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

Abstract

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 (Σ Δ G), the genetic, GCA and SCA variances (σG2 , σgca2 , σsca2) of the hybrid population, and prediction accuracy (rgca) 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 Ne= 20 out of 950 candidates in each parent population. Scenarios differed for heritability (h2) and the proportion τ=100×σsca2:σG2 of traits, training set (TS) size (NTS), and maize vs. wheat. Curves of Σ Δ G over selection cycles showed no crossing of both methods. If τ was high, Σ Δ G was generally higher for FS-RRGS than HS-RRGS due to higher rgca . In contrast, HS-RRGS was superior or on par with FS-RRGS, if τ or h2 and NTS were low. Σ Δ G showed a steeper increase and higher selection limit for scenarios with low τ , high h2 and large NTS . σgca2 and even more so σsca2 decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing σgca2 . 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.