Gloria M. Petersen

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.