Association of Common PALB2 Polymorphisms with Breast Cancer Risk

Cancer Prevention and Susceptibility

Association of Common PALB2 Polymorphisms with Breast Cancer Risk: A Case-Control Study Peizhan Chen,1Jie Liang,2 Zhanwei Wang,2 Xiaoyi Zhou,2 Lu Chen,1Mian Li,1Dong Xie,1 Zhibin Hu,2 Hongbing Shen,2 and Hui Wang1

Abstract Purpose: The PALB2 gene has an essential role in BRCA2-mediated DNA double-strand break repair and intra ^ S phase DNA damage checkpoint control, and its mutations are moderately associated with breast cancer susceptibility.This study was designed to investigate the common variants of PALB2 and theirassociation with breast cancerrisk. Experimental Design: Foursingle nucleotide polymorphisms (SNP; rs249954, rs249935, rs120963, and rs16940342) which tagged all 19 of the reported SNPs (minor allele frequency >0.05) covering PALB2 were selected and genotyped in 1,049 patients with breast cancer and 1,073 cancer-free controls in a female Chinese population. Results: Based on the multiple hypothesis testing with the Benjamini-Hochberg method, tagging SNPs (tSNP) rs249954, rs120963, and rs16940342 were found to be associated with an increase of breast cancer risk (false discovery rate ^ adjusted P values of 0.004, 0.028, and 0.049, respectively) under the dominant model. tSNP rs249954 was associated with a 36% increase of breast cancer risk [adjusted odds ratio (OR), 1.36; 95% confidence intervals (CI), 1.13 -1. 6 4; P = 0.001;TT/TC versus CC genotypes]. The adjusted OR for rs120963 was 1.25 (95% CI, 1.04-1.49; P = 0.014; CC/CT versusTTgenotypes). For rs16940342, the adjusted OR was1.21 (95% CI,1.02-1.45; P = 0.037; GG/GA versus AA genotypes). Based on an additive model, tSNPs rs249954 and rs120963 were associated with an increase of breast cancer risk (P = 0.005 and 0.019; respectively), with the false discovery rate ^ adjusted P values being 0.020 and 0.038, respectively. Conclusions: Ourdata suggest that the variants of PALB2 conferlow-penetrance breast cancer susceptibility in a Chinese population.

Breast cancer is the most common cancer of women, with an recent increase in the incidence of this disease (2, 3). The estimated 178,000 new cases and 40,000 deaths in the United incidence of breast cancer is about twice as common in women States in 2007 (1). Although the incidence of breast cancer in with first-degree relatives affected by the disease compared China is one-fifth of that in the United States, there has been a with the general population, and the risk ratio increases with increasing numbers of affected first-degree relatives. This indicates that hereditary factors are involved in the develop- BRCA1, 1 ment of breast cancer (4). In the 1990s, mutations of Authors’Affiliations: Key Laboratory of Nutrition and Metabolism, Institute for BRCA2 TP53 Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy , and , which confer a high risk (5- to 10-fold) of of Sciences; Graduate School of the ChineseAcademy of Sciences and 2Laboratory breast cancer, were identified (5–7). However, these muta- of Reproductive Medicine, Cancer Center, Nanjing Medical University, Nanjing, China tions account for less than 5% of all patients with breast cancer, Received 2/19/08; revised 5/15/08; accepted 5/15/08. and less than 25% of those with familial cancers (8). Moderate- Grant support: Knowledge Innovation Program of the Chinese Academy of CHEK2, BRIP1 ATM Sciences (KSCX-YW-R-73), Ministry of Science and Technology of China penetrance mutations of , and , which (2007CB947100), Science and Technology Commission of Shanghai Municipality are rare but interact with BRCA1 and/or TP53, confer a 2-fold (06DZ19021), PujiangTalent Program (06PJ14107), Food Safety Research Center risk of breast cancer, have also been identified (9–11). and Key Laboratory of Nutrition and Metabolism of the Chinese Academy of Breast cancer susceptibility is thought to be associated with a Sciences, Department of Education (Innovative Key Grant 705023), and Program for Changjiang Scholars and Innovative ResearchTeam in University (IRT0631). ‘‘polygenic’’ model, in which a number of loci contribute to The costs of publication of this article were defrayed in part by the payment of page susceptibility, with each locus contributing only a small effect charges. This article must therefore be hereby markedadvertisement in accordance (12). Obviously, common variants of the abovementioned six with 18 U.S.C. Section 1734 solely to indicate this fact. genes are good candidates for breast cancer susceptibility. Their Note: P. Chen and J. Liang contributed equally to this work. Requests for reprints: Hui Wang, Institute forNutritional Sciences, Shanghai associations with breast cancer have been evaluated by a number Institutes forBiological Sciences, Chinese Academy of Sciences, Shanghai of research groups, but studies have found conflicting results 200031, P.R. China. Phone: 86-21-5492-0941; E-mail: [email protected] or no significant association (13–20). To date, several genome- or Hongbing Shen, Laboratory of Reproductive Medicine, Cancer Center, wide association studies have been accomplished in which sev- Nanjing Medical University, Nanjing 210029, P.R. China. Phone: 86-25-8686- 2756; E-mail: [email protected]. eral novel susceptibility loci have been identified (21–24). F 2008 American Association for Cancer Research. Fanconi anemia (FA) is an autosomal recessive disorder doi:10.1158/1078-0432.CCR-08-0429 characterized by cellular hypersensitivity to DNA cross-linking

