1
Online supplementary Information
2
Polymorphisms in the Sialic Acid-binding Immunoglobulin-like lectin 8
3
(SIGLEC8) Gene Are Associated with Susceptibility to Asthma
4
Pei-Song Gao1*, Kenichi Shimizu2, Audrey V. Grant1, Nicholas Rafaels1, Lin-Fu
5
Zhou1,3, Sherry A. Hudson1, Satoshi Konno2, Nives Zimmermann8, Maria I. Araujo4,
6
EduardoV. Ponte5, Alvaro A. Cruz5, Masaharu Nishimura2, Song-Nan Su6, Nobuyuki
7
Hizawa2, Terry H. Beaty7, Rasika A. Mathias1, Marc E. Rothenberg8, Kathleen C.
8
Barnes1, Bruce S. Bochner1
9
10
1
11
Hopkins University School of Medicine, Baltimore, Maryland, USA
12
2
Department of Medicine, Hokkaido University School of Medicine, Sapporo, Japan
13
3
Department of Respiratory Medicine, The First Affiliated Hospital, Nanjing Medical
14
University, Nanjing, Jiangsu, China
15
4
16
Bahia, Brazil.
17
5
18
6
19
Taiwan.
20
7
21
Health,Baltimore, Maryland, USA
22
8
23
Hospital Medical Center, Cincinnati, Ohio, USA
Department of Medicine, Division of Allergy and Clinical Immunology, Johns
Servico de Imunologia, Hospital Universitario Professor Edgard Santos, Salvador,
ProAR - Federal University of Bahia School of Medicine, Salvador, Bahia, Brazil.
Department of Medical Research and Education, Taipei Veterans General Veterans,
Department of Epidemiology, Johns Hopkins Bloomberg School of Public
Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s
24
25
*Contact Information: Address correspondence and reprint requests to:
26
Pei-Song Gao, MD, PhD.
27
Johns Hopkins Asthma & Allergy Center
28
5501 Hopkins Bayview Circle
29
Baltimore, MD 21224 USA;
30
E-mail: pgao1@jhmi.edu
1
31
Diagnosis of asthma in the Japanese population
32
Asthma was diagnosed according to the following three criteria: (1) presence of at least
33
two symptoms (recurrent cough, wheezing, or dyspnea), (2) presence of reversible
34
airflow limitation [15% variability in forced expiratory volume in one second (FEV1) or
35
in peak expiratory flow rate, either spontaneously or with an inhaled short-acting β2-
36
agonist] or airway hyperresponsiveness to methacholine, and (3) absence of other
37
pulmonary diseases. Control subjects were selected from healthy volunteers with no
38
history of asthma.
39
Genotyping and quality control
40
We performed genotyping on genomic DNA extracted from blood samples using
41
MagAttract DNA blood Mini M48 kit (QIAGEN) on a Biorobot M48, according to the
42
manufacturer’s instructions. DNA quantification was performed using Pico-Green
43
(Pico-green, Molecular Probes).
44
Tagging SNPs were selected to provide coverage 5 kb upstream and 5 kb
45
downstream of the siglec-8 genes in the African American group. All available SNPs
46
were initially selected from the HapMap (http://www.hapmap.org/) to tag the linkage
47
disequilibrium (LD) blocks in the African American population.. Tagging was based on
48
the LDSelect algorithm (1, 2), with a minor allele frequency (MAF) 10% and an r2
49
threshold of 0.80 (as reported in HapMap) to ensure nearly perfect LD to infer
50
information on all SNPs captured by the tag set.
