Dataset Information

Bayesian copy number detection and association in large-scale studies.

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.

SUBMITTER: Cristiano S

PROVIDER: S-EPMC7487704 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Bayesian copy number detection and association in large-scale studies.

Cristiano Stephen S McKean David D Carey Jacob J Bracci Paige P Brennan Paul P Chou Michael M Du Mengmeng M Gallinger Steven S Goggins Michael G MG Hassan Manal M MM Hung Rayjean J RJ Kurtz Robert C RC Li Donghui D Lu Lingeng L Neale Rachel R Olson Sara S Petersen Gloria G Rabe Kari G KG Fu Jack J Risch Harvey H Rosner Gary L GL Ruczinski Ingo I Klein Alison P AP Scharpf Robert B RB

BMC cancer 20200907 1

<h4>Background</h4>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.<h4>Methods</h4>We developed an approach called CNPBayes to identify latent batch effects in genome-wide association studies involving copy number, to provide probabilistic estimates of in ...[more]

PMID: 32894098

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Project description:BACKGROUND: Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. RESULTS: We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. CONCLUSION: We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.

Dataset Information

Bayesian copy number detection and association in large-scale studies.

Background

Methods

Results

Conclusions

Publications

Bayesian copy number detection and association in large-scale studies.

Similar Datasets

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