Recent genome-wide association studies (GWAS) identified genetic loci associated with pigmentation, nevi, and skin cancer. We performed a review and meta-analysis of GWAS results, grouping them into four categories: (i) loci associated with pigmentation (hair, eye, and/or skin color), cutaneous UV-response (sun sensitivity and/or freckling), and skin cancer; (ii) loci associated with nevi and melanoma; (iii) loci associated with pigmentation and/or cutaneous UV-response but not skin cancer; and (iv) loci associated distinctly with skin cancer, mostly basal cell carcinoma, but not pigmentation or cutaneous UV-response. These findings suggest at least two pathways for melanoma development (via pigmentation and via nevi), and two pathways for basal cell carcinoma development (via pigmentation and independent of pigmentation). However, further work is necessary to separate the association with skin cancer from the association with pigmentation. As with any GWAS, the identified loci may not include the causal variants and may need confirmation by direct genome sequencing.
An important goal in improving our understanding of skin cancer is to identify mechanisms accounting for increased inherited susceptibility. Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the most common types of skin cancer, and melanoma accounts for the most skin cancer deaths. Several high-penetrance loci for melanoma and basal cell carcinoma have been identified in melanoma-prone families and in families with nevoid basal cell carcinoma syndrome (NBCCS), respectively, including cyclin-dependent kinase inhibitor 2A (CDKN2A coding for p16 and p14ARF) and cyclin-dependent kinase 4 (CDK4) for melanoma and the human homolog of the Drosophila segment polarity gene patched (PTCH1) for BCC. Other high-penetrance loci have been suggested for melanoma on chromosomes 1p22 and 1p36, but the causal genes have yet to be identified (Bale et al., 1989; Gillanders et al., 2003; Hussein et al., 2003). CDKN2A alterations account for approximately 20–40% of melanomas occurring in families with three or more affected individuals (Goldstein et al., 2006;Kefford et al., 1999), whereas CDK4- and PTCH1-related NBCCS account for only a small percentage of melanomas (Goldstein et al., 2006) and BCCs (Epstein, 2008), respectively. Thus, environmental and other genetic factors likely account for the remaining risk.
Subsequent studies examined other pigmentation genes. Animal studies identified associations with pigmentation for the agouti signaling protein (ASIP ) and solute carrier family 24, member 5 (SLC24A5 ) genes; candidate gene studies in humans confirmed these associations with pigmentation (Graf et al., 2005; Kanetsky et al., 2002; Lamason et al., 2005; Norton et al., 2007). Studies of the four types of oculocutaneous albinism, disorders of melanin synthesis characterized by light skin, hair, and eye pigmentation among other features, have identified additional pigmentation genes, including tyrosinase (TYR), tyrosinase-related protein 1 (TYRP1), oculocutaneous albinism type 2 gene (OCA2, previously called P gene), and solute carrier family 45, member 2 (SLC45A2, previously called membrane-associated transporter protein, MATP) (reviewed in Tomita and Suzuki, 2004). Patients with certain types of oculocutaneous albinism experience sun sensitivity and are at increased risk of skin cancer, particularly non-melanoma skin cancers; melanomas, often amelanotic, are much less commonly reported (Luande et al., 1985; Perry and Silverberg, 2001; Terenziani et al., 2003; Yakubu and Mabogunje, 1993). In population-based studies using candidate-gene approaches, an SLC45A2 variant was associated with dark hair, dark skin, and protection from melanoma (Fernandez et al., 2008; Graf et al., 2005; Guedj et al., 2008; Nakayama et al., 2002). OCA2 variants were associated with melanoma in other studies (Duffy et al., 2010;Jannot et al., 2005), and ASIP was found to modify melanoma risk in the presence of MC1R variants (Landi et al., 2005).
Genome-wide association studies (GWAS) were conducted to try to identify other genomic loci associated with pigmentation and skin cancer. GWAS can identify common, low-penetrance susceptibility loci without prior hypotheses about the role of specific genes. We performed a review and meta-analysis of summary results from GWAS and replication studies examining skin cancers and their major risk factors, including pigmentation, cutaneous UV-response (including sun sensitivity and/or freckling), and nevi. Using large populations with mostly Caucasian individuals, GWAS confirmed associations already known, such as MC1R with pigmentation and skin cancer, and also identified chromosomal regions previously not known to be associated with pigmentation and/or skin cancer. By including data from multiple studies in one analysis, we can better capture loci that have significant associations with pigmentation traits and susceptibility to melanoma and non-melanoma skin cancers distinctly and in combination. This allows further understanding of the complex interactions of pigmentation, nevi, and skin cancer.
cRelatives are all living descendants and spouses of 22 couples who lived in a southwest region of the Netherlands with at least six children in the 19th century. Distant relatives are ≥5 generations apart.
dSample numbers vary across the SNPs tested in the populations. The largest sample numbers are presented in this table.
GWAS of natural hair color in US women. Replication performed in four additional studies from the US and Australia with data on hair color and other pigmentation phenotypes.
2287 US women of European ancestryd
(1) 870 US women controls from a skin cancer case-control studyd (2) 3750 US women from a diabetes case-control studyd (3) 2405 US men from a diabetes case-control studyd (4) 1440 Parents of twins from a family-based Australian studyd
Expanded GWAS from Sulem et al. (2007) in Icelandic individuals. Eight GWAS: three for eye color (blue versus green, blue versus brown, and blue versus non-blue); two for hair color (red versus non-red and blond versus brown); and three for skin pigmentation traits (sun sensitivity versus no sun sensitivity, freckling versus no freckling, and sun sensitivity + presence of freckling ‘burning and freckling’ versus no ‘burning and freckling’).b
bNo information was provided on whether the melanoma lesions were in situ or invasive.
cEastern European sample includes individuals from Hungary, Romania, and Slovakia.
dPigmentation traits examined included eye color, hair color, propensity to freckle, and skin sensitivity to the sun (Fitzpatrick score).
