Abstract

Germline copy number variants (CNVs) increase risk for many diseases, yet detection of CNVs and quantifying their contribution to disease risk in large-scale studies is challenging. We developed an approach called CNPBayes to identify latent batch effects, to provide probabilistic estimates of integer copy number across the estimated batches, and to fully integrate the copy number uncertainty in the association model for disease. We demonstrate this approach in a Pancreatic Cancer Case Control study of 7,598 participants where the major sources of technical variation were not captured by study site and varied across the genome. Candidate associations aided by this approach include deletions of 8q24 near regulatory elements of the tumor oncogene MYC and of Tumor Supressor Candidate 3 (TUSC3). This study provides a robust Bayesian inferential framework for estimating copy number and evaluating the role of copy number in heritable diseases.