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PALB2 (partner and localizer of BRCA2), a nuclear partner Translational Relevance of BRCA2, is required for the intranuclear localization and stability of BRCA2 to execute its functions in error-free DNA Although many genetic and environmental factors have double-strand break repair by homologous recombination and been suggested to be involved in the etiology of breast checkpoint control in intra–S phase DNA damage processes cancer, the underlying molecular and genetic mechanisms (28). Reduction of the expression of PALB2 by small interfering are not fully understood. The present investigation is a RNA, similar to a BRCA2 small interfering RNA, sensitized continuation of ourefforts to determine the genetic risk HeLa cells to mitomycin C, and resulted in a phenotype typical factors involved in the development of human breast of FA (28). Because the defects in PALB2 were related to this cancer. In this study, we identified several commonPALB2 new FA subtype, and affected subjects had a tendency to polymorphisms that confer low-penetrance susceptibility develop childhood cancer, PALB2 is also known as FANCN (FA to breast cancer. Our findings support the concept that complementation group N). In a subsequent study in the genetic variants of the PALB2 gene conferbreastcancer United Kingdom, monoallelic truncating mutations of PALB2 susceptibility and that this gene can be a potential target were found to be associated with a 2.3-fold increased risk of for the prevention and treatment of breast cancer. This breast cancer in individuals with familial breast cancer (29). In discovery also offers an opportunity to use PALB2 as a Finland, a new PALB2 mutation, found in patients with or biomarker in disease prediction and diagnosis of breast without a family history of breast cancer, conferred a 4-fold cancer. In addition, PALB2 variants, as personal genetic/ increased risk (30). Other studies have also found that trun- genomic profiling factors, can be used to predict cancer cating mutations in PALB2 conferred a moderate risk (31, 32). predisposition, leading to personalized approaches to These data indicate that PALB2 mutations are moderate- cancerprevention and/orclinical management.Thus, these penetrance susceptibility factors for breast cancer. Still, these results may be applied to future clinical and basic cancer mutations are rare, they occur in less than 1% of unselected research and practice for improvements in the diagnosis, breast cancers, and in less than 3% of patients with familial prevention, and treatment of breast cancer. breast cancers (29–32). Considering the fact that PALB2 is important for FA and DNA double-strand break repair, common variants of PALB2 are candidates for genetic susceptibility to breast cancer. agents and predisposition to cancer (25). Biallelic BRCA2 However, no previous study has investigated common single mutations cause the D1 subtype of FA, and the phenotype of nucleotide polymorphisms (SNP) of PALB2 for association this subtype is different from other types, with a high risk of with breast cancer susceptibility. In the present study, we childhood solid tumors (26). Two research groups recently analyzed common SNPs of PALB2 and their associations identified a new subtype of FA, FA-N, which has a similar with breast cancer risk in a case-control study using a SNP- phenotype as FA-D1 but lacks BRCA2 mutations (26, 27). tagging method.

