomide, hydroxychloroquine and azathioprine. Methotrexate is usually the drug of

choice, however many patients fail to respond and only 55% of patients remain on the drug

after 2 years [1]. Patients failing to respond to MTX are subsequently prescribed biologic

DMARDs such as anti-tumour necrosis factor (TNF) agents which disrupt TNF-α in order to

reduce inflammation , indeed combinations of traditional and biologic DMARDs are often

prescribed. Several anti-TNF agents are used to treat RA including infliximab, adalimumab,

golimumab, etanercept and certolizumab, all of which inhibit the effects of TNF-α by

preventing it from binding to its receptor. Anti-TNF biologics are costly, approximately

£10,000 per patient per year, and also have a high rate of non-response (approximately 30-

40% of patients) [2, 3]. Other biologics, targeting different pathways are also now available,

for example rituximab which targets B-cells and Tocilizumab targeting the interleukin 6

pathway. Although there has been significant progress in the treatment of RA over the past 10

years a significant proportion of patients are refractory to current therapies meaning there is

still need for new therapies. The heritability of RA is estimated to be approximately 60%,

indicating that the most important factor in determining whether someone is likely to develop

RA is found in their genetic background. An individual’s genetic background is present

before the onset of the disease and therefore could potentially be used as both an early

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biomarker of disease or as a target for therapeutic intervention. Indeed a recent report

estimated that a therapeutic target with existing genetic evidence is twice as likely to be

brought to market [4]. This is supported by a review of the AstraZeneca drug discovery

pipeline, which indicated that in their trials 73% of the compounds which made it to phase II

were supported by genetic evidence, compared to only 43% without genetic support [5].

Understanding the genetics of disease susceptibility will therefore be key to elucidating the

mechanisms driving disease development and will have a major impact on therapeutic

development. Here we review how genetics can be used in a translational approach to inform

drug discovery using RA as an exemplar.

2.0 Genetics of Rheumatoid arthritis, the current state of play

Since 2007, advances in genotyping technologies and the advent of genome wide association

studies (GWAS), over 100 genetic susceptibility loci have been identified for RA adding to

the well- established associations to the human leukocyte antigen (HLA) locus and protein

tyrosine non receptor 22 (PTPN22). Parallel studies in other autoimmune and inflammatory

diseases have revealed a picture of shared genetic susceptibility loci across many diseases.

The Immunochip project used this overlap to its advantage, with a consortium of 12

autoimmune diseases designing a custom illumina SNP genotyping array containing

~200,000 SNPs at 186 loci previously implicated in these diseases allowing the fine mapping

of these loci. The study included 11475 RA cases and 15870 controls of European descent,

which were combined in a meta-analysis with previous independent GWAS data [6]. The

study identified 14 new susceptibility loci and refined the association to a single gene for 19

loci. A follow up study testing variants which demonstrated suggestive evidence of

significance in the Immunochip also identified two novel loci [7]. The most recent large

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meta-analysis used a trans-ethnic approach including >100,000 samples of European and

Asian ancestry, analysing over 10 million SNPs, increased the number of RA susceptibility to

101[8]. Indeed GWAS focus on common frequency variants, therefore little has been done to

investigate rare variants in RA. Several studies have suggested that the impact of rare variants

on the heritability of RA is minimal (refs), several large scale sequencing projects may help

to shed light on the contribution of these rare coding variants. Variants identified through

GWAS are not necessarily the true causal variants as they may be correlated with several

other variants through linkage disequilibrium. In addition although >100 susceptibility

variants have been identified the mechanism by which these variants contribute to disease is

largely unknown.

Given this wealth of genetic data emerging from recent studies the question remains as to

how this data can be used to impact on clinical practice. An understanding of how disease

associated genetics variants affect gene function has the potential to inform drug discovery,

through the identification of disease genes that are drugable targets or through the

identification of pathways that could be up or down regulated to modulate the disease

condition. In addition the elucidation of a genetic basis for the existence of disease subsets

will in turn inform a stratified medicine approach in which the different biological pathways

driving disease in patient subsets can be characterised and targeted. Before this can happen,

though, the next challenge is to determine precisely which of the associated variants have

functional consequences, on which genes and in which cell types these are effective in order

that the key biological pathways disrupted in disease can be identified.

3.0 Utilising biological pathways and multi “Omics” data to prioritize genetic loci

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The abundance of biological “omics” data, such as gene expression data, chromatin histone

marks, protein-protein interactions, biological pathways and GWAS results which can be

used to develop hypotheses and advance the findings from GWA studies to facilitate insight

into the pathological mechanisms of disease associated variants (Figure 1).

This was demonstrated in the most recent meta-analysis in RA to date, in which publically

available data was used to develop an in-silico pipeline based on functional annotations [8].

