Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems

Osval A. Montesinos-López, Abelardo Montesinos-López, José Crossa, José C. Montesinos-López, David Mota-Sanchez, Fermín Estrada-González, Jussi Gillberg, Ravi Singh, Suchismita Mondal, Philomin Juliana

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
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Abstract

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.
Original languageEnglish
Pages (from-to)131-147
JournalG3: Genes, Genomes, Genetics
Volume8
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • genomic information
  • item-based collaborative filtering
  • matrix factorization
  • genotype
  • environment interaction
  • prediction accuracy
  • collaborative filtering
  • GenPred
  • Shared Data Resources
  • Genomic Selection

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