Seven newly identified loci for autoimmune thyroid disease

Abstract

Autoimmune thyroid disease (AITD), including Graves' disease (GD) and Hashimoto's thyroiditis (HT), is one of the most common of the immune-mediated diseases. To further investigate the genetic determinants of AITD, we conducted an association study using a custom-made single-nucleotide polymorphism (SNP) array, the ImmunoChip. The SNP array contains all known and genotype-able SNPs across 186 distinct susceptibility loci associated with one or more immune-mediated diseases. After stringent quality control, we analysed 103 875 common SNPs (minor allele frequency >0.05) in 2285 GD and 462 HT patients and 9364 controls. We found evidence for seven new AITD risk loci (P < 1.12 × 10(-6); a permutation test derived significance threshold), five at locations previously associated and two at locations awaiting confirmation, with other immune-mediated diseases.

Full text

Seven newly identified loci for autoimmune thyroid
disease
Jason D. Cooper1,∗,
{
, Matthew J. Simmonds2,
{
, Neil M. Walker1, Oliver Burren1, Oliver J. Brand2,
Hui Guo1, Chris Wallace1, Helen Stevens1, Gillian Coleman1, Wellcome Trust Case Control
Consortium
{
, Jayne A. Franklyn3, John A. Todd1,
}
and Stephen C.L. Gough2,4,
}
1
Department of Medical Genetics, Cambridge Institute for Medical Research, Juvenile Diabetes Research Foundation/
Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, University of
Cambridge, Addenbrooke’s Hospital, Cambridge,
2
Oxford Centre for Diabetes, Endocrinology and Metabolism,
University of Oxford, Churchill Hospital, Oxford,
3
School of Clinical and Experimental Medicine, Institute of Biomedical
Research, University of Birmingham, Birmingham, and
4
NIHR Oxford Biomedical Research Centre, Churchill Hospital,
Oxford, UK
Received May 31, 2012; Revised August 6, 2012; Accepted August 20, 2012
Autoimmune thyroid disease (AITD), including Graves’ disease (GD) and Hashimoto’s thyroiditis (HT), is one
of the most common of the immune-mediated diseases. To further investigate the genetic determinants of
AITD, we conducted an association study using a custom-made single-nucleotide polymorphism (SNP)
array, the ImmunoChip. The SNP array contains all known and genotype-able SNPs across 186 distinct sus-
ceptibility loci associated with one or more immune-mediated diseases. After stringent quality control, we
analysed 103 875 common SNPs (minor allele frequency >0.05) in 2285 GD and 462 HT patients and 9364 con-
trols. We found evidence for seven new AITD risk loci (P<1.12 310
26
; a permutation test derived signifi-
cance threshold), five at locations previously associated and two at locations awaiting confirmation, with
other immune-mediated diseases.
INTRODUCTION
Genome-wide association (GWA) studies have had a dramatic
impact on susceptibility locus discovery and in addition, high-
lighted and extended the previously observed (1,2) commonal-
ity of loci between immune-mediated diseases, which has
ultimately resulted in the ImmunoChip project. As described
previously (3), the ImmunoChip project is a collaboration
between 12 immune-mediated disease groups [autoimmune
thyroid disease (AITD), ankylosing spondylitis (AS), celiac
disease (CeD), Crohn’s disease (CD), IgA deficiency, multiple
sclerosis (MS), primary biliary cirrhosis (PBC), psoriasis (PS),
rheumatoid arthritis (RA), systemic lupus erythematosus
(SLE), type 1 diabetes (T1D) and ulcerative colitis (UC)]
and the Wellcome Trust Case Control Consortium
(WTCCC). The main objective of the project was to provide
cost-effective genotyping of all known and genotype-able
single-nucleotide polymorphisms (SNPs) across confirmed
and distinct susceptibility loci from the 12 disease groups
(www.ImmunoBase.org). The result was the ImmunoChip, a
custom Illumina 200 K Infinium high-density array containing
186 distinct susceptibility loci (3), mainly derived from GWA
studies, which have been convincingly associated (required
P,5×10
28
) with one or more immune-mediated diseases.
