Background: Linkage analyses and association studies suggested that inherited genetic variations play a role in the development of differentiated thyroid carcinoma (DTC).

Methods: We combined the results from a genome-wide association study (GWAS) performed by our group and from published studies on DTC. With a first approach, we evaluated whether a SNP published as associated with the risk of DTC could replicate in our GWAS (using FDR as adjustment for multiple comparisons). With the second approach, meta-analyses were performed between literature and GWAS when both sources suggested an association, increasing the statistical power of the analysis.

Results: rs1799814 (CYP1A1), rs1121980 (FTO), and 3 SNPs within 9q22 (rs965513, rs7048394, and rs894673) replicated the associations described in the literature. In addition, the meta-analyses between literature and GWAS revealed 10 more SNPs within 9q22, six within FTO, two within SOD1, and single variations within HUS1, WDR3, UGT2B7, ALOX12, TICAM1, ATG16L1, HDAC4, PIK3CA, SULF1, IL11RA, VEGFA, and 1p31.3, 2q35, 8p12, and 14q13.

Conclusion: This analysis confirmed several published risk loci that could be involved in DTC predisposition.

Impact: These findings provide evidence for the role of germline variants in DTC etiology and are consistent with a polygenic model of the disease. Cancer Epidemiol Biomarkers Prev; 25(4); 700–13. ©2016 AACR.

Thyroid carcinoma is the most common malignancy of the endocrine system showing increasing incidences over the years (1–3), with age-standardized rates (ASR) of about 6/100,000 in the developed countries. Particularly elevated ASRs were observed in Lithuania (ASR = 15.5/100,000), Italy (ASR = 13.5/100,000), Austria (ASR = 12.4/100,000), and in the United States (ASR = 9.9/100,000; refs. 4, 5). Two main thyroid carcinoma histological types can be distinguished: the “medullary” and the “non-medullary” thyroid carcinoma, the former (MTC) originating from the para-follicular cells, the latter (NMTC) from the follicular cells. NMTC comprises the most frequent subtypes, papillary (PTC) and follicular (FTC) thyroid carcinomas (defined overall as “differentiated thyroid carcinomas”, DTC), accounting for 80% and 15% of the cases, respectively. Hürthle cells (or oxyphilic cells, 5%) and poorly differentiated carcinomas (1%–6%) are considered as not common entities (6, 7).

The great majority of DTCs behaves as a sporadic form, featured by somatic mutations within RET, RAS, BRAF, or NTRK1 genes and affecting the MAPK signaling pathway (8–11). However, approximately 5% of cases, mostly PTC, have a family history (12). Inherited genetic variations play an important role in both the familial and the sporadic forms, as supported by data from linkage analyses or case–control association studies (CCASs). In particular, CCASs, when carried out with an appropriate sample size, constitute the state-of-the-art for the identification of common genetic variants associated with complex traits (13). In the literature, a large number of gene-based studies have been published in which specific a priori hypotheses have been examined. Thus, SNPs within genes encoding for proteins involved in the DNA repair, cell-cycle control, thyroid physiology, or playing a role in other types of human cancer have been investigated (14). The major weakness of these studies is that unknown genes playing a relevant role in the etiology of the disease could have been missed. This limitation is solved by genome-wide association studies (GWASs) in which the whole genome is analyzed without formulating any a priori hypothesis. GWASs on DTC allowed discovering novel variants, including those near FOXE1, DIRC3, NKX2-1 (15–17) and, more recently, those near IMMP2L, RARRES1, SNAPC4/CARD9, ARSB, BATF, DHX35, SPATA13, GALNTL4, and FOXA2 (18–20). However, to ensure a high quality and to prevent false-positive findings, highly stringent criteria are applied in the GWASs with the disadvantage of excluding SNPs truly associated with the risk. In the present work, we investigated whether SNPs associated with the susceptibility to DTC in previous CCASs could replicate in an independent GWAS carried out by our research group. Moreover, we investigated whether SNPs showing a sub-threshold genome-wide statistical significance in our GWAS could improve their association following a meta-analysis with previously published data.

Ethics statement

Study participants were recruited according to the protocols approved by the institutional review boards in accordance with the Declaration of Helsinki. All subjects provided written informed consent to participate in the study and allowed the use of their biological samples.

Study participants of the GWAS

The group of cases comprised 701 histologically confirmed DTC patients from central and southern Italy, recruited at the Cisanello Hospital in Pisa, an important Italian referral center for thyroid diseases. The control group comprised 499 healthy individuals from the Meyer Hospital in Florence without known thyroid disease, of which 390 were blood donor volunteers and 109 were healthy individuals recruited during a routine health screening. Cases and controls were frequency matched by sex, age, body mass index (BMI), and smoking habits. The patient group consisted of 22.3% males and 77.7% females with a median age of 46; the control group consisted of 23.2% males and 76.8% females with a median age of 50. The median BMI was 24.5 in cases and 24.4 in controls. The proportion of smokers was 37% in cases and 40% in controls. All cases and controls were of Caucasian origin.

Genome-wide association study

Full details of the GWAS, including the genotyping process, quality control and statistical analysis were previously described (20). Briefly, samples were genotyped using Illumina HumanOmni1-Quad_v1-0_B 1M BeadChips and Illumina HumanOmniExpress-12v1_A 730K BeadChips. Genotype calling was performed using Illumina GenomeStudio 2010 (Illumina Inc.). After applying strict quality control criteria, the analysis was restricted to the subset of genotyped SNPs common to both Illumina arrays used. Hence, 572 042 SNPs were analyzed for association with DTC risk in 690 cases and 497 controls. The adequacy of the case–control matching and the possibility of differential genotyping of cases and controls were assessed using Q-Q plots of test statistics. The genomic control inflation factor λ was calculated using the standard method by the CRAN R package GAP (Genetic Analysis Package; https://cran.r-project.org/web/packages/gap/index.html; http://www.inside-r.org/packages/cran/gap/docs/gcontrol2).

The inflation factor λ was 1.0, excluding the possibility of hidden population substructure, relatedness among subjects or differential genotype calling. Statistical analysis was conducted using PLINK version 1.06 (21).

Search strategy and selection criteria

PubMed was searched from database inception until September 2013 to collect case–control studies investigating the association between SNPs and DTC. We used the keywords polymorph* AND (papillary OR follicular OR non-medullary OR “non medullary”) AND thyroid AND (cancer OR carcinoma) AND (susceptibility OR risk OR predisposition) to collect studies carried out on DTC or PTC. The major reasons for exclusion of the studies were (i) studies not in English language; (ii) studies without odds ratio (OR) and 95% confidence interval (95% CI); (iii) case–case studies; (iv) studies on benign thyroid disease. A total of 100 original articles and five meta-analyses met our criteria and were assessed. The list of citations is reported, for brevity, in the Supplementary data. The SNPs reported in these studies were recorded and searched in the present GWAS, allowing a direct comparison between the results published in the literature with the results from the GWAS. When an SNP was not found in the GWAS, the linkage disequilibrium (LD) block around the SNP was checked using the CEU data of the 1000 Genome Project (22), and the results of SNPs in high LD (r2 ≥ 0.8) were reported. All the collected data are reported in Supplementary Tables S1 and S2.

Statistical approaches

Two statistical approaches were used to reduce the number of false positives and to increase the power of the study. With the first approach, we performed a meta-analysis of published data when more than one study was carried out on a given SNP. Then, we evaluated whether SNPs previously associated with the risk of DTC (positive, at a nominal significance level of Pass < 0.05, either in a single study or in meta-analysis) were associated also with the risk of DTC in our GWAS. These SNPs were evaluated by calculating their allelic Pass in the GWAS. In order to adjust for multiple comparisons, the false-positive discovery rate correction (FDR; ref. 23) was applied to the list of Pass obtained in the GWAS and the associations with q < 0.05 were considered as statistically significant, i.e., considered as replicating the literature data.

With the second approach, results from SNPs positive in the literature (either in a single study or in the meta-analysis of the literature) were meta-analyzed with those of GWAS. Moreover, the meta-analyses were performed also when, for a given SNP, a suggestive evidence of association (Pass < 0.20, taken arbitrarily) was observed both in the literature and in the GWAS. Because the GWAS was performed on Caucasians, the meta-analysis first was carried out in Caucasians. When literature data were not available for Caucasians, the GWAS was meta-analyzed using literature data for the available population(s).

The pooled ORs were calculated for allelic model (a vs. A) and additive model (Aa vs. AA and aa vs. AA). In case only dominant or recessive model was reported in the literature, the same model was applied for the GWAS data.

The statistics are based on the absolute counts of variant and common alleles/genotypes among cases and controls. The χ2 based Q-test was used to assess heterogeneity across studies (Phet < 0.05) and I2 statistics was calculated to quantify the proportion of the total variation across studies due to heterogeneity. In case of no significant heterogeneity, OR and 95% CI were assessed using the fixed-effect model (the Mantel–Haenszel method); otherwise the random-effects model (DerSimonian–Laird method) was used. Meta-analyses were performed by MIX 1.7 freeware software. Also in this case, adjustment for multiple comparisons was performed by applying the FDR correction and q < 0.05 were considered as significant. A SNP associated with the risk of DTC in the literature was considered replicated when found with a q < 0.05 also in the GWAS. Moreover, an SNP was considered positively associated with the risk of DTC when found with a q < 0.05 in the meta-analysis.

One hundred published articles, reporting results for 316 SNPs belonging to 127 genes, met the selection criteria (see the reference list in the Supplementary data). Data collected included the reference of the literature, the gene name, the dbSNP identification number, the number of cases and controls investigated, and the OR with its 95% CI, of the allelic and additive models. In the first type of evaluation, the corresponding ORs and 95% CIs were also calculated for these SNPs based on the GWAS. In case only dominant or recessive model was reported in the literature, the same model was applied for the GWAS data. The results are reported side-by-side in Supplementary Tables S1 and S2, respectively, to allow a direct comparison. Among the 316 SNPs, 91 were associated with the risk of DTC in a statistically significant way according to the literature (Pass < 0.05). The meta-analysis of the literature data alone was performed on 46 SNPs and 13 were statistically significant at the 0.05 level (Supplementary Table S3). Fifteen of the 91 SNPs associated in any study were replicated in the GWAS at the same significance level, and the side-by-side comparison is shown in Table 1. However, only five SNPs, including CYP1A1 rs1799814, FTO rs1121980, and the GWAS identified SNPs on 9q22 (rs965513, rs7048394, and rs894673), were statistically significant after the application of FDR correction. Only one SNP (rs965513) showed to be associated in a statistically significant way in the meta-analysis of literature data and in the present GWAS (Table 1). In addition to these analyses, we adopted another approach. In order to ascertain whether an increase of statistical power could allow reaching a statistical significance, we selected SNPs showing a Pass < 0.2 (arbitrarily chosen, in any inheritance model) both in the literature and in the GWAS and we performed a meta-analysis. Moreover, we added the results of the present GWAS to meta-analyses from literature data, when these latter showed SNPs significantly associated with the risk of DTC.

Table 1.

List of SNPs associated with the risk of DTC in previous studies published in the literature

