Siying Chen

This paper presents the largest catalog of human protein-coding variation from 985,830 individuals, identifying rare variants and their functional implications for precision medicine.

The paper uses single-cell RNA-seq data to develop a machine learning method called A-risk to prioritize autism risk genes, identifying 71 candidates including new ones like CLCN4, PRKAR1B, and NR2F1.

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.