Modelling selection response in plant-breeding programs using crop models

 

 Plant-breeding programs are designed and executed over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for target population of environments.Within each cycle, selection is applied to the prevailing genetic variation within a structured reference population. It is the primary mechanism by which breeding programs make the desired genetic changes.

Selection for specific trait combinations require a quantitative link between the effects of the alleles of thegenes impacted by selection and the trait phenotypes of plants and their breeding value.3 Selection responses can be modeled using crop growth models which aid in establishing the geneto- phenotype link. A model was built for wheat cropping system using the APSIM package. Phenotypes for a wheat diversity panel segregating for a set of physiological parameters were simulated regulating phenology, biomass partitioning and the ability to capture environmental resources in three Australian environments with contrasting water deficit patterns. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. The impact of yield components on yield prediction accuracy was quantified.1 A novel and powerful computational procedure, Approximate Bayesian computation allows the incorporation of CGMs directly into the estimation of whole genome marker effects in Whole Genome Prediction (WGP). A study was conducted to compare the prediction accuracy of ABC with GBLUP. ABC method is more accurate than the GBLUP in predicting performance in environments represented in the estimation set for traits determined by non-additive gene effects. Incorporating biological knowledge using ABC for in the form of CGMs into WGP is a very promising approach in improving prediction accuracy for most challenging scenarios in plant breeding and applied genetics.4 Crop growth models & computer simulation in combination aid in overcoming some of the difficulties associated with the conventional approach for developing theoretical prediction models. Ultimately these should aid in modeling the selection response more efficiently. Precise modeling of selection response allows for advanced breeding strategies that improve breeders’ abilities to handle complex trait–environment interactions and develop target genotypes.2

References

1.BUSTOS-KORTOS, D., BOER, M.P., MALOSETTI, M., CHAPMAN, S., CHENU, K., ZHENG, B. AND VAN EUWIJK, F.A., 2019, Combining crop growth modeling and statistical genetic modeling to evaluate phenotyping strategies.Front. Pl. Sci., 10: 1491

2.COOPER, M., PODLICH, D.W., JENSEN, N.M., CHAPMAN, S.C. AND HAMMER, G.L., 1999, Modelling plant breeding programs. Trends in Agron., 2: 33-64.

3.COOPER, M., POWELL, O., VOSSSS-FELS, K,P., MESSINA, C.D., GHO, C., PODLICH, D.W., TECHNOW, F., CHAPMAN,

S.C., BEVERIDGE, C.A., ORTIZ-BARRIENTOS, D. AND HAMMER, G.L. 2020, Modelling selection response in plant-breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions. in silico Plants. 3(1): 16.

4.TECHNOW, F., MESSINA, C.D., TOTIR, L.R. AND COOPER, M., 2015. Integrating crop growth models with whole genome prediction through approximate Bayesian computation. PLOS one, 10(6): 855.