51
An additional 416 SNPs identified as ancestry informative markers (AIMs) were
52
selected for maximal difference between African and European populations and
53
genotyped to assess potential confounding due to population substructure using the
54
same Illumina platform. Briefly, the GoldenGate assay employs three primers designed
2
55
for each locus. Two are specific to each allele at the SNP site and a third hybridizes at a
56
downstream locus from the site. All three primers have regions complementary to both
57
genome and universal PCR primer sites. A total of 250 ng of high quality gDNA was
58
plated and then activated. The activated DNA, paramagnetic particles, assay oligos, and
59
hybridization buffer were combined in a hybridization step to allow DNA to bind to the
60
particles. Following hybridization of primers, plates were washed to reduce noise and
61
allele specific oligos were extended and ligated to the downstream locus specific
62
primer. This mix then served as a PCR template using the universal primers, P1, P2, and
63
P3. P1 and P2 are Cy3 and Cy5 labeled. After downstream processing, the single-
64
stranded dye-labeled PCR products were hybridized to their complement VeraCode
65
bead type. Plates were then scanned in the BeadXpress Reader for fluorescence and
66
code identification. Scanned data and oligo assignments were uploaded into Illumina’s
67
BeadStudio software for downstream genotype cluster analysis.
68
Except the SNPs included in the Illumina HumanHap650Y version 1 and 3
69
BeadChips, the rest of SNP genotyping was determined using custom-designed primers
70
and probes for the TaqMan Allelic Discrimination Assay (Applied Biosystems, Foster
71
City, CA, USA). Reactions were set up in 5 μl on 96 well plates in TaqMan Universal
72
Master Mix (Applied Biosystems) with 5 ng DNA, 1 μm of each primer, and 0.2 μm of
73
each probe. The thermal cycling reactions (50°C for 2 minutes, 95°C for 10 minutes,
74
followed by 40 cycles at 95°C for 15 seconds and 60C for 1 minute) were run and
75
analyzed on a 7900HT Sequence Detection System (Applied Biosystems) with Applied
76
Biosystems Genotyper software (SDS system, version 2.2).
77
Genotyping quality was high, with an average completion rate of 95% for the TaqMan
78
genotyping. For the TaqMan genotyping, each plate contained eight duplicated samples
3
79
that were randomly selected, as well as 4 blank wells as negative controls, with an
80
average error rate of 0.7%. Additionally, approximately 8% of randomly selected
81
samples were further validated by direct sequencing on the 7900HT Sequence Detection
82
System.
83
Power calculation:
84
Statistical power for the case-control study among African Americans and Japanese was
85
estimated using QUANTO (v1.2)125 (3). In the African American population, assuming
86
464 asthma cases and 471 controls in a log-additive genetic model, a range of allele
87
frequencies for the high-risk allele was examined. There is 90.2% power to detect an
88
odds ratio of 2.3 with a disease allele frequency of 0.05 (alpha=0.015) (Supplementary
89
Figure S2A). The same power calculation was also performed for the case-control study
90
among Japanese, assuming 468 asthma cases and 457 controls in a log-additive genetic
91
model, there is 90.0% power to detect an odds ratio of 2.1 with a disease allele
92
frequency of 0.05(alpha=0.0035)(Supplementary Figure S2B).
4
Table S1. Chromosomal location, position, and minor allele frequencies (MAF) of Siglec-8 SNPs (chromosome 19q13.33-q13.41) in four
different populations. SNP in bold indicates tagging SNP for African Americans. MAF in bold represents those SNPs not genotyped and data
from the International HapMap project (www.HapMap.org). “-“: no data available.
SNP ID
Location
Distance from
previous SNP (bp)
Minor allele frequency (MAF)
Position
Role
African American
Brazilian
Japanese
Caucasian
rs11672925
56659679
0
A/C
Promoter
0.19
-
0.38
0.47
rs36498
56657807
1872
C/T
Promoter
0.50
0.31
0.33
0.13
rs10420357
56656378
1429
A/T
Promoter
0.25
0.17
0.07
0.11
rs36496
56653828
2550
T/C
Promoter
0.04
-
0.23
0.16
rs10409962
56652752
1076
A/G
Ser/Pro
0.50
0.32
0.13
0.09
rs36495
56652037
715
G/A
Intron
0.36
-
0.33
0.72
rs10408249
56649489
2548
T/C
Intron
0.47
-
0.26
0.19
rs3829659
56649368
121
G/A
Arg/Gln
0.46
0.36
0.36
0.27
rs39711
56646670
2698
C/T
3' UTR
0.45
0.40
0.33
0.27
rs10518263
56646534
136
G/T
3' UTR
0.12
-
0.19
0.20
rs36489
56645906
628
A/G
Downstream
0.17
-
0.60
0.19
rs36487
56645106
800
G/A
Downstream
0.15
--
0.43
0.19
rs36485
56644552
554
C/T
Downstream
0.29
-
0.67
0.27
rs6509541
56643115
1437
G/T
Downstream
0.50
0.40
0.11
0.03
5
Table S2. Family based association tests (FBAT) for association between Siglec-8 polymorphisms and current asthma among 356 nuclear
Brazilian families. Z: Z score for FBAT.
