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A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation

Author

Listed:
  • Kaiqiong Zhao
  • Karim Oualkacha
  • Lajmi Lakhal‐Chaieb
  • Aurélie Labbe
  • Kathleen Klein
  • Antonio Ciampi
  • Marie Hudson
  • Inés Colmegna
  • Tomi Pastinen
  • Tieyuan Zhang
  • Denise Daley
  • Celia M.T. Greenwood

Abstract

Identifying disease‐associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high‐throughput methylation profiles at single‐base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long‐range correlations, data errors, and confounding from cell type mixtures. We propose a regression‐based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

Suggested Citation

  • Kaiqiong Zhao & Karim Oualkacha & Lajmi Lakhal‐Chaieb & Aurélie Labbe & Kathleen Klein & Antonio Ciampi & Marie Hudson & Inés Colmegna & Tomi Pastinen & Tieyuan Zhang & Denise Daley & Celia M.T. Green, 2021. "A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation," Biometrics, The International Biometric Society, vol. 77(2), pages 424-438, June.
    • Handle: RePEc:bla:biomet:v:77:y:2021:i:2:p:424-438
    DOI: 10.1111/biom.13307
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    References listed on IDEAS

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    1. Jelle J. Goeman & Sara A. Van De Geer & Hans C. Van Houwelingen, 2006. "Testing against a high dimensional alternative," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 477-493, June.
    2. Ryan Lister & Mattia Pelizzola & Robert H. Dowen & R. David Hawkins & Gary Hon & Julian Tonti-Filippini & Joseph R. Nery & Leonard Lee & Zhen Ye & Que-Minh Ngo & Lee Edsall & Jessica Antosiewicz-Bourg, 2009. "Human DNA methylomes at base resolution show widespread epigenomic differences," Nature, Nature, vol. 462(7271), pages 315-322, November.
    3. D. Oakes, 1999. "Direct calculation of the information matrix via the EM," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 479-482, April.
    4. Brendan Maher, 2008. "Personal genomes: The case of the missing heritability," Nature, Nature, vol. 456(7218), pages 18-21, November.
    5. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    6. Farhad Shokoohi & David A. Stephens & Guillaume Bourque & Tomi Pastinen & Celia M. T. Greenwood & Aurélie Labbe, 2019. "A hidden markov model for identifying differentially methylated sites in bisulfite sequencing data," Biometrics, The International Biometric Society, vol. 75(1), pages 210-221, March.
    7. Simon N. Wood & Matteo Fasiolo, 2017. "A generalized Fellner‐Schall method for smoothing parameter optimization with application to Tweedie location, scale and shape models," Biometrics, The International Biometric Society, vol. 73(4), pages 1071-1081, December.
    8. Andrew P. Feinberg, 2007. "Phenotypic plasticity and the epigenetics of human disease," Nature, Nature, vol. 447(7143), pages 433-440, May.
    9. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
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