Donghui Li

The paper uses a multi-layered approach combining GWAS, spatial autocorrelation, and Hi-C to identify novel genetic variants associated with pancreatic cancer susceptibility.

This paper develops a Bayesian method to detect copy number variants and assess their association with disease in large-scale studies, finding deletions near MYC and TUSC3 linked to pancreatic cancer.

This study assesses polygenicity and risk prediction using common variants across 14 cancers, finding varying numbers of susceptibility variants and risk score effectiveness.

This paper improves polygenic risk scores by applying winners curse correction and variable thresholds to GWAS summary data, enhancing prediction accuracy for some diseases.