Fig. 1. LD of the 19 common variants (MAF > 0.05) based on the HapMap CHB database. The numberin each square represents the correlation (r2) between each pairof SNPs; the squareswith no number represent r2 = 1. FourSNPs (rs120963, rs249935, rs249954, and rs16940342) were chosen to tag the 19 common variants.

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Table 1. Primary information of the genotyping procedure for tSNPs of the PALB2 gene

tSNP* Amplification PCR primer PCR product (bp) Extension primer (45bp)

rs120963 (T>C) 5¶-CACATTTATTAGTCTTAGGTATTGAAATTG-3¶ (sense) 155 5¶-GACCTGGGTGTCGATACCTATAAAT- 5¶-TCCGTCTCTATTACAAAAAAGATTAAA-3¶ (antisense) GTTAATACTTGTGGAAGAAT-3¶ rs249935(A>G) 5¶-CAGAAAATTTACCAAGCAATCAC-3¶ (sense) 151 5¶-AGATAGAGTCGATGCCAGCTATTGC- 5¶-TCTCATTAAGTGAGAGCAGTAAGTCA-3¶ (antisense) TATCCAATTTTGAAAAAAGA-3¶ rs249954 (C>T) 5¶-AAGAATATTCTCTTCCAGTGTCTCG-3¶ (sense) 133 5¶-GCGGTAGGTTCCCGACATATTCTTA- 5¶-AAATCTGTTTTTCTGAATCTGTTTACC-3¶ (antisense) CATATGAAAACTTTAATGAA-3¶ rs16940342 (A>G) 5¶-CAAGTTCCTAAAACAAAAAAACAAC-3¶ (sense) 100 5¶-ACGCACGTCCACGGTGATTTTGCA- 5¶-TAATTAGTCACAGCTTATAGACCAAGTC-3¶ (antisense) AAAATAGGTAGCAGGCTGCTC-3¶

*rs249935 tagged rs414000, rs447529, rs463804, rs484226, rs463399, rs249932, rs811892, rs513313, rs249942, rs425629, and rs465017; rs120963 tagged rs12162020 and rs3096145; rs249954 tagged rs420259; and rs16940342 tagged rs13330119, according to the HapMap data (Rel 23a/phase II Mar08), on NCBI B36 assembly, dbSNP b126 CHB database.

Materials and Methods history were marked as ‘‘no.’’ The study was approved by the Institutional Review Board of Nanjing Medical University. Selection of tag SNPs for PALB2. The PALB2 gene, which is f38 kb Study participants. In this study, 1,058 patients with clinically long and is located on chromosome 16p12.1, contains 13 exons and 12 confirmed breast cancer were recruited from the Departments of Breast introns and encodes 1,186 amino acids [f130 kDa; National Center Surgery of the Cancer Hospital of Jiangsu Province (Nanjing), the First for Biotechnology Information (NCBI) database]. The 5¶-end of PALB2 Affiliated Hospital of Nanjing Medical University, and the Nanjing is f130 bp from the 5¶-end of DCTN5 (dynactin 4), and the 3¶-end of Gulou Hospital, Jiangsu Province, China, between January 2004 and PALB2 is f7 kbfrom the NADH dehydrogenase 1 ( NOUFAB1). There May 2007. A vast majority of participating subjects were included in a have been 183 SNPs reported in the dbSNP database,3 none of which previously published study (33). All subjects were genetically unrelated are the common SNPs [i.e., minor allele frequency (MAF) > 0.05] in the ethnic Han Chinese women from hospitals in Nanjing City and coding regions or in the promoter region for Asian populations. Based surrounding regions served by these hospitals. Patients with a previous on the HapMap CHB database (HapMap data, Rel 22/phase II Apr07, history of cancer, metastatic cancer, or previous radiotherapy or on NCBI B36 assembly, dbSNP b126; population: Han Chinese in chemotherapy were excluded. The 1,078 cancer-free controls were Beijing, China; MAF are >0.05) and the NCBI database (MAF >0.05 in ethnic Han Chinese women frequency-matched to the cases based on the Chinese population), we identified 18 common SNPs in the PALB2 F age ( 5 years) and residential area (urban versus rural). These women gene (including the region f300 bp upstream from the 5¶-end, the were randomly selected from a pool of 20,000 individuals participating whole PALB2 gene sequence and f6.5 kbdownstream the 3 ¶-end; f45 in a community-based screening program for noninfectious diseases kbtotal). The HapMap databaseregion was chr16:23515500.23560521 conducted in Jiangsu Province from 2004 to 2005. All subjects provided for tagging SNPs (tSNP) selection. In addition, a new common variant, a 5-mL venous blood sample. After informed consent was obtained, rs13330119, was recently identified in this region based on the recently each participant was personally interviewed by trained interviewers updated HapMap database (HapMap data, Rel 23a/phase II Mar08, using a pretested questionnaire to obtain information about demo- on NCBI B36 assembly, dbSNP b126). We applied the criterion that graphic data, menstrual data, reproductive history, and the cancer history of first-degree relatives (parents, siblings, and children). If first- degree relatives had a history of any kind of cancer, it was recorded as ‘‘yes’’, whereas patients without any first-degree relatives with a cancer 3 http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId=79728&chooseRs=all

