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 (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.

Effectiveness of Recurrent Selection in Irrigated Rice Breeding

“…In RS programs, the rate of genetic gain per unit of time can be increased by speeding up the selection cycles, by intensifying the selection pressure, by improving the evaluation precision (thus increasing the heritability), or by any combination of such methods (Bernardo, 2010;Müller et al, 2017). In RS schemes based on progeny testing, highquality phenotyping and maintenance of high genetic variability in the population are factors that should be prioritized in the breeding program.…”

“…2): (i) a recombination cross of progenies using remaining S 2:3 seeds (five panicles per progeny) related to the selected S 1:3 progenies from the last selection cycle, in the off-season (sowing in August) of the first year; (ii) seed multiplication of S 0 plants, with harvest in bulk, in the off-season (sowing in July) of the second year; (iii) phenotypic selection of single S 1 plants in the cropping season (sowing in January) of the third year; (iv) sowing of S 1:2 progenies on favorable conditions for yield potential expression and phenotypic selection between progeny rows, with harvest depletion of genetic variability because of allele fixation, either by genetic drift or by directional selection (Pereira and Vencovsky, 1988). Therefore, the main challenge in RS breeding is to increase favorable allele frequency, while avoiding identity by descent, in a way that genetic recombination remains effective as a source of novel genetic variation (Morais, 1997;Bernardo, 2010;Müller et al, 2017). Therefore, the main challenge in RS breeding is to increase favorable allele frequency, while avoiding identity by descent, in a way that genetic recombination remains effective as a source of novel genetic variation (Morais, 1997;Bernardo, 2010;Müller et al, 2017).…”

Assessing Prediction Models for Different Traits in a Rice Population Derived from a Recurrent Selection Program

“…Other benefits may result from a reduction in phenotyping requirements at every selection cycle (Heffner et al, 2009;Legarra et al, 2014); however, those savings must balance with the additional genotyping costs. Another potential benefit of GS (over classical recurrent selection schemes) is promoting more frequent recombination, as a consequence of shortened selection-crossing cycles, generating novel and useful variation in the breeding population (Heffner et al, 2009;Müller et al, 2017). Another potential benefit of GS (over classical recurrent selection schemes) is promoting more frequent recombination, as a consequence of shortened selection-crossing cycles, generating novel and useful variation in the breeding population (Heffner et al, 2009;Müller et al, 2017).…”

“…Prediction accuracy rapidly erodes over recombination cycles, especially under directional selection (Müller et al, 2017). The persistence of accuracy is a very important factor to consider in GS, since it reduces the need for frequent retraining of the prediction equation.…”

“…The accuracy of Bayesian models tends to persist longer, because those methods are able to consider the co-segregation between markers and QTLs within the family, thus capturing Mendelian sampling (Daetwyler et al, 2010). Bayesian methods are therefore advantageous, even if they show equal or less accuracy in the training population, since greater persistence over generations is expected when applied in selection populations (Habier et al, 2013;Müller et al, 2017). Bayesian methods are therefore advantageous, even if they show equal or less accuracy in the training population, since greater persistence over generations is expected when applied in selection populations (Habier et al, 2013;Müller et al, 2017).…”