Literature DB >> 22383897

Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer.

Jirong Long1, Qiuyin Cai, Hyuna Sung, Jiajun Shi, Ben Zhang, Ji-Yeob Choi, Wanqing Wen, Ryan J Delahanty, Wei Lu, Yu-Tang Gao, Hongbing Shen, Sue K Park, Kexin Chen, Chen-Yang Shen, Zefang Ren, Christopher A Haiman, Keitaro Matsuo, Mi Kyung Kim, Ui Soon Khoo, Motoki Iwasaki, Ying Zheng, Yong-Bing Xiang, Kai Gu, Nathaniel Rothman, Wenjing Wang, Zhibin Hu, Yao Liu, Keun-Young Yoo, Dong-Young Noh, Bok-Ghee Han, Min Hyuk Lee, Hong Zheng, Lina Zhang, Pei-Ei Wu, Ya-Lan Shieh, Sum Yin Chan, Shenming Wang, Xiaoming Xie, Sung-Won Kim, Brian E Henderson, Loic Le Marchand, Hidemi Ito, Yoshio Kasuga, Sei-Hyun Ahn, Han Sung Kang, Kelvin Y K Chan, Hiroji Iwata, Shoichiro Tsugane, Chun Li, Xiao-Ou Shu, Dae-Hee Kang, Wei Zheng.   

Abstract

Genetic factors play an important role in the etiology of both sporadic and familial breast cancer. We aimed to discover novel genetic susceptibility loci for breast cancer. We conducted a four-stage genome-wide association study (GWAS) in 19,091 cases and 20,606 controls of East-Asian descent including Chinese, Korean, and Japanese women. After analyzing 690,947 SNPs in 2,918 cases and 2,324 controls, we evaluated 5,365 SNPs for replication in 3,972 cases and 3,852 controls. Ninety-four SNPs were further evaluated in 5,203 cases and 5,138 controls, and finally the top 22 SNPs were investigated in up to 17,423 additional subjects (7,489 cases and 9,934 controls). SNP rs9485372, near the TGF-β activated kinase (TAB2) gene in chromosome 6q25.1, showed a consistent association with breast cancer risk across all four stages, with a P-value of 3.8×10(-12) in the combined analysis of all samples. Adjusted odds ratios (95% confidence intervals) were 0.89 (0.85-0.94) and 0.80 (0.75-0.86) for the A/G and A/A genotypes, respectively, compared with the genotype G/G. SNP rs9383951 (P = 1.9×10(-6) from the combined analysis of all samples), located in intron 5 of the ESR1 gene, and SNP rs7107217 (P = 4.6×10(-7)), located at 11q24.3, also showed a consistent association in each of the four stages. This study provides strong evidence for a novel breast cancer susceptibility locus represented by rs9485372, near the TAB2 gene (6q25.1), and identifies two possible susceptibility loci located in the ESR1 gene and 11q24.3, respectively.

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Year:  2012        PMID: 22383897      PMCID: PMC3285588          DOI: 10.1371/journal.pgen.1002532

Source DB:  PubMed          Journal:  PLoS Genet        ISSN: 1553-7390            Impact factor:   5.917


Introduction

Breast cancer is one of the most common malignancies diagnosed among women worldwide, including those living in East Asian countries. Genetic factors play an important role in the etiology of both sporadic and familial breast cancer [1]. In the past two decades, more than 1,000 reports have been published addressing the association between variants in candidate genes and breast cancer risk. However, only a few genetic risk factors have been confirmed for this common malignancy [2]. Recent genome-wide association studies (GWAS) have identified approximately 20 common genetic susceptibility loci for breast cancer [3]–[14]. However, these newly-identified genetic factors, along with known high-penetrance breast cancer susceptibility genes explain less than 30% of the heritability for this cancer [2], [15]. Furthermore, most GWAS were conducted among women of European ancestry, and many of the variants discovered in European-ancestry populations showed only a weak or no association with breast cancer in other ethnic groups [16], [17]. For example, only 8 of 12 breast cancer risk SNPs identified in women of European ancestry were directly replicated in Chinese population [18]. Therefore, GWAS conducted in non-European women are needed to fully uncover the genetic basis for breast cancer susceptibility. Herein, we report results from a large GWAS of breast cancer conducted in East Asian women.

Results

A total of 19,091 female breast cancer cases and 20,606 female controls—including 23,891 Chinese, 11,907 Korean and 3,809 Japanese women—were included in the present study (Table 1). In Stage I, we analyzed 690,947 SNPs in 2,918 breast cancer cases and 2,324 community controls recruited from studies conducted in Shanghai, China (Figure 1, Text S1). Top 5,365 SNPs were investigated in Stage IIa including 1,613 Chinese cases and 1,800 Chinese controls recruited from studies conducted in Shanghai, China. Of the SNPs evaluated, 68 SNPs showed an association with breast cancer risk at P≤0.05 with the same direction as observed in Stage I. We performed a meta-analysis for the remaining 4,913 SNPs with data available from both Stage IIa and Stage IIb (2,359 Korean cases and 2,052 Korean controls). Twenty-six SNPs showed an association with breast cancer risk with Pmeta≤0.05 and the association was consistent among Stages I, IIa and IIb. These SNPs, along with the 68 SNPs mentioned above, were selected for Stage III replication in 4,712 cases and 4,496 controls. Finally, based on the results of the first three stages, 22 top SNPs were selected for Stage IV evaluation in 7,489 cases and 9,934 controls.
Table 1

Selected characteristics of studies participating in the Asia Breast Cancer Consortium.