Table 2. Characteristics of breast cancer patients and cancer-free controls and the distributions of select variables

Cases, n (%) Controls, n (%)

Variable Ntotal = 1,049 Ntotal = 1,073 P

Age, mean years (FSD) 51.59 F 11.18 51.77 F 11.10 0.712 Age at menarche, mean years (FSD) 15.29 F 1.94 16.03 F 1.92 <0.0001 Age at menopause, mean years (FSD)* 48.99 F 4.42 49.26 F 4.23 0.307 Age at first live birth, mean years (FSD)c 25.58 F 3.42 24.94 F 3.20 <0.0001 Menopause status 0.33 Premenopause 509 (48.52%) 498 (46.41%) Postmenopause 540 (51.48%) 575 (53.59%) First-degree relative family history of cancer <0.0001 Yes 305(29.08%) 231 (21.53%) No 744 (70.92%) 842 (78.47%)

*Information for age at menopause was available for 522 (49.76%) cases with breast cancer and for 518 (48.27%) controls. cAge at first live birth information was available for 1,006 (95.90%) cases with breast cancer and for 1,043 (97.20%) controls.

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Table 3. Summary data for correlation, call rate, and Hardy-Weinberg equilibrium test for each tSNP of the PALB2 gene

tSNP Genotype Cases Controls OR (95% CI) OR (95% CI)* Pc P-trend* Pb Call HWE n (%) n (%) rate Pb n = 1,010 n = 1,033 96.3%

rs249954 CC 331 (32.77) 412 (39.88) 1.00 (Reference) 1.00 (Reference) TC 524 (51.88) 477 (46.18) 1.36 (1.12-1.65) 1.37 (1.13-1.66) TT 155 (15.35) 144 (13.94) 1.32 (1.01-1.72) 1.33 (1.01-1.75) 0.005 0.006 0.020 0.701 TT/TC vs. CC 1.35(1.13-1.62) 1.36 (1.13-1.64) 0.001 0.004 (Dom T) TT vs. TC/CC 1.10 (0.86-1.41) 1.11 (0.87-1.43) 0.414 (Rec T)

n = 997 n = 1,008 94.5%

rs120963 TT 428 (42.93) 488 (48.41) 1.00 (Reference) 1.00 (Reference) CT 459 (46.04) 436 (43.25) 1.20 (1.00-1.45) 1.20 (0.99-1.45) CC 110 (11.03) 84 (8.33) 1.50 (1.10-2.04) 1.49 (1.08-2.05) 0.019 0.007 0.038 0.331 CC/CT vs. TT 1.25(1.05-1.49) 1.25(1.04-1.49) 0.014 0.028 (Dom T) CC vs. CT/TT 1.36 (1.01-1.84) 1.36 (1.01-1.85) 0.041 0.164 (Rec C)

