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DAIRRy-BLUP: a high-performance computing approach to genomic prediction

Arne De Coninck (UGent) , Jan Fostier (UGent) , Steven Maenhout (UGent) and Bernard De Baets (UGent)
(2014) GENETICS. 197(3). p.813-822
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Project
HPC-UGent: the central High Performance Computing infrastructure of Ghent University
Abstract
In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression–best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP marker effects are normally distributed, are uncorrelated, and have equal variances. We propose DAIRRy-BLUP, a parallel, Distributed-memory RR-BLUP implementation, based on single-trait observations (y), that uses the Average Information algorithm for restricted maximum-likelihood estimation of the variance components. The goal of DAIRRy-BLUP is to enable the analysis of large-scale data sets to provide more accurate estimates of marker effects and breeding values. A distributed-memory framework is required since the dimensionality of the problem, determined by the number of SNP markers, can become too large to be analyzed by a single computing node. Initial results show that DAIRRy-BLUP enables the analysis of very large-scale data sets (up to 1,000,000 individuals and 360,000 SNPs) and indicate that increasing the number of phenotypic and genotypic records has a more significant effect on the prediction accuracy than increasing the density of SNP arrays.
Keywords
high-performance computing, RIDGE-REGRESSION, genomic prediction, distributed-memory architecture, simulated data, variance component estimation, SELECTION, INFORMATION, GENETICS, SIMULATION, ALGORITHM

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Citation

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

Chicago
De Coninck, Arne, Jan Fostier, Steven Maenhout, and Bernard De Baets. 2014. “DAIRRy-BLUP: a High-performance Computing Approach to Genomic Prediction.” Genetics 197 (3): 813–822.
APA
De Coninck, A., Fostier, J., Maenhout, S., & De Baets, B. (2014). DAIRRy-BLUP: a high-performance computing approach to genomic prediction. GENETICS, 197(3), 813–822.
Vancouver
1.
De Coninck A, Fostier J, Maenhout S, De Baets B. DAIRRy-BLUP: a high-performance computing approach to genomic prediction. GENETICS. 2014;197(3):813–22.
MLA
De Coninck, Arne, Jan Fostier, Steven Maenhout, et al. “DAIRRy-BLUP: a High-performance Computing Approach to Genomic Prediction.” GENETICS 197.3 (2014): 813–822. Print.
@article{5672275,
  abstract     = {In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression--best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP marker effects are normally distributed, are uncorrelated, and have equal variances. We propose DAIRRy-BLUP, a parallel, Distributed-memory RR-BLUP implementation, based on single-trait observations (y), that uses the Average Information algorithm for restricted maximum-likelihood estimation of the variance components. The goal of DAIRRy-BLUP is to enable the analysis of large-scale data sets to provide more accurate estimates of marker effects and breeding values. A distributed-memory framework is required since the dimensionality of the problem, determined by the number of SNP markers, can become too large to be analyzed by a single computing node. Initial results show that DAIRRy-BLUP enables the analysis of very large-scale data sets (up to 1,000,000 individuals and 360,000 SNPs) and indicate that increasing the number of phenotypic and genotypic records has a more significant effect on the prediction accuracy than increasing the density of SNP arrays.},
  author       = {De Coninck, Arne and Fostier, Jan and Maenhout, Steven and De Baets, Bernard},
  issn         = {0016-6731},
  journal      = {GENETICS},
  language     = {eng},
  number       = {3},
  pages        = {813--822},
  title        = {DAIRRy-BLUP: a high-performance computing approach to genomic prediction},
  url          = {http://dx.doi.org/10.1534/genetics.114.163683},
  volume       = {197},
  year         = {2014},
}

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