Carcinogen metabolism, cigarette smoking, and breast cancer risk: a Bayes model averaging approach

BACKGROUND: Standard logistic regression with or without stepwise selection has the disadvantage of not incorporating model uncertainty and the dependency of estimates on the underlying model into the final inference. We explore the use of a Bayes Model Averaging approach as an alternative to analyze the influence of genetic variants, environmental effects and their interactions on disease. METHODS: Logistic regression with and without stepwise selection and Bayes Model Averaging were applied to a population-based case-control study exploring the association of genetic variants in tobacco smoke-related carcinogen pathways with breast cancer. RESULTS: Both regression and Bayes Model Averaging highlighted a significant effect of NAT1*10 on breast cancer, while regression analysis also suggested a significant effect for packyears and for the interaction of packyears and NAT2. CONCLUSIONS: Bayes Model Averaging allows incorporation of model uncertainty, helps reduce dimensionality and avoids the problem of multiple comparisons. It can be used to incorporate biological information, such as pathway data, into the analysis. As with all Bayesian analysis methods, careful consideration must be given to prior specification

SULT1A1 genotype, active and passive smoking, and breast cancer risk by age 50 years in a German case–control study

INTRODUCTION: Sulfotransferase 1A1 (encoded by SULT1A1) is involved in the metabolism of procarcinogens such as heterocyclic amines and polycyclic aromatic hydrocarbons, both of which are present in tobacco smoke. We recently reported a differential effect of N-acetyltransferase (NAT) 2 genotype on the association between active and passive smoking and breast cancer. Additional investigation of a common SULT1A1 genetic polymorphism associated with reduced enzyme activity and stability might therefore provide deeper insight into the modification of breast cancer susceptibility. METHODS: We conducted a population-based case–control study in Germany. A total of 419 patients who had developed breast cancer by age 50 years and 884 age-matched control individuals, for whom risk factor information and detailed smoking history were available, were included in the analysis. Genotyping was performed using a fluorescence-based melting curve analysis method. Multivariate logistic regression analysis was used to estimate breast cancer risk associated with the SULT1A1 Arg(213)His polymorphism alone and in combination with NAT2 genotype in relation to smoking. RESULTS: The overall risk for breast cancer in women who were carriers of at least one SULT1A1*2 allele was not significantly different from that for women with the SULT1A1*1/*1 genotype (adjusted odds ratio 0.83, 95% confidence interval 0.66–1.06). Risk for breast cancer with respect to several smoking variables did not differ substantially between carriers of the *2 allele and noncarriers. However, among NAT2 fast acetylators, the odds ratio associated with passive smoking only (3.23, 95% confidence interval 1.05–9.92) was elevated in homozygous carriers of the SULT1A1*1 allele but not in carriers of the SULT1A1*2 allele (odds ratio 1.28, 95% confidence interval 0.50–3.31). CONCLUSION: We found no evidence that the SULT1A1 genotype in itself modifies breast cancer risk associated with smoking in women up to age 50 years. In combination with NAT2 fast acetylator status, however, the SULT1A1*1/*1 genotype might increase breast cancer risk in women exposed to tobacco smoke

Estrogen metabolite ratio: Is the 2-hydroxyestrone to 16α-hydroxyestrone ratio predictive for breast cancer?

Experimental studies have shown that two main estrogen metabolites hydroxylated by CYP1A1 and CYP1B1 in the breast differentially affect breast cell proliferation and carcinogenesis. Although 16α-hydroxyestrone (16αOHE1) exerts estrogenic activity through covalent estrogen receptor (ER) binding, 2-hydroxyestrone (2OHE1) presumably has antiestrogenic capabilities. The ratio of 2OHE1 to 16αOHE1 represents the relative dominance of one pathway over the other and is believed to be modifiable by diet. It was hypothesized that women with or at high risk of breast cancer have a lower estrogen metabolite ratio (EMR) compared with women without breast cancer. We conducted a systematic review on the EMR as a predictor for breast cancer. A total of nine studies (six prospective and three retrospective) matched our inclusion criteria, comprising 682 premenopausal cases (1027 controls) and 1189 postmenopausal cases (1888 controls). For the highest compared with the lowest quantile of urinary EMR, nonsignificant associations suggested at best a weak protective effect in premenopausal but not in postmenopausal breast cancer (range of odds ratios: 0.50–0.75 for premenopausal and 0.71–1.31 for postmenopausal). Circulating serum/plasma EMR was not associated with breast cancer risk. Associations were inconclusive for receptor subtypes of breast cancer. Uncontrolled factors known to be involved in breast carcinogenesis, such as 4-hydroxyestrone (4OHE1) concentration, may have confounded results for EMR. Results of the prospective studies do not support the hypothesis that EMR can be used as a predictive marker for breast cancer risk. Future research should concentrate on profiles of estrogen metabolites, including 4OHE1, to gain a more complete picture of the relative importance of single metabolites for breast cancer

Haplotype-sharing analysis using Mantel statistics for combined genetic effects

We applied a new approach based on Mantel statistics to analyze the Genetic Analysis Workshop 14 simulated data with prior knowledge of the answers. The method was developed in order to improve the power of a haplotype sharing analysis for gene mapping in complex disease. The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes from case-control studies. The genetic similarity is measured as the shared length between haplotype pairs around a genetic marker. The phenotypic similarity is measured as the mean corrected cross-product based on the respective phenotypes. Cases with phenotype P1 and unrelated controls were drawn from the population of Danacaa. Power to detect main effects was compared to the X(2)-test for association based on 3-marker haplotypes and a global permutation test for haplotype association to test for main effects. Power to detect gene × gene interaction was compared to unconditional logistic regression. The results suggest that the Mantel statistics might be more powerful than alternative tests

