Ryan L Powles

TL;DR: AnnoPred is introduced, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases and achieves consistently improved prediction accuracy in both extensive simulations and real data.

TL;DR: This work introduces a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics and demonstrates that this method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies.

TL;DR: GenoSkyline is introduced, an unsupervised learning framework to predict tissue-specific functional regions through integrating high-throughput epigenetic annotations that can guide genetic studies at multiple resolutions and provide valuable insights in understanding complex diseases.

TL;DR: The GenoSkyline-Plus annotations demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases.

TL;DR: During acquisition of resistance to HER2 inhibition, metabolic rewiring of breast cancer cells favors reliance on exogenous FA uptake over de novo FA synthesis, defined as an essential survival mechanism in HER2-positive breast cancer.