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Biostatistics. 2015 Oct;16(4):713-26. doi: 10.1093/biostatistics/kxv019. Epub 2015 May 14.

Estimating the dynamics and dependencies of accumulating mutations with applications to HIV drug resistance.

Author information

1
Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland and SIB Swiss Institute of Bioinformatics, Basel 4058, Switzerland.
2
Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland.
3
Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland and SIB Swiss Institute of Bioinformatics, Basel, Switzerland niko.beerenwinkel@bsse.ethz.ch.

Abstract

We introduce a new model called the observed time conjunctive Bayesian network (OT-CBN) that describes the accumulation of genetic events (mutations) under partial temporal ordering constraints. Unlike other CBN models, the OT-CBN model uses sampling time points of genotypes in addition to genotypes themselves to estimate model parameters. We developed an expectation-maximization algorithm to obtain approximate maximum likelihood estimates by accounting for this additional information. In a simulation study, we show that the OT-CBN model outperforms the continuous time CBN (CT-CBN) (Beerenwinkel and Sullivant, 2009. Markov models for accumulating mutations. Biometrika 96: (3), 645-661), which does not take into account individual sampling times for parameter estimation. We also show superiority of the OT-CBN model on several datasets of HIV drug resistance mutations extracted from the Swiss HIV Cohort Study database.

KEYWORDS:

Conjunctive Bayesian networks; Expectation–maximization algorithm; Genetic progression; HIV drug resistance; Maximum likelihood estimation

PMID:
25979750
DOI:
10.1093/biostatistics/kxv019
[Indexed for MEDLINE]

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