Joint effects of mammographic density and other risk factors on breast cancer risk remain unclear.From The Singapore Breast Screening Project, we selected 491 cases and 982 controls. Mammographic density was measured quantitatively. Data analysis was by conditional logistic regression.P=0.02).Density was a significant risk factor, adjusting for other factors. Density of 76–100% had an odds ratio of 5.54 (95% CI 2.38–12.90) compared with 0–10%. Density had significant interactions with body mass index and oral contraceptive use (Percent density increases breast cancer risk in addition to effects of other risk factors, and modifies the effects of BMI and OCs. Radiologically dense breast tissue is associated with an increased risk of breast cancer  using the Tabar qualitative measure of breast density , higher BMI (P=0.03), low parity (P=0.005), early menarche (P=0.003), family history of breast cancer (P<0.001), high socioeconomic status as measured by education (P=0.001), later age at menopause (P=0.008) and ever use of hormone replacement therapy (HRT) (P=0.008). Significantly reduced risks were associated with breast feeding (P=0.006) and early age at first birth (P=0.01). In addition, a significantly increased risk (P=0.001) was observed with higher total dense area and a significantly reduced risk (P=0.002) with higher total breast area (data not shown). Percent breast density had a stronger effect on risk than total dense area or total breast area. We therefore used percent density in subsequent analyses.P<0.001). After multivariable adjustment, the 76–100% density category had an OR of 5.54 (95% CI 2.38–12.90) compared with 0–10%.After entering the nonmammographic variables into stepwise conditional logistic regression, breast feeding and socioeconomic status were no longer significant. The remaining factors were used for multivariable adjustment of the effect of density. We estimated the effect of percent density on risk of cancers by time since screen (detailed results available from the authors). The trend remained highly significant 6 or more years after the mammogram, although its magnitude showed a tendency to decrease with time.P<0.001), nulliparity and low parity (P<0.001), no history of breast feeding (P<0.001), HRT use (P=0.001), premenopausal status (P<0.001), later age at first birth (P=0.02), younger age (P<0.001) and being Chinese (P<0.001). After adjusting for the well-established confounders age, BMI and ethnicity, all associations remained significant or borderline significant except for age at first birth.We also tabulated percent density against various breast cancer risk factors within the controls (data available from the authors). Density was significantly increased in association with low BMI (P=0.02 in both cases). The increasing risk with density was stronger in those with high BMI , and in those with a history of OC use. Numbers of cases and controls in individual categories are available from the authors.Our main findings in this Asian population are that percent breast density, as measured by the Cumulus interactive threshold, is strongly associated with risk of breast cancer. Also, percent density was a stronger associate of risk than absolute dense area, remaining substantial and significant after adjustment for other risk factors. The adjusted odds of breast cancer for those with more than 75% density was around five times that of those with density 10% or less.R2 for absolute dense area was 1% compared with 7% for percent density. However, the negative confounding with BMI was only observed for percent density. Although the regression coefficient in the conditional logistic model for percent density increased by 20% after adjustment for BMI, the coefficient for absolute dense area decreased by 4%. Others have similarly found greater confounding with BMI for percent density than for absolute dense area (We found that percent density was a stronger predictor of risk than absolute density, in agreement with some (We found that density was positively associated with late first birth, lack of history of breast feeding and use of HRT, but negatively associated with other breast cancer risk factors, namely age, low parity and BMI, as reported by others (Although percent density was higher in Chinese women in our study, we could not confirm our previous finding of heterogeneity by ethnicity of the effect of density on risk , it did Our results confirm the independent effect of breast density on breast cancer risk. We found that percent breast density was a stronger risk factor than absolute dense area, but was also more confounded with BMI. Our results indicate that density is positively related to most breast cancer risk factors, with the notable exceptions of age and BMI, the effect being increased in women with high BMI or a history of OC use.


The elevated risk for higher mtDNA copy numbers was primarily seen for women with <3 years between blood draw and cancer diagnosis; ORs (95% CIs) for 2nd, 3rd, 4th, and 5th quintile of mtDNA copy number were 1.52 , 2.52 , 3.12 , and 3.06 , respectively, compared with the 1st quintile (Ptrend = 0.004). There was no association between mtDNA copy number and breast cancer risk among women who donated a blood sample ≥3 years before breast cancer diagnosis (Ptrend = 0.41). This study supports a prospective association between increased mtDNA copy number and breast cancer risk that is dependent on the time interval between blood collection and breast cancer diagnosis. Future studies are warranted to confirm these findings and to elucidate the biological role of mtDNA copy number in breast cancer risk.Mitochondrial DNA (mtDNA) copy number in peripheral blood is associated with increased risk of several cancers. However, data from prospective studies on mtDNA copy number and breast cancer risk are lacking. We evaluated the association between mtDNA copy number in peripheral blood and breast cancer risk in a nested case-control study of 183 breast cancer cases with pre-diagnostic blood samples and 529 individually matched controls among participants of the Singapore Chinese Health Study. The mtDNA copy number was measured using real time PCR. Conditional logistic regression analyses showed that there was an overall positive association between mtDNA copy number and breast cancer risk (P Breast cancer is the most common cancer among women in the United States and accounts for 29% of all cancers in women with approximately 226,870 new cases expected to occur in 2012 Mitochondria, whose principal function is to generate energy through aerobic respiration, are the major source and target of intracellular reactive oxygen species (ROS) that plays an important role in breast carcinogenesis The study participants were chosen from the Singapore Chinese Health Study (SCHS), a population-based prospective cohort of 63,257 Chinese women and men from two major dialect groups, the Hokkiens and Cantonese, aged 45–74 years who were enrolled in the study from April 1993 through December 1998 Blood and single-void urine specimens were requested from subjects drawn from a 3% random sample of enrollees in April 1994. All surviving members of the cohort were contacted to provide a biospecimen between January 2000 and April 2005 and biospecimens were obtained from more than 32,500 participants. This study was approved by the Institutional Review Boards of the National University of Singapore, University of Pittsburgh, and the University of Minnesota and written informed consent was obtained from all participants.Cases: The population-based cancer registry in Singapore was used to identify the incident breast cancer cases Controls: For each case, 3 control women were randomly selected among the cohort participants of the Singapore Chinese Health Study who donated a blood sample between January 2000 and April 2005 and were alive and remained free of cancer on the date of breast cancer diagnosis of the index case, The control women were individually matched with the breast cancer cases with respect to age at baseline interviews (±3 years), date of baseline interview (±2 years), dialect group (Cantonese vs. Hokkien), menopausal status at sample collection and date of biospecimen collection (±6 months). In total, 529 women who met these criteria were chosen as a comparison control group.ND1) and another primer pair specific for the nuclear DNA (18s) were designed for relative quantification for mtDNA copy number. The primer sequences for the mitochondrial ND1 gene were: forward primer (ND1-F), 5′- CCCTAAAACCCGCCACATCT-3′; reverse primer (ND1-R), 5′-GAGCGATGGTGAGAGCTAAGGT-3′. The primer pair used for the amplification of the nuclear gene 18s was as follows: forward primer (18s-F), 5′-TAGAGGGACAAGTGGCGTTC-3′; reverse primer (18s-R), 5′-CGCTGAGCCAGTCAGTGT-3′. Standard curves made by serial dilution of a reference DNA sample was used to determine the ratio of mtDNA copy number to the amount of nuclear DNA that is proportional to the mtDNA copy number in each cell. All samples were assayed in duplicate. A calibrator DNA  was used to standardize analytical variation between different mtDNA copy number assay runs. The 14 µL PCR mixture contained 1× SYBR Green mastermix , 215 nM ND1-R (or 18s-R) primer, 215 nM ND1-F (or 18s-F) primer, and 0.4 ng of genomic DNA for ND1 and 18s. The thermal cycling conditions were 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds, and 60°C for 1 minute for ND1 and 62°C for 1 minute for 18s. The efficiency of all quantitative PCR runs ranged from 99% to 110%.The R2 for all standard curves was ≥0.99. Standard deviations for the cycle of threshold (Ct) duplicates were ≤0.25. Based on analyses of 27 blinded duplicate samples analyzed on two different days the coefficient of variation (CV) was 14%.All DNA samples were extracted from 0.5 ml of buffy coat fraction of the whole blood using the QiaAMP 96 DNA blood kits . DNA samples of 183 breast cancers and 529 individually matched controls were retrieved from the biorepository of the Singapore Chinese Health Study. All DNA samples of a given matched set (containing the samples from the case and the three matched controls) were arranged in random order, identified only by unique codes, and tested in the same batch of laboratory assay for mitochondrial DNA (mtDNA) copy number. The details of the mtDNA copy number assay have been described previously 2 test and the t-test were used to compare the distributions of demographics, lifestyle and reproductive characteristics between breast cancer cases and controls. Pearson correlation coefficient was used to evaluate the correlation between mtDNA copy number and time interval between blood collection and breast cancer diagnosis. Differences in the relative mtDNA copy number across different groups of selected reproductive characteristics among control participants were examined using analysis of covariance (ANCOVA). These