Recessive
Minor allele
SNP
Additive
Risk allele
frequency (MAF)
Z
P-value
Z
P-value
Rs36498
T
0.31
2.47
0.013
1.28
0.201
Rs10420357
T
0.17
-1.29
0.197
-1.11
0.266
Rs10409962
G
0.32
1.72
0.085
1.16
0.248
Rs3829659
G
0.36
0.279
0.780
0.615
0.538
Rs39711
T
0.40
-0.413
0.680
-0.109
0.913
Rs6509541
T
0.31
-0.020
0.984
-0.937
0.349
6
Table S3. Association of Siglec-8 SNPs with eosinophilic esophagitis (EE) among the 298 Caucasians (166 EE and 132 controls).
Minor allele frequencies
SNP
Risk allele
EE
Control
OR (95% CI)
P value
RS36485
T
0.231
0.265
0.83 (0.56-1.24)
0.332
RS36487
A
0.181
0.189
0.95 (0.61-1.47)
0.791
RS36489
G
0.180
0.189
0.94 (0.61-1.46)
0.768
RS39711
T
0.239
0.265
0.87 (0.59-1.29)
0.450
RS3829659
G
0.238
0.265
0.86 (0.59-1.28)
0.447
RS36495
A
0.234
0.277
0.81 (0.54-1.18)
0.223
RS10409962
G
0.065
0.087
0.73 (0.38-1.42)
0.309
RS10420357
T
0.087
0.106
0.80 (0.45-1.45)
0.446
7
Legends to figures
Figure S1. Statistical power to detect genotype odds ratio (OR) for asthma in African
American subjects consisting of 464 cases and 471 controls (A) and Japanese subjects
of 468 asthma cases and 457 controls (B) with disease allele frequency 0.05, 0.1, 0.15,
and 0.2 (curves from right to left). Assumptions: log additive genetic model, with a
disease allele frequency of 0.05. Power calculations were performed using QUANTO
(V 1.2).
Figure S2. Gene structure and linkage disequilibrium (LD) estimation for 14 Siglec-8
SNPs among African American controls (A) and 8 SNPs among 122 Caucasian controls
(B). The top panel shows the Siglec-8 gene structure, with exon in blue and intron in
light purple. The value within each diamond is the pairwise LD statistic D’. Diamonds
without a number correspond to D’=1; shading represents the magnitude and
significance of pair-wise LD with a red to white gradient reflecting higher to lower LD
values (see Haploview online for further details).
8
Figure S1.
A: Japanese subjects
B. African
American subjects
9
Figure S2.
A: African Americans
Figure 1.
Siglec8, 16kb
Chr. 19q13.33-q13.41
3’
5’
10
B: Caucasians
Siglec8, 16kb
Chr. 19q13.33-q13.41
11
References:
1. Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA. Selecting
a maximally informative set of single-nucleotide polymorphisms for association
analyses using linkage disequilibrium. Am J Hum Genet 2004;74(1):106-20.
2. Howie BN, Carlson CS, Rieder MJ, Nickerson DA. Efficient selection of
tagging single-nucleotide polymorphisms in multiple populations. Hum Genet
2006;120(1):58-68.
3. Gauderman WJ. Sample size requirements for matched case-control studies of
gene-environment interaction. Stat Med 2002; 21:35-50.
12