Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk.

BACKGROUND: Mammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk. METHODS: Using 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously (per 1 standard deviation, SD) and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies. RESULTS: Adjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38-1.52), adjusted absolute dense area (OR = 1.34 per SD, 95% CI 1.28-1.41), and the 77-SNP PRS (OR = 1.52 per SD, 95% CI 1.45-1.59) were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio (P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile. CONCLUSIONS: The combined associations of the 77-SNP PRS and adjusted density measures are generally well described by multiplicative models, and both risk factors provide independent information on breast cancer risk.

Asthma status is associated with decreased risk of aggressive urothelial bladder cancer.

Previous studies suggested an association between atopic conditions and specific cancers. The results on the association with urothelial bladder cancer (UBC) are scarce and inconsistent. To evaluate the association between asthma and risk of UBC, we considered 936 cases and 1,022 controls from the Spanish Bladder Cancer/EPICURO Study (86% males, mean age 65.4 years), a multicenter and hospital-based case-control study conducted during 1998-2001. Participants were asked whether they had asthma and detailed information about occupational exposures, smoking habits, dietary factors, medical conditions and history of medication was collected through face-to-face questionnaires performed by trained interviewers. Since asthma and UBC might share risk factors, association between patients' characteristics and asthma was studied in UBC controls. Association between UBC and asthma was assessed using logistic regression unadjusted and adjusted for potential confounders. The complex interrelationships, direct and mediating effect of asthma on UBC, were appraised using counterfactual mediation models. Asthma was associated with a reduced risk of UBC (odds ratio (OR) = 0.54, 95% confidence interval (CI) 0.37, 0.79) after adjusting for a wide range of confounders. No mediating effect was identified. The reduced risk associated with asthma was restricted to patients with high-risk non-muscle invasive (OR = 0.25, 95%CI 0.10, 0.62) and muscle invasive UBC (OR = 0.32, 95%CI 0.15, 0.69). Our results support that asthma is associated with a decreased risk of UBC, especially among aggressive tumors. Further work on the relationship between asthma and other atopic conditions and cancer risk should shed light on the relationship between immune response mechanisms and bladder carcinogenesis.

DNA mismatch repair gene MSH6 implicated in determining age at natural menopause.

The length of female reproductive lifespan is associated with multiple adverse outcomes, including breast cancer, cardiovascular disease and infertility. The biological processes that govern the timing of the beginning and end of reproductive life are not well understood. Genetic variants are known to contribute to ∼50% of the variation in both age at menarche and menopause, but to date the known genes explain <15% of the genetic component. We have used genome-wide association in a bivariate meta-analysis of both traits to identify genes involved in determining reproductive lifespan. We observed significant genetic correlation between the two traits using genome-wide complex trait analysis. However, we found no robust statistical evidence for individual variants with an effect on both traits. A novel association with age at menopause was detected for a variant rs1800932 in the mismatch repair gene MSH6 (P = 1.9 × 10(-9)), which was also associated with altered expression levels of MSH6 mRNA in multiple tissues. This study contributes to the growing evidence that DNA repair processes play a key role in ovarian ageing and could be an important therapeutic target for infertility.

BMI and breast cancer risk around age at menopause.

BACKGROUND: A high body mass index (BMI, kg/m2) is associated with decreased risk of breast cancer before menopause, but increased risk after menopause. Exactly when this reversal occurs in relation to menopause is unclear. Locating that change point could provide insight into the role of adiposity in breast cancer etiology. METHODS: We examined the association between BMI and breast cancer risk in the Premenopausal Breast Cancer Collaborative Group, from age 45 up to breast cancer diagnosis, loss to follow-up, death, or age 55, whichever came first. Analyses included 609,880 women in 16 prospective studies, including 9956 who developed breast cancer before age 55. We fitted three BMI hazard ratio (HR) models over age-time: constant, linear, or nonlinear (via splines), applying piecewise exponential additive mixed models, with age as the primary time scale. We divided person-time into four strata: premenopause; postmenopause due to natural menopause; postmenopause because of interventional loss of ovarian function (bilateral oophorectomy (BO) or chemotherapy); postmenopause due to hysterectomy without BO. Sensitivity analyses included stratifying by BMI in young adulthood, or excluding women using menopausal hormone therapy. RESULTS: The constant BMI HR model provided the best fit for all four menopausal status groups. Under this model, the estimated association between a five-unit increment in BMI and breast cancer risk was HR=0.87 (95% CI: 0.85, 0.89) before menopause, HR=1.00 (95% CI: 0.96, 1.04) after natural menopause, HR=0.99 (95% CI: 0.93, 1.05) after interventional loss of ovarian function, and HR=0.88 (95% CI: 0.76, 1.02) after hysterectomy without BO. CONCLUSION: The BMI breast cancer HRs remained less than or near one during the 45-55 year age range indicating that the transition to a positive association between BMI and risk occurs after age 55.

Mosaic uniparental disomies and aneuploidies as large structural variants of the human genome.

Mosaicism is defined as the coexistence of cells with different genetic composition within an individual, caused by postzygotic somatic mutation. Although somatic mosaicism for chromosomal abnormalities is a well-established cause of developmental and somatic disorders and has also been detected in different tissues, its frequency and extent in the adult normal population are still unknown. We provide here a genome-wide survey of mosaic genomic variation obtained by analyzing Illumina 1M SNP array data from blood or buccal DNA samples of 1991 adult individuals from the Spanish Bladder Cancer/EPICURO genome-wide association study. We found mosaic abnormalities in autosomes in 1.7% of samples, including 23 segmental uniparental disomies, 8 complete trisomies, and 11 large (1.5-37 Mb) copy-number variants. Alterations were observed across the different autosomes with recurrent events in chromosomes 9 and 20. No case-control differences were found in the frequency of events or the percentage of cells affected, thus indicating that most rearrangements found are not central to the development of bladder cancer. However, five out of six events tested were detected in both blood and bladder tissue from the same individual, indicating an early developmental origin. The high cellular frequency of the anomalies detected and their presence in normal adult individuals suggest that this type of mosaicism is a widespread phenomenon in the human genome. Somatic mosaicism should be considered in the expanding repertoire of inter- and intraindividual genetic variation, some of which may cause somatic human diseases but also contribute to modifying inherited disorders and/or late-onset multifactorial traits.