analyses were adjusted for age and analytical run in which mtDNA copy number was measured (batch number) to control for variation in relative mtDNA copy number across different analytical runs. Conditional logistic regression models retaining original matched sets that controlled for matching factors and the laboratory batches for mtDNA assays were used to evaluate the association between relative mtDNA copy number and breast cancer risk after adjustment for body mass index (BMI), age at menarche and number of live birth. The study participants were grouped into quintiles according to the distribution of relative mtDNA copy number among control subjects. Additional subgroup analyses for the mtDNA-breast cancer risk were performed using conditional logistic regression models according to the time interval between blood sample collection and breast cancer diagnosis (≥3 years vs. <3 years) and breast cancer stage at the time of diagnosis. All P-values <0.05 were considered to be statistically significant.All statistical analyses were carried out using SAS software version 9.1.3 . The distribution of the relative mtDNA copy number  was markedly skewed toward high values and corrected to a large extent by transformation to logarithmic values. Therefore, all statistical tests were performed on logarithmically transformed values, and geometric means are presented. The χ2 or use hormone replacement therapy, although these differences did not reach statistical significance , 2.52 , 3.12 , and 3.06 , respectively, compared with the 1st quintile (p for trend = 0.004)  . Additio= 0.004) . In cont= 0.004) .This is the first study to demonstrate a prospective association between increased relative mtDNA copy number and increased breast cancer risk. The findings from the current study are in agreement with the findings from an earlier retrospective case-control study (100 breast cancer cases and 100 controls in a predominantly white population) This study found that mtDNA copy number was negatively correlated with the time between blood draw and breast cancer diagnosis, and that the association between relative mtDNA copy number and breast cancer risk was restricted only to those women who donated a blood sample within 3 years of breast cancer diagnosis. A previous case-control study by Xia et al showed the stage I breast cancers had a lower mtDNA copy number in peripheral blood while stage II–IV breast cancers had increased mtDNA copy number in peripheral blood Several studies examined mtDNA copy number in tumor tissues as well as adjacent normal tissues of various organs. Increased mtDNA copy number was observed in tumor tissues of the colorectum The biological mechanism for increased relative mtDNA copy number in peripheral blood with risk of breast cancer is not completely understood. Since breast tumor specific mtDNA mutations have been rarely detected in peripheral blood The strengths of the current study include comprehensive recording of breast cancer cases via the Singapore cancer registry since 1968


Genome-wide association studies typically target inherited autosomal variants, but less studied genetic mechanisms can play a role in complex disease. Sex-linked variants aside, three genetic phenomena can induce differential risk in maternal versus paternal lineages of affected individuals: 1. maternal effects, reflecting the maternal genome's influence on prenatal development; 2. mitochondrial variants, which are inherited maternally; 3. autosomal genes, whose effects depend on parent of origin. We algebraically show that small asymmetries in family histories of affected individuals may reflect much larger genetic risks acting via those mechanisms. We apply these ideas to a study of sisters of women with breast cancer. Among 5,091 distinct families of women reporting that exactly one grandmother had breast cancer, risk was skewed toward maternal grandmothers (p<0.0001), especially if the granddaughter was diagnosed between age 45 and 54. Maternal genetic effects, mitochondrial variants, or variant genes with parent-of-origin effects may influence risk of perimenopausal breast cancer. inter-lineage asymmetry: mothers and their progenitors have higher (or lower) risk than fathers and their progenitors, and descendants of female cases have higher (or lower) risk than descendants of male cases. We describe how certain non-standard genetic mechanisms might underlie that asymmetry and make substantial contributions to disease susceptibility. Besides variants on sex chromosomes, these mechanisms include variants in the mother's genome that influence fetal development and hence later risk, variants in the mitochondria that modulate risk, and susceptibility variants in particular inherited genes whose expression depends on whether the variant came from the mother or the father. Applying our ideas to a study of more than 30,000 families with breast cancer, we found that more maternal grandmothers of cases than paternal grandmothers of cases had breast cancer, giving evidence that such non-standard mechanisms may be important contributors to breast cancer risk.Genetic studies often collect family histories from diagnosed individuals. Some diseases exhibit  Genome-wide association studies (GWASs) that compare affected individuals and controls have identified many inherited genetic variants associated with complex diseases Four other genetic mechanisms (henceforth referred to as “nonstandard”) are overlooked in a typical GWAS. Sex-linked genetic variants on the Y or the X chromosome are often not considered, although polymorphic X loci may be relevant to breast cancer The mother's genome can also exert effects on the developing fetus, of consequence for both birth outcomes and adult phenotypes inter-lineage asymmetry as the presence of a higher (or lower) risk either for the mother and her progenitors compared to the father and his progenitors, or for descendants of female cases compared to descendants of male cases. Although father's progenitors if only the paternally inherited copy is expressed. The action of these understudied mechanisms can be discerned by studying family histories.Each of these nonstandard mechanisms produces asymmetry in family history data. We define The presence of disease in a proband statistically induces enrichment in their progenitors and progeny for risk-related alleles. For the mechanisms in prospectively for asymmetry, by comparing risk in the offspring of male versus female affected individuals Investigators have also looked Epidemiologic studies sometimes assemble very large case-control samples or cohorts at elevated risk The NIEHS Sister Study enrolled 50,884 women who each had a sister diagnosed with breast cancer. We here use data from that cohort to compare rates of breast cancer in maternal versus paternal grandmothers. We develop general results to relate the inter-lineage relative risks in progenitors to the inter-lineage relative risks in descendants. Under simplifying assumptions, we calculate how large a maternally-mediated prenatal effect or a parent-of-origin effect would have to be to explain any particular inter-lineage asymmetry and conclude that those causative relative risks would have to be substantial to produce the inter-lineage asymmetry evident in the Sister Study.We analyzed family history data for a large number of cases of breast cancer, to compare the risk of breast cancer in the maternal versus the paternal grandmother. The Sister Study To assess asymmetry, we calculated odds ratios  using families where exactly one of the two grandmothers had breast cancer, i.e., discordant grandmother pairs.Using algebra we then derived formulae to assess the likely strength of the mechanisms that could underlie an observed asymmetry in family history. Foreseeing applications beyond breast cancer, we similarly derived expressions to assess the degree of asymmetry that those nonstandard mechanisms would produce in maternal versus paternal progenitors, and the degree of asymmetry produced in the offspring of affected male versus female individuals.Our analysis used 32,929 women, each from a distinct family, where each woman was the full sister of a case and could report breast cancer history for both grandmothers. Of these grandmothers, 3046 on the maternal side (9%) and 2639 (8%) on the paternal side had developed breast cancer. These reported rates are in general agreement with expected rates for their birth cohort The inter-lineage odds ratio depended on the granddaughter's age at diagnosis , being mThe degree of asymmetry in progenitors of affected individuals and the degree of asymmetry in offspring of affected individuals are closely related. Assume that any negative effect the disease has on reproductive success is equivalent in males and females and also that the relative risk for mothers versus fathers is the same as that for women versus men, and the risk for maternal grandmothers (grandfathers) is the same as that for paternal grandmothers (grandfathers). Straightforward manipulation of conditional probabilities proves the following result .Result 1: Under the above-stated assumptions, a) the inter-lineage relative risk for mothers with an affected offspring versus fathers with an affected offspring is the same as the inter-lineage relative risk for offspring with an affected mother versus those with an affected father, that is,b), the inter-lineage relative risk for maternal grandmothers (grandfathers) with an affected grandchild versus paternal grandmothers (grandfathers) with an affected grandchild is the same as the inter-lineage relative risk for grandchildren with an affected maternal grandmother (grandfather) versus those with an affected paternal grandmother (grandfather). That is,c) Similarly, provided the inter-lineage relative risk for sons equals that for daughters, the inter-lineage son (daughter) relative risk is the same as the inter-lineage parental relative risk for mothers versus fathers of affected offspring. That is,Note that Result 1 refers to a particular offspring or grandchild  and thus the result does not apply to families selected because one out of some large number of progeny developed the disease.To quantify inter-lineage relative risks, we adopt some additional simplifying assumptions: a rare outcome, random mating , Mendelian inheritance, Hardy-Weinberg equilibrium (HWE), and that the locus is not in linkage disequilibrium with and does not interact with another risk-related locus.Let P be the 1×3 row vector containing an individual's autosomal genotype probabilities for minor allele counts 0, 1, 2 for a particular locus. Under Hardy-Weinberg Equilibrium (HWE), for a randomly sampled individual, not risk-relevant to the proband, in that their genotypes at that locus are unrelated to the proband's risk conditional on the genotypes of risk-relevant individuals. However, their genotype distributions may still