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Previous reports have suggested genetic and prenatal hormonal contributions to gender diversity, albeit using small datasets. To further examine this issue, we recruited 27 twin pairs from two Australian specialist gender clinics, and obtained details of zygosity and self-reported gender identity. To increase the power of our analysis, we pooled our observations with previously published twin data that used comparable methods to ours. The pooled sample of 463 twin pairs comprised 47/222 (21.2%) MZ and 21/241 (8.7%) DZ concordant pairs. Based on a 1% population prevalence estimate, the estimated relative risk ratios for transgender concordance were 21.2 for MZ pairs (95%CI 16.4–27.3), and 8.7 for DZ pairs (95%CI 5.8–13.1). These findings suggest a substantial genetic contribution to gender diversity. In contrast, the combined results for concordance among same and opposite sex DZ pairs were 12/131 (9.2%) and 9/110 (8.2%) respectively, and did not support a role for prenatal sex hormones in the occurrence of gender diversity.

Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers cannot perform at the level of multi-reader systems used by screening programs in countries such as Australia, Sweden, and the UK. Therefore, implementation demands human-AI collaboration. Here, we use a large, high-quality retrospective mammography dataset from Victoria, Australia to conduct detailed simulations of five potential AI-integrated screening pathways, and examine human-AI interaction effects to explore automation bias. Operating an AI reader as a second reader or as a high confidence filter improves current screening outcomes by 1.9–2.5% in sensitivity and up to 0.6% in specificity, achieving 4.6–10.9% reduction in assessments and 48–80.7% reduction in human reads. Automation bias degrades performance in multi-reader settings but improves it for single-readers. This study provides insight into feasible approaches for AI-integrated screening pathways and prospective studies necessary prior to clinical adoption. Successful human-AI collaboration could greatly contribute to breast cancer mammographic screening. Here, the authors use a large-scale retrospective mammography dataset to simulate and compare five plausible AI-integrated screening pathways, finding optimal ways in which human-AI collaboration could be implemented in real-world settings.

Background While the UK Biobank has been widely used for cancer research, its representativeness of the population in terms of cancer incidence has not been thoroughly investigated. Methods We conducted a prospective cohort study of 466,163 UK Biobank participants who were cancer-free at recruitment. Standardised incidence ratios (SIRs) were calculated for all cancers combined and for 25 cancers, by comparing incidences for the participants with the UK national incidences. Variations in SIR by age, sex and deprivation measures were investigated. Results Over a median follow-up period of 12 years, 47,535 participants had a cancer diagnosis. The SIR for all cancers combined was 0.90 (95% CI: 0.89, 0.91). The SIR increased with age and deprivation ( P  = 10 −9 ). The SIRs of 17 cancers differed from 1 (Bonferroni-adjusted P  < 0.05): for prostate cancer and melanoma the SIRs were 1.2 and for the other 15 cancers the SIRs ranged from 0.43 to 0.93. The SIRs of 13 cancers differed by deprivation: the greater the deprivation, the lower the SIRs for prostate cancer and melanoma, and the higher the SIRs for the other 11 cancers. Conclusions The overall cancer incidence was 10% lower for the UK Biobank participants compared with the population, with most cancers having a lower incidence that increased with deprivation. Irrespective of their causes, the inconsistencies could bias UK Biobank research results related to absolute cancer risks, such as the development and/or validation of cancer risk models and penetrance estimates for cancer susceptibility genes.

Polygenic risk scores (PRSs) are a promising approach to accurately predict an individual’s risk of developing disease. The area under the receiver operating characteristic curve (AUC) of PRSs in their population are often only reported for models that are adjusted for age and sex, which are known risk factors for the disease of interest and confound the association between the PRS and the disease. This makes comparison of PRS between studies difficult because the genetic effects cannot be disentangled from effects of age and sex (which have a high AUC without the PRS). In this study, we used data from the UK Biobank and applied the stacked clumping and thresholding method and a variation called maximum clumping and thresholding method to develop PRSs to predict coronary artery disease, hypertension, atrial fibrillation, stroke and type 2 diabetes. We created case-control training datasets in which age and sex were controlled by design. We also excluded prevalent cases to prevent biased estimation of disease risks. The maximum clumping and thresholding PRSs required many fewer single-nucleotide polymorphisms to achieve almost the same discriminatory ability as the stacked clumping and thresholding PRSs. Using the testing datasets, the AUCs for the maximum clumping and thresholding PRSs were 0.599 (95% confidence interval [CI]: 0.585, 0.613) for atrial fibrillation, 0.572 (95% CI: 0.560, 0.584) for coronary artery disease, 0.585 (95% CI: 0.564, 0.605) for type 2 diabetes, 0.559 (95% CI: 0.550, 0.569) for hypertension and 0.514 (95% CI: 0.494, 0.535) for stroke. By developing a PRS using a dataset in which age and sex are controlled by design, we have obtained true estimates of the discriminatory ability of the PRSs alone rather than estimates that include the effects of age and sex.

