Skip to main content
construction release_alert
Scholars@Duke will be undergoing maintenance April 11-15. Some features may be unavailable during this time.
cancel

A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors.

Publication ,  Journal Article
Murgas, KA; Ma, Y; Shahidi, LK; Mukherjee, S; Allen, AS; Shibata, D; Ryser, MD
Published in: Bioinformatics
December 22, 2021

MOTIVATION: Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels. RESULTS: We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model's ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Bioinformatics

DOI

EISSN

1367-4811

Publication Date

December 22, 2021

Volume

38

Issue

1

Start / End Page

22 / 29

Location

England

Related Subject Headings

  • Humans
  • Genomics
  • Genome
  • Gene Expression Regulation, Neoplastic
  • DNA Methylation
  • Colorectal Neoplasms
  • Bioinformatics
  • Bayes Theorem
  • 49 Mathematical sciences
  • 46 Information and computing sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Murgas, K. A., Ma, Y., Shahidi, L. K., Mukherjee, S., Allen, A. S., Shibata, D., & Ryser, M. D. (2021). A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors. Bioinformatics, 38(1), 22–29. https://doi.org/10.1093/bioinformatics/btab637
Murgas, Kevin A., Yanlin Ma, Lidea K. Shahidi, Sayan Mukherjee, Andrew S. Allen, Darryl Shibata, and Marc D. Ryser. “A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors.Bioinformatics 38, no. 1 (December 22, 2021): 22–29. https://doi.org/10.1093/bioinformatics/btab637.
Murgas KA, Ma Y, Shahidi LK, Mukherjee S, Allen AS, Shibata D, et al. A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors. Bioinformatics. 2021 Dec 22;38(1):22–9.
Murgas, Kevin A., et al. “A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors.Bioinformatics, vol. 38, no. 1, Dec. 2021, pp. 22–29. Pubmed, doi:10.1093/bioinformatics/btab637.
Murgas KA, Ma Y, Shahidi LK, Mukherjee S, Allen AS, Shibata D, Ryser MD. A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors. Bioinformatics. 2021 Dec 22;38(1):22–29.

Published In

Bioinformatics

DOI

EISSN

1367-4811

Publication Date

December 22, 2021

Volume

38

Issue

1

Start / End Page

22 / 29

Location

England

Related Subject Headings

  • Humans
  • Genomics
  • Genome
  • Gene Expression Regulation, Neoplastic
  • DNA Methylation
  • Colorectal Neoplasms
  • Bioinformatics
  • Bayes Theorem
  • 49 Mathematical sciences
  • 46 Information and computing sciences