Study Stagea EthnicityNo. of casesNo. of controlsageb Menopause (%)c ER+ (%)
Stage I
Shanghai-IChinese2,9182,32451.7/50.3d 42.9/41.765.3
Stage II
Shanghai-II (IIa)Chinese1,6131,80053.2/53.450.2/55.162.5
SeBCS-I (IIb)Korean2,3592,05248.1/51.737.9/52.061.9
Stage III
Shanghai-IIIChinese2,6012,38653.8/55.1d 50.3/52.664.9
TaiwanChinese1,0661,06551.5/47.5d 52.3/39.966.1
NagoyaJapanese64464451.4/51.148.5/48.572.8
NaganoJapanese40140153.8/54.054.9/65.374.6
Stage IV
NanjingChinese1,7861,83750.6/50.251.3/47.655.7
TianjinChinese1,2971,58551.9/51.951.9/55.544.2
GuangzhouChinese83886549.0/49.241.8/51.971.6
NCCKorean50550449.0/49.149.5/45.365.0
SeBCS-IIKorean7771,10447.5/47.736.3/37.363.0
KOHBRA/KoGESKorean1,3973,20940.5/50.3d 23.3/62.8
MECJapanese88983066.5/66.585.3
Total 19,09120,606

See the methods section for the full names of participating studies.

Mean value for cases/controls.

Percentage for cases/controls.

Significant at α = 0.01 level.

Figure 1

Overview of the study design.

See the methods section for the full names of participating studies. Mean value for cases/controls. Percentage for cases/controls. Significant at α = 0.01 level. SNP rs9485372 showed a statistically significant association with breast cancer risk in each of the four stages (Table 2). The OR (95% CI) per A allele was 0.88 (0.81–0.95), 0.86 (0.81–0.92), 0.94 (0.88–1.00) and 0.90 (0.85–0.94), respectively, for stages I to IV. The association with this SNP was remarkably consistent across all but one small study (Figure 2A). Pooled analysis of samples from all studies produced OR (95% CI) of 0.90 (0.87–0.92) and P-value of 3.8×10−12, which is substantially lower than the conventional genome-wide significance level of 5×10−8 based on conservative Bonferroni adjustment of multiple comparisons at α = 0.05, providing strong evidence for an association of this SNP with breast cancer risk.
Table 2

Summary of results for the three SNPs showing a statistically or marginally significant association in all four stages with breast cancer risk, the Asia Breast Cancer Consortium.

SNPa Positionb StudyNo. of Cases/ControlsEAF (%)c Per allele OR (95%CI) dP valued
rs9485372 (A/G)149650567 (6q25.1)
Stage I2,770/2,17543.50.88(0.81–0.95)1.4×10−3
Stage II3,930/3,81847.10.86(0.81–0.92)6.3×10−6
Stage III4,081/4,07443.20.94(0.88–1.00)0.05
Stage IV5,186/7,44046.20.90(0.85–0.94)4.2×10−5
All stages15,967/17,50745.40.90(0.87–0.92)3.8×10−12
rs9383951 (C/G)152337306 (6q25.1)
Stage I2,916/2,31911.40.82(0.73–0.93)2.4×10−3
Stage II3,948/3,83610.10.90(0.81–1.00)0.06
Stage III4,581/4,4339.70.91(0.82–1.00)0.06
Stage IV6,117/8,2969.60.88(0.81–0.96)3.3×10−3
All stages17,562/18,88410.00.88(0.84–0.93)1.9×10−6
rs7107217 (C/A)128978900 (11q24.3)
Stage I2,916/2,31931.41.13(1.04–1.23)3.6×10−3
Stage II3,929/3,83934.81.11(1.04–1.18)2.1×10−3
Stage III4,606/4,42435.21.07(1.00–1.14)0.04
Stage IV7,348/9,83137.41.05(1.01–1.10)0.02
All stages18,799/20,41335.81.08(1.05–1.11)4.6×10−7

Effect/reference alleles based on forward strand.

From NCBI genome build 36.

Effect allele frequency in controls.

Adjusted for age and study sites.

Figure 2

ORs per risk allele and 95% CIs for breast cancer associated with three SNPs by study site and ethnicity.

A: rs9485372, B: rs9383951; and C: rs7107217.

ORs per risk allele and 95% CIs for breast cancer associated with three SNPs by study site and ethnicity.

A: rs9485372, B: rs9383951; and C: rs7107217. Effect/reference alleles based on forward strand. From NCBI genome build 36. Effect allele frequency in controls. Adjusted for age and study sites. Two other SNPs, rs9383951 and rs7107217, were also consistently replicated in each of the three replication sets. The C allele of rs9383951 was associated with decreased risk with OR (95% CI) of 0.82 (0.73–0.93), 0.90 (0.81–1.00), 0.91 (0.82–1.00), and 0.88 (0.81–0.96), respectively, for stages I to IV (Table 2). The P-value reached 1.9×10−6 in the pooled analysis of samples from all four stages. For SNP rs7107217, the ORs (95% CI) per C allele were 1.13 (1.04–1.23), 1.11 (1.04–1.18), 1.07 (1.00–1.14) and 1.05 (1.01–1.10), respectively, for stages I to IV, respectively (Table 2). Analyses with all subjects combined showed OR (95% CI) of 1.08 (1.05–1.11) and P value of 4.6×10−7. Again, the association of breast cancer risk with these two SNPs was very consistent across the vast majority of participating studies (Figure 2B and 2C). Stratified analyses showed that the associations with these three SNPs were consistent in all three East Asian populations, although the association for SNPs rs9485372 and rs7107217 was not significant for Japanese subjects, probably due to a small sample size (Table 3). Associations of these three SNPs with breast cancer risk were similar when stratified by menopausal or estrogen receptor status and none of the heterogeneity tests was statistically significant (Table S1). No significant interaction was observed with other risk factors (Table S1). After adjusted for the top 5 or 10 principal components, the results did not change significantly (Table S2).
Table 3

Association of SNPs with breast cancer risk by ethnic groups, the Asia Breast Cancer Consortium.

