Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea
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.
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Nature Research
2018
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, CHICKPEAS, GENOMICS, GENOTYPE ENVIRONMENT INTERACTION, |
Online Access: | https://hdl.handle.net/10883/19600 |
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dig-cimmyt-10883-196002023-01-24T14:29:17Z Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea Roorkiwal, M. Jarquín, D. Muneendra K. Singh Pooran M. Gaur Chellapilla Bharadwaj Rathore, A. Howard, R. Samineni Srinivasan Ankit Jain Garg, V. Sandip Kale Annapurna Chitikineni Shailesh Tripathi Jones, E. Robbins, K. Crossa, J. Varshney, R.K. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY CHICKPEAS GENOMICS GENOTYPE ENVIRONMENT INTERACTION 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. 2018-09-05T21:39:49Z 2018-09-05T21:39:49Z 2018 Article 2045-2322 https://hdl.handle.net/10883/19600 10.1038/s41598-018-30027-2 English 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. Open Access PDF London Nature Research Springer Nature art. 11701 8 Scientific Reports |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY CHICKPEAS GENOMICS GENOTYPE ENVIRONMENT INTERACTION AGRICULTURAL SCIENCES AND BIOTECHNOLOGY CHICKPEAS GENOMICS GENOTYPE ENVIRONMENT INTERACTION |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY CHICKPEAS GENOMICS GENOTYPE ENVIRONMENT INTERACTION AGRICULTURAL SCIENCES AND BIOTECHNOLOGY CHICKPEAS GENOMICS GENOTYPE ENVIRONMENT INTERACTION Roorkiwal, M. Jarquín, D. Muneendra K. Singh Pooran M. Gaur Chellapilla Bharadwaj Rathore, A. Howard, R. Samineni Srinivasan Ankit Jain Garg, V. Sandip Kale Annapurna Chitikineni Shailesh Tripathi Jones, E. Robbins, K. Crossa, J. Varshney, R.K. Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea |
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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. |
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Article |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY CHICKPEAS GENOMICS GENOTYPE ENVIRONMENT INTERACTION |
author |
Roorkiwal, M. Jarquín, D. Muneendra K. Singh Pooran M. Gaur Chellapilla Bharadwaj Rathore, A. Howard, R. Samineni Srinivasan Ankit Jain Garg, V. Sandip Kale Annapurna Chitikineni Shailesh Tripathi Jones, E. Robbins, K. Crossa, J. Varshney, R.K. |
author_facet |
Roorkiwal, M. Jarquín, D. Muneendra K. Singh Pooran M. Gaur Chellapilla Bharadwaj Rathore, A. Howard, R. Samineni Srinivasan Ankit Jain Garg, V. Sandip Kale Annapurna Chitikineni Shailesh Tripathi Jones, E. Robbins, K. Crossa, J. Varshney, R.K. |
author_sort |
Roorkiwal, M. |
title |
Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea |
title_short |
Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea |
title_full |
Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea |
title_fullStr |
Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea |
title_full_unstemmed |
Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea |
title_sort |
genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea |
publisher |
Nature Research |
publishDate |
2018 |
url |
https://hdl.handle.net/10883/19600 |
work_keys_str_mv |
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