Record Details

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

OAR@ICRISAT

View Archive Info
 
 
Field Value
 
Relation http://oar.icrisat.org/12592/
https://acsess.onlinelibrary.wiley.com/doi/full/10.1002/tpg2.20194
https://doi.org/10.1002/tpg2.20194
 
Title Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding
 
Creator Montesinos-López, O A
Montesinos-Lopez, A
Acosta, R
Varshney, R K
Bentley, A
Crossa, J
 
Description Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.
 
Publisher Crop Science Society of America
 
Date 2022-02-16
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_by_nc_nd
 
Identifier http://oar.icrisat.org/12592/1/The%20Plant%20Genome_15_1_1-24_2022.pdf
Montesinos-López, O A and Montesinos-Lopez, A and Acosta, R and Varshney, R K and Bentley, A and Crossa, J (2022) Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding. The Plant Genome (TSI), 15 (1). pp. 1-24. ISSN 1940-3372