be distorted away from HWE, due to their relationship with the proband. The following result, proven in We need to introduce the concept of “risk relevant” genotypes. For maternally-mediated prenatal effects, those where the maternal genome affects the offspring's risk through prenatal influences on fetal development, the risk-relevant genotype at that locus is that of the affected proband's mother; and for autosomal genes with parent-of-origin effects the risk-relevant genotypes are those of both the proband and the proband's parents. Those risk-relevant genotypes will be distorted away from HWE for a locus related to risk. Other family members who are the progenitors or descendants of risk-relevant individuals are Result 2: Assume that the population meets the assumptions stated earlier. Let + is not risk-relevant to the proband, then If The elements of the matrix V are the conditional probabilities for the offspring or parent to have a certain allele count, conditional on the index person having a certain allele count. Note that Result 2 holds under our assumptions even if the locus under study is not related to risk. This result allows the extrapolation of enrichment of the risk allele back to progenitors and forward to descendants.Suppose a genetic variant carried by the mother has a prenatal effect on fetal development, hence on later risk to her offspring. We quantify the asymmetry induced by such a maternal effect .Let the relative risk for the offspring of a mother with one (two) copies of the variant allele be henever  . Note thThis attenuation of their mothers, i.e., the sampled proband case's great-grandmothers  were due entirely to a maternally-acting SNP with a frequency of 10% and the effect obeyed a log-additive risk model, the relative risk for an offspring whose mother carries one copy of that SNP would be about 4.0.Most diseases affect both males and females, so that, even if risk is maternally mediated, both grandmothers and grandfathers can contribute to the asymmetry analysis. In that event, the inter-lineage grandparental odds ratio (relative risk) is estimated by dividing the number of discordant pairs where the affected grandparent (either grandmother or grandfather) is on the maternal side by the number of discordant pairs where the affected grandparent is on the paternal side. If the condition is not rare so that some families contribute two discordant pairs, a within-cluster resampling approach I for offspring who carry a maternally-inherited copy of the variant compared to a risk of I are the same for both sexes, they could be sex-specific) (I) is 5.Another plausible source of asymmetry in family history involves parent-of-origin effects such as genetic imprinting. Such an effect was reported for breast cancer by Kong, et al pecific) . For a pcific) ( . Again tcific)  can also produce asymmetry in families. A recent report found such a variant to be related to breast cancer risk in African-American women While case-control GWASs have revealed many SNPs related to susceptibility to complex diseases, nonstandard genetic mechanisms may also play a role. We considered three such mechanisms that can produce asymmetry in family histories: maternal genetic effects that influence risk via the prenatal environment; parent-of-origin effects, for example where the expression of an imprinted polymorphic gene variant depends on parental source; and effects of variants in the mitochondrial DNA, which are exclusively inherited from mothers. Our algebraic quantification of the relationship between inter-lineage asymmetry and its driving cause led us to conclude that, although the driving effect would be small if due to a mitochondrial variant, if a single variant acted through maternally-mediated prenatal effects or was subject to parent-of-origin effects, then that cause would be associated with a large relative risk, at least on the order of 4 or 5, for breast cancer.Although a single-allele scenario seems unlikely, we wondered whether a single allele acting through a maternally-mediated genetic mechanism could explain the known increased risk seen in sisters. Under our simplifying homogeneity assumptions, one can show with a little added algebra that a single, log-additive maternal effect with a single-copy relative risk of 4.0 involving an allele with frequency about 0.07 would produce about the observed two-fold increased risk for sisters of cases.Although we have focused our methods and analysis on progenitors and descendants, siblings, aunts and uncles would also be informative. A prenatal maternal effect and a parent-of-origin effect where only the maternal allele is expressed share an interesting feature: A half sibling of a case would have risk similar to that of a full sibling if the shared parent is the mother, but no increased risk if the shared parent is the father. A Danish study reported that pattern of asymmetry in half-brothers of cases with the birth defect cryptorchidism Several small studies have compared rates of breast cancer in maternal and paternal relatives. A registry-based Swedish study found no difference in maternal and paternal grandmothers but included fewer than a thousand breast cancer cases in situ) of 1251.Although our sample is large, with almost 33,000 families represented, cases reported among sisters and grandmothers were not generally validated clinically. Nevertheless, our participants are sisters of women with breast cancer; and they have proven themselves an informed and dedicated cohort, providing bio-samples, completing lengthy questionnaires and maintaining commitment to follow-up with a very low dropout rate. Moreover, though the study staff did not obtain medical records for cases whose sister enrolled in the Sister Study, we did request medical records for 1422 affected sisters who joined our family-based add-on “Two Sister Study” We made some simplifying assumptions  and thus our figures depict idealized settings, which are not fully appropriate for the Sister Study. There are also effects secondary to ascertainment. The Sister Study is more likely to enroll unaffected women from larger families who have lower genetic risks. Consider two families, each with a single daughter with breast cancer and suppose one family has 10 daughters and the second has only two daughters. The first family is more likely to be in our study because any one of the 9 unaffected daughters can join, but the same large family has demonstrated lower genetic risk with only one of 10 affected, compared to the second family with 1 of 2 affected. The Sister Study consequently would have sampled families with less genetic enrichment for the allele under study than the two-fold increase presumed. Because this ascertainment effect should distort the inter-lineage asymmetry toward the null, the estimate of 4 for a maternally-mediated prenatal effect may be too low.We implicitly assumed that the reported father is the biological father, but reported paternity can be incorrect. However, out of a subset of 602 families in the Two Sister Study where DNA was acquired, only 5 fathers failed the paternity test. Moreover, the observed strong pattern where asymmetry was related to the age at diagnosis of the granddaughter could not be explained by misidentified paternity. Another issue is that in a large series of cases such as the Sister Study, some affected sisters may unknowingly have been adopted; however, we expect that proportion to be small. Also, unaware adoptees would report the wrong history on both the maternal and the paternal side, driving estimates toward symmetry.Reporting bias and self-selection may be at work. One might expect a woman with both a sister and a mother with breast cancer to be more likely to join the Sister Study than a woman with only an affected sister. Also, women whose mother had breast cancer may undergo more regular screening. However, the rate of breast cancer reported for mothers was about 18%, which does not exceed expectation based on having a first degree relative with breast cancer.With combined data including as many as 45,000 cases, large consortial efforts are underway Although we saw evidence for inter-lineage asymmetry based on the family histories from the Sister Study participants, with more breast cancer in the maternal lineage, one cannot differentiate among the three nonstandard mechanisms using only phenotypic family histories of affected individuals. Only family-based genotype data will enable an investigator to identify the genetic mechanism and identify relevant variants In summary, susceptibility to complex disease can be influenced by inherited autosomal gene variants, as most GWASs assume, but can also be influenced by sex-linked genes, maternally-mediated prenatal effects, parent-of-origin effects, and mitochondrial variants. These under-studied genetic mechanisms are best explored through family studies. Yet even without access to genetic data on family members, evidence that those phenomena play a role can be adduced through careful analyses of family history data from large assemblages of cases. Breast cancer appears to be subject to genetic mechanisms that produce family history asymmetry, particularly when diagnosed in the perimenopausal years.Table S1Probability of each case-parents triad genotype in a population with random mating, Mendelian inheritance and Hardy-Weinberg equilibrium at the locus under study.(DOCX)Click here for additional data file.Table S2Probability of each case-parents triad genotype selected from a population with random mating, Mendelian inheritance and Hardy-Weinberg equilibrium at the locus under study conditional on the presence of an affected child when risk to the child depends only on the maternal genotype with risk vector (DOCX)Click here for additional data file.Table S3Probability of each case-parents triad genotype selected from a population with random mating, Mendelian inheritance and Hardy-Weinberg equilibrium at the locus under study conditional on the presence of an affected child when risk to a child depends only having inherited a copy of the variant allele from its mother with risk vector (DOCX)Click here for additional data file.Text S1Relationship between asymmetry in progenitors and progeny.(DOCX)Click here for additional data file.Text S2Carrying genotype distributions across generations.(DOCX)Click here for additional data file.Text S3Asymmetry in parents and grandparents due to maternal effect.(DOCX)Click here for additional data file.Text S4Relationship between parent and grandparent asymmetry induced by a maternal effect.(DOCX)Click here for additional data file.Text S5Asymmetry produced by an imprinted genetic variant.(DOCX)Click here for additional data file.Text S6Relationship between parental asymmetry induced by maternal effects and grandparental asymmetry induced by a parent-of-origin effect.(DOCX)Click here for additional data file.


