An ImmunoChip Study of Multiple Sclerosis Risk

An ImmunoChip study of multiple sclerosis riskin African Americans

Noriko Isobe,1,2 Lohith Madireddy,1 Pouya Khankhanian,1 Takuya Matsushita,1,3

Stacy J. Caillier,1 Jayaji M. More,1 Pierre-Antoine Gourraud,1 Jacob L. McCauley,4

Ashley H. Beecham,4 International Multiple Sclerosis Genetics Consortium, Laura Piccio,5

Joseph Herbert,6 Omar Khan,7 Jeffrey Cohen,8 Lael Stone,8 Adam Santaniello,1

Bruce A. C. Cree,1 Suna Onengut-Gumuscu,9 Stephen S. Rich,9 Stephen L. Hauser,1

Stephen Sawcer10,* and Jorge R. Oksenberg1,*

*These authors contributed equally to this work.

The aims of this study were: (i) to determine to what degree multiple sclerosis-associated loci discovered in European populations

also inuence susceptibility in African Americans; (ii) to assess the extent to which the unique linkage disequilibrium patterns in

African Americans can contribute to localizing the functionally relevant regions or genes; and (iii) to search for novel African

American multiple sclerosis-associated loci. Using the ImmunoChip custom array we genotyped 803 African American cases with

multiple sclerosis and 1516 African American control subjects at 130 135 autosomal single nucleotide polymorphisms. We con-

ducted association analysis with rigorous adjustments for population stratication and admixture. Of the 110 non-major histo-

compatibility complex multiple sclerosis-associated variants identied in Europeans, 96 passed stringent quality control in our

African American data set and of these, 470% (69) showed over-representation of the same allele amongst cases, including 21with nominally signicant evidence for association (one-tailed test P5 0.05). At a further eight loci we found nominally signicantassociation with an alternate correlated risk-tagging single nucleotide polymorphism from the same region. Outside the regions

known to be associated in Europeans, we found seven potentially associated novel candidate multiple sclerosis variants (P5 104),one of which (rs2702180) also showed nominally signicant evidence for association (one-tailed test P = 0.034) in an independent

second cohort of 620 African American cases and 1565 control subjects. However, none of these novel associations reached

genome-wide signicance (combined P = 6.3 105). Our data demonstrate substantial overlap between African American andEuropean multiple sclerosis variants, indicating common genetic contributions to multiple sclerosis risk.

1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA2 Division of Neurology, Department of Internal Medicine, Saga University Faculty of Medicine, Saga, Saga 849-8501, Japan3 Department of Neurological Therapeutics, Neurological Institute, Graduate School of Medical Sciences, Kyushu University,

Fukuoka, Fukuoka 812-8582, Japan4 John P. Hussman Institute for Human Genomics and The Dr John T Macdonald Foundation Department of Human Genetics,

University of Miami, Miller School of Medicine, Miami, FL 33136, USA5 Department of Neurology, Washington University School of Medicine, St. Louis, MO 63108, USA6 Department of Neurology, New York University School of Medicine, New York, NY 10016, USA7 Multiple Sclerosis Centre and The Sastry Foundation Advanced Imaging Laboratory, Department of Neurology, Wayne State

University School of Medicine, Detroit, MI 48201, USA8 Mellen Centre for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH 44195, USA9 Centre for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA10 Department of Clinical Neurosciences, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK

doi:10.1093/brain/awv078 BRAIN 2015: 138; 15181530 | 1518

Received November 20, 2014. Revised January 5, 2015. Accepted January 26, 2015. Advance Access publication March 28, 2015

The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.For Permissions, please email: [email protected]

by guest on June 15, 2015D

ownloaded from

Correspondence to: Jorge R. Oksenberg, PhD,

Department of Neurology, School of Medicine,

University of California,

San Francisco,

675 Nelson Rising Lane,

Room 215C,

San Francisco,

CA 94158, USA

E-mail: [email protected]

Keywords: multiple sclerosis; African Americans; ImmunoChip; linkage disequilibrium

Abbreviations: GWAS = genome-wide association study; IMSGC = International Multiple Sclerosis Genetics Consortium;MHC = major histocompatibility complex; SNP = single nucleotide polymorphism; WTCCC2 = Wellcome Trust Case ControlConsortium 2

IntroductionMultiple sclerosis is a chronic, inammatory disease of the

CNS and a common cause of neurological disability in

young adults (Hauser and Goodin, 2012). Its modest her-

itability reects complex polygenic effects and, most likely,

gene-environment interactions [Simon et al., 2011;

International Multiple Sclerosis Genetics Consortium

(IMSGC), 2013a; Sawcer et al., 2014]. The results from

genome-wide association studies (GWAS) have claried im-

portant aspects of multiple sclerosis pathogenesis and pro-

vided strong empirical support for a model of inheritance

driven primarily by allelic variants that are relatively

common in the general population [Oksenberg and

Baranzini, 2010; IMSGC and Wellcome Trust Case

Control Consortium 2 (WTCCC2), 2011]. The strongest

susceptibility signal genome-wide maps to HLA-DRB1 in

the class II region of the major histocompatibility complex

(MHC, 6p21.3) and explains up to 10.5% of the genetic

variance underlying risk. The HLA association implies that

mechanistically, multiple sclerosis clusters with other anti-

gen-specic autoimmune diseases, a hypothesis supported

by the observation that the non-MHC associated variants

appear to locate predominantly in or near genes inuencing

the function of the adaptive immune system (IMSGC and

WTCCC2, 2011). Interestingly, some of the non-MHC al-

lelic variants associated with multiple sclerosis have also

emerged in GWAS of other autoimmune diseases (IMSGC

and WTCCC2, 2011; Cotsapas et al., 2011), suggesting

that common underlying risk mechanisms might exist

across multiple immune-related conditions. To better

describe this overlap and rene the regions of interest in

susceptibility loci, a mega-consortium was established to

conduct cost-effective candidate loci association studies

across multiple autoimmune diseases using a common,

high-coverage single nucleotide polymorphisms (SNPs)

array known as the ImmunoChip (Cortes and Brown,

2011). The chip was designed in 2010 and 207728 vari-

ants were considered for inclusion, of which 196 524

passed manufacturing quality control (192 402 autosomal,

1595 X-linked, 1735 Y-linked, 791 pseudoautosomal and

one mitochondrial). The multiple sclerosis input to the con-

tent came from two sources: an early analysis of a well-

powered GWAS (IMSGC and WTCCC2, 2011) and a

meta-analysis of previously published smaller GWAS

(Patsopoulos et al., 2011).

Typing the ImmunoChip in a new independent data set

identied 48 novel multiple sclerosis susceptibility variants

with genome-wide signicance (IMSGC, 2013b). These re-

sults considerably enhanced the roster of validated risk loci

and demonstrated the discovery power of this array that

has been similarly effective in other autoimmune diseases

(Trynka et al., 2011; Cooper et al., 2012; Eyre et al., 2012;

Jostins et al., 2012; Juran et al., 2012; Liu et al., 2012;

Tsoi et al., 2012; Hinks et al., 2013). However, the utility

of this platform in non-Europeans remains to be addressed.

In addition, consistent with their longer evolutionary his-

tory, populations of African origin are known to have, on

average, characteristically smaller blocks of linkage disequi-

librium compared to populations with European ancestry

(Tishkoff and Kidd, 2004), implying that the study of

populations of African origin could help to narrow the re-

gions of interest and assist in identifying causative variants

(Buyske et al., 2012; Gong et al., 2013).

Notwithstanding difculties in surveillance, multiple

sclerosis is almost non-existent in black Africans and

early estimates suggested that the disease was signicantly

less prevalent in African Americans than in European

Americans (relative risk of 0.64; Wallin et al., 2004).

However, contemporary studies are challenging the long-

held belief that African Americans are at a reduced risk

for developing multiple sclerosis (Wallin et al., 2012;

Langer-Gould et al., 2013). Furthermore, compared with

whites, African Americans are more likely to have a more

severe disease course, which at least in part appears to be

genetically determined (Buchanan et al., 2004; Cree et al.,

2004, 2009; Boster et al., 2009; Kimbrough et al., 2014).

