Hum Genet (2012) 131:1023–1037

DOI 10.1007/s00439-011-1137-z

REVIEW PAPER

Unraveling the genetic component of systemic sclerosis

José Ezequiel Martín · Lara Bossini-Castillo · Javier Martín

Received: 24 October 2011 / Accepted: 21 December 2011 / Published online: 5 January 2012© Springer-Verlag 2012

Abstract Systemic sclerosis (SSc) is a severe connectivetissue disorder characterized by extensive Wbrosis, vasculardamage, and autoimmune events. During the last years, thenumber of genetic markers convincingly associated withSSc has exponentially increased. In this report, we aim tooVer an updated review of the classical and novel geneticassociations with SSc, analyzing the Wrmest and replicatedsignals within HLA and non-HLA genes, identiWed by bothcandidate gene and genome-wide association (GWA) stud-ies. We will also provide an insight into the future perspec-tives and approaches that might shed more light into thecomplex genetic background underlying SSc. In spite ofthe remarkable advance in the Weld of SSc genetics duringthe last decade, the use of the new genetic technologiessuch as next generation sequencing (NGS), as well as thedeep phenotyping of the study cohorts, to fully characterizethe genetic component of this disease is imperative.

Introduction

Systemic sclerosis (SSc) is a multisystem life-threateningconnective tissue disorder characterized by extensive Wbro-sis in the skin and internal organs, vascular damage andimmune imbalance with autoantibody production (espe-cially anticentromere (ACA) and antitopoisomerase I(ATA) (Gabrielli et al. 2009). The commonly acceptedclassiWcation of SSc patients comprises two subtypes: thelimited cutaneous (lcSSc) subset and the diVuse cutaneous

(dcSSc) subset, an earlier and more progressing form of thedisease (LeRoy et al. 1988).

The reported prevalence for SSc, excluding some spatio-temporal clustering studies, ranges from 7/million to 700/million due to important inter-population diVerences (Ran-que and Mouthon 2010). In fact, SSc is more prevalent inthe US than in the European population, in which a north–south increasing gradient has been described (Ranque andMouthon 2010).

Systemic sclerosis, as other related autoimmune diseases(AIDs), aVects female more frequently than male (Voskuhl2011; Svyryd et al 2010), with a mean sex ratio around 3:1(Ranque and Mouthon 2010). This female-biased ratio inAIDs has been proposed to be inXuenced by diVerent fac-tors, i.e., X-chromosome dosage (Invernizzi 2009), skewedX-chromosome inactivation (Uz et al. 2008), hormonemilieu (Rubtsov et al. 2010), etc., but none of them hasbeen accepted as uniquely responsible for the deviation.

Ethnicity is also known to greatly aVect SSc prevalence.In fact, prevalence is higher in blacks than in white and Japa-nese populations, and Hispanics and Native Americans havemore severe disease outcome than whites (Mayes et al. 2003;Reveille 2003). Of special interest is the Choctaw population,a Native American tribe which bears the highest-describedethnic-speciWc prevalence (660/million) (Arnett et al. 1996).

Interestingly, twin-studies revealed a high concordanceof autoantibody production in monozygotic twins (Feghali-Bostwick et al. 2003), familial relative risks in SSc patientWrst-degree relatives is 13-fold higher than in the generalpopulation, and 15-fold higher in siblings (Arnett et al.2001), and multi-case SSc families are concordant for auto-antibody production and HLA-haplotypes (Assassi et al.2007). Hence, considering all supporting evidence for thecontribution of genetic factors to the disease, SSc was deW-ned as a complex disease in which genetic susceptibility

J. E. Martín · L. Bossini-Castillo · J. Martín (&)Instituto de Parasitología y Biomedicina López-Neyra, IPBLN-CSIC, Consejo Superior de Investigaciones CientíWcas, Parque Tecnológico Ciencias de la Salud, Avenida del Conocimiento s/n 18100-Armilla, Granada, Spaine-mail: martin@ipb.csic.es

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1024 Hum Genet (2012) 131:1023–1037

and diVerent environmental triggers interact both in thepathogenesis and in the prognosis of the disease (Gabrielliet al. 2009). Interestingly, some environmental factors havebeen related to high prevalence of SSc in certain regions,i.e., inhaled silica dust, exposure to solvents or infectiousagents (Mora 2009). However, as a result of the lack of areliable quantiWcation of exposure to diVerent environmen-tal insults and the phenotypic heterogeneity of the patients,the link between environmental factors and SSc develop-ment remains uncovered.

Due to the involvement of our group in both the pre-GWAS and GWAS phases of SSc genetic studies, thegreat scientiWc eVort carried out by diVerent groups, andthe growing knowledge on the genetic basis of SScgenetic susceptibility (Tables 1, 2; Figs. 1, 2) we weremotivated to oVer an updated review of the describedgenetic associations with SSc. In this report, we will ana-lyze the Wrmest genetic associations with SSc, identiWedby either candidate gene studies or in the latest SScgenome-wide association studies (GWASs) (Zhou et al.2009; Radstake et al. 2010; Allanore et al. 2011) and phe-notype-dependent GWAS analysis (Gorlova et al. 2011),and we will describe the classical and the novel reportedHLA associations. Finally, we will consider the futureperspectives and approaches for the research of the SScgenetic background.

Considering the immune-related functions of the estab-lished SSc genetic factors and in order to provide a didacticoverview of SSc non-HLA genetic factors, we will classifythem according to their possible role in the innate or adap-tive immune response in the SSc pathogenesis.

Prior to proceeding with the analysis of the genetic vari-ants associated with SSc, we would like to remark that, todate, the most relevant genetic risk factors for SSc areshared among diVerent AIDs (especially systemic lupuserythematosus, SLE). It is also worth mentioning that pre-viously reported association with an AID is one of the mostrelevant criteria for candidate gene selection, which proba-bly biased the initial reports towards shared autoimmunitypathways. Nevertheless, SSc patients are characterized byvascular damage, extensive Wbrosis, and autoimmuneevents, giving this disease its own entity. Furthermore, thefact of diVerent AIDs sharing genetic susceptibility factorsdoes not entail that the eVects of these loci are of equalmagnitude. In addition, it does not imply that a particularlocus is associated in the same direction (increased suscep-tibility or protection) in diVerent AIDs, or even that theobserved association corresponds to the same variants. Aclear example of this divergence in the associated variantsis the HLA region. While the association with HLA genesis a common feature of AIDs, no HLA class I gene has beenreported to be associated with SSc, and the alleles of HLAgene that have been reported to be associated with SSc,

especially the HLA-DPB1 locus, are diVerent from the onesassociated with SLE and other related-AIDs. Then, we pro-pose that hypothesis-free approaches (such as GWAS),increasing size of the studied cohorts, deeper phenotypiccharacterization of the patients and replication of somerecently reported associations with diVerent clinical fea-tures might provide further insights into the SSc speciWcgenetic component.

