Genomic and pedigree-based predictive ability for quality traits in tea (Camellia sinensis (L.) O. Kuntze)

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.

Automated summary

By comparing six genomic prediction models, researchers found that some tea quality traits have high prediction accuracies, while others are lower. All GS models performed similarly, with BRR, BayesA, and others slightly outperforming. Increasing trait heritability and training population size can improve prediction accuracies.