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 teabreeding 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.
Genetic improvement of quality traits in tea (Camellia sinensis (L.) O. Kuntze) through conventional breeding methods has been limited, because tea quality is a difficult and expensive trait to measure. Genomic selection (GS) is suitable for predicting such complex traits, as it uses genome wide markers to estimate the genetic values of individuals. We compared the prediction accuracies of six genomic prediction models including Bayesian ridge regression (BRR), genomic best linear unbiased prediction (GBLUP), BayesA, BayesB, BayesC and reproducing kernel Hilbert spaces models incorporating the pedigree relationship namely; RKHS-pedigree, RKHS-markers and RKHS markers and pedigree (RKHS-MP) to determine the breeding values for 12 tea quality traits. One hundred and three tea genotypes were genotyped using genotyping-by-sequencing and phenotyped using nuclear magnetic resonance spectroscopy in replicated trials. We also compared the effect of trait heritability and training population size on prediction accuracies. The traits with the highest prediction accuracies were; theogallin (0.59), epicatechin gallate (ECG) (0.56) and theobromine (0.61), while the traits with the lowest prediction accuracies were theanine (0.32) and caffeine (0.39). The performance of all the GS models were almost the same, with BRR (0.53), BayesA (0.52), GBLUP (0.50) and RKHS-MP (0.50) performing slightly better than the others. Heritability estimates were moderate to high (0.35–0.92). Prediction accuracies increased with increasing training population size and trait heritability. We conclude that the moderate to high prediction accuracies observed suggests GS is a promising approach in tea improvement and could be implemented in breeding programmes.
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