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Needles: toward large-scale genomic prediction with marker-by-environment interaction

(2016) GENETICS. 203(1). p.543-555
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Abstract
Genomic prediction relies on genotypic marker information to predict the agronomic performance of future hybrid breeds based on trial records. Because the effect of markers may vary substantially under the influence of different environmental conditions, marker-by-environment interaction effects have to be taken into account. However, this may lead to a dramatic increase in the computational resources needed for analyzing large-scale trial data. A high-performance computing solution, called Needles, is presented for handling such data sets. Needles is tailored to the particular properties of the underlying algebraic framework by exploiting a sparse matrix formalism where suited and by utilizing distributed computing techniques to enable the use of a dedicated computing cluster. It is demonstrated that large-scale analyses can be performed within reasonable time frames with this framework. Moreover, by analyzing simulated trial data, it is shown that the effects of markers with a high environmental interaction can be predicted more accurately when more records per environment are available in the training data. The availability of such data and their analysis with Needles also may lead to the discovery of highly contributing QTL in specific environmental conditions. Such a framework thus opens the path for plant breeders to select crops based on these QTL, resulting in hybrid lines with optimized agronomic performance in specific environmental conditions.
Keywords
IBCN, GenPred, shared data resource, genomic selection, genomic prediction, marker-by-environment interaction, high-performance computing, variance component estimation, simulated data, QUANTITATIVE TRAITS, FLEXIBLE SIMULATION, ASSISTED SELECTION, MOLECULAR MARKERS, BREEDING VALUES, COMPLEX TRAITS, GENOTYPE, INFORMATION, MODELS, MAIZE

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Citation

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MLA
De Coninck, Arne et al. “Needles: Toward Large-scale Genomic Prediction with Marker-by-environment Interaction.” GENETICS 203.1 (2016): 543–555. Print.
APA
De Coninck, A., De Baets, B., Kourounis, D., Verbosio, F., Schenk, O., Maenhout, S., & Fostier, J. (2016). Needles: toward large-scale genomic prediction with marker-by-environment interaction. GENETICS, 203(1), 543–555.
Chicago author-date
De Coninck, Arne, Bernard De Baets, Drosos Kourounis, Fabio Verbosio, Olaf Schenk, Steven Maenhout, and Jan Fostier. 2016. “Needles: Toward Large-scale Genomic Prediction with Marker-by-environment Interaction.” Genetics 203 (1): 543–555.
Chicago author-date (all authors)
De Coninck, Arne, Bernard De Baets, Drosos Kourounis, Fabio Verbosio, Olaf Schenk, Steven Maenhout, and Jan Fostier. 2016. “Needles: Toward Large-scale Genomic Prediction with Marker-by-environment Interaction.” Genetics 203 (1): 543–555.
Vancouver
1.
De Coninck A, De Baets B, Kourounis D, Verbosio F, Schenk O, Maenhout S, et al. Needles: toward large-scale genomic prediction with marker-by-environment interaction. GENETICS. 2016;203(1):543–55.
IEEE
[1]
A. De Coninck et al., “Needles: toward large-scale genomic prediction with marker-by-environment interaction,” GENETICS, vol. 203, no. 1, pp. 543–555, 2016.
@article{8069439,
  abstract     = {Genomic prediction relies on genotypic marker information to predict the agronomic performance of future hybrid breeds based on trial records. Because the effect of markers may vary substantially under the influence of different environmental conditions, marker-by-environment interaction effects have to be taken into account. However, this may lead to a dramatic increase in the computational resources needed for analyzing large-scale trial data. A high-performance computing solution, called Needles, is presented for handling such data sets. Needles is tailored to the particular properties of the underlying algebraic framework by exploiting a sparse matrix formalism where suited and by utilizing distributed computing techniques to enable the use of a dedicated computing cluster. It is demonstrated that large-scale analyses can be performed within reasonable time frames with this framework. Moreover, by analyzing simulated trial data, it is shown that the effects of markers with a high environmental interaction can be predicted more accurately when more records per environment are available in the training data. The availability of such data and their analysis with Needles also may lead to the discovery of highly contributing QTL in specific environmental conditions. Such a framework thus opens the path for plant breeders to select crops based on these QTL, resulting in hybrid lines with optimized agronomic performance in specific environmental conditions.},
  author       = {De Coninck, Arne and De Baets, Bernard and Kourounis, Drosos and Verbosio, Fabio and Schenk, Olaf and Maenhout, Steven and Fostier, Jan},
  issn         = {0016-6731},
  journal      = {GENETICS},
  keywords     = {IBCN,GenPred,shared data resource,genomic selection,genomic prediction,marker-by-environment interaction,high-performance computing,variance component estimation,simulated data,QUANTITATIVE TRAITS,FLEXIBLE SIMULATION,ASSISTED SELECTION,MOLECULAR MARKERS,BREEDING VALUES,COMPLEX TRAITS,GENOTYPE,INFORMATION,MODELS,MAIZE},
  language     = {eng},
  number       = {1},
  pages        = {543--555},
  title        = {Needles: toward large-scale genomic prediction with marker-by-environment interaction},
  url          = {http://dx.doi.org/10.1534/genetics.115.179887},
  volume       = {203},
  year         = {2016},
}

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