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Graphics Processing Unit-Accelerated Quantitative Trait Loci Detection

Guillaume Chapuis, Olivier Filangi, Dominique Lavenier, Jean Michel J. M. Elsen, Pascale Leroy
GenScale Team, INRIA Rennes, Rennes, France
Journal of Computational Biology 20, 9, 672-686, 2013

@article{chapuis:hal-00903794,

   hal_id={hal-00903794},

   url={http://hal.inria.fr/hal-00903794},

   title={Graphics Processing Unit-Accelerated Quantitative Trait Loci Detection},

   author={Chapuis, Guillaume and Filangi, Olivier and Lavenier, Dominique and Elsen, Jean Michel, J. M. and Leroy, Pascale},

   keywords={QTL, bioinformatics, GPU},

   language={Anglais},

   affiliation={GENSCALE – INRIA – IRISA , INRA},

   publisher={Mary Ann Liebert, Inc. publishers},

   pages={672-686},

   journal={Journal of Computational Biology},

   volume={20},

   number={9},

   audience={internationale},

   doi={10.1089/cmb.2012.0136},

   year={2013},

   month={Sep},

   pdf={http://hal.inria.fr/hal-00903794/PDF/cmb2013.pdf}

}

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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.
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