Supplementary methods
Description of the GWAS on BCC
Description of Study Populations
Nurses’ Health Study (NHS): The NHS was established in 1976, when 121,700 female
U.S. registered nurses between the ages of 30 and 55, residing in 11 larger U.S. states,
completed and returned an initial self-administered questionnaire on their medical
histories and baseline health-related exposures, forming the basis for the NHS cohort.
Biennial questionnaires with collection of exposure information on risk factors have been
collected prospectively. Overall, follow-up has been very high; after more than 20 years
approximately 90% of participants continue to complete questionnaires. From May 1989
through September 1990, we collected blood samples from 32,826 participants in the
NHS cohort.
Health Professionals Follow-up Study (HPFS): In 1986, 51,529 men from all 50 U.S.
states in health professions (dentists, pharmacists, optometrists, osteopath physicians,
podiatrists, and veterinarians) aged 40-75 answered a detailed mailed questionnaire,
forming the basis of the study. The average follow-up rate for this cohort over 10 years
is >90%. Between 1993 and 1994, 18,159 study participants provided blood samples by
overnight courier.
The five GWAS sets included in the BCC GWAS: Detailed descriptions for five
GWAS sets were published previously (Hunter et al. 2007; Qi et al. 2010; Rimm et al.
1991).
1) Postmenopausal invasive breast cancer case-control study nested within the NHS:
Eligible cases in this study consisted of women with pathologically confirmed incident
breast cancer from the subcohort who gave a blood specimen. Cases with a diagnosis
after blood collection up to June 1, 2000 with no previously diagnosed cancer except for
non-melanoma skin cancer were included. One control for each case was randomly
selected among women who gave a blood sample and were free of diagnosed cancer
(excluding non-melanoma skin cancer) up to and including the interval in which the case
was diagnosed. Controls were matched to cases on year of birth, menopausal status,
recent post-menopausal hormone (PMH) use, month of blood return, time of day of blood
collection, and fasting status at blood draw (Hunter et al. 2007). All breast cancer cases
were excluded from the current study.
2) Type 2 diabetes (T2D) case-control study nested within the NHS and HPFS:
Diabetes cases were defined as self-reported incident diabetes confirmed by a validated
supplementary questionnaire. For cases before 1998, diagnosis was made using criteria
consistent with those proposed by the National Diabetes Data Group (NDDG). For cases
during the 1998 and 2000 cycles, the American Diabetes Association’s diagnostic criteria
were used for the diagnosis of diabetes cases. The nondiabetic control subjects were
matched to cases on age, month and year of blood draw, and fasting status (Qi et al.
2010).
3) Coronary heart disease (CHD) case-control study nested within the NHS and
HPFS: In both the NHS and HPFS, participants who had reported an incident CHD event
on the follow-up questionnaire were contacted for confirmation and permission to review
medical records was requested. Medical records for deceased participants were also
sought for deaths that were identified by families and postal officials and through the
National Death Index. Physicians blinded to the participant’s questionnaire reports
reviewed all medical records. Fatal CHD cases were identified primarily through review
of medical records, as previously described (Rimm et al. 1991). Among participants who
provided blood samples and who were without cardiovascular disease or cancer at blood
draw, incident CHD cases occurring after blood draw were selected as cases. Controls
were selected, in a 2:1 ratio matched to cases on age, smoking, and month of blood return.
References:
Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, Hankinson SE, Wacholder S, Wang
Z, Welch R, Hutchinson A, Wang J, Yu K, Chatterjee N, Orr N, Willett WC,
Colditz GA, Ziegler RG, Berg CD, Buys SS, McCarty CA, Feigelson HS, Calle
EE, Thun MJ, Hayes RB, Tucker M, Gerhard DS, Fraumeni JF, Jr., Hoover RN,
Thomas G, Chanock SJ (2007) A genome-wide association study identifies alleles
in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat
Genet 39: 870-4
Qi L, Cornelis MC, Kraft P, Stanya KJ, Linda Kao WH, Pankow JS, Dupuis J, Florez JC,
Fox CS, Pare G, Sun Q, Girman CJ, Laurie CC, Mirel DB, Manolio TA, Chasman
DI, Boerwinkle E, Ridker PM, Hunter DJ, Meigs JB, Lee CH, Hu FB, van Dam
RM (2010) Genetic variants at 2q24 are associated with susceptibility to type 2
diabetes. Hum Mol Genet 19: 2706-15
Rimm EB, Giovannucci EL, Willett WC, Colditz GA, Ascherio A, Rosner B, Stampfer
MJ (1991) Prospective study of alcohol consumption and risk of coronary disease
in men. Lancet 338: 464-8
Table S1. Association results for all the 99 tested KEGG pathways.