Objective: we conducted a case-control study to investigate the association between dietary folate, vitamin B6 and vitamin B12 intake, MTHFR and MTR genotype, and breast cancer risk.Methods: Genotyping for MTHFR C677T and A1298C and MTR A2756G polymorphisms were performed using polymerase chain reaction-restriction fragment length polymorphism analysis (PCR-RFLP) method. The intake of folate, vitamin B6 and vitamin B12 were calculated by each food item from questionnaire.Results: Subjects with breast cancer tended to have more first-degree relatives  and have high intake of folate  and Vitamin B6 . Compared to the reference group, women with MTHFR 677 TT genotype and T allele had a significantly increased risk of breast cancer, with ORs (95%CI) of 1.8(1.08-2.27) and 1.39(1.02-1.92), respectively. For those who had folate intake＜450 ug/day, MTHFR 667TT genotype was associated with a higher risk of breast cancer . Similarly, subjects with Vitamin B6 intake＜0.84 mg/day and MTHFR 667T allele genotype was correlated with a marginally increased risk of breast cancer. A significant interaction was observed between MTHFR C667T polymorphism and folate intake on the risk of breast cancer (P for interaction was 0.025).Conclusion: This case-control study found a significant association between MTHFR C667T polymorphism, folate intake and vitamin B6 and breast cancer risk, and a significant interaction was observed between MTHFR C667T polymorphism and folate intake on the risk of breast cancer. Previous studies have suggested an inverse association between high consumption of folate intake and methyl-related B vitamins and risk of breast cancer in different populations.Subjects: A total of 325 cases with breast cancer who were newly diagnosed and histologically confirmed were enrolled at Xinxiang Medical College and Guangxi Medical University from March 2009 to November 2011. Three hundred thirty eligible controls who entered the hospital for health check-ups during the same period were enrolled into control group. The protocol of our study was approved by the institutional review board at Guangxi Medical University and Xinxiang Medical College, and conducted in accordance with the Declaration of Helsinki. Written informed consent was collected from all patients.6 and vitamin B12 were calculated by each food item from questionnaire.A self-designed questionnaire was used to collect the clinical and demographic characteristics, such as age, age at menarche, age of menopausal status, family history of cancer, and dietary intake habits with 62 food terms. The intake of folate, vitamin BGenotype of polymorphisms: Peripheral blood samples were obtained from each case and control subject, and stored at -80℃ until analysis. Genomic DNA samples were extracted using TIANamp blood DNA kit  in accordance with the manufacturer’s protocol. Genotyping for MTHFR C677T and A1298C and MTR A2756G polymorphisms were performed using polymerase chain reaction-restriction fragment length polymorphism analysis (PCR-RFLP) method. Primer and probes of MTHFR C677T and A1298C and MTR A2756G were designed by Sequenom® Assay Design, Version 3.1 software (Sequenom®). The digested PCR products were separated on 3% agarose gel electrophoresis, stained by ethidium bromide and visualized by UV transilluminator. For quality control, 10% of cases and controls were randomly selected, and repeat analysis was conducted to verify the reproducibility. The results were 100% concordant.Statistical Analysis: Continuous variables and Categorical variables were presented as mean ± SD and n (%) of subjects, respectively, and analyzed using the independent-samples t-test and χ2-test, respectively. The association between dietary folate, vitamin B6 and vitamin B12 intake, MTHFR and MTR genotype, and breast cancer risk were assessed by unconditional logistical regression model with Odds ratios (ORs) and 95% confidence intervals (CIs). Stratified analyses were used to evaluate the potential modifying effect of modifying effect of folate intake, vitamin B6 and vitamin B12 on breast cancer risk with MTHFR genotypes. All analyses were performed by using Stata version 8 . All P-values were two sided, and a  P-value <0.05 was considered statistically significant.2=30.77, P<0.001) and have high intake of folate  and Vitamin B6 . Of 325 eligible breast cancer cases, 296 patients agreed to participate into our study, with a participation rate of 91.08%. Three hundred thirty controls were enrolled into our study, and 306 of them agreed to join this study (92.73%) . SubjectThe genotype distributions of MTHFR C677T and A1298C and MTR A2756G were in line with Hardy-Weinberg equilibrium in the control group. Compared to the reference group, women with MTHFR 677 TT genotype and T allele had a significantly increased risk of breast cancer, with ORs (95%CI) of 1.8(1.08-2.27) and 1.39(1.02-1.92), respectively . But the6 with breast cancer risk are presented in 6 intake＜0.84 mg/day and MTHFR 667T allele genotype was correlated with a marginally increased risk of breast cancer. A significant interaction was observed between MTHFR C667T polymorphism and folate intake on the risk of breast cancer (P for interaction was 0.025).The associations of MTHFR C667T polymorphism and intake of folate and Vitamin B6 was associated with a significantly increased risk of breast cancer, and MTHFR 677TT genotype was correlated with increased risk of breast cancer. In addition, a significant interaction was observed between MTHFR C667T polymorphism and folate intake on the risk of breast cancer. However, we did not find statistically significant association between the MTHFR A1298C and MTR A2756G polymorphisms and breast cancer risk. In the present study, we found high intake of folate and Vitamin B17-19 Although the high intake of folate may be beneficial in populations who are deficient of this nutrient, high intake of folate may provide further benefit or harmful for women with already-sufficient levels.20 In our study, we observed that high folate intake contributes to prevent the development of tumor. However, previous study indicated that an increased risk of breast cancer was found in premenopausal women with high folate intake,4 and another study reported no significant association between dietary intake of folate and breast cancer risk in Japanese women.4 Folate levels may play a protective role in breast cancer risk among those with low level of folate, but contribute to tumorigenesis in those who have been already sufficient in foalte intake group.It is reported that folate plays a protective role in the development of cancer before preneoplastic lesion, and an inverse association was found between intake of folate and tumorigenesis.6 had an association with a decreased risk of breast cancer in Chinese and Brazilian female populations.4,21 However, Lin et al. conducted a case-control study with 848 cases and 848 controls, and reported folate, vitamin B6, and vitamin B12 may confer litter or no reduction in overall risk of developing breast cancer.17 The inconsistency of these studies may be induced by differences in ethnicities, source of control subjects, sample size and etc. Further their confirmation of existing findings is still needed in future studies.Moreover, previous two studies indicated that high intake of vitamin B6. Our findings are consistent with previous studies.22-24 A meta-analysis reported no overall significant or reverse association between MTHFR C677T polymorphism and breast cancer risk, or between the MTHFR A1298C polymorphism and risk.25 Ma et al. also reported the lack of association between MTHFR C677T polymorphism and breast cancer risk in Brazilian women.4 Another meta-analysis with pooling 37 studies indicated that MTHFR 677TT variant genotype was associated with icnreased breast cancer risk. Because these studies were conducted in different populations, direct comparisons between them are difficult. The discrepancies of these studies may be due to differences in variant frequencies between races.Our study found a significant association between MTHFR C677T polymorphism and breast cancer risk, and MTHFR C677T genetic variation has interaction with vitamin B6 and breast cancer risk, and a significant interaction was observed between MTHFR C667T polymorphism and folate intake on the risk of breast cancer. Our finding indicated that folate and other methyl-related B vitamins have a role in developing breast cancer. Further multi-center studies are needed to elucidate the etiology of breast cancer. In summary, this case-control study found a significant association between MTHFR C667T polymorphism, folate intake and vitamin B


DICER, rs3803012 in 3′UTR of RAN and rs10773771 in 3′UTR of HIWI) with a case-control study including 397 HNC cases and 900 controls matched by age and sex in Chinese. Although none of three SNPs was significantly associated with overall risk of HNC, rs1057035 in 3′UTR of DICER was associated with a significantly decreased risk of oral cancer . Furthermore, luciferase activity assay showed that rs1057035 variant C allele led to significantly lower expression levels as compared to the T allele, which may be due to the relatively high inhibition of hsa-miR-574-3p on DICER mRNA. These findings indicated that rs1057035 located at 3′UTR of DICER may contribute to the risk of oral cancer by affecting the binding of miRNAs to DICER. Large-scale and well-designed studies are warranted to validate our findings.MicroRNAs (miRNAs) have been reported to play a key role in oncogenesis. Genetic variations in miRNA processing genes and miRNA binding sites may affect the biogenesis of miRNA and the miRNA-mRNA interactions, hence promoting tumorigenesis. In the present study, we hypothesized that potentially functional polymorphisms in miRNA processing genes may contribute to head and neck cancer (HNC) susceptibility. To test this hypothesis, we genotyped three SNPs at miRNA binding sites of miRNA processing genes (rs1057035 in 3′UTR of  Head and neck cancer (HNC), especially squamous cell carcinoma of the head and neck (SCCHN), is the sixth most common malignancy worldwide, accounting for an estimated 650 000 new cancer cases and 350 000 cancer deaths every year MicroRNAs (miRNAs) are a class of non-coding, single-stranded RNAs of ∼22 nucleotides and act as gene regulators through binding to the 3′-untranslated region (UTR) of protein-coding transcripts, in turn triggering messenger RNA degradation or translational repression DICER significantly increased the risk of OPLs in Americans XPO5 and rs14035 in 3′UTR of RAN were associated with risk of esophageal cancer DICER, rs3803012 in RAN and rs10773771 in HIWI) with a case-control study of 397 HNC cancer cases and 900 healthy controls in Han Chinese and evaluated their individual and joint effect on HNC risk. To clarify the functional relevance of the selected SNPs with HNC cancer, we also examined the luciferase activity of DICER rs1057035  by the cloning and reporter gene assay, assessing the effects of the SNP on the binding of special miRNAs to the 3′UTR of DICER.More recently, it has been reported that the presence of single nucleotide polymorphisms (SNPs) in microRNA genes, their processing machinery and target binding sites affects the susceptibility to cancers by modulating expression levels of miRNAs and miRNA–mRNA interactions This study was approved by the institutional review board of Nanjing Medical University. The design and performance of current study involving human subjects were clearly described in a research protocol. All participants were voluntary and would complete the informed consent in written before taking part in this research.All newly and histologically confirmed HNC patients were consecutively recruited from Jiangsu Stomatological Hospital and the First Affiliated Hospital of Nanjing Medical University, Nanjing, China, since January 2009 to April 2011. Exclusion criteria included second HNC primary tumors or metastasized cancer from other organs. 