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Genomic prediction in hybrid breeding: II. Reciprocal recurrent genomic selection with full-sib and half-sib families

KEY MESSAGE: Genomic prediction of GCA effects based on model training with full-sib rather than half-sib families yields higher short- and long-term selection gain in reciprocal recurrent genomic selection for hybrid breeding, if SCA effects are important. ABSTRACT: Reciprocal recurrent genomic sel...

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Detalles Bibliográficos
Autores principales: Melchinger, Albrecht E., Frisch, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10471712/
https://www.ncbi.nlm.nih.gov/pubmed/37653062
http://dx.doi.org/10.1007/s00122-023-04446-3
Descripción
Sumario:KEY MESSAGE: Genomic prediction of GCA effects based on model training with full-sib rather than half-sib families yields higher short- and long-term selection gain in reciprocal recurrent genomic selection for hybrid breeding, if SCA effects are important. ABSTRACT: Reciprocal recurrent genomic selection (RRGS) is a powerful tool for ensuring sustainable selection progress in hybrid breeding. For training the statistical model, one can use half-sib (HS) or full-sib (FS) families produced by inter-population crosses of candidates from the two parent populations. Our objective was to compare HS-RRGS and FS-RRGS for the cumulative selection gain ([Formula: see text] ), the genetic, GCA and SCA variances ([Formula: see text] ,[Formula: see text] , [Formula: see text] ) of the hybrid population, and prediction accuracy ([Formula: see text] ) for GCA effects across cycles. Using SNP data from maize and wheat, we simulated RRGS programs over 10 cycles, each consisting of four sub-cycles with genomic selection of [Formula: see text] out of 950 candidates in each parent population. Scenarios differed for heritability [Formula: see text] and the proportion [Formula: see text] of traits, training set (TS) size ([Formula: see text] ), and maize vs. wheat. Curves of [Formula: see text] over selection cycles showed no crossing of both methods. If [Formula: see text] was high, [Formula: see text] was generally higher for FS-RRGS than HS-RRGS due to higher [Formula: see text] . In contrast, HS-RRGS was superior or on par with FS-RRGS, if [Formula: see text] or [Formula: see text] and [Formula: see text] were low. [Formula: see text] showed a steeper increase and higher selection limit for scenarios with low [Formula: see text] , high [Formula: see text] and large [Formula: see text] . [Formula: see text] and even more so [Formula: see text] decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing [Formula: see text] . Since the TS for FS-RRGS can additionally be used for hybrid prediction, we recommend this method for achieving simultaneously the two major goals in hybrid breeding: population improvement and cultivar development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-023-04446-3.