Pathway
Gene
enriched1
Pathway Enrichment pvalue
Autoimmune thyroid disease
25
<0.001
Allograft rejection
24
0.019
Antigen processing and presentation
47
0.028
Chronic myeloid leukemia
40
0.082
B cell receptor signaling pathway
31
0.052
Type I diabetes mellitus
29
0.054
Systemic lupus erythematosus
34
0.06
Leukocyte transendothelial migration
55
0.032
JAK-STAT signaling pathway
Cell adhesion molecules (CAMs)
60
63
0.02
0.068
Graft-versus-host disease
27
0.073
Focal adhesion
Glycerophospholipid metabolism
74
29
0.009
0.058
Cell Communication
28
0.12
Toll-like receptor signaling pathway
48
0.07
Phosphatidylinositol signaling system
44
0.068
Regulation of actin cytoskeleton
91
0.029
Glycerolipid metabolism
VEGF signaling pathway
23
33
0.081
0.21
Neurodegenerative Diseases
Natural killer cell mediated cytotoxicity
25
58
0.046
0.098
Melanoma
25
0.097
Inositol phosphate metabolism
28
0.299
Sphingolipid metabolism
21
0.153
Renal cell carcinoma
30
0.206
Fc epsilon RI signaling pathway
mTOR signaling pathway
35
25
0.115
0.321
Pancreatic cancer
Oxidative phosphorylation
35
73
0.265
0.343
FDR
2
0.24
2
0.59
9
0.51
2
0.42
7
0.46
5
0.59
3
0.57
3
0.52
6
0.48
8
0.51
0.49
1
0.50
2
0.56
0.53
6
0.57
7
0.54
6
0.52
3
0.49
8
0.55
0.61
7
0.62
0.59
4
0.59
2
0.65
1
0.79
9
0.77
8
0.75
0.76
9
0.74
Epithelial cell signaling in Helicobacter pylori
infection
38
0.161
PPAR signaling pathway
26
0.427
Tyrosine metabolism
22
0.334
Ubiquitin mediated proteolysis
77
0.259
Fatty acid metabolism
32
0.397
Ribosome
42
0.245
Purine metabolism
82
0.292
GnRH signaling pathway
41
0.277
Acute myeloid leukemia
29
0.507
Hematopoietic cell lineage
36
0.363
Porphyrin and chlorophyll metabolism
20
0.459
Starch and sucrose metabolism
37
0.387
Glioma
30
0.353
Glycan structures - biosynthesis 2
46
0.41
Adherens junction
35
0.391
Aminoacyl-tRNA biosynthesis
31
0.543
Homologous recombination
21
0.648
Pathogenic Escherichia coli infection
26
0.611
EPEC
26
0.611
Non-small cell lung cancer
29
0.642
Melanogenesis
45
0.672
Pyrimidine metabolism
Tryptophan metabolism
65
34
0.474
0.622
Tight junction
SNARE interactions in vesicular transport
65
28
0.501
0.274
T cell receptor signaling pathway
39
0.373
Drug metabolism - cytochrome P450
24
0.61
Long-term potentiation
32
0.625
6
0.81
8
0.84
2
0.82
7
0.84
9
0.84
2
0.86
1
0.90
2
0.90
3
0.88
6
0.90
6
0.92
2
0.91
9
0.91
5
0.89
6
0.90
2
0.90
8
0.94
6
0.92
1
0.92
1
0.94
1
0.93
3
0.92
3
0.93
0.91
3
0.91
0.90
1
0.89
3
0.90
7
Metabolism of xenobiotics by cytochrome
P450
24
0.654
Cytokine-cytokine receptor interaction
93
0.308
Valine, leucine and isoleucine degradation
Vibrio cholerae infection
33
33
0.581
0.728
Endometrial cancer
28
0.805
Gap junction
39
0.724
Apoptosis
42
0.415
Small cell lung cancer
36
0.452
Fructose and mannose metabolism
24
0.499
Glutathione metabolism
31
0.777
Adipocytokine signaling pathway
32
0.651
TGF-beta signaling pathway
42
0.696
Neuroactive ligand-receptor interaction
56
0.706
DNA replication
23
0.697
Lysine degradation
27
0.562
Folate biosynthesis
22
0.557
Cell cycle
68
0.786
Bladder cancer
Complement and coagulation cascades
22
24
0.742
0.817
Arachidonic acid metabolism
20
0.804
p53 signaling pathway
39
0.794
ErbB signaling pathway
37
0.579
Axon guidance
49
0.819
Insulin signaling pathway
65
0.797
Glycolysis / Gluconeogenesis
29
0.839
Propanoate metabolism
26
0.869
Colorectal cancer
43
0.887
MAPK signaling pathway
Base excision repair
109
22
0.78
0.699
0.90
2
0.89
6
0.88
6
0.92
0.93
4
0.92
1
0.91
6
0.96
6
0.95
7
0.94
5
0.95
7
0.97
5
0.96
2
0.95
2
0.96
6
0.96
8
0.96
1
0.97
4
0.97
0.96
6
0.95
4
0.94
4
0.93
2
0.92
6
0.94
5
0.93
4
0.92
5
0.92
2
0.92
Calcium signaling pathway
60
0.856
Prostate cancer
49
0.887
ECM-receptor interaction
23
0.834
Long-term depression
33
0.84
Butanoate metabolism
27
0.715
Thyroid cancer
21
0.687
Glycan structures - biosynthesis 1
65
0.7
Nucleotide excision repair
33
0.724
Selenoamino acid metabolism
22
0.711
Glycine, serine and threonine metabolism
25
0.925
Pyruvate metabolism
22
0.806
Wnt signaling pathway
67
0.861
N-Glycan biosynthesis
23
0.875
1
2
The number of the genes with eSNPs, which are used for the enrichment in the pathways;
Based on 1000 permutations and 99 pathways tested in total.3 Based on 1000 permutations and 99 tested pathway
0.91
3
0.92
7
0.93
3
0.92
7
0.91
8
0.91
2
0.90
4
0.90
3
0.90
1
0.91
6
0.91
3
0.96
1
0.96
6
Table S2. The representative eSNP information for the two pathways with
significant enrichement in the BCC GWAS.