Here, we applied the ImmunoChip to a well-curated

African American multiple sclerosis data set to investigate:

(i) whether European multiple sclerosis-associated variants

ImmunoChip in African Americans with MS BRAIN 2015: 138; 15181530 | 1519


ownloaded from

are also associated with the disease in African Americans;

(ii) whether the smaller haplotype blocks, characteristic of

African American genomes, can contribute to better map-

ping of the functionally relevant variants driving the asso-

ciation; and (iii) whether the array can identify novel

multiple sclerosis-associated loci in African American

patients.

Materials and methods

Patients and control subjects

The core screening data set consists of 842 de-identied DNAsamples from African American cases and 498 AfricanAmerican controls. All multiple sclerosis subjects met estab-lished diagnostic criteria (McDonald et al., 2001; Polmanet al., 2011). Ascertainment protocols and clinical and demo-graphic characteristics have been summarized elsewhere (Creeet al., 2004; Oksenberg et al., 2004). All study participants areself-reported African Americans. Additionally, data from 1114African American control subjects were provided by theInternational Consortium on the Genetics of Systemic LupusErythematosus (SLEGEN), totalling 842 cases and 1612 con-trols. The University of California at San FranciscoInstitutional Review Board approved this study.

SNP genotyping and quality control

SNP genotyping was conducted using the ImmunoChip, anIllumina Innium HD custom array at the Wellcome TrustSanger Institute, Hinxton, Cambridge, UK for the cases andat the Centre for Public Health Genomics, University ofVirginia, Charlottesville, VA, USA for the controls. Standardquality control measures were implemented using PLINKv1.07 (Purcell et al., 2007). SNPs with missing rates higherthan 2%, Hardy-Weinberg proportion test P5105 in con-trols and P5108 in cases, and distinct missing proportionbetween cases and controls with P5 103 were excluded. Forthe further analysis, 130 248 autosomal SNPs remained,including 96 of 110 SNPs known to be associated inEuropeans (IMSGC, 2013b). Samples were excluded for miss-ing genotyping rates exceeding 2%, extreme autosomal hetero-zygosity of 43 standard deviations (SD), or excessive IdentityBy Descent (IBD) with PI_HAT 40.20.

Population stratification

Principal component (PC) analyses were used to assess ances-try and control for the effects of population stratication.Principal component analysis was conducted using prunedautosomal non-MHC SNPs with minor allele frequency41% and pairwise r25 0.1 with a window size of 100SNPs (25 408 SNPs). The scree plot indicated that PC1 ex-plained the vast majority of the variance in the AfricanAmerican data set (Supplementary Fig. 1A). According to theplot, PC1 were used to remove two outlier samples with thevalues outside 6 SD and all following association analyseswere conducted using PC1 as a covariate. PC1 values werehighly correlated with the previously reported percentage ofAfrican ancestry (r = 0.99, P52.2 1016) (Reich et al.,

2005; Isobe et al., 2013). When the PC1 components of indi-vidual samples were compared with each other, the controlsamples from SLEGEN had higher PC1 values compared tothe UCSF cases and controls (P = 2.53 1027 and1.77 1016, respectively), suggesting the proximity ofSLEGEN controls to African ancestry (Supplementary Fig.1B). Thus, to eliminate association signals derived from thedifferent population admixture levels between the two controlgroups, association was analysed between the two with PC1 asa covariate, which identied 113 SNPs with P-values 5103

to be removed. Finally, 130 135 autosomal SNPs remained forthe following analysis. Another principal component analysiswas conducted including samples from the 1000 GenomeProject (The 1000 Genomes Project Consortium, 2010) as areference, with commonly available autosomal SNPs prunedwith the same criteria as above (24 994 SNPs). From thisprincipal component analysis, an additional 22 sampleslocated far from the relevant reference populations wereremoved (Fig. 1). Ultimately, 803 African American multiplesclerosis cases and 1516 healthy African American controlsubjects remained for further analysis. Principal componentanalyses were performed using the R package SNPRelate(Zheng et al., 2012).

Association analysis

Following quality control analyses, association tests were con-ducted assuming the additive effect of the allele for the affect-ation status with PC1 as a covariate to control for populationstratication and admixture. First, the replication status of 96European multiple sclerosis SNPs (IMSGC, 2013b) was eval-uated using one-tailed tests. For each multiple sclerosis variant,the Cochrane Heterogeneity Q Test was also performed to testeffect size differences between African Americans andEuropeans. Additionally, SNPs with association P-values5104 locating outside 2Mb (1Mb centromeric and 1Mbtelomeric) anking the European multiple sclerosis-associatedSNPs were nominated as candidates for novel multiple scler-osis-associated variants in African Americans. All associationtests for genotyped SNPs were conducted using PLINK (v1.07)(Purcell et al., 2007). Power calculations were performed usingBioconductors GeneticsDesign package version 1.28.0(Warnes et al., 2010).

Fine mapping with imputation

For multiple sclerosis SNPs, regardless of evidence of replicationin African Americans, we assessed whether there was a morestrongly associated risk-tagging SNP in the region anking themultiple sclerosis SNP by testing for association amongst thegenotyped SNPs from these regions, including those assessableby imputation. The regions of multiple sclerosis SNPs weredened as the range of chromosomal positions where SNPsin linkage disequilibrium around the multiple sclerosisSNPs with r240.5 locate in the European populations of the1000 Genome Project. Imputation was performed usingIMPUTE2 (v2.3.0) (Howie et al., 2009) and the 1000Genomes Phase 1 integrated haplotypes (released in September2013) were used as a reference panel. In addition to thepreviously conducted quality control measures, those AT/GCSNPs with failed alignment were removed. Missing genotypesfor the genotyped SNPs were not imputed. To increase the

1520 | BRAIN 2015: 138; 15181530 N. Isobe et al.


ownloaded from

imputation accuracy we did not pre-phase genotypes and thenumber of Markov chain Monte Carlo iteration (-iter) was

increased to 60 with burnin 20. Association tests were con-

ducted with the frequentist test using SNPTEST (v2.5b) assum-ing an additive effect of each SNP (Marchini and Howie,

2010). Similar to the analysis in the genotyped SNPs, the PC1

component was included as a covariate in the analysis. SNPswere removed when they had poor imputation accuracy with

the INFO score obtained from IMPUTE2 lower than 0.7,

minor allele frequency 51%, or a Hardy-WeinbergEquilibrium test violation (P5105 in controls andP5 108 in cases). Regions were also considered replicatedwhen a neighbouring SNP had an association with false dis-

covery rate (FDR) P5 0.05 corrected with P-values of all theSNPs, both genotyped and imputed, within the region. To cal-

culate linkage disequilibrium parameters between the original

European multiple sclerosis SNP and the optimal risk-taggingSNP of African Americans, we used the 1000 Genome Project

population data set (The 1000 Genomes Project Consortium,

2010) for Europeans and our case-control data set for African

Americans. GTOOL (v0.7.5) (Freeman and Marchini, 2007)was used to convert post-imputed genetic data to PLINK-

format data to be ready to calculate linkage disequilibrium in

PLINK. Imputation was also performed for the linkage disequi-librium region of the candidate novel multiple sclerosis variants

using a maximum range of positions where proxy SNPs

(r24 0.5 in African Americans) of the candidate SNPs locateto capture the possible causative variants. ANNOVAR

(Wang et al., 2010), SNPnexus (Dayem Ullah et al., 2013),and RegulomeDB (Boyle et al., 2012) were used to annotateobtained variants. We plotted the association test results

using LocusZoom (Pruim et al., 2010) with neededmodications.

Replication study of unreportedmultiple sclerosis variants

For the candidate multiple sclerosis loci previously unreportedin Europeans, a replication study was conducted on an inde-pendent African American group consisting of 620 multiplesclerosis cases and 1565 controls by genotyping the topSNPs after imputation in the region of interest. SNP genotyp-ing was completed in the replication data set using predesignedand custom TaqMan SNP Genotyping Assays. TaqMan

SNP genotyping assays were conducted in 384-well plates onan ABI 7900HT Sequence Detection System using SDS 2.3software. Association P-values were provided with one-tailedtest. We also performed meta-analysis under a xed-effectsmodel with effect sizes and standards errors from the AfricanAmerican ImmunoChip (discovery) data set and the replicationstudy. Here a SNP was considered to have replicated when thereplication P5 0.05 (one-tailed test) and the combined P-valueof meta-analysis is more signicant than the discovery P-value.For the replication study, no adjustment of population admix-ture was conducted.

