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

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Prediction of multiple-trait and multiple-environment genomic data using recommender systems
 
Identifier https://hdl.handle.net/11529/11099
 
Creator Montesinos-Lopez, Osval A.
Montesinos-Lopez, Abelardo
Crossa, Jose
Montesinos-Lopez, Jose C.
Mota-Sanchez, David
Estrada-Gonzalez, Fermin
Gilberg, Jussi
Singh, Ravi
Mondal, Suchismita
Juliana, Philomin
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description 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, while researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although statistical models are usually mathematically elegant, they are also computatio
nally 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: a) item-based collaborative filtering (IBCF; method M1) and b) the matrix factorization algorithm (method M2) in the context of multiple traits and multiple environments. The IBCF and matrix factorization 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 (method M1) was slightly better in terms of prediction accuracy than the two conventional methods and the matrix factorization 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.
 
Subject Agricultural Sciences
Agricultural research
Maize
Zea Mays
Bread wheat
Triticum aestivum
Crop performance
Genomic Prediction
Genomic Selection
Genomic selection models
 
Language English
 
Date 2017
 
Contributor Shrestha, Rosemary