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Published in: BMC Cancer 1/2020

Open Access 01-12-2020 | Pancreatic Cancer | Research article

Bayesian copy number detection and association in large-scale studies

Authors: Stephen Cristiano, David McKean, Jacob Carey, Paige Bracci, Paul Brennan, Michael Chou, Mengmeng Du, Steven Gallinger, Michael G. Goggins, Manal M. Hassan, Rayjean J. Hung, Robert C. Kurtz, Donghui Li, Lingeng Lu, Rachel Neale, Sara Olson, Gloria Petersen, Kari G. Rabe, Jack Fu, Harvey Risch, Gary L. Rosner, Ingo Ruczinski, Alison P. Klein, Robert B. Scharpf

Published in: BMC Cancer | Issue 1/2020

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Abstract

Background

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 due to biological and technical sources of heterogeneity that vary across the genome within and between samples.

Methods

We developed an approach called CNPBayes to identify latent batch effects in genome-wide association studies involving copy number, 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.

Results

Applying a hidden Markov model (HMM) to identify CNVs in a large multi-site Pancreatic Cancer Case Control study (PanC4) of 7598 participants, we found CNV inference was highly sensitive to technical noise that varied appreciably among participants. Applying CNPBayes to this dataset, we found that the major sources of technical variation were linked to sample processing by the centralized laboratory and not the individual study sites. Modeling the latent batch effects at each CNV region hierarchically, we developed probabilistic estimates of copy number that were directly incorporated in a Bayesian regression model for pancreatic cancer risk. Candidate associations aided by this approach include deletions of 8q24 near regulatory elements of the tumor oncogene MYC and of Tumor Suppressor Candidate 3 (TUSC3).

Conclusions

Laboratory effects may not account for the major sources of technical variation in genome-wide association studies. This study provides a robust Bayesian inferential framework for identifying latent batch effects, estimating copy number, and evaluating the role of copy number in heritable diseases.
Appendix
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Metadata
Title
Bayesian copy number detection and association in large-scale studies
Authors
Stephen Cristiano
David McKean
Jacob Carey
Paige Bracci
Paul Brennan
Michael Chou
Mengmeng Du
Steven Gallinger
Michael G. Goggins
Manal M. Hassan
Rayjean J. Hung
Robert C. Kurtz
Donghui Li
Lingeng Lu
Rachel Neale
Sara Olson
Gloria Petersen
Kari G. Rabe
Jack Fu
Harvey Risch
Gary L. Rosner
Ingo Ruczinski
Alison P. Klein
Robert B. Scharpf
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2020
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-020-07304-3

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