Optimizing sparse testing for genomic prediction of plant breeding crops

dc.creatorMontesinos-Lopez, O.A.
dc.creatorSaint Pierre, C.
dc.creatorGezan, S.A.
dc.creatorBentley, A.R.
dc.creatorMosqueda-Gonzalez, B.A.
dc.creatorMontesinos-Lopez, A.
dc.creatorEeuwijk, F.A. van
dc.creatorBeyene, Y.
dc.creatorGowda, M.
dc.creatorGardner, K.A.
dc.creatorGerard, G.S.
dc.creatorCrespo-Herrera, L.A.
dc.creatorCrossa, J.
dc.date2023-06-22T20:20:12Z
dc.date2023-06-22T20:20:12Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:38Z
dc.date.available2023-07-17T20:10:38Z
dc.identifierhttps://hdl.handle.net/10883/22625
dc.identifier10.3390/genes14040927
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514368
dc.descriptionWhile sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1–M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15–85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis.
dc.languageEnglish
dc.publisherMDPI
dc.relationhttps://hdl.handle.net/11529/10548813
dc.relationAccelerated Breeding
dc.relationBill & Melinda Gates Foundation
dc.relationUnited States Agency for International Development
dc.relationFoundation for Research Levy on Agricultural Products
dc.relationAgricultural Agreement Research Fund
dc.relationCGIAR Trust Fund
dc.relationhttps://hdl.handle.net/10568/130894
dc.rightsCIMMYT 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
dc.rightsOpen Access
dc.source4
dc.source14
dc.source2073-4425
dc.sourceGenes
dc.source927
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectSparse Testing
dc.subjectGenomic Prediction
dc.subjectMAIZE
dc.subjectTESTING
dc.subjectWHEAT
dc.subjectPLANT BREEDING
dc.subjectCROPS
dc.subjectWheat
dc.titleOptimizing sparse testing for genomic prediction of plant breeding crops
dc.typeArticle
dc.typePublished Version
dc.coverageBasel (Switzerland)


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