The authors combined genetic risk loci with data including; functional annotation of SNPs,

cis acting eQTLs, risk variants for other traits, text mining, epigenetic motifs, protein-protein

interactions, knockout mouse phenotypes and annotated molecular pathways to identify the

candidate genes at the RA risk loci. Studying chromatin marks in 34 cell types, the authors

found that the RA loci were enriched for tri-methylation of histone H3 at lysine 4

(H3K4me3) in primary CD4+ regulatory T cells. H3K4me3 is a histone modification

associated with open and active chromatin as it has been correlated with increased gene

expression and nucleosome loss [9], suggesting RA loci, although mostly located in

intergenic or intronic regions, are located in open active sites. For 98 of the 101 loci,

candidate risk genes were identified. Forty four of these loci showed evidence of being

expression quantitative trait loci (eQTLs) acting in cis, in data obtained from peripheral blood

mononuclear cells, CD4+ T-cells and CD14+ and CD16+ monocytes. Pathway analysis

supported these findings, demonstrating enrichment of risk loci in T-cell related pathways, B-

cell pathways and cytokine signalling pathways.

4.0 Genetic studies reveal Disease Pathways

Using genetics to identify critical pathways involved in disease is one method by which

genetics can be utilised in drug discovery (Figure 1). Once a pathway has been identified it

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would then be assumed that if a risk variant increased the expression of a gene that positively

regulates a particular pathway, inhibition of that gene or signalling pathway may be effective

in treating the disease, a high throughput drug screen of the pathway could then be

conducted. Okada et al used a library of drugs currently approved for use in experimental or

in clinical trial therapies for human diseases which target specific genes and screened them

for the presence of the identified RA candidate genes or any genes from their direct protein-

protein interaction networks[8]. Twenty seven of the approved drug target genes overlapped

the RA candidate genes, demonstrating the utility of this approach.

Genes mapping to RA loci have been shown to cluster into three pathways, NFKB, T cell

signalling and JAK-STAT pathways [10]. Candidate genes at several RA loci are involved in

NFKB signalling, including REL, TNFAIP3, PRKCQ, TRAF1, TRAF6, CCL19/CCL21,

IRAK1 and CD40. A study by Li et al investigated the CD40 locus as a potential therapeutic

target; fine mapping and exon sequencing of the locus, refined the association to a single

SNP, the authors then show that this SNP is an eQTL for CD40 expression in PBMCs, this

was supported by flow cytometry results which showed that individuals who were

homozygous for the risk allele had increased amounts of CD40 on the cell surface of CD19+

B-cells compared to those homozygous for the non-risk allele. Perturbation experiments

showed a direct correlation between the amount of CD40 on the cells surface and the

phosphorylation of RelA, a subunit of the NFKB transcription factor. A high throughput

drug screen identified two novel compounds as potential therapies [11].

Another interesting RA susceptibility locus is the PADI4 locus. PADI4 encodes an enzyme

which catalyses protein citrullination, converting arginine residues to citrulline which are the

target of anti citrullinated peptide antibodies (ACPA). Indeed the association at this locus is

specific to RA, where as many of the other RA loci are shared across other autoimmune

diseases. Cigarette smoking has been associated with an increased risk of RA and causes

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protein citrullination, ncreased expression of PADI enzymes has been found in

bronchoalveolar lavage cells from smokers compared to non-smokers; suggesting that the

PADI4 locus may be important in the development of RA, representing an interesting drug

target.

5.0 Support for using genetics in drug discovery; proof of concept

The identification of variants associated with susceptibility to RA that are already the targets

of successful therapeutics (IL6, CTLA4, TYK2) provides a proof of concept that genetics

could be successful in identifying novel targets. A variant within the interleukin 6 receptor

(IL6R) gene has recently been associated with RA and IL6R is the target of the monoclonal

antibody tocilizumab, an approved RA therapy. IL6 is a pro-inflammatory cytokine,

overproduction of which causes systemic inflammation and elevated levels have been

observed in the serum and synovial fluid of RA patients [12]. IL6R exists as both soluble and

membrane bound forms, where the RA disease associated variant, leads to increased cleavage

of the membrane-bound protein, increasing the amount of the soluble form of the IL6

receptor. Tocilizumab is a humanised monoclonal antibody which binds to both the

membrane bound and soluble forms of the IL6 receptor prohibiting IL6 from exerting its pro-

inflammatory effects. A new therapy Sirukumab which blocks IL-6 as opposed to the IL6

receptor is now in phase II trials after promising results in Phase II, where low doses of the

drug showed significant improvements in symptoms [13]. Other approved RA therapies

targeting candidate genes include abatacept, which targets the CTLA4 gene and tofacitinib

targeting the JAK-STAT pathway. Tyrosine kinase 2 (TYK2) is a key molecule in the JAK-

STAT signalling pathway implicated in cytokine signalling responses associated with

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immunological diseases, a variant in this gene has been associated with RA [6]. Mouse

models have also provided further supporting evidence that TYK2 is a genetic target for RA,

with Tyk2 deficient mice shown to be resistant to experimental arthritis [14]. These

examples provide compelling evidence that genetics can significantly contribute to the

identification of drug targets. Currently less than 5% of drug molecules that begin phase I

trials are approved as safe effective therapeutics [15]. It has been shown that most failures

occur during phase II trials mainly as a result of lack of efficacy or toxicity. The application

of human genetics to drug discovery has been demonstrated to reduce the number of

compounds which fail at phase II trials, potentially by increasing the probability that

therapeutically modulating the target will result in a safe and effective drug. The majority of

the RA susceptibility loci identified to date confer small effect sizes, raising the concern that

they may not be sufficient therapeutic targets, however the examples described above

demonstrate that pharmacological modulation of disease associated genes conferring small

effects on risk (OR<1.4) has an observable clinical impact.