Impact of genotyping errors on the type I error rate and the power of haplotype-based association methods

<p>Abstract</p> <p>Background</p> <p>We investigated the influence of genotyping errors on the type I error rate and empirical power of two haplotype based association methods applied to candidate regions. We compared the performance of the Mantel Statistic Using Haplotype Sharing and the haplotype frequency based score test with that of the Armitage trend test.</p> <p>Our study is based on 1000 replication of simulated case-control data settings with 500 cases and 500 controls, respectively. One of the examined markers was set to be the disease locus with a simulated odds ratio of 3. Differential and non-differential genotyping errors were introduced following a misclassification model with varying mean error rates per locus in the range of 0.2% to 15.6%.</p> <p>Results</p> <p>We found that the type I error rate of all three test statistics hold the nominal significance level in the presence of nondifferential genotyping errors and low error rates. For high and differential error rates, the type I error rate of all three test statistics was inflated, even when genetic markers not in Hardy-Weinberg Equilibrium were removed. The empirical power of all three association test statistics remained high at around 89% to 94% when genotyping error rates were low, but decreased to 48% to 80% for high and nondifferential genotyping error rates.</p> <p>Conclusion</p> <p>Currently realistic genotyping error rates for candidate gene analysis (mean error rate per locus of 0.2%) pose no significant problem for the type I error rate as well as the power of all three investigated test statistics.</p

Representation of genetic association via attributable familial relative risks in order to identify polymorphisms functionally relevant to rheumatoid arthritis

The results from association studies are usually summarized by a measure of evidence of association (frequentist or Bayesian probability values) that does not directly reflect the impact of the detected signals on familial aggregation. This article investigates the possible advantage of a two-dimensional representation of genetic association in order to identify polymorphisms relevant to disease: a measure of evidence of association (the Bayes factor, BF) combined with the estimated contribution to familiality (the attributable sibling relative risk, λs). Simulation and data from the North American Rheumatoid Consortium (NARAC) were used to assess the possible benefit under several scenarios. Simulation indicated that the allele frequencies to reach the maximum BF and the maximum attributable λs diverged as the size of the genetic effect increased. The representation of BF versus attributable λs for selected regions of NARAC data revealed that SNPs involved in replicated associations clearly departed from the bulk of SNPs in these regions. In the 12 investigated regions, and particularly in the low-recombination major histocompatibility region, the ranking of SNPs according to BF differed from the ranking of SNPs according to attributable λs. The present results should be generalized using more extensive simulations and additional real data, but they suggest that a characterization of genetic association by both BF and attributable λs may result in an improved ranking of variants for further biological analyses

Accelerometry and physical activity questionnaires - a systematic review

Abstract Background The aim of this study is to review accelerometer wear methods and correlations between accelerometry and physical activity questionnaire data, depending on participant characteristics. Methods We included 57 articles about physical activity measurement by accelerometry and questionnaires. Criteria were to have at least 100 participants of at least 18 years of age with manuscripts available in English. Accelerometer wear methods were compared. Spearman and Pearson correlation coefficients between questionnaires and accelerometers and differences between genders, age categories, and body mass index (BMI) categories were assessed. Results In most investigations, requested wear time was seven days during waking hours and devices were mostly attached on hips with waist belts. A minimum of four valid days with wear time of at least ten hours per day was required in most studies. Correlations (r = Pearson, ρ = Spearman) of total questionnaire scores against accelerometer measures across individual studies ranged from r = 0.08 to ρ = 0.58 (P < 0.001) for men and from r = −0.02 to r = 0.49 (P < 0.01) for women. Correlations for total physical activity among participants with ages ≤65 ranged from r = 0.04 to ρ = 0.47 (P < 0.001) and from r = 0.16 (P = 0.02) to r = 0.53 (P < 0.01) among the elderly (≥65 years). Few studies investigated stratification by BMI, with varying cut points and inconsistent results. Conclusion Accelerometers appear to provide slightly more consistent results in relation to self-reported physical activity among men. Nevertheless, due to overall limited consistency, different aspects measured by each method, and differences in the dimensions studied, it is advised that studies use both questionnaires and accelerometers to gain the most complete physical activity information

The effect of family history on screening procedures and prognosis in breast cancer patients - Results of a large population-based case-control study

Background: The potential benefit of additional breast cancer screening examinations in moderate risk patients (patients with a history of breast cancer in one or two family members) remains unclear.Methods: A large population-based case-control study on breast cancer in postmenopausal women in Germany recruited 2002-2005 (3813 cases and 7341 age-matched controls) was used to assess the association of family history with breast cancer risk. Analysis of family history, participation in screening procedures, and tumor size regarding prognosis in patients was based on follow-up data until 2015.Results: A first degree family history of breast cancer was associated with higher breast cancer risk (OR 1.39, p &lt; 0.001). Patients with a first degree family history of breast cancer were more likely to have had &gt;10 mammograms (MG) (42.7% vs. 24.9%, p &lt; 0.001) and showed a higher rate of imaging-detected tumors (MG or ultrasound) (45.8% vs. 31.9%, p &lt; 0.001). A smaller tumor size at initial diagnosis (below 2 cm) was more likely in patients with a positive family history (OR 1.45, p &lt; 0.001) and a higher number of MG (&gt;= 10 MG: OR 2.29). After accounting for tumor characteristics, mammogram regularity (HR 0.72, p &lt; 0.001) and imaging-assisted tumor detection (HR 0.66, p &lt; 0.001) were associated with better overall survival but not with a positive family history.Discussion: Patients with a positive family history had a higher rate of imaging detected tumors with smaller size at initial diagnosis compared to patients without affected family members. Screening was associated with improved survival after a breast cancer diagnosis, irrespective of a positive family history. (C) 2020 The Authors. Published by Elsevier Ltd.</p