Non-HLA associations

Innate immune response

IRF5

Type I interferon (IFN) is a critical regulator in the innateimmune response and has pleiotropic functions in virtuallyall somatic cell types (Hall and Rosen 2010). Interestingly,IFN-inducible genes are similarly altered in SLE and in SSc(Assassi et al. 2010). Moreover, IFN regulatory factor 5(IRF5) gene polymorphisms have been associated with sus-ceptibility for SLE and other AIDs (Graham et al. 2007;Sigurdsson et al. 2005; Graham et al. 2006). Similarly, theIRF5 gene (rs20046040 and an rs377385-rs2004640-rs10954213 haplotype) was signiWcantly associated withSSc, the dcSSc subset and Wbrosing alveolitis (Dieude et al.2009a, 2010a). Interestingly, the IRF5 association with SScwas replicated in a Japanese cohort (Ito et al. 2009). Inaddition, the Wrst Caucasian SSc GWAS, which comprised2,296 SSc patients and 5,171 controls from the US andEurope, revealed rs10488631 IRF5 variant as the strongestnon-HLA genetic factor for SSc (Radstake et al.2010).Then, this association was conWrmed in an indepen-dent European SSc GWAS (Allanore et al. 2011).

MIF

Macrophage inhibitory factor, MIF, participates as aninnate and adaptive immune response mediator acting as aproinXammatory and immunoregulatory cytokine (Calan-dra and Roger 2003). MIF gene has been associated to sev-eral AIDs and is considered as a common autoimmunefactor (Gregersen and Bucala 2003). The Wrst report of MIFassociation with SSc tested two AID-related MIF promoterpolymorphisms, ¡173 MIF (rs755622) SNP and ¡794CATTn repeat, in US population (Wu et al. 2006). Thisstudy revealed a lower frequency of ¡173*C MIF allele inthe lcSSc group than in the dcSSc group of patients, andalso an association of C7, a high-expression haplotypecomprising both the ¡173*C MIF allele and seven repeatsof ¡794 CATT, with the lcSSc subset of patients (Wu et al.2006).

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Hum Genet (2012) 131:1023–1037 1025

Tab

le1

Non

-exh

aust

ive

list

of

mos

t rel

evan

t non

-HL

A g

enet

ic a

ssoc

iatio

ns f

ound

to d

ate

with

SSc

or

its s

ubph

enot

ypes

sus

cept

ibili

ty

aC

ontr

adic

tory

or

inco

nsis

tent

res

ults

exi

st a

mon

g th

e diV

eren

t stu

dies

whi

ch in

vest

igat

ed th

is g

ene’

s as

soci

atio

nb

The

eV

ect r

epor

ted

of r

s844

8644

in S

Sc is

pro

tect

ive

Gen

eG

enet

ic v

aria

tion

(s)

App

roac

hPo

pula

tion

(s)

Phen

otyp

e(s)

Rep

lica

ted

Ris

k/pr

otec

tion

Fun

ctio

nal r

elev

ance

Ref

eren

ce(s

)

CD

247

rs20

5662

6G

WA

SC

auca

sian

SSc

Yes

Pro

tect

ion

–R

adst

ake

etal

. 201

0;

All

anor

e et

al. 2

011;

D

ieud

e et

al. 2

010a

TN

IP1

rs22

3328

7, r

s495

8881

, rs

3792

783

GW

AS

Cau

casi

anS

Sc

No

Ris

k–

All

anor

e et

al. 2

011

RH

OB

rs13

0214

01, r

s342

070

GW

AS

Cau

casi

anSS

cN

oR

isk

–A

llan

ore

etal

. 201

1

IRF

5rs

2004

6040

, rs1

0954

213,

rs

2280

714,

rs1

0488

631,

rs

1253

7284

, rs4

7281

42,

rs37

5738

5, r

s109

5421

3

Can

dida

te g

ene

(conW

rmed

in G

WA

S)

Cau

casi

an, A

sian

dcS

Sc/

AT

A+

/FA

+Y

esR

isk

–R

adst

ake

etal

. 201

0;

All

anor

e et

al. 2

011;

D

ieud

e et

al. 2

009a

, 201

0a;

Ito

etal

. 200

9

STA

T4

rs75

7486

5, r

s118

8934

1,

rs81

7967

3, r

s101

8165

6,

rs67

5277

0, r

s382

1236

Can

dida

te G

ene

(conW

rmed

in G

WA

S)

Cau

casi

an, A

sian

SSc

Yes

Ris

k–

Rad

stak

e et

al. 2

010;

A

llan

ore

etal

. 201

1;

Rue

da e

tal.

2009

; T

such

iya

etal

. 200

9;

Gou

rh e

tal.

2009

; D

ieud

e et

al. 2

009b

BA

NK

1rs

1051

6487

, rs3

7331

97,

rs17

2665

94C

andi

date

gen

eC

auca

sian

dcSS

c/A

TA

+Y

esC

ontr

adic

tory

rs10

5164

87: n

on-s

ynon

ymou

s,

alte

red

gene

exp

ress

ion

and

prot

ein

loca

liza

tion

. rs

3733

197:

non

-syn

onym

ous

Rue

da e

tal.

2010

; D

ieud

e et

al. 2

009c

BL

Krs

2736

340,

rs1

3277

113

Can

dida

te g

ene

Cau

casi

an, A

sian

lcSS

c/A

CA

+Y

esR

isk

Ris

k H

aplo

type

rel

ated

to

B-c

ell d

isru

pted

gen

e ex

pres

sion

in

the

NF

kB s

igna

ling

pat

hway

Gou

rh e

tal.