GWAS finding could also highlight target genes where there is the possibility of

repositioning drugs already approved for other diseases, short-cutting the long process of

major clinical trials. This has proven successful in the past, for example both methotrexate

and rituximab were originally used to treat cancer, and early indications suggest GWAS may

provide more compelling targets. In addition, the emerging genetic sharing, known as

pleiotropy, between autoimmune diseases suggests that drug re-positioning between these

genetically related diseases may be a feasible option.

A recent study has estimated the cost of bringing a prescription drug to market to be $2.6

billion dollars [16], given the low success rate of molecules entering phase I trials that

successfully complete this journey, a huge financial loss is incurred by failing drugs. Not

only can genetics provide novel, or re-positioned targets for RA, but by fully understanding

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the mechanistic action of genetic variants we can model the effects of modifying a target

within patients. This then provides both the magnitude and direction (agonist/antagonist) that

the therapy must mimic to be effective. As discussed earlier the variants associated with RA

in IL6R have the same effect as the approved drug tocilizumab, inhibiting the IL6 receptor.

The observation of the effects of the protective risk allele on the IL6 receptor provides

validation of IL6 as a suitable drug target. This could be referred to as a natural experiment,

and has been successful in the development of several approved therapies, for example statins

as a treatment for high cholesterol. Families with hypercholesterolemia carrying mutations in

the low density lipoprotein receptor gene (LDLR) demonstrated high LD cholesterol and

increased risk of heart disease, a relationship was seen between the number/type of mutations

and the blood levels of LDL cholesterol levels, corresponding to a range of individual

disease severity [17, 18]. This series of alleles resulting in different extremes of the

phenotype allowed the relationship between LDL and increased risk of heart attack to be

defined. 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) was known to be critical to

the synthesis of LDL and this ultimately led to the development of HMG-CoA reductase

inhibitors (statins) to treat high cholesterol. The identification of a series of alleles associated

with a disease affecting a single gene or pathway ranging from complete inhibition of the

target to an increase in expression, would allow us to model the effects of these alleles on the

disease phenotype in a dose response manner. For example, loss of function variants would

allow us to observe the effects of target inhibition on the phenotype and could mimic the

effects of a potential therapeutic, including off-target and detrimental effects. This would

require in in depth knowledge of the causal alleles and their function. The identification of

such allelic series may require large scale sequencing studies in order to identify the full

range of variants.

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PCSK9 is an example of the success of an allelic series leading to treatment; a rare gain of

function mutation in the PCSK9 gene was found in two families with an increased risk of

coronary artery disease (CAD) hypercholesterolemia [19]. Further studies found that a loss

of function variant at PCSK9 correlated with low LDL levels and reduced occurrence of CAD

[20-22]. In vivo studies then revealed a mechanistic link between PCSK9 and LDL levels

demonstrating that overexpression of PCSK9 in mice resulted in hypercholesterolemia. This

was determined to be due to a post-transcriptional modification causing a reduction in the

expression of the LDL receptor protein [23, 24]. Clinical trials of a PCSK9 monoclonal

antibody significantly reduced LDL cholesterol levels in both healthy individuals and

individuals with hypercholesterolemia [25]. Subsequently, two cholesterol lowering PCSK9

monoclonal antibodies, repatha and praluent, have been approved by the FDA.

A further indication of how genetic discoveries can aid therapeutic intervention can be

demonstrated with a SNP within TNFRSF1A a gene encoding tumour necrosis factor receptor

1 (TNFR1), that has been associated with multiple sclerosis but not with other AID, such as

RA. A study has shown that the MS risk allele regulates the expression of a novel soluble

form of TNFR1 that can block TNF [26]. Interestingly anti-TNF drugs have been shown to

aggravate or even promote the onset of MS, but conversely have been successful in the

treatment of other AID where there is no association, including RA. The TNFRSF1A variant

mimics the effect of anti-TNF therapies and suggests their mechanism of action in

ameliorating disease, showing how insight can be gained by comparing genetic loci across

related AID and determining the functional consequences.

6.0 Disease prediction and stratified medicine

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One of the objectives of complex disease genetics is the ability to predict an individual’s risk

of developing disease with a view to development of interventions such as lifestyle

modifications, close monitoring or the use of therapies to delay or prevent onset. Statistical

models incorporating RA genetic loci into genetic risk scores (GRS) have been developed to

predict who is at risk of developing disease, with limited success (Area under the curve

(AUC) 0.72-0.79), although this is increased in males who smoke (AUC 0.86) [27, 28].

