Probabilistic graphical models for genetic association studies - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Briefings in Bioinformatics Année : 2012

Probabilistic graphical models for genetic association studies

Résumé

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.

Dates et versions

hal-00573325 , version 1 (03-03-2011)

Identifiants

Citer

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

Altmetric

Partager

Gmail Facebook X LinkedIn More