Genome-wide CNV analysis replicates the association between GSTM1 deletion and bladder cancer: a support for using continuous measurement from SNP-array data.

BACKGROUND: Structural variations such as copy number variants (CNV) influence the expression of different phenotypic traits. Algorithms to identify CNVs through SNP-array platforms are available. The ability to evaluate well-characterized CNVs such as GSTM1 (1p13.3) deletion provides an important opportunity to assess their performance. RESULTS: 773 cases and 759 controls from the SBC/EPICURO Study were genotyped in the GSTM1 region using TaqMan, Multiplex Ligation-dependent Probe Amplification (MLPA), and Illumina Infinium 1 M SNP-array platforms. CNV callings provided by TaqMan and MLPA were highly concordant and replicated the association between GSTM1 and bladder cancer. This was not the case when CNVs were called using Illumina 1 M data through available algorithms since no deletion was detected across the study samples. In contrast, when the Log R Ratio (LRR) was used as a continuous measure for the 5 probes contained in this locus, we were able to detect their association with bladder cancer using simple regression models or more sophisticated methods such as the ones implemented in the CNVtools package. CONCLUSIONS: This study highlights an important limitation in the CNV calling from SNP-array data in regions of common aberrations and suggests that there may be added advantage for using LRR as a continuous measure in association tests rather than relying on calling algorithms.

Likelihood ratio test for detecting gene (G)-environment (E) interactions under an additive risk model exploiting G-E independence for case-control data.

There has been a long-standing controversy in epidemiology with regard to an appropriate risk scale for testing interactions between genes (G) and environmental exposure (E ). Although interaction tests based on the logistic model-which approximates the multiplicative risk for rare diseases-have been more widely applied because of its convenience in statistical modeling, interactions under additive risk models have been regarded as closer to true biologic interactions and more useful in intervention-related decision-making processes in public health. It has been well known that exploiting a natural assumption of G-E independence for the underlying population can dramatically increase statistical power for detecting multiplicative interactions in case-control studies. However, the implication of the independence assumption for tests for additive interaction has not been previously investigated. In this article, the authors develop a likelihood ratio test for detecting additive interactions for case-control studies that incorporates the G-E independence assumption. Numerical investigation of power suggests that incorporation of the independence assumption can enhance the efficiency of the test for additive interaction by 2- to 2.5-fold. The authors illustrate their method by applying it to data from a bladder cancer study.

Folate metabolism genes, vegetable intake and renal cancer risk in central Europe.

In a multicenter case-control study of renal cell carcinoma (RCC) conducted in central and eastern Europe, we reported a strong inverse association with high vegetable intake and RCC risk. The odds ratio (OR) for high compared to the lowest tertile of vegetable intake was OR = 0.67; (95% confidence interval (CI): 0.53-0.83; p-trend < 0.001). We hypothesized that variation in key folate metabolism genes may modify this association. Common variation in 5 folate metabolism genes (CBS: Ex9+33C > T (rs234706), Ex13 +41C > T (rs1801181), Ex18 -391 G > A (rs12613); MTHFR: A222V Ex5+79C > T (rs1801133), Ex8-62A > C (rs1801131); MTR: Ex26 20A > G (rs1805087), MTRR: Ex5+136 T > C (rs161870), and TYMS:IVS2-405 C > T (rs502396), Ex8+157 C > T (rs699517), Ex8+227 A > G (rs2790)) were analyzed among 1,097 RCC cases and 1,555 controls genotyped in this study. Having at least 1 variant T allele of MTHFR A222V was associated with higher RCC risk compared to those with 2 common (CC) alleles (OR = 1.44; 95% CI: 1.17-1.77; p = 0.001). After stratification by tertile of vegetable intake, the higher risk associated with the variant genotype was only observed in the low and medium tertiles (p-trend = 0.001), but not among those in the highest tertile (p-interaction = 0.22). The association remained robust after calculation of the false discovery rate (FDR = 0.05). Of the 3 TYMS SNPs examined, only the TYMS IVS2 -405 C (rs502396) variant was associated with a significantly lower risk compared to the common genotype (OR = 0.73; 95% CI: 0.57-0.93). Vegetable intake modified the association between all 3 TYMS SNPs and RCC risk (p-interaction < 0.04 for all). In summary, these findings suggest that common variation in MTHFR and TYMS genes may be associated with RCC risk, particularly when vegetable intake is low.

Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States.

IMPORTANCE: An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention. OBJECTIVE: To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors. DESIGN, SETTING, AND PARTICIPANTS: Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality. EXPOSURES: Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors. MAIN OUTCOMES AND MEASURES: Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking). RESULTS: The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who were at the lowest and highest deciles of nonmodifiable risks, the 5th and 95th percentile range of the risk distribution associated with 4 modifiable factors was 2.9% to 5.0% and 15.5% to 25.0%, respectively. For women in the highest decile of risk owing to nonmodifiable factors, those who had low BMI, did not drink or smoke, and did not use MHT had risks comparable to an average woman in the general population. CONCLUSIONS AND RELEVANCE: This model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States. The model can also identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. The effectiveness of this model for individual risk communication needs further investigation.