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

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 (ΣΔG), the genetic, GCA and SCA variances (σ2G,σ2gca, σ2sca) of the hybrid population, and prediction accuracy (rgca) for GCA effects across cycles.

Albrecht E. Melchinger, Matthias Frisch

Theoretical and Applied Genetics; September 2023, vol. 136:203

https://doi.org/10.1007/s00122-023-04446-3

 

 

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 (ΣΔGΣΔ�), the genetic, GCA and SCA variances (σ2Gσ�2,σ2gcaσ���2, σ2scaσ���2) 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��=20 out of 950 candidates in each parent population. Scenarios differed for heritability (h2)(ℎ2) and the proportion τ=100×σ2sca:σ2G�=100×σ���2:σ�2 of traits, training set (TS) size (NTS���), and maize vs. wheat. Curves of ΣΔGΣΔ� over selection cycles showed no crossing of both methods. If τ� was high, ΣΔGΣΔ� 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 h2ℎ2 and NTS��� were low. ΣΔGΣΔ� showed a steeper increase and higher selection limit for scenarios with low τ�, high h2ℎ2 and large NTS���. σ2gcaσ���2 and even more so σ2scaσ���2 decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing σ2gcaσ���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.

 

See https://link.springer.com/article/10.1007/s00122-023-04446-3

 

Figure 1: Production of hybrids (green) for the training set using doubled-haploid lines sampled from parent populations ΠF (females, yellow) and ΠM (males, blue). Top: Half-sib reciprocal recurrent genomic selection (HS-RRGS) with NTS/2 half-sib families in each population produced by crossing the candidates with an inbred tester from the opposite population. Bottom: Full-sib reciprocal recurrent genomic selection (FS-RRGS) with NTS single-cross hybrids obtained from paired crosses of DH lines. DH lines in the prediction set of ΠF and ΠM are also shown.

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