Androgen Exposure and Sensation-Seeking in Young Males

Testosterone is thought to be associated with short attention spans, increased novelty, and thrill seeking and behavioral disinhibition in men. However, there is little empirical evidence for such associations among normal males. Here we test three separate measures of androgenicity; salivary testosterone (current exposure), facial masculinity (pubertal exposure), and 2D:4D digit ratio (prenatal exposure) as predictors of sensation-seeking in young men. Participants were 98 young men between the ages of 18 and 23. An unstimulated saliva sample was collected for determination of testosterone and participants completed a questionnaire, including Zuckerman’s sensation-seeking scale. Facial photos were obtained and rated for masculinity by anonymous observers. Both right and left hands were scanned to determine 2D:4D digit ratios. In univariate analyses, salivary testosterone (n = 91; r = 0.22; P = 0.04) and facial masculinity (n = 91; r = 0.23; P = 0.04) were significant predictor of boredom susceptibility, but not the disinhibition, thrill-seeking, experience seeking subscales. 2D:4D was not associated with any of measure of sensation-seeking. In multivariate models, facial masculinity was a significant predictor of boredom susceptibility (n = 82; b = 0.35; P = 0.001), controlled for left 2D:4D and salivary testosterone. These results suggest that among men, current and pubertal testosterone exposure are associated with greater need for immediate stimulation, but not other forms of sensation-seeking. This result is consistent with previous findings of a positive relationship between salivary testosterone and delayed discounting, and may reflect the impact of testosterone on dopaminergic reward mechanisms.AnthropologyHuman Evolutionary Biolog

The androgen receptor CAG repeat polymorphism and modification of breast cancer risk in BRCA1 and BRCA2 mutation carriers.

INTRODUCTION: The androgen receptor (AR) gene exon 1 CAG repeat polymorphism encodes a string of 9-32 glutamines. Women with germline BRCA1 mutations who carry at least one AR allele with 28 or more repeats have been reported to have an earlier age at onset of breast cancer. METHODS: A total of 604 living female Australian and British BRCA1 and/or BRCA2 mutation carriers from 376 families were genotyped for the AR CAG repeat polymorphism. The association between AR genotype and disease risk was assessed using Cox regression. AR genotype was analyzed as a dichotomous covariate using cut-points previously reported to be associated with increased risk among BRCA1 mutation carriers, and as a continuous variable considering smaller allele, larger allele and average allele size. RESULTS: There was no evidence that the AR CAG repeat polymorphism modified disease risk in the 376 BRCA1 or 219 BRCA2 mutation carriers screened successfully. The rate ratio associated with possession of at least one allele with 28 or more CAG repeats was 0.74 (95% confidence interval 0.42-1.29; P = 0.3) for BRCA1 carriers, and 1.12 (95% confidence interval 0.55-2.25; P = 0.8) for BRCA2 carriers. CONCLUSION: The AR exon 1 CAG repeat polymorphism does not appear to have an effect on breast cancer risk in BRCA1 or BRCA2 mutation carriers

A PALB2 mutation associated with high risk of breast cancer

Introduction: As a group, women who carry germline mutations in partner and localizer of breast cancer 2 susceptibility protein (PALB2) are at increased risk of breast cancer. Little is known about by how much or whether risk differs by mutation or family history, owing to the paucity of studies of cases unselected for family history.Methods: We screened 1,403 case probands for PALB2 mutations in a population-based study of Australian women with invasive breast cancer stratified by age at onset. The age-specific risk of breast cancer was estimated from the cancer histories of first- and second-degree relatives of mutation-carrying probands using a modified segregation analysis that included a polygenic modifier and was conditioned on the carrier case proband. Further screening for PALB2 c.3113G > A (W1038X) was conducted for 779 families with multiple cases of breast cancer ascertained through family cancer clinics in Australia and New Zealand and 764 population-based controls.Results: We found five independent case probands in the population-based sample with the protein-truncating mutation PALB2 c.3113G > A (W1038X); 2 of 695 were diagnosed before age 40 years and 3 of 708 were diagnosed when between ages 40 and 59 years. Both of the two early-onset carrier case probands had very strong family histories of breast cancer. Further testing found that the mutation segregated with breast cancer in these families. No c.3113G > A (W1038X) carriers were found in 764 population-based unaffected controls. The hazard ratio was estimated to be 30.1 (95% confidence interval (CI), 7.5 to 120; P A mutation appears to be associated with substantial risks of breast cancer that are of clinical relevance. © 2010 Southey et al.; licensee BioMed Central Ltd