Given the low population prevalence of RA, it is unlikely that general population screening

would be of use; instead such models would be targeted to populations of individuals with

other risk factors such as first degree relatives of patients with RA.

In some areas such as cancer, knowledge gained from genetics has huge potential to inform

personalised medicine, where healthcare can be tailored to each individual patient based on

predictive markers, allowing the most effective drug to be selected for each individual. In the

case of a complex, polygenic disease such as RA, with hundreds of contributing genetic

factors, medicines targeted to the individual may be overly ambitious, however the

identification of genetically defined sub-groups of patients, the so-called stratified medicine

approach is more likely to be attainable.

Indeed we can already begin to sub-divide RA based on the presence of ACPA, and many

people consider ACPA positive and ACPA negative RA to be two distinct diseases [29-31].

Genetic studies to date have focused on ACPA positive individuals, although ACPA negative

individuals make up between 50 and 10% of the RA patients depending on their stage in

disease [32]. A recent study of ACPA negative patients identified associations in the HLA

region, accounted for by two amino acid positions, position 11 at HLA-DRB1 and position 9

at HLA-B, both of which have previously been associated with ACPA positive disease.

However, interestingly, the specific amino acid residues at each position conferring risk were

different, with a serine residue at position 11 HLA-DRB1 conferring risk of ACPA negative

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disease, but being protective against ACPA positive disease [33]. The high heritability of

ACPA negative disease [34] and the low number of associated loci [35] highlights the need

for larger studies to identify seronegative specific loci.

Stratified medicine could also Identify groups of patients more or less likely to respond to

treatment or develop an adverse reaction, for example, testing for thiopurine

methyltransferase (TMPT) gene polymorphisms before beginning azathioprine therapy is

now included in the prescribing guidelines from the British Society of Rheumatology (BSR)

and the US Food and Drug Administration (FDA). Individuals with genetic polymorphisms

in TMPT exhibit reduced/deficiency of the enzyme which metabolises 6-mercaptopurine, a

product of azathioprine, which can lead to toxicity [36]. Knowing the genotype at this locus

can help clinicians to determine the most appropriate course of treatment for the patient.

It is well known that in order to reduce joint damage and prevent disability it is important to

treat patients as soon as possible, indeed it has been shown that the introduction of early

effective treatment can markedly improve long term outcomes [37-39]. However there are

still no true reliably validated biomarkers of treatment response and therefore treatments are

prescribed in a process of first line and second line therapies representing a trial and error

strategy.

The majority of genetic studies in RA have focused on susceptibility however some work on

treatment response has been carried out. To date only two genetic associations with treatment

response have been identified, and neither hold enough predictive value to be informative in

clinic; Firstly a variant in the CD84 gene (which encodes leukocyte differentiation antigen

CD84) has been found to be predictive of response to etanercept (an anti TNF inhibitor) [40].

Secondly, the PDE3A-SLCO1C1 locus has been associated with response to the TNF

inhibitors etanercept, infliximab and adalimumab [41].

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7.0 Identifying RA target genes

Although over 100 susceptibility loci have been identified for RA the task remains to

determine their functional effects and how they contribute to disease before their potential

benefit as a therapeutic target can be fully realised.

For only a handful of RA loci have the functional variants of disease genes been fully

characterised, as has been found in many other complex autoimmune diseases, the majority

of susceptibility variants identified reside outside the coding regions of the genome, and

therefore their function is as yet unknown. Although variants may lie in intergenic or intronic

regions, they can still affect gene regulation, transcription or splicing events, and evidence

suggests that some regions of the genomes may be transcribed and not translated for example

regions encoding microRNA (miRNA) or long non coding RNAs (lncRNAs), which can in

turn affect gene regulation. An example of this is a non-coding variant associated with

plasma low density lipoprotein levels and myocardial infarction [42]. The SNP was found to

create a transcription factor binding site altering the expression of the SORT1 gene, which

directly altered plasma levels of LDL, demonstrating that non-coding variants can have a

direct impact on a disease phenotype.

Integration of GWAS results and gene expression data has shown that disease associated loci

are enriched for expression quantitative trait loci (eQTLS), suggesting they have a regulatory

role [43-46]. Many eQTLs act universally across tissues; however some are cell type specific

and require specific stimulatory conditions. It will be crucial to identify the cell type in

which disease associated loci are exerting their effects as many regulatory mechanisms are

cell type specific. The investigation of the innate immune cells, monocytes and dendritic

cells, in two separate studies, identified eQTLs that were only revealed after specific

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stimulations [47, 48]. One study stimulated monocytes with lipopolysaccharide (LPS), or

interferon γ (IFNγ) [47] , while a second stimulated dendritic cells with E.coli, LPS, influenza

or interferon-β (IFNβ) [48], and measured the resulting gene expression. The patterns of

eQTLs were not only cell type specific but interestingly were dependent on the cell

stimulation and were termed response QTLs (reQTLs). Both studies showed an enrichment of

reQTLs at GWAS loci. These studies add a further level of complexity to understanding the

regulatory role of disease associated variants, illustrating that not only must we identify the

critical cell types, but some eQTLs may only be revealed after identification of the relevant

stimulus.