Results

Replication study of the multiplesclerosis-associated SNPs innon-MHC regions

We screened 130 135 autosomal SNPs in 803 African

American multiple sclerosis cases and 1516 African

American control subjects. Figure 2 shows a Circos plot

summarizing the results from this screen; as anticipated,

the strongest association was observed in the MHC

region on chromosome 6p21.3 (P = 2.75 108). InEuropeans 110 SNPs from 103 discrete loci outside the

MHC region have been established as risk variants in mul-

tiple sclerosis (IMSGC, 2013b); in our African American

screen, results passing stringent quality control were avail-

able for 96 of these SNPs (including rs3190930 a proxy

SNP for rs802734, Supplementary Table 1). Amongst these

96 we found that 470% (69/96) had the same allele over-represented in cases as in European multiple sclerosis cases,

a highly signicant excess of concordance (one-tailed bino-

mial test P = 1.07 105). For 21 of these 69 the excessfrequency in cases was nominally signicant (one-tailed test

P5 0.05) (Table 1); for all of these the effect sizes inAfrican Americans were statistically indistinguishable from

those observed in Europeans (heterogeneity test P40.05,Supplementary Table 1). Even including unreplicated mul-

tiple sclerosis SNP, the obtained effect sizes of multiple

sclerosis variants in African Americans were generally cor-

related with those in Europeans (Supplementary Fig. 2). To

estimate the level of concordance that might be expected if

effects were the same in African Americans as in

Europeans, we estimated for each of the 96 SNPs the

power of a study with 803 cases and 1516 control subjects

Figure 1 Principal component analysis of the African

American Immunochip data set with reference to samples

from the 1000 Genome Project. African American (AfAm)

case/control samples are shown as + and reference samples from

the 1000 Genome Project are shown as closed circles. YRI and

LWK = Africans; ASW = African Americans; CEU, TSI, FIN, GBR

and IBS = Europeans; MXL, PUR, CLM = Central-South Americans;

CHB, CHS and JPN = Asians.



ownloaded from

to identify nominally signicant association (one-tailed test

P50.05 or half the power to observe two-tailed testP50.1), assuming effect sizes equivalent to those seen inthe European screen (IMSGC, 2013b) and the risk allele

frequencies observed in our African American control

population (Supplementary Table 1). Across the 96 variants

we found that the average power was 18.6%, with values

ranging from 5.5% (at rs2028597) to 49.9% (at

rs6677309). Based on this average value we would antici-

pate seeing nominally signicant association (one-tailed test

P5 0.05) at between 12 and 24 SNPs with the same riskallele as in Europeans. Our observation of 21 such SNPs is

thus entirely consistent with these variants exerting equiva-

lent effect in African Americans and Europeans.

Figure 2 Circos plot of ImmunoChip in African Americans. The outermost track shows the autosomal chromosomes. The second track

indicates the genes closest to the non-MHC multiple sclerosis-associated variants. Gene names of replicated loci in African Americans with

identical SNPs as Europeans are shown in bold black, replicated loci with alternate variants are shown in bold blue, and unreplicated loci are

shown in grey. The replicated novel multiple sclerosis locus in African Americans is indicated in bold red. The innermost track indicates log10(P)

(two-tailed test) of association tests for each ImmunoChip SNP from African Americans (dark blue) and Europeans (light blue). The range of y-axis

is 08, excluding the peaks of Europeans with higher significance. The dark red line in the middle of the plot represents log10(P) = 4.



ownloaded from

Tab

le1

Rep

licate

d21

SN

Ps

inA

fric

an

Am

eri

can

so

ut

of

96

no

n-M

HC

mu

ltip

lesc

lero

sis

susc

ep

tib

ilit

yvari

an

tso

fE

uro

pean

s

Ch

rrs

IDP

osi

tio

nG

en

ea

Fu

ncti

on

RA

Eu

rop

ean

sbA

fric

an

Am

eri

can

sh

et.P

dP

ow

er

RA

FO

R(9

5%

CI)

PR

AF

RA

FO

R(9

5%

CI)

Pc

(co

nt.

)(c

ase

s)(c

on

t.)

(on

e-t

ailed

)

1rs

6677309

117080166

CD58

Intr

onic

A0.8

79

1.3

4(1

.271.4

1)

1.5

10

28

0.5

47

0.4

71

1.2

3(1

.091.3

9)

4.7

10

04

2.2

10

01

0.4

99

16

rs12927355

11194771

CLEC16A

Intr

onic

G0.6

78

1.2

1(1

.171.2

6)

8.2

10

27

0.7

87

0.7

47

1.2

8(1

.101.4

8)

5.3

10

04

4.8

10

01

0.4

21

19

rs11554159

18285944

IFI30

Exonic

G0.7

30

1.1

5(1

.111.2

0)

2.6

10

13

0.7

95

0.7

55

1.2

7(1

.091.4

7)

8.7

10

04

2.1

10

01

0.3

07

5rs

71624119

55440730

ANKRD55

Intr

onic

G0.7

55

1.1

2(1

.081.1

7)

2.7

10

09

0.9

43

0.9

35

1.4

0(1

.081.8

2)

6.1

10

03

1.0

10

01

0.1

16

7rs

917116

28172739

JAZF1

Intr

onic

C0.2

05

1.1

2(1

.071.1

6)

2.1

10

08

0.7

15

0.7

02

1.2

0(1

.041.3

7)

6.2

10

03

3.8

10

01

0.2

56

3rs

1920296

121543577

IQCB1

Intr

onic

C0.6

44

1.1

4(1

.111.1

8)

6.8

10

15

0.7

28

0.7

00

1.1

9(1

.041.3

6)

7.1

10

03

5.7

10

01

0.3

07

1rs

2050568

157770241

FCRL1

Intr

onic

G0.5

34

1.0

8(1

.051.1

2)

1.3

10

06

0.2

02

0.1

56

1.2

1(1

.031.4

2)

1.1

10

02

1.8

10

01

0.1

20

12

rs1800693

6440009

TNFRSF1A

Intr

onic

G0.3

98

1.1

4(1

.111.1

8)

6.9

10

16

0.3

99

0.3

62

1.1

6(1

.021.3

1)

1.1

10

02

8.2

10

01

0.3

29

10

rs2688608

75658349

(C10orf55)

Inte

rgenic

A0.5

49

1.0

7(1

.031.1

0)

6.4

10

05

0.3

07

0.2

54

1.1

7(1

.021.3

4)

1.3

10

02

2.2

10

01

0.1

26

10

rs2104286

6099045

IL2RA

Intr

onic

A0.7

22

1.2

1(1

.161.2

6)

7.6

10

23

0.9

39

0.9

33

1.3

3(1

.031.7

1)

1.4

10

02

4.8

10

01

0.2

23

18

rs7238078

56384192

MALT1

Intr

onic

A0.7

70

1.0

5(1

.021.1

0)

6.3

10

03

0.7

77

0.7

43

1.1

7(1

.021.3

5)

1.5

10

02

1.4

10

01

0.0

90

20

rs2248359

52791518

(CYP2

4A1)

Inte

rgenic

G0.5

99

1.0

7(1

.031.1

0)

9.8

10

05

0.3

99

0.3

54

1.1

5(1

.011.3

0)

1.7

10

02

2.9

10

01

0.1

40

3rs

2028597

105558837

CBLB

Intr

onic

G0.9

20

1.0

4(0

.981.1

1)

1.8

10

01

0.9

72

0.9

62

1.4

6(1

.022.0

8)

1.9

10

02

6.6

10

02

0.0

55

7rs

201847125

50325567

(IKZF1

)In

terg

enic

G0.6

95

1.1

1(1

.071.1

5)

2.9

10

08

0.9

01

0.8

93

1.2

4(1

.011.5

3)

1.9

10

02

2.9

10

01

0.1

38

1rs

7552544

101240893

(VCAM1)

Inte

rgenic

A0.5

58

1.0

8(1

.051.1

2)

3.7

10

06

0.8

10

0.8

00

1.1

8(1

.011.3

8)

2.0

10

02

2.8

10

01

0.1

31

3rs

2255214

121770539

(CD86)

Inte

rgenic

C0.5

18

1.1

1(1

.081.1

5)

