Advanced search
1 file | 293.88 KB

Including explicit marker-by-environment interaction for large-scale genomic prediction

Author
Organization
Project
HPC-UGent: the central High Performance Computing infrastructure of Ghent University
Project
Bioinformatics: from nucleotids to networks (N2N)
Abstract
Genomic prediction for plants is heavily influenced by the environment. Not only do the environmental conditions influence the phenotypic traits directly, genetic effects may also vary across different environments. Therefore, it is essential to include marker-by-environment interactions in the linear mixed models used for analyzing the genomic data. However, when every genetic marker is coupled to every environmental covariate, the problem size grows dramatically. Luckily, information about marker-by-environment interaction is only sparsely present in data sets, since each plant is tested in a limited number of environmental conditions only. In contrast, the genotypes of plants are a dense source of information and thus including marker effects and their interaction with environment in a single-step genomic prediction setting requires the coupling of sparse and dense matrix algebra. Our implementation of this strategy uses distributed computing techniques together with an optimized library for sparse matrix manipulations (PARDISO) to efficiently use a high performance computing cluster for the analysis of large-scale data sets.
Keywords
genomic prediction, high performance computing, marker-by-environment interaction

Downloads

    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 293.88 KB

Citation

Please use this url to cite or link to this publication:

Chicago
De Coninck, Arne, Drosos Kourounis, Fabio Verbosio, Olaf Schenk, Bernard De Baets, Steven Maenhout, and Jan Fostier. 2015. “Including Explicit Marker-by-environment Interaction for Large-scale Genomic Prediction.” In Communications in Agricultural and Applied Biological Sciences, ed. Xavier Draye, 80:117–121.
APA
De Coninck, A., Kourounis, D., Verbosio, F., Schenk, O., De Baets, B., Maenhout, S., & Fostier, J. (2015). Including explicit marker-by-environment interaction for large-scale genomic prediction. In X. Draye (Ed.), COMMUNICATIONS IN AGRICULTURAL AND APPLIED BIOLOGICAL SCIENCES (Vol. 80, pp. 117–121). Presented at the 20th National symposium on Applied Biological Sciences.
Vancouver
1.
De Coninck A, Kourounis D, Verbosio F, Schenk O, De Baets B, Maenhout S, et al. Including explicit marker-by-environment interaction for large-scale genomic prediction. In: Draye X, editor. COMMUNICATIONS IN AGRICULTURAL AND APPLIED BIOLOGICAL SCIENCES. 2015. p. 117–21.
MLA
De Coninck, Arne, Drosos Kourounis, Fabio Verbosio, et al. “Including Explicit Marker-by-environment Interaction for Large-scale Genomic Prediction.” Communications in Agricultural and Applied Biological Sciences. Ed. Xavier Draye. Vol. 80. 2015. 117–121. Print.
@inproceedings{5929345,
  abstract     = {Genomic prediction for plants is heavily influenced by the environment. Not only do the environmental conditions influence the phenotypic traits directly, genetic effects may also vary across different environments. Therefore, it is essential to include marker-by-environment interactions in the linear mixed models used for analyzing the genomic data. However, when every genetic marker is coupled to every environmental covariate, the problem size grows dramatically. Luckily, information about marker-by-environment interaction is only sparsely present in data sets, since each plant is tested in a limited number of environmental conditions only. In contrast, the genotypes of plants are a dense source of information and thus including marker effects and their interaction with environment in a single-step genomic prediction setting requires the coupling of sparse and dense matrix algebra. Our implementation of this strategy uses distributed computing techniques together with an optimized library for sparse matrix manipulations (PARDISO) to efficiently use a high performance computing cluster for the analysis of large-scale data sets.},
  author       = {De Coninck, Arne and Kourounis, Drosos and Verbosio, Fabio and Schenk, Olaf and De Baets, Bernard and Maenhout, Steven and Fostier, Jan},
  booktitle    = {COMMUNICATIONS IN AGRICULTURAL AND APPLIED BIOLOGICAL SCIENCES},
  editor       = {Draye, Xavier},
  issn         = {1379-1176},
  keyword      = {genomic prediction,high performance computing,marker-by-environment interaction},
  language     = {eng},
  location     = {Louvain-La-Neuve, Belgium},
  number       = {1},
  pages        = {117--121},
  title        = {Including explicit marker-by-environment interaction for large-scale genomic prediction},
  volume       = {80},
  year         = {2015},
}