Gene or locusdbSNP ID; variantAuthor, yearLiterature best PassMeta-analysis of the literatureGWAS allelic PassGWAS allelic q
DNA repair 
 ALKBH3 rs10838192 Neta et al. 2011 (25) 9.42 × 10−4 — 0.79 0.92 
 BRCA1 rs799917 Xu et al. 2012 (26) 0.04 0.07 0.69 0.92 
 BRIP1 rs2048718; −1918G>A Sigurdson et al. 2009 (27) 0.01 — 0.21 0.70 
 HUS1 rs2708906 Neta et al. 2011 (25) 2.18 × 10−4 — 0.26 0.70 
 RAD52 rs11226; *744C>T Siraj et al. 2008 (28) 8.52 × 10−4 — 0.87 0.95 
 XRCC1 rs1799782; Arg194Trp Chiang et al. 2008 (29) 0.02 0.63 0.64 0.92 
 XRCC1 rs25489 García-Qu. et al. 2011 (30) 0.03 0.73 0.24 0.70 
 XRCC1 rs25487 Ho et al. 2009 (31) 0.01 0.24 0.13 0.53 
 XRCC3 rs861539; Thr241Met Sturgis et al. 2005 (32) 2.48 × 10−3 0.13 0.34 0.74 
 XRCC7 rs7830743 Rahimi et al. 2012 (33) 1.17 × 10−4 0.01 0.66 0.92 
Cell-cycle regulation and apoptosis 
 BAK1 rs493871 Neta et al. 2011 (25) 1.42 × 10−4 — 0.36 0.74 
 BCL2 rs1801018; Thr7Thr Eun et al. 2011 (34) 0.04 — 0.96 0.99 
 BCL2 rs2279115 Wang et al. 2012 (35) 0.01 0.07 0.77 0.92 
 CDKN2A rs3731217 Zhang et al. 2013 (36) 1.39 × 10−3 — 0.82 0.92 
 MDM2 rs2279744 Zhang et al. 2013 (36) 0.01 — 0.28 0.70 
 TGFB1 rs1800472; Thr263Ile Sigurdson et al. 2009 (27) 0.01 — 0.38 0.74 
 TP53 rs1042522; Pro72Arg Granja et al. 2004 (37) 3.12 × 10−3 0.05 0.50 0.85 
 WDR3 rs4658973 Baida el al.,2008 (38) 3.91 × 10−8 7.07 × 10−6 0.04 0.24 
Xenobiotic metabolism 
 CYP1A1 rs4646903; 3801T>C Bufalo et al. 2006 (39) 0.01 0.11 0.09 0.43 
 CYP1A1 rs1799814; 4887C>A Siraj et al. 2008 (40) 1.50 × 10−5 — 1.00 × 10−3 0.018 
 CYP19A1 rs4774585 Schonfeldet al. 2012 (41) 2.64 × 10−3 — 0.74 0.92 
 CYP19A1 rs1004984 Schonfeldet al. 2012 (41) 0.01 — 0.19 0.68 
 CYP19A1 rs7163193 Schonfeldet al. 2012 (41) 0.04 — 0.89 0.95 
 CYP19A1 rs2414099 Schonfeldet al. 2012 (41) 0.01 — 0.12 0.51 
 CYP8B1 rs6788947 Asc.-Kilfoy et al. 2012 (42) 6.58 × 10−4 — 0.82 0.92 
 CYP8B1 rs7614670 Asc.-Kilfoy et al. 2012 (42) 0.02 — 0.71 0.92 
 CYP8B1 rs11715464 Asc.-Kilfoy et al. 2012 (42) 4.71 × 10−3 — 0.93 0.98 
 FMO3 rs10911641 Asc.-Kilfoy et al. 2012 (42) 0.03 — 0.46 0.83 
 GSTP1 rs1695 Granja et al. 2004 (43) 3.21 × 10−4 0.77 0.47 0.83 
 MTF2 rs549938 Asc.-Kilfoy et al. 2012 (42) 0.01 — 0.34 0.74 
 MTHFR rs1801133 Prasad et al. 2011 (44) 0.04 0.03 0.24 0.70 
 NAT2 rs1799929; Leu161Leu Hernández et al. 2008 (45) 0.01 — 0.73 0.92 
 NAT2 rs1041983; Tyr94Tyr Guilhen et al. 2009 (46) 1.76 × 10−9 — 0.69 0.92 
 NAT2 rs1208; Arg268Lys Guilhen et al. 2009 (46) 0.01 — 0.97 0.99 
 SOD1 rs1041740 Asc.-Kilfoy et al. 2012 (42) 2.00 × 10−3 — 0.23 0.70 
 SOD1 rs12626475 Asc.-Kilfoy et al. 2012 (42) 3.39 × 10−3 — 0.27 0.70 
 UGT2B7 rs3924194 Asc.-Kilfoy et al. 2012 (42) 0.01 — 0.28 0.70 
Thyroid function 
 TG rs180223; Ser734Ala Akdi et al. 2011 (47) 4.26 × 10−4 — 0.28 0.70 
 TG rs853326; Met1028Val Akdi et al. 2011 (47) 3.92 × 10−4 — 0.44 0.83 
 THRA rs939348 Pastor et al. 2012 (48) 0.02 — 0.80 0.92 
 TPO rs732609; Thr725Pro Cipollini et al. 2013 (49) 5.90 × 10−3 0.12 0.65 0.92 
 TPO rs2048722 Cipollini et al. 2013 (49) 0.03 0.26 0.84 0.93 
MAPK pathway 
 HRAS rs12628; 81T>C Khan et al. 2013 (50) 7.66 × 10−16 — 0.02 0.16 
 PDGFRA rs6554162; −1309A>G Kim et al. 2012 (51) 6.62 × 10−4 — 0.66 0.92 
 PDGFRA rs1800812; −635G>T Kim et al. 2012 (51) 0.01 — 0.63 0.92 
 RET rs1800860; Ala432Ala Ho et al. 2005 (52) 0.01  0.38 0.74 
Immune response and inflammation 
 ALOX12 rs1126667; Gln261Arg Prasad et al. 2012 (53) 7.79 × 10−8 — 0.94 0.98 
 IL1B rs1143627; −31C>T Ban et al. 2012 (54) 0.04 — 0.62 0.92 
 IL1B rs1143643 Ban et al. 2012 (54) 0.02 — 0.89 0.95 
 IL11RA rs1061758; −106A>G Eun et al. 2012 (55) 2.21 × 10−3 — 0.03 0.21 
 MASP1 rs850316 Brenner al.,2013 (56) 1.31 × 10−3 — 0.28 0.70 
 SERPINA5 rs6112; Pro159Pro Brenner al.,2013 (56) 4.27 × 10−4 — 0.81 0.92 
 SERPINA5 rs6108 Brenner al.,2013 (56) 8.41 × 10−5 — 0.69 0.92 
 TICAM1 rs2292151; Asp557Asp Brenner al.,2013 (56) 0.01 — 0.04 0.24 
Other cancer genes 
 ATG16L1 rs2241880; Thr300Ala Huijbers et al. 2012 (57) 0.02 — 0.03 0.21 
 BMP3 rs3733549; Arg192Gln Kim et al. 2013 (58) 0.02 — 0.47 0.83 
 CDH1 rs16260; −160C>A Wang et al. 2012 (59) 3.32 × 10−3 — 0.35 0.74 
 COL11A1 rs1763347; Gly1516Gly Park et al. 2011 (60) 4.67 × 10−3 — 0.59 0.92 
 COL11A1 rs2229783; Ile1602Ile Park et al. 2011 (60) 0.01 — 0.70 0.92 
 ESR1 rs2228480; Thr594Thr Rebaï et al. 2009 (61) 1.51 × 10−16 — 0.05 0.28 
 FTO rs17817288 Kitahara et al. 2012 (62) 9.75 × 10−4 — 0.005 0.064 
 FTO rs11642841 Kitahara et al. 2012 (62) 0.01 — 0.006 0.067 
 FTO rs1121980 Kitahara et al. 2012 (62) 4.86 × 10−3 — 0.001 0.018 
 FTO rs9939609 Kitahara et al. 2012 (62) 4.36 × 10−3 — 0.004 0.06 
 FTO rs1477196 Kitahara et al. 2012 (62) 3.44 × 10−3 — 0.23 0.70 
 GNB3 rs5443; 825C>T Sheu et al. 2007 (63) 0.03 — 0.51 0.85 
 HDAC4 rs6749348 Neta et al. 2011 (25) 1.37 × 10−4 — 0.29 0.71 
 HDAC4 rs7584828 Neta et al. 2011 (25) 1.36 × 10−3 — 0.10 0.45 
 HER2 rs1801200; Ile655Val Rebaï et al. 2009 (64) 0.01 — 0.99 0.99 
 IGF1R rs2229765; Glu1043Glu Cho et al. 2012 (65) 3.49 × 10−3 — 0.51 0.85 
 IGFBP3 rs2132572 Xu et al. 2012 (66) 3.87 × 10−3 — 0.37 0.74 
 IGFBP3 rs2854744 Xu et al. 2012 (66) 0.04 — 0.99 0.99 
 INSR rs919275 Kitahara et al. 2012 (62) 0.02 — 0.73 0.92 
 ITGA6 rs11895564; Ala380Thr Kim et al. 2011 (67) 5.00 × 10−3 — 0.16 0.63 
 ITGB2 rs2070946; −149A>G Eun et al. 2013 (68) 1.27 × 10−3 — 0.63 0.92 
 MDR1 rs1045642; Ile1145Ile Ozdemir et al. 2013 (69) 5.01 × 10−4 — 0.09 0.43 
 OPN rs11730582; −443C>T Mu et al. 2013 (70) 0.03 — 0.69 0.92 
 PTPRJ rs4752904; Asp872Glu Iuliano et al. 2010 (71) 5.00 × 10−3 — 0.43 0.82 
 SULF1 rs6472462 Schonfeldet al. 2012 (41) 0.01 — 0.35 0.74 
 VEGFA rs699947; −2578C>A Hsiao et al. 2007 (72) 0.01 — 0.06 0.32 
 WWOX rs3764340; Pro282Ala Cancemi et al. 2011 (73) 5.40 × 10−3 — 0.36 0.74 
GWAS or intergenic regions 
 1p12-13 rs2145418 Baida et al. 2008 (38) 8.78 × 10−10 — 0.77 0.92 
 1p31.3 rs334725 Gudmundsson et al. 2012 (15) 6.60 × 10−3 — 0.11 0.45 
 2q35 rs966423 Gudmundsson et al. 2012 (15) 1.30 × 10−9 1.06 × 10−6 0.009 0.09 
 5q24 rs2910164 Jazdzewski et al. 2008 (74) 1.13 × 10−5 0.10 0.60 0.92 
 8p12 rs2439302 Gudmundsson et al. 2012 (15) 2.00 × 10−9 — 0.19 0.68 
 8q24 rs6983267 Jones et al. 2012 (75) 4.66 × 10−3 0.07 0.82 0.92 
 9q22 rs965513 Gudmundsson et al. 2009 (16) 1.70 × 10−27 <10−20 2.67 × 10−10 2.40 × 108 
 9q22 rs7048394 Landa et al. 2009 (76) 2.40 × 10−4 — 2.41 × 10−6 7.23 × 105 
 9q22 rs894673 Landa et al. 2009 (76) 2.20 × 10−4 — 1.45 × 10−8 6.53 × 107 
 14q13 rs944289 Gudmundsson et al. 2009 (16) 2.00 × 10−9 <10−20 0.01 0.09 
Gene or locusdbSNP ID; variantAuthor, yearLiterature best PassMeta-analysis of the literatureGWAS allelic PassGWAS allelic q
DNA repair 
 ALKBH3 rs10838192 Neta et al. 2011 (25) 9.42 × 10−4 — 0.79 0.92 
 BRCA1 rs799917 Xu et al. 2012 (26) 0.04 0.07 0.69 0.92 
 BRIP1 rs2048718; −1918G>A Sigurdson et al. 2009 (27) 0.01 — 0.21 0.70 
 HUS1 rs2708906 Neta et al. 2011 (25) 2.18 × 10−4 — 0.26 0.70 
 RAD52 rs11226; *744C>T Siraj et al. 2008 (28) 8.52 × 10−4 — 0.87 0.95 
 XRCC1 rs1799782; Arg194Trp Chiang et al. 2008 (29) 0.02 0.63 0.64 0.92 
 XRCC1 rs25489 García-Qu. et al. 2011 (30) 0.03 0.73 0.24 0.70 
 XRCC1 rs25487 Ho et al. 2009 (31) 0.01 0.24 0.13 0.53 
 XRCC3 rs861539; Thr241Met Sturgis et al. 2005 (32) 2.48 × 10−3 0.13 0.34 0.74 
 XRCC7 rs7830743 Rahimi et al. 2012 (33) 1.17 × 10−4 0.01 0.66 0.92 
Cell-cycle regulation and apoptosis 
 BAK1 rs493871 Neta et al. 2011 (25) 1.42 × 10−4 — 0.36 0.74 
 BCL2 rs1801018; Thr7Thr Eun et al. 2011 (34) 0.04 — 0.96 0.99 
 BCL2 rs2279115 Wang et al. 2012 (35) 0.01 0.07 0.77 0.92 
 CDKN2A rs3731217 Zhang et al. 2013 (36) 1.39 × 10−3 — 0.82 0.92 
 MDM2 rs2279744 Zhang et al. 2013 (36) 0.01 — 0.28 0.70 
 TGFB1 rs1800472; Thr263Ile Sigurdson et al. 2009 (27) 0.01 — 0.38 0.74 
 TP53 rs1042522; Pro72Arg Granja et al. 2004 (37) 3.12 × 10−3 0.05 0.50 0.85 
 WDR3 rs4658973 Baida el al.,2008 (38) 3.91 × 10−8 7.07 × 10−6 0.04 0.24 
Xenobiotic metabolism 
 CYP1A1 rs4646903; 3801T>C Bufalo et al. 2006 (39) 0.01 0.11 0.09 0.43 
 CYP1A1 rs1799814; 4887C>A Siraj et al. 2008 (40) 1.50 × 10−5 — 1.00 × 10−3 0.018 
 CYP19A1 rs4774585 Schonfeldet al. 2012 (41) 2.64 × 10−3 — 0.74 0.92 
 CYP19A1 rs1004984 Schonfeldet al. 2012 (41) 0.01 — 0.19 0.68 
 CYP19A1 rs7163193 Schonfeldet al. 2012 (41) 0.04 — 0.89 0.95 
 CYP19A1 rs2414099 Schonfeldet al. 2012 (41) 0.01 — 0.12 0.51 
 CYP8B1 rs6788947 Asc.-Kilfoy et al. 2012 (42) 6.58 × 10−4 — 0.82 0.92 
 CYP8B1 rs7614670 Asc.-Kilfoy et al. 2012 (42) 0.02 — 0.71 0.92 
 CYP8B1 rs11715464 Asc.-Kilfoy et al. 2012 (42) 4.71 × 10−3 — 0.93 0.98 
 FMO3 rs10911641 Asc.-Kilfoy et al. 2012 (42) 0.03 — 0.46 0.83 
 GSTP1 rs1695 Granja et al. 2004 (43) 3.21 × 10−4 0.77 0.47 0.83 
 MTF2 rs549938 Asc.-Kilfoy et al. 2012 (42) 0.01 — 0.34 0.74 
 MTHFR rs1801133 Prasad et al. 2011 (44) 0.04 0.03 0.24 0.70 
 NAT2 rs1799929; Leu161Leu Hernández et al. 2008 (45) 0.01 — 0.73 0.92 
 NAT2 rs1041983; Tyr94Tyr Guilhen et al. 2009 (46) 1.76 × 10−9 — 0.69 0.92 
 NAT2 rs1208; Arg268Lys Guilhen et al. 2009 (46) 0.01 — 0.97 0.99 
 SOD1 rs1041740 Asc.-Kilfoy et al. 2012 (42) 2.00 × 10−3 — 0.23 0.70 
 SOD1 rs12626475 Asc.-Kilfoy et al. 2012 (42) 3.39 × 10−3 — 0.27 0.70 
 UGT2B7 rs3924194 Asc.-Kilfoy et al. 2012 (42) 0.01 — 0.28 0.70 
Thyroid function 
 TG rs180223; Ser734Ala Akdi et al. 2011 (47) 4.26 × 10−4 — 0.28 0.70 
 TG rs853326; Met1028Val Akdi et al. 2011 (47) 3.92 × 10−4 — 0.44 0.83 
 THRA rs939348 Pastor et al. 2012 (48) 0.02 — 0.80 0.92 
 TPO rs732609; Thr725Pro Cipollini et al. 2013 (49) 5.90 × 10−3 0.12 0.65 0.92 
 TPO rs2048722 Cipollini et al. 2013 (49) 0.03 0.26 0.84 0.93 
MAPK pathway 
 HRAS rs12628; 81T>C Khan et al. 2013 (50) 7.66 × 10−16 — 0.02 0.16 
 PDGFRA rs6554162; −1309A>G Kim et al. 2012 (51) 6.62 × 10−4 — 0.66 0.92 
 PDGFRA rs1800812; −635G>T Kim et al. 2012 (51) 0.01 — 0.63 0.92 
 RET rs1800860; Ala432Ala Ho et al. 2005 (52) 0.01  0.38 0.74 
Immune response and inflammation 
 ALOX12 rs1126667; Gln261Arg Prasad et al. 2012 (53) 7.79 × 10−8 — 0.94 0.98 
 IL1B rs1143627; −31C>T Ban et al. 2012 (54) 0.04 — 0.62 0.92 
 IL1B rs1143643 Ban et al. 2012 (54) 0.02 — 0.89 0.95 
 IL11RA rs1061758; −106A>G Eun et al. 2012 (55) 2.21 × 10−3 — 0.03 0.21 
 MASP1 rs850316 Brenner al.,2013 (56) 1.31 × 10−3 — 0.28 0.70 
 SERPINA5 rs6112; Pro159Pro Brenner al.,2013 (56) 4.27 × 10−4 — 0.81 0.92 
 SERPINA5 rs6108 Brenner al.,2013 (56) 8.41 × 10−5 — 0.69 0.92 
 TICAM1 rs2292151; Asp557Asp Brenner al.,2013 (56) 0.01 — 0.04 0.24 
Other cancer genes 
 ATG16L1 rs2241880; Thr300Ala Huijbers et al. 2012 (57) 0.02 — 0.03 0.21 
 BMP3 rs3733549; Arg192Gln Kim et al. 2013 (58) 0.02 — 0.47 0.83 
 CDH1 rs16260; −160C>A Wang et al. 2012 (59) 3.32 × 10−3 — 0.35 0.74 
 COL11A1 rs1763347; Gly1516Gly Park et al. 2011 (60) 4.67 × 10−3 — 0.59 0.92 
 COL11A1 rs2229783; Ile1602Ile Park et al. 2011 (60) 0.01 — 0.70 0.92 
 ESR1 rs2228480; Thr594Thr Rebaï et al. 2009 (61) 1.51 × 10−16 — 0.05 0.28 
 FTO rs17817288 Kitahara et al. 2012 (62) 9.75 × 10−4 — 0.005 0.064 
 FTO rs11642841 Kitahara et al. 2012 (62) 0.01 — 0.006 0.067 
 FTO rs1121980 Kitahara et al. 2012 (62) 4.86 × 10−3 — 0.001 0.018 
 FTO rs9939609 Kitahara et al. 2012 (62) 4.36 × 10−3 — 0.004 0.06 
 FTO rs1477196 Kitahara et al. 2012 (62) 3.44 × 10−3 — 0.23 0.70 
 GNB3 rs5443; 825C>T Sheu et al. 2007 (63) 0.03 — 0.51 0.85 
 HDAC4 rs6749348 Neta et al. 2011 (25) 1.37 × 10−4 — 0.29 0.71 
 HDAC4 rs7584828 Neta et al. 2011 (25) 1.36 × 10−3 — 0.10 0.45 
 HER2 rs1801200; Ile655Val Rebaï et al. 2009 (64) 0.01 — 0.99 0.99 
 IGF1R rs2229765; Glu1043Glu Cho et al. 2012 (65) 3.49 × 10−3 — 0.51 0.85 
 IGFBP3 rs2132572 Xu et al. 2012 (66) 3.87 × 10−3 — 0.37 0.74 
 IGFBP3 rs2854744 Xu et al. 2012 (66) 0.04 — 0.99 0.99 
 INSR rs919275 Kitahara et al. 2012 (62) 0.02 — 0.73 0.92 
 ITGA6 rs11895564; Ala380Thr Kim et al. 2011 (67) 5.00 × 10−3 — 0.16 0.63 
 ITGB2 rs2070946; −149A>G Eun et al. 2013 (68) 1.27 × 10−3 — 0.63 0.92 
 MDR1 rs1045642; Ile1145Ile Ozdemir et al. 2013 (69) 5.01 × 10−4 — 0.09 0.43 
 OPN rs11730582; −443C>T Mu et al. 2013 (70) 0.03 — 0.69 0.92 
 PTPRJ rs4752904; Asp872Glu Iuliano et al. 2010 (71) 5.00 × 10−3 — 0.43 0.82 
 SULF1 rs6472462 Schonfeldet al. 2012 (41) 0.01 — 0.35 0.74 
 VEGFA rs699947; −2578C>A Hsiao et al. 2007 (72) 0.01 — 0.06 0.32 
 WWOX rs3764340; Pro282Ala Cancemi et al. 2011 (73) 5.40 × 10−3 — 0.36 0.74 
GWAS or intergenic regions 
 1p12-13 rs2145418 Baida et al. 2008 (38) 8.78 × 10−10 — 0.77 0.92 
 1p31.3 rs334725 Gudmundsson et al. 2012 (15) 6.60 × 10−3 — 0.11 0.45 
 2q35 rs966423 Gudmundsson et al. 2012 (15) 1.30 × 10−9 1.06 × 10−6 0.009 0.09 
 5q24 rs2910164 Jazdzewski et al. 2008 (74) 1.13 × 10−5 0.10 0.60 0.92 
 8p12 rs2439302 Gudmundsson et al. 2012 (15) 2.00 × 10−9 — 0.19 0.68 
 8q24 rs6983267 Jones et al. 2012 (75) 4.66 × 10−3 0.07 0.82 0.92 
 9q22 rs965513 Gudmundsson et al. 2009 (16) 1.70 × 10−27 <10−20 2.67 × 10−10 2.40 × 108 
 9q22 rs7048394 Landa et al. 2009 (76) 2.40 × 10−4 — 2.41 × 10−6 7.23 × 105 
 9q22 rs894673 Landa et al. 2009 (76) 2.20 × 10−4 — 1.45 × 10−8 6.53 × 107 
 14q13 rs944289 Gudmundsson et al. 2009 (16) 2.00 × 10−9 <10−20 0.01 0.09 

NOTE: Only SNPs associated in a statistically significant way at the 0.05 level or below are reported. The best Pass represents the lowest published P value of association for any model tested (i.e., dominant, additive, recessive, and allelic). When more than one study was published on the same SNP, a meta-analysis of the literature data was performed and the best Pass in any model is reported, as well. For each SNP the allelic Pass from the present GWAS and its q-value after FDR correction is also reported. q < 0.05 are highlighted in bold.

*Complete references are shown in the Supplementary data.

Results from the meta-analyses are shown in Tables 2 and 3. Below, we summarize the results according to the biological processes.

Table 2.

