Strengthening public breeding pipelines by emphasizing quantitative genetics principles and Open Source data management
The strategic goals of the “Consultative Group on International Agricultural Research” (CGIAR), which serves small-scale agricultural producers in the developing world, include the increase of nutrition and food security, the reduction of poverty, and the reduction of the “environmental footprint” of agricultural production systems (https://www.cgiar.org/how-we-work/strategy/). For each of these goals, progress can be made by breeding new crop varieties with increased productivity, stress resilience, nutritional value, and reduced requirement for fertilizer or agrochemicals. Despite the great success of CGIAR breeding in the last decades, we posit that quantitative genetics principles must be more strongly emphasized in breeding strategies to keep pace with the accelerated demand and with changes in production conditions resulting in a growing demand for food, climate change and newly introduced breeding objectives -such as nutritional quality. Traditionally, molecular breeding approaches focused on the identification of major genes, often for disease resistance, and the introgression of these alleles into elite material. This has been a fruitful strategy to prevent or mitigate production losses since disease resistances are essential traits for most target populations of environments (TPEs). However, the focus on major genes for disease resistances may also have slowed down genetic gain for yield in some programs. We advocate the redesign of breeding pipelines with a stronger orientation on quantitative genetics principles, optimizing the components of the “breeder’s equation” to deliver a high selection response for quantitative traits like yield. Moreover, to improve the basis on which selection decisions are made, we propose an open-source breeding approach in which individual public and private institutions collaborate, align their activities, and share data to enhance efficiency for all participants. We will briefly present the breeder’s equation and highlight the terms that can be manipulated to increase genetic gain per time and per dollar invested. We will also present some guidelines recommended by the Excellence in Breeding (EiB) platform to optimize the selection response in a classical breeding scheme. We then discuss how genome-assisted prediction methods (genomic selection, GS) can be used for further optimization.