In addition, the ImmunoChip contained wildcard SNPs provided
by the 12 disease groups and SNPs for replication found in non-
immunological disease GWA studies conducted by the WTCCC
(www.wtccc.org.uk/ccc2/wtccc2_studies.shtml).
The AITDs are caused by an autoimmune attack in which
antibodies directed against components of the thyroid gland
∗
To whom correspondence should be addressed. Tel: +44 1223762107; Fax: +44 1223762102; Email jason.cooper@cimr.cam.ac.uk
†
Joint first authors.
‡
A full list of the Wellcome Trust Case Control Consortium investigators involved in this study are included in the Supplementary Information.
}
Joint senior authors.
#The Author 2012. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/
licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is prop-
erly cited.
Human Molecular Genetics, 2012, Vol. 21, No. 23 5202–5208
doi:10.1093/hmg/dds357
Advance Access published on August 24, 2012

lead to either thyroid hormone excess and clinical features of
hyperthyroidism as seen in people with Graves’ disease (GD) or
thyroid gland damage, reduced thyroid hormone production and
clinical features of hypothyroidism as seen in people with
Hashimoto’s thyroiditis (HT). AITD shows a strong female pre-
ponderance with estimated incidence rates of GD in females as
80 per 100 000 person-years and in males as 8 per 100 000
person-years, and of HT in females as 350 per 100 000
person-years and in males as 8 per 100 000 person-years (4).
Although many regions of the genome have been reported
as AITD susceptibility loci, convincing evidence for associ-
ation has been limited to four regions: PTPN22/1q13.2,
CTLA4/2q33.2, HLA/6p21, and TSHR/14q31.1 (5). These sus-
ceptibility loci all fall within the antigen presentation/T-cell
receptor signalling pathway. In addition, there is mounting
evidence for a susceptibility locus in FCRL3/1q23.1 (5–7),
SCGB3A2/5q32 (8,9) and IL2RA/10p15.1 (10,11), and a
recent GWA and replication study in subjects recruited from
the Chinese Han population identified two novel loci at 4p14
and RNASET2-FGFR1OP-CCR6/6q27 (5).
Here, we used the ImmunoChip to further investigate the
genetic architecture of AITD in 2374 GD and 474 HT patients,
and 9953 controls.
RESULTS
After consideration of the number of AITD patients available,
we focused on susceptibility locus discovery using common
SNPs [minor allele frequency (MAF) ≥0.05] outside of the
HLA, which reduced the number of tests performed and con-
sequently, allowed us to use a less stringent significance
threshold. We adopted a permutation approach to correct the
0.05 level of significance for multiple testing and for correl-
ation between SNP genotypes, and obtained a threshold P¼
1.12 ×10
26
(see Materials and Methods). After stringent
quality control (see Materials and Methods), we analysed
103 875 common SNP genotypes in 2285 GD and 462 HT
patients, and 9364 controls. As the vast majority of our
AITD patients have been previously analysed in either discov-
ery or confirmation studies of the reported AITD susceptibility
loci, we initially used the AITD susceptibility loci on the
ImmunoChip as positive controls and as expected, found evi-
dence of association (P,1.12 ×10
26
and good genotype
signal intensity clusters; Supplementary Material, Table S1)
for PTPN22 (P¼9.7 ×10
223
), CTLA4 (P¼2.1 ×10
223
),
IL2RA (P¼2.7 ×10
27
) and TSHR (P¼1.3 ×10
238
in GD
patients/controls). We note that the AITD risk locus
SCGB3A2 was not on the ImmunoChip, but had previously
been associated in our AITD patients (P¼0.007; (8)). In add-
ition, we found some evidence of association for FCRL3 (P¼
1.1 ×10
25
in GD patients/controls; Supplementary Material,
Table S1) and found further evidence of association for the
recently reported RNASET2–FGFR1OP –CCR6/6q27
locus (5) (imm_6_167 338101 P¼1.6 ×10
27
;r
2
between
imm_6_167338101 and rs9355610 (5) in controls ¼0.72;
Table 1; Supplementary Material, Fig. S1) in subjects with
European ancestry. The recently reported 4p14 locus (5) was
not on the ImmunoChip.
