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Enhancing hybrid prediction in pearl millet using genomic and/or multi-environment phenotypic information of inbreds

Creator: Jarquín, D.
Creator: Howard, R.
Creator: Zhikai Liang
Creator: Shashi K. Gupta
Creator: Schnable, J.C.
Creator: Crossa, J.
Year: 2020
URI: https://hdl.handle.net/10883/21064
Language: English
Publisher: Frontiers Media
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: Switzerland
Volume: 10
DOI: 10.3389/fgene.2019.01294
Keywords: Genomic Selection
Keywords: Hybrid Prediction
Keywords: Conventional and Tunable GBS
Description: Genomic selection (GS) is an emerging methodology that helps select superior lines among experimental cultivars in plant breeding programs. It offers the opportunity to increase the productivity of cultivars by delivering increased genetic gains and reducing the breeding cycles. This methodology requires inexpensive and sufficiently dense marker information to be successful, and with whole genome sequencing, it has become an important tool in many crops. The recent assembly of the pearl millet genome has made it possible to employ GS models to improve the selection procedure in pearl millet breeding programs. Here, three GS models were implemented and compared using grain yield and dense molecular marker information of pearl millet obtained from two different genotyping platforms (C [conventional GBS RAD-seq] and T [tunable GBS tGBS]). The models were evaluated using three different cross-validation (CV) schemes mimicking real situations that breeders face in breeding programs: CV2 resembles an incomplete field trial, CV1 predicts the performance of untested hybrids, and CV0 predicts the performance of hybrids in unobserved environments. We found that (i) adding phenotypic information of parental inbreds to the calibration sets improved predictive ability, (ii) accounting for genotype-by-environment interaction also increased the performance of the models, and (iii) superior strategies should consider the use of the molecular markers derived from the T platform (tGBS).
Agrovoc: MARKER-ASSISTED SELECTION
Agrovoc: GENOTYPE ENVIRONMENT INTERACTION
Agrovoc: COMBINING ABILITY
Related Datasets: https://doi.org/10.6084/m9.figshare.5969230
Related Datasets: https://doi.org/10.6084/m9.figshare.5566843
ISSN: 1664-8021
Journal: Frontiers in Genetics
Article number: 1294


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

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