Show simple item record

Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.)

Creator: Tomar, V.
Creator: Singh, D.
Creator: Dhillon, G.S.
Creator: Yong Suk Chung
Creator: Poland, J.A.
Creator: Singh, R.P.
Creator: Joshi, A.K.
Creator: Gautam, Y.
Creator: Tiwari, B.S.
Creator: Kumar, U.
Year: 2021
URI: https://hdl.handle.net/10883/21722
Language: English
Publisher: Frontiers
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: 12
DOI: 10.3389/fpls.2021.720123
Keywords: Single-Environment
Keywords: Multi-Environment
Keywords: Genotyping by Sequencing
Keywords: Genomic Selection
Keywords: Genomic Prediction
Keywords: Best Linear Unbiased Prediction
Description: Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.
Agrovoc: MARKER-ASSISTED SELECTION
Agrovoc: GENOMICS
Agrovoc: WHEAT
Related Datasets: https://figshare.com/collections/Increased_Predictive_Accuracy_of_Multi-Environment_Genomic_Prediction_Model_for_Yield_and_Related_Traits_in_Spring_Wheat_Triticum_aestivum_L_/5653156
ISSN: 1664-462X
Journal: Frontiers in Plant Science
Article number: 720123


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

Show simple item record