SNPStudyNo. of Cases/ControlsEAF (%)a OR (95% CI)b P valueb
HeterozygoteHomozygote
rs9485372
Chinese9,922/9,64443.20.90(0.84–0.96)0.83(0.76–0.90)3.5×10−6
Korean5,006/6,82548.20.87(0.79–0.95)0.76(0.68–0.85)6.0×10−7
Chinese+Korean14,928/16,46945.20.89(0.85–0.94)0.80(0.75–0.85)9.4×10−12
Japanese1,039/1,03847.50.93(0.76–1.13)0.84(0.66–1.07)0.15
All studies15,967/17,50745.40.89(0.85–0.94)0.80(0.75–0.86)3.8×10−12
rs9383951
Chinese10,625/10,18010.70.86(0.80–0.92)0.87(0.67–1.13)3.4×10−5
Korean5,011/6,8339.70.92(0.83–1.02)0.79(0.52–1.19)0.06
Chinese+Korean15,636/17,01310.30.88(0.83–0.93)0.86(0.69–1.07)1.3×10−5
Japanese1,926/1,8716.80.86(0.71–1.05)0.40(0.14–1.13)0.05
All studies17,562/18,88410.00.88(0.83–0.93)0.83(0.67–1.03)1.9×10−6
rs7107217
Chinese11,887/11,71932.31.09(1.03–1.15)1.14(1.05–1.25)2.2×10−4
Korean4,987/6,82438.71.13(1.04–1.23)1.19(1.06–1.34)7.1×10−4
Chinese+Korean16,874/18,54334.61.10(1.05–1.15)1.16(1.08–1.24)6.4×10−7
Japanese1,925/1,87047.31.09(0.94–1.27)1.09(0.91–1.31)0.33
All studies18,799/20,41335.81.10(1.05–1.15)1.15(1.08–1.22)4.6×10−7

Effect allele frequency in controls.

Adjusted for age and study sites.

Effect allele frequency in controls. Adjusted for age and study sites. Both SNPs rs9485372 and rs9383951 are located at chromosome 6q25.1, approximately 2.34 Mb and 350 kb from the SNP rs2046210 that we previously reported for breast cancer risk [8]. None of these three SNPs, however, are in LD (r2<0.1) in any of the three populations (Asian, European and Africans) as determined using data generated in the HapMap or any of the study populations included in the current study (Table S3 and Figure S1). In an analysis including all 30,153 subjects who were genotyped for three SNPs in 6q25.1, all three SNPs remained strongly associated with breast cancer risk after mutual adjustment of the other 2 SNPs with P values of 1.4×10−12, 1.3×10−4, and 6.0×10−39 for SNPs rs9485372, rs9383951 and rs2046210, respectively (Table S4). No significant interaction was observed for these three SNPs (Table S5). We also created a genetic risk score (GRS) to evaluate the combined effect of three SNPs located in 6q25.1 (Table S6). Compared with women carrying 0–1 risk variants, women carrying 6 variants had over two-fold increased risk with an OR (95% CI) of 2.36 (1.89–2.96) and a P value of 1.3×10−47. A total of 376 SNPs were successfully imputed in the LD blocks including rs2046210 and rs9485372 and the whole ESR1 gene with RSQ≥0.3 and minor allele frequency (MAF)≥0.05. Among them, 27 SNPs showed an association with breast cancer risk with P≤0.05 after adjusted for age, rs9485372, rs9383951 and rs2046210 (Table S7). With the exception of rs4591859 and rs7776340 in the locus of rs2046210 and rs7768330 in the locus of rs9383921, all other SNPs are in the same LD block within the ESR1 gene (Figure S2). No additional SNP in the rs9485372 locus showed an association with breast cancer risk at p<0.05 after adjusted for rs9485372, rs2046210, and rs9383921.

Discussion

In this large GWAS conducted in East-Asian women including 19,091 cases and 20,606 controls, we provided strong evidence for a novel breast cancer susceptibility locus represented by rs9485372 and suggestive evidence for two other loci, represented by SNPs rs9383951 and rs7107217. We previously reported a genetic susceptibility locus at 6q25.1, represented by rs2046210, for breast cancer risk [8]. The newly identified SNPs, rs9485372 and rs9383951, also are located at chromosome 6q25.1. However, these three SNPs are not in LD and are thus representing independent breast cancer susceptibility loci. All of them were associated with breast cancer risk after mutual adjustment of the other two SNPs. SNP rs9485372 is approximately 31 Kb upstream of the TGF-β activated kinase 1/MAP3K7 binding protein 2 (TAB2) gene (Figure 3). The protein encoded by this gene is an activator of MAP3K7/TAK1, which is required for the IL-1 induced activation of NF-κB and MAPK8/JNK. The TGF-β pathway plays a major role in breast cancer development and progression [19]. The MAP kinases pathway is critical in regulating cell growth and cell death [20] and may contribute to the development of cancer [20]. Furthermore, the TAB2 protein is required for DNA damage-induced TAK1 activation, suggesting that TAB2 may play a role in DNA damage repair [21]. Other genes in the region identified in the study included SUMO4, LATS1, PPIL4, and UST. However, given the proximity of the TAB2 gene with rs9485372 and the important role of this gene in breast carcinogenesis, it is possible that the association between rs9485372 and breast cancer risk may be mediated through the TAB2 gene. It is also possible that the association may be mediated through regulating the ESR1 gene, located approximately 2.5 Mb from rs9485372. This possibility was highlighted by a recent study showing that several open reading frames in the 6q25.1 regions co-expressed with ESR1 [22]. Further research is warranted to clarify the mechanism of the association identified in the study.
Figure 3

A regional plot of the −log10P-values for SNPs at 6q25.1.

The LD is estimated using data from HapMap Asian population. Also shown are the SNP Build 36 coordinates in kilobases (Kb), recombination rates in centimorgans (cM) per megabase (Mb) and genes in the region (below) based on the March 2006 UCSC genome browser assembly.

A regional plot of the −log10P-values for SNPs at 6q25.1.