We found evidence of association between AITD and seven
genomic locations (P,1.12 ×10
26
; Table 1). Five of these
new AITD risk loci were at locations previously associated
with at least one other immune-mediated disease (Table 1; Supple-
mentary Material, Figs S2–S6); three of which included the most
AITD-associated SNPs being located within MMEL1/1p36.32,
LPP/3q28 and BACH2/6q15 genes. However, only BACH2 has
a known autoimmune function as a regulator of nucleic acid-
triggered antiviral responses in human cells (12). The remaining
two new AITD risk loci were unconfirmed risk loci: rs1534422
at 2p25.1 is an unconfirmed T1D risk locus (T1D P¼2.1 ×
10
26
;(13); Supplementary Material, Fig. S7); and rs4409785 at
11q21 is an unconfirmed MS risk locus (MS P¼6.3 ×10
27
;
(14); Supplementary Material, Fig. S8).
We tested for heterogeneity in disease association before
combining the GD and HT patients (Table 1and Supplemen-
tary Material, Table S1). After allowing for multiple testing,
only the disease association of the TSHR/14q31.1 locus was
significantly different between the two patient groups. This
difference between GD and HT patients was expected as
the TSHR represents the primary autoantigen in hyperthy-
roidism of GD (15). As we focused on susceptibility loci
discovery using common SNPs (MAF .0.05) outside of
the HLA to reduce the number of tests performed, which
was necessary given the number of GD and HT patients
available in this study, we did not proceed to test whether
the most disease-associated SNP alone was sufficient to
model the association signal in AITD risk loci with dense
SNP coverage.
We examined the overlap between AITD and the other 11
immune-mediated diseases that provided susceptibility loci
to the ImmunoChip in turn, by testing whether the pattern of
AITD association across immune-mediated disease regions
differed according to whether those regions had previously
been associated with each specific immune-mediated disease
or not (Table 2;(16)). We restricted the analysis to
non-HLA SNPs from densely mapped regions associated
with at least one immune-mediated disease. We found that
SNPs within RA-associated regions were significantly (P¼
6.56 ×10
25
) more likely to be associated with AITD than
SNPs within regions not associated with RA. In addition,
there was marginal evidence of an overlap between AITD
and T1D-associated regions (P¼0.0327). In contrast, we
found that SNPs within regions associated with AS, UC and
PS showed significantly less evidence of association with
AITD than SNPs within regions known to be associated
with each disease (P¼3.27 ×10
24
, 1.69 ×10
23
and
4.08 ×10
23
, respectively). There was also marginal evidence
of an overlap between AITD and regions not associated with
CD (P¼0.0250). We note that an important caveat about
interpreting these test results is that the susceptibility loci
for each disease are incomplete and the extent of the incom-
pleteness varies between diseases. This prevents us, for
example, from drawing any conclusions that ‘AITD is less
like AS than UC’. However, for the majority of the immune-
mediated diseases listed in Table 2, we can assume that the
common loci with the largest effects are known and that sig-
nificant results here are true representations of AITD-disease
overlap.
Human Molecular Genetics, 2012, Vol. 21, No. 23 5203

Table 1. Single locus test evidence for the seven newly identified AITD non-HLA susceptibility loci and 6q27 on the ImmunoChip.
