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 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 ( ΣΔ G ), the genetic, GCA and SCA variances ( σ_G^2 , σ_gca^2 , σ_sca^2 ) of the hybrid population, and prediction accuracy ( r_gca ) 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 N_e = 20 out of 950 candidates in each parent population. Scenarios differed for heritability ( h^2) and the proportion τ = 100 ×σ_sca^2 :σ_G^2 of traits, training set (TS) size ( N_TS ), and maize vs. wheat. Curves of ΣΔ G over selection cycles showed no crossing of both methods. If τ was high, ΣΔ G was generally higher for FS-RRGS than HS-RRGS due to higher r_gca . In contrast, HS-RRGS was superior or on par with FS-RRGS, if τ or h^2 and N_TS were low. ΣΔ G showed a steeper increase and higher selection limit for scenarios with low τ , high h^2 and large N_TS . σ_gca^2 and even more so σ_sca^2 decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing σ_gca^2 . 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.