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

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 [Formula: see text] but little is known about how [Formula: see text] affects genomic selection (GS) in RS, especially the persistency of prediction accuracy ([Formula: see text]) and genetic gain. Synthetics were simulated by intermating [Formula: see text]= 2-32 parent lines from an ancestral population with short- or long-range linkage disequilibrium ([Formula: see text]) and subjected to multiple cycles of GS. We determined [Formula: see text] and genetic gain across 30 cycles for different training set (TS) sizes, marker densities, and generations of recombination before model training. Contributions to [Formula: see text] and genetic gain from pedigree relationships, as well as from cosegregation and [Formula: see text] 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 [Formula: see text] was high for small [Formula: see text] where predominantly cosegregation contributed to [Formula: see text], but also for large [Formula: see text] where [Formula: see text] 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 [Formula: see text] > 4, given long-range LD_A in the ancestral population. Although our scenarios suggest that information from pedigree relationships contributed to [Formula: see text] 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 ([Formula: see text]) and higher marker density improved persistency of [Formula: see text] and hence genetic gain, but additional recombinations could not increase genetic gain.

Keywords: GenPred; Genomic Selection; Shared Data Resources; genetic gain; genomic prediction; prediction accuracy; recurrent selection; synthetic populations.