The LD is estimated using data from HapMap Asian population. Also shown are the SNP Build 36 coordinates in kilobases (Kb), recombination rates in centimorgans (cM) per megabase (Mb) and genes in the region (below) based on the March 2006 UCSC genome browser assembly. SNP rs9383951 is located in intron 5 of the ESR1 gene, an important gene that has been documented to play a key role in breast cancer development and progression. Previous candidate gene studies have extensively evaluated two SNPs, rs2234693 (Pvull) and rs9340799 (XbaI), in the ESR1 gene in relation to breast cancer risk; the results, however, have been inconsistent [2]. Neither rs2234693 nor rs9340799 are in LD (r2<0.01) with the SNPs discovered in the present study. To follow-up the lead from our previous study reporting a susceptibility locus at 6q25.1 for breast cancer [8], two recent studies conducted among women of European descent identified rs3757318 and rs9397435 in relation to breast cancer risk [11], [23]. These two SNPs are in strong LD (r2>0.6 in Asians) with the SNP (rs2046210) we previously reported at 6q25.1 in East Asians but not in other populations. Again, these two SNPs are not in LD (r2<0.01 in Asian, European and African populations) with rs9383951 and rs9485372 identified in this study. Although the association with rs9383951 did not reach the conventional genome-wide significance, the fact that this SNP is located in the ESR1 gene strongly suggests a true association of this SNP with breast cancer risk. SNP rs7107217 also showed a consistent association in all four stages, although the pooled P-value did not reach the conventional genome-wide significance level. This SNP is located at 11q24.3, 152 Kb downstream of the BARX2 gene and 212 Kb upstream of the TMEM45B gene (Figure S3). BARX2 is a homeobox gene for which the mouse ortholog has been shown to influence cellular processes that control cell adhesion and cytoskeleton remodeling. It has been shown, BARX2 and estrogen receptor-alpha (ESR1) coordinately regulate the production of alternatively spliced ESR1 isoforms and control breast cancer cell growth and invasion [24]. BARX2 also acts in a tumor suppressor and loss of heterozygosity of this gene, lead to poorer survival in patients with ovarian cancer [25]. It could be ideal to increase the sample size in the discovery stage and simplify the replication stages of the study. However, like many other consortium projects, financial constraints and some logistical issues prevented us for achieving the maximum statistical power. Nevertheless, with approximately 40,000 cases and controls, our study represents the largest breast cancer genetic association study in East Asian women. This consortium will continue to provide valuable resources to identify additional novel susceptibility loci for breast cancer. In summary, in this large GWAS conducted in East Asia women, we provided convincing evidence for an association with a novel independent susceptibility locus located at 6q25.1, near the TAB2 gene. Our study also suggests that genetic variants in the ESR1 gene and chromosome 11q24.3 may be related to breast cancer risk. Given that multiple independent breast cancer susceptibility loci have identified in our studies and studies conducted by others in 6q25.1 that harbors the ESR1 gene, it is possible that 6q25.1 may represent an important region for breast cancer susceptibility.

Methods

Study populations

Included in this consortium project were 19,091 cases and 20,606 controls from 14 studies (Table 1). Detailed descriptions of these participating studies and demographic characteristics of study participants are provided in Text S1. Briefly, the consortium included 23,981 Chinese women, 11,907 Korean women, 3,809 Japanese women. The Chinese women were from 8 studies: Shanghai [n = 13,642, Shanghai Breast Cancer Study, Shanghai Breast Cancer Survival Study (SBCSS), Shanghai Endometrial Cancer Study (SECS), Shanghai Women Health Study (SWHS)] [8], , Nanjing (n = 3,623) [27], Tianjin (n = 2,882) [28], Taiwan (n = 2,131) [29], and Guangzhou (n = 1,703). The Korean women were from four studies [Seoul Breast Cancer Study (SeBCS) (n = 6,292) [30], Korea NCC (n = 1,009), KoGES (n = 3,209) [31], and KOHBRA (n = 1,397) [32]]. The Japanese women were from three studies conducted in Hawaii and Los Angeles [n = 1,719; Multiethnic Cohort Study (MEC) [33]], Nagoya (n = 1,288) [34], and Nagano (n = 802) [35] (Table 1). Approval was granted from relevant institutional review boards in all study sites; all included subjects gave informed consent.

Genotyping methods

The Genotyping protocol for Stage I has been described previously [8]. Briefly, the initial 300 subjects were genotyped using the Affymetrix GeneChip Mapping 500K Array Set. The remaining 4,985 subjects were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. We included one negative control and at least three positive quality control (QC) samples from the Coriell Cell Repositories (http://ccr.coriell.org/) in each of the 96-well plates for Affymetrix SNP Array 6.0 genotyping. A total of 273 positive QC samples were successfully genotyped, and the average concordance rate was 99.9% with a median value of 100%. The sex of all study samples was confirmed to be female. Genetically identical, unexpected duplicated samples were excluded, as were close relatives with a pair-wise proportion of identify-by-descent (IBD) estimate greater than 0.25. All samples with a call rate<95% were excluded. The SNPs were excluded if: (i) MAF<1%, (ii) call rate<95%, or (iii) genotyping concordance rate<95% in quality control samples. The final dataset included 2,918 cases and 2,324 controls for 690,947 markers. There are 21,223 SNPs that were on Affymetrix 500K Array Set but not on the Affymetrix SNP Array 6.0. These SNPs were excluded. SNPs on the Affymetrix 6.0 array but not on the Affymetrix 500k array were treated as missing data for those samples genotyped on using the Affymetrix 500k array. Similar results were obtained after excluding women genotyped by Affymetrix 500K Array Set from the analyses. Genotyping for Stage IIa was completed using the Illumina iSelect platform. To compare the consistency between the Affymetrix and Illumina iSelect platforms, we also included 43 samples from Stage I that were genotyped by Affymetrix SNP 6.0. Similar to the QC procedures used in Stage I, the following criteria were used to exclude samples: (i) call rate<95%; or (ii) unexpected duplicated samples based on IBD estimate. SNPs were excluded if: (i) call rate<95%, or (ii) genotyping concordance rate<95% in quality control samples when compared with Affymetrix 6.0 data. After QC, the mean concordance rate was 99.85% between Illumina iSelect and Affymetrix 6.0 genotyping. Data for the SNPs analyzed in Stage IIb were extracted from the Korean GWAS genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0 chip. A total of 30 QC samples were successfully genotyped, and the concordance rate was 99.83%. The sex of all samples was confirmed to be female. The SNPs were excluded if: (1) genotype call rate<95%, (2) MAF<1% in either the cases or controls, (3) deviation from HWE at P-value<10−6, and (4) poor cluster plot in either the cases or controls. Genotyping for Stage III and all samples from Koreans in Stage IV was completed using the iPLEX Sequenom MassArray platform in the Vanderbilt Molecular Epidemiology Laboratory. Included in each 96-well plate as QC samples were one negative control (water), two blinded duplicates, and two samples from the HapMap project. To compare the consistency between the Affymetrix and Sequenom platforms, we also genotyped 45 samples included in Stage I. The mean concordance rate was 99.67% for the blind duplicates, 98.88% for HapMap samples, and 99.52% between Sequenom and Affymetrix 6.0 genotyping. Data quality from the Hong Kong study was low and thus data from the study were excluded for the current analysis. Genotyping for two Chinese studies (Nanjing and Guangzhou) in Stage IV was completed using the iPLEX Sequenom MassArray platform at the Fudan University, Shanghai, China. Blind duplicate QC samples were included and the mean concordance rate was 98.70%. Genotyping for the Tianjin study in Stage IV was performed using TaqMan assays. Genotyping assay protocols were developed and validated at the Vanderbilt Molecular Epidemiology Laboratory, and TaqMan genotyping assay reagents were provided to investigators of the Tianjin study (Tianjin Cancer Institute and Hospital). For the MEC study, data for the three SNPs presented in this study were extracted from the GWA scan data generated using Illumina 660W. For SNPs not included on the chip, imputed data using HapMap as reference were extracted. Genotype frequencies for SNP rs9485372 deviated from HWE in controls (P = 0.004), therefore, this SNP was excluded in data analyses. Not all SNPs for Stage IV were genotyped in all studies included in Stage IV due to genotyping failure or the use of different genotyping platforms (Table S8).