2010

a;

Ito

etal

. 201

0;

Cou

stet

eta

l. 20

11b

TN

FSF

4rs

1234

314,

rs2

2059

60,

rs84

4864

4, r

s120

3990

4,

rs84

4665

, rs8

4464

8

Can

dida

te g

ene

Cau

casi

anSS

cY

esR

iskb

Ris

k hi

gh-e

xpre

ssio

n ha

plot

ype

rela

ted

to d

estr

ucti

on

of r

epre

ssor

bin

ding

sit

e

Gou

rh e

tal.

2010

b;

Bos

sini

-Cas

till

o et

al. 2

011b

TN

FA

IP3

rs50

2993

9C

andi

date

gen

eC

auca

sian

SSc

No

Ris

kL

inka

ge d

iseq

uili

briu

m w

ith

mis

sens

e SN

P rs

2230

926

Die

ude

etal

. 201

0b

MIF

rs75

5622

Can

dida

te g

ene

Cau

casi

anSS

cY

esR

isk

Hig

h-ex

pres

sion

hap

loty

peW

u et

al. 2

006;

B

ossi

ni-C

asti

llo

etal

. 201

1a

ITG

AM

rs11

4367

9C

andi

date

gen

eC

auca

sian

, H

ispa

nic

SS

cY

esa

Ris

kPr

otei

n st

ruct

ural

alt

erat

ion

Car

mon

a et

al. 2

011;

C

oust

et e

tal.

2011

a;

Ana

ya e

tal.2

006

PT

PN

22rs

2476

601

Can

dida

te g

ene

Cau

casi

anSS

cY

esa

Ris

kN

on-s

ynon

ymou

s: d

isru

pts

Lyp

-Csk

bin

ding

, sup

pres

sing

T

-cel

l act

ivat

ion

Bal

ada

etal

. 200

6;

Gou

rh e

tal.

2006

; D

ieud

e et

al. 2

008;

D

iaz-

Gal

lo e

tal.

2011

123

1026 Hum Genet (2012) 131:1023–1037

Table 2 Genetic associations found to date within the xMHC region, both HLA alleles and non-HLA genes within the xMHC

Gene Genetic variation Population(s) Phenotype Replicated Reference(s)

HLA-DPB1 HLA-DPB1*1301 Caucasian ATA+ Yes Muller-Hilke 2009; Simeon et al. 2009

HLA-DPB1*0402 Asian ACA+ No Gilchrist et al. 2001

HLA-DPB1*0901 Asian ATA+ No Gilchrist et al. 2001

HLA-DQA1 HLA-DQA1*0501 Black SSc No Muller-Hilke 2009

HLA-DQA1*0201 Black, Caucasian SSc No Muller-Hilke 2009

HLA-DQB1 HLA-DQB1*03(02) Asian, Caucasian ARA+ Yes Muller-Hilke 2009; Gilchrist et al. 2001

HLA-DQB1*05(01) Asian, Caucasian ACA+ Yes Muller-Hilke 2009; Arnett et al. 2010; Gilchrist et al. 2001; Rands et al. 2000

HLA-DQB1*0201 Caucasian ARA+ No Kuwana et al. 1999

HLA-DQB1*0202 Caucasian SSc No Muller-Hilke 2009

HLA-DQB1*0301 Black, Caucasian SSc No Muller-Hilke 2009

HLA-DQB1*0601 Asian ATA+ No Gilchrist et al. 2001

HLA-DQB1*26epi Caucasian ACA+ Yes Muller-Hilke 2009; Simeon et al. 2009; Kuwana et al. 1995

HLA-DRB1 HLA-DRB1*01(01) Caucasian, Asian ACA+ Yes Arnett et al. 2010; Rands et al. 2000

HLA-DRB1*04 Caucasian ACA+ No Simeon et al. 2009

HLA-DRB1*0401 Asian ARA+ No Gilchrist et al. 2001

HLA-DRB1*0404 Caucasian ARA+ No Muller-Hilke 2009

HLA-DRB1*0701 Caucasian SSc Yes Muller-Hilke 2009; Arnett et al. 2010

HLA-DRB1*08(02) Asian, Black ARA+ Yes Muller-Hilke 2009; Gilchrist et al. 2001

HLA-DRB1*08(04) Black, Caucasian ACA+ Yes Muller-Hilke 2009; Simeon et al. 2009

HLA-DRB1*11 Caucasian ARA+ No Muller-Hilke 2009

HLA-DRB1*11(04) Caucasian ATA+ Yes Muller-Hilke 2009; Arnett et al. 2010; Simeon et al. 2009; Kuwana et al. 1999

HLA-DRB1*1501 Caucasian SSc No Muller-Hilke 2009

HLA-DRB1*1502 Asian ATA+ No Gilchrist et al. 2001

NOTCH4 rs443198 Caucasian ACA+ No Gorlova et al. 2011

rs9296015 Caucasian ATA+ No Gorlova et al. 2011

PSORS1C1 rs3130573 Caucasian SSc No Allanore et al. 2011

Fig. 1 Cumulative number of genes to date which have been associated with SSc or any of its sub-phenotypes (Blue) and cumulative number of genetic associations which have been replicated (Red) from 2002 to 2011

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Hum Genet (2012) 131:1023–1037 1027

A recent replication study strengthened the under-repre-sentation of ¡173*C MIF allele in the lcSSc subgroup in apooled-analysis of diVerent SSc Caucasian Europeancohorts and highlighted the risk eVect of this allele for thedcSSc patients (Bossini-Castillo et al. 2011a).

ITGAM

The �M�2-integrin plays an important role in activation,adherence, and migration of leukocytes, phagocytosis, andneutrophil apoptosis (Fagerholm et al. 2006). The � subunitof this heterodimeric integrin, encoded by ITGAM (alsoknown as CD11b), is considered a genetic risk factor forSLE, but not for other AIDs (Nath et al. 2008). Veryrecently, Carmona et al. tested the association of ITGAMrs1143679 genetic variant, due to its functional relevance inSLE. This variant is a non-synonymous polymorphism inexon 3 which aVects the structure of the protein and is themost likely causal variant for the association of this genewith SLE (Carmona et al. 2011). This study showed signiW-cant association of this SNP with SSc and lcSSc, and atrend of association with the ACA positive subgroup ofpatients (Carmona et al. 2011).