Meta-analyses of published data on Caucasians with data from present GWAS

Gene or locusdbSNP IDReferencePublished OR (allelic model)Allelic OR (present GWAS)Meta-analysisPassqPublished OR (additive model)OR of the additive model (present GWAS)Meta-analysisPassq
DNA repair 
ATM rs664677 Akulevich et al. 2009 (77) 1.08 (0.87–1.33) 1.09 (0.90–1.32) 1.09 (0.94–1.25) 0.25 0.31 1.06 (0.76–1.47)a 1.07 (0.83–1.38)a 1.07 (0.87–1.30)a 0.53 0.57 
        1.20 (0.75–1.91)b 1.23 (0.78–1.93)b 1.22 (0.88–1.69)b 0.24 0.40 
BRCA1 rs16942 Sturgis et al. 2005 (32) 0.73 (0.51–1.05) 1.04 (0.86–1.25) 0.92 (0.81–1.06) 0.24 0.30 0.80 (0.49–1.29)a 1.40 (0.80–1.34)a 0.88 (0.74–1.06)a 0.18 0.29 
        0.41 (0.15–1.10)b 1.08 (0.72–1.61)b 0.91 (0.68–1.23)b 0.56 0.63 
  Xu et al. 2012 (26) 0.85 (0.68–1.06)          
HUS1 rs2708906 Neta et al. 2011 (25) — 1.11 (0.93–1.32)    1.55 (1.11–2.18)a 1.21 (0.91–1.61)a 1.34 (1.08–1.67)a 8.8 × 10−3 0.04 
        2.40 (1.51–3.82)b 1.20 (0.92–1.57)b 1.52 (1.16–2.00)b 2.5 × 10−3 0.01 
XRCC1 rs25487 Siraj et al. 2008 (28) 0.72 (0.41–1.26) 0.87 (0.72–1.05) 0.92 (0.85–0.99) 0.04 0.06 — 0.76 (0.59–0.99)a 0.91 (0.82–1.02)a 0.11 0.22 
        — 0.87 (0.58–1.34)b 0.85 (0.71–1.02)b 0.07 0.17 
  Ho et al. 2009 (31) 0.70 (0.56–0.89)     0.76 (0.55–1.05)a     
        0.47 (0.27–0.82)b     
  Sigurdson et al. 2009 (27) 1.05 (0.91–1.21)     1.18 (0.97–1.44)a     
        0.95 (0.68–1.32)b     
  Akulevich et al. 2009 (77) 0.86 (0.69–1.07)     0.68 (0.50–0.94)a     
        0.90 (0.56–1.45)b     
  García-Qu. et al. 2011 (30) 1.00 (0.82–1.22)     1.12 (0.84–1.50)a     
        0.91 (0.59–1.40)b     
  Fard-Esf. et al. 2011 (78) 0.87 (0.63–1.20)     0.73 (0.47–1.15)a     
        0.90 (0.44–1.85)b     
  Santos et al. 2012 (79) 0.96 (0.69–1.35)     0.90 (0.55–1.47)a     
        0.98 (0.46–2.10)b     
XRCC3 rs1799796 García-Qu. et al. 2011 (30) 0.85 (0.68–1.06) 0.86 (0.70–1.06) 0.86 (0.73–1.00) 0.04 0.06 0.92 (0.69–1.21)a 0.80 (0.62–1.03)a 0.85 (0.71–1.03)a 0.46 0.52 
        0.60 (0.33–1.11)b 0.87 (0.51–1.48)b 0.74 (0.50–1.11)b 0.37 0.44 
XRCC7 rs7830743 Siraj et al. 2008 (28) 0.99 (0.65–1.49) 1.07 (0.78–1.47) 1.24 (1.01–1.54) 0.04 0.06 0.96 (0.60–1.54)a 1.07 (0.77–1.50)a 1.30 (1.03–1.64)a 0.03 0.09 
        1.11 (0.27–4.49)b 1.12 (0.27–4.72)b 1.13 (0.49–2.61)b 0.78 0.81 
  Rahimi et al. 2012 (33) 1.90 (1.29–2.79)     2.42 (1.55–3.81)a     
        1.16 (0.25–5.29)b     
ZNF350 rs2278420 Sigurdson et al. 2009 (27) 0.99 (0.84–1.16) 0.85 (0.67–1.09) 0.95 (0.83–1.08) 0.27 0.31 1.05 (0.86–1.28)a 0.86 (0.65–1.13)a 0.98 (0.84–1.15)a 0.81 0.83 
        0.83 (0.53–1.32)b 0.70 (0.29–1.66)b 0.80 (0.53–1.20)b 0.28 0.41 
Cell-cycle regulation and apoptosis 
WDR3 rs4658973 Baida et al. 2008 (38) 0.35 (0.25–0.47) 0.83 (0.70–1.00) 0.71 (0.61–0.82) 5.7 × 10−6 1.8 × 10−6 0.40 (0.26–0.62)a 0.65 (0.49–0.86)a 0.60 (0.48–0.74)a 3.7 × 10−6 7.8 × 10−5 
        0.07 (0.03–0.18)b 0.74 (0.52–1.06)b 0.64 (0.47–0.87)b 4.5 × 10−3 1.7 × 10−2 
  Akdi et al. 2010 (80) 1.09 (0.77–1.55)     0.81 (0.46–1.41)a     
        1.29 (0.63–2.64)b     
Xenobiotic metabolism 
CYP1A1 rs4646903 Siraj et al. 2008 (40) 1.42 (0.88–2.30) 1.28 (0.95–1.73) —   1.16 (0.60–2.24)a 1.32 (0.96–1.80)a 1.29 (0.97–1.71)a 0.12 0.23 
        2.42 (0.86–6.83)b 1.40 (0.26–7.70)b 2.09 (0.86–5.06)b 0.29 0.41 
CYP1A1 rs1799814 Siraj et al. 2008 (40) 1.87 (1.44–2.42) 1.85 (1.27–2.70) 1.86 (1.50–2.30) 1.3 × 10−8 4.4 × 10−8 1.91 (1.36–2.70)a 1.77 (1.20–2.60)a 1.85 (1.43–2.39)a 2.7 × 10−6 7.8 × 10−5 
        3.48 (1.74–6.96)b 5.03 (062–41.1)b 3.61 (1.87–6.97)b 1.3 × 10−4 1.1 × 10−3 
CYP26B1 rs12622950 Asc.-Kilfoy et al. 2012 (42) — 1.10 (0.88–1.38) —   1.32 (0.96–1.83)a 1.04 (0.79–1.36)a 1.14 (0.93–1.41)a 0.20 0.29 
        1.69 (0.72–3.95)b 1.41 (0.76–2.60)b 1.50 (0.91–2.46)b 0.11 0.24 
CYP26B1 rs7606254 Asc.-Kilfoy et al. 2012 (42) — 1.14 (0.89–1.46) —   1.07 (0.76–1.53)a 1.11 (0.84–1.46)a 1.10 (0.88–1.36)a 0.41 0.48 
        2.07 (0.80–5.34)b 1.57 (0.64–3.87)b 1.79 (0.93–3.44)b 0.68 0.73 
CYP26B1 rs707718 Asc.-Kilfoy et al. 2012 (42) — 0.99 (0.79–1.25) —   0.80 (0.57–1.13)a 0.90 (0.69–1.17)a 0.86 (0.70–1.06)a 0.16 0.28 
        2.05 (0.86–4.91)b 1.51 (0.68–3.35)b 1.74 (0.96–3.31)b 0.07 0.17 
MTHFR rs1801133 Siraj et al. 2008 (40) 1.47 (0.87–2.47) 1.11 (0.93–1.33) 1.18 (1.00–1.69) 0.05 0.08 1.77 (0.96–3.29)a 1.20 (0.90–1.60)a 1.34 (1.05–1.73)a 0.02 0.07 
        0.95 (0.12–7.54)b 1.21 (0.86–1.71)b 1.22 (0.87–1.71)b 0.26 0.40 
  Prasad et al. 2011 (44) 2.20 (1.00–4.86)     2.21 (0.92–5.30)a     
        2.65 (0.16–42.9)b     
NAT2 rs1799929 Hernández et al. 2008 (45) 0.70 (0.51–0.96) 0.97 (0.81–1.16) 0.89 (0.76–1.05) 0.16 0.23 0.64 (0.37–1.10)a 0.85 (0.64–1.11)a 0.80 (0.63–1.02)a 0.07 0.17 
        0.51 (0.27–0.96)b 0.99 (0.69–1.43)b 0.83 (0.60–1.15)b 0.26 0.40 
SOD1 rs1041740 Asc.-Kilfoy et al. 2012 (42) 1.42 (1.14–1.76) 1.12 (0.93–1.34) 1.23 (1.08–1.42) 2.7 × 10−3 5.5 × 10−3 1.48 (1.09–2.00)a 1.20 (0.92–1.57)a 1.32 (1.08–1.61)a 7.1 × 10−3 0.04 
        1.86 (1.15–3.02)b 1.17 (0.80–1.71)b 1.40 (1.04–1.88)b 0.03 0.09 
SOD1 rs12626475 Asc.-Kilfoy et al. 2012 (42) 1.33 (1.08–1.64) 1.10 (0.92–1.33) 1.20 (1.04–1.38) 0.01 0.02 1.33 (0.98–1.79)a 1.18 (0.91–1.54)a 1.24 (1.02–1.52)a 0.03 0.09 
        1.73 (1.09–2.73)b 1.16 (0.80–1.69)b 1.36 (1.02–1.82)b 0.04 0.11 
UGT2B7 rs3924194 Asc.-Kilfoy et al. 2012 (42) 0.66 (0.49–0.88) 0.84 (0.61–1.16)    0.74 (0.53–1.05)a 0.82 (0.59–1.14)a 0.73 (0.57–0.94)a 0.01 0.04 
        0.31 (0.12–0.85)b 0.97 (0.16–5.82)b 0.37 (0.15–0.91)b 0.03 0.09 
Thyroid function 
THRB rs826377 Pastor et al. 2012 (48) 1.01 (0.79–1.29) 1.00 (0.81–1.24) 1.06 (0.94–1.21) 0.35 0.38 1.08 (0.81–1.45)a 1.13 (0.87–1.46)a 1.11 (0.91–1.34)a 0.30 0.39 
        0.80 (0.67–1.73)b 0.76 (0.44–1.32)b 0.77 (0.49–1.21)b 0.26 0.40 
TPO rs1042589 Cipollini et al. 2013 (49) 0.94 (0.84–1.05) 0.89 (0.74–1.06) 0.93 (0.85–1.02) 0.12 0.17 0.98 (0.81–1.18)a 0.76 (0.56–1.03)a 0.92 (0.79–1.06)a 0.24 0.33 
        0.87 (0.69–1.10)b 0.78 (0.54–1.11)b 0.86 (0.73–1.03)b 0.10 0.23 
  Cipollini et al. 2013 (49) 0.94 (0.78–1.14)     0.88 (0.65–1.19)a     
        0.90 (0.62–1.32)b     
TRHR rs4129682 Akdi et al. 2011 (47) 0.99 (0.82–1.19) 0.88 (0.74–1.05) 0.93 (0.82–1.37) 0.27 0.31 1.15 (0.83–1.58)a 1.09 (0.83–1.44)a 1.12 (0.90–1.38)a 0.31 0.39 
        0.96 (0.65–1.41)b 0.74 (0.52–1.04)b 0.83 (0.64–1.07)b 0.15 0.32 
TRHR rs7823804 Akdi et al. 2011 (47) 0.94 (0.77–1.14) 0.94 (0.77–1.14) 0.94 (0.82–1.08) 0.37 0.39 0.91 (0.69–1.22)a 1.03 (0.80–1.34)a 0.97 (0.80–1.19)a 0.80 0.83 
        0.89 (0.57–1.41)b 0.80 (0.52–1.22)b 0.84 (0.62–1.15)b 0.27 0.41 
TSHR rs11845164 Pastor et al. 2012 (48) 1.08 (0.83–1.41) 1.21 (0.91–1.61) 1.14 (0.94–1.38) 0.19 0.26 0.96 (0.71–1.31)a 1.37 (1.00–1.88)a 1.14 (0.92–1.43)a 0.24 0.33 
        2.22 (0.81–6.07)b 0.70 (0.26–1.89)b 1.24 (0.61–2.51)b 0.55 0.62 
TSHR rs8019570 Pastor et al. 2012 (48) 1.09 (0.83–1.42) 1.21 (0.91–1.61) 1.14 (0.94–1.39) 0.17 0.23 0.99 (0.73–1.35)a 1.37 (1.00–1.88)a 1.16 (0.93–1.45)a 0.19 0.29 
        2.04 (0.73–5. 7)b 0.70 (0.26–1.89)b 1.18 (0.58–2.40)b 0.65 0.72 
HRAS rs12628 Khan et al. 2013 (50) 5.82 (3.80–8.93) 1.23 (1.02–1.48) 2.64 (0.58–12.1) 0.21 0.27 6.66 (3.66–12.1)a 1.51 (1.16–1.96)a 3.09 (0.72–13.2)a 0.13 0.24 
        9.86 (4.08–23.8)b 1.31 (0.89–1.92)b 3.45 (0.48–24.9)b 0.22 0.40 
RET rs1799939 Ho et al. 2005 (52) 0.79 (0.50–1.25) 1.14 (0.92–1.41) 1.05 (0.93–1.19) 0.43 0.44 0.67 (0.38–1.19)a 1.29 (0.99–1.67)a 1.08 (0.92–1.25)a 0.35 0.43 
        0.97 (0.32–3.07)b 0.93 (0.51–1.67)b 1.04 (0.72–1.50)b 0.84 0.86 
  Sigurdson et al. 2009 (27) 1.04 (0.88–1.23)     1.02 (0.84–1.25)a     
        1.15 (0.69–1.93)b     
Immune response and inflammation 
ALOX12 rs1126667 Prasad et al. 2012 (53) 2.06 (1.45–2.93) 0.99 (0.83–1.89) 1.74 (1.28–2.37) 4.0 × 10−4 8.9 × 10−4 3.01 (1.88–4.82)a 1.34 (1.02–1.75)a 1.63 (1.29–2.06)a 4.3 × 10−5 6.0 × 10−4 
        2.75 (0.49–15.6)b 0.85 (0.59–1.22)b 0.89 (0.63–1.27)b 0.53 0.62 
SERPINA5 rs6115 Brenner et al. 2013 (56) 1.72 (1.40–2.12) 1.02 (0.85–1.24) 1.32 (0.79–2.21) 0.28 0.31 1.76 (1.29–2.41)a 1.09 (0.84–1.41)a 1.37 (0.86–2.19)a 0.19 0.29 
        2.52 (1.66–3.83)b 0.98 (0.65–1.49)b 1.57 (0.62–3.97)b 0.34 0.43 
SERPINA5 rs6112 Brenner et al. 2013 (56) 1.61 (1.31–1.99) 1.02 (0.85–1.24) 1.28 (0.82–2.00) 0.28 0.31 1.76 (1.30–2.37)a 1.09 (0.84–1.41)a 1.38 (0.86–2.20)a 0.18 0.29 
        2.74 (1.63–4.62)b 0.98 (0.65–1.49)b 1.62 (0.59–4.43)b 0.35 0.43 
SERPINA5 rs6108 Brenner et al. 2013 (56) 1.48 (1.20–1.81) 1.04 (0.86–1.26) 1.24 (0.88–1.75) 0.22 0.28 1.39 (1.02–1.89)a 1.09 (0.84–1.41)a 1.21 (0.96–1.54)a 0.11 0.22 
        2.41 (1.53–3.78)b 1.02 (0.67–1.56)b 1.56 (0.67–3.63)b 0.30 0.41 
TICAM1 rs2292151 Brenner et al. 2013 (56) 1.46 (1.16–1.84) 1.09 (0.89–1.34) 1.24 (1.06–1.45) 7.1 × 10−3 0.01 1.43 (1.06–1.93)a 0.91 (0.70–1.18)a 1.10 (0.91–1.34)a 0.32 0.42 
        2.15 (1.19–3.88)b 1.69 (0.99–2.91)b 1.89 (1.27–2.82)b 1.8 × 10−3 0.01 
Other cancer genes 
ATG16L1 rs2241880 Huijbers et al. 2012 (57) 0.76 (0.60–0.98) 0.83 (0.70–0.99) 0.81 (0.70–0.93) 3.6 × 10−3 7.6 × 10−3 0.67 (0.44–1.01)a 0.97 (0.73–1.30)a 0.86 (0.68–1.08)a 0.19 0.29 
        0.57 (0.35–0.93)b 0.67 (0.47–0.95)b 0.63 (0.48–0.84)b 1.7 × 10−3 0.01 
FTO rs17817288 Kitahara et al. 2012 (62) 1.37 (1.12–1.68) 1.28 (1.07–1.53) 1.32 (1.15–1.51) 6.4 × 10−5 1.6 × 10−4 1.46 (1.01–2.11)a 1.31 (0.99–1.74)a 1.36 (1.09–1.71)a 6.8 × 10−3 0.04 
        1.98 (1.30–3.02)b 1.63 (1.15–2.30)b 1.76 (1.35–2.30)b 3.2 × 10−5 6.7 × 10−4 
FTO rs11642841 Kitahara et al. 2012 (62) 0.74 (0.60–0.91) 0.78 (0.65–0.93) 0.76 (0.67–0.87) 3.8 × 10−5 1.2 × 10−4 0.64 (0.47–0.87)a 0.88 (0.67–1.16)a 0.76 (0.62–0.94)a 9.7 × 10−3 0.04 
        0.61 (0.40–0.94)b 0.59 (0.42–0.84)b 0.60 (0.45–0.79)b 3.7 × 10−4 2.2 × 10−3 
FTO rs1121980 Kitahara et al. 2012 (62) 0.76 (0.62–0.93) 0.75 (0.63–0.89) 0.75 (0.66–0.86) 2.0 × 10−5 5.7 × 10−5 0.70 (0.51–0.96)a 0.84 (0.63–1.19)a 0.76 (0.60–0.96)a 0.02 0.07 
        0.60 (0.39–0.92)b 0.55 (0.39–0.78)b 0.57 (0.43–0.75)b 7.5 × 10−5 7.9 × 10−4 
FTO rs8050136 Kitahara et al. 2012 (62) 0.77 (0.62–0.94) 0.75 (0.63–0.89) 0.76 (0.67–0.86) 1.6 × 10−5 4.8 × 10−5 0.73 (0.54–1.00)a 0.84 (0.63–1.19)a 0.78 (0.62–0.98)a 0.03 0.09 
        0.59 (0.38–0.93)b 0.55 (0.39–0.78)b 0.56 (0.43–0.74)b 2.8 × 10−5 6.7 × 10−4 
FTO rs9939609 Kitahara et al. 2012 (62) 0.77 (0.62–0.94) 0.77 (0.65–0.93) 0.77 (0.67–0.88) 1.7 × 10−4 3.9 × 10−4 0.74 (0.54–1.00)a 0.88 (0.67–1.16)a 0.81 (0.66–1.00)a 0.05 0.13 
        0.60 (0.38–0.93)b 0.58 (0.41–0.83)b 0.59 (0.45–0.78)b 2.8 × 10−4 1.9 × 10−3 
FTO rs7202116 Kitahara et al. 2012 (62) 0.77 (0.63–0.95) 0.75 (0.63–0.89) 0.76 (0.66–0.87) 9.8 × 10−5 2.5 × 10−4 0.74 (0.55–1.01)a 0.84 (0.63–1.19)a 0.78 (0.62–0.99)a 0.04 0.11 
        0.60 (0.38–0.93)b 0.55 (0.39–0.78)b 0.57 (0.43–0.75)b 7.5 × 10−5 7.9 × 10−4 
HDAC4 rs6749348 Neta et al. 2011 (25)  0.85 (0.62–1.16) —   0.41 (0.26–0.65)a 0.78 (0.56–1.08)a 0.58 (0.31–1.08)a 0.09 0.20 
        0.28 (0.03–2.46)b 1.92 (0.39–9.56)b 0.97 (0.27–3.54)b 0.97 0.97 
HDAC4 rs7584828 Neta et al. 2011 (25)  0.82 (0.64–1.05)    0.55 (0.38–0.79)a 0.76 (0.58–1.00)a 0.68 (0.54–0.84)a 4.0 × 10−4 4.2 × 10−3 
        0.33 (0.08–1.28)b 0.96 (0.41–2.25)b 0.71 (0.35–1.46)b 0.35 0.43 
IGFBP3 rs2132572 Xu et al. 2012 (66)c 0.60 (0.40–0.80) 0.86 (0.66–1.13) 0.77 (0.61–0.96) 0.02       
PIK3CA rs17849071 Xing et al. 2012 (81) — 0.71 (0.51–0.99) —   0.52 (0.23–1.19)a 0.67 (0.47–0.96))a 0.64 (0.46–0.90)a 8.8 × 10−3 0.04 
        — 0.79 (0.21–2.95)b   
SULF1 rs6472462 Schonfeld et al. 2012 (41) 1.28 (1.05–1.56) 1.09 (0.91–1.30) 1.17 (1.03–1.33) 0.02 0.03 1.40 (0.97–2.02)a 0.92 (0.69–1.23)a 1.08 (0.86–1.36)a 0.50 0.55 
        1.67 (1.11–2.50)b 1.19 (0.84–1.68)b 1.37 (1.06–1.78)b 0.02 0.07 
GWAS or intergenic regions 
1p12–13 rs4659200 Baida et al. 2008 (38) 0.84 (0.61–1.15) 0.97 (0.81–1.17) 0.93 (0.80–1.10) 0.41 0.43 0.99 (0.62–1.60)a 1.15 (0.88–1.50)a 1.11 (0.88–1.40)a 0.38 0.46 
        0.66 (0.35–1.26)b 0.85 (0.58–1.24)b 0.80 (0.58–1.10)b 0.17 0.34 
1p12–13 rs7515409 Baida et al. 2008 (38) 1.02 (0.78–1.34) 0.94 (0.79–1.12) 0.96 (0.83–1.12) 0.61 0.61 0.84 (0.54–1.31)a 0.80 (0.60–1.07)a 0.81 (0.64–1.04)a 0.09 0.20 
        1.11 (0.62–1.97)b 0.89 (0.63–1.26)b 0.94 (0.70–1.27)b 0.71 0.75 
1p12–13 rs1241 Baida et al. 2008 (38) 0.90 (0.65–1.25) 0.92 (0.76–1.10) 0.92 (0.78–1.07) 0.27 0.31 0.69 (0.44–1.10)a 1.09 (0.84–1.42)a 0.98 (0.77–1.23)a 0.83 0.83 
        0.74 (0.48–1.16)b 0.73 (0.49–1.08)b 0.79 (0.55–1.13)b 0.20 0.38 
1p31.3 rs334725 Gudmundsson et al. 2012 (15) 1.31 (1.08–1.60) 1.39 (0.91–2.13) 1.32 (1.10–1.59) 2.4 × 10−3 5.1 × 10−3  1.33 (0.86–2.05)a    
         2.74 (0.30–24.6)b    
2q35 rs966423 Gudmundsson et al. 2012 (15) 1.34 (1.22–1.47) 1.26 (1.06–1.51) 1.27 (1.19–1.35) 1.0 × 10−13 1.3 × 10−12  0.98 (0.74–1.28)a    
         1.74 (1.21–2.50)b    
  Liyanarachchi et al. 2013 (82) 1.30 (1.12–1.51)          
             