n = 1,031 n = 1,052 98.2%

rs16940342 AA 561 (54.41) 620 (58.94) 1.00 (Reference) 1.00 (Reference) GA 423 (41.03) 377 (35.84) 1.24 (1.04-1.48) 1.25 (1.04-1.50) GG 47 (4.56) 55 (5.23) 0.95 (0.63-1.42) 0.97 (0.64-1.46) 0.050 0.115 0.067 0.813 GG/GA vs. AA 1.20 (1.01-1.43) 1.21 (1.02-1.45) 0.037 0.049 (Dom G) GG vs. GA/AA 0.87 (0.58-1.29) 0.88 (0.59-1.33) 0.479 (Rec G)

n = 1,035 n = 1,058 98.6%

rs249935AA 720 (69.5 7) 762 (72.02) 1.00 (Reference) 1.00 (Reference) GA 290 (28.02) 264 (24.95) 1.16 (0.96-1.41) 1.14 (0.93-1.39) GG 25 (2.42) 32 (3.02) 0.83 (0.49-1.41) 0.88 (0.51-1.52) 0.221 0.453 0.123 GG/GA vs. AA 1.13 (0.93-1.36) 1.11 (0.92-1.35) 0.216 (Dom G) GG vs. GA/AA 0.79 (0.47-1.35) 0.85 (0.50-1.46) 0.392 (Rec G)

Abbreviations: Rec, recessive; Dom, dominant; HWE, Hardy-Weinberg equilibrium. *Adjusted by age, age at menarche, menopause status, and first-degree relative family history of cancer. cTwo-sided m2 test. bFDR-adjusted P value for multiple hypothesis testing using the Benjamini-Hochberg method.

the r2 threshold must be >0.8 in a ‘‘pairwise tagging only’’ mode with including each tSNP site was amplified from the genomic DNA using the Haploview v.3.32 software program4 to select tSNPs. Four SNPs HotStar Taq DNA Polymerase (Qiagen, Germany) in a multiplex PCR. (rs120963, rs249935, rs249954, and rs16940342) were selected as Primers for each tSNP are listed in Table 1. The PCR products were tSNPs for the association analysis with a mean r2 being 0.958 according purified with exonuclease I (U.S. Biochemical Corporation) and shrimp to the updated database. The linkage disequilibrium (LD) between the alkaline phosphatase (U.S. Biochemical Corporation). After the 19 common variants is shown in Fig. 1. purification step, an extension primer (Table 1) for each tSNP was DNA isolation and genotyping. Genomic DNA was extracted from used, together with the extension mix (Beckman Coulter) to make a leukocytes by proteinase K digestion followed by phenol-chloroform specific extension product, which was then hybridized with the extraction and ethanol precipitation. The tSNPs of PALB2 were geno- SNPware Plate (Beckman Coulter). The plate was screened by the typed using the GenomeLabSNPstream 12-plex Genotyping System specialized imager of this SNPstream system, and the genotype data (Beckman Coulter, Inc.) following the manufacturer’s instructions. were analyzed by GenomeLab SNPstream version 2.2 software (Beck- This platform uses a single base pair extension reaction to incor- man Coulter). To ensure genotyping quality, we added 24 (6.25%) porate two-color fluorescence terminal nucleotides which are detected randomly selected replications and 4 blanks for each 384-well plate and by a specialized imager (34). This high-throughput method has been found a concordance rate of >99%. For each loci assayed, nine cases previously validated (35, 36). Briefly, a 100 to 150-bp DNA sequence and five controls were excluded in further analyses for fail genotyping, the final analyses included 1,049 cases and 1,073 controls. Statistical methods. Differences in demographic characteristics and 4 http://www.hapmap.org/ selected variables were evaluated with the m2 test (for categorical