eCases included in situ and invasive melanoma. Icelandic sample excluded mucosal and ocular melanoma; histology types not described for other samples.
fSelf-reported pigmentation data were used.
gSample numbers vary across the SNPs tested and phenotypes assessed. The largest sample numbers are presented in this table.
h480 of these BCC cases were added using in silico genotyping, where known genotypes of relatives are used to provide information on BCC cases not genotyped.
iIncludes some cases used in the initial GWAS through in silico genotyping.
jPigmentation traits examined included eye color, hair color, skin color, skin sensitivity to the sun (Fitzpatrick score and reaction to acute and chronic sun exposure), and propensity to freckle.
kPigmentation data were obtained from Icelandic samples for all SNPs except one around SLC45A2, where pigmentation data were presented in samples from Iceland, Eastern Europe, Spain (including melanoma cases only), and the US.
lA total of 34 998 Icelandic controls were used for the initial GWAS and replication, although numbers for each were not specified.
mNevus count was evaluated using a similar protocol in initial and replication studies.
pMelanoma cases and controls from the GenoMEL consortium used in the initial GWAS include individuals of European ancestry from Sweden, Australia, the UK, the Netherlands, France, Italy, and Spain. Melanoma cases and controls from the GenoMEL consortium used for replication include individuals of European ancestry from Sweden, Australia, the Netherlands, France, Italy, Eastern Europe, and Israel.
GWA pooling study performed in a melanoma population-based case-control sample of European descendants from Australia. Replication performed in: (i) same population as the initial GWAS, (ii) melanoma cases and controls independently sampled from the same population, and (iii) melanoma cases and controls from a population-based study of melanoma diagnosed before age 40 in Australians of European descent.a,b
864 Melanoma cases from Australia (pooled)
864 Controls from Australia (pooled)
Melanoma cases from: (i) 789 Australia, (ii) 725 Australia, (iii) 505 Australia
Controls from: (i) 854 Australia (ii) 797 Australia (iii) 454 Australia
GWAS performed in a BCC case-control study in Iceland. Replication performed in additional BCC case-control studies in (i) Iceland and (ii) Eastern Europe.c Significant loci were also examined for association in melanoma cases and controls from (i) Iceland, (ii) Sweden, and (iii) Spain as well as for association with pigmentation traits in 5130 Icelandic controls from Sulem et al. (2007, 2008).d–g
930 BCC cases from Iceland
33 117 Controls from Iceland
BCC cases from: (i) 703 Iceland, (ii) 513 Eastern Europe Melanoma cases from: (i) 565 Iceland, (ii) 1062 Sweden, (iii) 277 Spain
Controls for BCC study from: (i) 2329 Iceland (ii) 515 Eastern Europe Controls for melanoma study from: (i) 32061 Iceland (ii) 538 Sweden (iii) 1292 Spain
Illumina HumanHap 300 and HumaCNV 370-duo Bead Arrays
All variants tested for association with pigmentation, sun sensitivity, and freckling; no associations found so results not adjusted
Follow-up study of Stacey et al. (2008) where GWAS of BCC case-control study was repeated with an increased sample size. Replication of the third strongest signal (rs401681) was performed in additional BCC case-control samples from (i) Iceland and (ii) Eastern Europe, and a melanoma case-control sample from (i) Iceland, (ii) Sweden, and (iii) Spain.b,c
1505 BCC cases from Icelandh
28 890 Controls from Iceland
BCC cases from: (i) 744 Icelandi, (ii) 525 Eastern Europe Melanoma cases from: (i) 577 Iceland, (ii) 1056 Sweden, (iii) 748 Spain
Controls for BCC study from: (i) 515 Eastern Europe controls Controls for melanoma study from: (i) 28 890 Iceland controls, (ii) 522 Sweden controls, (iii) 1427 Spain controls
Illumina HumanHap 300 and HumanCNV 370 -duo Bead Arrays
Follow-up study of Stacey et al. (2008) and Rafnar et al. (2009) where 30 more high-ranking SNPs from the GWAS were investigated by genotyping in additional BCC cases from (i) Iceland and (ii) a case-control sample from Eastern Europe controls. SNPs at three loci (KRT5, CDKN2A/B, and 7q32) were genotyped in BCC case-control samples from (iii) the US and (iv) Spain. Two other variants (in TERT-CLPTM1L and SLC45A2) were tested for association with BCC. These five loci were examined for associations with squamous cell carcinoma (SCC) (in two populations), melanoma (in six populations), and fair pigmentation traits in 6200 Icelandic individuals.e,g,j,k This study was included in this table (not Table 3) because it reported GWAS data that were not previously presented in Stacey et al. (2008) or Rafnar et al. (2009). These data were further combined with genotype data from an additional Icelandic population.