  Liyanarachchi et al. 2013 (82) 1.14 (1.01–1.29)          
             
5q24 rs2910164 Jazdzewski et al. 2008 (74) 1.14 (0.96–1.34) 0.95 (0.78–1.16) 1.01 (0.93–1.09) 0.89 0.92 1.55 (1.25–1.91)a 0.94 (0.73–1.22)a 1.07 (0.97–1.19)a 0.19 0.29 
        0.50 (0.28–0.89)b 0.91 (0.56–1.47)b 0.88 (0.73–1.07)b 0.19 0.36 
  Jones et al. 2012 (75) 1.00 (0.88–1.14)     1.01 (0.86–1.19)a     
        0.98 (0.70–1.38)b     
  Wei et al. 2013 (83) 0.95 (0.82–1.09)     0.88 (0.71–1.10)a     
        0.93 (0.70–1.24)b     
8p12 rs2439302 Gudmundsson et al. 2012 (15) 1.36 (1.23–1.50) 1.12 (0.94–1.34) 1.30 (1.23–1.39) 4.0 × 10−17 1.2 × 10−15  1.10 (0.83–1.45)a    
         1.28 (0.90–1.83)b    
  Liyanarachchi et al. 2013 (82) 1.46 (1.26–1.70)          
             
  Liyanarachchi et al. 2013 (82) 1.23 (1.09–1.38)          
             
8q24 rs6983267 Akdi et al. 2011 (47) 0.98 (0.81–1.18) 1.02 (0.86–1.22) 1.06 (0.99–1.14) 0.09 0.13 1.14 (0.83–1.57)a 1.15 (0.56–1.54)a 1.03 (0.91–1.17)a 0.65 0.70 
        0.94 (0.65–1.36)b 1.03 (0.73–1.46)b 1.11 (0.96–1.27)b 0.15 0.32 
  Jones et al. 2012 (75) 1.14 (1.03–1.27)     1.01 (0.83–1.23)a     
        1.27 (1.03–1.57)b     
  Wang et al. 2013 (84) 1.01 (0.88–1.15)     0.99 (0.80–1.21)a     
        1.01 (0.78–1.32)b     
9q22 rs965513 Gudmundsson et al. 2009 (16) 1.75 (1.59–1.94) 1.78 (1.48–2.14) 1.85 (1.76–1.95) <10−20 <10−20  1.80 (1.37–2.35)a    
         3.08 (2.10–4.53)b    
  Takahashi et al. 2010 (17) 1.65 (1.43–1.91)          
             
  Jones et al. 2012 (75) 1.96 (1.76–2.18)     2.12 (1.77–2.55)a     
        3.89 (3.10–4.86)b     
  Tomaz et al. 2012 (85) 2.81 (1.87–4.22)          
             
  Liyanarachchi et al. 2013 (82) 2.09 (1.80–2.42)          
             
  Liyanarachchi et al. 2013 (82) 1.81 (1.59–2.06)          
             
9q22 rs7048394 Landa et al. 2009 (76) 1.46 (1.19–1.78) 1.55 (1.29–1.87) 1.51 (1.31–1.73) 6.3 × 10−9 2.3 × 10−8  1.39 (1.07–1.80)a    
         2.78 (1.80–4.28)b    
9q22 rs894673 Landa et al. 2009 (76) 1.39 (1.17–1.65) 1.65 (1.38–1.97) 1.51 (1.33–1.71) 1.3 × 10−10 8.3 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs3758249 Landa et al. 2009 (76) 1.39 (1.17–1.66) 1.65 (1.38–1.97) 1.51 (1.34–1.72) 1.3 × 10−10 8.3 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs907577 Landa et al. 2009 (76) 1.39 (1.17–1.65) 1.65 (1.38–1.97) 1.51 (1.34–1.71) 1.3 × 10−10 8.3 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs3021526 Landa et al. 2009 (76) 1.32 (1.11–1.58) 1.55 (1.29–1.87) 1.46 (1.29–1.66) 4.0 × 10−9 1.6 × 10−8  1.39 (1.07–1.80)a    
         2.78 (1.80–4.28)b    
  Tomaz et al. 2012 (85) 1.89 (1.27–2.82)          
             
9q22 rs10119760 Landa et al. 2009 (76) 1.47 (1.23–1.75) 1.65 (1.38–1.97) 1.56 (1.34–1.76) 1.6 × 10−10 9.7 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs1867277 Takahashi et al. 2010 (17) 1.48 (1.27–1.71) 1.65 (1.38–1.97) 1.55 (1.38–1.73) 2.9 × 10−14 4.9 × 10−13  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
  Jones et al. 2012 (75) 1.75 (1.57–1.94)     1.99 (1.64–2.41)a     
        3.08 (2.46–3.84)b     
  Tomaz et al. 2012 (85) 1.76 (1.18–2.62)          
             
9q22 rs7849497 Tomaz et al. 2012 (85) 2.45 (1.60–3.76) 1.55 (1.29–1.87) 1.67 (1.41–1.98) 3.2 × 10−9 1.4 × 10−8  1.39 (1.07–1.80)a    
         2.78 (1.80–4.28)b    
9q22 rs1867278 Tomaz et al. 2012 (85) 1.76 (1.18–2.62) 1.65 (1.38–1.97) 1.67 (1.42–1.96) 4.4 × 10−10 2.2 × 10−9  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs1867279 Tomaz et al. 2012 (85) 2.52 (1.64–3.86) 1.55 (1.29–1.87) 1.90 (1.19–3.04) 0.01 0.02  1.39 (1.07–1.80)a    
         2.78 (1.80–4.28)b    
9q22 rs1867280 Tomaz et al. 2012 (85) 1.68 (1.13–2.49) 1.65 (1.38–1.97) 1.65 (1.41–1.95) 1.4 × 10−9 6.5 × 10−9  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs3021523 Tomaz et al. 2012 (85) 2.39 (1.56–3.67) 1.65 (1.38–1.97) 1.74 (1.48–2.05) 2.7 × 10−11 2.8 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
14q13 rs944289 Gudmundsson et al. 2009 (16) 1.37 (1.24–1.52) 1.25 (1.05–1.49) 1.25 (1.17–1.33) 0.01 0.02  1.13 (0.81–1.58)a    
         1.48 (1.04–2.09)b    
  Takahashi et al. 2010 (17) 1.13 (0.95–1.36)          
             
  Jones et al. 2012 (75) 1.33 (1.19–1.49)     1.31 (1.02–1.68)a     
        1.76 (1.37–2.25)b     
  Liyanarachchi et al. 2013 (82) 1.25 (1.08–1.46)          
             
  Liyanarachchi et al. 2013 (82) 1.22 (1.09–1.38)          
             
