We introduce GeneSIS, a statistical learning framework for modeling variant-level gene-by-sex interaction effects, and show it improves polygenic score prediction across 32 traits …
Led by Bill Li, this work introduces cross-trait polygenic prediction as a strategy for using phenome-wide PGS libraries in deeply phenotyped cohorts to reveal multiple polygenic …
Led by Ishir Rao, this work introduces sc4D, a spatio-temporal single-cell transcriptomics framework for modeling disease progression with embedded optimal transport.
In this study, led by Xiaohe (Lucy) Tian, we showed that ancestry-aware integration of tissue-specific genomic annotations enhances the transferability of polygenic scores (PGS).
