New insights into trait introgression with the look-ahead intercrossing strategy

Area of Science:

• Plant breeding
• Quantitative genetics
• Genomic selection

Background:

• Trait introgression (TI) is crucial for crop improvement but traditionally relies on time-consuming backcrossing (BC).
• Achieving desired allele introduction and genetic background recovery in TI programs presents significant challenges.
• Optimizing TI strategies is essential for efficient development of improved crop varieties.

Purpose of the Study:

• To evaluate the advantages of incorporating intercrossing (IC) into TI programs compared to sole reliance on BC.
• To introduce and assess a novel look-ahead intercrossing (LAS-IC) method for enhanced TI efficiency.
• To compare the performance of different TI strategies under varying resource availabilities.

Main Methods:

• Simulation of a TI breeding pipeline using traditional BC and modified strategies incorporating IC.
• Implementation of three selection strategies for BC and three modified strategies for IC.
• Development of the look-ahead intercrossing (LAS-IC) method combining Monte Carlo simulations and selection criteria.
• Comparison of strategy efficiency across five resource availability levels.

Main Results:

• Inclusion of intercrossing (IC) substantially increases TI program efficiency and probability of success.
• The proposed LAS-IC method demonstrates superior performance across various scenarios.
• LAS-IC achieves higher success probabilities with reduced resource requirements compared to BC-only strategies.
• Intercrossing accelerates the achievement of major QTL introgression and desired background recovery.

Conclusions:

• Intercrossing is a valuable addition to traditional trait introgression programs.
• The LAS-IC method offers a computationally efficient and highly effective approach for TI.
• Optimized TI strategies, including IC, can significantly reduce the time and cost of developing improved crop varieties.