Graphics Processing Unit-Accelerated Quantitative Trait Loci Detection

Guillaume Chapuis 1 Olivier Filangi 2 Dominique Lavenier 1 Jean Michel Elsen 3 Pascale Leroy 2
1 GenScale - Scalable, Optimized and Parallel Algorithms for Genomics
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
3 SAGA
INRA
Abstract : Mapping quantitative trait loci (QTL) using genetic marker information is a time-consuming analysis that has interested the mapping community in recent decades. The increasing amount of genetic marker data allows one to consider ever more precise QTL analyses while increasing the demand for computation. Part of the difficulty of detecting QTLs resides in finding appropriate critical values or threshold values, above which a QTL effect is considered significant. Different approaches exist to determine these thresholds, using either empirical methods or algebraic approximations. In this article, we present a new implementation of existing software, QTLMap, which takes advantage of the data parallel nature of the problem by offsetting heavy computations to a graphics processing unit (GPU). Developments on the GPU were implemented using Cuda technology. This new implementation performs up to 75 times faster than the previous multicore implementation, while maintaining the same results and level of precision (Double Precision) and computing both QTL values and thresholds. This speedup allows one to perform more complex analyses, such as linkage disequilibrium linkage analyses (LDLA) and multiQTL analyses, in a reasonable time frame.
Keywords : QTL bioinformatics GPU
Type de document :
Article dans une revue
Journal of Computational Biology, Mary Ann Liebert, 2013, 20 (9), pp.672-686. 〈10.1089/cmb.2012.0136〉
Liste complète des métadonnées

Littérature citée [17 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00903794
Contributeur : Dominique Lavenier <>
Soumis le : mercredi 13 novembre 2013 - 10:18:34
Dernière modification le : mardi 16 janvier 2018 - 15:54:20
Document(s) archivé(s) le : vendredi 14 février 2014 - 15:35:39

Fichier

cmb2013.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Citation

Guillaume Chapuis, Olivier Filangi, Dominique Lavenier, Jean Michel Elsen, Pascale Leroy. Graphics Processing Unit-Accelerated Quantitative Trait Loci Detection. Journal of Computational Biology, Mary Ann Liebert, 2013, 20 (9), pp.672-686. 〈10.1089/cmb.2012.0136〉. 〈hal-00903794〉

Partager

Métriques

Consultations de la notice

465

Téléchargements de fichiers

138