Model training across multiple breeding cycles significantly improves genomic prediction accuracy in...
Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.)
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Key message
Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors.
In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvem...
Alternative Titles
Full title
Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.)
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5069347
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5069347
Other Identifiers
ISSN
0040-5752
E-ISSN
1432-2242
DOI
10.1007/s00122-016-2756-5
