Roorkiwal, M and Jarquin, D and Singh, M K and Gaur, P M and Bharadwaj, C and Rathore, A and Howard, R and Srinivasan, S and Jain, A and Garg, V and Kale, S M and Chitikineni, A and Tripathi, S and Jones, E and Robbins, K R and Crossa, J and Varshney, R K (2018) Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea. Scientific Reports (TSI), 8 (1) (11701). pp. 1-11. ISSN 2045-2322
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Abstract
Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve prediction accuracy hence aid in selection of lines across target environments. Phenotypic data on 320 chickpea breeding lines for eight traits for three seasons at two locations were recorded. These lines were genotyped using DArTseq (1.6 K SNPs) and Genotyping-by-Sequencing (GBS; 89 K SNPs). Thirteen models were fitted including main effects of environment and lines, markers, and/or naïve and informed interactions to estimate prediction accuracies. Three cross-validation schemes mimicking real scenarios that breeders might encounter in the fields were considered to assess prediction accuracy of the models (CV2: incomplete field trials or sparse testing; CV1: newly developed lines; and CV0: untested environments). Maximum prediction accuracies for different traits and different models were observed with CV2. DArTseq performed better than GBS and the combined genotyping set (DArTseq and GBS) regardless of the cross validation scheme with most of the main effect marker and interaction models. Improvement of GS models and application of various genotyping platforms are key factors for obtaining accurate and precise prediction accuracies, leading to more precise selection of candidates.
| Item Type: | Article |
|---|---|
| Divisions: | Research Program : Asia Research Program : Genetic Gains |
| CRP: | CGIAR Research Program on Grain Legumes and Dryland Cereals (GLDC) |
| Uncontrolled Keywords: | Chickpea, genomic enabled prediction models, genotype environment, genomic selection, genotyping platforms, phenotypic data, impact of environment, genetics |
| Subjects: | Mandate crops > Chickpea Others > Genetics and Genomics |
| Depositing User: | Mr Ramesh K |
| Date Deposited: | 06 Aug 2018 08:30 |
| Last Modified: | 06 Aug 2018 08:30 |
| URI: | http://oar.icrisat.org/id/eprint/10821 |
| Official URL: | http://dx.doi.org/10.1038/s41598-018-30027-2 |
| Projects: | UNSPECIFIED |
| Funders: | Bill & Melinda Gates Foundation (Tropical Legumes III [OPP124589], Genomic Open-source Breeding Informatics Initiative (GOBII)), and Department of Agriculture Cooperation & Farmers Welfare (DAC&FW), Govt. of India |
| Acknowledgement: | The authors are thankful to Bill & Melinda Gates Foundation (Tropical Legumes III [OPP124589], Genomic Open-source Breeding Informatics Initiative (GOBII)), and Department of Agriculture Cooperation & Farmers Welfare (DAC&FW), Govt. of India for financial assistance. The work reported in this article was undertaken as a part of the CGIAR Research Program on Grain Legumes and Dryland Cereals (GLDC). ICRISAT is a member of the CGIAR. |
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