Variational Inference for Coupled Hidden Markov Models Applied to the Joint Detection of Copy Number Variations

Wang Xiaoqiang (), Lebarbier Emilie (), Aubert Julie () and Robin Stéphane ()
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Wang Xiaoqiang: School of Mathematics and Statistics, Shandong University (Weihai), Weihai, Shandong, China
Lebarbier Emilie: UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, Paris, France
Aubert Julie: UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, Paris, France
Robin Stéphane: UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, Paris, France

The International Journal of Biostatistics, 2019, vol. 15, issue 1, 15

Abstract: Hidden Markov models provide a natural statistical framework for the detection of the copy number variations (CNV) in genomics. In this context, we define a hidden Markov process that underlies all individuals jointly in order to detect and to classify genomics regions in different states (typically, deletion, normal or amplification). Structural variations from different individuals may be dependent. It is the case in agronomy where varietal selection program exists and species share a common phylogenetic past. We propose to take into account these dependencies inthe HMM model. When dealing with a large number of series, maximum likelihood inference (performed classically using the EM algorithm) becomes intractable. We thus propose an approximate inference algorithm based on a variational approach (VEM), implemented in the CHMM R package. A simulation study is performed to assess the performance of the proposed method and an application to the detection of structural variations in plant genomes is presented.

Keywords: copy number variation; coupled Hidden Markov models; variational approximation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/ijb-2018-0023

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