420 cases were initially contacted for participation and approximately 95% of eligible cases (397 cases) agreed to participate in the study. Cancer-free controls frequency-matched to the cases on age (±5 years) and sex were randomly selected from a cohort of more than 30,000 participants in a community-based screening program for non-infectious diseases in the Jiangsu Province, China. All participants were genetically unrelated, ethnic Han Chinese population. When written informed consent was obtained, a structured questionnaire was used by trained interviewers to collect information on demographic data and environmental exposure history, such as age, sex, smoking and drinking consumption. After interview, approximately 5-ml venous blood sample was collected from each participant. Finally, a total of 397 cases and 900 controls completed the interview and donated the blood sample.DROSHA, DGCR8, XPO5, DICER, HIWI, GEMIN3, GEMIN4 and RAN) were selected through an extensive literature search. For each gene, we first used the NCBI dbSNP database (http://www.ncbi.nlm.nih.gov/ build 131) to search SNPs that localized at 3′ UTR of genes and found 17 SNPs with minor allele frequency (MAF)>0.05 in Chinese population. Then, two web-based analysis tools were used to predict the effect of 17 SNPs on miRNA binding  http://www.mirbase.org/search.shtml) showed that this SNP was not located in the binding region for any miRNA. As a result, we identified 3 miRNA-binding SNPs in three genes . Among those, rs1057035 was predicted to influence has-miR-574-3p binding, rs3803012 influencing has-miR-199a-3p binding and 10773771 influencing has-miR-1264 binding.Eight key miRNA biogenesis genes . Genotyping was performed without knowing the subjects′ case or control status, and two negative controls in each 384-well format were used for quality control. The genotyping results were determined by using SDS 2.3 Allelic Discrimination Software (Applied Biosystems). The concordance achieved 100% for the duplicates of 5% of samples for each SNP.DICER containing the putative recognition site rs1057035 was amplified from the sample DNA, then cloned into the pMIR-REPORTTM (Applied Biosystems) vector with Mlu I and Hind III digestions. The primers were: GTAGACGCGTTCAACAGCAAACTTCAGCC (sense) and TCCAAAGCTTGGCAGGGTATCAGAATCTTT (antisense). Plasmids containing the different alleles of rs1057035 were generated using site-specific mutagenesis. All constructs used in this study were restriction mapped and sequenced to confirm their authenticity.The 3′UTR region of 5 cells per well in 24-well plates . Twenty-four hours after the plating, cells were transfected by Lipofectamine 2000 according to manufacturer's suggestion (Invitrogen). The DICER 3′UTR luciferase plasmids  and chemically synthesized mature hsa-miR-574-3p were cotransfected respectively. The pRL-SV40 plasmid (Promega) was cotransfected as a normalizing control. Six replicates for each group and the experiment repeated at least three times. After 24 hours of incubation, cells were collected and analyzed for luciferase activity with the Dual-Luciferase Reporter Assay System (Promega).Tca-8113 2 test to compare the observed genotype frequencies with the expected ones among the control subjects. Distributions of selected demographic variables, risk factors, and frequencies of variant genotypes between the cases and the controls were evaluated by using the χ2 test. The associations of variant genotypes with HNC risk were estimated by computing odds ratios (ORs) and 95% confidence intervals (CIs) from both univariate and multivariate logistic regression analyses. The heterogeneity between subgroups was assessed with the Chi-square-based Q test and the heterogeneity was considered significant when P<0.05. The possible gene-gene and gene-environment (i.e. smoking and drinking status) interactions were evaluated by the logistic regression models. All the statistical analyses were performed with Statistical Analysis System software . Two-sided tests were generally used for statistical analysis and P<0.05 was considered as the level of statistical significance.The Hardy-Weinberg equilibrium was tested by a goodness-of-fit χP = 0.637, 0.263 and 0.580, respectively), and the mean age was similar (59.98±11.94 years for the cases and 60.65±7.99 years for the controls). As expected, more drinkers were observed in the case group compared with that in the control group . Of the 397 cases, 293(73.8%) were with the tumor of oral cavity, 6(1.5%) with oropharynx, 86(22.2%) with larynx and 10(2.5%) with others. Furthermore, 335 cases (84.4%) presented with squamous cell carcinoma.The selected characteristics of the cases and the controls are shown in P = 0.14 for rs1057035 and P = 0.73 for rs3803012, respectively) except rs10773771 (P = 0.01). The genotype and allele distributions of DICER rs1057035, HIWI rs10773771 and RAN rs3803012 in cases and controls are shown in P = 0.080 for rs1057035, P = 0.397 for rs3803012 and P = 0.539 for rs10773771, respectively). To further investigate the modifying effects of three variants on risk of HNC with different tumor sites, we conducted the stratification analysis by oral cavity and non-oral cavity cancers. As shown in P = 0.019; TC/CC vs. TT: adjusted OR  = 0.65, 95% CI  = 0.46–0.92, adjusted P = 0.016; additive model: adjusted OR  = 0.67, 95% CI  = 0.48–0.93; adjusted P = 0.018). None of significant associations was observed between the genotypes of two other SNPs  and risk of HNC with different sites (SNPs were at Hardy-Weinberg equilibrium (nt sites Table2.P>0.05) in every two stratums. We also evaluated the effect of the addition of alcohol, smoking and gender on ORs and found that these were no differences if these variables were included in the models or not included (Table S1). Additionally, no significant results were observed for gene-gene or gene-environment  interactions (data not shown).We further conducted the stratification analysis on the associations between rs1057035 and oral cancer risk by age, sex, smoking, drinking and histology. As shown in DICER mRNA in vitro. Thus, we generated reporter genes for DICER 3′UTR  that were cotransfected with chemically synthesized mature hsa-miR-574-3p in Tca8113 oral squamous cell carcinoma cell lines. As shown in P = 0.032; Hela cell line: 0.041±0.007 for C allele versus 0.357±0.257 for the T allele, P = 0.030; Tca8113 cell line: 0.072±0.067 for C allele versus 0.833±0.120 for the T allele, P<0.001). The results suggested that the rs1057035 variant allele could differentially affect the targeting of hsa-miR-574-3p to 3′UTR of DICER in oral cancer cells.Because rs1057035 showed significant association with oral cancer risk and was predicted to locate at the putative miRNA binding site (hsa-miR-574-3p), it is interesting to test whether rs1057035 influences the targeting of hsa-miR-574-3p to DICER may play a role in the development of HNC, particularly of those tumors arising at oral cavity.In this case-control study of 397 HNC patients and 900 cancer-free controls in a Chinese population, we investigated the associations between three SNPs in 3′UTR of miRNA biosynthesis genes and risk of HNC. Although we did not find evidence for a main effect of each SNP on overall HNC risk, the subgroup analysis of HNC showed that variant genotypes of rs1057035 were associated with the risk of HNC arising at oral cavity. Additional functional analyses showed that rs1057035 C allele led to significantly lower luciferase activity, compared with the T allele. These findings suggested, for the first time, that potentially functional polymorphisms of DICER may have different roles in the tumorigenesis of different cancers. For example, some studies showed that lower levels of DICER mRNA expression were associated with the development of lung cancer DICER were correlated with increased cell proliferation of oral cancer cells DICER and exhibited a protective effect on oral cancer susceptibility, which were consistent with the findings in oral cancer cells P>0.05) between every two stratums, implying that there was no risk effect modification by these variables under investigation.DICER is a enzyme responsible for the cleavage of miRNA precursors and has previously been implicated in the oncogenic process of several cancers DICER and predicted to affect the binding of hsa-miR-574-3p. Hsa-miR-574-3p is evolutionarily conserved at the nucleotide level from flies to humans. Although the function of hsa-miR-574-3p is not clear, it is predicted to target several hundreds of human genes DICER rs1057035 C allele might affect the targeting of hsa-miR-574-3p and result in the decreased expression of DICER mRNA in oral cancer cells, which may be a possible underlying mechanism for the observed association between rs1057035 and the decreased risk of oral cancer. However, we did not find any association between rs1057035 and overall risk of HNC cancer or non-oral cancer risk in our study. The pathogenesis of oral cancer may be slightly different from other cancers arising at head and neck. For example, recent epidemiologic and molecular studies have identified high-risk types of human papillomavirus (HPV) as the potential etiologic factor for HNC, but it is more prominent in the oropharynx cancer than that in the oral cavity cancer The significant SNP (rs1057035) identified in our study is located in the 3′ UTR of RAN and HIWI and HNC risk. RAN is a unique member of the Ras superfamily of GTPases, which is essential to the transportation of pre-miRNAs from nucleus to cytoplasm through the nuclear pore complex in a GTP-dependent manner RAN rs3803012 and HIWI rs10773771 variant genotypes were not associated with risk of HNC, suggesting these SNPs might not modulate the susceptibility of HNC in Chinese population.Additionally, we also found no significant associations between SNPs in 3′UTR of Several potential limitations of the present study warrant considerations. First of all, a relatively small sample size may limit the statistical power of our study, especially in stratification analysis by tumor sites. We evaluated the statistical power for the effect of rs1057035 by the PS software  and found that in the case of OR = 0.65 (dominant model), our sample could reach 78.8% of the statistical efficacy. Secondly, it is a hospital-based case-control study and inherent selection bias cannot be completely excluded. However, we applied a rigorous epidemiological design in selecting study subjects and used further statistical adjustment for known risk factors to minimize potential biases. Thirdly, since the intensity and duration of smoking and drinking were absent in this study, it was difficult to do future analysis for such exposure variables. Last, we only selected miRNA-binding SNPs for genotyping and could not evaluate the relationship between other potentially functional SNPs in miRNA processing genes and HNC risk. Thus, larger, well-designed epidemiological studies with ethnically diverse populations are warranted to confirm and expand our findings.Table S1The analysis for the effect of variables on ORs in models. NOTE: a Likelihood ratio test was used to check the difference between −2*log Likelihood of the two models. b Compared to the first model including age, gender, smoking and drinking.(DOCX)Click here for additional data file.