Log odds of carrying an Ancestral Mutation in BRCA1 or BRCA2 for a Defined personal and family history in an Ashkenazi Jewish woman (LAMBDA)

INTRODUCTION: Ancestral mutations in BRCA1 and BRCA2 are common in people of Ashkenazi Jewish descent and are associated with a substantially increased risk of breast and ovarian cancer. Women considering mutation testing usually have several personal and family cancer characteristics, so predicting mutation status from one factor alone could be misleading. The aim of this study was to develop a simple algorithm to estimate the probability that an Ashkenazi Jewish woman carries an ancestral mutation, based on multiple predictive factors. METHODS: We studied Ashkenazi Jewish women with a personal or family history of breast or ovarian cancer and living in Melbourne or Sydney, Australia, or with a previous diagnosis of breast or ovarian cancer and living in the UK. DNA samples were tested for the germline mutations 185delAG and 5382insC in BRCA1, and 6174delT in BRCA2. Logistic regression was used to identify, and to estimate the predictive strength of, major determinants. RESULTS: A mutation was detected in 64 of 424 women. An algorithm was developed by combining our findings with those from similar analyses of a large study of unaffected Jewish women in Washington. Starting with a baseline score, a multiple of 0.5 (based on the logistic regression estimates) is added for each predictive feature. The sum is the estimated log odds ratio that a woman is a carrier, and is converted to a probability by using a table. There was good internal consistency. CONCLUSIONS: This simple algorithm might be useful in the clinical and genetic counselling setting. Comparison and validation in other settings should be sought

A comprehensive evaluation of interaction between genetic variants and use of menopausal hormone therapy on mammographic density.

INTRODUCTION: Mammographic density is an established breast cancer risk factor with a strong genetic component and can be increased in women using menopausal hormone therapy (MHT). Here, we aimed to identify genetic variants that may modify the association between MHT use and mammographic density. METHODS: The study comprised 6,298 postmenopausal women from the Mayo Mammography Health Study and nine studies included in the Breast Cancer Association Consortium. We selected for evaluation 1327 single nucleotide polymorphisms (SNPs) showing the lowest P-values for interaction (P int) in a meta-analysis of genome-wide gene-environment interaction studies with MHT use on risk of breast cancer, 2541 SNPs in candidate genes (AKR1C4, CYP1A1-CYP1A2, CYP1B1, ESR2, PPARG, PRL, SULT1A1-SULT1A2 and TNF) and ten SNPs (AREG-rs10034692, PRDM6-rs186749, ESR1-rs12665607, ZNF365-rs10995190, 8p11.23-rs7816345, LSP1-rs3817198, IGF1-rs703556, 12q24-rs1265507, TMEM184B-rs7289126, and SGSM3-rs17001868) associated with mammographic density in genome-wide studies. We used multiple linear regression models adjusted for potential confounders to evaluate interactions between SNPs and current use of MHT on mammographic density. RESULTS: No significant interactions were identified after adjustment for multiple testing. The strongest SNP-MHT interaction (unadjusted P int <0.0004) was observed with rs9358531 6.5kb 5' of PRL. Furthermore, three SNPs in PLCG2 that had previously been shown to modify the association of MHT use with breast cancer risk were found to modify also the association of MHT use with mammographic density (unadjusted P int <0.002), but solely among cases (unadjusted P int SNP×MHT×case-status <0.02). CONCLUSIONS: The study identified potential interactions on mammographic density between current use of MHT and SNPs near PRL and in PLCG2, which require confirmation. Given the moderate size of the interactions observed, larger studies are needed to identify genetic modifiers of the association of MHT use with mammographic density.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s13058-015-0625-

A genome-wide linkage study of mammographic density, a risk factor for breast cancer

