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(2015)23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING (PDP 2015) .p.747-750 Mark abstract Genomic prediction for plant breeding requires taking into account environmental effects and variations of genetic effects across environments. The latter can be modelled by estimating the effect of each genetic marker in every possible environmental condition, which leads to a huge amount of effects to be estimated. Nonetheless, the information about these effects is only sparsely present, due to the fact that plants are only tested in a limited number of environmental conditions. In contrast, the genotypes of the plants are a dense source of information and thus the estimation of both types of effects in one single step would require as well dense as sparse matrix formalisms. This paper presents a way to efficiently apply a high performance computing infrastructure for dealing with large-scale genomic prediction settings, relying on the coupling of dense and sparse matrix algebra.

Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-6934376



Autor: Arne De Coninck, Drosos Kourounis, Fabio Verbosio, Olaf Schenk, Bernard De Baets , Steven Maenhout and Jan Fostier

Fuente: https://biblio.ugent.be/publication/6934376



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