930 BCC cases from Iceland
34 998 Controls from Icelandl
BCC cases from: (i) 1843 Iceland, (ii) 528 Eastern Europe, (iii) 930 US, (iv) 186 Spain Melanoma cases from: (i) 589 Iceland, (ii) 749 the Netherlands, (iii) 1065 Sweden, (iv) 152 Austria, (v) 816 Spain, (vi) 564 Italy SCC cases from: (i) 438 Iceland, (ii) 710 US
Controls for BCC study from: (i) 34998 Icelandl (ii) 533 Eastern Europe, (iii) 849 US, (iv) 1758 Spain Controls for melanoma study from: (i) 34998 Iceland, (ii) 1831 the Netherlands, (iii) 2631 Sweden (iv) 376 Austria, (v) 1703 Spain, (vi) 368 Italy Controls for SCC study from: (i) 34998 Iceland (ii) 849 US
Illumina HumanHap 300 and HumanCNV 370 -duo Bead Arrays
All variants tested for association with pigmentation, sun sensitivity, and freckling; association present only for SLC45A2 variant, but results not adjusted
GWAS for nevi performed in adult female twins from the Twins UK registry. Replication performed in an independent sample of adolescent twins of European ancestry from the Brisbane Twin Nevus Studym and two melanoma case-control studies: (i) melanoma cases and controls of northern European descendants from Australia, and (ii) 1734 incident melanoma cases + 123 melanoma cases with a positive family history from a Leeds melanoma case-control study of North UK and controls from the same study + controls from the Wellcome Trust.n,o
1524 Adult female twins from Australia for nevi
Melanoma cases from: (i) 1734 Australia, (ii) 1397 UK For nevi: 4107 Adolescent twins from Australia
Controls for melanoma study from: (i) 1811 Australia, (ii) 1070 (Leeds)+ 1395 (Wellcome Trust)
Illumina HumanHap 300
Number of nevi was not associated with number of sunburns over lifetime and Fitzpatrick score in either sample, so results not adjusted
GWAS of melanoma cases from GenoMEL international consortium enriched by family history, multiple primary melanomas, or early onset of disease. Replication performed in two additional populations: (i) melanoma cases and controls from GenoMEL and (ii) melanoma cases and controls from a population-based study from Leeds, UK. Findings from three other GWAS studies (Brown et al., 2008; Falchi et al., 2009; Gudbjartsson et al., 2008) were also replicated.b,o
1650‘Enriched’ melanoma cases from GenoMELp
4336 Controls from France, UK, and GenoMEL
Melanoma cases from: (i) 1149 GenoMEL (‘enriched cases’)p, (ii) 1163 UK
Controls for melanoma study from: (i) 964 GenoMELp (ii) 903 UK
Illumina HumanHap 300
Table 3. Replication of findings from pigmentation and skin cancer GWAS
Study population and description
Adjustment for known skin cancer risk factors
aMelanoma cases include invasive and in situ melanoma. In Gudbjartsson et al. (2008), melanoma cases from Spain include invasive melanoma only.
bSample numbers vary across the SNPs tested and phenotypes assessed. The largest sample numbers are presented.
cEastern European sample includes individuals from Hungary, Romania, and Slovakia.
dPigmentation traits examined included eye color, hair color, and skin color. Pigmentation traits examined only in subset of 1438 cases and 3098 controls with 100% Northern European ancestry.
eNo information was provided on whether the melanoma lesions were in situ or invasive.
fPigmentation traits examined in 803 melanoma, BCC, and SCC cases and 870 controls included hair color, skin color, and tanning ability in childhood and adolescence.
gAdditional skin cancer risk factors include: family history of skin cancer, number of lifetime severe sunburns that blistered, sunlamp or tanning salon use, cumulative sun exposure while wearing a bathing suit, and geographic regions.
hPigmentation traits examined in 990 NHL cases and 828 controls included eye color, hair color, skin color, tanning (skin reaction to first sun of season), and hours in mid-day sun in the last 10 years.
Replication study of 11 variants at eight loci recently identified from GWAS on hair, eye, and skin pigmentation (Sulem et al., 2007, 2008) and eight MC1R variants tested for association with melanoma and BCC.a Variants at three of the eight loci (ASIP, TYR, and TYRP1) were further tested in an Eastern European sample of BCC cases and controls.b,c
(i) 810 from Iceland, (ii) 1033 from Sweden, (iii) 278 from Spain
(i) 36 723 from Iceland, (ii) 2650 from Sweden, (iii) 1297 from Spain
(i) 1649 from Iceland, (ii) 514 from Eastern Europe
(i) 33824 from Iceland, (ii) 522 from Eastern Europe
Significant variants were adjusted for hair and eye color, freckling, and sun sensitivity in Icelandic individuals only
Study of SNPs in the IRF4 gene tested for association with pigmentation and sun sensitivity phenotypesh and non-Hodgkin lymphoma (NHL) in non-Hispanic Caucasian controls from a multi-center US study.
1818 from USh
Table 4. SNPs significantly associated with hair color, eye color, and skin color
Hair color (P-value)
Red hair color (P-value)
Eye color (P-value)
Skin color (P-value)
SNPs included are those that were significant in the initial GWAS and replicated at least once within the same study. P-values presented are the combined P-values from the initial GWAS and at least one replication within the same study. SNPs in bold are those with novel associations with pigmentation.
SNPs included are those from GWAS that have been replicated either in the same study or additional studies and are statistically significant (P < 10−7). We also report SNPs from replication studies if novel significant associations with BCC and/or SCC were identified. In some cases, studies used different reference alleles for the same SNP; the reference alleles are listed to help interpret the direction of association. SNPs in bold are those with novel associations with BCC and/or SCC.
GWAS, genome-wide association study; OR, odds ratio; CI, confidence interval; P, P-value; ‘Combined’, pooled results reported in the original publications.
Other replications include studies that further tested the association with these SNPs and BCC and/or SCC.
aStacey et al. (2008) initial GWAS in Icelandic population; replicated in the first phase with an independent Icelandic sample and in the second phase with an Eastern European sample.
fIn Stacey et al. (2009), this SNP was also genotyped, and the association had genome-wide significance for Iceland only but not Icelandic and Eastern European populations combined (P = 1.3 × 10−5); therefore, results shown are for Iceland only. In this study, when the results were adjusted for rs401681[C], rs2736098 [A] was no longer significant.
gIn Stacey et al. (2009), for US BCC and SCC samples, rs157935 could not be typed; therefore rs125124 was used as a surrogate SNP. Odds ratios for rs125124 in these samples were included in the analysis here for rs157935.
hIn Stacey et al. (2009), the effect of this SNP was dependent on paternal origin of the risk allele.
iIn Gudbjartsson et al. (2008), these variants were identified as being associated with pigmentation and were tested for association with BCC.
jIn Stacey et al. (2009), the investigation of KRT5 was expanded to include six more exonic polymorphisms common in Europeans that were genotyped in Icelandic, Eastern European and Spain BCC and control samples and were associated with BCC. The SNP rs641615 is in the same linkage disequilibrium block as rs11170164; in a multivariate analysis, the signal from rs641615 remained significant when adjusted for the effect of rs11170164.
kGudbjartsson et al. (2008) data from Icelandic population only. Other non-synonymous MC1R variants (D294H, R160W, D84E, R163Q, I155T, V92M, and V60L) tested were not significantly associated with BCC in the Icelandic sample; however, in a grouped analysis, the presence of any of these variants, including R151C, was significantly associated with BCC (P = 7.5 × 10−7).
lThe ASIP haplotype was also examined in Nan et al. (2009a) in BCC and SCC cases and controls. P-values for association were not reported.
