Optimizing self-pollinated crop breeding employing genomic selection: from schemes to updating training sets

Abstract

Long-term breeding schemes employing genomic selection (GS) can boost the response to selection per year. Although several studies show that GS delivers a higher response to selection, only a few analyze the best strategy to employ it, specifically how often and in what manner the training set (TS) should be updated. Therefore, we used stochastic simulation to compare in a long-term breeding program of a hypothetical self-pollinated crop five different strategies to implement GS in the line fixation stage and four methods and sizes to update the TS. Moreover, among breeding schemes, we proposed a new approach for using GS to select the best individuals in each F2 progeny based on genomic estimated breeding and divergence and crossed them to generate a new recombination event. Finally, we compared these schemes to the traditional phenotypic selection and drift. Our results showed that using GS in F2 followed by the phenotypic selection of new parentals in F4 was the best scenario. Furthermore, adding a new set of training data every cycle (over 800) to update the TS maintains the accuracy at satisfactory levels for many more generations, showing that more data is better than optimizing the genetic relationship between TS and the targeted population in a closed system. Hence, we believe that these results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.

Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance

“…Several studies have reported an increase in response to selection in breeding schemes using GS (Gorjanc et al 2018;Muleta et al 2019;DoVale et al 2021;Sabadin et al 2021). However, to increase the GS-based breeding program e ciency, it is essential to decide how and at which stage to apply this tool (Sabadin et al 2021). Usually, this increase is due to the shortening of the breeding cycle, which can be logistically challenging.…”

“…In this context, our results showed that even using a low-density marker set, it is possible to keep the accuracy at levels that provide genetic gain over many breeding cycles however there is a need to update the TS properly. Furthermore, as described by (Sabadin et al 2021), the GPO updating method promotes more accurate estimates of LD between markers and QTL since, in long-term breeding schemes, recombination between marker and QTL causes an LD decrease. In contrast, selection and drift generate new LD or tighten the LD between closely linked loci.…”

“…Consequently, it may create a plateau of faster selection response due to the inbreeding effect. Also, the xation of the alleles in the heterotic groups becomes partly due to random factors (Gerke et al 2015;DoVale et al 2021). Reducing inbreeding can be done by increasing the number of parents (breeding sizes) or conserving population genetic diversity via optimal parental contribution (Cowling et al 2020).…”

“…Markers' effects were estimated using the ridge-regression best linear unbiased prediction (RRBLUP), considering the respective functions in the AlphaSimR package for A and A + D (Gaynor et al 2021). Moreover, we adopted the GPO strategy to update the TS to keep the accuracy at reasonable levels for more breeding cycles (Sabadin et al 2021).…”