Five-year absolute breast cancer risk prediction models are required to comply with national guidelines regarding risk reduction regimens. Models including the Gail model are under-utilized in the general population for various reasons, including difficulty in accurately completing some clinical fields. The purpose of this study was to determine if a streamlined risk model could be designed without substantial loss in performance. Only the clinical risk factors that were easily answered by women will be retained and combined with an objective validated polygenic risk score (PRS) to ultimately improve overall compliance with professional recommendations. We first undertook a review of a series of 2,339 Caucasian, African American and Hispanic women from the USA who underwent clinical testing. We first used deidentified test request forms to identify the clinical risk factors that were best answered by women in a clinical setting and then compared the 5-year risks for the full model and the streamlined model in this clinical series. We used OPERA analysis on previously published case-control data from 11,924 Gail model samples to determine clinical risk factors to include in a streamlined model: first degree family history and age that could then be combined with the PRS. Next, to ensure that the addition of PRS to the streamlined model was indeed beneficial, we compared risk stratification using the Streamlined model with and without PRS for the existing case-control datasets comprising 1,313 cases and 10,611 controls of African-American (n = 7421), Caucasian (n = 1155) and Hispanic (n = 3348) women, using the area under the curve to determine model performance. The improvement in risk discrimination from adding the PRS risk score to the Streamlined model was 52%, 46% and 62% for African-American, Caucasian and Hispanic women, respectively, based on changes in log OPERA. There was no statistically significant difference in mean risk scores between the Gail model plus risk PRS compared to the Streamlined model plus PRS . This study demonstrates that validated PRS can be used to streamline a clinical test for primary care practice without diminishing test performance. Importantly, by eliminating risk factors that women find hard to recall or that require obtaining medical records, this model may facilitate increased clinical adoption of 5-year risk breast cancer risk prediction test in keeping with national standards and guidelines for breast cancer risk reduction.

Colorectal cancer risk stratification is crucial to improve screening and risk-reducing recommendations, and consequently do better than a one-size-fits-all screening regimen. Current screening guidelines in the UK, USA and Australia focus solely on family history and age for risk prediction, even though the vast majority of the population do not have any family history. We investigated adding a polygenic risk score based on 45 single-nucleotide polymorphisms to a family history model (combined model) to quantify how it improves the stratification and discriminatory performance of 10-year risk and full lifetime risk using a prospective population-based cohort within the UK Biobank. For both 10-year and full lifetime risk, the combined model had a wider risk distribution compared with family history alone, resulting in improved risk stratification of nearly 2-fold between the top and bottom risk quintiles of the full lifetime risk model. Importantly, the combined model can identify people (n = 72,019) who do not have family history of colorectal cancer but have a predicted risk that is equivalent to having at least one affected first-degree relative (n = 44,950). We also confirmed previous findings by showing that the combined full lifetime risk model significantly improves discriminatory accuracy compared with a simple family history model 0.673 (95% CI 0.664–0.682) versus 0.666 (95% CI 0.657–0.675), p = 0.0065. Therefore, a combined polygenic risk score and first-degree family history model could be used to improve risk stratified population screening programs.

Background The association between body mass index (BMI) and risk of breast cancer depends on time of life, but it is unknown whether this association depends on a woman’s familial risk. Methods We conducted a prospective study of a cohort enriched for familial risk consisting of 16,035 women from 6701 families in the Breast Cancer Family Registry and the Kathleen Cunningham Foundation Consortium for Research into Familial Breast Cancer followed for up to 20 years (mean 10.5 years). There were 896 incident breast cancers (mean age at diagnosis 55.7 years). We used Cox regression to model BMI risk associations as a function of menopausal status, age, and underlying familial risk based on pedigree data using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), all measured at baseline. Results The strength and direction of the BMI risk association depended on baseline menopausal status ( P  < 0.001); after adjusting for menopausal status, the association did not depend on age at baseline ( P  = 0.6). In terms of absolute risk, the negative association with BMI for premenopausal women has a much smaller influence than the positive association with BMI for postmenopausal women. Women at higher familial risk have a much larger difference in absolute risk depending on their BMI than women at lower familial risk. Conclusions The greater a woman’s familial risk, the greater the influence of BMI on her absolute postmenopausal breast cancer risk. Given that age-adjusted BMI is correlated across adulthood, maintaining a healthy weight throughout adult life is particularly important for women with a family history of breast cancer.

Mendelian-like inheritance of germline DNA methylation in cancer susceptibility genes has been previously reported. We aimed to scan the genome for heritable methylation marks associated with breast cancer susceptibility by studying 25 Australian multiple-case breast cancer families. Here we report genome-wide DNA methylation measured in 210 peripheral blood DNA samples provided by family members using the Infinium HumanMethylation450. We develop and apply a new statistical method to identify heritable methylation marks based on complex segregation analysis. We estimate carrier probabilities for the 1000 most heritable methylation marks based on family structure, and we use Cox proportional hazards survival analysis to identify 24 methylation marks with corresponding carrier probabilities significantly associated with breast cancer. We replicate an association with breast cancer risk for four of the 24 marks using an independent nested case–control study. Here, we report a novel approach for identifying heritable DNA methylation marks associated with breast cancer risk. DNA methylation is associated with breast cancer risk. Here the authors measure DNA methylation in the blood of individuals from 25 Australian families with multiple cases of breast cancer but not known mutations associated with breast cancer risk to identify possible heritable methylation markers.

Background Monozygotic twins have long been studied to estimate heritability and explore epigenetic influences on phenotypic variation. The phenotypic and epigenetic similarities of monozygotic twins have been assumed to be largely due to their genetic identity. Results Here, by analyzing data from a genome-scale study of DNA methylation in monozygotic and dizygotic twins, we identified genomic regions at which the epigenetic similarity of monozygotic twins is substantially greater than can be explained by their genetic identity. This “epigenetic supersimilarity” apparently results from locus-specific establishment of epigenotype prior to embryo cleavage during twinning. Epigenetically supersimilar loci exhibit systemic interindividual epigenetic variation and plasticity to periconceptional environment and are enriched in sub-telomeric regions. In case-control studies nested in a prospective cohort, blood DNA methylation at these loci years before diagnosis is associated with risk of developing several types of cancer. Conclusions These results establish a link between early embryonic epigenetic development and adult disease. More broadly, epigenetic supersimilarity is a previously unrecognized phenomenon that may contribute to the phenotypic similarity of monozygotic twins.