OR = 1.27 (CI = 1.15, 1.41), P = 1.9 × 10−6a
1. OR = 1.23 (CI = 1.08, 1.40), P = 1.6 × 10−3a 2. OR = 1.33 (CI = 1.11, 1.59), P = 2.1 × 10−3a
1. OR = 1.14 (CI = 1.06, 1.23), P = 6.1 × 10−4i 2. OR = 1.04 (CI = 0.84, 1.27), P = 0.74d
OR = 1.35 (CI = 1.23, 1.50), P = 2.1 × 10−9b,j
rs1805007 [T] (R151C)
OR = 1.33 (CI = 1.17, 1.50), P = 6.8 × 10−6i,k
ASIP Haplotype (rs1015362[G] and rs4911414[T])
1. OR = 1.09 (CI = 0.87, 1.38)l
1. OR = 1.35 (CI = 1.20, 1.53), P = 1.2 × 10−6i 2. OR = 1.25 (CI = 0.99, 1.58)l
rs16891982 [G] (L374F)
OR = 2.71(CI = 1.88, 3.92) P = 1.0 × 10−7b
OR = 0.76 (CI = 0.43, 1.34), P = 0.34d
The significant loci from the GWAS were grouped into four categories: (i) loci associated with pigmentation (hair, eye, and/or skin color), cutaneous UV-response (sun sensitivity and/or freckling), and skin cancer; (ii) loci associated with nevi and melanoma; (iii) loci associated with pigmentation and/or cutaneous UV-response, but not skin cancer; and (iv) loci associated with skin cancers, mostly basal cell carcinoma (BCC), but not pigmentation or cutaneous UV-response. Table 6 shows the overall associations with pigmentation, sun sensitivity, freckling, nevi, and skin cancer phenotypes for the genes located in the chromosomal regions identified by GWAS and replication studies; both significant and null associations are included for a complete synthesis of all results reported to date.
Table 6. Associations with pigmentation, sun sensitivity, freckling, nevi, and skin cancer for genes located in chromosomal regions identified by GWAS and replication studies
Loci associated with pigmentation, cutaneous UV-response, and skin cancer
GWAS confirmed the known association of SNPs in MC1R, ASIP, TYR, TYRP1, SLC45A2, and OCA2 gene regions with pigmentation factors. SNPs in MC1R were associated with hair color (both red and light hair color), sun sensitivity, and freckling in the GWAS (Table 4; Supporting Information Tables S1, S2 and S5). In replication studies, Gudbjartsson et al. (2008) confirmed the previously known associations of melanoma and BCC with MC1R, and Duffy et al. (2010) confirmed associations with melanoma for two MC1R RHC variants, R151C and R160W (Table 5; Supporting Information Table S7). However, the associations were not significant in Duffy et al.’s study (2010) after adjustment for pigmentation.
SNPs in ASIP were associated with red hair color, sun sensitivity, and freckling (Table 4; Supporting information Tables S2 and S5). SNPs around MC1R and ASIP gene regions were the only ones significantly associated with red versus non-red hair color. A novel association with melanoma was found in the GWAS for loci around ASIP (Supporting Information Table S7). Replication studies further tested the association of ASIP SNPs with melanoma. In Gudbjartsson et al. (2008) and in a subset of cases and controls with 100% Northern European ancestry in Duffy et al. (2010), ASIP SNPs were associated with melanoma even after adjustment for pigmentation; Gudbjartsson et al. (2008) tested the ASIP haplotype (AH) rs1015362 and rs4911414, and Duffy et al. (2010) tested rs4911442. In Duffy et al. (2010), there was a suggestive pattern of interaction for MC1R and ASIP, as previously observed (Landi et al., 2005). In a third replication study, Nan et al. (2009a) tested the association of melanoma with three ASIP SNPs (rs1015362, rs4911414, and rs6058017) and AH (rs1015362 and rs4911414). The rs1015362 and rs6058017 SNPs and the ASIP haplotype had null associations with melanoma; rs4911414 had a suggestive association with melanoma risk but this was not statistically significant.
Gudbjartsson et al. (2008) identified a novel association for BCC with the ASIP haplotype, while Nan et al. (2009a) tested the association of rs1015362, rs4911414, rs6058017, and AH with BCC and SCC (Table 5). These latter authors found that whereas rs1015362 and AH were not associated, rs4911414 and rs6058017 were nearly significantly associated with risk of BCC (in opposite directions); however, neither was significant after Bonferroni correction for multiple testing. The rs4911414 SNP was associated with SCC and there was a suggestive association of rs6058017 with SCC, but these associations were not significant after Bonferroni correction (Nan et al., 2009a).
In GWAS, SNPs in TYR were associated with eye color, skin color, sun sensitivity, and freckling; in addition, a novel association with melanoma was found (Bishop et al., 2009; Falchi et al., 2009) (Table 4; Supporting information Tables S3–5 and S7). The association of TYR with melanoma was tested in replication studies. In Gudbjartsson et al. (2008), the TYR SNP rs1126809 (R402Q) was associated with melanoma after adjustment for pigmentation factors. In two other replication studies, however, although the direction of association was the same, this TYR SNP and rs1042602 (S192Y) were not associated with melanoma when adjusted for pigmentation factors (Duffy et al., 2010;Nan et al., 2009a). In one replication study, the TYR SNP rs1126809 was significantly associated with BCC (Gudbjartsson et al., 2008) (Table 5), whereas in Nan et al. (2009a) the association with BCC followed the same direction but was not significant. The TYR SNP rs1042602 was marginally associated with SCC, but the association was further diminished after Bonferroni correction (Nan et al., 2009a).