1.7

10

10

0.7

26

0.7

12

1.1

5(1

.001.3

2)

2.4

10

02

6.5

10

01

0.2

25

7rs

1843938

3113034

(CARD11)

Inte

rgenic

A0.4

38

1.0

8(1

.051.1

2)

2.2

10

06

0.4

35

0.4

11

1.1

3(1

.001.2

9)

2.8

10

02

4.8

10

01

0.1

71

5rs

4976646

176788570

RGS1

4In

tronic

G0.3

40

1.1

3(1

.091.1

7)

1.0

10

12

0.5

58

0.5

37

1.1

3(1

.001.2

7)

3.0

10

02

9.6

10

01

0.3

14

11

rs7120737

47702395

AGBL2

Intr

onic

G0.1

45

1.1

3(1

.081.1

8)

7.6

10

08

0.2

86

0.2

66

1.1

4(0

.991.3

0)

3.2

10

02

9.3

10

01

0.2

75

3rs

9282641

121796768

CD86

Utr

5G

0.9

19

1.1

2(1

.051.1

9)

5.9

10

04

0.9

66

0.9

56

1.3

5(0

.971.8

8)

3.7

10

02

2.8

10

01

0.0

96

19

rs34536443

10463118

TYK2

Exonic

C0.9

51

1.2

8(1

.181.4

0)

1.2

10

08

0.9

96

0.9

94

2.1

2(0

.875.1

8)

4.9

10

02

2.7

10

01

0.0

80

Posi

tion

isbas

ed

on

hum

ange

nom

e19

and

dbSN

P137.

aW

hen

SNPs

loca

tein

terg

enic

,th

ecl

ose

stge

nes

are

show

nin

bra

ckets

.bR

esu

lts

of

Euro

pean

sar

eori

ginat

ed

from

IMSG

C,2013b.

cSN

Ps

with

asso

ciat

ionP-

valu

es5

0.0

5(o

ne-t

aile

dte

st)

were

show

n.

dC

och

rane

Hete

roge

neity

Qte

st.

AA

=A

fric

anA

meri

cans;

Chr

=ch

oro

moso

me;C

I=

confidence

inte

rval

;co

nt.

=co

ntr

ols

;het.

=hete

roge

neity;

RA

=ri

skal

lele

;R

AF

=ri

skal

lele

frequency

.



ownloaded from

Unsurprisingly, the two SNPs with the most signicant as-

sociation in the African Americans were those with the

strongest effects in Europeans, rs6677309 (CD58) andrs12927355 (CLEC16A).

Among the 21 replicated variants, two exonic multiple

sclerosis SNPs in IFI30 (rs11554159) and TYK2

(rs34536443), respectively, are predicted as probably

damaging. For rs34536443 in TYK2, the protective allele

drives T lymphocyte differentiation towards a Th2 pheno-

type (Couturier et al., 2011). This variant was also found

to be associated with juvenile idiopathic arthritis, primary

biliary cirrhosis, psoriasis and rheumatoid arthritis (Eyre

et al., 2012; Liu et al., 2012; Tsoi et al., 2012; Hinks

et al., 2013). Other replicated SNPs included rs1800693

in TNFRSF1A, which functionality mimics the effect of

TNF blocking drugs (Gregory et al., 2012), and

rs2104286 in IL2RA, which seems to increase the ratio

of soluble/membrane IL2RA and inhibits IL2 signalling

(Maier et al., 2009). Both rs1800693 (TNFRSF1A) andrs2104286 (IL2RA) accounted for 450% of posteriorprobability of association in Europeans (IMSGC, 2013b).

In a previous study using an overlapping, albeit larger

sample set of African Americans, we reported signicant

associations for 8 of 74 tested non-MHC multiple sclerosis

risk SNPs with a two-tailed test P 5 0.01 threshold,whereas (coincidentally) 21 variants exceeded the one-

tailed test P5 0.05 threshold (Isobe et al., 2013). Whencompared to this study, all of these variants had the same

direction of association despite the limited statistical power

of both studies (Supplementary Table 2). However, associ-

ations in this study did not reach statistical signicance in

seven loci due most likely to the relatively low effect sizes

of these variants. Additionally, lower minor allele frequency

of the updated multiple sclerosis SNPs compared to the

previous SNPs prevented replication for two loci

(MMEL1 and IRF8). On the other hand, the TYK2 locus

was replicated, this time with a different risk-tagging SNP

from our previous study (rs8112449, not replicated; Isobe

et al., 2013).

Given the possibility of allelic heterogeneity across ances-

tral groups, we reasoned that SNPs lying close to the

European lead SNP have increased prior odds even if not

in linkage disequilibrium. To look for such effects we

searched in the African American data set for nominally

associated SNPs within the intervals anking each of the

110 European SNPs (with boundaries of the anking inter-

vals dened by the most distant SNP in linkage disequilib-

rium with the European lead SNP; r24 0.5 in the 1000Genome data set of Europeans). We considered rst the

21 intervals containing European lead SNPs that showed

nominal evidence of association, and found more signi-

cantly associated SNPs in 20 (data not shown). Among

them only eight were in linkage disequilibrium (r240.5)with the European lead SNP. In the remaining 89 regions

( = 110 21), after correction for independent testing ateach locus, and setting a FDR of 0.05, we found eight

regions that contained SNPs showing nominally signicant

evidence for association (Table 2, Figs 2 and 3, and

Supplementary Fig. 3). One of the variants (rs1861842)

in the PVT1/MIR1208 locus shows modest linkage disequi-librium (r2 = 0.409) with the corresponding lead European

SNP (rs759648) in the European population but rather

little linkage disequilibrium with that SNP in African

Americans (r2 = 0.142), suggesting that these two SNPs

(rs759648 and rs1861842) tag the same signal in

Europeans while only rs1861842 is correlated with the

signal in African Americans, consistent with this SNP

being a better tag for the functionally relevant variant

(Fig. 3A).

Taking advantage of the unique linkage disequilibrium

patterns in the African American genome enabled us to

possibly narrow two additional disease-association regions,

MMEL1 at 1p36 and ZFP36L1 at 14q22-q24. In the

MMEL1 locus, the linkage disequilibrium block in

African Americans (r24 0.5) anking rs111375644(lowest P-value in African Americans) is 2 494 8162 728 455bp and is 3.5 kb smaller than linkage disequilib-

rium block in Europeans anking rs3748817 (lowest

P-value in Europeans), excluding LOC115110 from the

candidate disease-associated genes (Fig. 3B). Furthermore,

the narrow linkage disequilibrium region (r24 0.8) aroundrs3748817 spreads across 237 kb in Europeans and in-

cludes ve genes, whereas the size of the high linkage dis-

equilibrium region in African Americans for rs111375644

was 16 kb and includes a single gene (TNFRSF14). In the

ZFP36L1 locus, the linkage disequilibrium region in

African Americans around the most signicantly associated

SNP rs8011424, was 25.6 kb smaller (69 265 911

69 310210bp) than that in Europeans anking the estab-

lished multiple sclerosis SNP (rs2236262), highlighting the

upstream region of ZFP36L1 (Supplementary Fig. 3D).

However, as these variants are monomorphic or show no

signicant linkage disequilibrium with their respective

European lead SNP even in the European population,

they may represent additional risk alleles rather than suc-

cessful ne mapping of European signals. Lastly, in the

IRF8 locus, the optimal SNP in African Americans after

imputation (rs13333054) coincides with the previously re-

ported SNP in the 2011 GWAS (IMSGC and WTCCC2,

2011) rather than the ImmunoChip (IMSGC, 2013b)(Supplementary Fig. 3E).

In this African American cohort 4 of 96 European lead

SNPs showed nominally signicant evidence of association

with the alternate allele to that seen in Europeans

(Supplementary Table 1), raising the possibility that these

variants might be exerting different, even opposite effects in

this population. However, this number of seemingly oppos-

ite effects is consistent with that expected to result from

random sampling variation overwhelming genuine but

modest signals. In a study of this size and considering 96

variants, we would anticipate seeing up to ve apparently

reversed effects by chance alone. A similar low frequency of

apparently reversed signals was seen in our previous

African American study (Isobe et al., 2013) and also in



ownloaded from

Tab

le2

Alt

ern

ate

SN

Ps

inre

plicati

ng

kn

ow

nsu

scep

tib

ilit

yre

gio

ns

(A)

Eu

rop

ean

mu

ltip

lesc

lero

sis-

ass

ocia

ted

SN

Ps

(B)

To

pS

NP

inA

fric

an

Am

eri

can

sL

Din

fo(b

etw

een

Aan

dB

)e

rsID

(LD

regio

na)

Gen

eR

AA

fAm

rsID

cG

en

eR

AR

AF

OR

(95%

CI)

raw

pF

DRP

(No

.P

)dE

UR

AfA

m

OR

Pb

case

sco

nt.

