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Probabilistic graphical models for genetic association studies

Abstract : Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review paper, we cover the applications of these models to the population association studies' context, such as linkage disequilibrium modeling, fine mapping and candidate-gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field.
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Contributor : Raphaël Mourad <>
Submitted on : Thursday, March 3, 2011 - 2:08:54 PM
Last modification on : Wednesday, July 10, 2019 - 7:18:03 PM

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Raphaël Mourad, Christine Sinoquet, Philippe Leray. Probabilistic graphical models for genetic association studies. Briefings in Bioinformatics, Oxford University Press (OUP), 2012, 13 (1), pp.20-33. ⟨10.1093/bib/BBR015⟩. ⟨hal-00573325⟩



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