A previous report on the inXuence of ITGAM in SScrevealed no evidence of signiWcant association of the SLE-associated ITGAM variants (Coustet et al. 2011a). How-ever, a novel meta-analysis including these two previousstudies and a new independent European ancestry SSccohort conWrmed ITGAM as a SLE and SSc shared geneticrisk factor (Anaya et al. 2006).

Adaptive immune response

STAT4

STAT4 is one of the seven members of the signal transduc-ers and activators of transcription (STAT) family, whichare critical for T-cell diVerentiation and signaling (Lim andCao 2006). Especially, STAT4 is implicated in regulation ofthe Th1/Th2 (Lim and Cao 2006) and Th17/Treg cytokinebalance (Mukasa et al. 2010) and it is induced by IL-12,IL-23, and type I IFNs (Watford et al. 2004; O’Shea 1997).

STAT4 rs7574865 SNP has been associated with severalAIDs (Martinez et al. 2008), and its association with SScwas described by our group and collaborators (Rueda et al.2009). In this study, rs7574865 T allele was associated with

Fig. 2 Firmest genetic associa-tions described in either SSc, its two common subphenotypes (limited or diVuse cutaneous subtypes) or the most frequent autoantibody proWles (anticen-tromere or antitopoisomerase I autoantibodies). Bigger font means stronger genetic associa-tion

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1028 Hum Genet (2012) 131:1023–1037

increased susceptibility to the lcSSc form of the disease(Rueda et al. 2009). These Wndings were replicated in anindependent Japanese population (Tsuchiya et al. 2009).Then, a multiethnic US study showed the association ofseveral STAT4 genetic variants with the global disease(Gourh et al. 2009). And the association of STAT4 with SScwas again supported by an additional study in a diVerentCaucasian population (Dieude et al. 2009b). In the WrstCaucasian GWAS, STAT4 was a top GWAS-level associa-tion which reaYrmed this gene as a key player in SSc(Radstake et al. 2010). SigniWcant association of this locuswas also observed in the most recent SSc GWAS (Allanoreet al. 2011).

Interestingly, the key role of STAT4 in inXammation hasbeen conWrmed by the analysis of diVerent SSc mousemodels in STAT4 knock-out mice (Avouac et al. 2011).These authors reported that stat4 ¡/¡ mice showed protec-tion against the inXammatory processes triggered in the ble-omycin SSc-model, but collagen deposition was notdecreased in tight-skin mice models lacking STAT4.

BANK1

BANK1 gene encodes a signaling molecule expressedexclusively in B cells, the B-cell scaVold protein with anky-rin repeats (BANK1) (Yokoyama et al. 2002). Due to theBANK1 association with diVerent AIDs, it constituted agood candidate gene for SSc genetic predisposition(Orozco et al. 2009; Chang et al. 2009; Guo et al. 2009;Kozyrev et al. 2008).

Hence, two studies have been carried out in independentSSc cohorts. A multicenter study in six European populationsand a North-American population tested the association withSSc of three BANK1 SNPs, which had been previously asso-ciated with SLE and AR (rs10516487, rs17266594, andrs3733197) (Rueda et al. 2010). These analyses revealed theassociation of two out of three SNPs with SSc (rs10516487and rs17266594), and the association of the three tested vari-ants with the dcSSc and ATA positive subgroups of patients(Rueda et al. 2010). Similarly, Diudé et al. revealed the asso-ciation of two functional polymorphisms (rs3733197 andrs10516487) and a protective haplotype comprising bothvariants with dcSSc (Dieude et al. 2009c).

BLK

BLK gene encodes Blk, the only member of the Src familykinases (SFKs) that is exclusively expressed in B cells andthymocytes, and not in mature ��T-cells (Dymecki et al.1990; Islam et al. 1995). Blk is a BCR-associated transduc-ing molecule, plays a key role in B-cell development (Saijoet al. 2003; Tretter et al. 2003) and in IL-17-producing ��T-cells (Laird et al. 2010). BLK was Wrst identiWed as a SLE

susceptibility gene and later as a common autoimmunity fac-tor (Hom et al. 2008; Borowiec et al. 2009; Orozco et al.2011). This candidate gene has been analyzed in diVerentSSc cohorts. In the Wrst report of BLK inXuence in SScgenetic predisposition, the combined analysis of CaucasianUS and European cohorts revealed the association of theminor allele of two genetic variants (rs13277113 andrs2736340) with increased susceptibility to SSc, and speciW-cally with the lcSSc and ACA+ subsets (Gourh et al. 2010a).Moreover, the risk haplotype comprising both minor alleleswas associated with disrupted gene expression of peripheral-blood circulating B cells, especially NFkB signaling pathway(Gourh et al. 2010a). Analysis of rs13277113 polymorphismin a Japanese cohort showed association of this marker withthe whole disease independently of the subtype or autoanti-body subgroup (Ito et al. 2010). A recent meta-analysisrevealed an association or trend of association ofrs13277113 A allele with SSc and with every subtype orautoantibody stratum, with the strongest signiWcant subtypeassociation observed in the dcSSc (Coustet et al. 2011b). Inaddition, an additive eVect of BLK and BANK1 in the dcSScwas revealed (Coustet et al. 2011b).

TNFSF4

The OX40 ligand (OX40L) was a potential autoimmunityfactor due to its implication in B-cell proliferation andT-cell proliferation and survival (Gough and Weinberg2009). Genetic-association studies showed the implicationof diVerent polymorphisms in the promoter of OX40Lencoding gene, TNFSF4 (tumor necrosis factor ligandsuperfamily member 4), in SLE, cardiovascular disease andSjögren syndrome propensity (Cunninghame Graham et al.2008; Wang et al. 2005; Nordmark et al. 2011). Recently,TNFSF4 was also identiWed and conWrmed as a SSc geneticfactor (Gourh et al. 2010b; Bossini-Castillo et al. 2011b).Gourh et al. reported a risk eVect of three TNFSF4 variants,rs1234314, rs2205960, and rs844648, with SSc and itsdiVerent clinical and serological outcomes (Gourh et al.2010b). And a protective eVect of rs844644 minor allele inthe whole disease and the diVerent subgroups was alsorevealed (Gourh et al. 2010b). The authors suggested thatthe more prominent variants in the North-American popula-tion included in this study were rs2205960 and rs855648(Gourh et al. 2010b). The role of TNFSF4 in SSc was latersupported in the European Caucasian population (Bossini-Castillo et al. 2011b). The role of the four polymorphismswith the whole disease and the eVects of the associationswere consistent (Bossini-Castillo et al. 2011b). However,this replication study showed a lcSSc and ACA positiverestricted association of the TNFSF4 variants. Moreover, anew risk haplotype for these subgroups was reported(Bossini-Castillo et al. 2011b).

