FORESTS OF LATENT TREE MODELS FOR THE DETECTION OF GENETIC ASSOCIATIONS

Christine Sinoquet, Raphaël Mourad, Philippe Leray

Abstract

Together with the population aging concern, increasing health care costs require understanding the causal basis for common genetic diseases. The high dimensionality and complexity of genetic data hamper the detection of genetic associations. To alleviate the core risks (missing of the causal factor, spurious discoveries), machine learning offers an appealing alternative framework to standard statistical approaches. A novel class of probabilistic graphical models has recently been proposed - the forest of latent tree models - , to obtain a trade-off between faithful modeling of data dependences and tractability. In this paper, we evaluate the soundness of this modeling approach in an association genetics context. We have performed intensive tests, in various controlled conditions, on realistic simulated data. We have also tested the model on real data. Beside guaranteeing data dimension reduction through latent variables, the model is empirically proven able to capture indirect genetic associations with the disease, both on simulated and real data. 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 nodes.

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Paper Citation


in Harvard Style

Sinoquet C., Mourad R. and Leray P. (2012). FORESTS OF LATENT TREE MODELS FOR THE DETECTION OF GENETIC ASSOCIATIONS . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012) ISBN 978-989-8425-90-4, pages 5-14. DOI: 10.5220/0003703400050014


in Bibtex Style

@conference{bioinformatics12,
author={Christine Sinoquet and Raphaël Mourad and Philippe Leray},
title={FORESTS OF LATENT TREE MODELS FOR THE DETECTION OF GENETIC ASSOCIATIONS},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)},
year={2012},
pages={5-14},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003703400050014},
isbn={978-989-8425-90-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)
TI - FORESTS OF LATENT TREE MODELS FOR THE DETECTION OF GENETIC ASSOCIATIONS
SN - 978-989-8425-90-4
AU - Sinoquet C.
AU - Mourad R.
AU - Leray P.
PY - 2012
SP - 5
EP - 14
DO - 10.5220/0003703400050014