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

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