Karyotype
band
Most
disease-associated
SNP
Located within
the gene
MAF in
controls
Immune-mediated diseases previously
associated with regions (P,5×10
28
)
(reported SNP; r
2
with reported SNP in
controls)
2285 GD patients and 9364
controls
462 HT patients and 9364
controls
Heterogeneity in
disease association
2282 GD, 451 HT patients and
9364 controls
a
OR
(95% CI)
P-value OR (95% CI) P-value P-value OR (95% CI) P-value
1p36.32 rs2843403
C.T
MMEL1 (32 kb
upstream of
TNFRSF1)
0.362 CeD (26) (rs3748816/TNFRSF14-MMEL1;
r
2
¼0.99)
PBC (27) (rs3748816; r
2
¼0.99)
RA (28) (rs3890745/TNFRSF14-MMEL1;
r
2
¼0.90)
0.84 (0.79– 0.90) 7.94 310
27
0.97 (0.85–1.12) 0.696 0.0593 0.86 (0.81– 0.92) 5.63 ×10
26
2p25.1 rs1534422
A.G
216 kb upstream
of TRIB2
0.455 Unconfirmed T1D locus
b
1.16 (1.09– 1.24) 4.69 ×10
26
1.24 (1.08– 1.41) 1.64 ×10
23
0.394 1.17 (1.11– 1.25) 1.76 310
27
3q27.3/
3q28
rs13093110
C.T
LPP 0.452 CeD (26) (rs1464510; r
2
¼0.98) 1.18 (1.10– 1.26) 8.17 ×10
27
1.20 (1.05– 1.37) 7.09 ×10
23
0.797 1.19 (1.12– 1.26) 3.69 310
28
6q15 rs72928038
G.A
BACH2 0.177 CD (29) (rs1847472; r
2
¼0.39)
CeD (26) (rs10806425/BACH2-MAP3K7;
r
2
¼0.30)
T1D (21) (rs11755527; r
2
¼0.19)
c
1.21 (1.12– 1.32) 3.63 ×10
26
1.30 (1.11– 1.53) 1.36 ×10
23
0.417 1.23 (1.14– 1.32) 1.23 310
27
6q27 imm_6_167338101
A.C
FGFR1OP
(118 kb
upstream of
CCR6)
0.408 AITD (5) (rs9355610; r
2
¼0.72)
CD (30) (rs2301436/CCR6;r
2
¼0.61)
RA (31) (rs3093023/CCR6;r
2
¼0.25
Vitiligo (32) (rs2236313; r
2
¼0.22)
0.84 (0.79– 0.90) 3.30 ×10
27
0.88 (0.76–1.00) 0.0564 0.575 0.85 (0.80– 0.90) 1.64 310
27
11q21 rs4409785
T.C
0.173 Unconfirmed MS locus
d
1.21 (1.11– 1.31) 5.37 ×10
26
1.34 (1.14– 1.57) 3.54 ×10
24
0.234 1.23 (1.14– 1.33) 7.69 310
28
12q12 rs4768412
C.T
PRICKLE1 0.363 CD (30) (rs11175593/LRRK2-MUC19)
d
1.19 (1.11– 1.27) 3.30 310
27
1.00 (0.88– 1.15) 0.949 0.0234
e
1.16 (1.09– 1.23) 3.56 ×10
26
16p11.2 rs57348955
G.A
83 kb upstream
of ITGAM
0.396 SLE (33) (rs1143679/ITGAM;r
2
¼0.056)
T1D (13) (rs4988084/IL27)
f
0.83 (0.77– 0.89) 3.76 310
28
0.91 (0.80–1.05) 0.188 0.190 0.84 (0.79– 0.89) 5.13 ×10
28
The reported SNPs have a P-value of ,1.12 ×10
26
(see Materials and Methods) and were the most disease-associated SNP in the region with good genotype signal intensity plots (www.ImmunoB ase.org). For each SNP, the most
significant P-value is shown in bold. Note that the same controls were analysed in each analysis. AITD, autoimmune thyroid disease; MAF, minor allele frequency; HT, Hashimoto’s thyroiditis; GD, Graves’ disease; OR, Odds ratio for
the minor allele; CI, confidence interval; CD, Crohn’s disease; CeD, celiac disease; PBC, primary biliary cirrhosis; and SLE, systematic lupus erythematosus.
a
We excluded 14 second degree relatives or closer between disease groups (see Materials and Methods).
