Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

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dc.contributor.authorAtanda, Sikiru Adeniyi
dc.contributor.authorOlsen, Michael
dc.contributor.authorCrossa, Jose
dc.date.accessioned2023-04-30T12:14:14Z
dc.date.available2023-04-30T12:14:14Z
dc.date.issued2021-06-22
dc.descriptionFrontiers in Plant Science, Volume 12en_US
dc.description.abstractTo enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.en_US
dc.description.sponsorshipACE: Crop Improvementen_US
dc.identifier.citationAtanda, S. A., Olsen, M., Crossa, J., BurgueƱo, J., Rincent, R., Dzidzienyo, D., ... & Robbins, K. R. (2021). Scalable sparse testing genomic selection strategy for early yield testing stage. Frontiers in Plant Science, 1205.en_US
dc.identifier.issn1664-462X
dc.identifier.urihttps://doi.org/10.3389/fpls.2021.658978
dc.language.isoenen_US
dc.publisherFrontiersen_US
dc.subjectgenomic selectionen_US
dc.subjectfactor analyticen_US
dc.subjectpreliminary yield trialsen_US
dc.subjectprediction accuracyen_US
dc.subjectunstructured modelen_US
dc.subjectCDmeanen_US
dc.subjectJuan BurgueƱoen_US
dc.subjectRenaud Rincenten_US
dc.subjectDaniel Dzidzienyoen_US
dc.subjectYoseph Beyeneen_US
dc.subjectManje Gowdaen_US
dc.subjectKate Dreheren_US
dc.subjectPrasanna M. Boddupallien_US
dc.subjectPangirayi Tongoonaen_US
dc.subjectEric Yirenkyi Danquahen_US
dc.subjectGbadebo Olaoyeen_US
dc.subjectKelly R. Robbinsen_US
dc.subjectAgricultureen_US
dc.subjectUniversity of Ghanaen_US
dc.titleScalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stageen_US
dc.typeArticleen_US
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