Introduction Physical activity may affect health via DNA methylation. The epigenetic influences of sedentary behaviors such as television viewing are unknown. We performed a genomewide study of DNA methylation in peripheral blood in relation to physical activity and television viewing time. Methods DNA methylation was measured using the Illumina Infinium HumanMethylation450K BeadChip array in blood samples collected at baseline (N = 5513) and follow-up (N = 1249) from participants in the Melbourne Collaborative Cohort Study. At baseline, times per week of leisure-time physical activity were self-reported. At follow-up, the International Physical Activity Questionnaire was used to assess MET-hours per week of total and leisure-time physical activity and hours per day of television viewing time. Linear mixed models were used to assess associations between physical activity and television viewing measures and DNA methylation at individual CpG sites, adjusted for potential confounders and batch effects. Results At follow-up, total physical activity was associated with DNA methylation at cg10266336 (P = 6.0 × 10−9), annotated to the SAA2 gene. Weaker evidence of associations (P < 1.0 × 10−5) were observed for an additional 14 CpG sites with total physical activity, for 7 CpG sites with leisure-time physical activity, and for 9 CpG sites with television viewing time. Changes in leisure-time physical activity between baseline and follow-up were associated with methylation changes (P < 0.05) at four of the seven CpG sites with weaker evidence of cross-sectional associations with leisure-time physical activity. Conclusion Physical activity and television viewing may be associated with blood DNA methylation, a potential pathway to chronic disease development. Further research using accelerometer data and larger sample sizes is warranted.

and white blood cell proportions; and similar study design. Of note, previous analyses of blood samples fromMCCS participants showed substantial replication of previously identified signals and discovered highly replicable novel associations for other health risk factors such as BMI (7), alcohol consumption (8), and tobacco smoking (9). Taken together, our data and those presented in Mandaviya et al. would mean that there is at best weak evidence for an association between FFQ-derived intakes of folate and vitamin B-12 and DNA methylation in peripheral blood. This suggests that blood DNA methylation might not mediate, nor be a useful marker of, the association between intake of these nutrients and disease risk and shows the complexity of the one-carbon metabolism pathway in terms of, e.g., the number of nutrients involved, their interactions, and existing interindividual differences in nutrient absorption and metabolism.

Background: DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart. Methods: Using the Illumina Infinium HumanMethylation450 BeadChip, DNA methylation measures were determined using baseline peripheral blood samples from 5,606 adult Melbourne Collaborative Cohort Study (MCCS) participants. For a subset of 1,088 of them, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models adjusted for batch effects and potential confounders. Independent data from the LOLIPOP (N=4,042) and KORA (N=1,662) cohorts were used to replicate associations discovered in the MCCS. Results: Cross-sectional analyses identified 1,414 CpGs associated with alcohol intake at P<10-7, 1,243 of which had not been reported previously. Of these 1,243 novel associations, 1,078 were replicated (P<0.05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated (P<0.05) 403 of 518 associations that had been reported previously. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1,414 CpGs, 530 were differentially methylated (P<0.05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1,414 cross-sectional associations. Conclusion: Our study indicates that, for middle-aged and older adults, alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with changes in alcohol consumption.

Abstract Background We applied machine learning to find a novel breast cancer predictor based on information in a mammogram. Methods Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case–control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index. Results Combining studies, almost all textural features were associated with case–control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all P < 10−6). For the Cirrus measure derived from combining studies, the OR was 1.90 (95% confidence interval [CI] = 1.73 to 2.09), equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24; P = 4 × 10−5). Conclusions A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.

Abstract In this note, we develop a novel algorithm for generating random numbers from a distribution with a probability density function proportional to and Our algorithm is highly efficient and is based on rejection sampling where the envelope distribution is an appropriately chosen beta distribution. An example application illustrating how the new algorithm can be used to generate random correlation matrices is discussed.