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TNFAIP3

The TNFAIP3 (tumor necrosis factor alpha-induced protein3 or intracellular ubiquitin-editing protein A20) participatesin the negative feedback regulation of NFkB pathway andB cell survival and controls TNF-mediated apoptosis(Vereecke et al. 2009; Tavares et al. 2010). TNFAIP3genetic variability has been related to a number of AIDs,including SSc (Vereecke et al. 2009; Dieude et al. 2010b).The association of TNFAIP3 rs5029939 SNP (previouslyrelated with SLE) with SSc and more prominently with thedcSSc, Wbrosing alveolitis and pulmonary arterial hyperten-sion, was revealed in two Caucasian European cohorts(Dieude et al. 2010b).

PTPN22

The protein tyrosine phosphatase non-receptor 22 gene,PTPN22, encodes LYN, a lymphoid-speciWc phosphatasewhich negatively regulates the TCR-signaling (Mustelinet al. 2003). Genetic studies classically associated thePTPN22 R620W (C1858T, rs2476601) polymorphismwith autoimmunity, and recently also a novel PTPN22polymorphism, R263Q (G788A; rs33996649), has beenreported to have a protective role in SLE (Orru et al.2009). The R620W polymorphism has been indepen-dently analyzed in several European and US cohorts(Balada et al. 2006; Gourh et al. 2006; Dieude et al. 2008;Diaz-Gallo et al. 2011); and Wnally, meta-analysis of allthe studied populations conWrmed the association of the Tof this variant with increased SSc susceptibility (Lee et al.2011). The second autoimmune-related PTPN22 variant,R263Q, was tested by Díaz-Gallo et al. in diVerent Euro-pean Caucasian SSc populations, but no signiWcant asso-ciation was observed in the pooled analysis (Diaz-Galloet al. 2011).

The GWAS contribution

CD247

The Wrst GWAS in Caucasian SSc patients introduced anew susceptibility gene in the SSc genetic Weld, CD247.Remarkably, CD247 encodes the TCR-CD3 complex zetachain, CD3� (Call and Wucherpfennig 2004), and mightplay a critical role in the imbalanced immune response typ-ical of AIDs. CD247 reached GWAS-level signiWcanceassociation with SSc and was internally replicated in thisstudy (Radstake et al. 2010). Moreover, this gene had pre-viously been described as a genetic risk factor for SLE andother AIDs (Radstake et al. 2010). The role of CD247 inSSc has been recently supported by an independent replica-tion study (Dieude et al. 2011a).

TNIP1, PSORS1C1, and RHOB

A recent GWAS by Allanore et al. in Caucasian EuropeanSSc patients added three new genes in the growing puzzleof SSc genetic background, TNIP1, PSORS1C1 and RHOB(Allanore et al. 2011). TNIP1 encodes the TNFAIP3 inter-acting protein 1, which plays an important role in the regu-lation of the NFkB signaling pathway. DiVerent geneticvariants in this locus have also been associated with AIDs(Gateva et al. 2009). Interestingly, Allanore et al. showedthat TNIP1 expression was decreased in SSc Wbroblasts andskin; and TNIP1 levels correlated with in vitro collagendeposition (Allanore et al. 2011). RHOB (Ras homologgene family member B) locates in early endosomes and thepre-lysosomal compartment and had never been associatedwith autoimmune disease before (Adamson et al. 1992).The association of PSORS1C1 (psoriasis susceptibility 1candidate 1), in spite of its location near the HLA-DQB1locus, was independent from the HLA association in thisstudy. Although PSORS1C1 is a conWrmed psoriasis riskfactor, this GWAS is the Wrst report on SSc susceptibility(Nair et al. 2000). To date, none of these novel associationshas been replicated in independent SSc cohorts.

Novel associations yet to conWrm and non-replicated associations

To date multiple loci have been reported as SSc genetic riskfactors, but they have not yet been independently repli-cated. Some of these loci have been related to speciWc clini-cal or serological subset of patients, i.e., IRF8 and GRB10(associated with lcSSc), SOX5 and RPL41/ESYT1 (associ-ated with ACA positive and dcSSc, respectively) detectedthrough a sub-phenotype based GWAS analysis (Gorlovaet al. 2011); Hypoxia-inducible factor 1A, HIF1A (associ-ated with SSc but especially with lcSSc and ACA positivepatients) (WipV et al. 2009); IL2 (related to the lcSSc form)(Mattuzzi et al. 2007); IL2RA (associated with ACA posi-tive patients) (Martin et al. 2011); endothelin receptor typeB, ENDRB (associated with dcSSc subgroup) (Fonsecaet al. 2006); endothelin receptor type A, ENDRA (associ-ated with positive anti-RNA polymerase auto-antibodiestiters) (Fonseca et al. 2006); interferon regulatory factor 7,IRF7 (associated with ACA positive patients) (Carmonaet al. 2012). Other genes have been related to vascular dam-age and digital ulcers, i.e., Fibrinogen (Vettori et al. 2010);Stromal cell-derived factor 1, SDF-1/CXCL12 (Manettiet al. 2009). Analysis of the genetic networks underlyingmicrovascular changes in the diseases with scleroderma-type capillaroscopic pattern, such as mixed connective tis-sue disease, undiVerentiated connective tissue disease,overlap syndromes, and dermatomyositis, or even diabetesmellitus (in which microangiopathy is a speciWc complication)

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and cardiovascular disease (which has been recently relatedwith SSc) might reveal genetic association of yet unex-plored loci with SSc-related vascular-damage events.