R2

D

R2

D

rs3748817

(1:

24842721)

MMEL1

A1.0

52.4

10

01

rs111375644

TNFRSF14

(dis

t=

15959),

FAM213B

(dis

t=

6963)

G0.0

65

0.0

39

1.9

2(1

.432.5

8)

1.5

10

05

9.7

10

03

(664)

f

0.0

22

1.0

0

rs41286801

(1:

9268093373)

EVI5

A0.9

65.8

10

01

rs115126543

EVI5

(dis

t=

10883),

RPL5

(dis

t=

28750)

G0.0

22

0.0

08

3.0

6(1

.765.3

3)

7.9

10

05

2.4

10

02

(2697)

f

0.0

00

0.8

5

rs4679081

(3:

3297133014)

CCR4

(dis

t=

17080),

GLB1

(dis

t=

24617)

GN

Ars78553800

TRIM

71

(dis

t=

53040),

CCR4

(dis

t=

6255)

C0.9

56

0.9

26

1.5

8(1

.22-2

.06)

4.8

10

04

4.8

10

02

(268)

0.0

02

1.0

00.0

32

0.9

6

rs941816

(6:

36346-3

6384)

PXT1

G1.0

82.0

10

01

rs79281846

PXT1

A0.0

80

0.0

57

1.5

7(1

.222.0

3)

5.5

10

04

2.4

10

02

(98)

0.0

03

0.7

70.0

11

1.0

0

rs759648

(8:

129155129222)

PVT1

(dis

t=

45446),

MIR1208

(dis

t=

3417)

C0.9

67.3

10

01

rs1861842

MIR1208

(dis

t=

46226),

LINC00977

(dis

t=

1020053)

T0.3

92

0.3

28

1.3

0(1

.141.4

8)

8.5

10

05

4.3

10

03

(499)

0.4

09

0.9

90.1

42

0.6

3

rs2236262

(14:

6923369302)

ZFP36L1

A1.1

08.3

10

02

rs8011424

ZFP36L1

(dis

t=

9296),

ACTN1

(dis

t=

68584)

A0.2

53

0.2

14

1.3

5(1

.161.5

7)

8.4

10

05

1.7

10

02

(371)

0.0

19

1.0

00.1

24

1.0

0

rs35929052

(16:

8599486018)

IRF8

(dis

t=

38273),

LOC146513

(dis

t=

325553)

G1.0

63.7

10

01

rs13333054

IRF8

(dis

t=

54822),

LINC01082

(dis

t=

218754)

T0.2

55

0.2

04

1.3

6(1

.171.5

8)

6.0

10

05

8.7

10

03

(228)

0.0

36

1.0

00.0

08

1.0

0

rs1077667

(19:

66626676)

TNFSF14

G1.0

24.3

10

01

rs12150912

TNFSF14

(dis

t=

3775),

C3

(dis

t=

3472)

G0.6

51

0.6

29

1.2

4(1

.071.4

5)

5.2

10

03

2.5

10

03

(19)

0.3

44

0.9

90.0

71

1.0

0

aR

egi

ons

are

defined

asra

nge

ofposi

tions

where

SNPs

inlin

kag

edis

equili

bri

um

(r24

0.5

)w

ith

the

multip

lesc

lero

sis-

asso

ciat

ed

SNPs

inEuro

pean

softh

e1000

Genom

ePro

ject

loca

te.C

hro

moso

me

num

ber

and

genom

icposi

tions

(hum

ange

nom

e19,

kb)

are

show

n.

bO

ne-t

aile

dte

st.

cIm

pute

dSN

Ps

are

show

nin

ital

ics.

dFa

lse

dis

cove

ryra

teP-

valu

es

are

show

nw

ith

the

num

ber

of

SNPP-

valu

es

inlin

kag

edis

equili

bri

um

regi

ons

inbra

ckets

.eLin

kag

edis

equili

bri

um

info

rmat

ion

inEuro

pean

sori

ginat

ed

from

Euro

pean

dat

ase

tof

the

1000

Genom

ePro

ject

and

that

inA

fric

anA

meri

cans

are

from

our

Afr

ican

Am

eri

can

dat

ase

t.f T

op

SNP

inlin

kag

edis

equili

bri

um

regi

on

(B)

ism

onom

orp

hic

inEuro

pean

s.

AfA

m=

Afr

ican

Am

eri

cans;

Chr

=ch

rom

oso

me;C

I=

confidence

inte

rval

;dis

t=

dis

tance

;EU

R=

Euro

pean

s;LD

=lin

kag

edis

equili

bri

um

;M

AF

=m

inor

alle

lefr

equency

;N

A=

not

avai

lable

;O

R=

odds

ratio;R

A=

risk

alle

lein

Euro

pean

s;

SE=

stan

dar

derr

or.



ownloaded from

Figure 3 Narrowing in the causative region using African American data set. Comparative association plots for the loci of (A) PVT1/

MIR1208 and (B) MMEL1 of (i) African Americans after fine mapping with imputation; and (ii) the discovery data set of European ImmunoChip.

SNPs with the top association P-values are shown in purple with SNP IDs. For the plots of African Americans, genotyped SNPs are shown in

closed circles and imputed ones in closed triangles. Colours of the marks represent linkage disequilibrium (r2) with the top SNP in each

population. Chromosomal positions are based on human genome 19. Note differences in the scales of y-axis.



ownloaded from

our study of European multiple sclerosis variants in an

Indian data set (Pandit et al., 2011). Although such allele

ipping could theoretically have resulted from unusual

population-based differences in allele frequencies (Lin

et al., 2007; Zaykin and Shibata, 2008; Clarke and

Cardon, 2010), no such differences are apparent in the

1000 Genomes data set (The 1000 Genomes Project

Consortium, 2010), where the minor allele for these four

SNPs is the same in both populations.

Exploring potential associationoutside the established multiplesclerosis-associated loci

Recognizing the limited power of the data set, we never-

theless explored the evidence for association seen at SNPs

mapping outside the designated 110 multiple sclerosis loci

and outside the MHC region. In this analysis we identied

seven regions containing at least one SNP with P5 104

(Supplementary Table 3). Only one of these (rs11123495)

showed nominally signicant association in the European

ImmunoChip data set (P = 1.98 102) but the directionof the association was opposite from that in African

Americans (IMSGC, 2013b). When analysing these seven

regions in an independent replication cohort (620 African

American cases with multiple sclerosis and 1565 control

subjects), we found evidence of association for only one

variant (rs2702180 in SMG7) (one-tailed test P = 0.034,

Table 3). However, in a combined analysis across both

African American data sets, this SNP failed to reach

genome-wide signicance (P = 6.3 105).

DiscussionThe recent completion of the ImmunoChip project raised to

110 the number of non-MHC multiple sclerosis risk DNA

variants in Europeans (IMSGC, 2013b). In aggregate, the

proportion of the genetic variance accounting for disease

risk explained by these polymorphisms, including the

MHC, is roughly 27% (IMSGC, 2013b). Our main goal

was to assess the transferability of this updated multiple

sclerosis genetic map to African Americans. The number

of replicated variants (21 of 96) was within the range of

expectation given the power of our study, suggesting that

most, if not all of the multiple sclerosis risk SNPs dis-

covered in Europeans are also relevant in African

Americans and possibly in other non-white populations as

well. An excess of concordant direction for allelic effects of

the European multiple sclerosis SNPs in African Americans

is consistent with this generalization.