Lifestyle factors, including food and nutrition, physical activity, body composition and reproductive factors, and single nucleotide polymorphisms (SNPs) are associated with breast cancer risk, but few studies of these factors have been performed in the Japanese population. Thus, the goals of this study were to validate the association between reported SNPs and breast cancer risk in the Japanese population and to evaluate the effects of SNP genotypes and lifestyle factors on breast cancer risk.A case–control study in 472 patients and 464 controls was conducted from December 2010 to November 2011. Lifestyle was examined using a self-administered questionnaire. We analyzed 16 breast cancer-associated SNPs based on previous GWAS or candidate-gene association studies. Age or multivariate-adjusted odds ratios (OR) and 95% confidence intervals (95% CI) were estimated from logistic regression analyses.High BMI and current or former smoking were significantly associated with an increased breast cancer risk, while intake of meat, mushrooms, yellow and green vegetables, coffee, and green tea, current leisure-time exercise, and education were significantly associated with a decreased risk. Three SNPs were significantly associated with a breast cancer risk in multivariate analysis: rs2046210 , rs3757318 (OR = 1.33[1.05-1.69]), and rs3803662 (OR = 1.28 [1.07-1.55]). In 2046210 risk allele carriers, leisure-time exercise was associated with a significantly decreased risk for breast cancer, whereas current smoking and high BMI were associated with a significantly decreased risk in non-risk allele carriers.In Japanese women, rs2046210 and 3757318 located near the ESR1 gene are associated with a risk of breast cancer, as in other Asian women. However, our findings suggest that exercise can decrease this risk in allele carriers. Data in the National Statistics of Cancer Registries by Region 1975–2004) indicate that the prevalence of breast cancer in Japan has increased steadily since 1975. More than 60,000 patients had breast cancer in 2008 and the mammary gland is the most common site of a malignant tumor in Japanese women [975–2004 The relationship of lifestyle factors, including food and nutrition, physical activity, body composition, environmental factors, and reproductive factors, with breast cancer risk have been widely studied, mainly in Europe and the United States, and much evidence linking cancer to these factors has been accumulated. According to the 2007 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Second Expert Report, the evidence that breastfeeding decreases the breast cancer risk and that alcohol increases this risk is described as “convincing” . In postIn the United States, a breast cancer risk assessment tool based on a statistical model known as the “Gail model” has been produced by the National Cancer Institute (NCI) ,5. HowevRegarding genetic factors, genome-wide association studies (GWAS) have identified several breast cancer susceptibility single nucleotide polymorphisms (SNPs) . HoweverCurrent findings suggest that the interactions between breast cancer susceptibility SNPs and breast cancer risk are not as strong as those for BRCA1 or BRCA2 gene mutation. However, carriers of risk SNP alleles are more common compared with carriers of BRCA1 or BRCA2 mutation. Evaluation of the need to incorporate SNPs into a breast cancer risk model requires examination of the influence of these SNPs and established breast cancer risk factors to determine whether these are mutually confounding factors. Moreover, such findings might allow risk allele carriers to reduce their incidence of breast cancer through guidance on lifestyle habits.The current study was performed to add to the relatively small number of studies that have examined genomic factors such as SNPs in combination with non-genomic factors such as those associated with lifestyle. We first aimed to validate whether reported breast cancer susceptibility SNPs are applicable in the Japanese population. We then examined the possible confounding effects on breast cancer risk of SNPs and lifestyle factors such as food, nutrition, physical activity, body composition, environment factors and reproductive factors.A multicenter population-based case–control study was conducted between December 2010 and November 2011 in Japan. The subjects were consecutive patients with non-invasive or invasive breast cancer aged over 20 years old who were treated at Okayama University Hospital, Okayama Rousai Hospital and Mizushima Kyodo Hospital in Okayama and at Kagawa Prefecture Central Hospital in Kagawa. The controls were women aged over 20 years old without a history of breast cancer who underwent breast cancer screening at Mizushima Kyodo Hospital and Okayama Saiseikai Hospital in Okayama and at Kagawa Prefectural Cancer Detection Center in Kagawa. All subjects gave written informed consent before enrollment in the study. A blood sample (5 ml) used for SNP analysis was collected from each subject. Subjects were also given questionnaires that they completed at home and mailed back to Okayama University Hospital. The study was approved by the institutional ethics committee on human research at Okayama University.A survey of lifestyle was performed using an 11-page self-administered questionnaire that included questions on age, height and body weight (current and at 18 years old), cigarette smoking, alcohol drinking, intake of 15 foods items, intake of 4 beverages, leisure-time exercise (current and at 18 years old), menstruation status, age at first menstruation, age at first birth, parity, breastfeeding, age at menopause, hormone replacement therapy (HRT), history of benign breast disease, familial history of breast cancer, and education. Controls answered the survey based on their current status and patients referred to their prediagnostic lifestyle.Body mass index (BMI) was calculated as body weight/square of height. Former or current alcohol drinkers were asked to give the frequency per week and type of drink usually consumed . The alcoholic content of each drink was taken to be 8.8 g per glass (200 ml) of beer, and 20 g per glass of sake (180 ml), wine (180 ml), shochu (110 ml) and whisky (60 ml) , to alloSixteen breast cancer-associated SNPs were identified from previous GWAS  and candGenomic DNA was isolated from whole blood with a Taq-Man® Sample-to-SNP™ kit . Samples were analyzed by a TaqMan genotyping assay using the StepOne™ real-time polymerase chain reaction (PCR) system (Applied Biosystems) in a 96-well array plate that included four blank wells as negative controls. The PCR profile consisted of an initial denaturation step at 95°C for 10 min, 40 cycles of 92°C for 15 sec, and 60°C for 1 min. PCR products were analyzed by StepOne™ Software Ver2.01 (Applied Biosystems). To assess the quality of genotyping, we conducted re-genotyping of a randomly selected 5% of samples and obtained 100% agreement.For all analyses, significance was defined as a p-value <0.05. Associations between lifestyle and breast cancer risk were estimated by computing age adjusted odds ratios (OR) and their 95% confidence intervals (CI) from logistic regression analyses. Height was categorized as ≤150, 151–155, 156–160 and >160 according to quartile. Weight was categorized as <50, 50–54.9, 55–59.9 and ≥60 according to quartile. BMI was categorized as ≤20, 20–21.9, 22–23 and ≥24 according to quartile. Alcohol intake per day (g/day) was categorized as 0, <5, 5–10 and ≥10 g/day according to quartile. Food intake, including meat, fish, egg, soy, milk, fruits, green and yellow vegetables and mushrooms, was categorized as ≤1, 2–4 and 5 times/week. Beverage intake including coffee and green tea was categorized as ≤1, 2–3 and ≥3 cups/day. Intensity of physical activity in leisure time was categorized as 0, <6, 6–11.9, 12–23.9 and ≥24 METs/week. Age at menarche was classified as ≤12, 13 and ≥14 years old, parity as 0, 1–2 and ≥3, and age at first childbirth as <25, 25–29 and ≥30 years old. Education level was categorized as high school or less, two-year college, and university or higher.In analysis of SNPs, accordance with the Hardy-Weinberg equilibrium was checked in controls using a chi-squared test. The associations between genotype and the risk of breast cancer were estimated by computing OR and the 95% CI from logistic regression analyses. Per allele OR was calculated using 0, 1 or 2 copies of the risk allele (a) as a continuous variable. The reported OR and 95% CI denote the risk difference when increasing the number of risk alleles by one. Two models of analyses were performed, with the first model adjusted only for age and the second model adjusted for factors that were significantly associated with breast cancer risk in this study (multivariate adjustment).For SNPs associated with breast cancer, we classified subjects as risk allele carriers or non-risk allele carriers and examined associations of lifestyle factors with breast cancer risk in these subgroups. Two models were also used in this analysis, with the second model adjusted for factors that were significantly associated with breast cancer risk in the first model.All statistical analyses were performed with Statistical Analysis System software JMP version 9.0.3 (SAS Institute).A total of 515 patients and 527 controls agreed to participate in the study and gave written informed consent. Of these women, 476 patients (92.4%) and 464 controls (88.8%) returned self-administered questionnaires. In 2 cases, blood samples could not be obtained because of brittle vessels and in another 2 cases SNP genotyping could not be performed because of poor DNA amplification. Thus, the final data set for analysis included 472 patients and 464 controls with completed questionnaires and SNP genotyping.Adjusted OR with 95% CIs for lifestyle factors are shown in Table In analysis of SNPs, deviation from the Hardy-Weinberg equilibrium (P <0.05 by chi square test) was found for rs1800054 and rs1045485, and thus these SNPs were excluded from analysis. The minor allele frequencies were <0.05 for rs4666451 and rs104548, and these SNPs were also excluded, leaving 12 SNPs for analysis. Multivariate ORs were adjusted for factors that were found to be significantly associated with breast cancer: BMI, smoking status, meat intake, mushroom intake, yellow and green vegetable intake, coffee intake, green tea intake, leisure-time exercise and education level.Age adjusted ORs and multivariate ORs with 95% CIs for independent SNPs in all subjects and in subjects stratified by menopausal status are shown in Table A subgroup analysis was performed for rs2046210 and rs3757318. For rs2046210, leisure time exercise was associated with a significantly decreased breast cancer risk in risk allele carriers (AA + AG), but not in non-risk allele carriers (GG). In contrast, BMI ≥ 24 and current smoking were associated with a significantly increased breast cancer in non-risk allele carriers (GG), but not in risk allele carriers (AA + AG). Intensity of physical activity in leisure exercise of 12.0-23.9 METS/week and university education were associated with breast cancer risk in risk allele and non-risk allele carriers . ESR1 encodes an estrogen receptor (ERα), a ligand-activated transcription factor composed of several domains important for hormone binding, DNA binding, and activation of transcription . ERα is Turnbull et al. first reported a significant association of rs3757318 with breast cancer risk . rs37573We also found that SNPs associated with breast cancer differed with regard to menses state, with rs2046210 and rs3803662 associated with breast cancer risk in premenopausal women. rs3803662 lies 8 kb upstream of TNRC9 and was found to have a significant association with breast cancer risk by Easton et al. . TNRC9 iThis case–control study showed that rs2046210 and rs3757318 located near the ESR1 gene and rs3808662 located on TNRC9 are associated with breast cancer risk in Japanese women. Our results suggest that leisure-time exercise can reduce the breast cancer risk in rs2046210 risk allele carriers, whereas smoking and obesity may increase the breast cancer risk in non-risk allele carriers. Further studies are required to confirm the validity of the association of these SNPs and lifestyle factors with breast cancer risk in the Japanese population.SNPs: Single nucleotide polymorphisms; WCRF/AICR: World Cancer Research Fund/American Institute for Cancer Research; NIC: National Cancer Institute; GWAS: Genome-wide association studies; LD: Linkage disequilibrium; BMI: Body mass index; MET: Metabolic equivalent; OR: Odds ratio; CI: Confidence interval; ERα: estrogen receptor α.The authors declare that they have no competing interests.NT designed the study. TM carried out genotyping, performed statistical analysis, and wrote the manuscript with NT. KN participated in genotyping and statistical analysis. TN, TI, TM, TS, JM, HD, SI, HK, KK, YI and YO obtained informed consent from subjects, collected blood samples and data from subjects, and provided advice on the study. YK designed the study and served as an advisor. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/13/565/prepub


