Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

Abstract

Key message Optimal cross selection increases long-term genetic gain of two-part programs with rapid recurrent genomic selection. It achieves this by optimising efficiency of converting genetic diversity into genetic gain through reducing the loss of genetic diversity and reducing the drop of genomic prediction accuracy with rapid cycling. AbstractThis study evaluates optimal cross selection to balance selection and maintenance of genetic diversity in two-part plant breeding programs with rapid recurrent genomic selection. The two-part program reorganises a conventional breeding program into a population improvement component with recurrent genomic selection to increase the mean value of germplasm and a product development component with standard methods to develop new lines. Rapid recurrent genomic selection has a large potential, but is challenging due to genotyping costs or genetic drift. Here we simulate a wheat breeding program for 20 years and compare optimal cross selection against truncation selection in the population improvement component with one to six cycles per year. With truncation selection we crossed a small or a large number of parents. With optimal cross selection we jointly optimised selection, maintenance of genetic diversity, and cross allocation with AlphaMate program. The results show that the two-part program with optimal cross selection delivered the largest genetic gain that increased with the increasing number of cycles. With four cycles per year optimal cross selection had 78% (15%) higher long-term genetic gain than truncation selection with a small (large) number of parents. Higher genetic gain was achieved through higher efficiency of converting genetic diversity into genetic gain; optimal cross selection quadrupled (doubled) efficiency of truncation selection with a small (large) number of parents. Optimal cross selection also reduced the drop of genomic selection accuracy due to the drift between training and prediction populations. In conclusion optimal cross selection enables optimal management and exploitation of population improvement germplasm in two-part programs.Electronic supplementary materialThe online version of this article (10.1007/s00122-018-3125-3) contains supplementary material, which is available to authorized users.

Optimising Genomic Selection in Wheat: Effect of Marker Density, Population Size and Population Structure on Prediction Accuracy

“…The results presented here show that incorporating more breeding cohorts in the training set is beneficial in a conventional breeding program with a long generation interval. A recent study by Gorjanc et al (2018) investigates the response in a rapid cycling program which uses genomic selection to quickly identify parents.…”

Genomic Selection at Preliminary Yield Trial Stage: Training Population Design to Predict Untested Lines

“…simulation study also suggests that the probability of success in a breeding program could be achieved with fewer crosses more carefully chosen and bigger segregating populations. This topic has recently been discussed by Gorjanc et al [36], when they simulated the results of applying an optimal cross selection scheme to balance selection and maintenance of genetic diversity for breeding programs under a recurrent genomic selection scheme. They showed the benefits of the optimal cross selection, and the positive implications for maintaining genetic diversity and genomic prediction accuracy in the long term.…”

Modelling illustrates that genomic selection provides new opportunities for intercrop breeding

“…Stochastic simulations are becoming widely used to explore the efficiency of genomic selection in single species breeding only (e.g. Muleta et al 2019;Gorjanc et al 2018;Gaynor et al 2017), but to our knowledge our use of simulations to explore genomic selection's value for intercrop breeding is unique. Through simulations, we have shown that intercrop breeding programs using genomic selection can produce faster genetic gain than intercrop breeding programs which use phenotypic selection, working to a common cost basis that reflects the resources available for a mediumly-invested breeding initiative.…”

“…While these requirements can be easily met within a simulation framework, practical application of a maximum avoidance crossing scheme may be more challenging, as breeders might introduce new genetic material to their breeding population, and not every crossing event might produce seed. Other, more complex, strategies might be more suitable to reduce the loss of genetic variation in real-world breeding programs, such as optimal contribution selection and crossing (Gorjanc et al 2018;Akdemir & Sánchez, 2016;Sonesson et al 2012;Meuwissen, 1997), and exploring these could be a feature of future work.…”