For several of the established loci even though we failed

to see evidence for signicant association with the

European lead SNP, we did nd evidence of association

with independent anking variants. Most of these new vari-

ants were uncorrelated with the European lead SNP in both Tab

le3

Rep

licati

on

of

can

did

ate

no

vel

mu

ltip

lesc

lero

sis-

ass

ocia

ted

loci

inA

fric

an

Am

eri

can

s

To

pS

NP

saft

er

imp

uta

tio

nR

ep

licati

on

stu

dy

Meta

-an

laysi

saR

egio

nal

top

SN

Pin

Eu

rop

ean

ICb

Ch

rrs

IDc

Po

siti

on

Gen

ed

RA

RA

FO

R(9

5%

CI)

Pe

RA

Ff

OR

Pg

OR

(95%

CI)

Prs

IDP

osi

tio

nR

AO

R(9

5%

CI)

Ph

case

sco

nt.

case

sco

nt.

1rs

2702180

183517971

SMG7

T0.7

40.7

01.3

1(1

.141.5

0)

1.3

10

04

0.7

40.7

11.1

53.4

10

02

1.2

3(1

.111.3

6)

5.3

10

05

rs789176

183512840

A1.0

5(1

.001.1

0)

5.6

10

02

2rs1438856

119570839

(EN1)

A0.3

20.2

61.4

0(1

.211.6

2)

6.2

10

06

0.2

50.2

80.8

39.9

10

01

1.0

9(0

.981.2

1)

1.1

10

01

rs11123495

119577577

A1.0

5(1

.011.0

9)

2.0

10

02

8rs

6986146

i6197182

(LOC100287015)

T0.2

60.2

01.3

5(1

.161.5

6)

6.3

10

05

0.2

10.2

20.9

08.9

10

01

1.1

2(1

.011.2

5)

3.6

10

02

rs6986146

6197182

G1.0

1(0

.981.0

4)

6.1

10

01

12

rs10848633

2316019

CACNA1C

G0.2

80.2

11.4

2(1

.231.6

4)

6.9

10

06

0.2

20.2

11.0

52.7

10

01

1.2

3(1

.11.3

7)

2.0

10

04

rs11062158

2325978

G1.1

0(1

.001.2

1)

4.1

10

02

12

rs10860696

101559080

SLC5A8

T0.2

90.2

41.4

0(1

.211.6

2)

7.2

10

06

0.2

80.2

80.9

85.9

10

01

1.1

7(1

.061.3

0)

2.7

10

03

rs12313149

101584223

C1.0

1(0

.971.0

6)

6.8

10

01

20

rs6047667

21827351

(PAX1)

G0.3

60.2

91.4

3(1

.231.6

6)

2.6

10

06

0.3

00.3

10.9

57.8

10

01

1.1

5(1

.041.2

7)

8.0

10

03

rs2180439

21853100

G1.0

1(0

.981.0

5)

5.0

10

01

Posi

tion

isbas

ed

on

hum

ange

nom

e19

and

dbSN

P137.Lis

tof

genoty

ped

top

SNPs

with

asso

ciat

ionP5

10

4ar

esh

ow

nin

Supple

menta

ryTab

le3.A

ssay

faile

din

one

of

the

seve

nca

ndid

ate

loci

.aFi

xed

modelis

applie

d.

bR

egi

ons

are

defined

asra

nge

of

posi

tions

where

SNPs

inlin

kag

edis

equili

bri

um

(r24

0.5

)w

ith

the

top

SNPs

afte

rim

puta

tion

inA

fric

anA

meri

cans

of

the

1000

Genom

ePro

ject

loca

te.

cIm

pute

dSN

Ps

are

show

nin

ital

ics.

dW

hen

SNPs

loca

tein

terg

enic

,th

ecl

ose

stge

nes

are

show

nin

bra

ckets

.eSN

PT

EST

was

use

dfo

rth

eas

soci

atio

nte

stin

the

impute

ddat

ase

tw

hile

PLIN

Kw

asuse

dfo

rth

ete

stin

the

genoty

ped

dat

ase

tbefo

reim

puta

tion.H

ere

,P-

valu

es

from

SNPT

EST

were

show

n.

f Ris

kal

lele

sar

eth

ose

inth

edis

cove

rydat

ase

t.gO

ne-t

aile

dte

st.

hW

hen

multip

leco

rrect

ion

was

conduct

ed

for

the

SNPs

avai

lable

ineac

hlin

kag

edis

equili

bri

um

regi

on

with

FDR

meth

ods,

the

FDRP-

valu

es

(no.SN

Ps

ineac

hlo

cus)

were

P=

7.7

10

01

(n=

133)

for

rs789176,P

=2.1

10

02

(n=

2)

for

rs11123495,P

=6.1

10

01

(n=

1)

for

rs6986146,P

=8.3

10

01

(n=

78)

for

rs11062158,P

=7.3

10

01

(n=

3)

for

rs12313149,an

dP

=5.0

10

01

(n=

1)

for

rs2180439.

i As

replic

atio

nof

the

top

SNP

afte

rim

puta

tion,rs

6986480

faile

d,th

eto

pge

noty

ped

SNP

was

use

dfo

rth

ere

plic

atio

nst

udy.

AA

=A

fric

anA

meri

cans;

Chr

=ch

rom

oso

me;C

I=

confidence

inte

rval

;co

nt.

=co

ntr

ol;

LD

=lin

kag

edis

equili

bri

um

;O

R=

odds

ratio;R

A=

risk

alle

le;R

AF

=ri

skal

lele

frequency

.



ownloaded from

the African American and European populations, suggest-

ing that these must be different effects although the possi-

bility of artefacts cannot be excluded. However, for one

variant (rs1861842 adjacent to MIR1208) we did see link-

age disequilibrium in the European population but not in

the African American population, indicating that the ori-

ginal disease risk signal has been successfully ne mapped

beyond what was possible within the European population.

Altogether, our data conrm that the potential of less ex-

tensive linkage disequilibrium structure present in African

Americans to aid in ne mapping is only likely to be ad-

vantageous if the study has adequate power to demonstrate

signicant genome-wide association.

The signal we identied in the region of MMEL1, locates

telomeric to MMEL1, between FAM213B and TNFRSF14,

whereas the European signal locates to the 18th intron of

MMEL1 itself. This increases the evidence supporting a

role for these other genes. FAM213B is associated with

biosynthesis of prostaglandin F2 and cyclooxygenase path-

way whereas TNFRSF14, a member of the TNF receptor

superfamily ts well within the activatory/inhibitory inam-

mation pathways mechanistically associated with multiple

sclerosis (IMSGC and WTCCC2, 2011). Mutations in

TNFRSF14 have also been associated with B cell lymph-

oma (Morin et al., 2011; Lohr et al., 2012), consistent with

an increasing appreciation that disordered B cell function is

intimately associated with multiple sclerosis pathogenesis

(Hauser and Goodin, 2012). Interestingly, a recent net-

work-based pathway analysis also ranked TNFRSF14

higher compared to MMEL1 as a multiple sclerosis suscep-

tibility locus (IMSGC, 2013a). Similarly, for EVI5 on

chromosome 1, which is a well-established risk locus with

relatively large effect size (odds ratio = 1.20) (IMSGC,

2013b), the reported top risk-tagging SNP in Europeans

locates at the 3 untranslated region (UTR) of EVI5,while in African Americans, the associated SNP

(rs115126543) identied in this study locates 11 kb up-

stream of the gene itself within transcription factor binding

sites and a DNase I hypersensitive site (Bernstein et al.,

2012), suggesting a role for transcriptional regulatory

mechanisms mediating risk. However, it is notable that

risk allele frequencies for both these variants are low and

therefore so is power. As neither is identied with clear

signicance additional studies will be required to conrm

the relevance of these observations.

The screen identied seven novel regions containing at

least one SNP with suggestive evidence of association, of

which only one (rs2702180 in SMG7 on chromosome 1)

replicated in an independent data set using relatively lenient

but predetermined replication criteria. SMG7 encodes a

protein that is essential for nonsense-mediated mRNA

decay, a process linked to autoimmunity (Bachmann

et al., 2006). Interestingly, the risk-tagging SNP was re-

ported to be associated with the expression level of

SMG7 in brain tissues (Gibbs et al., 2010) and in lympho-

blastoid cells (Stranger et al., 2007). In SLE, NCF2 adjacent

to SMG7 is reported to be associated with the disease

(Gateva et al., 2009; Cunninghame Graham et al., 2011;

Jacob et al., 2012) but a multi-ethnic study pointed out that

SLE-associated variants located in NCF2 were signicantly

associated with the expression of SMG7 (Kim-Howard

et al., 2014). Additional studies will be required to validate

the association in an independent African American data

set with larger sample size and to determine if the associ-

ation with this locus can also be observed in European

populations. The SMG7 region locates outside the highest

peak in a genome-wide admixture scan, which may par-

tially explain why the multiple sclerosis association with

SMG7/NCF2 was found in African Americans but not in

Europeans (Reich et al., 2005).

