Jingning Zhang

The paper develops polygenic risk scores to stratify lung cancer and smoking cessation risk, showing significant odds ratios in diverse populations and planning clinical trials to evaluate interventions.

This paper introduces PROSPER, an ensemble penalized regression method for multi-ancestry polygenic risk prediction, showing improved performance across diverse populations.

MUSSEL improves polygenic risk prediction across populations by integrating GWAS summary statistics and using Bayesian hierarchical modeling with ensemble learning.

The paper introduces CT-SLEB, a method for multi-ancestry polygenic prediction that improves performance in non-European populations using GWAS summary statistics and simulation studies.

The paper identifies cis-pQTLs and develops models for proteome-wide association studies using plasma proteome data from European and African ancestry populations.