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

Abstract

Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.

Evaluating breeding for broad versus narrow adaptation for cassava in Nigeria using stochastic simulation

“…The realized genetic gains from GS‐enabled breeding programs were greater than those of a conventional breeding program. This finding was consistent with previous simulation studies in other crop species, including tea, rice, wheat, and sorghum (Lubanga et al., 2022; Muleta et al., 2019; Sabadin et al., 2022; Tessema et al., 2020), which affirmed that breeding programs implementing GS can achieve higher genetic gain than phenotypic selection breeding programs. These findings revealed that the use of the GS technique can enhance the efficiency of the breeding program even in the presence of G × E due to information sharing among genotyped clones over years and locations (Heffner et al., 2009).…”

Genomic-inferred cross-selection metrics for multi-trait improvement in a recurrent selection breeding program

“…Pea has limited genetic diversity (Yang et al, 2022) and presumably has Me less than the 500 random QTN selected in our study. This aligns with several simulation studies that predominantly assume polygenic traits are controlled by 500 or greater QTN (Wientjes et al, 2015;Yao et al, 2018;Peters et al, 2020;Sabadin et al, 2022).…”

“…Recently, Sabadin et al (2022) also emphasized the relationship between the number of parents and the effective population size ( N e ). In the simulation study, the author reported greater resilience to the loss of genetic variance over the long term, involving 48 parents compared to 24 parents.…”

Approaching 25 years of progress towards Fusarium head blight resistance in southern soft red winter wheat (Triticum aestivum L.)

“…In the To enhance the utility of predictions in earlier generations, studies should now assess the feasibility of genotyping greater numbers of plants, reducing generation time, and accelerating the average rate of genetic gain. While many studies have shown the benefits of reducing generation time in simulations (Bernardo, 2020;Sabadin et al, 2022), evaluation of real-world efficacy will rely on understanding the costs of restructuring breeding programmes to handle increased labour, the efficacy of within-family predictions using models trained on historic data, and increased genotyping costs associated with greater sampling. As lines are selected earlier in the breeding programme pipeline, a greater emphasis will be placed on withinfamily than among-family selection.…”