Gene or locusdbSNP IDReferencePublished OR (allelic model)Allelic OR (present GWAS)Meta-analysisPassqPublished OR (additive model)OR of the additive model (present GWAS)Meta-analysisPassq
DNA repair 
ATM rs664677 Akulevich et al. 2009 (77) 1.08 (0.87–1.33) 1.09 (0.90–1.32) 1.09 (0.94–1.25) 0.25 0.31 1.06 (0.76–1.47)a 1.07 (0.83–1.38)a 1.07 (0.87–1.30)a 0.53 0.57 
        1.20 (0.75–1.91)b 1.23 (0.78–1.93)b 1.22 (0.88–1.69)b 0.24 0.40 
BRCA1 rs16942 Sturgis et al. 2005 (32) 0.73 (0.51–1.05) 1.04 (0.86–1.25) 0.92 (0.81–1.06) 0.24 0.30 0.80 (0.49–1.29)a 1.40 (0.80–1.34)a 0.88 (0.74–1.06)a 0.18 0.29 
        0.41 (0.15–1.10)b 1.08 (0.72–1.61)b 0.91 (0.68–1.23)b 0.56 0.63 
  Xu et al. 2012 (26) 0.85 (0.68–1.06)          
HUS1 rs2708906 Neta et al. 2011 (25) — 1.11 (0.93–1.32)    1.55 (1.11–2.18)a 1.21 (0.91–1.61)a 1.34 (1.08–1.67)a 8.8 × 10−3 0.04 
        2.40 (1.51–3.82)b 1.20 (0.92–1.57)b 1.52 (1.16–2.00)b 2.5 × 10−3 0.01 
XRCC1 rs25487 Siraj et al. 2008 (28) 0.72 (0.41–1.26) 0.87 (0.72–1.05) 0.92 (0.85–0.99) 0.04 0.06 — 0.76 (0.59–0.99)a 0.91 (0.82–1.02)a 0.11 0.22 
        — 0.87 (0.58–1.34)b 0.85 (0.71–1.02)b 0.07 0.17 
  Ho et al. 2009 (31) 0.70 (0.56–0.89)     0.76 (0.55–1.05)a     
        0.47 (0.27–0.82)b     
  Sigurdson et al. 2009 (27) 1.05 (0.91–1.21)     1.18 (0.97–1.44)a     
        0.95 (0.68–1.32)b     
  Akulevich et al. 2009 (77) 0.86 (0.69–1.07)     0.68 (0.50–0.94)a     
        0.90 (0.56–1.45)b     
  García-Qu. et al. 2011 (30) 1.00 (0.82–1.22)     1.12 (0.84–1.50)a     
        0.91 (0.59–1.40)b     
  Fard-Esf. et al. 2011 (78) 0.87 (0.63–1.20)     0.73 (0.47–1.15)a     
        0.90 (0.44–1.85)b     
  Santos et al. 2012 (79) 0.96 (0.69–1.35)     0.90 (0.55–1.47)a     
        0.98 (0.46–2.10)b     
XRCC3 rs1799796 García-Qu. et al. 2011 (30) 0.85 (0.68–1.06) 0.86 (0.70–1.06) 0.86 (0.73–1.00) 0.04 0.06 0.92 (0.69–1.21)a 0.80 (0.62–1.03)a 0.85 (0.71–1.03)a 0.46 0.52 
        0.60 (0.33–1.11)b 0.87 (0.51–1.48)b 0.74 (0.50–1.11)b 0.37 0.44 
XRCC7 rs7830743 Siraj et al. 2008 (28) 0.99 (0.65–1.49) 1.07 (0.78–1.47) 1.24 (1.01–1.54) 0.04 0.06 0.96 (0.60–1.54)a 1.07 (0.77–1.50)a 1.30 (1.03–1.64)a 0.03 0.09 
        1.11 (0.27–4.49)b 1.12 (0.27–4.72)b 1.13 (0.49–2.61)b 0.78 0.81 
  Rahimi et al. 2012 (33) 1.90 (1.29–2.79)     2.42 (1.55–3.81)a     
        1.16 (0.25–5.29)b     
ZNF350 rs2278420 Sigurdson et al. 2009 (27) 0.99 (0.84–1.16) 0.85 (0.67–1.09) 0.95 (0.83–1.08) 0.27 0.31 1.05 (0.86–1.28)a 0.86 (0.65–1.13)a 0.98 (0.84–1.15)a 0.81 0.83 
        0.83 (0.53–1.32)b 0.70 (0.29–1.66)b 0.80 (0.53–1.20)b 0.28 0.41 
Cell-cycle regulation and apoptosis 
WDR3 rs4658973 Baida et al. 2008 (38) 0.35 (0.25–0.47) 0.83 (0.70–1.00) 0.71 (0.61–0.82) 5.7 × 10−6 1.8 × 10−6 0.40 (0.26–0.62)a 0.65 (0.49–0.86)a 0.60 (0.48–0.74)a 3.7 × 10−6 7.8 × 10−5 
        0.07 (0.03–0.18)b 0.74 (0.52–1.06)b 0.64 (0.47–0.87)b 4.5 × 10−3 1.7 × 10−2 
  Akdi et al. 2010 (80) 1.09 (0.77–1.55)     0.81 (0.46–1.41)a     
        1.29 (0.63–2.64)b     
Xenobiotic metabolism 
CYP1A1 rs4646903 Siraj et al. 2008 (40) 1.42 (0.88–2.30) 1.28 (0.95–1.73) —   1.16 (0.60–2.24)a 1.32 (0.96–1.80)a 1.29 (0.97–1.71)a 0.12 0.23 
        2.42 (0.86–6.83)b 1.40 (0.26–7.70)b 2.09 (0.86–5.06)b 0.29 0.41 
CYP1A1 rs1799814 Siraj et al. 2008 (40) 1.87 (1.44–2.42) 1.85 (1.27–2.70) 1.86 (1.50–2.30) 1.3 × 10−8 4.4 × 10−8 1.91 (1.36–2.70)a 1.77 (1.20–2.60)a 1.85 (1.43–2.39)a 2.7 × 10−6 7.8 × 10−5 
        3.48 (1.74–6.96)b 5.03 (062–41.1)b 3.61 (1.87–6.97)b 1.3 × 10−4 1.1 × 10−3 
CYP26B1 rs12622950 Asc.-Kilfoy et al. 2012 (42) — 1.10 (0.88–1.38) —   1.32 (0.96–1.83)a 1.04 (0.79–1.36)a 1.14 (0.93–1.41)a 0.20 0.29 
        1.69 (0.72–3.95)b 1.41 (0.76–2.60)b 1.50 (0.91–2.46)b 0.11 0.24 
CYP26B1 rs7606254 Asc.-Kilfoy et al. 2012 (42) — 1.14 (0.89–1.46) —   1.07 (0.76–1.53)a 1.11 (0.84–1.46)a 1.10 (0.88–1.36)a 0.41 0.48 
        2.07 (0.80–5.34)b 1.57 (0.64–3.87)b 1.79 (0.93–3.44)b 0.68 0.73 
CYP26B1 rs707718 Asc.-Kilfoy et al. 2012 (42) — 0.99 (0.79–1.25) —   0.80 (0.57–1.13)a 0.90 (0.69–1.17)a 0.86 (0.70–1.06)a 0.16 0.28 
        2.05 (0.86–4.91)b 1.51 (0.68–3.35)b 1.74 (0.96–3.31)b 0.07 0.17 
MTHFR rs1801133 Siraj et al. 2008 (40) 1.47 (0.87–2.47) 1.11 (0.93–1.33) 1.18 (1.00–1.69) 0.05 0.08 1.77 (0.96–3.29)a 1.20 (0.90–1.60)a 1.34 (1.05–1.73)a 0.02 0.07 
        0.95 (0.12–7.54)b 1.21 (0.86–1.71)b 1.22 (0.87–1.71)b 0.26 0.40 
  Prasad et al. 2011 (44) 2.20 (1.00–4.86)     2.21 (0.92–5.30)a     
        2.65 (0.16–42.9)b     
NAT2 rs1799929 Hernández et al. 2008 (45) 0.70 (0.51–0.96) 0.97 (0.81–1.16) 0.89 (0.76–1.05) 0.16 0.23 0.64 (0.37–1.10)a 0.85 (0.64–1.11)a 0.80 (0.63–1.02)a 0.07 0.17 
        0.51 (0.27–0.96)b 0.99 (0.69–1.43)b 0.83 (0.60–1.15)b 0.26 0.40 
SOD1 rs1041740 Asc.-Kilfoy et al. 2012 (42) 1.42 (1.14–1.76) 1.12 (0.93–1.34) 1.23 (1.08–1.42) 2.7 × 10−3 5.5 × 10−3 1.48 (1.09–2.00)a 1.20 (0.92–1.57)a 1.32 (1.08–1.61)a 7.1 × 10−3 0.04 
        1.86 (1.15–3.02)b 1.17 (0.80–1.71)b 1.40 (1.04–1.88)b 0.03 0.09 
SOD1 rs12626475 Asc.-Kilfoy et al. 2012 (42) 1.33 (1.08–1.64) 1.10 (0.92–1.33) 1.20 (1.04–1.38) 0.01 0.02 1.33 (0.98–1.79)a 1.18 (0.91–1.54)a 1.24 (1.02–1.52)a 0.03 0.09 
        1.73 (1.09–2.73)b 1.16 (0.80–1.69)b 1.36 (1.02–1.82)b 0.04 0.11 
UGT2B7 rs3924194 Asc.-Kilfoy et al. 2012 (42) 0.66 (0.49–0.88) 0.84 (0.61–1.16)    0.74 (0.53–1.05)a 0.82 (0.59–1.14)a 0.73 (0.57–0.94)a 0.01 0.04 
        0.31 (0.12–0.85)b 0.97 (0.16–5.82)b 0.37 (0.15–0.91)b 0.03 0.09 
Thyroid function 
THRB rs826377 Pastor et al. 2012 (48) 1.01 (0.79–1.29) 1.00 (0.81–1.24) 1.06 (0.94–1.21) 0.35 0.38 1.08 (0.81–1.45)a 1.13 (0.87–1.46)a 1.11 (0.91–1.34)a 0.30 0.39 
        0.80 (0.67–1.73)b 0.76 (0.44–1.32)b 0.77 (0.49–1.21)b 0.26 0.40 
TPO rs1042589 Cipollini et al. 2013 (49) 0.94 (0.84–1.05) 0.89 (0.74–1.06) 0.93 (0.85–1.02) 0.12 0.17 0.98 (0.81–1.18)a 0.76 (0.56–1.03)a 0.92 (0.79–1.06)a 0.24 0.33 
        0.87 (0.69–1.10)b 0.78 (0.54–1.11)b 0.86 (0.73–1.03)b 0.10 0.23 
  Cipollini et al. 2013 (49) 0.94 (0.78–1.14)     0.88 (0.65–1.19)a     
        0.90 (0.62–1.32)b     
TRHR rs4129682 Akdi et al. 2011 (47) 0.99 (0.82–1.19) 0.88 (0.74–1.05) 0.93 (0.82–1.37) 0.27 0.31 1.15 (0.83–1.58)a 1.09 (0.83–1.44)a 1.12 (0.90–1.38)a 0.31 0.39 
        0.96 (0.65–1.41)b 0.74 (0.52–1.04)b 0.83 (0.64–1.07)b 0.15 0.32 
TRHR rs7823804 Akdi et al. 2011 (47) 0.94 (0.77–1.14) 0.94 (0.77–1.14) 0.94 (0.82–1.08) 0.37 0.39 0.91 (0.69–1.22)a 1.03 (0.80–1.34)a 0.97 (0.80–1.19)a 0.80 0.83 
        0.89 (0.57–1.41)b 0.80 (0.52–1.22)b 0.84 (0.62–1.15)b 0.27 0.41 
TSHR rs11845164 Pastor et al. 2012 (48) 1.08 (0.83–1.41) 1.21 (0.91–1.61) 1.14 (0.94–1.38) 0.19 0.26 0.96 (0.71–1.31)a 1.37 (1.00–1.88)a 1.14 (0.92–1.43)a 0.24 0.33 
        2.22 (0.81–6.07)b 0.70 (0.26–1.89)b 1.24 (0.61–2.51)b 0.55 0.62 
TSHR rs8019570 Pastor et al. 2012 (48) 1.09 (0.83–1.42) 1.21 (0.91–1.61) 1.14 (0.94–1.39) 0.17 0.23 0.99 (0.73–1.35)a 1.37 (1.00–1.88)a 1.16 (0.93–1.45)a 0.19 0.29 
        2.04 (0.73–5. 7)b 0.70 (0.26–1.89)b 1.18 (0.58–2.40)b 0.65 0.72 
HRAS rs12628 Khan et al. 2013 (50) 5.82 (3.80–8.93) 1.23 (1.02–1.48) 2.64 (0.58–12.1) 0.21 0.27 6.66 (3.66–12.1)a 1.51 (1.16–1.96)a 3.09 (0.72–13.2)a 0.13 0.24 
        9.86 (4.08–23.8)b 1.31 (0.89–1.92)b 3.45 (0.48–24.9)b 0.22 0.40 
RET rs1799939 Ho et al. 2005 (52) 0.79 (0.50–1.25) 1.14 (0.92–1.41) 1.05 (0.93–1.19) 0.43 0.44 0.67 (0.38–1.19)a 1.29 (0.99–1.67)a 1.08 (0.92–1.25)a 0.35 0.43 
        0.97 (0.32–3.07)b 0.93 (0.51–1.67)b 1.04 (0.72–1.50)b 0.84 0.86 
  Sigurdson et al. 2009 (27) 1.04 (0.88–1.23)     1.02 (0.84–1.25)a     
        1.15 (0.69–1.93)b     
Immune response and inflammation 
ALOX12 rs1126667 Prasad et al. 2012 (53) 2.06 (1.45–2.93) 0.99 (0.83–1.89) 1.74 (1.28–2.37) 4.0 × 10−4 8.9 × 10−4 3.01 (1.88–4.82)a 1.34 (1.02–1.75)a 1.63 (1.29–2.06)a 4.3 × 10−5 6.0 × 10−4 
        2.75 (0.49–15.6)b 0.85 (0.59–1.22)b 0.89 (0.63–1.27)b 0.53 0.62 
SERPINA5 rs6115 Brenner et al. 2013 (56) 1.72 (1.40–2.12) 1.02 (0.85–1.24) 1.32 (0.79–2.21) 0.28 0.31 1.76 (1.29–2.41)a 1.09 (0.84–1.41)a 1.37 (0.86–2.19)a 0.19 0.29 
        2.52 (1.66–3.83)b 0.98 (0.65–1.49)b 1.57 (0.62–3.97)b 0.34 0.43 
SERPINA5 rs6112 Brenner et al. 2013 (56) 1.61 (1.31–1.99) 1.02 (0.85–1.24) 1.28 (0.82–2.00) 0.28 0.31 1.76 (1.30–2.37)a 1.09 (0.84–1.41)a 1.38 (0.86–2.20)a 0.18 0.29 
        2.74 (1.63–4.62)b 0.98 (0.65–1.49)b 1.62 (0.59–4.43)b 0.35 0.43 
SERPINA5 rs6108 Brenner et al. 2013 (56) 1.48 (1.20–1.81) 1.04 (0.86–1.26) 1.24 (0.88–1.75) 0.22 0.28 1.39 (1.02–1.89)a 1.09 (0.84–1.41)a 1.21 (0.96–1.54)a 0.11 0.22 
        2.41 (1.53–3.78)b 1.02 (0.67–1.56)b 1.56 (0.67–3.63)b 0.30 0.41 
TICAM1 rs2292151 Brenner et al. 2013 (56) 1.46 (1.16–1.84) 1.09 (0.89–1.34) 1.24 (1.06–1.45) 7.1 × 10−3 0.01 1.43 (1.06–1.93)a 0.91 (0.70–1.18)a 1.10 (0.91–1.34)a 0.32 0.42 
        2.15 (1.19–3.88)b 1.69 (0.99–2.91)b 1.89 (1.27–2.82)b 1.8 × 10−3 0.01 
Other cancer genes 
ATG16L1 rs2241880 Huijbers et al. 2012 (57) 0.76 (0.60–0.98) 0.83 (0.70–0.99) 0.81 (0.70–0.93) 3.6 × 10−3 7.6 × 10−3 0.67 (0.44–1.01)a 0.97 (0.73–1.30)a 0.86 (0.68–1.08)a 0.19 0.29 
        0.57 (0.35–0.93)b 0.67 (0.47–0.95)b 0.63 (0.48–0.84)b 1.7 × 10−3 0.01 
FTO rs17817288 Kitahara et al. 2012 (62) 1.37 (1.12–1.68) 1.28 (1.07–1.53) 1.32 (1.15–1.51) 6.4 × 10−5 1.6 × 10−4 1.46 (1.01–2.11)a 1.31 (0.99–1.74)a 1.36 (1.09–1.71)a 6.8 × 10−3 0.04 
        1.98 (1.30–3.02)b 1.63 (1.15–2.30)b 1.76 (1.35–2.30)b 3.2 × 10−5 6.7 × 10−4 
FTO rs11642841 Kitahara et al. 2012 (62) 0.74 (0.60–0.91) 0.78 (0.65–0.93) 0.76 (0.67–0.87) 3.8 × 10−5 1.2 × 10−4 0.64 (0.47–0.87)a 0.88 (0.67–1.16)a 0.76 (0.62–0.94)a 9.7 × 10−3 0.04 
        0.61 (0.40–0.94)b 0.59 (0.42–0.84)b 0.60 (0.45–0.79)b 3.7 × 10−4 2.2 × 10−3 
FTO rs1121980 Kitahara et al. 2012 (62) 0.76 (0.62–0.93) 0.75 (0.63–0.89) 0.75 (0.66–0.86) 2.0 × 10−5 5.7 × 10−5 0.70 (0.51–0.96)a 0.84 (0.63–1.19)a 0.76 (0.60–0.96)a 0.02 0.07 
        0.60 (0.39–0.92)b 0.55 (0.39–0.78)b 0.57 (0.43–0.75)b 7.5 × 10−5 7.9 × 10−4 
FTO rs8050136 Kitahara et al. 2012 (62) 0.77 (0.62–0.94) 0.75 (0.63–0.89) 0.76 (0.67–0.86) 1.6 × 10−5 4.8 × 10−5 0.73 (0.54–1.00)a 0.84 (0.63–1.19)a 0.78 (0.62–0.98)a 0.03 0.09 
        0.59 (0.38–0.93)b 0.55 (0.39–0.78)b 0.56 (0.43–0.74)b 2.8 × 10−5 6.7 × 10−4 
FTO rs9939609 Kitahara et al. 2012 (62) 0.77 (0.62–0.94) 0.77 (0.65–0.93) 0.77 (0.67–0.88) 1.7 × 10−4 3.9 × 10−4 0.74 (0.54–1.00)a 0.88 (0.67–1.16)a 0.81 (0.66–1.00)a 0.05 0.13 
        0.60 (0.38–0.93)b 0.58 (0.41–0.83)b 0.59 (0.45–0.78)b 2.8 × 10−4 1.9 × 10−3 
FTO rs7202116 Kitahara et al. 2012 (62) 0.77 (0.63–0.95) 0.75 (0.63–0.89) 0.76 (0.66–0.87) 9.8 × 10−5 2.5 × 10−4 0.74 (0.55–1.01)a 0.84 (0.63–1.19)a 0.78 (0.62–0.99)a 0.04 0.11 
        0.60 (0.38–0.93)b 0.55 (0.39–0.78)b 0.57 (0.43–0.75)b 7.5 × 10−5 7.9 × 10−4 
HDAC4 rs6749348 Neta et al. 2011 (25)  0.85 (0.62–1.16) —   0.41 (0.26–0.65)a 0.78 (0.56–1.08)a 0.58 (0.31–1.08)a 0.09 0.20 
        0.28 (0.03–2.46)b 1.92 (0.39–9.56)b 0.97 (0.27–3.54)b 0.97 0.97 
HDAC4 rs7584828 Neta et al. 2011 (25)  0.82 (0.64–1.05)    0.55 (0.38–0.79)a 0.76 (0.58–1.00)a 0.68 (0.54–0.84)a 4.0 × 10−4 4.2 × 10−3 
        0.33 (0.08–1.28)b 0.96 (0.41–2.25)b 0.71 (0.35–1.46)b 0.35 0.43 
IGFBP3 rs2132572 Xu et al. 2012 (66)c 0.60 (0.40–0.80) 0.86 (0.66–1.13) 0.77 (0.61–0.96) 0.02       
PIK3CA rs17849071 Xing et al. 2012 (81) — 0.71 (0.51–0.99) —   0.52 (0.23–1.19)a 0.67 (0.47–0.96))a 0.64 (0.46–0.90)a 8.8 × 10−3 0.04 
        — 0.79 (0.21–2.95)b   
SULF1 rs6472462 Schonfeld et al. 2012 (41) 1.28 (1.05–1.56) 1.09 (0.91–1.30) 1.17 (1.03–1.33) 0.02 0.03 1.40 (0.97–2.02)a 0.92 (0.69–1.23)a 1.08 (0.86–1.36)a 0.50 0.55 
        1.67 (1.11–2.50)b 1.19 (0.84–1.68)b 1.37 (1.06–1.78)b 0.02 0.07 
GWAS or intergenic regions 
1p12–13 rs4659200 Baida et al. 2008 (38) 0.84 (0.61–1.15) 0.97 (0.81–1.17) 0.93 (0.80–1.10) 0.41 0.43 0.99 (0.62–1.60)a 1.15 (0.88–1.50)a 1.11 (0.88–1.40)a 0.38 0.46 
        0.66 (0.35–1.26)b 0.85 (0.58–1.24)b 0.80 (0.58–1.10)b 0.17 0.34 
1p12–13 rs7515409 Baida et al. 2008 (38) 1.02 (0.78–1.34) 0.94 (0.79–1.12) 0.96 (0.83–1.12) 0.61 0.61 0.84 (0.54–1.31)a 0.80 (0.60–1.07)a 0.81 (0.64–1.04)a 0.09 0.20 
        1.11 (0.62–1.97)b 0.89 (0.63–1.26)b 0.94 (0.70–1.27)b 0.71 0.75 
1p12–13 rs1241 Baida et al. 2008 (38) 0.90 (0.65–1.25) 0.92 (0.76–1.10) 0.92 (0.78–1.07) 0.27 0.31 0.69 (0.44–1.10)a 1.09 (0.84–1.42)a 0.98 (0.77–1.23)a 0.83 0.83 
        0.74 (0.48–1.16)b 0.73 (0.49–1.08)b 0.79 (0.55–1.13)b 0.20 0.38 
1p31.3 rs334725 Gudmundsson et al. 2012 (15) 1.31 (1.08–1.60) 1.39 (0.91–2.13) 1.32 (1.10–1.59) 2.4 × 10−3 5.1 × 10−3  1.33 (0.86–2.05)a    
         2.74 (0.30–24.6)b    
2q35 rs966423 Gudmundsson et al. 2012 (15) 1.34 (1.22–1.47) 1.26 (1.06–1.51) 1.27 (1.19–1.35) 1.0 × 10−13 1.3 × 10−12  0.98 (0.74–1.28)a    
         1.74 (1.21–2.50)b    
  Liyanarachchi et al. 2013 (82) 1.30 (1.12–1.51)          
             
  Liyanarachchi et al. 2013 (82) 1.14 (1.01–1.29)          
             
5q24 rs2910164 Jazdzewski et al. 2008 (74) 1.14 (0.96–1.34) 0.95 (0.78–1.16) 1.01 (0.93–1.09) 0.89 0.92 1.55 (1.25–1.91)a 0.94 (0.73–1.22)a 1.07 (0.97–1.19)a 0.19 0.29 
        0.50 (0.28–0.89)b 0.91 (0.56–1.47)b 0.88 (0.73–1.07)b 0.19 0.36 
  Jones et al. 2012 (75) 1.00 (0.88–1.14)     1.01 (0.86–1.19)a     
        0.98 (0.70–1.38)b     
  Wei et al. 2013 (83) 0.95 (0.82–1.09)     0.88 (0.71–1.10)a     
        0.93 (0.70–1.24)b     
8p12 rs2439302 Gudmundsson et al. 2012 (15) 1.36 (1.23–1.50) 1.12 (0.94–1.34) 1.30 (1.23–1.39) 4.0 × 10−17 1.2 × 10−15  1.10 (0.83–1.45)a    
         1.28 (0.90–1.83)b    
  Liyanarachchi et al. 2013 (82) 1.46 (1.26–1.70)          
             
  Liyanarachchi et al. 2013 (82) 1.23 (1.09–1.38)          
             
8q24 rs6983267 Akdi et al. 2011 (47) 0.98 (0.81–1.18) 1.02 (0.86–1.22) 1.06 (0.99–1.14) 0.09 0.13 1.14 (0.83–1.57)a 1.15 (0.56–1.54)a 1.03 (0.91–1.17)a 0.65 0.70 
        0.94 (0.65–1.36)b 1.03 (0.73–1.46)b 1.11 (0.96–1.27)b 0.15 0.32 
  Jones et al. 2012 (75) 1.14 (1.03–1.27)     1.01 (0.83–1.23)a     
        1.27 (1.03–1.57)b     
  Wang et al. 2013 (84) 1.01 (0.88–1.15)     0.99 (0.80–1.21)a     
        1.01 (0.78–1.32)b     
9q22 rs965513 Gudmundsson et al. 2009 (16) 1.75 (1.59–1.94) 1.78 (1.48–2.14) 1.85 (1.76–1.95) <10−20 <10−20  1.80 (1.37–2.35)a    
         3.08 (2.10–4.53)b    
  Takahashi et al. 2010 (17) 1.65 (1.43–1.91)          
             
  Jones et al. 2012 (75) 1.96 (1.76–2.18)     2.12 (1.77–2.55)a     
        3.89 (3.10–4.86)b     
  Tomaz et al. 2012 (85) 2.81 (1.87–4.22)          
             
  Liyanarachchi et al. 2013 (82) 2.09 (1.80–2.42)          
             
