Identification of a BRCA2-Specific modifier locus at 6p24 related to breast cancer risk

Common genetic variants contribute to the observed variation in breast cancer risk for BRCA2 mutation carriers; those known to date have all been found through population-based genome-wide association studies (GWAS). To comprehensively identify breast cancer risk modifying loci for BRCA2 mutation carriers, we conducted a deep replication of an ongoing GWAS discovery study. Using the ranked P-values of the breast cancer associations with the imputed genotype of 1.4 M SNPs, 19,029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211,155 SNPs as part of a multi-consortial project. DNA samples from 3,881 breast cancer affected and 4,330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis. We replicated previously reported breast cancer susceptibility alleles in these BRCA2 mutation carriers and for several regions (including FGFR2, MAP3K1, CDKN2A/B, and PTHLH) identified SNPs that have stronger evidence of association than those previously published. We also identified a novel susceptibility allele at 6p24 that was inversely associated with risk in BRCA2 mutation carriers (rs9348512; per allele HR = 0.85, 95% CI 0.80-0.90, P = 3.9×10−8). This SNP was not associated with breast cancer risk either in the general population or in BRCA1 mutation carriers. The locus lies within a region containing TFAP2A, which encodes a transcriptional activation protein that interacts with several tumor suppressor genes. This report identifies the first breast cancer risk locus specific to a BRCA2 mutation background. This comprehensive update of novel and previously reported breast cancer susceptibility loci contributes to the establishment of a panel of SNPs that modify breast cancer risk in BRCA2 mutation carriers. This panel may have clinical utility for women with BRCA2 mutations weighing options for medical prevention of breast cancer

Genetically Predicted Body Mass Index and Breast Cancer Risk : Mendelian Randomization Analyses of Data from 145,000 Women of European Descent

Background Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. Methods We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases = 46,325, controls = 42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. Results In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR] = 0.65 per 5 kg/m(2) increase, 95% confidence interval [CI]: 0.56-0.75, p = 3.32 x 10(-10)). The associations were similar for both premenopausal (OR = 0.44, 95% CI: 0.31-0.62, p = 9.91x10(-8)) and postmenopausal breast cancer (OR = 0.57, 95% CI: 0.46-0.71, p = 1.88x10(-8)). This association was replicated in the data from the DRIVE consortium (OR = 0.72, 95% CI: 0.60-0.84, p = 1.64 x 10(-7)). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p <0.05; for 16 of them, the allele associated with elevated BMI was associated with reduced breast cancer risk. Conclusions BMI predicted by genome-wide association studies (GWAS)-identified variants is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons for this discrepancy may reveal insights into the complex relationship of genetic determinants of body weight in the etiology of breast cancer.Peer reviewe

Improving Snowfall Forecasting by Diagnosing Snow Density

Current prediction of snowfall amounts is accomplished either by using empirical techniques or by using a standard modification of liquid equivalent precipitation such as the 10-to-1 rule. This rule, which supposes that the depth of the snowfall is 10 times the liquid equivalent (a snow ratio of 10:1, reflecting an assumed snow density of 100 kg m�3), is a particularly popular technique with operational forecasters, although it dates from a limited nineteenth-century study. Unfortunately, measurements of freshly fallen snow indicate that the snow ratio can vary from on the order of 3:1 to (occasionally) 100:1. Improving quantitative snowfall forecasts requires, in addition to solutions to the significant challenge of forecasting liquid precipitation amounts, a more robust method for forecasting the density of snow. A review of the microphysical literature reveals that many factors may contribute to snow density, including in-cloud (crystal habit and size, the degree of riming and aggregation of the snowflake), subcloud (melting and sublimation), and surface processes (compaction and snowpack metamorphism). Despite this complexity, the paper explores the sufficiency of surface and radiosonde data for the classification of snowfall density. A principal component analysis isolates seven factors that influence the snow ratio: solar radiation (month), low- to midlevel temperature, mid- to upper-level temperature, low- to midlevel relative humidity, midlevel relative humidity, upper-level relative humidity, and external compaction (surfac