Background Folate and other one-carbon metabolism nutrients are essential to enable DNA methylation to occur, but the extent to which their dietary intake influences methylation in adulthood is unclear. Objective We assessed associations between dietary intake of these nutrients and DNA methylation in peripheral blood, overall and at specific genomic locations. Design We conducted a cross-sectional study using baseline data and samples from 5186 adult participants in the Melbourne Collaborative Cohort Study (MCCS). Nutrient intake was estimated from a food-frequency questionnaire. DNA methylation was measured by using the Illumina Infinium HumanMethylation450 BeadChip array (HM450K). We assessed associations of intakes of folate, riboflavin, vitamins B-6 and B-12, methionine, choline, and betaine with methylation at individual cytosine-guanine dinucleotides (CpGs), and with median (genome-wide) methylation across all CpGs, CpGs in gene bodies, and CpGs in gene promoters. We also assessed associations with methylation at long interspersed nuclear element 1 (LINE-1), satellite 2 (Sat2), and Arthrobacter luteus restriction endonuclease (Alu) repetitive elements for a subset of participants. We used linear mixed regression, adjusting for age, sex, country of birth, smoking, energy intake from food, alcohol intake, Mediterranean diet score, and batch effects to assess log-linear associations with dietary intake of each nutrient. In secondary analyses, we assessed associations with low or high intakes defined by extreme quintiles. Results No evidence of log-linear association was observed at P < 10-7 between the intake of one-carbon metabolism nutrients and methylation at individual CpGs. Low intake of riboflavin was associated with higher methylation at CpG cg21230392 in the first exon of PROM1 (P = 5.0 × 10-8). No consistent evidence of association was observed with genome-wide or repetitive element measures of methylation. Conclusion Our findings suggest that dietary intake of one-carbon metabolism nutrients in adulthood, as measured by a food-frequency questionnaire, has little association with blood DNA methylation. An association with low intake of riboflavin requires replication in independent cohorts. This study was registered at http://www.clinicaltrials.gov as NCT03227003.

With advances in genetic epidemiology, increasingly large amounts of pedigree-related information are being collected by family studies, including twin studies. To date, biomedical data management systems that cater for family data have usually done so as part of their standard (non-family-centric) data model. Consequently, data managers with computing expertise are needed to extract family datasets and perform family-centric operations. We present a robust approach to handling large family datasets. Our approach is implemented as a new module which extends the capabilities of The Ark, an open-source web-based biomedical data management tool. Using an algorithm designed by the authors, the pedigree module dynamically infers family relationships for any selected subject (not necessarily the proband). A web interface allows researchers to create, update, delete and navigate parental and twin relationships between subjects, and bulk import/export pedigrees. Consanguineous relationships can be captured, and configurable pedigree visualizations generated. A web services interface provides interoperability.

DNA methylation can mimic the effects of germline mutations in cancer predisposition genes. Recently, we identified twenty‐four heritable methylation marks associated with breast cancer risk. As breast and prostate cancer share genetic risk factors, including rare, high‐risk mutations (eg, in BRCA2), we hypothesized that some of these heritable methylation marks might also be associated with the risk of prostate cancer.

Background Clustering of breast and colorectal cancer has been observed within some families and cannot be explained by chance or known high-risk mutations in major susceptibility genes. Potential shared genetic susceptibility between breast and colorectal cancer, not explained by high-penetrance genes, has been postulated. We hypothesized that yet undiscovered genetic variants predispose to a breast-colorectal cancer phenotype. Methods To identify variants associated with a breast-colorectal cancer phenotype, we analyzed genome-wide association study (GWAS) data from cases and controls that met the following criteria: cases (n = 985) were women with breast cancer who had one or more first- or second-degree relatives with colorectal cancer, men/women with colorectal cancer who had one or more first- or second-degree relatives with breast cancer, and women diagnosed with both breast and colorectal cancer. Controls (n = 1769), were unrelated, breast and colorectal cancer-free, and age- and sex- frequency-matched to cases. After imputation, 6,220,060 variants were analyzed using the discovery set and variants associated with the breast-colorectal cancer phenotype at P<5.0E-04 (n = 549, at 60 loci) were analyzed for replication (n = 293 cases and 2,103 controls). Results Multiple correlated SNPs in intron 1 of the ROBO1 gene were suggestively associated with the breast-colorectal cancer phenotype in the discovery and replication data (most significant; rs7430339, Pdiscovery = 1.2E-04; rs7429100, Preplication = 2.8E-03). In meta-analysis of the discovery and replication data, the most significant association remained at rs7429100 (P = 1.84E-06). Conclusion The results of this exploratory analysis did not find clear evidence for a susceptibility locus with a pleiotropic effect on hereditary breast and colorectal cancer risk, although the suggestive association of genetic variation in the region of ROBO1, a potential tumor suppressor gene, merits further investigation.