SNPs in TYRP1 were associated with eye color, and a novel association with melanoma was found in GWAS (Bishop et al., 2009; Falchi et al., 2009; Table 4; Supporting information Tables S3 and S7). Three replication studies also tested this association. In an Icelandic sample, the TYRP1 SNP rs1408799 was significantly associated with melanoma risk even after adjustment for pigmentation factors (Gudbjartsson et al., 2008). The association of the same TYRP1 SNP with melanoma was confirmed, but the association was not statistically significant, after Bonferroni correction for multiple comparisons in Nan et al. (2009a) and after adjustment for hair, eye, and skin color in Duffy et al. (2010).
In GWAS, SNPs in SLC45A2 were associated with light hair and skin color and sun sensitivity (Table 4; Supporting Information Tables S1, S4 and S5). Stacey et al. (2009) confirmed a previously identified association with melanoma (Supporting Information Table S7). These results were confirmed in Duffy et al. (2010), with three SNPs showing significant associations with melanoma; after adjustment for pigmentation and Bonferroni correction, the SLC45A2 SNP rs16891982 remained significantly associated with melanoma. Nan et al. (2009a) also tested the association of SLC45A2 SNPs and melanoma; one SNP, rs13289, was associated with melanoma risk, but the association was not significant after Bonferroni correction. Stacey et al. (2009) identified novel associations of the SLC45A2 SNP rs16891982 with BCC and SCC (Table 5); in that study, the SLC45A2 SNP rs16891982 was associated with pigmentation factors, but results of the association with skin cancer were not adjusted for pigmentation. A subsequent replication study found no association for three SLC45A2 SNPs with either BCC or SCC (Nan et al., 2009a).
OCA2 SNPs were associated with eye color in GWAS (Table 4; Supporting Information Table S3), confirming previous studies demonstrating that variation in eye color is linked to the OCA2 region (Duffy et al., 2007). OCA2 SNPs were not associated with melanoma in any GWAS. In a replication study, the OCA2 SNP rs1800407 was associated with melanoma but was not statistically significant after Bonferroni correction for multiple testing (Duffy et al., 2010). Other studies found an association with melanoma for this SNP and other OCA2 SNPs, but these studies were small (Fernandez et al., 2009;Jannot et al., 2005). In Nan et al. (2009a), this OCA2 SNP was associated with BCC, but the association was not statistically significant after Bonferroni correction.
Loci associated with nevi and melanoma
Two loci around the methylthioadenosine phosphorylase (MTAP, near CDKN2A/CDKN2B) and phospholipase A2, group VI (PLA2G6) genes were associated with nevi and melanoma (Supporting Information Tables S6 and S7). The association between these genes and melanoma was no longer significant after adjustment for nevus count, suggesting that susceptibility loci for nevus count were mediating melanoma risk in this population. Nevus count has been associated with the 9p21 locus containing MTAP and CDKN2A in prior linkage studies (Falchi et al., 2006;Zhu et al., 2007). GWAS did not confirm variants or loci previously associated with nevi by other methods, such as genome-wide linkage and candidate gene analysis on chromosomes 1, 2, 4, 6, 8, 16, 17 (Zhu et al., 2007), 5q31-32, and 2p24 (Falchi et al., 2006), and OCA2 and myosin VIIA (MYO7A) (Fernandez et al., 2009).
Loci associated with pigmentation and/or cutaneous UV-response
The loci associated with pigmentation and/or cutaneous UV-response but not melanoma or BCC included five genes, namely solute carrier family 24, member 4 (SLC24A4), two-pore segment channel 2 (TPCN2), interferon regulatory factor 4/exocyst complex component 2 (IRF4/EXOC2), kit ligand (KITLG), and hect domain and RCC1-like domain 2 (HERC2) (Table 4). Three of these, SLC24A4, TPCN2 and IRF4, are novel potential ‘pigmentation genes.’ Loci in and around SLC24A4 were associated with hair and eye color, and sun sensitivity (Supporting Information Tables S1, S3 and S5). The association of SLC24A4 SNPs with light hair color was replicated by Duffy et al. (2010), and a new association with blue eye color was reported in that study. SLC24A4 is in the same solute carrier family as the other ‘pigmentation genes’SLC45A2 and SLC24A5, which are potassium-dependent sodium/calcium exchangers (Lamason et al., 2005 and Sulem et al., 2007). SNPs in SLC24A5 were associated with skin color but were not tested for association with skin cancers in these GWAS (Supporting Information Table S4). In a replication study, SNPs around SLC24A5 were not associated with melanoma, BCC, or SCC (Nan et al., 2009a). In GWAS, SNPs around TPCN2 were associated with hair color only (Supporting information Table S1). Interestingly, TPCN2 encodes a protein also involved in calcium transport (Sulem et al., 2008). The relationship between solute transport and pigmentation, however, is not well-defined.