It is well known that in the past disease associated regions of the genome were assigned

candidate genes based on the gene which is closest to the associated variants, or the gene in

the region with the most biologically plausible role given the phenotype. Candidate genes are

now assigned to variants based on biological omics evidence such as the presence of

regulatory regions, binding sites and chromatin state. However this designation is constrained

to some extent by our knowledge of genomics. It has been demonstrated through functional

studies that the gene closest to the associated genetic variant is not always the target; indeed

variants can have effect in both cis and trans on genes many megabases away. A region on

chromosome 16p13 has been associated with several autoimmune diseases including T1D

and MS, the disease associated variants are located within the c-type lectin domain family 16

(CLEC16A) gene, and were assumed to exert their function on the gene in which they reside.

However functional studies found that an intron of the CLEC16A gene, containing disease

associated variants, was acting as a regulatory sequence for the neighbouring gene

dexamethasone induced protein (DEXI). The disease alleles that conferred protection against

T1D and MS were correlated with an increase in the expression of DEXI in monocytes.

Furthermore chromatin conformation capture (3C) experiments determined a physical

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interaction between the promoter of DEXI and the intron of CLEC16A containing risk

variants, demonstrating that the DNA had looped out in order to allow these regions to be in

close proximity [49].

A recent study attempted to determine the target genes of disease associated variants using

chromatin conformation capture technology (Capture Hi-C) [50-52], linking disease SNPs

with the disease causing genes [53]. The 3d conformation of the genome means that it is

possible for sections of DNA many megabases apart, or even on different chromosomes, to

interact. Hi-C uses formaldehyde to fix the conformation of DNA in a specific cell at a

specific time point, the crosslinked DNA is then digested and re-ligated so that only

fragments of DNA which were covalently liked form ligation products. A biotin label is

incorporated into the ligation product allowing the products to be selected for and then

sequenced. Capture Hi-C, as used in the Martin et al study utilised this technique in two

complementary experiments, firstly using region capture targeting regions associated with

disease (rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA), type 1 diabetes (T1D)

and psoriatic arthritis (PsA)) and secondly promoter capture, capturing all gene promoters

within 500kb of each disease associated variant. This technique identifies physical

interactions between the selected disease associated fragments (region capture) or promoter

fragments (promoter capture) and the rest of the genome. The study investigated chromatin

interaction in both T and B cell lines and found that the majority of interactions were cell

type specific with only 20% found in both cell lines.

Several of the confirmed interactions further demonstrated the importance of functional

studies to determine where the SNP of interest is exerting its effect, as SNPs were shown to

interact with distant promoters, sometimes over a megabase away. For example SNPs

proximal to the EOMES gene associated with RA had a physical interaction spanning 640kb

with the promoter of AZI2 involved in NFKB activation. Additionally SNPs within the

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COG6 gene associated with RA and JIA also demonstrated long range interaction with the

promoter of FOXO1 over 1Mb away.

Interestingly the authors also found that loci from different AID which would typically be

classed as separate loci may act on the same promoter. Using the traditional method of

assigning the nearest candidate gene, loci where the lead disease associated SNPs from two

AID map some distance apart would therefore be assigned to different candidate genes.

SNPs associated with PsA located within the DENND1B gene physically interacted with a

region in the PTPRC gene over 1Mb away, involved in T and B cell signalling, which is also

associated with RA. A further example showed interaction between an enhancer region in

RAD51B containing RA associated variants and the promoter of ZFP36L1 containing JIA

associated variants, this interaction was only observed in B cells which is appropriate as

ZFP36L1 encodes a transcription factor associated with B cell differentiation to plasma cells.

The study has further demonstrated the complex regulatory landscape of the genome where

promoters can interact with many different enhancers and vice versa. The way we think

about disease associated loci and how we often speculate on their mechanisms must be

broadened to incorporate the bigger picture. SNPs do not need to be in close proximity or in

LD to expend their effects on the same gene. Further investigating the key findings of this

study will be pivotal in our understanding of the role of genetics in RA.

8.0 Conclusion

We have presented evidence to support the use of genetics in the development of new

therapeutics for RA. Only when the true target of a disease associated variant has been

determined and characterised within the relevant cell type under the appropriate stimulatory

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conditions can pathway based approaches to inform therapeutic target investigation be used

to best effect.

9.0 Expert opinion

Although huge advances have been made in RA genetics, a substantial amount of work is still

required in order to better understand how these disease associated loci contribute to

pathogenesis. Experiments investigating each disease associated variant in specific cell types

under different environmental conditions will be essential to determine the way in which such

variants influence the disease process. A thorough investigation of the regulatory role of

these loci should then be undertaken. This will be challenging as experiments in many cell

types will be required with data collected over time course experiments and under different

stimulatory conditions to reveal potential eQTLs and reQTLs. Identification of the relevant

cell type will allow further more complex functional experiments which can incur substantial

financial costs, including experiments such as RNA-seq, chromatin conformation capture

experiments, chromatin immunoprecipitation (CHIP) assays and luciferase reporter assays.

