Show simple item record

Prediction of multiple-trait and multiple-environment genomic data using recommender systems

Author: Montesinos-Lopez, O.A.
Author: Montesinos-Lopez, A.
Author: Crossa, J.
Author: Montesinos-López, J.C.
Author: Mota-Sanchez, D.
Author: Estrada-González, F.
Author: Gillberg, J.
Author: Singh, R.G.
Author: Mondal, S.
Author: Juliana, P.
Year: 2018
URI: http://hdl.handle.net/10883/19119
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: itembased 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.
Format: PDF
Language: English
Publisher: Genetics Society of America
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: Bethesda, MD
Pages: 131-147
Issue: 1
Volume: 8
DOI: 10.1534/g3.117.300309
Keywords: Genomic Information
Keywords: Matrix Factorization
Keywords: Prediction Accuracy
Keywords: Collaborative Foltering
Keywords: GenPred
Keywords: Shared Data Resources
Agrovoc: GENOMICS
Agrovoc: GENOTYPE ENVIRONMENT INTERACTION
Agrovoc: STATISTICAL METHODS
Related Datasets: http://hdl.handle.net/11529/11099
Journal: G3: Genes, Genomes, Genetics


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

Show simple item record