  Liyanarachchi et al. 2013 (82) 1.81 (1.59–2.06)          
             
9q22 rs7048394 Landa et al. 2009 (76) 1.46 (1.19–1.78) 1.55 (1.29–1.87) 1.51 (1.31–1.73) 6.3 × 10−9 2.3 × 10−8  1.39 (1.07–1.80)a    
         2.78 (1.80–4.28)b    
9q22 rs894673 Landa et al. 2009 (76) 1.39 (1.17–1.65) 1.65 (1.38–1.97) 1.51 (1.33–1.71) 1.3 × 10−10 8.3 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs3758249 Landa et al. 2009 (76) 1.39 (1.17–1.66) 1.65 (1.38–1.97) 1.51 (1.34–1.72) 1.3 × 10−10 8.3 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs907577 Landa et al. 2009 (76) 1.39 (1.17–1.65) 1.65 (1.38–1.97) 1.51 (1.34–1.71) 1.3 × 10−10 8.3 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs3021526 Landa et al. 2009 (76) 1.32 (1.11–1.58) 1.55 (1.29–1.87) 1.46 (1.29–1.66) 4.0 × 10−9 1.6 × 10−8  1.39 (1.07–1.80)a    
         2.78 (1.80–4.28)b    
  Tomaz et al. 2012 (85) 1.89 (1.27–2.82)          
             
9q22 rs10119760 Landa et al. 2009 (76) 1.47 (1.23–1.75) 1.65 (1.38–1.97) 1.56 (1.34–1.76) 1.6 × 10−10 9.7 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs1867277 Takahashi et al. 2010 (17) 1.48 (1.27–1.71) 1.65 (1.38–1.97) 1.55 (1.38–1.73) 2.9 × 10−14 4.9 × 10−13  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
  Jones et al. 2012 (75) 1.75 (1.57–1.94)     1.99 (1.64–2.41)a     
        3.08 (2.46–3.84)b     
  Tomaz et al. 2012 (85) 1.76 (1.18–2.62)          
             
9q22 rs7849497 Tomaz et al. 2012 (85) 2.45 (1.60–3.76) 1.55 (1.29–1.87) 1.67 (1.41–1.98) 3.2 × 10−9 1.4 × 10−8  1.39 (1.07–1.80)a    
         2.78 (1.80–4.28)b    
9q22 rs1867278 Tomaz et al. 2012 (85) 1.76 (1.18–2.62) 1.65 (1.38–1.97) 1.67 (1.42–1.96) 4.4 × 10−10 2.2 × 10−9  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs1867279 Tomaz et al. 2012 (85) 2.52 (1.64–3.86) 1.55 (1.29–1.87) 1.90 (1.19–3.04) 0.01 0.02  1.39 (1.07–1.80)a    
         2.78 (1.80–4.28)b    
9q22 rs1867280 Tomaz et al. 2012 (85) 1.68 (1.13–2.49) 1.65 (1.38–1.97) 1.65 (1.41–1.95) 1.4 × 10−9 6.5 × 10−9  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
9q22 rs3021523 Tomaz et al. 2012 (85) 2.39 (1.56–3.67) 1.65 (1.38–1.97) 1.74 (1.48–2.05) 2.7 × 10−11 2.8 × 10−10  1.46 (1.09–1.95)a    
         2.92 (2.02–4.22)b    
14q13 rs944289 Gudmundsson et al. 2009 (16) 1.37 (1.24–1.52) 1.25 (1.05–1.49) 1.25 (1.17–1.33) 0.01 0.02  1.13 (0.81–1.58)a    
         1.48 (1.04–2.09)b    
  Takahashi et al. 2010 (17) 1.13 (0.95–1.36)          
             
  Jones et al. 2012 (75) 1.33 (1.19–1.49)     1.31 (1.02–1.68)a     
        1.76 (1.37–2.25)b     
  Liyanarachchi et al. 2013 (82) 1.25 (1.08–1.46)          
             
  Liyanarachchi et al. 2013 (82) 1.22 (1.09–1.38)          
             

NOTE: Meta-analyses were performed when both sources showed a Pass < 0.2 (arbitrary chosen in any inheritance model) or when a meta-analysis of data from the literature alone was statistically significant. Statistically significant results at a nominal level of Pass < 0.05 are highlighted in bold.

aHeterozygotes; brare homozygotes; conly dominant model available.

Table 3.

Meta-analyses between the non-Caucasian population(s) from the literature and present GWAS

Gene or locusdbSNP IDReferencePublished OR (allelic model)Allelic OR (present GWAS)Meta-analysisPassqPublished OR (additive model)OR of the additive model (present GWAS)Meta-analysisPassq
Cell-cycle regulation and apoptosis 
MDM2 rs2279744 Zhang et al. 2013 (36)c 1.50 (1.10–2.00) 1.27 (0.91–1.77) 1.40 (1.12–1.74) 2.6 × 103       
Xenobiotic metabolism 
GPX3 rs3792796 Lin et al. 2009 (86) 1.15 (0.90–1.46) 1.08 (0.90–1.29) 1.10 (0.96–1.27) 0.17 0.24 1.25 (0.90–1.74)a 1.02 (0.78–1.33)a 1.10 (0.90–1.36)a 0.35 0.45 
        1.19 (0.66–2.16)b 1.19 (0.83–1.72)b 1.19 (0.87–1.63)b 0.28 0.40 
Immune response and inflammation 
IL11RA rs1061758 Eun et al. 2012 (55) 1.62 (1.14–2.28) 1.29 (1.02–1.65) 1.39 (1.14–1.70) 1.0 × 10−3 0.01 3.03 (1.52–6.06)a 1.24 (0.94–1.64)a 1.41 (1.08–1.82)a 0.01 0.09 
        3.16 (1.42–7.04)b 1.87 (0.89–3.93)b 2.38 (1.38–4.11)b 1.8 × 10−3 0.02 
TLR6 rs3775073 Kim et al. 2013 (87) 1.28 (0.91–1.81) 1.14 (0.94–1.38) 1.17 (0.99–1.38) 0.06 0.17 1.21 (0.74–1.99)a 1.12 (0.86–1.44)a 1.14 (0.91–1.42)a 0.26 0.41 
        1.67 (0.80–3.47)b 1.34 (0.87–2.06)b 1.42 (0.98–2.05)b 0.06 0.20 
Other cancer genes 
FOSB rs12373539 Han et al. 2012 (88) 0.79 (0.55–1.14) 0.84 (0.63–1.12) 0.82 (0.65–1.03) 0.09 0.20 0.74 (0.45–1.22)a 0.84 (0.63–1.11)a 0.82 (0.64–1.04)a 0.10 0.18 
        0.66 (0.27–1.62)b 0.87 (0.39–1.91)b 0.77 (0.43–1.39)b 0.39 0.46 
HER2 rs1801200 Rebaї et al. 2009 (64) 1.88 (1.18–3.01) 1.00 (0.79–1.28) 1.15 (0.92–1.43) 0.22 0.24 1.36 (0.78–2.37)a 0.96 (0.73–1.26)a 1.03 (0.80–1.31)a 0.83 0.83 
         1.32 (0.53–3.31)b    
IGF1R rs2229765 Cho et al. 2012 (65) 0.56 (0.39–0.80) 0.94 (0.79–1.13) 0.84 (0.72–0.99) 0.04 0.15 0.56 (0.35–0.90)a 0.86 (0.66–1.13)a 0.77 (0.61–0.98)a 0.03 0.09 
        0.28 (0.11–0.76)b 0.94 (0.64–1.38)b 0.80 (0.56–1.15)b 0.23 0.40 
ITGA6 rs11895564 Kim et al. 2011 (67) 2.04 (1.24–3.37) 1.15 (0.95–1.39) 1.46 (0.84–2.54) 0.18 0.24 1.96 (1.12–3.43)a 1.01 (0.78–1.31)a 1.34 (0.71–2.55)a 0.37 0.45 
        7.03 (0.64–78.5)b 1.44 (0.96–2.18)b 2.00 (0.57–7.05)b 0.28 0.40 
ITGB1 rs2230396 Eun et al. 2013 (68) 0.90 (0.63–1.28) 0.85 (0.65–1.12) 0.87 (0.70–1.08) 0.20 0.24 0.67 (0.39–1.15)a 0.78 (0.58–1.06)a 0.75 (0.58–0.98)a 0.04 0.09 
        0.97 (0.46–2.06)b 1.16 (0.43–3.17)b 1.03 (0.57–1.89)b 0.91 0.91 
OPN rs17524488 Mu et al. 2013 (70) 0.86 (0.71–1.05) 0.95 (0.78–1.14) 0.91 (0.76–1.04) 0.16 0.24 0.82 (0.59–1.15)a 0.81 (0.63–1.04)a 0.81 (0.67–0.99)a 0.04 0.09 
        0.74 (0.48–1.11)b 1.08 (0.70–1.66)b 0.88 (0.66–1.19)b 0.41 0.46 
OPN rs11730582 Mu et al. 2013 (70) 2.14 (1.74–2.62) 1.04 (0.87–1.24) 1.49 (0.73–3.02) 0.27 0.27 2.05 (1.46–2.90)a 0.80 (0.59–1.08)a 1.28 (0.51–3.21)a 0.61 0.67 
        4.31 (2.85–6.52)b 1.08 (0.76–1.54)b 2.15 (0.55–8.34)b 0.27 0.40 
VEGFA rs699947 Hsiao et al. 2007 (72) 1.66 (1.11–2.50) 1.19 (0.99–1.42) 1.22 (1.05–1.41) 8.6 × 10−3 0.05 1.89 (1.08–3.32)a 1.23 (0.94–1.63)a 1.26 (1.01–1.57)a 0.04 0.09 
        2.30 (0.87–6.13)b 1.38 (0.97–1.96)b 1.42 (1.04–1.94)b 0.03 0.15 
Gene or locusdbSNP IDReferencePublished OR (allelic model)Allelic OR (present GWAS)Meta-analysisPassqPublished OR (additive model)OR of the additive model (present GWAS)Meta-analysisPassq
Cell-cycle regulation and apoptosis 
MDM2 rs2279744 Zhang et al. 2013 (36)c 1.50 (1.10–2.00) 1.27 (0.91–1.77) 1.40 (1.12–1.74) 2.6 × 103       
Xenobiotic metabolism 
GPX3 rs3792796 Lin et al. 2009 (86) 1.15 (0.90–1.46) 1.08 (0.90–1.29) 1.10 (0.96–1.27) 0.17 0.24 1.25 (0.90–1.74)a 1.02 (0.78–1.33)a 1.10 (0.90–1.36)a 0.35 0.45 
        1.19 (0.66–2.16)b 1.19 (0.83–1.72)b 1.19 (0.87–1.63)b 0.28 0.40 
Immune response and inflammation 
IL11RA rs1061758 Eun et al. 2012 (55) 1.62 (1.14–2.28) 1.29 (1.02–1.65) 1.39 (1.14–1.70) 1.0 × 10−3 0.01 3.03 (1.52–6.06)a 1.24 (0.94–1.64)a 1.41 (1.08–1.82)a 0.01 0.09 
        3.16 (1.42–7.04)b 1.87 (0.89–3.93)b 2.38 (1.38–4.11)b 1.8 × 10−3 0.02 
TLR6 rs3775073 Kim et al. 2013 (87) 1.28 (0.91–1.81) 1.14 (0.94–1.38) 1.17 (0.99–1.38) 0.06 0.17 1.21 (0.74–1.99)a 1.12 (0.86–1.44)a 1.14 (0.91–1.42)a 0.26 0.41 
        1.67 (0.80–3.47)b 1.34 (0.87–2.06)b 1.42 (0.98–2.05)b 0.06 0.20 
Other cancer genes 
FOSB rs12373539 Han et al. 2012 (88) 0.79 (0.55–1.14) 0.84 (0.63–1.12) 0.82 (0.65–1.03) 0.09 0.20 0.74 (0.45–1.22)a 0.84 (0.63–1.11)a 0.82 (0.64–1.04)a 0.10 0.18 
        0.66 (0.27–1.62)b 0.87 (0.39–1.91)b 0.77 (0.43–1.39)b 0.39 0.46 
HER2 rs1801200 Rebaї et al. 2009 (64) 1.88 (1.18–3.01) 1.00 (0.79–1.28) 1.15 (0.92–1.43) 0.22 0.24 1.36 (0.78–2.37)a 0.96 (0.73–1.26)a 1.03 (0.80–1.31)a 0.83 0.83 
         1.32 (0.53–3.31)b    
IGF1R rs2229765 Cho et al. 2012 (65) 0.56 (0.39–0.80) 0.94 (0.79–1.13) 0.84 (0.72–0.99) 0.04 0.15 0.56 (0.35–0.90)a 0.86 (0.66–1.13)a 0.77 (0.61–0.98)a 0.03 0.09 
        0.28 (0.11–0.76)b 0.94 (0.64–1.38)b 0.80 (0.56–1.15)b 0.23 0.40 
ITGA6 rs11895564 Kim et al. 2011 (67) 2.04 (1.24–3.37) 1.15 (0.95–1.39) 1.46 (0.84–2.54) 0.18 0.24 1.96 (1.12–3.43)a 1.01 (0.78–1.31)a 1.34 (0.71–2.55)a 0.37 0.45 
        7.03 (0.64–78.5)b 1.44 (0.96–2.18)b 2.00 (0.57–7.05)b 0.28 0.40 
ITGB1 rs2230396 Eun et al. 2013 (68) 0.90 (0.63–1.28) 0.85 (0.65–1.12) 0.87 (0.70–1.08) 0.20 0.24 0.67 (0.39–1.15)a 0.78 (0.58–1.06)a 0.75 (0.58–0.98)a 0.04 0.09 
        0.97 (0.46–2.06)b 1.16 (0.43–3.17)b 1.03 (0.57–1.89)b 0.91 0.91 
OPN rs17524488 Mu et al. 2013 (70) 0.86 (0.71–1.05) 0.95 (0.78–1.14) 0.91 (0.76–1.04) 0.16 0.24 0.82 (0.59–1.15)a 0.81 (0.63–1.04)a 0.81 (0.67–0.99)a 0.04 0.09 
        0.74 (0.48–1.11)b 1.08 (0.70–1.66)b 0.88 (0.66–1.19)b 0.41 0.46 
OPN rs11730582 Mu et al. 2013 (70) 2.14 (1.74–2.62) 1.04 (0.87–1.24) 1.49 (0.73–3.02) 0.27 0.27 2.05 (1.46–2.90)a 0.80 (0.59–1.08)a 1.28 (0.51–3.21)a 0.61 0.67 
        4.31 (2.85–6.52)b 1.08 (0.76–1.54)b 2.15 (0.55–8.34)b 0.27 0.40 
VEGFA rs699947 Hsiao et al. 2007 (72) 1.66 (1.11–2.50) 1.19 (0.99–1.42) 1.22 (1.05–1.41) 8.6 × 10−3 0.05 1.89 (1.08–3.32)a 1.23 (0.94–1.63)a 1.26 (1.01–1.57)a 0.04 0.09 
        2.30 (0.87–6.13)b 1.38 (0.97–1.96)b 1.42 (1.04–1.94)b 0.03 0.15 

aHeterozygotes; brare homozygotes; conly recessive model available. Statistically significant results at a nominal level of Pass < 0.05 are highlighted in bold.

SNPs within DNA repair genes

A total of 64 SNPs located within 27 genes involved in DNA repair pathways were investigated so far in the context of DTC. Of them, 10 were associated with the disease in the literature with at least one genetic model but none was replicated in the present GWAS under the allelic model (Table 1). However, a statistical significance was observed for rs25487 within XRCC1 (OR = 0.76, 95% CI, 0.59–0.99 for heterozygotes in the additive model). When this result was combined in meta-analysis with seven previous studies carried out on Caucasians, an OR of 0.92 (95% CI, 0.85–0.99) was found in the allelic model; however, this result was not significant after FDR correction (q = 0.06). Moreover, the meta-analyses revealed an increased risk for rs2708906, at 5′ region near HUS1 (OR = 1.34; 95% CI, 1.08–1.64; q = 0.04 for heterozygotes and OR = 1.52; 95% CI, 1.16–2.00; q = 0.01 for homozygotes; Table 2).

SNPs within cell-cycle regulation and apoptosis genes

Thirty-three common SNPs in 15 genes involved in the cell-cycle regulation or in apoptosis were collected. A total of nine significance associations were published so far, but only rs4658973 (WDR3) was replicated in the GWAS (allelic model: OR = 0.83; 95% CI, 0.70–1.00). Meta-analysis on Caucasians again suggested the role of this variant in DTC etiology with OR = 0.71 (95% CI, 0.61–0.82, allelic model), remaining significant after FDR correction (q = 1.8 × 10−6; Tables 1 and 2). Moreover, when the GWAS association on Caucasians was combined with a mixed population from a previous study, a statistical significance was found for rs2279744 (MDM2, OR = 1.40, 95% CI, 1.12–1.74; only recessive model was available for meta-analysis; Table 3).

SNPs within genes encoding for xenobiotic metabolism enzymes

Through PubMed search, 67 SNPs within 19 genes encoding for xenobiotic metabolism enzymes (XME) were collected. Overall, 19 positive associations were reported in the literature. Interestingly, rs1799814 (CYP1A1) showed a strong association in GWAS and it remained statistically significant after FDR correction (Table 1). In the meta-analysis, a high risk was found associated with the rare allele (OR = 1.86, 95% CI, 1.50–2.30, q = 4.4 × 10−8). Besides rs1799814, meta-analyses on Caucasians, after multiple testing correction, revealed a possible role also for rs1041740 (SOD1, q = 5.5 × 10−3; allelic model), rs12626475 (3′ region near SOD1, q = 0.02; allelic model), and rs3924194 (UGT2B7, q = 0.04; for heterozygotes; Table 2).

SNPs within genes involved in thyroid function

Seven genes playing a key role in thyroid function were assessed in DTC studies by genotyping 21 SNPs. Only five SNPs were reported as significantly associated with DTC, and none of them was significant in the GWAS. Thus, the meta-analyses did not confirm the role of these variants in DTC etiology (Tables 1 and 2).