Unlike other ‘pigmentation genes’ that are associated with light (or dark) pigmentation traits uniformly (i.e. light hair, light eye and skin color, and poor tanning), the IRF4 minor variant rs12203592 [T] was associated with dark hair color but light eye and skin color, and sun sensitivity in GWAS (Table 4; Supporting information Tables S1, S3–5). The locus on chromosome 6p25.3 near IRF4 [between IRF4 and EXOC2 (exocyst complex component 2)] was also associated with freckling in another GWAS (Supporting information Table S5). Two studies further tested these associations (Duffy et al., 2010;Gathany et al., 2009). Both studies replicated the significant association with dark hair color; Duffy et al. (2010) also replicated the significant associations with light eye and skin color for IRF4, but these associations were not significant in Gathany et al. (2009), perhaps due to the smaller sample size (Supporting information Tables S1, S3 and S4). A meta-analysis for the association of the IRF4 SNP rs12203592 with hair color confirmed this finding (Supporting information Table S1). This observation suggests that for this locus, the mechanism of pigmentation control for hair color may differ from that for eye and skin color. Gudbjartsson et al. (2008) found that the association of melanoma and BCC with the chromosome 6p25.3 locus was not significant. Duffy et al. (2010) found a near significant association of the IRF4 SNP rs12203592 with melanoma in the Northern European subset, but the association was not significant after adjustment for pigmentation (Supporting Information Table S7). The IRF4 gene encodes a B-cell proliferation/differentiation protein and is a member of the interferon regulatory factor family of transcription factors, which are involved in regulating gene expression in response to interferon and other cytokines. IRF4 is also called MUM1 and was used in one study to detect melanocytic lesions (nevi and melanoma) pathologically (Sundram et al., 2003). In another study, IRF4 stained hematolymphoid neoplasms and melanomas but not breast, prostate, or GI tumors (Natkunam et al., 2001).
In GWAS, SNPs around KITLG loci were associated with hair color only (Supporting Information Table S1). Prior to GWAS, KITLG was known to have a role in pigmentation. KITLG functions in melanogenesis and was found to affect pigmentation in animal models (Hultman et al., 2007; reviewed in Wehrle-Haller, 2003). The association of KITLG polymorphisms with pigmentation in humans was confirmed in a study of skin color (Miller et al., 2007). Furthermore, gain of function mutations in KITLG were recently found to cause familial progressive hyperpigmentation, an autosomal dominant syndrome characterized by hyperpigmented patches of the skin that expand and increase with age (Wang et al., 2009).
SNPs in HERC2, a gene located close to OCA2, were associated with hair, eye and skin color, and sun sensitivity (Supporting Information Tables S1, S3–5). Different SNPs from the same locus were also associated with eye color in a candidate gene study (Eiberg et al., 2008). Previously, OCA2 variants were thought to be the primary determinants of blue eye color; however, the HERC2 SNP rs12913832 predicted eye color significantly better than was predicted by any OCA2 haplotype (Sturm et al., 2008). Variants in HERC2 are thought to lead to a decrease in expression of the adjacent OCA2 gene, especially within iris melanocytes (Sturm et al., 2008). Duffy et al. (2010) found no association with melanoma for HERC2.
Loci associated with skin cancer only
Four novel loci identified from GWAS were distinctly associated with BCC and one with BCC and melanoma, but not with pigmentation, sun sensitivity, freckling, or other skin cancers. The loci associated with BCC only are located around the following genes: peptidylarginine deiminase, type VI (PADI6 ), ras homolog gene family, member u (RHOU ), kruppel-like factor 14 (KLF14 ), and keratin 5 (KRT5 ) (Table 5). The mechanisms by which these loci increase the risk for BCC are unknown, but these findings suggest a pathway or pathways independent of pigmentation and sun sensitivity. Cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B ) was associated with BCC but not with melanoma in GWAS. However, CDKN2A is a high penetrance melanoma susceptibility gene, as discussed previously, and in some families with CDKN2A mutations, there is an increased risk of pancreatic cancer, breast cancer, and neural tumors in addition to melanoma (Bahuau et al., 1998; Borg et al., 2000; Goldstein et al., 2006; Hewitt et al., 2002; Petronzelli et al., 2001; Randerson-Moor et al., 2001; Rizos et al., 2001).
In GWAS, one SNP around telomerase reverse transcriptase-CLPTM1-like protein (TERT-CLPTM1L) was associated with an increased risk of BCC and a protective effect on melanoma. The TERT-CLPTM1L locus is particularly interesting in that it is associated with multiple malignancies, including glioma and cancer of the lung, urinary bladder, prostate, cervix, and pancreas (Landi et al., 2009; Petersen et al., 2010; Rafnar et al., 2009; Shete et al., 2009; Wang et al., 2008). While this locus is associated with increased risk of these malignancies, it is protective for melanoma in cases from Iceland, Sweden, and Spain (Rafnar et al., 2009; Stacey et al., 2009) (Supporting information Table S7). Variants in this locus are hypothesized to affect telomere length (Rafnar et al., 2009); in one study, short telomeres were associated with an increased risk of BCC, whereas long telomeres were associated with an increased risk of melanoma (Han et al., 2009). Further exploration of the functional correlates of this locus is required to understand the mechanisms responsible for these associations.
We performed a meta-analysis across the GWAS and replication studies to refine previous associations and potentially identify new loci for a given phenotype. When phenotypes were defined uniformly across the GWAS, as for red hair color and melanoma, the results for SNPs identified in more than one GWAS and/or replication study were combined in a meta-analysis (Supporting information Tables S2 and S7). Although few new SNPs reached genome-wide significance in the meta-analysis and previous associations were refined, no new loci emerged as significant in the meta-analysis for either phenotype. Forest plots for the melanoma meta-analysis are shown for SNPs identified in at least three studies in Figure 1. The meta-analysis was limited by several factors. First, the phenotypic definitions varied across studies. For example, non-red hair color, eye color, skin color, and sun sensitivity were defined differently across GWAS, limiting our ability to combine results for these phenotypes. Secondly, manuscripts reported only major GWAS results, often limited to statistically significant associations, precluding the possibility of combining results that could have reached significance in a meta-analysis. Thus, using the available published data and collaboration with the primary investigators of each study, we were able to combine results only for red hair color and melanoma.