Abstract Introduction Mammographic breast density is a highly heritable (h2 > 0.6) and strong risk factor for breast cancer. We conducted a genome-wide linkage study to identify loci influencing mammographic breast density (MD). Methods Epidemiological data were assembled on 1,415 families from the Australia, Northern California and Ontario sites of the Breast Cancer Family Registry, and additional families recruited in Australia and Ontario. Families consisted of sister pairs with age-matched mammograms and data on factors known to influence MD. Single nucleotide polymorphism (SNP) genotyping was performed on 3,952 individuals using the Illumina Infinium 6K linkage panel. Results Using a variance components method, genome-wide linkage analysis was performed using quantitative traits obtained by adjusting MD measurements for known covariates. Our primary trait was formed by fitting a linear model to the square root of the percentage of the breast area that was dense (PMD), adjusting for age at mammogram, number of live births, menopausal status, weight, height, weight squared, and menopausal hormone therapy. The maximum logarithm of odds (LOD) score from the genome-wide scan was on chromosome 7p14.1-p13 (LOD = 2.69; 63.5 cM) for covariate-adjusted PMD, with a 1-LOD interval spanning 8.6 cM. A similar signal was seen for the covariate adjusted area of the breast that was dense (DA) phenotype. Simulations showed that the complete sample had adequate power to detect LOD scores of 3 or 3.5 for a locus accounting for 20% of phenotypic variance. A modest peak initially seen on chromosome 7q32.3-q34 increased in strength when only the 513 families with at least two sisters below 50 years of age were included in the analysis (LOD 3.2; 140.7 cM, 1-LOD interval spanning 9.6 cM). In a subgroup analysis, we also found a LOD score of 3.3 for DA phenotype on chromosome 12.11.22-q13.11 (60.8 cM, 1-LOD interval spanning 9.3 cM), overlapping a region identified in a previous study. Conclusions The suggestive peaks and the larger linkage signal seen in the subset of pedigrees with younger participants highlight regions of interest for further study to identify genes that determine MD, with the goal of understanding mammographic density and its involvement in susceptibility to breast cancer

Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures.

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.ABCFS: The Australian Breast Cancer Family Registry (ABCFR; 1992-1995) was supported by the Australian NHMRC, the New South Wales Cancer Council, and the Victorian Health Promotion Foundation (Australia), and by grant UM1CA164920 from the USA National Cancer Institute. The Genetic Epidemiology Laboratory at the University of Melbourne has also received generous support from Mr B. Hovey and Dr and Mrs R.W. Brown to whom we are most grateful. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Breast Cancer Susceptibility Variants and Mammographic Density 5 Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. BBCC: This study was funded in part by the ELAN-Program of the University Hospital Erlangen; Katharina Heusinger was funded by the ELAN program of the University Hospital Erlangen. BBCC was supported in part by the ELAN program of the Medical Faculty, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg. EPIC-Norfolk: This study was funded by research programme grant funding from Cancer Research UK and the Medical Research Council with additional support from the Stroke Association, British Heart Foundation, Department of Health, Research into Ageing and Academy of Medical Sciences. MCBCS: This study was supported by Public Health Service Grants P50 CA 116201, R01 CA 128931, R01 CA 128931-S01, R01 CA 122340, CCSG P30 CA15083, from the National Cancer Institute, National Institutes of Health, and Department of Health and Human Services. MCCS: Melissa C. Southey is a National Health and Medical Research Council Senior Research Fellow and a Victorian Breast Cancer Research Consortium Group Leader. The study was supported by the Cancer Council of Victoria and by the Victorian Breast Cancer Research Consortium. MEC: National Cancer Institute: R37CA054281, R01CA063464, R01CA085265, R25CA090956, R01CA132839. MMHS: This work was supported by grants from the National Cancer Institute, National Institutes of Health, and Department of Health and Human Services. (R01 CA128931, R01 CA 128931-S01, R01 CA97396, P50 CA116201, and Cancer Center Support Grant P30 CA15083). Breast Cancer Susceptibility Variants and Mammographic Density 6 NBCS: This study has been supported with grants from Norwegian Research Council (#183621/S10 and #175240/S10), The Norwegian Cancer Society (PK80108002, PK60287003), and The Radium Hospital Foundation as well as S-02036 from South Eastern Norway Regional Health Authority. NHS: This study was supported by Public Health Service Grants CA131332, CA087969, CA089393, CA049449, CA98233, CA128931, CA 116201, CA 122340 from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services. OOA study was supported by CA122822 and X01 HG005954 from the NIH; Breast Cancer Research Fund; Elizabeth C. Crosby Research Award, Gladys E. Davis Endowed Fund, and the Office of the Vice President for Research at the University of Michigan. Genotyping services for the OOA study were provided by the Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096. OFBCR: This work was supported by grant UM1 CA164920 from the USA National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. SASBAC: The SASBAC study was supported by Märit and Hans Rausing’s Initiative against Breast Cancer, National Institutes of Health, Susan Komen Foundation and Agency for Science, Technology and Research of Singapore (A*STAR). Breast Cancer Susceptibility Variants and Mammographic Density 7 SIBS: SIBS was supported by program grant C1287/A10118 and project grants from Cancer Research UK (grant numbers C1287/8459). COGS grant: Collaborative Oncological Gene-environment Study (COGS) that enabled the genotyping for this study. Funding for the BCAC component is provided by grants from the EU FP7 programme (COGS) and from Cancer Research UK. Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692), the National Institutes of Health (CA128978) and Post- Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAMEON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund.This is the author accepted manuscript. The final version is available via American Association for Cancer Research at http://cancerres.aacrjournals.org/content/early/2015/04/10/0008-5472.CAN-14-2012.abstract