Fibrosis is unquestionably a SSc hallmark, and althoughsome SSc well-established risk factors have been associ-ated with Wbrotic processes (i.e., IRF5, TNIP1, TNFAIP3),the genetic network of the SSc Wbrotic events is still miss-ing. Analysis of genetic polymorphisms associated withdiVerent connective tissue Wbrotic disorders, such as mor-phea and primary biliary cirrhosis, might increase ourunderstanding of the causes for SSc excessive collagendeposition. Since Wbrosis of the internal organs, especiallythe lungs, is a main clinical concern, genetic studies haverecently included stratiWcation by lung Wbrosis status of thepatients: CD226 (with reported signiWcant associationswith SSc, dcSSc, ATA positive patients and SSc-relatedWbrosing alveolitis); NLR family pyrin domain containing1, NLRP1 (risk factor for ATA positive status and Wbrosingalveolitis development in SSc patients), matrix metallopro-teinase 12, MMP12, and Toll-like receptor 2, TLR2 (bothassociated with dcSSc, ATA positive status and lung Wbro-sis) and IL1B (associated with restrictive lung physiology)(Dieude et al. 2011b, c; Manetti et al. 2010; Broen et al.2011; Beretta et al. 2007).

Due to its impact in the survival rates of SSc patients,there has been great interest in deWning genetic risk factorsassociated with pulmonary arterial hypertension, PAH.Recently, some loci have been proposed as PAH geneticmarkers, i.e., potassium voltage-gated channel shaker-relatedsubfamily member 5, KCNA5 (associated with SSc andPAH) (WipV et al. 2010); Endoglin, ENG (associated withPAH in SSc patients) (WipV et al. 2007); Urokinase-typeplasminogen activator receptor, uPAR (associated with PAHand also with digital ulcers appearance) (Manetti et al. 2011);NOS2 (associated with PAH) (Kawaguchi et al. 2006).

The MHC region

HLA genes

Since SSc is a disease with a strong autoimmune compo-nent and the production of autoantibodies is a major hall-mark, it is expected that genetic variations in the MHCgenetic region in chromosome 6 would play a major role inthe genetic component of this disease (Muller-Hilke 2009).Many studies have described diVerent genetic associationsof MHC alleles with SSc, being these the ones conferringthe highest risk (or protection). Furthermore, most associa-tions of MHC alleles have been found speciWcally with theautoantibody producing subgroup of patients (Arnett et al.2010; Simeon et al. 2009; Gilchrist et al. 2001; Kuwanaet al. 1999; Fanning et al. 1998; Rands et al. 2000). Never-

theless, no associations have been described to datebetween HLA class I alleles and SSc or its sub-phenotypes,and thus, all described association belong to the HLA classII genes. Also, population genetic heterogeneity has beenfound in the HLA alleles associated with the disease, e.g.,in the case of the association found between HLA-DRB1*0804 and the disease in black American populationfound by Arnett et al. which has not been observed in Cau-casian or Asian populations (Arnett et al. 2010).

In Table 2 are shown the diVerent HLA class II allelesassociated with SSc, although most of them are heteroge-neous and poorly or not replicated at all. To date, the mostsolid association with the overall disease are the HLA-DRB1*1104 and the HLA-DRB1*0701 haplotypes, caus-ing risk and protection, respectively (Arnett et al. 2010).Nevertheless, even these associations have been reported tobelong into autoantibody positive subgroups rather than theoverall disease (e.g., HLA-DRB1*1104), hence supportingthe hypothesis that MHC class II genes associationsobserved in the overall disease are residual from the auto-antibody positive subgroups. Furthermore, Kuwana et al.(1999) suggested that the associations within the xMHC ofHLA class II genes was exclusively conWned to the autoan-tibody proWles and not to the disease susceptibility, in con-trast to the Wndings in the more recent works (Arnett et al.2010; Simeon et al. 2009). Hence, most of the strongestassociations are found within the major autoantibody posi-tive subgroup of patients (Table 2). The most solid, repli-cated associations with the autoantibody subgroups are theHLA-DQB1*0501/HLA-DRB1*0101 haplotype with theproduction of ACA (Arnett et al. 2010; Simeon et al. 2009;Kuwana et al. 1999; Kuwana et al. 1995), HLA-DRB1*1104and HLA-DPB1*1301 with the production of ATA(Simeon et al. 2009; Gilchrist et al. 2001; Fanning et al.1998) and HLA-DQB1*0302 with the production of ARA(Arnett et al. 2010; Kuwana et al. 1999). It is noteworthythat the Wrm association of the absence of a polar aminoacidin the position 26 of the HLA-DQB1 molecule with thepresence of ACA which has been only found in populationsof Caucasian origin (Arnett et al. 2010; Gilchrist et al.2001; McHugh et al. 1994; Beretta et al. 2011).

Above all these, there is one main methodological prob-lem in these studies, which is the high cost of HLA typingat high resolution (four digits), which in turn can be tra-duced to low sample size. Indeed, the best well-poweredHLA alleles study to date included 1,300 cases and 1,000controls from three diVerent ethnicities (961 and 539 fromCaucasian origin) (Arnett et al. 2010). This, together withthe low frequency of most HLA alleles in the populations,leads to a decreased statistical power in most studies, andthis problem is accentuated when attending to autoantibodyproWles. In other complex diseases in which the HLA sys-tem also plays a role, studies in which SNPs (much faster

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and cheaper to study) extracted from GWASs are used totag HLA alleles have been really successful (de Bakkeret al. 2006; Monsuur et al. 2008).

Recently, a genome-wide study in the subgroups of SScdescribed SNP associations within the xMHC which tagsome of the HLA alleles previously described (Gorlovaet al. 2011). The most signiWcant Wndings in the xMHCregion in this work included the association of SNPsrs6457617 and rs9275390 tagging the association of HLA-DQB1*0501 with ACA and a haplotype of three SNPs(rs987870*C, rs3135021*A, and rs6901221*C) tagging theassociation of HLA-DPB1*1301 with ATA (See Table 2).

Nevertheless, the association of the variation in the HLAalleles and their tagging genetic variants is, at best, hetero-geneous, both in replication terms and in phenotype ofassociation terms (Table 2). More studies within the MHCregion with larger cohorts and better HLA typing for allHLA molecules are needed. Furthermore, even DNA andaminoacid sequence are needed since associations found inhigh resolution HLA alleles are most likely caused by somespeciWc positions in the HLA molecules, as in the case ofthe association of the shared epitope with RA (Gregersenet al. 1987).