Pathway
Autoimmune thyroid disease
JAK-STAT signaling pathway
Genes with eSNP
HLA-B
HLA-G
HLA-DQB1
HLA-DRB33
HLA-DRB1
HLA-DOB
IFNA14
HLA-DPA1
HLA-DRB4
HLA-C
HLA-A
HLA-DMA
HLA-DPB1
HLA-DOA
HLA-E
CD80
CD86
HLA-F
HLA-DRA
CD40
HLA-DMB
CTLA4
IL21R
STAM2
IFNA14
IL15RA
CSF2RB
IL6ST
PIK3CB
EP300
CBLB
IFNGR2
IL2RB
IL3RA
STAT4
IL6R
SOCS1
SOS1
CRLF2
AKT3
STAT1
EPOR
STAT6
STAT5A
CCND1
IL11RA
IL28RA
eSNP1
rs3093562
rs805262
rs2736171
rs2856683
rs7536479
rs10947342
rs9299397
rs13215763
rs7775228
rs2854050
rs9461216
rs2857211
rs3130171
rs6920606
rs13209817
rs4330287
rs7627354
rs1265156
rs8052975
rs13037326
rs12928665
rs13100355
rs4787976
rs10930939
rs9299397
rs8177600
rs2284027
rs17730330
rs6439828
rs4821998
rs9821956
rs2834212
rs2281094
rs9311913
rs4853551
rs4845617
rs3862469
rs12473092
rs2431744
rs10927041
rs6689496
rs9392697
rs10506348
rs789056
rs11228554
rs2381163
rs10903038
eSNP PBCC2
1.21E-04
5.15E-04
7.53E-04
8.19E-04
1.92E-03
1.92E-03
4.09E-03
4.11E-03
4.72E-03
5.10E-03
5.38E-03
5.54E-03
6.26E-03
7.29E-03
1.01E-02
1.16E-02
1.59E-02
4.59E-02
1.49E-01
1.59E-01
3.26E-01
7.95E-01
2.93E-03
2.96E-03
4.09E-03
7.43E-03
9.25E-03
9.47E-03
9.68E-03
1.03E-02
1.18E-02
1.32E-02
1.89E-02
2.59E-02
2.63E-02
2.69E-02
2.71E-02
2.84E-02
3.23E-02
3.24E-02
3.48E-02
3.84E-02
3.85E-02
4.05E-02
4.11E-02
4.33E-02
4.73E-02
CSF2RA
IL9R
IL12RB1
JAK2
IL10RB
IL19
PIAS4
SPRY3
IL15
GRB2
IFNAR2
CNTFR
CCND2
LEPR
IL2RA
ISGF3G
PRLR
SPRED2
CLCF1
PIK3R3
STAT5B
STAT2
IL12A
IFNAR1
IL12RB2
CCND3
IFNGR1
CBL
SPRY2
STAM
OSMR
PTPN11
IL2RG
TYK2
IL13RA1
1
2
3
rs3762474
rs10912203
rs426132
rs10974985
rs2243511
rs6673928
rs10402864
rs4943241
rs973431
rs9914731
rs2834153
rs1448370
rs4765769
rs3790436
rs11597536
rs1943321
rs7728551
rs840963
rs4370716
rs222616
rs4890112
rs17118403
rs2243140
rs2243600
rs3828069
rs4623235
rs10457655
rs1918
rs558366
rs12770533
rs16869234
rs7335152
rs12859844
rs409782
rs11215633
7.96E-02
8.01E-02
8.57E-02
9.21E-02
9.88E-02
1.01E-01
1.07E-01
1.15E-01
1.24E-01
1.32E-01
1.35E-01
1.47E-01
1.57E-01
1.92E-01
2.36E-01
2.98E-01
3.05E-01
3.12E-01
3.18E-01
3.42E-01
3.74E-01
4.19E-01
4.68E-01
5.07E-01
5.51E-01
5.68E-01
6.97E-01
7.89E-01
7.94E-01
8.28E-01
8.50E-01
9.06E-01
9.18E-01
9.38E-01
9.91E-01
Denotes genes with the smallest PBCC;
PBCC represents p values of eSNPs for BCC association;
The four genes HLA-DRB3, HLA-DRB5, HLA-DQA1, and HLA-DQA2 have the same representative eSNP: rs2856683.