SNP selection for replication

SNP selection for Stage II replication: Promising SNPs were selected for replication in Stage II based on the following criteria: 1) minor allele frequency (MAF)≥5%; 2) P<0.02 in Stage I; 3) Hardy-Weinberg equilibrium (HWE) test P>1.0×10−6 in controls; 4) not in strong linkage disequilibrium (LD) (r2<0.5) with any of the previously confirmed breast cancer genetic risk variants or SNPs evaluated in our previous studies [8], [12]; and 5) high genotyping quality as indicated by very clear genotyping clusters checked manually. When multiple SNPs are in LD with r2≥0.5, one SNP with the lowest P-value was selected. In total, 6,303 SNPs were selected for replication. A total of 5,906 SNPs (93.7%) were successfully designed by Illumina and included in the iSelect array. After stringent QC procedures, data from 5,365 SNPs were considered high quality for association analyses in Stage IIa, which include 1,613 breast cancer patients and 1,800 controls recruited from Shanghai studies. SNP selection for Stage III replication: Among the 5,365 SNPs successfully genotyped in Stage IIa, 68 SNPs were selected for Stage III replication in an independent set of 5,203 cases and 5,138 controls recruited from Shanghai and several other East Asian populations (Table 1 and Text S1). The selection criteria are: 1) an association with breast cancer risk in Stage IIa with P≤0.05; 2) the direction of the association consistent in both stages; and 3) P≤0.001 in the merged data of Stage I and IIa. During the course of Stage III genotyping, genome-wide association scan data from 2,359 cases and 2,052 controls were obtained from the Seoul Breast Cancer GWAS (Stage IIb). Therefore, we performed a meta-analysis of Stage IIa and IIb data. Of the 5,297 SNPs which were not selected initially for Stage III replication based on Stage IIa data alone, data were available for 4,913 SNPs in Stage IIb. Meta-analyses of these 4,913 SNPs from Stage IIa and IIb yielded 26 additional SNPs that showed an association at P≤0.05 and in the same direction among stages I, IIa, and IIb. These 26 SNPs were then added to the list of SNPs to be genotyped in Stage III. SNP selection for Stage IV replication: Based on the results of the first three stages, 22 top SNPs were selected for Stage IV evaluation and genotyped in up to 17,423 additional subjects (7,489 cases and 9,934 controls) (Table 1 and Text S1).

Statistical analyses

Case-control differences in selected demographic characteristics and major risk factors were evaluated using t-tests (for continuous variables) and Chi-square tests (for categorical variables). Associations between SNPs and breast cancer risk were assessed using odds ratios (ORs) and 95% confidence intervals (CIs) derived from logistic regression models. ORs were estimated for heterozygote and homozygote for the variant allele compared with homozygotes for the common allele. ORs were also estimated for the variant allele based on a log-additive model and adjusted for age, and study site, when appropriate. Stratified analyses by ethnicity, menopausal status, and estrogen receptor (ER) status were carried out. PLINK version 1.06 was used to analyze genome-wide data obtained in Stage I and the replication data in Stage IIa. Results from Stage IIb were also obtained from PLINK version 1.06. Meta-analyses of Stage IIa and Stage IIb were performed using a weighted z-statistics method, where weights were proportional to the square root of the number of individuals in each sample and standardized such that the weights added up to one. The z-statistic summarizes the magnitude and direction of the effect relative to the reference allele. An overall z-statistic and p value were then calculated from the weighted average of the individual statistics. Calculations were implemented in the METAL package (http://www.sph.umich.edu/csg/abecasis/Metal). Individual data were obtained from each study for Stage IV SNPs for a pooled analysis, which were conducted using SAS, version 9.2, with the use of two-tailed tests. We first investigated the population structure by estimating inflation factor λ using all 690,947 SNPs SNPs that passed the QC. The inflation factor λ was estimated to be 1.042, suggesting that any population substructure, if present, should not have any appreciable effect on the results. Among the final 690,947 SNPs obtained in Stage I after QC, we generated a list of 196,471 SNPs with pairwise LD<0.2 by using plink (http://pngu.mgh.harvard.edu/~purcell/plink/). Then, principal components were estimated based on these 196,471 SNPs using EIGENSTRAT [36]. We then drew a plot for all Stage I and HapMap II subjects based on the first two principal components (Figure 4). All study participants in Stage I were clustered very closely with HapMap Asians. The first 5 or 10 principal components were adjusted in the logistic regression analyses for evaluating associations of SNPs and breast cancer risk.
Figure 4

Principal Component Analysis (PCA) based on the first two eigenvectors obtained by PCA.