Menarche and menopause mark the onset and cessation, respectively, of ovarian activity associated with reproduction, and affect breast cancer risk. Our aim was to assess the strengths of their effects and determine whether they depend on characteristics of the tumours or the affected women.Individual data from 117 epidemiological studies, including 118 964 women with invasive breast cancer and 306 091 without the disease, none of whom had used menopausal hormone therapy, were included in the analyses. We calculated adjusted relative risks (RRs) associated with menarche and menopause for breast cancer overall, and by tumour histology and by oestrogen receptor expression.Breast cancer risk increased by a factor of 1·050  for every year younger at menarche, and independently by a smaller amount , for every year older at menopause. Premenopausal women had a greater risk of breast cancer than postmenopausal women of an identical age . All three of these associations were attenuated by increasing adiposity among postmenopausal women, but did not vary materially by women's year of birth, ethnic origin, childbearing history, smoking, alcohol consumption, or hormonal contraceptive use. All three associations were stronger for lobular than for ductal tumours (p<0·006 for each comparison). The effect of menopause in women of an identical age and trends by age at menopause were stronger for oestrogen receptor-positive disease than for oestrogen receptor-negative disease (p<0·01 for both comparisons).The effects of menarche and menopause on breast cancer risk might not be acting merely by lengthening women's total number of reproductive years. Endogenous ovarian hormones are more relevant for oestrogen receptor-positive disease than for oestrogen receptor-negative disease and for lobular than for ductal tumours.Cancer Research UK. Menarche and menopause are markers of onset and cessation, respectively, of ovarian and related endocrine activity associated with reproduction. During women's reproductive years (broadly the time between menarche and menopause) the ovary produces steroid hormones that directly affect development and function of the breast. Early menarche and late menopause are known to increase women's risk of developing breast cancer. To assess reliably the strengths of these associations and whether they vary by tumour subtype or by characteristics of affected women requires large numbers, and we address these questions by combining information from more than 100 epidemiological studies. Combining individual participant data from many studies not only increases statistical power but also permits similar definitions and similar analytical methods to be used across studies.This collaboration began in 1992, and has published on breast cancer risk associated with use of hormonal therapies and childbearing practices.Cases were women with invasive breast cancer and controls were women without breast cancer. So that similar analytical methods could be used across studies, we incorporated cohort studies using a nested case–control design, in which up to four controls were selected at random, matched at follow-up to age of the case at cancer diagnosis and, where appropriate, by broad geographical region. Data for a range of sociodemographic, reproductive, and other behavioural factors, covering the time period to onset of disease for cases and to an equivalent time for controls, were sought from principal investigators .We included studies in these analyses if individual data had been provided for women's menopausal status, age at menarche and, if appropriate, age at menopause, and whether or not they had had a hysterectomy or a bilateral oophorectomy. Women who had had a natural menopause or who had had a bilateral oophorectomy before their natural menopause were classified as postmenopausal, but those who had had a hysterectomy without bilateral oophorectomy before their natural menopause were classified as being of unknown menopausal status (because hysterectomy can mask cessation of ovarian activity). Otherwise, we took definitions used by principal investigators to classify each woman by her age at menarche, menopausal status and, for postmenopasual women, by her age at menopause. Women with unknown menopausal status or unknown ages at menarche or menopause were excluded from analyses, as were women who had used menopausal hormone therapy, since such use can mask the onset of menopause and modify associations between hormonal factors and breast cancer risk.We also sought information about tumour characteristics—ie, about oestrogen receptor status and about tumour histology. We used information provided by principal investigators to classify tumours as oestrogen receptor-positive or oestrogen receptor-negative, and as ductal, lobular, or of other histology.We did all analyses using conditional logistic regression, similar in principle to the Mantel-Haenszel stratification technique used in previous reports from this collaboration.2, 25–29 kg/m2, ≥30 kg/m2); by height ; by smoking ; and by alcohol consumption per week (<50 g and ≥50 g). Women with unknown values for any adjustment variable were assigned to separate strata.Analyses of the association between various factors and women's age at menarche and age at menopause were restricted to controls, and we calculated ORs stratifying by study, by centre within study, and where appropriate by age at diagnosis ; by year of birth ; by parity and age when first child was born ; by current body-mass index  were also examined. We restricted analyses comparing breast cancer risk in premenopausal, perimenopausal, and postmenopausal women to women aged 45–54 years (since each category is represented in this narrow age band) and we used the same stratifications as described above, except that age was stratified by single years. We stratified analyses relating to tumour characteristics by study, centre within study, age at diagnosis in single years, and year of birth, and adjusted by parity, age at first birth, BMI, smoking, and alcohol consumption, also using the same categories as described previously.2 tests for heterogeneity, calculated from the change in log likelihood on addition of extra terms. Significance tests for heterogeneity by tumour subtype were based on analyses within cases only , stratified by age, study, and BMI and adjusted for other variables listed previously. We did our analyses with STATA (version 11).We calculated RRs for breast cancer per 1 year younger at menarche and per 1 year older at menopause by linear regression, using the mean age within each category. We made comparisons across different subgroups of women using standard χWhen results for large numbers of subgroups are presented in the figures 99% CIs (or group-specific 99% CIs) are given, to take account of multiple testing. In the text all CIs quoted are 95% CIs.The funders had no role in study design, data collection, analysis, or interpretation of data, preparation of the report, or decision to publish. All members of the analysis and writing committee  had access to the raw data and are responsible for the final submission for publication.Overall 117 studies, together including 118 964 women with breast cancer (cases) and 306 091 without the disease (controls), were included in these analyses. The younger women were at menarche, the greater was their subsequent risk of breast cancer, the RR increasing by a factor of 1·050  for every year younger at menarche . ResultsTo assess consistency of findings, we calculated the RR of breast cancer per year younger at menarche for 34 subgroups of women, subdivided by 14 of their personal characteristics: year of birth, age at diagnosis and menopausal status, ethnic origin, parity, age at first birth, height, BMI as a young adult, current BMI, use of oral contraceptives, smoking, alcohol consumption, family history of breast cancer, menopausal status and, for postmenopausal women, their age at menopause . Both amOf the 117 contributing studies, 85 provided some information about tumour characteristics , and figOvarian production of hormones decreases rapidly at around the time of menopause, as is shown in 2) had a lower risk of breast cancer than leaner women (BMI <25 kg/m2), whereas the reverse was observed among postmenopausal women. Hence, the RR of breast cancer after the menopause decreases more rapidly in lean than in overweight or obese women (p<0·0001).The consistency of the finding of a greater risk of breast cancer among premenopausal than postmenopausal women of the same age was examined across 30 subgroups of women, subdivided by 13 of their characteristics . There wThe proportion of different types of breast cancer varies by age and by menopausal status . For tumIn analyses restricted to postmenopausal women, the RR of breast cancer increased by a factor of 1·029  for every year older at menopause . The finThe association between age at menopause and breast cancer risk was examined in 30 subgroups and did not vary significantly across 11 of the 13 characteristics examined . The assvs 1·029 [1·025–1·032]; heterogeneity p=0·001).Among postmenopausal women, for whom the effect on breast cancer risk per year younger at menarche can be directly compared to the effect per year older at menopause, breast cancer risk increased by a significantly greater amount per year of menarche than per year of menopause , supplemented by hand searching in review articles. Eligible studies needed to have obtained individual information about reproductive factors and about use of hormonal therapies, from at least 400 women with breast cancer and similar information from controls without the disease. Studies that had obtained relevant data, but had not published any results for breast cancer were sought by correspondence with colleagues, by discussions at collaborators meetings, and by electronic searches using additional terms “cohort”, “prospective”, “women”, and “cancer risk”.117 eligible studies were included and principal investigators contributed information about almost 120 000 women with breast cancer who had never used menopausal hormonal therapies to this individual-participant meta-analysis. We report on the relation between menarche and menopause and breast cancer risk, overall, and by oestrogen receptor status and by histological subtypes of the tumours, adjusting for possible confounding factors. Subgroup results are also shown by various sociodemographic and personal characteristics, including year of birth, ethnic origin, parity, age at first birth, smoking, alcohol consumption, and body-mass index.InterpretationBreast cancer risk increased by a significantly greater factor for every year younger at menarche than for every year older at menopause, indicating that menarche and menopause may not affect breast cancer risk merely by extending women's total reproductive years. Endogenous ovarian hormones are more relevant for oestrogen receptor-positive disease than for oestrogen receptor-negative disease and for lobular than for ductal tumours.This worldwide collaboration has brought together and reanalysed individual participant data for 120 000 women with breast cancer and 300 000 controls without the disease from 117 epidemiological studies in 35 countries .PanelResWhile confirming that