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Published (Version of Record)CC BY-NC-ND V4.0, Open Access
Journal article
Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.)
The plant genome, Vol.16(4), e20332
2023
PMID: 37122189
Abstract
In wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and β-carotenoid concentrations) using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013–2014 from Powerkheda (Madhya Pradesh) and for 2014–2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.
Details
- Title
- Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.)
- Authors/Creators
- Prabina Kumar Meher - Indian Agricultural Statistics Research InstituteAjit Gupta - Indian Agricultural Statistics Research InstituteSachin Rustgi - Clemson UniversityReyazul Rouf Mir - Sher-e-Kashmir University of Agricultural Sciences and Technology of KashmirAnuj Kumar - Shantou University Medical CollegeJitendra Kumar - National Agri-Food Biotechnology InstituteHarindra Singh Balyan - Chaudhary Charan Singh UniversityPushpendra Kumar Gupta - Chaudhary Charan Singh University
- Publication Details
- The plant genome, Vol.16(4), e20332
- Publisher
- Wiley
- Number of pages
- 13
- Grant note
- S009 / NIFAHatch/Multistate Indian Council of Agricultural Research (ICAR) under Lal Bahadur Shastri Outstanding Young Scientist Award Scheme
- Identifiers
- 991005722668907891
- Copyright
- © 2023 The Authors.
- Murdoch Affiliation
- Food Futures Institute
- Language
- English
- Resource Type
- Journal article
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- Domestic collaboration
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- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.51 Dairy & Animal Sciences
- 3.51.115 Livestock Reproduction
- Web Of Science research areas
- Genetics & Heredity
- Plant Sciences
- ESI research areas
- Plant & Animal Science