In conclusion, we show the extensive replication of

European multiple sclerosis variants in African Americans,

consistent with a shared genetic architecture for multiple

sclerosis susceptibility across these different populations.

However, as the ImmunoChip design was mainly based

on reference European populations (Cortes and Brown,

2011), the utility of the array to genotype non-European

populations, potentially lacking tag SNPs for some haplo-

types and the full range of cross-ancestral genetic plei-

otropy, remains unknown. Our results suggest that

ImmunoChip-like platforms have substantial potential to

ne-map regions of interest by taking advantage of differ-

ent haplotypic structures, but the need for very large

sample sizes and functional studies is still evident. Even

with arrays capable of tagging variation in both popula-

tions, very large sample sizes would be necessary to exclude

any modest effect of a European variant in an African

American population and vice versa. Trans-ancestral stu-

dies are also likely to help in the discovery of new genes

and pathways vital to disease susceptibility (Diabetes

Genetics Replication and Meta-analysis Consortium et al.,

2014). In addition, the clinical expression of multiple scler-

osis, including its severity, is known to have a genetic basis,

but to date no disease modiers have been convincingly

identied. The severe clinical course and treatment-resist-

ance typical of multiple sclerosis in African Americans

highlights an additional opportunity, i.e. to identify modi-

ers of disease severity and progression that could lead to

much-needed therapeutic opportunities.

Supplementary materialSupplementary material is available at Brain online.

AcknowledgementsThe authors thank the multiple sclerosis patients and

healthy controls who participated in this study. The au-

thors acknowledge the contributions of H. Mousavi and

R. Guerrero (UCSF) for sample processing and manage-

ment, and the genotyping teams at the Wellcome Trust

Sanger Institute, UK, the Cambridge NIHR Biomedical



ownloaded from

Research Centre, UK and the Centre for Public Health

Genomics, University of Virginia, Charlottesville, VA,

USA. This manuscript is dedicated to the memory of

Joseph Herbert, in recognition of his contributions and

leadership in multiple sclerosis research, and committed

dedication to people aficted with the disease.

FundingThis study is supported by grants from the National

Institute of Health (R01NS076492, R01NS046297 and

R01NS049477) and the UK Multiple Sclerosis Society

(898/08). Recruitment of study participants and sample ac-

quisition was supported by National Multiple Sclerosis

Society (RG2899-D11 and RC2 GM093080). N.I. was sup-

ported by Postdoctoral Fellowship for Research Abroad

from Japan Society for the Promotion of Science (JSPS)

and is currently a JSPS Research Fellow. L.P. is a Harry

Weaver Neuroscience Scholar of the National Multiple

Sclerosis Society (JF2144A2/1).

ReferencesBachmann MP, Bartsch H, Gross JK, Maier SM, Gross TF,

Workman JL, et al. Autoimmunity as a result of escape from

RNA surveillance. J Immunol 2006; 177: 1698707.

Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M.

An integrated encyclopedia of DNA elements in the human genome.

Nature 2012; 489: 5774.

Boster AL, Endress CF, Hreha SA, Caon C, Perumal JS, Khan OA.

Pediatric-onset multiple sclerosis in African-American black and

European-origin white patients. Pediatr Neurol 2009; 40: 313.Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA,

Kasowski M, et al. Annotation of functional variation in personal

genomes using RegulomeDB. Genome Res 2012; 22: 17907.

Buchanan RJ, Martin RA, Zuniga M, Wang S, Kim M. Nursing home

residents with multiple sclerosis: comparisons of African American

residents to white residents at admission. Mult Scler 2004; 10:

6607.Buyske S, Wu Y, Carty CL, Cheng I, Assimes TL, Dumitrescu L, et al.

Evaluation of the metabochip genotyping array in African

Americans and implications for ne mapping of GWAS-identied

loci: the PAGE study. PLoS One 2012; 7: e35651.

Clarke GM, Cardon LR. Aspects of observing and claiming allele ips

in association studies. Genet Epidemiol 2010; 34: 26674.

Cooper JD, Simmonds MJ, Walker NM, Burren O, Brand OJ, Guo H,

et al. Seven newly identied loci for autoimmune thyroid disease.

Hum Mol Genet 2012; 21: 52028.

Cortes A, Brown MA. Promise and pitfalls of the Immunochip.

Arthritis Res Ther 2011; 13: 101.

Cotsapas C, Voight BF, Rossin E, Lage K, Neale BM, Wallace C, et al.

Pervasive sharing of genetic effects in autoimmune disease. PLoS

Genet 2011; 7: e1002254.

Couturier N, Bucciarelli F, Nurtdinov RN, Debouverie M, Lebrun-

Frenay C, Defer G, et al. Tyrosine kinase 2 variant inuences T

lymphocyte polarization and multiple sclerosis susceptibility. Brain

2011; 134: 693703.

Cree BA, Khan O, Bourdette D, Goodin DS, Cohen JA, Marrie RA,

et al. Clinical characteristics of African Americans vs Caucasian

Americans with multiple sclerosis. Neurology 2004; 63: 203945.

Cree BA, Reich DE, Khan O, De Jager PL, Nakashima I, Takahashi T,

et al. Modication of Multiple Sclerosis Phenotypes by African

Ancestry at HLA. Arch Neurol 2009; 66: 22633.

Cunninghame Graham DS, Morris DL, Bhangale TR, Criswell LA,

Syvanen AC, Ronnblom L, et al. Association of NCF2, IKZF1,

IRF8, IFIH1, and TYK2 with systemic lupus erythematosus. PLoS

Genet 2011; 7: e1002341.

Dayem Ullah AZ, Lemoine NR, Chelala C. A practical guide for the

functional annotation of genetic variations using SNPnexus. Brief

Bioinform 2013; 14: 43747.

Diabetes Genetics Replication And Meta-analysis (DIAGRAM)

Consortium, Asian Genetic Epidemiology Network Type 2

Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes

(SAT2D) Consortium, Mexican American Type 2 Diabetes

(MAT2D) Consortium, and Type 2 Diabetes Genetic Exploration

by Next-generation sequencing in multi-Ethnic Samples (T2D-

GENES) ConsortiumGenome-wide trans-ancestry meta-analysis

provides insight into the genetic architecture of type 2 diabetes

susceptibility. Nat Genet 2014; 46: 23444.

Eyre S, Bowes J, Diogo D, Lee A, Barton A, Martin P, et al. High-

density genetic mapping identies new susceptibility loci for rheuma-

toid arthritis. Nat Genet 2012; 44: 133640.

Freeman C, Marchini J. GTOOL. 2007. http://www.well.ox.ac.uk/~

cfreeman/software/gwas/gtool.html

Gateva V, Sandling JK, Hom G, Taylor KE, Chung SA, Sun X, et al. A

large-scale replication study identies TNIP1, PRDM1, JAZF1,

UHRF1BP1 and IL10 as risk loci for systemic lupus erythematosus.

Nat Genet 2009; 41: 122833.

Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA,

Lai SL, et al. Abundant quantitative trait loci exist for DNA methy-

lation and gene expression in human brain. PLoS Genet 2010; 6:

e1000952.

Gong J, Schumacher F, Lim U, Hindorff LA, Haessler J, Buyske S,

et al. Fine Mapping and Identication of BMI Loci in African

Americans. Am J Hum Genet 2013; 93: 66171.

Gregory AP, Dendrou CA, Atteld KE, Haghikia A, Xifara DK,

Butter F, et al. TNF receptor 1 genetic risk mirrors outcome of

anti-TNF therapy in multiple sclerosis. Nature 2012; 488: 50811.

Hauser SL, Goodin DS. Multiple Sclerosis and other demyelinating

diseases. In: Longo DL, Fauci AS, Kasper DL, Hauser SL,

Jameson JL, Loscalzo J, editors. Harrisons principle of in-

ternal medicine. 18th edn. New York: McGraw Hill; 2012.

p. 3395409.

