Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection

Abstract

Abstract Recurrent selection (RS) has been used in plant breeding to successively improve synthetic and other multiparental populations. Synthetics are generated from a limited number of parents (Np), but little is known about how Np affects genomic selection (GS) in RS, especially the persistency of prediction accuracy (rg,g^) and genetic gain. Synthetics were simulated by intermating Np= 2–32 parent lines from an ancestral population with short- or long-range linkage disequilibrium (LDA) and subjected to multiple cycles of GS. We determined rg,g^ and genetic gain across 30 cycles for different training set (TS) sizes, marker densities, and generations of recombination before model training. Contributions to rg,g^ and genetic gain from pedigree relationships, as well as from cosegregation and LDA between QTL and markers, were analyzed via four scenarios differing in (i) the relatedness between TS and selection candidates and (ii) whether selection was based on markers or pedigree records. Persistency of rg,g^ was high for small Np, where predominantly cosegregation contributed to rg,g^, but also for large Np, where LDA replaced cosegregation as the dominant information source. Together with increasing genetic variance, this compensation resulted in relatively constant long- and short-term genetic gain for increasing Np > 4, given long-range LDA in the ancestral population. Although our scenarios suggest that information from pedigree relationships contributed to rg,g^ for only very few generations in GS, we expect a longer contribution than in pedigree BLUP, because capturing Mendelian sampling by markers reduces selective pressure on pedigree relationships. Larger TS size (NTS) and higher marker density improved persistency of rg,g^ and hence genetic gain, but additional recombinations could not increase genetic gain.