b
rs1534422 at 2p25.1 is an unconfirmed T1D risk locus (T1D P¼2.1 ×10
26
;(13)).
c
We used CEU data from the 1000 Genomes Project to estimate r
2
as the T1D SNP, rs11755527, was not available in the ImmunoChip data.
d
rs4409785 at 11q21 is an unconfirmed MS risk locus [MS P¼6.3 ×10
27
;(14)].
e
Assuming 13 [8 (Table 1) and 5 (Supplementary Material, Table S1)] independent tests, the adjusted P-value was 3.85 ×10
23
for the 0.05 level of significance based on the Bonferroni correction for multiple testing.
f
No available genotype data which included both SNPs.
5204 Human Molecular Genetics, 2012, Vol. 21, No. 23

DISCUSSION
We have found evidence for seven risk loci not previously
associated with AITD and expect that even more AITD
risk loci will be discovered as more AITD patients are gen-
otyped on the ImmunoChip. In this study, as the Immuno-
Chip contains densely mapped immune-mediated disease
loci representing only a small proportion, 1%, of the
genome, we used a permutation approach to derive a signifi-
cance level rather than adopting a genome-wide significance
level. Importantly, regardless of which discovery significance
level was adopted, confirmation of the new AITD risk loci
will require independent replication evidence. We found
some statistical evidence that AITD, outside of the HLA,
has a shared genetic architecture with both RA and T1D.
AITD often co-exists with other immune-mediated diseases
and a recent study estimating the prevalence of these
co-existing disorders found that RA was the most common,
occurring in 3.15% of GD patients and 4.24% of HT patients
studied; T1D occurred in 1.11% of GD patients and 1.01% of
HT patients (17). We note that the prevalence study (17)
included vast majority of our AITD patients, and that
AITD patients were recruited from endocrine clinics and
not rheumatology clinics.
The implicated candidate genes for the established and
seven new AITD risk loci indicate the importance of T
lymphocyte signalling (PTPN22), T-regulatory cell function
(CTLA4 and IL2RA), lymphocyte trafficking (CCR6) and a
newly identified possible link with the anti-viral immune re-
sponse (BACH2). Nevertheless, other AITD-associated
regions suggest candidate genes of uncertain or unknown
functions or roles in autoimmunity of AITD, such as LPP or
TRIB2. Two regions, 11q21 and 12q12, have no annotated
protein-coding candidate genes, indicating how much of the
aetiology of this and other immune-mediated diseases
remain to be explained.
MATERIALS AND METHODS
Subjects
The British AITD cases consisted of 2374 (390 males and 1984
females) GD and 474 (69 males and 405 females) HT patients
(18). The British control population consisted of 6894 subjects
drawn from the British 1958 Birth Cohort (1958BC; http://
wwww.cls.ioe.ac.uk/studies.asp?section=000100020003) and
3059 subjects drawn from the UK Blood Services Common
Control Collection (UKBS-CC) (6,19). All subjects were of
white European ancestry with written informed consent and
Ethics Committee/Institutional Review Board approval.
SNP selection
The dense SNP map for 186 confirmed and distinct loci,
mainly derived from GWA studies, which have been asso-
ciated (required P,5×10
28
) with one or more autoimmune
diseases, consisted of all known SNPs, and small insertion
deletions, in the dbSNP database, in the 1000 Genomes
project (February 2010 release) and in additional sequencing
data provided by collaborators. The ImmunoChip contains
196 524 polymorphisms (718 insertion/deletions and 195 806
SNPs) (3,20).
Genotyping
Samples were genotyped using a custom Illumina 200K Infi-
nium high-density array according to the manufacturer’s
protocol at the Wellcome Trust Sanger Institute (WTSI) in
Hinxton, UK, and University of Virginia (UVA) in Charlottes-
ville, USA. In WTSI, 2374 GD, 474 HT, 1478 1958BC and
3059 UKBS-CC samples were processed, and in UVA, 5416
1958BC samples were processed.
