Abstract
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.
Highlights
While several studies comparing the performance of different prediction models have been reported in many crops (Grattapaglia et al 2018; Kwong et al 2017; Lozada et al 2019), our objective was to compare 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-P), RKHS-markers (RKHS-M) and RKHS markers and pedigree (RKHS-MP), to determine the breeding values for 12 tea quality traits measured in two different environments using Nuclear Magnetic Resonance (NMR)
Negative values in the marker-based relationship matrix imply that the detection of an allele in one genotype makes it less likely to be detected in the other genotype, zero indicate absence of dependence, while positive values indicate an increased likelihood of an allele being detected in the other genotype
The RKHS-MP model performed well in most of the traits, and had the highest accuracies in epicatechin gallate (ECG) and theogallin, it did not perform significantly better than Bayesian ridge regression (BRR) and GBLUP
Summary
Kuntze) quality is an important attribute in a tea breeding programme It is the main determinant of price at the tea auction and is measured based on the flavour and colour of the liquor (hue) along with appearance of dry tea (leaf) (Zheng et al 2016). Mouthfeel, colour and aroma are important tea quality traits for consumer and are key targets for selection in breeding programmes. These tea attributes originate from biochemical compounds present in fresh tea shoots such as catechins, alkaloids, amino acids and volatile compounds (Borse 2012; Chen et al 2018a)
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