Hinks A, Cobb J, Marion MC, Prahalad S, Sudman M, Bowes J, et al.

Dense genotyping of immune-related disease regions identies 14

new susceptibility loci for juvenile idiopathic arthritis. Nat Genet

2013; 45: 6649.

Howie BN, Donnelly P, Marchini J. A exible and accurate genotype

imputation method for the next generation of genome-wide associ-

ation studies. PLoS Genet 2009; 5: e1000529.

International Multiple Sclerosis Genetics Consortium (IMSGC)

Network-based multiple sclerosis pathway analysis with GWAS

data from 15,000 cases and 30,000 controls. Am J Hum Genet

2013a; 92: 85465.International Multiple Sclerosis Genetics Consortium (IMSGC)

Analysis of immune-related loci identies 48 new susceptibility vari-

ants for multiple sclerosis. Nat Genet 2013b; 45: 135360.International Multiple Sclerosis Genetics Consortium (IMSGC),

Wellcome Trust Case Control Consortium 2 (WTCCC2). Genetic

risk and a primary role for cell-mediated immune mechanisms in

multiple sclerosis. Nature 2011; 476: 2149.Isobe N, Gourraud PA, Harbo HF, Caillier SJ, Santaniello A,

Khankhanian P, et al. Genetic risk variants in African Americans

with multiple sclerosis. Neurology 2013; 81: 21927.Jacob CO, Eisenstein M, Dinauer MC, Ming W, Liu Q, John S, et al.

Lupus-associated causal mutation in neutrophil cytosolic factor 2

(NCF2) brings unique insights to the structure and function of

NADPH oxidase. Proc Natl Acad Sci USA 2012; 109: E5967.



ownloaded from

Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY,

et al. Host-microbe interactions have shaped the genetic architecture

of inammatory bowel disease. Nature 2012; 491: 11924.

Juran BD, Hirscheld GM, Invernizzi P, Atkinson EJ, Li Y, Xie G,

et al. Immunochip analyses identify a novel risk locus for primary

biliary cirrhosis at 13q14, multiple independent associations at four

established risk loci and epistasis between 1p31 and 7q32 risk vari-

ants. Hum Mol Genet 2012; 21: 520921.

Kimbrough DJ, Sotirchos ES, Wilson JA, Al-Louzi O, Conger A,

Conger D, et al. Retinal damage and vision loss in African-

American multiple sclerosis patients. Ann Neurol 2014; 77: 22836.

Kim-Howard X, Sun C, Molineros JE, Maiti AK, Chandru H,

Adler A, et al. Allelic heterogeneity in NCF2 associated with sys-

temic lupus erythematosus (SLE) susceptibility across four ethnic

populations. Hum Mol Genet 2014; 23: 165668.

Langer-Gould A, Brara SM, Beaber BE, Zhang JL. Incidence of mul-

tiple sclerosis in multiple racial and ethnic groups. Neurology 2013;

80: 17349.

Lin PI, Vance JM, Pericak-Vance MA, Martin ER. No gene is an

island: the ip-op phenomenon. Am J Hum Genet 2007; 80:

5318.

Liu JZ, Almarri MA, Gaffney DJ, Mells GF, Jostins L, Cordell HJ,

et al. Dense ne-mapping study identies new susceptibility loci for

primary biliary cirrhosis. Nat Genet 2012; 44: 113741.

Lohr JG, Stojanov P, Lawrence MS, Auclair D, Chapuy B, Sougnez C,

et al. Discovery and prioritization of somatic mutations in diffuse

large B-cell lymphoma (DLBCL) by whole-exome sequencing. Proc

Natl Acad Sci USA 2012; 109: 387984.

Maier LM, Anderson DE, Severson CA, Baecher-Allan C, Healy B,

Liu DV, et al. Soluble IL-2RA levels in multiple sclerosis subjects

and the effect of soluble IL-2RA on immune responses. J Immunol

2009; 182: 15417.

Marchini J, Howie B. Genotype imputation for genome-wide associ-

ation studies. Nat Rev Genet 2010; 11: 499511.

McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP,

Lublin FD, et al. Recommended diagnostic criteria for multiple scler-

osis: guidelines from the International Panel on the diagnosis of

multiple sclerosis. Ann Neurol 2001; 50: 1217.

Morin RD, Mendez-Lago M, Mungall AJ, Goya R, Mungall KL,

Corbett RD, et al. Frequent mutation of histone-modifying genes

in non-Hodgkin lymphoma. Nature 2011; 476: 298303.

Oksenberg JR, Baranzini SE. Multiple sclerosis geneticsis the glass

half full, or half empty? Nat Rev Neurol 2010; 6: 42937.

Oksenberg JR, Barcellos LF, Cree BA, Baranzini SE, Bugawan TL,

Khan O, et al. Mapping multiple sclerosis susceptibility to the

HLA-DR locus in African Americans. Am J Hum Genet 2004; 74:

1607.

Pandit L, Ban M, Sawcer S, Singhal B, Nair S, Radhakrishnan K, et al.

Evaluation of the established non-MHC multiple sclerosis loci in an

Indian population. Mult Scler 2011; 17: 13943.

Patsopoulos NA, Esposito F, Reischl J, Lehr S, Bauer D, Heubach J,

et al. Genome-wide meta-analysis identies novel multiple sclerosis

susceptibility loci. Ann Neurol 2011; 70: 897912.

Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M,

et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the

McDonald criteria. Ann Neurol 2011; 69: 292302.

Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP,

et al. LocusZoom: regional visualization of genome-wide association

scan results. Bioinformatics 2010; 26: 23367.

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D,

et al. PLINK: a tool set for whole-genome association and popula-

tion-based linkage analyses. Am J Hum Genet 2007; 81: 55975.Reich D, Patterson N, De Jager PL, McDonald GJ, Waliszewska A,

Tandon A, et al. A whole-genome admixture scan nds a candidate

locus for multiple sclerosis susceptibility. Nat Genet 2005; 37:

11138.Sawcer S, Franklin RJ, Ban M. Multiple sclerosis genetics. Lancet

Neurol 2014; 13: 7009.

Simon KC, Munger KL, Ascherio A. XVI European Charcot

Foundation lecture: nutrition and environment: can MS be pre-

vented? J Neurol Sci 2011; 311: 18.

Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, et al.

Population genomics of human gene expression. Nat Genet 2007;

39: 121724.

The 1000 Genomes Project ConsortiumA map of human genome vari-

ation from population-scale sequencing. Nature 2010; 467:

106173.Tishkoff SA, Kidd KK. Implications of biogeography of human popu-

lations for race and medicine. Nat Genet 2004; 36: S217.

Trynka G, Hunt KA, Bockett NA, Romanos J, Mistry V, Szperl A,

et al. Dense genotyping identies and localizes multiple common and

rare variant association signals in celiac disease. Nat Genet 2011;

43: 1193201.

Tsoi LC, Spain SL, Knight J, Ellinghaus E, Stuart PE, Capon F, et al.

Identication of 15 new psoriasis susceptibility loci highlights the

role of innate immunity. Nat Genet 2012; 44: 13418.

Wallin MT, Culpepper WJ, Coffman P, Pulaski S, Maloni H,

Mahan CM, et al. The Gulf War era multiple sclerosis cohort: age

and incidence rates by race, sex and service. Brain 2012; 135:

177885.

Wallin MT, Page WF, Kurtzke JF. Multiple sclerosis in US veterans of

the Vietnam era and later military service: race, sex, and geography.

Ann Neurol 2004; 55: 6571.

Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of

genetic variants from high-throughput sequencing data. Nucleic

Acids Res 2010; 38: e164.Warnes G, Duffy D, Man M, Qiu W, Lazarus R. GeneticsDesign:

Functions for designing genetics studies. R package version 1.28.0.

2010. http://www.bioconductor.org/packages/release/bioc/html/Gen

eticsDesign.htmlZaykin DV, Shibata K. Genetic ip-op without an accompanying

change in linkage disequilibrium. Am J Hum Genet 2008; 82:

7946.

Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A high-

performance computing toolset for relatedness and principal compo-

nent analysis of SNP data. Bioinformatics 2012; 28: 33268.



ownloaded from

An ImmunoChip Study of Multiple Sclerosis Risk

Documents

Transcript of An ImmunoChip Study of Multiple Sclerosis Risk