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International Joint Conference on Biomedical Engineering Systems and Technologies

BIOSTEC 2012: Biomedical Engineering Systems and Technologies pp 113–134Cite as

Forests of Latent Tree Models to Decipher Genotype-Phenotype Associations

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 357))

Abstract

Genome-wide association studies have revolutionized the search for genetic influences on common genetic diseases such as diabetes, obesity, asthma, cardio-vascular diseases and some cancers. In particular, together with the population aging concern, increasing health care costs require that further investigations are pursued to design scalable and efficient tools. The high dimensionality and complexity of genetic data hinder the detection of genetic associations. To decrease the risks of missing the causal factor and discovering spurious associations, machine learning offers an attractive framework alternative to classical statistical approaches. A novel class of probabilistic graphical models (PGMs) has recently been proposed - the forest of latent tree models (FLTMs) - , to reach a trade-off between faithful modeling of data dependences and tractability. In this chapter, we assess the great potentiality of this model to detect genotype-phenotype associations. The FLTM-based contribution is first put into the perspective of PGM-based works meant to model the dependences in genetic data; then the contribution is considered from the technical viewpoint of LTM learning, with the vital objective of scalability in mind. We then present the systematic and comprehensive evaluation conducted to assess the ability of the FLTM model to detect genetic associations through latent variables. Realistic simulations were performed under various controlled conditions. In this context, we present a procedure tailored to correct for multiple testing. We also show and discuss results obtained on real data. Beside guaranteeing data dimension reduction through latent variables, the FLTM model is empirically proven able to capture indirect genetic associations with the disease: strong associations are evidenced between the disease and the ancestor nodes of the causal genetic marker node, in the forest; in contrast, very weak associations are obtained for other latent variables. Finally, we discuss the prospects of the model for association detection at genome scale.

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Author information

Authors and Affiliations

  1. LINA, UMR CNRS 6241, Université de Nantes, 2 rue de la Houssinière, BP 92208, 44322, Nantes Cedex, France

    Christine Sinoquet

  2. Center for Computational Biology and Bioinformatics, Department of Molecular and Medical Genetics, Indiana University, Indianapolis, IN, 46002, U.S.A.

    Raphaël Mourad

  3. UMR CNRS 6241, Ecole Polytechnique de l’Université de Nantes, rue Christian Pauc, BP 50609, 44306, Nantes Cedex 3, France

    Philippe Leray

Authors
  1. Christine Sinoquet
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  2. Raphaël Mourad
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  3. Philippe Leray
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Editor information

Editors and Affiliations

  1. University of Porto, Portugal

    Joaquim Gabriel

  2. Institute of Information Theory and Automation of the ASCR, Pod vodárenskou věží 4, CZ-182 08, Prague 8, Czech Republic

    Jan Schier

  3. Dept. of Electrical Engineering, ESAT-SCD(SISTA), Katholieke Universiteit Leuven, Belgium

    Sabine Van Huffel

  4. University of Toulouse, France

    Emmanuel Conchon

  5. University of Coimbra, Portugal

    Carlos Correia

  6. IST - Technical University of Lisbon,, Av.Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Ana Fred

  7. Institute of Telecommunication, Lisboa, Portugal

    Hugo Gamboa

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Sinoquet, C., Mourad, R., Leray, P. (2013). Forests of Latent Tree Models to Decipher Genotype-Phenotype Associations. In: Gabriel, J., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2012. Communications in Computer and Information Science, vol 357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38256-7_8

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  • DOI: https://doi.org/10.1007/978-3-642-38256-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38255-0

  • Online ISBN: 978-3-642-38256-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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