A: all individuals from Stage I and HapMap; B: breast cancer cases and controls from Stage I.

Principal Component Analysis (PCA) based on the first two eigenvectors obtained by PCA.

A: all individuals from Stage I and HapMap; B: breast cancer cases and controls from Stage I. To evaluate the combined effect of SNPs located in chromosome 6q25.1 on breast cancer risk, we created a genetic risk score (GRS) by summing the number (0–2) of risk alleles that each woman carried for each of the three SNPs, including rs9383951, rs9485372, rs2046210. The GRS was constructed among those who had complete data for all three SNPs. We also did imputation using MACH (http://www.sph.umich.edu/csg/abecasis/MACH/index.html) with HapMap II Asian data as reference. LD structure was estimated from the flanking 100 kb of these three SNPs and the ESR1 gene using data from HapMap II Asians (Figure S1). All SNPs in the LD blocks including rs9485372, rs2046210 and rs9383951 and SNPs inside the ESR1 gene were analyzed in relation to breast cancer risk with age, rs9485372, rs9383951 and rs2046210 adjusted. Estimates of pairwise LD (r2) for common SNPs from HapMap II Asians for the SNPs located in 6q25.1. A: LD plot for the flanking 100 kb of SNP rs9485372. B: LD plot for the upstream 100 kb of SNP rs2046210 and the ESR1 gene. (TIF) Click here for additional data file. Estimates of pairwise LD (r2) from HapMap II Asian for the SNPs showing significant associations after adjusted for rs9485372, rs9383951 and rs2046210. (TIF) Click here for additional data file. A regional plot of the −log10P-values for SNPs at 11q24.3. The LD is estimated using data from HapMap Asian population. Also shown are the SNP Build 36 coordinates in kilobases (Kb), recombination rates in centimorgans (cM) per megabase (Mb) and genes in the region (below) based on the March 2006 UCSC genome browser assembly. (TIF) Click here for additional data file. Association of SNPs with breast cancer risk by menopause and ER status. (DOCX) Click here for additional data file. Association results adjusted for the top principal components in Stage I. (DOCX) Click here for additional data file. LD between the 3 SNPs that are associated with breast cancer and are located in 6q25.1. (DOCX) Click here for additional data file. Conditional analyses for SNPs located on 6q25.1. (DOCX) Click here for additional data file. Association results of SNP-SNP interaction. (DOCX) Click here for additional data file. Associations of breast cancer risk with the genetic risk score for the three SNPs located in chromosome 6q25.1, the Asia Breast Cancer Consortium. (DOCX) Click here for additional data file. SNPs in 6q25.1 showed association after adjusted for rs9485372, rs9383951 and rs2046210. (DOCX) Click here for additional data file. Sample size for the SNPs included in Stage IV. (DOCX) Click here for additional data file. Supplementary Methods. (DOCX) Click here for additional data file.
  36 in total

1.  Principal components analysis corrects for stratification in genome-wide association studies.

Authors:  Alkes L Price; Nick J Patterson; Robert M Plenge; Michael E Weinblatt; Nancy A Shadick; David Reich
Journal:  Nat Genet       Date:  2006-07-23       Impact factor: 38.330

2.  Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33.

Authors:  Bert Gold; Tomas Kirchhoff; Stefan Stefanov; James Lautenberger; Agnes Viale; Judy Garber; Eitan Friedman; Steven Narod; Adam B Olshen; Peter Gregersen; Kristi Kosarin; Adam Olsh; Julie Bergeron; Nathan A Ellis; Robert J Klein; Andrew G Clark; Larry Norton; Michael Dean; Jeff Boyd; Kenneth Offit
Journal:  Proc Natl Acad Sci U S A       Date:  2008-03-07       Impact factor: 11.205

3.  A cytoplasmic ATM-TRAF6-cIAP1 module links nuclear DNA damage signaling to ubiquitin-mediated NF-κB activation.

Authors:  Michael Hinz; Michael Stilmann; Seda Çöl Arslan; Kum Kum Khanna; Gunnar Dittmar; Claus Scheidereit
Journal:  Mol Cell       Date:  2010-10-08       Impact factor: 17.970

Review 4.  Architecture of inherited susceptibility to common cancer.

Authors:  Olivia Fletcher; Richard S Houlston
Journal:  Nat Rev Cancer       Date:  2010-05       Impact factor: 60.716

5.  Evaluation of 11 breast cancer susceptibility loci in African-American women.

Authors:  Wei Zheng; Qiuyin Cai; Lisa B Signorello; Jirong Long; Margaret K Hargreaves; Sandra L Deming; Guoliang Li; Chun Li; Yong Cui; William J Blot
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-09-29       Impact factor: 4.254

6.  Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1.

Authors:  Wei Zheng; Jirong Long; Yu-Tang Gao; Chun Li; Ying Zheng; Yong-Bin Xiang; Wanqing Wen; Shawn Levy; Sandra L Deming; Jonathan L Haines; Kai Gu; Alecia Malin Fair; Qiuyin Cai; Wei Lu; Xiao-Ou Shu
Journal:  Nat Genet       Date:  2009-02-15       Impact factor: 38.330

7.  Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer.