Sample quality control
Samples were excluded based on per-sample call rate (Supple-
mentary Material, Figs S9 – S11), outlying autosomal heterozy-
gosity (Supplementary Material, Figs S9 – S11), inconsistent
recorded and genotype-inferred sex, non-European ancestry
(21) (Supplementary Material, Figs S12 – S14), duplication
and being closely related to another sample in the study. Re-
latedness between the study participants was estimated by a
identity by state (IBS) statistic, we defined duplicates, or mono-
zygotic twins, as having an IBS .0.98 and second degree rela-
tives or closer, as having an IBS .0.1875 (22); the sample with
the lower call rate was excluded from further analyses. Finally,
we checked control sample’s identity using the 169 intentional
duplicates between the WTSI and the UVA, and using in-house
genotype data. We identified 256 samples, which had incorrect
identifiers as a result of manifest misreading, plate rotation and
chip-related issues, and excluded 95 samples from unknown
subjects (i.e. could not be assigned to identifiers). In addition,
we found ten additional duplicates between the genotyping
centers. All potential sample identity errors were discussed
with the relevant genotyping centers by Neil M. Walker. After
sample quality control, 2285 GD, 462 HT, 6541 1958BC and
Table 2. The overlap between AITD and the 11 other immune-mediated dis-
eases that provided susceptibility loci to the ImmunoChip. A negative
Z-value indicates that SNPs within disease-associated regions were more likely
to be associated with AITD than SNPs within regions not known to be asso-
ciated with the disease. A positive Z-value indicates that SNPs within regions
not known to be associated with the disease were more likely to be associated
with AITD than SNPs within associated regions.
Immune-mediated
disease
Number of
non-HLA
loci
Number of
SNPs within
loci
Wilcoxon rank test
ZP
AS 8 2079 3.593 3.27 ×10
24
CeD 30 9866 0.147 0.883
CD 66 22 585 2.242 0.0250
IgA deficiency 1 122 21.208 0.227
MS 26 7147 20.277 0.782
PBC 1 209 20.840 0.401
PS 25 6767 2.872 4.08 ×10
23
RA 25 7742 23.992 6.56 ×10
25
SLE 35 9099 20.726 0.468
T1D 41 12 658 22.146 0.0327
UC 28 8901 3.140 1.69 ×10
23
Human Molecular Genetics, 2012, Vol. 21, No. 23 5205

2823 UKBS-CC samples were available for analysis. When we
analysed GD and HT patients together, we excluded 14 second
degree relatives or closer between the disease groups, which
resulted in 2282 GD and 451 HT samples.
SNP quality control
SNP genotypes were called using Illumina GenomeStudio
GenTrain 2.0 algorithm and genotypes with a gencall score
(a quality metric for the distance a sample is from the center
of the nearest genotype cluster) ,0.15 were not assigned gen-
otypes. SNPs were excluded if the MAF fell ,5% in controls,
if they deviated from Hardy – Weinberg equilibrium (HWE;
z-score .5) in controls or if the per-SNP call rate fell
,95% in cases or controls. We note that SNP quality
control was performed in WTSI and UVA-processed controls
separately. In addition, for SNPs from the pseudoautosomal
regions on chromosome X, we tested for deviation from
HWE in male and female controls separately after discovering
SNP genotype frequency differences between sexes in patients
and controls (e.g. rs306891). We excluded SNPs from the
HLA (NCBI build 36 coordinates chr6:29690000-33498585).
