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.
Acknowledgements
This work was supported by the CNV-Maize program funded by the french National Research Agency (ANR-10-GENM-104) and France Agrimer (11000415). Xiaoqiang Wang was financed by CNV-Maize project and National Natural Science Foundation of China (11601286). We are grateful to Stéphane Nicolas for providing the maize dataset.
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Supplementary Material
The online version of this article offers supplementary material (DOI:https://doi.org/10.1515/ijb-2018-0023).
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