Not Just a Hashtag: Using Black Twitter to Engage in Critical Visual Pedagogy

[First Paragraph] We live in a global society in which we are constantly exposed to new technologies, people, and situations that transform our perceptions and worldviews. As we are exposed to these new experiences, it is increasingly necessary to maintain a critical eye and question what we are seeing. It is not enough for higher education merely to teach material; instructors should also teach the responsibilities and ethics that coincide with it. Encouraging criticality in higher education helps learners to develop a deeper understanding of social justice, inequality, and oppressive systems, and it teaches learners how to combat those issues in their own lives (Chatelier, 2015; Muhammad, 2018). To do so, higher education should seek to adopt a transformative educational lens through which learning is grounded in learners’ lived experiences. This can be achieved through the integration of critical pedagogy, which seeks to develop awareness of power structures and one’s own position within them, creating the opportunity to implement constructive forms of action (Freire, 2006). Anderson and Keehn (2019) argue that the foundational value of critical pedagogy is the identification and confrontation of power structures that do not support all people. And as Bradshaw (2017) postulates, critical pedagogy necessitates a steadfast and constant review of our daily experiences to ensure that they are responsive to diverse learner needs and experiences. By aligning educational practices with students’ life experiences, teachers can teach more meaningful material

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase