title: 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 betweenTSand 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 LDAin 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. LargerTSsize ([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.
authors.affiliation: Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, 70599 Stuttgart, Germany.
authors.forename: Dominik
authors.initials: D
authors.lastname: Müller
authors.affiliation: Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, 70599 Stuttgart, Germany.
authors.forename: Pascal
authors.initials: P
authors.lastname: Schopp
authors.affiliation: Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, 70599 Stuttgart, Germany melchinger@uni-hohenheim.de.
authors.forename: Albrecht E
authors.initials: AE
authors.lastname: Melchinger
mesh_terms: D001947:Breeding; D003198:Computer Simulation; D014644:Genetic Variation; D018745:Genome, Plant; D023281:Genomics; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011995:Recombination, Genetic / Q000235:genetics; D012641:Selection, Genetic; D003313:Zea mays / Q000235:genetics
keywords: GenPred; Genomic Selection; Shared Data Resources; genetic gain; genomic prediction; prediction accuracy; recurrent selection; synthetic populations