Assessing the Risk Stratification of Breast Cancer Polygenic Risk Scores in a Brazilian Cohort.

Polygenic risk scores (PRSs) for breast cancer have a clear clinical utility in risk prediction. PRS transferability across populations and ancestry groups is hampered by population-specific factors, ultimately leading to differences in variant effects, such as linkage disequilibrium and differences in variant frequency (allele frequency differences). Thus, locally sourced population-based phenotypic and genomic data sets are essential to assess the validity of PRSs derived from signals detected across populations. This study assesses the transferability of a breast cancer PRS composed of 313 risk variants (313-PRS) in a Brazilian trihybrid admixed ancestries (European, African, and Native American) whole-genome sequenced cohort, the Rare Genomes Project. 313-PRS was computed in the Rare Genomes Project (n = 853) using the UK Biobank (UKBB; n = 264,307) as reference. The Brazilian cohorts have a high European ancestry (EA) component, with allele frequency differences and to a lesser extent linkage disequilibrium patterns similar to those found in EA populations. The 313-PRS distribution was found to be inflated when compared with that of the UKBB, leading to potential overestimation of PRS-based risk if EA is taken as a standard. However, case controls lead to equivalent predictive power when compared with UKBB-EA samples with area under the receiver operating characteristic curve values of 0.66 to 0.62 compared with 0.63 for UKBB.

Time encoding migrates from prefrontal cortex to dorsal striatum during learning of a self-timed response duration task.

Although time is a fundamental dimension of life, we do not know how brain areas cooperate to keep track and process time intervals. Notably, analyses of neural activity during learning are rare, mainly because timing tasks usually require training over many days. We investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter engages as animals become proficient in the task.