ClinCancerRes2008;14(18)September15,2008 5934 www.aacrjournals.org Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2008 American Association for Cancer Research. PALB2 Polymorphisms and Breast Cancer variables) and Student’s t test (for continuous variables). The m2 test No significant association between rs249935 and breast cancer (1 df) was used to assess the Hardy-Weinberg equilibrium for each tSNP risk was detected under either of the assumptions (P = 0.216 PALB2 of the gene. Unconditional logistic regression was used to and 0.392, respectively; Table 3). compare the genotype frequencies of each tSNP between cases and We stratified the three positively associated tSNPs by selected controls. The common homozygote was used as the reference to factors: age, age at menarche (below or above 16), menopausal calculate genotype-specific odds ratio (OR) and its 95% confidence intervals (CI) with adjustment for age, menopausal status, age at status (premenopausal or postmenopausal), and whether the menarche, and first-degree relatives family history under the three subject had a first-degree relative with a history of cancer (yes assumed (additive, dominant, and recessive) models. The trend test was or no) for further exploration of the association of PALB2 also carried out in the analysis. We carried out multiple hypothesis variants with breast cancer risk based on a dominant model testing using the Benjamini-Hochberg method to control the false (Table 4). We found that the association of rs249954 with discovery rate (FDR) in the unconditional logistic regression analysis breast cancer risk was more evident in the subjects with an age (37). An FDR of 0.05 was used as a critical value to assess whether the at menarche of >16 (adjusted OR, 1.54; 95% CI, 1.19-2.00) and P obtained values were significant. For the three tSNPs with FDR- those without a first-degree relative with a history of cancer P adjusted < 0.05, their association with breast cancer risk was analyzed (adjusted OR, 1.37; 95% CI, 1.10-1.69). The associations with stratification by age, menopausal status (premenopausal or between rs120963 or rs16940342 and breast cancer risk were postmenopausal), age at menarche (below or above 16), and first- degree relative cancer history (yes or no) under the dominant model. also stronger in participants with an age at menarche of >16 All statistical analyses were done with Statistical Analysis System (adjusted OR, 1.29; 95% CI, 1.00-1.66 for rs120963 and software (v.9.1.3e; SAS Institute). adjusted OR, 1.32; 95% CI, 1.03-1.69 for rs16940342) and those without a first-degree relative with a history of cancer (adjusted OR, 1.26; 95% CI, 1.02-1.56 for rs120963 and adjusted OR, Results 1.23; 95% CI, 1.00-1.51 for rs16940342). On the other hand, only rs120963 was strongly associated with premenopausal In general, we found a significant difference between cancer women (adjusted OR, 1.32; 95% CI, 1.02-1.72). patients and controls with regard to the age at menarche (P < 0.0001), age at their first live birth (P < 0.0001), and the existence of a first-degree relative with a history of cancer Discussion (P < 0.0001; Table 2). The call rate of each tSNP was >90%, and the Hardy-Weinberg equilibrium test for each tSNP passed at We used a tagging approach to evaluate the associations the P = 0.05 level (Table 3). between the common variants of PALB2 and breast cancer risk. Three tSNPs were significantly associated with breast cancer Based on the HapMap CHB database, a region of about 45 kb risk