2q36.3 is associated with prognosis for oestrogen receptor-negative breast cancer patients treated with chemotherapy

Large population-based registry studies have shown that breast cancer prognosis is inherited. Here we analyse single-nucleotide polymorphisms (SNPs) of genes implicated in human immunology and inflammation as candidates for prognostic markers of breast cancer survival involving 1,804 oestrogen receptor (ER)-negative patients treated with chemotherapy (279 events) from 14 European studies in a prior large-scale genotyping experiment, which is part of the Collaborative Oncological Gene-environment Study (COGS) initiative. We carry out replication using Asian COGS samples (n=522, 53 events) and the Prospective Study of Outcomes in Sporadic versus Hereditary breast cancer (POSH) study (n=315, 108 events). Rs4458204-A near CCL20 (2q36.3) is found to be associated with breast cancer-specific death at a genome-wide significant level (n=2,641, 440 events, combined allelic hazard ratio (HR)=1.81 (1.49-2.19); P for trend=1.90 × 10 â ̂'9). Such survival-associated variants can represent ideal targets for tailored therapeutics, and may also enhance our current prognostic prediction capabilities

Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the breast cancer association consortium: a combined case-control study.

INTRODUCTION: Several common breast cancer genetic susceptibility variants have recently been identified. We aimed to determine how these variants combine with a subset of other known risk factors to influence breast cancer risk in white women of European ancestry using case-control studies participating in the Breast Cancer Association Consortium. METHODS: We evaluated two-way interactions between each of age at menarche, ever having had a live birth, number of live births, age at first birth and body mass index (BMI) and each of 12 single nucleotide polymorphisms (SNPs) (10q26-rs2981582 (FGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312 (MAP3K1), 16q12-rs3803662 (TOX3), 2q35-rs13387042, 5p12-rs10941679 (MRPS30), 17q23-rs6504950 (COX11), 3p24-rs4973768 (SLC4A7), CASP8-rs17468277, TGFB1-rs1982073 and ESR1-rs3020314). Interactions were tested for by fitting logistic regression models including per-allele and linear trend main effects for SNPs and risk factors, respectively, and single-parameter interaction terms for linear departure from independent multiplicative effects. RESULTS: These analyses were applied to data for up to 26,349 invasive breast cancer cases and up to 32,208 controls from 21 case-control studies. No statistical evidence of interaction was observed beyond that expected by chance. Analyses were repeated using data from 11 population-based studies, and results were very similar. CONCLUSIONS: The relative risks for breast cancer associated with the common susceptibility variants identified to date do not appear to vary across women with different reproductive histories or body mass index (BMI). The assumption of multiplicative combined effects for these established genetic and other risk factors in risk prediction models appears justified.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are