Authors:  Simon N Stacey; Andrei Manolescu; Patrick Sulem; Steinunn Thorlacius; Sigurjon A Gudjonsson; Gudbjörn F Jonsson; Margret Jakobsdottir; Jon T Bergthorsson; Julius Gudmundsson; Katja K Aben; Luc J Strobbe; Dorine W Swinkels; K C Anton van Engelenburg; Brian E Henderson; Laurence N Kolonel; Loic Le Marchand; Esther Millastre; Raquel Andres; Berta Saez; Julio Lambea; Javier Godino; Eduardo Polo; Alejandro Tres; Simone Picelli; Johanna Rantala; Sara Margolin; Thorvaldur Jonsson; Helgi Sigurdsson; Thora Jonsdottir; Jon Hrafnkelsson; Jakob Johannsson; Thorarinn Sveinsson; Gardar Myrdal; Hlynur Niels Grimsson; Steinunn G Sveinsdottir; Kristin Alexiusdottir; Jona Saemundsdottir; Asgeir Sigurdsson; Jelena Kostic; Larus Gudmundsson; Kristleifur Kristjansson; Gisli Masson; James D Fackenthal; Clement Adebamowo; Temidayo Ogundiran; Olufunmilayo I Olopade; Christopher A Haiman; Annika Lindblom; Jose I Mayordomo; Lambertus A Kiemeney; Jeffrey R Gulcher; Thorunn Rafnar; Unnur Thorsteinsdottir; Oskar T Johannsson; Augustine Kong; Kari Stefansson
Journal:  Nat Genet       Date:  2008-04-27       Impact factor: 38.330

8.  Genetic variants of BLM interact with RAD51 to increase breast cancer susceptibility.

Authors:  Shian-Ling Ding; Jyh-Cherng Yu; Shou-Tung Chen; Giu-Cheng Hsu; Shou-Jen Kuo; Yu Hsin Lin; Pei-Ei Wu; Chen-Yang Shen
Journal:  Carcinogenesis       Date:  2008-10-28       Impact factor: 4.944

9.  Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study.

Authors:  Olivia Fletcher; Nichola Johnson; Nick Orr; Fay J Hosking; Lorna J Gibson; Kate Walker; Diana Zelenika; Ivo Gut; Simon Heath; Claire Palles; Ben Coupland; Peter Broderick; Minouk Schoemaker; Michael Jones; Jill Williamson; Sarah Chilcott-Burns; Katarzyna Tomczyk; Gemma Simpson; Kevin B Jacobs; Stephen J Chanock; David J Hunter; Ian P Tomlinson; Anthony Swerdlow; Alan Ashworth; Gillian Ross; Isabel dos Santos Silva; Mark Lathrop; Richard S Houlston; Julian Peto
Journal:  J Natl Cancer Inst       Date:  2011-01-24       Impact factor: 13.506

10.  Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2.

Authors:  Shahana Ahmed; Gilles Thomas; Maya Ghoussaini; Catherine S Healey; Manjeet K Humphreys; Radka Platte; Jonathan Morrison; Melanie Maranian; Karen A Pooley; Robert Luben; Diana Eccles; D Gareth Evans; Olivia Fletcher; Nichola Johnson; Isabel dos Santos Silva; Julian Peto; Michael R Stratton; Nazneen Rahman; Kevin Jacobs; Ross Prentice; Garnet L Anderson; Aleksandar Rajkovic; J David Curb; Regina G Ziegler; Christine D Berg; Saundra S Buys; Catherine A McCarty; Heather Spencer Feigelson; Eugenia E Calle; Michael J Thun; W Ryan Diver; Stig Bojesen; Børge G Nordestgaard; Henrik Flyger; Thilo Dörk; Peter Schürmann; Peter Hillemanns; Johann H Karstens; Natalia V Bogdanova; Natalia N Antonenkova; Iosif V Zalutsky; Marina Bermisheva; Sardana Fedorova; Elza Khusnutdinova; Daehee Kang; Keun-Young Yoo; Dong Young Noh; Sei-Hyun Ahn; Peter Devilee; Christi J van Asperen; R A E M Tollenaar; Caroline Seynaeve; Montserrat Garcia-Closas; Jolanta Lissowska; Louise Brinton; Beata Peplonska; Heli Nevanlinna; Tuomas Heikkinen; Kristiina Aittomäki; Carl Blomqvist; John L Hopper; Melissa C Southey; Letitia Smith; Amanda B Spurdle; Marjanka K Schmidt; Annegien Broeks; Richard R van Hien; Sten Cornelissen; Roger L Milne; Gloria Ribas; Anna González-Neira; Javier Benitez; Rita K Schmutzler; Barbara Burwinkel; Claus R Bartram; Alfons Meindl; Hiltrud Brauch; Christina Justenhoven; Ute Hamann; Jenny Chang-Claude; Rebecca Hein; Shan Wang-Gohrke; Annika Lindblom; Sara Margolin; Arto Mannermaa; Veli-Matti Kosma; Vesa Kataja; Janet E Olson; Xianshu Wang; Zachary Fredericksen; Graham G Giles; Gianluca Severi; Laura Baglietto; Dallas R English; Susan E Hankinson; David G Cox; Peter Kraft; Lars J Vatten; Kristian Hveem; Merethe Kumle; Alice Sigurdson; Michele Doody; Parveen Bhatti; Bruce H Alexander; Maartje J Hooning; Ans M W van den Ouweland; Rogier A Oldenburg; Mieke Schutte; Per Hall; Kamila Czene; Jianjun Liu; Yuqing Li; Angela Cox; Graeme Elliott; Ian Brock; Malcolm W R Reed; Chen-Yang Shen; Jyh-Cherng Yu; Giu-Cheng Hsu; Shou-Tung Chen; Hoda Anton-Culver; Argyrios Ziogas; Irene L Andrulis; Julia A Knight; Jonathan Beesley; Ellen L Goode; Fergus Couch; Georgia Chenevix-Trench; Robert N Hoover; Bruce A J Ponder; David J Hunter; Paul D P Pharoah; Alison M Dunning; Stephen J Chanock; Douglas F Easton
Journal:  Nat Genet       Date:  2009-03-29       Impact factor: 38.330

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  85 in total

1.  Association of five single nucleotide polymorphisms at 6q25.1 with breast cancer risk in northwestern China.

Authors:  Long Zhou; Na He; Tian Feng; Tingting Geng; Tianbo Jin; Chao Chen
Journal:  Am J Cancer Res       Date:  2015-07-15       Impact factor: 6.166

Review 2.  Genome-Wide Association Studies of Cancer in Diverse Populations.

