Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat

Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat

Background: Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential...

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Bibliographic Details
Main Authors: Avci, M. (Author), Babar, M.A (Author), Bai, G. (Author), Foulkes, J. (Author), Guo, J. (Author), Khan, J. (Author), Khan, N. (Author), McBreen, J. (Author), Pradhan, S. (Author), Reynolds, M. (Author), Shahi, D. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
article
breeding
canopy
Canopy temperature
crop
cross validation
fruiting
Fruiting efficiency
genetic gain
Genomic prediction
genotype
genotyping
grain yield
harvest index
Multi-trait genomic prediction
NDVI
nonhuman
phenotype
prediction
season
single nucleotide polymorphism
spike
Spike partitioning index
validation process
vegetation
wheat
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