Once the causal genetic variant is known and we have determined the relevant cell type, the

regulated target gene and how the variant works mechanistically (e.g. up or down regulate),

the plausibility of the locus as a drug target should become apparent. We believe that our

knowledge of the genetic component of RA holds great potential for the development of new

therapeutics. Indeed we have discussed the advantages of having genetic evidence for a

target, and it is hoped that conducting these functional experiments will elucidate

mechanisms which will provide biological validation of a gene as a drug target, in so called

natural experiments. The aim will then be to increase collaborations with industrial partners

in order to take these candidates into clinical trials. An advantage of such natural

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experiments is that it should provide safer targets with a reduced likelihood of toxicity and

adverse events, with an increased likelihood of efficacy, which in turn should reduce attrition

rates. Okada et al have demonstrated, with proof of concept, the potential which lies within

pathway analysis and combining genetic and bioinformatics data[8]

Perhaps one of the most exciting oppourtunities on the horizon is the use of genome editing

technologies where it is relevant to introduce “genetic corrections” into precursor stem cells.

Clustered Regulatory Interspaced Palindromic Repeats (CRISPR) type II system is a

modified bacterial immune system used for genome editing. Which can target specific

modifications to a specific site in the genome allowing single base changes or large insertions

or deletions. Another use of CRIPSR is to use a nuclease dead Cas9 molecule known as dead

Cas9 (dCas9) which cannot cleave DNA. The dCas9 molecule can bind to DNA and can be

tagged with transcriptional activators or repressors, therefore targeting this to a promoter or

enhancer sequence can act to enhance or repress gene transcription. CRISPR has the

capability to introduce single base changes into the human germline or act as a novel

therapeutic to increase or decrease gene expression. There is enormous potential for this

technology to revolutionise the treatment or even cure monogenic diseases. For example

CRISPR has been used in mice with a dominant mutation in the Crygc gene which causes

cataracts. [54]. In humans, CRISPR has been used to correct a mutation in the CFTR gene

causing cystic fibrosis (CF) [55]. These examples provide proof of concept for gene

correction in single gene disorders. Although RA is a complex disease with many genetic

loci, we have shown that perturbation of a single locus is significant for treatment of the

disease. CRISPR is currently in its infancy and more understanding is needed about the

potential for off target effects and the specificity of the DNA repair stage. Currently CRISPR

provides an exciting new tool for in vitro studies aimed at understanding how disease

associated variants or combinations of variants work at a cellular level, but excitingly for

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many diseases it immediately offers revolutionary new opportunities for therapies the

possibilities of which only arise following the elucidation of the genetic mechanism

underpinning disease.

Article highlights box

Drug molecules with genetic support are more likely to progress to phase II clinical

trials.

Proof of concept for the utility of genetics in drug development lies within current

approved RA therapies targeting susceptibility loci; e.g. Tocilizumab and IL6R.

Determining the mechanistic action of GWAS hits and their contribution to disease

pathogenesis through functional experiments will be critical to identify potential

therapeutic targets.

Not only can genetics provide novel, or re-positioned targets for RA, by fully

understanding the mechanistic action of genetic variants we can model the effects of

modifying a target within patients. This then provides both the magnitude and

direction (agonist/antagonist) that the therapy must mimic to be effective.

In the future genome editing techniques such as CRISPR may represent an alternative

to therapeutic intervention.

Figure 1: Pipeline utilising genetics to identify therapeutic targets

Reference List

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1. Barrera P, van der Maas A, van Ede AE, et al. Drug survival, efficacy and toxicity of monotherapy with a fully human anti-tumour necrosis factor-alpha antibody compared with methotrexate in long-standing rheumatoid arthritis. Rheumatology (Oxford) 2002;41(4):430-9

2. Dixon WG, Hyrich KL, Watson KD, et al. Drug-specific risk of tuberculosis in patients with rheumatoid arthritis treated with anti-TNF therapy: results from the British Society for Rheumatology Biologics Register (BSRBR). Ann Rheum Dis 2010;69(3):522-8

3. Coenen MJ, Toonen EJ, Scheffer H, et al. Pharmacogenetics of anti-TNF treatment in patients with rheumatoid arthritis. Pharmacogenomics 2007;8(7):761-73

4. Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nat Genet 2015;47(8):856-60

*This paper provides support for the use of genetics in drug discovery

5. Cook D, Brown D, Alexander R, et al. Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 2014;13(6):419-31

6. Eyre S, Bowes J, Diogo D, et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat Genet 2012;44:1336-40

7. McAllister K, Yarwood A, Bowes J, et al. Identification of BACH2 and RAD51B as rheumatoid arthritis susceptibility loci in a meta-analysis of genome-wide data. Arthritis Rheum 2013;65:3058-62

8. Okada Y, Wu D, Trynka G, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 2014;%20;506(7488):376-81

** A large scale GWAS and meta analysis of RA, which utilises a bioinformatic pipeline

to combine GWAS results with biological and pathway data. The authors

conduct a high throughput drug screen to identify drug targets from GWAS

results providing proof of concept for the utility of genetics in drug discovery.