early menarche and late menopause increase breast cancer risk, we showed that these effects were not equivalent, in that the excess risk associated with lengthening women's reproductive years by one year at menarche was greater than the excess associated with one year's lengthening at menopause. We also found that oestrogen receptor-positive and lobular breast cancers are strongly affected by women's menopausal status, and by their age.The production of steroid hormones by the ovary begins at around the time of menarche and decreases rapidly at around the time of menopause. Most women become menopausal between the ages of 45 and 54 years. By restricting analyses to the 31 000 women with breast cancer in this narrow age range and stratifying by single years of age , valid comparisons can be made of the short-term effect of the menopause, and breast cancer risk was about 40% higher in premenopausal than in postmenopausal women of the same age. Since the postmenopausal women had reached their menopause only an average 4·6 years previously, the findings indicate a rapid decline in breast cancer risk in women of identical ages soon after menopause. This finding probably explains the flattening of the age-incidence curve at around age 50 years, the so-called Clemmesen's hook,There is accumulating evidence that oestrogen receptor-positive and lobular breast cancers are more sensitive to ovarian hormones than are oestrogen receptor-negative and ductal cancers. Not only are oestrogen receptor-positive and lobular tumours strongly affected by the menopause, as we have shown, but postmenopausal women who use hormone therapy have a greater increase in oestrogen receptor-positive than oestrogen receptor-negative tumours and in lobular than ductal breast cancers.Women's adiposity was consistently shown to attenuate associations between menopause and breast cancer risk. Circulating oestradiol concentrations increase as postmenopausal women's BMI increases.Although a woman's age at menarche does not coincide precisely with the onset of breast development, the two are highly correlated.These findings confirm that young age at menarche and old age at menopause increase breast cancer risk. Many factors known to affect breast cancer risk, including childbearing patterns, height, and BMI, are also associated both with women's age at menarche and with their age at menopause. To ensure as much comparability as possible between women with breast cancer and controls, and thus to minimise potential confounding, analyses were stratified by these factors, and by study, age, year of birth, smoking, and alcohol consumption. Additional adjustment by ethnic origin, hormonal contraceptive use, and family history of breast cancer altered the excess RR estimates by less than 1%. The associations did not vary materially by women's personal characteristics, other than BMI among postmenopausal women.This meta-analysis of individual-participant data includes almost all available epidemiological evidence for the association between menarche and menopause and breast cancer risk. Large numbers of cases were required to assess reliably whether the associations differ by tumour type and by characteristics of the affected women. Combining individual data from many studies has the advantage of increasing statistical power and also of ensuring that definitions across studies are as similar as possible. Single studies do not have sufficient power to examine these associations in detail, so reviews based solely on the limited published evidence could well be susceptible to publication bias. Although availability of information about oestrogen receptor status varied substantially over time, with most studies done before 1990 contributing relatively little information, analyses of all available data combined were sufficiently powered to describe associations separately within both oestrogen receptor-positive and oestrogen receptor-negative disease. Another important advantage of this meta-analysis is that it seeks to review both published and unpublished findings, thus avoiding unduly selective emphasis on published results or on only some studies.Women included in these analyses generally reported their ages at menarche and at menopause many years after the events had occurred. Comparisons of information recorded at around the time of menarche and menopause with that recalled many years later has shown substantial regression to the mean over time, especially for recalled age at menarche,Since the effect on breast cancer risk of 1 year younger at menarche is significantly greater than that of 1 year older at menopause, these findings suggest that the effects of each may not be acting merely by lengthening the total duration of women's reproductive years. In most populations women's average age at menarche has been declining in successive birth cohorts,


SHBG rs6259, ESR1 rs2234693, CYP19 rs10046 and rs4775936, and UGT1A1 rs8175347, or the progesterone pathway, PGR rs1042838, with the risk of breast cancer. Our results suggest that these genetic variants do not have a strong effect on breast cancer risk.Sex hormones play a key role in the development of breast cancer. Certain polymorphic variants (SNPs and repeat polymorphisms) in hormone-related genes are associated with sex hormone levels. However, the relationship observed between these genetic variants and breast cancer risk has been inconsistent. We conducted a case-control study nested within two prospective cohorts to assess the relationship between specific genetic variants in hormone-related genes and breast cancer risk. In total, 1164 cases and 2111 individually-matched controls were included in the study. We did not observe an association between potential functional genetic polymorphisms in the estrogen pathway,  Genetic variants in hormone-related genes have been shown to be associated with sex hormone levels ESR1 gene (rs2046210 and rs12662670) that were significantly associated with risk in several genome wide association studies Epidemiological evidence indicates a key role for sex hormones in breast cancer development. High circulating levels of estrogens and androgens have been consistently associated with increased breast cancer risk in postmenopausal women SHBG), which binds to, and reduces the biological availability of estrogens and androgens; the progesterone receptor (PGR); the estrogen receptor alpha (ESR1); aromatase (CYP19), which converts androgens to estrogens; and UDP-glucuronosyltransferase 1A1 (UGT1A1), which glucuronidates estrogens, thereby facilitating their excretion in urine. Candidate polymorphisms in these genes were selected based on their potential functional role  and reported association with breast cancer risk at the time of initiation of our study. We selected polymorphisms that were associated with breast cancer risk in some, but not all, studies. The following polymorphisms were genotyped: SHBG rs6259 PGR rs1042838 ESR1 rs2234693 CYP19 rs10046 and rs4775936 UGT1A1 rs8175347 We selected several genes that encode for proteins involved in hormone signaling and metabolism: sex hormone-binding globulin  For the NYUWHS participants, DNA was extracted from blood clots or cell precipitates (prepared by centrifugation of whole blood collected at blood donation) for 42% of participants. For the remainder of NYUWHS participants, DNA was extracted from serum. Samples were genotyped using the TaqMan® approach www.genotyping.se) for five SNPs . Four of these SNPs  were assayed using the GenomeLab SNPStream 12plex-system (Beckman Coulter) and one (rs1042838) using the FP-TDI system. UGT1A1 rs8175347 was assayed at the German Cancer Research Center in Heidelberg using fluorescent fragment analysis on an ABI PRISM 3100 Genetic analyzer with the GeneMapper software version 3.0 (Applied Biosystems). The percentage of samples with successful calls was ≥98% for all polymorphisms. We conducted a pilot study to assess genotype concordance across duplicate samples from NSMSC participants (n = 164 duplicates). The concordance between samples from the same participant was ≥99% for all loci.For the NSMSC participants, DNA was isolated from buffy coats. Genotyping was performed at the SNP Technology Platform at Uppsala University Hospital  were included on each plate and were interspersed throughout the plate with the case-control samples. Laboratory personnel were blinded as to case-control status and the identity of the quality control samples.We assessed deviation from Hardy-Weinberg equilibrium (HWE) in each cohort for each genetic variant with a chi-square test. Odds ratios and 95% confidence intervals for breast cancer risk were estimated using the conditional logistic regression model, as appropriate for the matched study design. All models were adjusted for race/ethnicity  and through matching, were also adjusted for age at blood donation, duration of sample storage, and menopausal status. Multivariate-adjusted models included other known risk factors for breast cancer: family history of breast cancer (in a first degree relative), age at menarche, age at first birth/parity , ever use of hormone replacement therapy, and body mass index (BMI). For covariates with missing data , we performed multiple imputation of missing data for each cohort separately using a fully conditional specification model Descriptive statistics for the cases and controls are shown in SHBG rs6259, PGR rs1042838, CYP19 rs10046 and rs4775936, or UGT1A1 rs8175347 variants ESR1 rs2234693  that did not observe an association for CYP19 rs10046 and rs4775936 and UGT1A1 rs8175347. CYP19 encodes for the enzyme aromatase, which converts androgens to estrogens. The rs10046 T allele and the rs4775936 A allele have been shown to be associated with higher levels of circulating estrogen or estrogen to androgen ratios in several studies UGT1A1 rs8175347 is associated with lower transcriptional activity and may result in reduced glucuronidation of estrogens CYP19 and UGT1A1 and breast cancer risk Three of the genetic variants we selected directly influence estrogen levels: SHBG rs6259 influences estrogen bioavailability and the A allele is associated with higher SHBG levels (suggesting lower estrogen bioavailability) in most PGR rs1042838 influences progesterone signaling and the T allele is in high linkage disequilibrium with the PROGINS allele, which reduces the PGR transcript stability and may decrease the response to progesterone PGR rs1042838 and breast cancer risk, in agreement with nearly all previous studies ESR1 rs2234693 SNP has not been clearly demonstrated, though the C allele produces a binding site for the B-myb transcription factor, which may result in an alternative form and/or altered expression of the estrogen receptor, and thus influence estrogen signaling ESR1 rs2234693, consistent with the findings of most other studies (reviewed in ESR1 rs2234693 is not in linkage disequilibrium with the SNPs near the ESR1 gene that were associated with breast cancer risk in several genome wide association studies The function of the There is substantial evidence that higher levels of circulating estrogens are associated with an increased risk of breast cancer in postmenopausal women. A pooled analysis of nine prospective epidemiologic studies found that for a doubling in estradiol levels, there was about a 29% increase in risk The ages at enrollment into the parent cohorts were 34–65 for the NYUWHS and 40–69 for the NSMSC. While generalizability of our results to other populations requires caution, it is unlikely that the relationship between these common genetic variants and breast cancer risk is limited to women in the same age range as in our study.SHBG rs6259, PGR rs1042838, ESR1 rs2234693, CYP19 rs10046 and rs4775936, and UGT1A1 rs8175347 and breast cancer risk. The effects of other genetic variants in these or other hormone-related genes, individually or in combination, may have an effect on breast cancer risk. In conclusion, our study provides supportive evidence that these genetic variants in hormone-related genes are not likely to have a strong effect on breast cancer risk.In agreement with the largest meta-analyses of epidemiological studies to date, we did not observe an association for