Improving snowfall forecasting by accounting for the climatological variability of snow density

ABSTRACT Accurately forecasting snow depth is a challenge. In particular, one poorly understood component of snow-depth forecasting is determining the snow ratio. The snow ratio is the ratio of snow depth to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 &lt; ratio &lt; 9:1), average (9:1&lt;=ratio&lt;=15:1), and light (ratio&gt;15:1). A web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural networks is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snow depth, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snow depth, given a forecasted value of liquid equivalent. The surface wind speed cannot be used as a sole discriminator for snow ratios. At Buffalo, New York, and Sault Ste. Marie, Michigan, locations susceptible to lake-effect snowstorms, wind speeds greater than 6 m s , however, only small differences exist between lake-effect and nonlake-effect events. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible.

Improving Snowfall Forecasting by Accounting for the Climatological Variability of Snow Density

ABSTRACT Accurately forecasting snow depth is a challenge. In particular, one poorly understood component of snow-depth forecasting is determining the snow ratio. The snow ratio is the ratio of snow depth to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 &lt; ratio &lt; 9:1), average (9:1&lt;=ratio&lt;=15:1), and light (ratio&gt;15:1). A web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural networks is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snow depth, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snow depth, given a forecasted value of liquid equivalent. The surface wind speed cannot be used as a sole discriminator for snow ratios. At Buffalo, New York, and Sault Ste. Marie, Michigan, locations susceptible to lake-effect snowstorms, wind speeds greater than 6 m s , however, only small differences exist between lake-effect and nonlake-effect events. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible.

Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment

Abstract Background: Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients. Methods: We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP &amp; 0.15). Results: Evidence of associations with breast cancer-specific survival was observed in three patient subgroups, with variant rs5934618 in patients with grade 3 tumors (15-year-hazard ratio (HR) [95% confidence interval (CI)] 1.32 [1.20, 1.45], P = 1.4E−08, BFDP = 0.01, per G allele); variant rs4679741 in patients with ER-positive tumors treated with endocrine therapy (15-year-HR [95% CI] 1.18 [1.11, 1.26], P = 1.6E−07, BFDP = 0.09, per G allele); variants rs1106333 (15-year-HR [95% CI] 1.68 [1.39,2.03], P = 5.6E−08, BFDP = 0.12, per A allele) and rs78754389 (5-year-HR [95% CI] 1.79 [1.46,2.20], P = 1.7E−08, BFDP = 0.07, per A allele), in patients with ER-negative tumors treated with chemotherapy. Conclusions: We found evidence of four loci associated with breast cancer-specific survival within three patient subgroups. There was limited evidence for the existence of associations in other patient subgroups. However, the power for many subgroups is limited due to the low number of events. Even so, our results suggest that the impact of common germline genetic variants on breast cancer-specific survival might be limited

Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses

Abstract Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype. To identify novel loci, we performed a genome-wide association study including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. We identified 32 novel susceptibility loci (P &lt; 5.0 × 10−8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate &lt; 0.05). Five loci showed associations (P &lt; 0.05) in opposite directions between luminal and non-luminal subtypes. In silico analyses showed that these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 54.2% for luminal A-like disease and 37.6% for triple-negative disease. The odds ratios of polygenic risk scores, which included 330 variants, for the highest 1% of quantiles compared with middle quantiles were 5.63 and 3.02 for luminal A-like and triple-negative disease, respectively. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores

Common variants in breast cancer risk loci predispose to distinct tumor subtypes

Abstract Background: Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. Methods: Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. Results: Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate &lt; 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p &lt; 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. Conclusion: This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction

Evidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation (vol 5, 4999, 2014)

10.1038/ncomms16193NATURE COMMUNICATIONS9