under the additive model (Table 3). The association for covering the whole PALB2 gene was analyzed; the SNPs tSNP rs249935 was not significant (P = 0.221), whereas the identified in the PALB2 region were consistent with those association between tSNP rs249954 and breast cancer risk was reported in the NCBI database.3 the strongest (adjusted OR, 1.33; 95% CI, 1.01-1.75 for the TT For the present study, four SNPs with a mean correlation of genotype and adjusted OR, 1.37; 95% CI, 1.13-1.66 for the TC 0.959 were selected from 19 tagged common SNPs. Our data genotype; Table 3), compared with the common homozygote showed that tSNPs rs249954 and rs120963 were associated CC (P = 0.005 and P-trend = 0.006). The tSNP rs120963 was with breast cancer risk when an additive model was used. Based also associated with an increased risk of breast cancer on a dominant model, three of the four tSNPs (rs249954, (adjusted OR, 1.49; 95% CI, 1.08-2.05 for CC and adjusted rs120963, and rs16940342) were significantly associated with OR, 1.20; 95% CI, 0.99-1.45 for CT compared with the TT breast cancer risk in the single-locus analysis. Even after genotype; P = 0.019; Table 3) in a dose-dependent manner adjustment for multiple testing, these three tSNPs were still (P-trend = 0.007). The tSNP rs16940342 showed a marginal significantly associated with breast cancer risk, with a FDR- association with breast cancer risk (P = 0.050) without an adjusted P < 0.05. obvious trend (P-trend = 0.115). However, after being SNP rs249954 tags SNP rs420259 (r2 =1,D¶ = 1), which determined by multiple hypothesis testing, only rs249954 was reported to be associated with the risk of bipolar disorder and rs120963 were found to be associated with breast cancer (P = 6.29 Â 10-8) following a genome-wide association study risk, with the FDR-adjusted P values being 0.020 and 0.038, of cases from the United Kingdom (38). This indicates that respectively. diseases may share some fraction of genetic factors. Because Under the assumption of a dominant model, tSNPs rs249954 SNP rs249954 is located in intron 6 and rs420259 is in intron 9 and rs129063 were associated with increased breast cancer risk of the PALB2 gene, it is uncertain which one of these two (P = 0.001 and 0.014, respectively; Table 3) with FDR-adjusted variants is the cause of the increase in breast cancer risk. In P < 0.05 (adjusted P = 0.004 and P = 0.028 for rs249954 and order to identify additional SNPs that may be in high LD with rs120963, respectively). tSNP rs16940342 also showed a these two sites that could be associated with breast cancer risk, significant risk association (adjusted OR, 1.21; 95% CI, 1.02- we screened all of the common variants (with MAF > 0.05) 1.45; P = 0.037; Table 3), and had a FDR-adjusted P < 0.05 within an approximately 200-kb-long region around these two (adjusted P = 0.0493), indicating that G is another allele that sites (f100 kbupstream and f100 kbdownstream of these associated with breast cancer risk. Under the assumption of loci) based on the CHB HapMap data resource (HapMap data, a recessive model, only tSNP rs120963 showed a marginal Rel 23a/phase II Mar08, on NCBI B36 assembly, dbSNP b126). association (P = 0.041) with an adjusted OR of 1.37 (95% CI, We did not find any SNPs that correlated with these two loci 1.01-1.85). However, based on the multiple hypothesis testing, under the ‘‘r2 > 0.80’’ level. This indicates that the association this association was not significant (FDR-adjusted P = 0.164). identified here may be unique.