Authors:  Sungshim L Park; Iona Cheng; Christopher A Haiman
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2017-06-21       Impact factor: 4.254

Review 3.  BRCA and Breast Cancer-Related High-Penetrance Genes.

Authors:  Sang-Ah Han; Sung-Won Kim
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

4.  Genome-wide association study in East Asians identifies two novel breast cancer susceptibility loci.

Authors:  Mi-Ryung Han; Jirong Long; Ji-Yeob Choi; Siew-Kee Low; Sun-Seog Kweon; Ying Zheng; Qiuyin Cai; Jiajun Shi; Xingyi Guo; Keitaro Matsuo; Motoki Iwasaki; Chen-Yang Shen; Mi Kyung Kim; Wanqing Wen; Bingshan Li; Atsushi Takahashi; Min-Ho Shin; Yong-Bing Xiang; Hidemi Ito; Yoshio Kasuga; Dong-Young Noh; Koichi Matsuda; Min Ho Park; Yu-Tang Gao; Hiroji Iwata; Shoichiro Tsugane; Sue K Park; Michiaki Kubo; Xiao-Ou Shu; Daehee Kang; Wei Zheng
Journal:  Hum Mol Genet       Date:  2016-06-27       Impact factor: 6.150

5.  Breast Cancer Family History and Contralateral Breast Cancer Risk in Young Women: An Update From the Women's Environmental Cancer and Radiation Epidemiology Study.

Authors:  Anne S Reiner; Julia Sisti; Esther M John; Charles F Lynch; Jennifer D Brooks; Lene Mellemkjær; John D Boice; Julia A Knight; Patrick Concannon; Marinela Capanu; Marc Tischkowitz; Mark Robson; Xiaolin Liang; Meghan Woods; David V Conti; David Duggan; Roy Shore; Daniel O Stram; Duncan C Thomas; Kathleen E Malone; Leslie Bernstein; Jonine L Bernstein
Journal:  J Clin Oncol       Date:  2018-04-05       Impact factor: 44.544

6.  Breast Cancer-Related Low Penetrance Genes.

Authors:  Daehee Kang; Ji-Yeob Choi
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

7.  A Comprehensive cis-eQTL Analysis Revealed Target Genes in Breast Cancer Susceptibility Loci Identified in Genome-wide Association Studies.

Authors:  Xingyi Guo; Weiqiang Lin; Jiandong Bao; Qiuyin Cai; Xiao Pan; Mengqiu Bai; Yuan Yuan; Jiajun Shi; Yaqiong Sun; Mi-Ryung Han; Jing Wang; Qi Liu; Wanqing Wen; Bingshan Li; Jirong Long; Jianghua Chen; Wei Zheng
Journal:  Am J Hum Genet       Date:  2018-05-03       Impact factor: 11.025

8.  Breast cancer risk prediction using a clinical risk model and polygenic risk score.

Authors:  Yiwey Shieh; Donglei Hu; Lin Ma; Scott Huntsman; Charlotte C Gard; Jessica W T Leung; Jeffrey A Tice; Celine M Vachon; Steven R Cummings; Karla Kerlikowske; Elad Ziv
Journal:  Breast Cancer Res Treat       Date:  2016-08-26       Impact factor: 4.872

9.  Common genetic determinants of breast-cancer risk in East Asian women: a collaborative study of 23 637 breast cancer cases and 25 579 controls.

Authors:  Wei Zheng; Ben Zhang; Qiuyin Cai; Hyuna Sung; Kyriaki Michailidou; Jiajun Shi; Ji-Yeob Choi; Jirong Long; Joe Dennis; Manjeet K Humphreys; Qin Wang; Wei Lu; Yu-Tang Gao; Chun Li; Hui Cai; Sue K Park; Keun-Young Yoo; Dong-Young Noh; Wonshik Han; Alison M Dunning; Javier Benitez; Daniel Vincent; Francois Bacot; Daniel Tessier; Sung-Won Kim; Min Hyuk Lee; Jong Won Lee; Jong-Young Lee; Yong-Bing Xiang; Ying Zheng; Wenjin Wang; Bu-Tian Ji; Keitaro Matsuo; Hidemi Ito; Hiroji Iwata; Hideo Tanaka; Anna H Wu; Chiu-chen Tseng; David Van Den Berg; Daniel O Stram; Soo Hwang Teo; Cheng Har Yip; In Nee Kang; Tien Y Wong; Chen-Yang Shen; Jyh-Cherng Yu; Chiun-Sheng Huang; Ming-Feng Hou; Mikael Hartman; Hui Miao; Soo Chin Lee; Thomas Choudary Putti; Kenneth Muir; Artitaya Lophatananon; Sarah Stewart-Brown; Pornthep Siriwanarangsan; Suleeporn Sangrajrang; Hongbing Shen; Kexin Chen; Pei-Ei Wu; Zefang Ren; Christopher A Haiman; Aiko Sueta; Mi Kyung Kim; Ui Soon Khoo; Motoki Iwasaki; Paul D P Pharoah; Wanqing Wen; Per Hall; Xiao-Ou Shu; Douglas F Easton; Daehee Kang
Journal:  Hum Mol Genet       Date:  2013-03-27       Impact factor: 6.150

10.  APOBEC3 deletion polymorphism is associated with breast cancer risk among women of European ancestry.

Authors:  Dennis Xuan; Guoliang Li; Qiuyin Cai; Sandra Deming-Halverson; Martha J Shrubsole; Xiao-Ou Shu; Mark C Kelley; Wei Zheng; Jirong Long
Journal:  Carcinogenesis       Date:  2013-05-28       Impact factor: 4.944

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