9. Barski A, Cuddapah S, Cui K, et al. High-resolution profiling of histone methylations in the human genome. Cell 2007;129(4):823-37

10. Messemaker TC, Huizinga TW, Kurreeman F. Immunogenetics of rheumatoid arthritis: Understanding functional implications. J Autoimmun 2015;64:74-81

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11. Li G, Diogo D, Wu D, et al. Human genetics in rheumatoid arthritis guides a high-throughput drug screen of the CD40 signaling pathway. PLoS Genet 2013;9:e1003487

* A systematic study of the CD40 locus associated with RA. The authors use eQTL and

pertubation studies to show direct correlation between associated variants, gene

expression and cell surface protein expression. A high throughput drug screen

identifies potential therapeutic inhibitors.

12. Nishimoto N. Interleukin-6 in rheumatoid arthritis. Curr Opin Rheumatol 2006;18(3):277-81

13. Smolen JS, Weinblatt ME, Sheng S, et al. Sirukumab, a human anti-interleukin-6 monoclonal antibody: a randomised, 2-part (proof-of-concept and dose-finding), phase II study in patients with active rheumatoid arthritis despite methotrexate therapy. Ann Rheum Dis 2014;73(9):1616-25

14. Shaw MH, Boyartchuk V, Wong S, et al. A natural mutation in the Tyk2 pseudokinase domain underlies altered susceptibility of B10.Q/J mice to infection and autoimmunity. Proc Natl Acad Sci U S A 2003;100(20):11594-9

15. Plenge RM, Scolnick EM, Altshuler D. Validating therapeutic targets through human genetics. Nat Rev Drug Discov 2013;12(8):581-94

16. Avorn J. The $2.6 billion pill--methodologic and policy considerations. N Engl J Med 2015;372(20):1877-9

17. Brown MS, Goldstein JL. Expression of the familial hypercholesterolemia gene in heterozygotes: mechanism for a dominant disorder in man. Science 1974;185(4145):61-3

18. Rader DJ, Cohen J, Hobbs HH. Monogenic hypercholesterolemia: new insights in pathogenesis and treatment. J Clin Invest 2003;111(12):1795-803

19. Abifadel M, Varret M, Rabes JP, et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet 2003;34(2):154-6

20. Cohen J, Pertsemlidis A, Kotowski IK, et al. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nat Genet 2005;37(2):161-5

21. Kotowski IK, Pertsemlidis A, Luke A, et al. A spectrum of PCSK9 alleles contributes to plasma levels of low-density lipoprotein cholesterol. Am J Hum Genet 2006;78(3):410-22

22. Cohen JC, Boerwinkle E, Mosley TH, Jr., Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med 2006;354(12):1264-72

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23. Maxwell KN, Breslow JL. Adenoviral-mediated expression of Pcsk9 in mice results in a low-density lipoprotein receptor knockout phenotype. Proc Natl Acad Sci U S A 2004;101(18):7100-5

24. Park SW, Moon YA, Horton JD. Post-transcriptional regulation of low density lipoprotein receptor protein by proprotein convertase subtilisin/kexin type 9a in mouse liver. J Biol Chem 2004;279(48):50630-8

25. Stein EA, Mellis S, Yancopoulos GD, et al. Effect of a monoclonal antibody to PCSK9 on LDL cholesterol. N Engl J Med 2012;366(12):1108-18

26. Gregory AP, Dendrou CA, Attfield KE, et al. TNF receptor 1 genetic risk mirrors outcome of anti-TNF therapy in multiple sclerosis. Nature 2012;488(7412):508-11

27. Scott IC, Seegobin SD, Steer S, et al. Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. PLoS Genet 2013;9:e1003808

* uses genetics to attempt to predict RA

28. Yarwood A, Han B, Raychaudhuri S, et al. A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk. Ann Rheum Dis 2013;

* uses genetics to attempt to predict RA

29. Daha NA, Toes RE. Rheumatoid arthritis: Are ACPA-positive and ACPA-negative RA the same disease? Nat Rev Rheumatol 2011;7:202-3

30. van der Helm-van Mil AH, Huizinga TW. Advances in the genetics of rheumatoid arthritis point to subclassification into distinct disease subsets. Arthritis Res Ther 2008;10:205

31. Barrera P, van der Maas A, van Ede AE, et al. Drug survival, efficacy and toxicity of monotherapy with a fully human anti-tumour necrosis factor-alpha antibody compared with methotrexate in long-standing rheumatoid arthritis. Rheumatology (Oxford) 2002;41(4):430-9