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Table 4. Stratified analysis of the three significantly associated loci of PALB2

Stratification factors rs249954 Genotype Cases, Controls, OR (95% CI)* n = 1,010 n = 1,033

Age <50 CC 172 205 1.00 TC + TT 342 302 1.35(1.04-1.76) z50 CC 159 205 1.00 TC + TT 337 321 1.38 (1.06-1.79) Age at menarche <16 CC 186 177 1.00 TC + TT 393 242 1.54 (1.19-2.00) z16 CC 145233 1.00 TC + TT 286 381 1.23 (0.95-1.60) Menopause status Premenopause CC 162 196 1.00 TC + TT 327 282 1.39 (1.07-1.82) Postmenopause CC 169 214 1.00 TC + TT 352 341 1.33 (1.03-1.72) First-degree relative, family history of cancer No CC 231 317 1.00 TC + TT 487 494 1.37 (1.10-1.69) Yes CC 100 93 1.00 TC + TT 192 129 1.35(0.93-1.94)

*Adjusted by age, age at menarche, menopausal status, and the first-degree relative family history of cancer (excluding the stratified factors in each stratum).

tSNP rs120963 is f6.5 kbfrom the 3 ¶-end of PALB2,but the study, the definition of cancer family history used in the it is in complete LD with SNP rs3096145 (r2 =1,D¶ =1) study may be too broad. Positive definition of family and in high LD with rs12162020 (r2 = 0.896, D¶ = 0.947) of history—if first-degree relatives had a history of any kind of the 3¶-end near the PALB2 gene; therefore, it is highly likely cancers, family history was recorded as ‘‘yes’’—may be too that the 3¶-end near gene SNP rs3096145 of PALB2 may be broad and makes it difficult to interpret the subsequent the causal variant. Nevertheless, we cannot exclude the observation of an increased breast cancer risk of ‘‘at-risk’’ possibility that SNPs in other genes (such as rs152460 and genotypes (under the dominant model) in individuals with no rs152454 in gene UBPH, f30 kbfrom PALB2) found to be family history of cancer. In future studies, a better-defined in high LD (r2 > 0.80) with rs120963 within a 200 kbregion family history should be used. around this locus may be potential sources for the observed In summary, there were low-penetrance effects of PALB2 effect. In our study, tSNP rs16940342 (in intron 4) was only variants on breast cancer risk in a Chinese population. Because marginally associated with breast cancer risk (P = 0.05, the etiologic roles of these variants are unknown, their P-trend = 0.115) in an additive model. However, under the biological functions, and the functions of the variants in LD assumption of a dominant model, it showed a stronger with the investigated variants, require further validation and association with breast cancer risk (adjusted OR, 1.21; 95% mechanistic studies. To the best of our knowledge, this is the CI, 1.02-1.45; P = 0.037) with a FDR-adjusted P < 0.05. The first study to evaluate the common variants in PALB2 and their newly identified common variant rs13330119 is tagged by association with breast cancer risk. To confirm our findings, rs16940342 with a pairwise r2 of 0.941 and may also be the larger epidemiologic studies with ethnically diverse populations causal variant. SNP rs16940342 is located in intron 4, and are warranted. rs13330119 is within intron 10 of the PALB2 gene; therefore, it is uncertain which of these two variants underlies the association with breast cancer risk. We also did not find any Disclosure of Potential Conflicts of Interest other SNPs in high LD with these two SNPs within a 200-kb- long region based on the HapMap data (Rel 23a/phase II No potential conflicts of interest were disclosed. Mar08), on the NCBI B36 assembly dbSNP b126 CHB database. Therefore, the association we found here may be caused by one single SNP or by the combined effects of these Acknowledgments two loci. However, further analysis is needed to validate this finding. We thank ourcollaboratorsin the Department of General Surgery, Jiangsu Cancer Hospital (Jinhai Tang, Jianwei Qin) and in the Department of General Surgery (Shui Of note, some intricate effects were found with the variants Wang),The First Affiliated Hospital of Nanjing Medical University. We also thank in the stratification study. Although we attempted to control Drs. Qingyi Wei, Donald L. Hill, and Elizabeth Rayburn for assistance in editing this the confounding factors for the cases and controls used in manuscript.

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Table 4. Stratified analysis of the three significantly associated loci of PALB2 (Cont’d)

rs120963 rs16940342 Genotype Cases, Controls, OR (95% CI)* Genotype Cases, Controls, OR (95% CI)* n = 997 n = 1,008 n = 1,031 n = 1,052

TT 228 243 1.00 AA 282 311 1.00 CT + CC 280 249 1.24 (0.96-1.60) GA + GG 240 209 1.26 (0.98-1.62) TT 200 2451.00 AA 279 309 1.00 CT + CC 289 271 1.28 (0.99-1.65) GA + GG 230 223 1.18 (0.92-1.52)

TT 253 204 1.00 AA 332 250 1.00 CT + CC 323 203 1.27 (0.98-1.64) GA + GG 263 1751.14 (0.88-1.47) TT 175284 1.00 AA 229 370 1.00 CT + CC 246 317 1.29 (1.00-1.66) GA + GG 207 257 1.32 (1.03-1.69)

TT 216 237 1.00 AA 271 293 1.00 CT + CC 270 226 1.32 (1.02-1.72) GA + GG 228 202 1.23 (0.95-1.58) TT 212 251 1.00 AA 290 327 1.00 CT + CC 299 294 1.19 (0.92-1.52) GA + GG 242 230 1.21 (0.95-1.55)

TT 300 374 1.00 AA 391 483 1.00 CT + CC 405 406 1.26 (1.02-1.56) GA + GG 342 346 1.23 (1.00-1.51) TT 128 114 1.00 AA 170 137 1.00 CT + CC 164 114 1.20 (0.84-1.71) GA + GG 128 86 1.17 (0.81-1.67)

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Peizhan Chen, Jie Liang, Zhanwei Wang, et al.

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