Previously, despite differences in subject’s age and in DNA
processing between 1958BC and UKBS-CC control subjects,
few differences in SNP allele frequencies between 1958BC
and UKBS-CC controls were observed (19), which justified
the combination of these two control groups into a single
control group. To assess the possible differential genotyping
errors between the WTSI and UVA genotyping centers,
we tested for differences in SNP allele frequencies in controls
between genotyping centers; the test was stratified by
geographical region of Great Britain (23). The quantile – quan-
tile plot for the WTSI control versus UVA control 1
degree-of-freedom (1-df) association tests (Supplementary
Material, Fig. S15) showed a slight inflation of the test statistic
[inflation factor (
l
) was 1.017; as the inflation factor scales
with sample size, a more informative and comparable
measure of inflation is the inflation factor for an equivalent
study of 1000 cases and 1000 controls,
l
1000
(24), which
was 1.004 for the WTSI control/UVA control analysis]. As
the ImmunoChip was designed to include all known and
genotype-able SNPs across 186 distinct immune-mediated
disease susceptibility loci, there was inflation in the test statis-
tic for the AITD patient versus control association tests: HT
patient/control
l
1000
¼1.06 (l¼1.05); GD patient/control
l
1000
¼1.08 (
l
¼1.28); and HT and GD patient/control
l
1000
¼1.08 (l¼1.33). We note that
l
was based on
non-HLA common SNPs from regions not previously asso-
ciated with AITD (excluded regions reported in Supplemen-
tary Material, Table S1) that passed quality control. We also
estimated the inflation factor based on the WTCCC replication
SNPs for reading and maths ability, and psychosis endopheno-
types: HT patient/control
l
1000
¼1.02 (
l
¼1.02); GD patient/
control
l
1000
¼1.03 (
l
¼1.11); and HT and GD patient/
control
l
1000
¼1.04 (
l
¼1.17). We note that 2605 WTCCC
replication SNPs passed quality control and had a MAF
.0.05, and that the observed inflation of the test statistic
was a result of the replication SNPs overlapping some of the
immune-mediated disease loci, for example, the T1D loci
COBL/7p12.1 and GLIS3/9p24.2. As the inflation factor
l
1000
of the test statistics from the WTSI control/UVA
control analysis was just over 1 and we found a similar infla-
tion of the test statistics for GD patient/controls and for HT
patient/controls at 6 – 8% in the main analysis and at 2 – 3%
in the subset of WTCCC replication SNPs, population struc-
ture is unlikely to a major source of inflation. After SNP
quality control, 103 875 common SNPs were available for
analysis.
Statistical analysis
All statistical analyses were performed in R (http://www.r-p
roject.org) using the snpStats package available from the Bio-
conductor project (http://www.bioconductor.org). After apply-
ing sample and SNP quality control exclusions, we analysed
the case/control data using the 1-df Cochran-Armitage trend
test. SNPs were modelled using a numerical indicator variable
coded 0, 1 and 2, representing the occurrences of the minor
allele and assuming multiplicative allelic effects. We also
tested the disease-associated SNPs reported in Table 1for dif-
ferences between the multiplicative allelic effects model and
the full genotype model, and no significant differences were
found. We analysed SNPs on the X-chromosome using the
method proposed by Clayton (25).
We initially analysed the GD and HT patients separately,
but against the same controls. Before combining the GD and
HT patients into a single patient group, as we had analysed
GD and HT patients against the same controls, we tested for
disease association heterogeneity between the two AITDs by
testing for differences in SNP allele frequencies between GD
and HT patient groups. In the absence of disease association
heterogeneity, we proceeded to combine the GD and HT
patients and re-analysed.
As GD and HT patients, unlike the controls, were not
recruited from all 12 geographical regions of Great Britain
(seven and six regions, respectively), we did not stratify the ana-
lysis by geographical region to allow for population structure.
We note that previously, little difference was reported
between analyses with or without regional stratification (19).
However, to check that we were gaining power with the add-
itional controls rather than false-positive findings through ig-
noring the population structure, we tested for population
heterogeneity in SNP genotype frequencies across the regions
to identify SNPs that differentiate between the regions (Supple-
mentary Material, Table S2). None of the seven new or previ-
ously reported AITD risk loci showed large allele frequency
differences between controls from the 12 regions.
To adjust P-values for the multiple tests and to allow for the
correlation between SNP genotypes, we adopted a permutation
test approach. We performed 100 000 permutation tests of the
common SNPs on the ImmunoChip, permuting the disease
status label for each test, to provide the distribution of the
test statistic under the null hypothesis of no association
between SNP genotypes and disease status. The adjusted
P-value for 0.05 was 1.12 ×10
26
.
