Simulating rapid permafrost degradation and erosion processes under a warming climate

Current model approaches used to simulate the degradation of permafrost under a warming climate are highly simplistic since they only consider one-dimensional (top-down) thawing and ignore lateral processes such as soil erosion and mass wasting which are the most abundant forms of thaw in many regions. Thus, current model assessments are most likely far too conservative in their estimates of permafrost thaw impacts (Rowland & Coon, 2015). It therefore remains uncertain how climate warming and permafrost thaw will affect (i) the intensity of erosion and mass wasting processes and (ii) essential ecosystem functions, landscape characteristics, and infrastructure. It also remains unclear (iii) whether any erosion-induced landscape changes further accelerate permafrost thaw. In order to answer these critical questions, land surface models (LSMs) require a new level of realism in order to adequately project permafrost thaw dynamics. Within the PermaRisk project, the permafrost model CryoGrid3 is extended with an erosion scheme that allows to represent lateral mass movement processes within the limited framework of one dimensional LSMs. The new model will be applied and validated at three Arctic sites in Alaska, Canada, and northern Siberia. Furthermore, 21st century climate impact projections for the key sites are scheduled as a basis for thorough risk analyses concerning potential damages to critical ecosystem functions/services and infrastructure. We will present first simulations on rapid permafrost degradation processes with a special focus on thaw slumps at a test site in northern Canada. We expect the results to demonstrate the capabilities and the limitations of the new model

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.NovartisEli Lilly and CompanyAstraZenecaAbbViePfizer UKCelgeneEisaiGenentechMerck Sharp and DohmeRocheCancer Research UKGovernment of CanadaArray BioPharmaGenome CanadaNational Institutes of HealthEuropean CommissionMinistère de l'Économie, de l’Innovation et des Exportations du QuébecSeventh Framework ProgrammeCanadian Institutes of Health Researc

Genome-wide association study of germline variants and breast cancer-specific mortality

BACKGROUND: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry. METHODS: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10