In the meta-analysis for red hair color (Supporting Information Table S2), the strongest signals for red versus non-red hair color were attributed to loci on chromosome 16 near MC1R, particularly the well-known MC1R‘red hair color’ (RHC) alleles (R151C, R160W, and D294H) known to result in diminished function of the protein (reviewed in García-Borrón et al., 2005). The melanoma meta-analysis also includes replication studies, which tested SNPs identified in previous GWAS for associations with melanoma. In the melanoma meta-analysis, there is some redundancy of the study populations for several SNPs, as noted in Supporting information Table S7, which could not be quantified. For example, both Bishop et al. (2009) and Falchi et al. (2009) replicated GWAS findings in samples from the Leeds melanoma case-control study; however, as Falchi et al. (2009) used more Leeds samples, we excluded the replication data from Bishop et al. (2009) in the meta-analysis calculations. Also, Brown et al. (2008) and Bishop et al. (2009) used samples from the Q-MEGA study, and the redundancy could not be corrected; therefore, for SNPs with data from these studies, the meta-analysis P-values may be slightly reduced. For example, SNPs around ASIP had the strongest associations with melanoma (Figure 1), but the P-values could be affected by the inclusion of the same subjects in some of the studies contributing to the meta-analysis.
Genome-wide association studies of pigmentation, sun sensitivity, and skin cancer phenotypes have identified new loci associated with these phenotypes, some of which are shared and some of which are distinct. There are two main subsets of SNPs associated with melanoma risk, one associated with melanoma and pigmentation, and one associated with melanoma and nevus count. SNPs in these subsets did not overlap, suggesting that there may be unique pathways to melanoma development. There also appear to be at least two pathways to BCC development: one pigmentation-dependent and one pigmentation-independent. Interestingly, all of the SNPs associated with both BCC and melanoma, except the one around TERT-CLPTM1L, are also associated with pigmentation, including SLC45A2, TYR, MC1R, and ASIP, suggesting that BCC and melanoma share a common development pathway via pigmentation. An alternative possibility is that these SNPs are associated with pigmentation traits that predispose to skin cancer (melanoma and BCC) but that they do not themselves mechanistically lead to the development of skin cancer. A major limitation for the GWAS reviewed in this manuscript is that all but two skin cancer GWAS (Stacey et al., 2008, 2009) did not examine or adjust for skin cancer risk factors when reporting risk loci. Replication studies that adjusted for pigmentation did not consistently confirm associations for some SNPs with melanoma and BCC, including TYRP1, TYR, and ASIP (Duffy et al., 2010;Nan et al., 2009a). Thus, future studies of these genes should focus on whether subjects carrying these SNPs are at increased risk of melanoma and/or BCC risk through pigmentation alone or through pathways independent of pigmentation.
In the GWAS of nevus count, it did appear that melanoma risk was mediated by nevi, as the association between SNPs and melanoma risk was diminished following adjustment for nevus count (Falchi et al., 2009). In this study, the number of nevi was not associated with frequency of sunburn or skin type; however, other pigmentation factors were not tested for association with nevus count. Similarly, SNPs found associated with nevus count were not tested for association with pigmentation or cutaneous UV-response. Therefore, we cannot definitively conclude that the association between nevus count and melanoma is independent of pigmentation or cutaneous UV-response. In future studies, pigmentation factors and other risk factors for melanoma should be fully accounted for so as to conclusively determine whether the genetic associations with skin cancer are direct or are mediated by pigmentation, nevi, or both. Further studies of the SNPs associated with BCC but not pigmentation may uncover novel pathways for BCC development. Finally, SNPs associated with pigmentation but not other phenotypes in these GWAS should be further examined and tested for skin cancer associations in additional populations.
GWAS have been a powerful tool for identifying novel loci associated with pigmentation and skin cancer phenotypes. Interestingly, many novel associations were found when significant loci identified in one GWAS were replicated for a different phenotype. It was difficult to perform a meta-analysis given varying phenotypic definitions of pigmentation and sun sensitivity traits, suggesting that future studies should employ consistent definitions and categorizations, when possible, to allow for data harmonization.
GWAS do have two major intrinsic limitations. First, only a relatively small number of SNPs are contained on the chip used for genotyping, leaving large numbers of SNPs untested for associations. Secondly, the identified loci may not be the causal variants, so associated SNPs are not necessarily causally related to the examined phenotype. Studies with MC1R demonstrate these limitations. The MC1R RHC alleles are not on the common genotyping platforms that were used for these GWAS. As noted for GWAS of red hair color, in all GWAS reviewed here, a signal on chromosome 16 near MC1R prompted further genotyping of the RHC alleles and multivariable analyses, demonstrating that the signals were due to RHC alleles (Han et al., 2008; Nan et al., 2009b; Sulem et al., 2007, 2008). Interestingly, some of the MC1R SNPs identified in GWAS were not in linkage disequilibrium with the RHC alleles, suggesting that they acted independently; however, in multivariable analyses, only the association with RHC alleles remained significant. Therefore, GWAS results must be interpreted with caution: significant GWAS findings do not necessarily identify the causal variants or SNPs in linkage disequilibrium with the causal variants. Supporting this cautionary note is a recent study showing that the most significant SNP from a GWAS of a well-studied disease, sickle cell anemia, was located 9 kb from the known causal variant; furthermore, these authors suggest that rare causal mutations may create ‘synthetic’ associations in GWAS that are credited to common variants (Dickson et al., 2010). This underlines the importance of prior knowledge of risk factors (like MC1R for melanoma), even in an apparently unbiased, or agnostic, approach and indicates that other tools such as direct genomic sequencing examining rarer variants should be considered in future studies.
In conclusion, although there are limitations, GWAS have provided important information regarding loci associated with pigmentation phenotypes and skin cancer. These data can guide future studies using sequencing and other techniques to identify causal variants, and generate hypotheses regarding biological mechanisms and functional consequences of the identified variants.