To examine the overlap between AITD and the other 11
immune-mediated diseases that provided susceptibility loci
to the ImmunoChip, we tested whether the distribution of
AITD test statistics varied between SNPs within regions asso-
ciated with another immune-mediated disease and within
5206 Human Molecular Genetics, 2012, Vol. 21, No. 23

regions not associated with that disease. We adopted a gene set
enrichment approach based on the Wilcoxon rank test as
implemented in the R package wgsea (Chris Wallace,
www-gene.cimr.cam.ac.uk/staff/wallace) (16). SNPs within
the non-HLA immune-mediated disease loci were divided
into two groups for each immune-mediated disease: (i) SNPs
within regions associated with disease at the time the Immuno-
Chip was designed; and (ii) SNPs within regions not asso-
ciated with that disease. We then tested the null hypothesis
that the distribution of P-values describing the evidence for as-
sociation of each SNP with AITD was the same in the two
groups using a Wilcoxon rank test with a permutation-based
variance estimate to allow for the considerable between SNP
correlation (16). We used 10 000 permutations to estimate
the variance of the Wilcoxon rank test.
SUPPLEMENTARY MATERIAL
Supplementary Material is available at HMG online.
ACKNOWLEDGEMENTS
We would like to thank all AITD patients and control subjects
for participating in this study. We thank nurses and doctors
for recruiting AITD patients into the AITD National Collection.
We thank the members of each disease consortium who initiated
and sustained the cross-disease ImmunoChip project. We thank
Jeffrey Barrett for assistance with ImmunoChip SNP selection
and for ImmunoChip-related correspondence. We thank Jenni-
fer Stone for co-ordinating the ImmunoChip design and produc-
tion at Illumina. The authors wish to acknowledge Sarah Edkins,
Emma Gray, Doug Simpkin, Sarah Hunt and staff of the Sanger
Institute’s Sample Logistics, Genotyping and Variation Inform-
atics facilities, respectively. The Diabetes and Inflammation La-
boratory is funded by the Juvenile Diabetes Research
Foundation International, the Wellcome Trust and the National
Institute for Health Research Cambridge Biomedical Centre.
The Cambridge Institute for Medical Research (CIMR) is in
receipt of a Wellcome Trust Strategic Award (079895). Core in-
frastructure support to the Wellcome Trust Centre for Human
Genetics, Oxford was provided by grant 090532/Z/09/Z. The
WTCCC is funded by Wellcome Trust awards 076113 and
083948. CW is supported by the Wellcome Trust (089989).
We acknowledge use of DNA from The UK Blood Services col-
lection of Common Controls (UKBS-CC collection), which is
funded by the Wellcome Trust grant 076113/C/04/Z and by
the USA National Institute for Health Research program grant
to the National Health Service Blood and Transplant
(RP-PG-0310-1002). We acknowledge the use of DNA from
the British 1958 Birth Cohort collection, which is funded by
the UK Medical Research Council grant G0000934 and the
Wellcome Trust grant 068545/Z/02. This research utilized
resources provided by the Type 1 Diabetes Genetics Consor-
tium, a collaborative clinical study sponsored by the National
Institute of Diabetes and Digestive and Kidney Diseases, the
National Institute of Allergy and Infectious Diseases, the Na-
tional Human Genome Research Institute, the National Institute
of Child Health and Human Development and the Juvenile
Diabetes Research Foundation International and is supported
by the USA National Institutes of Health grant U01-DK062418.
Conflict of Interest statement. None declared.
FUNDING
The research leading to these results has received funding from
the European Union’s 7th Framework Programme (FP7/2007 –
2013) under grant agreement No. 241447 [Natural immunomo-
dulators as novel immunotherapies for type 1 diabetes
(NAIMIT)]. Funding to pay the Open Access publication
charges for this article was provided by the Wellcome Trust.
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