SNPs within MAPK pathway genes

Of 17 SNPs within 8 genes of the MAPK pathway, four predisposing variants were reported. GWAS replicated the significant association found for rs12628 (HRAS, OR = 1.23, 95% CI, 1.02–1.48 in the allelic model), but a high heterogeneity was found between the study population previously analyzed and the present study (Phet < 0.0001). Thus, no significant evidence of association was identified in the meta-analysis using the random-effect model (Table 2).

SNPs within immune response and inflammation genes

Fifteen genes and 33 SNPs involved in immunity or in inflammation pathways were analyzed to identify susceptibility variants for DTC and eight significantly associated SNPs were published (Table 1). The present GWAS replicated the possible role of rs1126667 (ALOX12, OR = 1.34, 95% CI, 1.02–1.75; heterozygotes) and rs2292151 (TICAM1, OR = 1.69, 95% CI, 0.99–2.91; homozygotes; Table 2), as well as for rs1061758 (IL11RA, OR = 1.29, 95% CI, 1.02–1.65; allelic model; Table 3). The involvement of these SNPs in increasing the risk of DTC was further suggested by the meta-analyses. In particular, an OR = 1.74 (95% CI, 1.28–2.37, q = 8.9 × 10−4; allelic model) for rs1126667, and OR = 1.24 (95% CI, 1.06–1.45, q = 0.01; allelic model) for rs2292151 was observed in the meta-analysis with Caucasians studies, and an OR = 1.39 (95% CI, 1.14–1.70, q= 0.01; allelic model) for 1061758 was observed in the meta-analysis with an Asian study (Tables 2 and 3).

SNPs within other cancer genes

Fifty-five SNPs in 28 other genes related to cancer were investigated in relation to DTC risk and 37 SNPs were associated according to the literature. Of them, SNPs within ATG16L1 and FTO showed a strong association in GWAS under the allelic model and rs1121980 (within FTO) remained associated also after FDR correction (Table 1). According to the present meta-analysis of the published results and our GWAS data on Caucasians, SNPs rs2241880 (ATG16L1, OR = 0.81, 95% CI, 0.70–0.93, q = 7.6 × 10−3; allelic model), rs11642841 (FTO, OR = 0.76, 95% CI, 0.67–0.87, q = 1.2 × 10−4; allelic model), rs1121980 (FTO, OR = 0.75, 95% CI, 0.66–0.86, q = 5.7 × 10−5; allelic model), rs8050136 (FTO, OR = 0.76, 95% CI, 0.67–0.86, q = 4.8 × 10−5; allelic model), rs9939609 (FTO, OR = 0.77, 95% CI, 0.67–0.88, q = 3.9 × 10−4; allelic model), rs7202116 (FTO, OR = 0.76, 95% CI, 0.66–0.87, q = 2.5 × 10−4; allelic model), rs7584828 (HDAC4, OR = 0.68, 95% CI, 0.54–0.84, q = 4.2 × 10−3; heterozygotes), rs2132572 (5′ region near IGFBP3, OR = 0.77, 95% CI, 0.61–0.96, only dominant model was available for meta-analysis), and rs17849071 (PIK3CA, OR = 0.64, 95% CI, 0.46–0.90, q = 0.04; heterozygotes) were associated with a reduced risk of DTC, whereas SNPs rs17817288 (FTO, OR = 1.32, 95% CI, 1.15–1.51, q = 1.6 × 10−4; allelic model) and rs6472462 (5′ region near SULF1, OR = 1.17, 95% CI, 1.03–1.33, q = 0.03; allelic model) were associated with increased risks (Table 2). When the meta-analyses were extended to other available populations, four more SNPs showed an evidence of association, although not significantly after FDR correction: rs2229765 (IGF1R, OR = 0.77, 95% CI, 0.61–0.98, q = 0.09; heterozygotes), rs2230396 (ITGB1, OR = 0.75, 95% CI, 0.58–0.98, q = 0.09; heterozygotes), rs17524488 (5′ region near OPN, OR = 0.81, 95% CI, 0.67–0.99, q = 0.09; heterozygotes), and rs699947 (5′ region near VEGFA, OR = 1.22, 95% CI, 1.05–1.41, q = 0.05; allelic model; Table 3).

SNPs previously studied in relation to DTC risks from genome-wide association studies or studies focused on specific intergenic regions

Genetic variants on 1p31.3, 2q35, 8p12, 9q22, and 14q13.3 were associated with DTC risk by using genome-wide approaches. Three LD blocks (defined by rs965513, rs7048394, and rs894673) on chromosome 9q22 near FOXE1 were associated with DTC risk so far. These SNPs also showed a strong association in the present GWAS, where the allelic Pass remained statistically significant also after FDR correction (Table 1). Moreover, these associations were strengthened in the meta-analyses on Caucasians in the allelic model, with OR = 1.85 (95% CI, 1.76–1.95, q < 10−20) for rs965513, OR = 1.51 (95% CI, 1.31–1.73, q = 2.3 × 10−8) for rs7048394 and OR = 1.51 (95% CI, 1.33–1.71, q = 8.3 × 10−10) for rs894673. Moreover, the present meta-analysis points rs334725 (1p13.3, OR = 1.32, 95% CI, 1.10–1.59, q = 5.1 × 10−3), rs966423 (2q35, OR = 1.27, 95% CI, 1.19–1.35, q = 1.3 × 10−12), rs2439302 (8p12, OR = 1.30, 95% CI, 1.23–1.39, q = 1.2 × 10−15), and rs944289 (14q13, OR = 1.25, 95% CI, 1.17–1.33, q = 0.02) as associated with the risk of DTC (Table 2).

Current scientists' knowledge on DTC genetic risk factors is based on a series of association studies on genes involved in different cellular mechanisms that could lead to malignant transformation of thyroid cells. Typically these studies were performed according to candidate-gene approaches, and rarely the findings were replicated using similar samples in terms of ethnicity and thyroid carcinoma histological type. Furthermore, to date, only few GWASs were performed, and a small number of genomic loci were associated with the risk of the disease by using this approach.

In order to gain further insights into the role of SNPs previously associated with DTC, in the present work we carefully analyzed the results of our GWAS and we performed meta-analyses with the previous studies. The associations between DTC and well-established GWAS-identified SNPs, including rs965513, rs7048394, and rs894673 near FOXE1 (9q22), were replicated using our GWAS data. Furthermore, rs944289 near NKX2-1 (14q13.3), rs966423 within DIRC3 (2q35), rs334725 within NFIA (1p31.3), and rs2439302 within NRG1 (8p12) showed an evidence of association in the meta-analysis of the GWAS results and previous published data. The role of these loci in DTC etiology was already discussed in previous works and will be not discussed here.

Although in the present work most of the SNPs assayed in previously published hypothesis-driven studies were not associated with the risk of DTC, it is noteworthy to observe that several of them actually did associate. In particular, rs1799814 within CYP1A1 and rs1121980 within FTO were replicated on our GWAS data after the application of multiple testing corrections. The meta-analysis–based approach provided an evidence of association of several additional variants, including SNPs in the DNA repair gene HUS1 (rs2708906), cell-cycle regulation gene WDR3 (rs4658973), xenobiotic metabolism genes SOD1 (rs1041740, rs12626475) and UGT2B7 (rs3924194), the immune response and inflammation genes ALOX12 (rs1126667), TICAM1 (rs2292151), and IL11RA (rs1061758), as well as other cancer genes ATG16L1 (rs2241880), FTO (rs17817288, rs11642841, rs9939609), HDAC4 (rs7584828), IGFBP3 (rs2132572), PIK3CA (rs17849071), SULF1 (rs6472462), IGF1R (rs2229765), OPN (rs17524488), and VEGFA (rs699947). All these SNPs were previously investigated in hypothesis-driven studies, underlying the importance of CCASs also in the era of GWAS. In particular, we highlighted the role of rs17849071 and rs17524488, whose association was not significant in previous studies but became statistically significant after increasing the sample size with the present meta-analysis. Overall, our in-depth analysis showed that some a priori hypotheses formulated in previous studies were confirmed and could have realistic bases for shedding some lights in the etiology of DTC. Our GWAS had an adequate statistical power to detect small size effects (>85% of power for SNPs with MAF>0.05, relative risk of 1.4 and type I error α = 0.05), that is reinforced with the data already published through the meta-analysis. However, we cannot exclude that other SNPs could be associated with DTC but failed to replicate in the present study. For example, it is worth mentioning that 13 SNPs (Supplementary Table S3) were found associated with the risk of DTC in the meta-analysis of literature data alone, although all but rs965513 were not significant in the present GWAS. Ideally, all these SNPs should be replicated in a large and independent series of cases and controls to further confirm their involvement in DTC predisposition (24). In conclusion, our findings provide additional evidence that common genetic variants have a role in DTC initiation and/or progression. Further cutting-edge studies, as novel GWASs, next-generation sequencing analysis, fine-mapping or genome-wide interactions studies, are needed to characterize all the predisposing risk factors for DTC.

No potential conflicts of interest were disclosed.

Conception and design: R. Elisei, C. Romei, K. Hemminki, F. Gemignani, A. Försti, S. Landi

Development of methodology: G. Figlioli, A. Försti

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): G. Figlioli, R. Elisei, F. Bambi, B. Chen, A. Cristaudo, K. Hemminki, A. Försti

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): G. Figlioli, R. Elisei, O. Melaiu, B. Chen, A. Köhler, K. Hemminki, A. Försti

Writing, review, and/or revision of the manuscript: G. Figlioli, C. Romei, M. Cipollini, A. Cristaudo, F. Gemignani, A. Försti, S. Landi