32. Viatte S, Plant D, Han B, et al. Association of HLA-DRB1 haplotypes with rheumatoid arthritis severity, mortality, and treatment response. JAMA 2015;313(16):1645-56

33. Han B, Diogo D, Eyre S, et al. Fine Mapping Seronegative and Seropositive Rheumatoid Arthritis to Shared and Distinct HLA Alleles by Adjusting for the Effects of Heterogeneity. Am J Hum Genet 2014;94:522-32

34. van der WD, Houwing-Duistermaat JJ, Toes RE, et al. Quantitative heritability of anti-citrullinated protein antibody-positive and anti-citrullinated protein antibody-negative rheumatoid arthritis. Arthritis Rheum 2009;60:916-23

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35. Viatte S, Plant D, Bowes J, et al. Genetic markers of rheumatoid arthritis susceptibility in anti-citrullinated peptide antibody negative patients. Ann Rheum Dis 2012;71:1984-90

36. Reuther LO, Vainer B, Sonne J, Larsen NE. Thiopurine methyltransferase (TPMT) genotype distribution in azathioprine-tolerant and -intolerant patients with various disorders. The impact of TPMT genotyping in predicting toxicity. Eur J Clin Pharmacol 2004;59(11):797-801

* an example of where genetics is used in clinic to inform treatment

37. Finckh A, Liang MH, van Herckenrode CM, de PP. Long-term impact of early treatment on radiographic progression in rheumatoid arthritis: A meta-analysis. Arthritis Rheum 2006;55(6):864-72

38. Scire CA, Verstappen SM, Mirjafari H, et al. Reduction of long-term disability in inflammatory polyarthritis by early and persistent suppression of joint inflammation: results from the Norfolk Arthritis Register. Arthritis Care Res (Hoboken ) 2011;63(7):945-52

39. Scire CA, Lunt M, Marshall T, et al. Early remission is associated with improved survival in patients with inflammatory polyarthritis: results from the Norfolk Arthritis Register. Ann Rheum Dis 2014;73(9):1677-82

40. Cui J, Stahl EA, Saevarsdottir S, et al. Genome-wide association study and gene expression analysis identifies CD84 as a predictor of response to etanercept therapy in rheumatoid arthritis. PLoS Genet 2013;9(3):e1003394

41. Acosta-Colman I, Palau N, Tornero J, et al. GWAS replication study confirms the association of PDE3A-SLCO1C1 with anti-TNF therapy response in rheumatoid arthritis. Pharmacogenomics 2013;14(7):727-34

42. Musunuru K, Strong A, Frank-Kamenetsky M, et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 2010;466:714-9

* an example of a non-coding variant directly affecting disease phenotype

43. Fairfax BP, Makino S, Radhakrishnan J, et al. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat Genet 2012;44:502-10

44. Lango AH, Estrada K, Lettre G, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 2010;467(7317):832-8

45. Nica AC, Montgomery SB, Dimas AS, et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet 2010;6:e1000895

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46. Nicolae DL, Gamazon E, Zhang W, et al. Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS. PLoS Genet 2010;6:e1000888

47. Fairfax BP, Humburg P, Makino S, et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 2014;343:1246949

* This study highlights the importance of identifying the cell type in which your variant

of interest exerts its effects showing the presence of response QTLs in monocytes.

48. Lee MN, Ye C, Villani AC, et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 2014;343:1246980

* This study highlights the importance of identifying the cell type in which your variant

of interest exerts its effects showing the presence of response QTLs in dendritic

cells.

49. Davison LJ, Wallace C, Cooper JD, et al. Long-range DNA looping and gene expression analyses identify DEXI as an autoimmune disease candidate gene. Hum Mol Genet 2012;21:322-33

* an example of a long range interaction, where disease suceptibility variants in

CLEC16A act on a neighbouring gene, DEXI.

50. Dryden NH, Broome LR, Dudbridge F, et al. Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C. Genome Res 2014;24(11):1854-68

51. Jager R, Migliorini G, Henrion M, et al. Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci. Nat Commun 2015;19:6178

52. Mifsud B, Tavares-Cadete F, Young AN, et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat Genet 2015;47(6):598-606

53. Martin P, McGovern A, Orozco G, et al. Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci. Nat Commun 2015;6:10069

** The first publication to systematically investigate the potential interaction between

the disease associated variants of four autoimmune diseases and their functional target

genes. The study has redefined the way candidate genes are allocated to genetic loci and

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demonstrated that loci from different disease can interact with a common regulatory

element.

54. Wu Y, Liang D, Wang Y, et al. Correction of a genetic disease in mouse via use of CRISPR-Cas9. Cell Stem Cell 2013;13(6):659-62

** CRISPR is used to correct a mutation inmice which causes cataracts. The

corrected allele was shown to be passes on to offspring. Provides proof of

concept for correction for single gene disorders using CRISPR.

55. Schwank G, Koo BK, Sasselli V, et al. Functional repair of CFTR by CRISPR/Cas9 in intestinal stem cell organoids of cystic fibrosis patients. Cell Stem Cell 2013;13(6):653-8

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