NDLI: Genomic selection across multiple breeding cycles in applied bread wheat breeding

Content Provider	Springer Nature Link
Author	Michel, Sebastian Ametz, Christian Gungor, Huseyin Epure, Doru Grausgruber, Heinrich Löschenberger, Franziska Buerstmayr, Hermann
Copyright Year	2016
Abstract	We evaluated genomic selection across five breeding cycles of bread wheat breeding. Bias of within-cycle cross-validation and methods for improving the prediction accuracy were assessed. The prospect of genomic selection has been frequently shown by cross-validation studies using the same genetic material across multiple environments, but studies investigating genomic selection across multiple breeding cycles in applied bread wheat breeding are lacking. We estimated the prediction accuracy of grain yield, protein content and protein yield of 659 inbred lines across five independent breeding cycles and assessed the bias of within-cycle cross-validation. We investigated the influence of outliers on the prediction accuracy and predicted protein yield by its components traits. A high average heritability was estimated for protein content, followed by grain yield and protein yield. The bias of the prediction accuracy using populations from individual cycles using fivefold cross-validation was accordingly substantial for protein yield (17–712 %) and less pronounced for protein content (8–86 %). Cross-validation using the cycles as folds aimed to avoid this bias and reached a maximum prediction accuracy of $$r_{\text{GS}}$$ = 0.51 for protein content, $$r_{\text{GS}}$$ = 0.38 for grain yield and $$r_{\text{GS}}$$ = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to $$r_{\text{GS}}$$ = 0.41 as estimated by cross-validation, while dropping outlier environments did not have a significant effect on the prediction accuracy. Independent validation suggests, on the other hand, that careful consideration is necessary before an outlier correction is undertaken, which removes lines from the training population. Predicting protein yield by multiplying genomic estimated breeding values of grain yield and protein content raised the prediction accuracy to $$r_{\text{GS}}$$ = 0.19 for this derived trait.
Starting Page	1179
Ending Page	1189
Page Count	11
File Format	PDF
ISSN	00405752
Journal	Theoretical and Applied Genetics
Volume Number	129
Issue Number	6
e-ISSN	14322242
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-04-11
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Plant Breeding/Biotechnology Plant Genetics & Genomics Agriculture Plant Biochemistry Biochemistry Biotechnology
Content Type	Text
Resource Type	Article
Subject	Genetics Medicine Biotechnology Agronomy and Crop Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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