Non-HLA genes

The xMHC region has a high density of genes, and manydo not belong functionally to either HLA class I, II, or IIIeven though they are located in the same region. Geneticsusceptibility found is such genes must be taken cautiouslyand importantly needs conWrmation from independentcohorts and research groups. The associations found withinthe xMHC region are likely to be dependent to those causedby the HLA alleles through the extensive haplotypic blockspresent. Hence, data on HLA class II alleles are necessaryin the same samples in order to perform statistical depen-dency tests and determine whether the association is condi-tioned to these. In SSc, two non-HLA associations havebeen found within the xMHC: PSORS1C1 (Allanore et al.2011) and NOTCH4 (Gorlova et al. 2011). Even thoughboth of them have interesting functional roles in the patho-genesis of SSc, and have been previously reported to beassociated with other AIDs (Valdes and Thomson 2009;Cheung et al. 2011; Rahman et al. 2005), none of theseresults have been independently replicated in SSc.

New perspectives

Phenotypic characterization

Starting from a homogenous and properly characterizedgroup of patients is essential in case–control genetic associ-

ation studies. However, SSc patients show high phenotypi-cal heterogeneity and relevant diVerences in terms ofinternal organ involvement, treatment regimens, and prog-nosis. Considering the increasing understanding of thepathophysiological pathways involved in the developmentof the disease and the diminished sensibility of the actualcriteria to detect certain subtypes (only 80% of the lcSScpatients), it is now evident that SSc classiWcation criteriashould be updated.

A joint international, collaborative initiative performedby the Scleroderma Clinical Trials Consortium (SCTC),the EULAR Scleroderma Trials and Research group(EUSTAR) and a group of international SSc expertsresulted in a list of 23 items of relevance for SSc classiW-cation. These criteria have been evaluated in terms ofvalidity of the items to measure what they purport to mea-sure, to discriminate SSc patients and mimicker patients,and to measure the relationship of the item to other mea-sures that are believed to be part of the same phenomenon(Johnson 2011). Since the results have been encouraging,the pruning of these initial set and its application mayhelp in the classiWcation of the patients into more homo-geneous groups.

Moreover, it has been suggested that autoimmune mech-anisms might predominate in the early stages of SSc, whileWbrosis and vasculopathy may be the leading causes of thelater stages (Ramos-Casals et al. 2010). Hence, the geneticcomponent of the advanced disease might be weaker andbetter prognostic classiWcation will be needed to deWne awider spectrum of speciWc genetic markers for vasculardamage and Wbrosis.

Moreover, as far as we know, there is no currently vali-dated activity and damage index accepted for SSc like forSLE (SELENA-SLEDAI, SLE-SDI) and there are no Wrmcriteria regarding the deWnition of some critical SSc clini-cal outcomes, such as pulmonary Wbrosis, pulmonary arte-rial hypertension, or vascular damage. It should be alsoconsidered that stratiWcation of the patients according tospeciWc clinical features will result in the study small sub-groups of the initial cohorts, which decreases the power ofthe studies to identify genetic association. Hence, greateVort on increasing the number of analyzed patientsshould be done.

In addition, it is well-known that more than one autoim-mune disease may coexist in a single patient (polyautoim-munity or co-autoimmunity) or in the same nuclear family(familial autoimmunity). Approximately 20–30% of SScpatients develop at least one additional AID (Caramaschiet al. 2007; Hudson et al. 2008; Avouac et al. 2010), whatcould inXate the association of SSc with common AID riskloci. Hence, controlling for the eVect of the combination ofparticular AIDs with SSc might reveal unique SSc geneticrisk factors.

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The use of better classiWcation criteria, together withestablished quantitative measures for clinical parametersand co-autoimmunity considerations might contribute todeWning clear SSc and subtype speciWc genetic markers.

Meta-GWAS

In most GWASs, one of the main pitfalls is the statisticalpower, with around 1–2,000 samples, low-frequency varia-tions, population heterogeneity, and the analysis of sub-phenotypes, the lack of power is a common issue. In thestudy of the genetic component of some autoimmune disor-ders is becoming popular the meta-analysis of two or morepublished GWASs in order to greatly increase the statisticalpower. This approach has been taken for example in RA(Raychaudhuri et al. 2008), leading to the discovery of Wvenovel susceptibility loci. In SSc, a far rarer disease thanRA, the recruitment of big cohorts for GWASs have beenmore challenging, but as of now, two GWAS have beenalready published in Caucasian and another one in Asianethnicity (Zhou et al. 2009), thus making possible the meta-GWAS approach for SSc.

Pan-autoimmunity meta-GWAS

There are increasing evidences of all the autoimmune disor-ders being one disease with diVerent clinical manifestationsdepending on the speciWc genetic variants and environmentalfactors involved. Following this line, recent studies haveapproached the analysis of the genetic component of AIDsfrom the angle of the meta-GWAS, that is, meta-analyzingGWAS performed in diVerent autoimmune disorders.Attending to the already known common autoimmunitymarkers, such as PTPN22, STAT4, IRF5, or CD247, it isexpected that the meta-GWAS based in diVerent disorderswould Wnd new variants associated to the diseases analyzed.Indeed to date, two such studies have been performed inautoimmune disorders, one comparing Crohn’s disease andceliac disease (Festen et al. 2011), and the other comparingceliac disease and rheumatoid arthritis (Zhernakova et al.2011). Among these two studies, a total of 18 new commonsusceptibility loci were identiWed, which is a signiWcantadvance in the study of the genetic component of diseasesalone and in the knowledge of autoimmunity with no newGWAS-level genotyping expense. In the case of SSc no pan-autoimmunity meta-GWAS have been performed, but thereare other autoimmune disorders in which GWAS have beenperformed and the comparison should be suitable due to thesimilarities between them, such as SLE. In fact, among themost relevant genetic associations with SSc only RHOB andPSORS1C1 have not been reported to be associated withSLE (Fig. 3, adapted from Delgado-Vega et al. (2010)).Hence, the pan-autoimmunity meta-GWAS approach might

provide a deeper mosaic of both shared and unique patholog-ical pathways for these closely related-AIDs.

