Genomic selection strategies to increase genetic gain in tea breeding programs

Tea [Camellia sinensis (L.) O. Kuntze] is mainly grown in low‐ to middle‐income countries (LMIC) and is a global commodity. Breeding programs in these countries face the challenge of increasing genetic gain because the accuracy of selecting superior genotypes is low and resources are limited. Phenotypic selection (PS) is traditionally the primary method of developing improved tea varieties and can take over 16 yr. Genomic selection (GS) can be used to improve the efficiency of tea breeding by increasing selection accuracy and shortening the generation interval and breeding cycle. Our main objective was to investigate the potential of implementing GS in tea‐breeding programs to speed up genetic progress despite the low cost of PS in LMIC. We used stochastic simulations to compare three GS‐breeding programs with a Pedigree and PS program. The PS program mimicked a practical commercial tea‐breeding program over a 40‐yr breeding period. All the GS programs achieved at least 1.65 times higher genetic gains than the PS program and 1.4 times compared with Seed‐Ped program. Seed‐GSc was the most cost‐effective strategy of implementing GS in tea‐breeding programs. It introduces GS at the seedlings stage to increase selection accuracy early in the program and reduced the generation interval to 2 yr. The Seed‐Ped program outperformed PS by 1.2 times and could be implemented where it is not possible to use GS. Our results indicate that GS could be used to improve genetic gain per unit time and cost even in cost‐constrained tea‐breeding programs. Core Ideas Genomic selection strategies achieved higher genetic gains than phenotypic and pedigree‐based selection programs. Cost‐constrained Seedlings Genomic Selection was the most promising and cost‐effective strategy. The Seedlings Genomic Selection program introduced genomic selection at the seedlings stage and had 1.7 times more genetic gain than phenotypic selection. The pedigree program outperformed phenotypic selection and can be implemented in cases where genomic selection cannot be used.