Using the terms ‘genome wide melanoma’, ‘genome wide sun sensitivity’, ‘genome wide pigmentation’, ‘genome wide nevus’, ‘genome wide nevi’, ‘genome wide basal cell carcinoma’, ‘genome wide squamous cell carcinoma’, and ‘genome wide skin cancer’, we conducted a PubMed literature search and identified 12 studies that performed genome-wide analyses examining pigmentation and cutaneous UV-response traits, nevi, melanoma, basal cell carcinoma, and squamous cell carcinoma of the skin in human populations during the time from the first study (Stokowski et al., 2007) through 1 November 2009. We also identified four studies that expanded upon or replicated findings from GWAS within the same time frame. We reported associations that were significant at a GWAS level corresponding to a P-value <1 × 10−7 and were replicated in at least one other sample. In studies replicating GWAS findings, a P-value <0.05 was considered significant unless otherwise specified in the study. Given different phenotypic definitions, a meta-analysis was only possible for red hair color and melanoma phenotypes.
Statistical methods for meta-analysis
For each given SNP associated with red hair color and/or melanoma, a meta-analysis was performed to combine odds ratios for a reference allele weighted by the estimation certainty under a fixed effects model. In all of the studies except those using pooling, the test statistics were corrected for population stratification using principal component analysis or multidimensional scaling. For all studies reviewed in this manuscript, with the exception of Gudbjartsson et al. (2008), Rafnar et al. (2009), Stacey et al. (2008, 2009), and Sulem et al. (2007, 2008), there was no strong evidence of cryptic relatedness or substructure based on genome control (GC) values; hence, we have used the original odds ratios (ORs) and P-values in the meta-analysis. The studies performed by deCODE Genetics (Gudbjartsson et al., 2008; Rafnar et al., 2009; Stacey et al., 2008, 2009; Sulem et al., 2007, 2008) reported P-values adjusted by GC values because of strong evidence of cryptic relatedness and substructure. We have used the reported, GC-corrected test results in the meta-analysis.
Many papers reported ORs, confidence interval (CIs) of ORs, and P-values. In some cases, studies used different reference alleles for the same SNP; the reference alleles are listed in the tables to help interpret the direction of association. Where original studies reported different alleles, the reference alleles used for the meta-analyses are specified in bold in the ‘Meta-analysis’ column of the tables (Supporting Information Tables S2 and S7). The natural weights used for combining ORs are their standard deviations (SDs), which were derived from the reported CIs. The approach is appropriate if the P-values were derived from Wald tests based on ORs and their SDs. However, in our situation, the P-values in some papers were computed based on the likelihood ratio statistics (LRT) (Rafnar et al., 2009; Stacey et al., 2008, 2009; Stokowski et al., 2007). The LRT produces much more accurate P-values than the Wald test statistic for studies with a very high proportion of controls. In this case, the meta-analysis based on ORs produces incorrect P-values. Therefore, we performed a meta-analysis by combining the reported P-values in four steps:
1 We converted the P-value Pi in study i into a normal quantile zi, with and the sign determined by the direction of the tested allele. Here, Φ is the cumulative density function of the standard normal distribution.
2 We computed the positive weight as .
3 We computed the Z test statistic for the meta-analysis as The two-sided P-value for the meta-analysis is calculated as P = 2Φ(−|Z|).
4 We computed the OR for the meta-analysis as
The meta-analysis plots reporting the association between SNPs and melanoma risk (Figure 1) were obtained using the r statistical package ‘rmeta.’
Heterogeneity and random effects meta-analysis
We computed Cochran’s Q statistic (Cochran, 1954) and I2 statistic (Higgins and Thompson, 2002) to quantify the heterogeneity effect across studies. Under the null hypothesis of no heterogeneity, the Cochran’s Q statistic follows a χ2 distribution with n–1 degree of freedom, where n is the number of studies to be combined. For SNPs with suggestive evidence of heterogeneity (I2 > 0%), we performed a meta-analysis under a random effects model (Higgins and Thompson, 2002). As expected, the P-values under the fixed effects model and the random effects model are very different when there is strong evidence of heterogeneity.
Our analysis included only reported results for most of the reviewed SNPs. For a given SNP reported here, one or more GWAS may have failed to report the test results because the SNP was not genotyped, because the association between the SNP and the disease had been previously demonstrated to be significant, or because the SNP was not among the most significant in that study. For the latter two cases, potential reporting bias could be present in this analysis. However, for the SNPs reported in this manuscript, as existing data have provided very strong evidence of association, the potential reporting bias is unlikely to change the conclusion. Another limitation is that not all studies included in the meta-analysis reported data on all of the same SNPs; thus, we could only combine the SNPs that were reported in more than one study. For example, there were six studies that performed melanoma GWAS (Bishop et al., 2009; Brown et al., 2008; Falchi et al., 2009; Rafnar et al., 2009; Stacey et al., 2008, 2009), and four replication studies (Duffy et al. 2010; Gathany et al., 2009; Gudbjartsson et al., 2008; Nan et al., 2009a); however, for most SNPs, we could combine data across only a few of the studies as these studies did not report data on all of the same SNPs. Only SNPs with novel and/or significant associations for melanoma are included in Supporting Information Table S7, and only SNPs reported in at least three studies are included in Figure 1. For the red hair color meta-analysis, only two GWAS examined this phenotype and were thus included: Sulem et al. (2008) and Han et al. (2008). The combined results across the two studies are reported (Supporting information Table S2). Both studies reported many of the same SNPs. However, because for some SNPs only P-values were reported, the direction of the association could not be inferred; therefore, the meta-analysis for red hair color was possible for most but not all reported significant SNPs.
This study was supported by the Intramural Research Program of NIH, National Cancer Institute, Division of Cancer Epidemiology and Genetics. The authors thank Drs. David Duffy, Hongmei Nan, Jiali Han, Mario Falchi, and Patrick Sulem for sharing data that allowed meta-analysis and refined associations for specific loci, and Barbara Rogers, William Wheeler, and Sara De Matteis for help with the graphical items.