Next generation sequencing: rare variants

As it has been pinpointed before (Frazer et al. 2009), com-mon genetic variation has a role in genetic susceptibility ofcomplex traits such as autoimmune disorders, expectingmany common variants associated with minor eVect of eachone and complex interactions. Nevertheless, after 6 years ofcommon variation analysis through GWAS, only a smallpercentage of the expected genetic component has beenexplained so far. Thus, with the advent of the next genera-tion sequencing (NGS) technologies, eyes are starting to Wxin the rare genetic variations (with less than 1% frequencyin populations) which may cause higher genetic risk toautoimmune disorders. In this path, the new generation ofsequencing technology is to be used to Wnd such rare, oreven private, genetic variants (Davey et al. 2011; Bansalet al. 2010). Despite the fact that NGS technologies willprovide great amount of novel rare variants, three mainissues will challenge the performance of reliable associa-tion test studies: management of the large number of identi-Wed genetic variants, a high proportion of sequence errors,and large proportion of missing data (as imputation of thesedata may not be appropriate).

Copy number variations

SNPs are an attractive candidate for genetic susceptibilitystudies for many reasons, but, as mentioned above, the com-plexity of the genetic component of autoimmune disordersexplained by common SNPs analyzed in current GWASs

Fig. 3 Established common and unique genetic risks factors for SScand SLE

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and candidate gene studies is little. Through NGS technolo-gies we might be able to address rarer SNPs, but also non-SNP genetic variation must be accounted for. In this regard,copy number variations (CNVs), are the second most fre-quent genetic variation in the human genome (Zhang et al.2009; Alkan et al. 2011). Indeed, CNVs have been tackledin the past in some autoimmune disorders, but in most caseswith little success due to the diYculties that the genotypingof these variations present. Further eVorts must be made,either through NGS or other methods, to address the role, ifany, of CNVs in autoimmune disorders and in SSc speciW-cally. In addition to SNPs and CNVs, there is a growingknowledge of the role of micro-RNAs (or miRNAs) as regu-lators of the immune system (O’Connell et al. 2010). Thus,genetic variations aVecting the target sites of this miRNAscould have a major impact in the immune homeostasis andplay a role in autoimmunity susceptibility.

Epigenetic modiWcations

In addition to SNPs, there is another source of phenotypicvariability known to inXuence complex traits: the epige-nome (Rakyan et al. 2011). The methylation, phosphoryla-tion, adenylation, and other modiWcations of histones andDNA are important players in many human traits, and areknown to play a major role in human disorders as theAngelman syndrome and the Prader–Willi syndrome.Indeed, there are studies that point to a role of epigeneticsin SSc (Lei et al. 2009; Wang et al. 2006). Indeed, epige-netic changes are supposed to aVect AIDs at diVerentlevels: dynamics of endogenous retrovirus, X-chromosomeinactivation defects, altered methylation of T- and B-cellsleading to autoimmunity, etc. (Brooks et al. 2010). Inter-estingly, the recent reports have established a linkbetween epigenetics and SSc involving, for example:skewed X-chromosome inactivation patterns, abnormalmethylation in CD4+ T-cells and repression of collagensuppression genes (Uz et al. 2008; Lei et al. 2009; Wanget al. 2006).

Gene–gene and gene–environment interactions

Due to methodological and technical limitations, quiteoften analyses are single-variant centered, albeit no geneticvariant acts alone in the raise of complex traits. Complextraits as autoimmune disease arise from the interaction ofmany genetic variants, epigenetic changes, and environ-mental variables, and thus, the genetic portion of these dis-eases cannot be addressed without gene–gene interaction(Cordell 2009) and gene–environment interaction analyses(Thomas 2010). As the number of described SSc suscepti-bility loci increases, the necessity of understanding howthese relate to each other and to environment does so.

Conclusion

Much has been advanced in the Weld of SSc genetics inthe past 10 years. Now, with the overwhelming advancesin sequencing technologies, bioinformatics, statistics,and the raw power of new computers, the gap betweenthe “existent complexity” and the “analyzable complex-ity” is being shortened in the understanding of complexhuman traits, and so in autoimmune disorders. With therecent GWASs and more than 40 susceptibility loci (tak-ing into account the non-replicated ones) (Fig. 1), inter-action, combination, and other more complex analysesare becoming feasible in SSc. According to the currentguidelines (Manolio et al. 2009), we propose that in orderto fully address all elements that give rise to complex dis-orders and contribute to increasing the knowledge of theyet unexplained SSc missing heritability, it is imperativethat the SSc genetic research addresses: (1) increasingsample sizes and focusing on subtypes and clinical char-acteristics with relatively small sample sizes (meta-GWAS); (2) inclusion of non-Caucasian populations inhigh-throughput genotyping studies; (3) comparisonamong related-AIDs (pan-autoimmunity meta-GWAS);(4) improvement of phenotyping by subtler and morequantitative or precise phenotypes; (5) capture largerproportion of variation in implicated genes (NGS); (6)enhancing the investigation of the X chromosome; (7)investigating gene–gene interactions; (8) investigatinggene–environment interactions: measure environmentrigorously and analyze it against GWA data: examinerare exposures, consider including GWA in monozygotictwins or migrant studies, conduct large prospectivecohort studies with GWA genotyping and reproduciblereliable exposure measures at baseline; (9) measuringepigenetic variants in appropriate tissues or cells; and(10) reliably measuring CNVs. These new strategies(added to functional studies) would contribute in bothunraveling new genetic risk factors and identifying thecausal variants for the known associations.

In spite of the advances in deciphering the pathogene-sis of SSc, and the association of some biomarkers (i.e.,autoantibody proWle) and genetic variants with speciWcsubsets and internal organ involvement, it is still chal-lenging to identify patients at risk for adverse outcomesand to determine which patients are responding to cur-rent therapies (Hummers 2010). Unraveling the geneticcomponent of SSc should be considered as the basis forearly diagnosis, promising drug targets, and for person-alized medicine (Hudson 2011). Hopefully, geneticscreening of the patients might provide valuable help inpatient care in terms of predicting the eYcacy of drugsand the appearance toxic eVects even before the treat-ment starts.

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