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): G. Figlioli, A. Köhler

Study supervision: R. Elisei, K. Hemminki, F. Gemignani, S. Landi

This work was funded by the Istituto Toscano Tumori, grant system 2010.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Kilfoy
BA
,
Devesa
SS
,
Ward
MH
,
Zhang
Y
,
Rosenberg
PS
,
Holford
TR
, et al
Gender is an age-specific effect modifier for papillary cancers of the thyroid gland
.
Cancer Epidemiol Biomarkers Prev
2009
;
18
:
1092
100
.
2.
Aschebrook-Kilfoy
B
,
Grogan
RH
,
Ward
MH
,
Kaplan
E
,
Devesa
SS
. 
Follicular thyroid cancer incidence patterns in the United States, 1980–2009
.
Thyroid
2013
;
23
:
1015
21
.
3.
Yu
GP
,
Li
JC
,
Branovan
D
,
McCormick
S
,
Schantz
SP
. 
Thyroid cancer incidence and survival in the national cancer institute surveillance, epidemiology, and end results race/ethnicity groups
.
Thyroid
2010
;
20
:
465
73
.
4.
Steliarova-Foucher
E
,
O'Callaghan
M
,
Ferlay
J
,
Masuyer
E
,
Forman
D
,
Comber
H
, et al
European Cancer Observatory: Cancer Incidence, Mortality, Prevalence and Survival in Europe. Version 1.0 (September 2012) European Network of Cancer Registries
,
International Agency for Research on Cancer
. Available fromhttp://eco.iarc.fr, accessed on 03/03/2014.
5.
Ferlay
J
,
Soerjomataram
I
,
Ervik
M
,
Dikshit
R
,
Eser
S
,
Mathers
C
, et al
GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]
.
Lyon, France
:
International Agency for Research on Cancer
; 
2013
. Available from: http://globocan.iarc.fr, accessed on 03/03/2014.
6.
Grande
E
,
Diez
JJ
,
Zafon
C
,
Capdevila
J
. 
Thyroid cancer: molecular aspects and new therapeutic strategies
.
JThyroid Res
2012
;
2012
:
847108
.
7.
Jukkola
A
,
Bloigu
R
,
Ebeling
T
,
Salmela
P
,
Blanco
G
. 
Prognostic factors in differentiated thyroid carcinomas and their implications for current staging classifications
.
Endocr Relat Cancer
2004
;
11
:
571
9
.
8.
DeLellis
RA
. 
Pathology and genetics of thyroid carcinoma
.
J Surg Oncol
2006
;
94
:
662
9
.
9.
Fagin
JA
. 
Genetics of papillary thyroid cancer initiation: implications for therapy
.
Trans Am Clin Climatol Assoc
2005
;
116
:
259
69
.
10.
Kimura
ET
,
Nikiforova
MN
,
Zhu
Z
,
Knauf
JA
,
Nikiforov
YE
,
Fagin
JA
. 
High prevalence of BRAF mutations in thyroid cancer: genetic evidence for constitutive activation of the RET/PTC-RAS-BRAF signaling pathway in papillary thyroid carcinoma
.
Cancer Res
2003
;
63
:
1454
7
.
11.
Kondo
T
,
Ezzat
S
,
Asa
SL
. 
Pathogenetic mechanisms in thyroid follicular-cell neoplasia
.
Nat Rev Cancer
2006
;
6
:
292
306
.
12.
Malchoff
CD
,
Malchoff
DM
. 
Familial nonmedullary thyroid carcinoma
.
Cancer Control
2006
;
13
:
106
10
.
13.
Healy
DG
. 
Case–control studies in the genomic era: a clinician's guide
.
Lancet Neurol
2006
;
5
:
701
7
.
14.
Landa
I
,
Robledo
M
. 
Association studies in thyroid cancer susceptibility: are we on the right track?
J Mol Endocrinol
2011
;
47
:
R43
58
.
15.
Gudmundsson
J
,
Sulem
P
,
Gudbjartsson
DF
,
Jonasson
JG
,
Masson
G
,
He
H
, et al
Discovery of common variants associated with low TSH levels and thyroid cancer risk
.
Nat Genet
2012
;
44
:
319
22
.
16.
Gudmundsson
J
,
Sulem
P
,
Gudbjartsson
DF
,
Jonasson
JG
,
Sigurdsson
A
,
Bergthorsson
JT
, et al
Common variants on 9q22.33 and 14q13.3 predispose to thyroid cancer in European populations
.
Nat Genet
2009
;
41
:
460
4
.
17.
Takahashi
M
,
Saenko
VA
,
Rogounovitch
TI
,
Kawaguchi
T
,
Drozd
VM
,
Takigawa-Imamura
H
, et al
The FOXE1 locus is a major genetic determinant for radiation-related thyroid carcinoma in Chernobyl
.
Hum Mol Genet
2010
;
19
:
2516
23
.
18.
Figlioli
G
,
Chen
B
,
Elisei
R
,
Romei
C
,
Campo
C
,
Cipollini
M
, et al
Novel genetic variants in differentiated thyroid cancer and assessment of the cumulative risk
.
Sci Rep
2015
;
5
:
8922
.
19.
Figlioli
G
,
Kohler
A
,
Chen
B
,
Elisei
R
,
Romei
C
,
Cipollini
M
, et al
Novel genome-wide association study-based candidate loci for differentiated thyroid cancer risk
.
J Clin Endocrinol Metab
2014
:
jc20141734
.
20.
Kohler
A
,
Chen
B
,
Gemignani
F
,
Elisei
R
,
Romei
C
,
Figlioli
G
, et al
Genome-wide association study on differentiated thyroid cancer
.
J Clin Endocrinol Metab
2013
;
98
:
E1674
E81
.
21.
Purcell
S
,
Neale
B
,
Todd-Brown
K
,
Thomas
L
,
Ferreira
MAR
,
Bender
D
, et al
PLINK: a toolset for whole-genome association and population-based linkage analysis
.
Am J Hum Genet
2007
;
8
:
559
75
.
22.
1000 Genomes Project Consortium
,
Abecasis
GR
,
Auton
A
,
Brooks
LD
,
DePristo
MA
,
Durbin
RM
, et al
An integrated map of genetic variation from 1,092 human genomes
.
Nature
2012
;
49
:
56
65
.
23.
Benjamini
Y
,
Drai
D
,
Elmer
G
,
Kafkafi
N
,
Golani
I
. 
Controlling the false discovery rate in behavior genetics research
.
Behav Brain Res
2001
;
125
:
279
84
.
24.
Colhoun
HM
,
McKeigue
PM
,
Davey Smith
G
. 
Problems of reporting genetic associations with complex outcomes
.
Lancet
2003
;
361
:
865
72
.
25.
Neta
G
,
Brenner
AV
,
Sturgis
EM
,
Pfeiffer
RM
,
Hutchinson
AA
,
schebrook-Kilfoy
B
, et al
Common genetic variants related to genomic integrity and risk of papillary thyroid cancer
.
Carcinogenesis
2011
;
32
:
1231
7
.
26.
Xu
L
,
Doan
PC
,
Wei
Q
,
Liu
Y
,
Li
G
,
Sturgis
EM
. 
Association of BRCA1 functional single nucleotide polymorphisms with risk of differentiated thyroid carcinoma
.
Thyroid
2012
;
22
:
35
43
.
27.
Sigurdson
AJ
,
Land
CE
,
Bhatti
P
,
Pineda
M
,
Brenner
A
,
Carr
Z
, et al
Thyroid nodules, polymorphic variants in DNA repair and RET-related genes, and interaction with ionizing radiation exposure from nuclear tests in Kazakhstan
.
Radiat Res
2009
;
171
:
77
88
.
28.
Siraj
AK
,
Al-Rasheed
M
,
Ibrahim
M
,
Siddiqui
K
,
Al-Dayel
F
,
Al-Sanea
O
, et al
RAD52 polymorphisms contribute to the development of papillary thyroid cancer susceptibility in Middle Eastern population
.
J Endocrinol Invest
2008
;
31
:
893
9
.
29.
Chiang
FY
,
Wu
CW
,
Hsiao
PJ
,
Kuo
WR
,
Lee
KW
,
Lin
JC
, et al
Association between polymorphisms in DNA base excision repair genes XRCC1, APE1, and ADPRT and differentiated thyroid carcinoma
.
Clin Cancer Res
2008
;
14
:
5919
24
.
30.
Garcia-Quispes
WA
,
Perez-Machado
G
,
Akdi
A
,
Pastor
S
,
Galofre
P
,
Biarnes
F
, et al
Association studies of OGG1, XRCC1, XRCC2 and XRCC3 polymorphisms with differentiated thyroid cancer
.
Mutat Res
2011
;
709–710
:
67
72
.
31.
Ho
T
,
Li
G
,
Lu
J
,
Zhao
C
,
Wei
Q
,
Sturgis
EM
. 
Association of XRCC1 polymorphisms and risk of differentiated thyroid carcinoma: a case–control analysis
.
Thyroid
2009
;
19
:
129
35
.
32.
Sturgis
EM
,
Zhao
C
,
Zheng
R
,
Wei
Q
. 
Radiation response genotype and risk of differentiated thyroid cancer: a case–control analysis
.
Laryngoscope
2005
;
115
:
938
45
.
33.
Rahimi
M
,
Fayaz
S
,
Fard-Esfahani
A
,
Modarressi
MH
,
Akrami
SM
,
Fard-Esfahani
P
. 
The role of Ile3434Thr XRCC7 gene polymorphism in differentiated thyroid cancer risk in an Iranian population
.
Iran Biomed J
2012
;
16
:
218
22
.
34.
Eun
YG
,
Hong
IK
,
Kim
SK
,
Park
HK
,
Kwon
S
,
Chung
DH
, et al
A Polymorphism (rs1801018, Thr7Thr) of BCL2 is associated with papillary thyroid cancer in Korean population
.
Clin Exp Otorhinolaryngol
2011
;
4
:
149
54
.
35.
Wang
YX
,
Zhao
L
,
Wang
XY
,
Liu
CM
,
Yu
SG
. 
Role of Caspase 8, Caspase 9 and Bcl-2 polymorphisms in papillary thyroid carcinoma risk in Han Chinese population
.
Med Oncol
2012
;
29
:
2445
51
.
36.
Zhang
F
,
Xu
L
,
Wei
Q
,
Song
X
,
Sturgis
EM
,
Li
G
. 
Significance of MDM2 and P14 ARF polymorphisms in susceptibility to differentiated thyroid carcinoma
.
Surgery
2013
;
153
:
711
7
.
37.
Granja
F
,
Morari
J
,
Morari
EC
,
Correa
LA
,
Assumpcao
LV
,
Ward
LS
. 
Proline homozygosity in codon 72 of p53 is a factor of susceptibility for thyroid cancer
.
Cancer Lett
2004
;
210
:
151
7
.
38.
Baida
A
,
Akdi
M
,
Gonzalez-Flores
E
,
Galofre
P
,
Marcos
R
,
Velazquez
A
. 
Strong association of chromosome 1p12 loci with thyroid cancer susceptibility
.
Cancer Epidemiol Biomarkers Prev
2008
;
17
:
1499
504
.
39.
Bufalo
NE
,
Leite
JL
,
Guilhen
AC
,
Morari
EC
,
Granja
F
,
Assumpcao
LV
, et al
Smoking and susceptibility to thyroid cancer: an inverse association with CYP1A1 allelic variants
.
Endocr Relat Cancer
2006
;
13
:
1185
93
.
40.
Siraj
AK
,
Ibrahim
M
,
Al-Rasheed
M
,
Abubaker
J
,
Bu
R
,
Siddiqui
SU
, et al
Polymorphisms of selected xenobiotic genes contribute to the development of papillary thyroid cancer susceptibility in Middle Eastern population
.
BMC Med Genet
2008
;
9
:
61
.
41.
Schonfeld
SJ
,
Neta
G
,
Sturgis
EM
,
Pfeiffer
RM
,
Hutchinson
AA
,
Xu
L
, et al
Common genetic variants in sex hormone pathway genes and papillary thyroid cancer risk
.
Thyroid
2012
;
22
:
151
6
.
42.
Aschebrook-Kilfoy
B
,
Neta
G
,
Brenner
AV
,
Hutchinson
A
,
Pfeiffer
RM
,
Sturgis
EM
, et al
Common genetic variants in metabolism and detoxification pathways and the risk of papillary thyroid cancer
.
Endocr Relat Cancer
2012
;
19
:
333
44
.
43.
Granja
F
,
Morari
J
,
Morari
EC
,
Correa
LA
,
Assumpcao
LV
,
Ward
LS
. 
GST profiling may be useful in the screening for thyroid nodule malignancy
.
Cancer Lett
2004
;
209
:
129
37
.
44.
Prasad
VV
,
Wilkhoo
H
. 
Association of the functional polymorphism C677T in the methylenetetrahydrofolate reductase gene with colorectal, thyroid, breast, ovarian, and cervical cancers
.
Onkologie
2011
;
34
:
422
6
.
45.
Hernandez
A
,
Xamena
N
,
Surralles
J
,
Galofre
P
,
Velazquez
A
,
Creus
A
, et al
Role of GST and NAT2 polymorphisms in thyroid cancer
.
J Endocrinol Invest
2008
;
31
:
1025
31
.
46.
Guilhen
AC
,
Bufalo
NE
,
Morari
EC
,
Leite
JL
,
Assumpcao
LV
,
Tincani
AJ
, et al
Role of the N-acetyltransferase 2 detoxification system in thyroid cancer susceptibility
.
Clin Cancer Res
2009
;
15
:
406
12
.
47.
Akdi
A
,
Perez
G
,
Pastor
S
,
Castell
J
,
Biarnes
J
,
Marcos
R
, et al
Common variants of the thyroglobulin gene are associated with differentiated thyroid cancer risk
.
Thyroid
2011
;
21
:
519
25
.
48.
Pastor
S
,
Akdi
A
,
Gonzalez
ER
,
Castell
J
,
Biarnes
J
,
Marcos
R
, et al
Common genetic variants in pituitary-thyroid axis genes and the risk of differentiated thyroid cancer
.
Endocr Connect
2012
;
1
:
68
77
.
49.
Cipollini
M
,
Pastor
S
,
Gemignani
F
,
Castell
J
,
Garritano
S
,
Bonotti
A
, et al
TPO genetic variants and risk of differentiated thyroid carcinoma in two European populations
.
Int J Cancer
2013
;
133
:
2843
51
.
50.
Khan
MS
,
Pandith
AA
,
Ul
HM
,
Iqbal
M
,
Khan
NP
,
Wani
KA
, et al
Lack of mutational events of RAS genes in sporadic thyroid cancer but high risk associated with HRAS T81C single nucleotide polymorphism (case–control study)
.
Tumour Biol
2013
;
34
:
521
9
.
51.
Kim
MJ
,
Kim
SK
,
Park
HJ
,
Chung
DH
,
Park
HK
,
Lee
JS
, et al
PDGFRA promoter polymorphisms are associated with the risk of papillary thyroid cancer
.
Mol Med Rep
2012
;
5
:
1267
70
.
52.
Ho
T
,
Li
G
,
Zhao
C
,
Wei
Q
,
Sturgis
EM
. 
RET polymorphisms and haplotypes and risk of differentiated thyroid cancer
.
Laryngoscope
2005
;
115
:
1035
41
.
53.
Prasad
VV
,
Padma
K
. 
Non-synonymous polymorphism (Gln261Arg) of 12-lipoxygenase in colorectal and thyroid cancers
.
Fam Cancer
2012
;
11
:
615
21
.
54.
Ban
JY
,
Kim
MK
,
Park
SW
,
Kwon
KH
. 
Interleukin-1 beta polymorphisms are associated with lymph node metastasis in Korean patients with papillary thyroid carcinoma
.
Immunol Invest
2012
;
41
:
888
905
.
55.
Eun
YG
,
Shin
IH
,
Kim
MJ
,
Chung
JH
,
Song
JY
,
Kwon
KH
. 
Associations between promoter polymorphism -106A/G of interleukin-11 receptor alpha and papillary thyroid cancer in Korean population
.
Surgery
2012
;
151
:
323
9
.
56.
Brenner
AV
,
Neta
G
,
Sturgis
EM
,
Pfeiffer
RM
,
Hutchinson
A
,
Yeager
M
, et al
Common single nucleotide polymorphisms in genes related to immune function and risk of papillary thyroid cancer
.
PLoS One
2013
;
8
:
e57243
.
57.
Huijbers
A
,
Plantinga
TS
,
Joosten
LA
,
Aben
KK
,
Gudmundsson
J
,
den
HM
, et al
The effect of the ATG16L1 Thr300Ala polymorphism on susceptibility and outcome of patients with epithelial cell-derived thyroid carcinoma
.
Endocr Relat Cancer
2012
;
19
:
L15
L8
.
58.
Kim
YO
,
Hong
IK
,
Eun
YG
,
Nah
SS
,
Lee
S
,
Heo
SH
, et al
Polymorphisms in bone morphogenetic protein 3 and the risk of papillary thyroid cancer
.
Oncol Lett
2013
;
5
:
336
40
.
59.
Wang
YX
,
Zhao
L
,
Wang
XY
,
Liu
CM
,
Yu
SG
. 
Association between E-cadherin (CDH1) polymorphisms and papillary thyroid carcinoma risk in Han Chinese population
.
Endocrine
2012
;
41
:
526
31
.
60.
Park
HJ
,
Choe
BK
,
Kim
SK
,
Park
HK
,
Kim
JW
,
Chung
JH
, et al
Association between collagen type XI alpha1 gene polymorphisms and papillary thyroid cancer in a Korean population
.
Exp Ther Med
2011
;
2
:
1111
6
.
61.
Rebai
M
,
Kallel
I
,
Charfeddine
S
,
Hamza
F
,
Guermazi
F
,
Rebai
A
. 
Association of polymorphisms in estrogen and thyroid hormone receptors with thyroid cancer risk
.
J Recept Signal Transduct Res
2009
;
29
:
113
8
.
62.
Kitahara
CM
,
Neta
G
,
Pfeiffer
RM
,
Kwon
D
,
Xu
L
,
Freedman
ND
, et al
Common obesity-related genetic variants and papillary thyroid cancer risk
.
Cancer Epidemiol Biomarkers Prev
2012
;
21
:
2268
71
.
63.
Sheu
SY
,
Handke
S
,
Brocker-Preuss
M
,
Gorges
R
,
Frey
UH
,
Ensinger
C
, et al
The C allele of the GNB3 C825T polymorphism of the G protein beta3-subunit is associated with an increased risk for the development of oncocytic thyroid tumours
.
JPathol
2007
;
211
:
60
6
.
64.
Rebai
M
,
Kallel
I
,
Hamza
F
,
Charfeddine
S
,
Kaffel
R
,
Guermazi
F
, et al
Association of EGFR and HER2 polymorphisms with risk and clinical features of thyroid cancer
.
Genet Test Mol Biomarkers
2009
;
13
:
779
84
.
65.
Cho
SH
,
Kim
SK
,
Kwon
E
,
Park
HJ
,
Kwon
KH
,
Chung
JH
. 
Polymorphism of IGF1R is associated with papillary thyroid carcinoma in a Korean population
.
JInterferon Cytokine Res
2012
;
32
:
401
6
.
66.
Xu
L
,
Mugartegui
L
,
Li
G
,
Sarlis
NJ
,
Wei
Q
,
Zafereo
ME
, et al
Functional polymorphisms in the insulin-like binding protein-3 gene may modulate susceptibility to differentiated thyroid carcinoma in Caucasian Americans
.
Mol Carcinog
2012
;
51
Suppl 1
:
E158
E67
.
67.
Kim
SK
,
Kim
DK
,
Oh
IH
,
Song
JY
,
Kwon
KH
,
Choe
BK
, et al
A missense polymorphism (rs11895564, Ala380Thr) of integrin alpha 6 is associated with the development and progression of papillary thyroid carcinoma in Korean population
.
JKorean Surg Soc
2011
;
81
:
308
15
.
68.
Eun
YG
,
Kim
SK
,
Chung
JH
,
Kwon
KH
. 
Association study of integrins beta 1 and beta 2 gene polymorphism and papillary thyroid cancer
.
Am J Surg
2013
;
205
:
631
5
.
69.
Ozdemir
S
,
Uludag
A
,
Silan
F
,
Atik
SY
,
Turgut
B
,
Ozdemir
O
. 
Possible roles of the xenobiotic transporter P-glycoproteins encoded by the MDR1 3435 C>T gene polymorphism in differentiated thyroid cancers
.
Asian Pac J Cancer Prev
2013
;
14
:
3213
7
.
70.
Mu
G
,
Wang
H
,
Cai
Z
,
Ji
H
. 
OPN -443C>T genetic polymorphism and tumor OPN expression are associated with the risk and clinical features of papillary thyroid cancer in a Chinese cohort
.
Cell Physiol Biochem
2013
;
32
:
171
9
.
71.
Iuliano
R
,
Palmieri
D
,
He
H
,
Iervolino
A
,
Borbone
E
,
Pallante
P
, et al
Role of PTPRJ genotype in papillary thyroid carcinoma risk
.
Endocr Relat Cancer
2010
;
17
:
1001
6
.
72.
Hsiao
PJ
,
Lu
MY
,
Chiang
FY
,
Shin
SJ
,
Tai
YD
,
Juo
SH
. 
Vascular endothelial growth factor gene polymorphisms in thyroid cancer
.
JEndocrinol
2007
;
195
:
265
70
.
73.
Cancemi
L
,
Romei
C
,
Bertocchi
S
,
Tarrini
G
,
Spitaleri
I
,
Cipollini
M
, et al
Evidences that the polymorphism Pro-282-Ala within the tumor suppressor gene WWOX is a new risk factor for differentiated thyroid carcinoma
.
Int J Cancer
2011
;
129
:
2816
24
.
74.
Jazdzewski
K
,
Murray
EL
,
Franssila
K
,
Jarzab
B
,
Schoenberg
DR
,
de la
CA
. 
Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma
.
Proc Natl Acad Sci U S A
2008
;
105
:
7269
74
.
75.
Jones
AM
,
Howarth
KM
,
Martin
L
,
Gorman
M
,
Mihai
R
,
Moss
L
, et al
Thyroid cancer susceptibility polymorphisms: confirmation of loci on chromosomes 9q22 and 14q13, validation of a recessive 8q24 locus and failure to replicate a locus on 5q24
.
J Med Genet
2012
;
49
:
158
63
.
76.
Landa
I
,
Ruiz-Llorente
S
,
Montero-Conde
C
,
Inglada-Perez
L
,
Schiavi
F
,
Leskela
S
, et al
The variant rs1867277 in FOXE1 gene confers thyroid cancer susceptibility through the recruitment of USF1/USF2 transcription factors
.
PLoS Genet
2009
;
5
:
e1000637
.
77.
Akulevich
NM
,
Saenko
VA
,
Rogounovitch
TI
,
Drozd
VM
,
Lushnikov
EF
,
Ivanov
VK
, et al
Polymorphisms of DNA damage response genes in radiation-related and sporadic papillary thyroid carcinoma
.
Endocr Relat Cancer
2009
;
16
:
491
503
.
78.
Fard-Esfahani
P
,
Fard-Esfahani
A
,
Fayaz
S
,
Ghanbarzadeh
B
,
Saidi
P
,
Mohabati
R
, et al
Association of Arg194Trp, Arg280His and Arg399Gln polymorphisms in X-ray repair cross-complementing group 1 gene and risk of differentiated thyroid carcinoma in Iran
.
Iran Biomed J
2011
;
15
:
73
8
.
79.
Santos
LS
,
Branco
SC
,
Silva
SN
,
Azevedo
AP
,
Gil
OM
,
Manita
I
, et al
Polymorphisms in base excision repair genes and thyroid cancer risk
.
Oncol Rep
2012
;
28
:
1859
68
.
80.
Akdi
A
,
Gimenez
EM
,
Garcia-Quispes
W
,
Pastor
S
,
Castell
J
,
Biarnes
J
, et al
WDR3 gene haplotype is associated with thyroid cancer risk in a Spanish population
.
Thyroid
2010
;
20
:
803
9
.
81.
Xing
JC
,
Tufano
RP
,
Murugan
AK
,
Liu
D
,
Wand
G
,
Ladenson
PW
, et al
Single nucleotide polymorphism rs17849071 G/T in the PIK3CA gene is inversely associated with follicular thyroid cancer and PIK3CA amplification
.
PLoS One
2012
;
7
:
e49192
.
82.
Liyanarachchi
S
,
Wojcicka
A
,
Li
W
,
Czetwertynska
M
,
Stachlewska
E
,
Nagy
R
, et al
Cumulative risk impact of five genetic variants associated with papillary thyroid carcinoma
.
Thyroid
2013
;
23
:
1532
40
.
83.
Wei
WJ
,
Wang
YL
,
Li
DS
,
Wang
Y
,
Wang
XF
,
Zhu
YX
, et al
Association between the rs2910164 polymorphism in pre-Mir-146a sequence and thyroid carcinogenesis
.
PLoS One
2013
;
8
:
e56638
.
84.
Wang
YL
,
Feng
SH
,
Guo
SC
,
Wei
WJ
,
Li
DS
,
Wang
Y
, et al
Confirmation of papillary thyroid cancer susceptibility loci identified by genome-wide association studies of chromosomes 14q13, 9q22, 2q35 and 8p12 in a Chinese population
.
J Med Genet
2013
;
50
:
689
95
.
85.
Tomaz
RA
,
Sousa
I
,
Silva
JG
,
Santos
C
,
Teixeira
MR
,
Leite
V
, et al
FOXE1 polymorphisms are associated with familial and sporadic nonmedullary thyroid cancer susceptibility
.
Clin Endocrinol(Oxf)
2012
;
77
:
926
33
.
86.
Lin
JC
,
Kuo
WR
,
Chiang
FY
,
Hsiao
PJ
,
Lee
KW
,
Wu
CW
, et al
Glutathione peroxidase 3 gene polymorphisms and risk of differentiated thyroid cancer
.
Surgery
2009
;
145
:
508
13
.
87.
Kim
SK
,
Park
HJ
,
Hong
IK
,
Chung
JH
,
Eun
YG
. 
A missense polymorphism (rs11466653, Met326Thr) of toll-like receptor 10 (TLR10) is associated with tumor size of papillary thyroid carcinoma in the Korean population
.
Endocrine
2013
;
43
:
161
9
.
88.
Han
SA
,
Song
JY
,
Min
SY
,
Park
WS
,
Kim
MJ
,
Chung
JH
, et al
A genetic association analysis of polymorphisms, rs2282695 and rs12373539, in the FOSB gene and papillary thyroid cancer
.
Exp Ther Med
2012
;
4
:
519
23
.