Genomic predictions and genome-wide association studies based on RAD-seq of quality-related metabolites for the genomics-assisted breeding of tea plants

• Published in: Scientific reports Vol. 10; no. 1; pp. 17480 - 10
• Main Authors: Yamashita, Hiroto, Uchida, Tomoki, Tanaka, Yasuno, Katai, Hideyuki, Nagano, Atsushi J., Morita, Akio, Ikka, Takashi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.10.2020; Nature Portfolio
• Subjects: 631/114; 631/208; 631/449; 631/61; Camellia sinensis - genetics; Catechin - analogs & derivatives; Catechin - chemistry; Computational Biology; Genetic Association Studies; Genome, Plant; Genomics; Genotype; Humanities and Social Sciences; Linkage Disequilibrium; multidisciplinary; Phenotype; Plant Breeding; Polymorphism, Single Nucleotide; Science; Science (multidisciplinary); Sequence Analysis, DNA
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-74623-7

Effectively using genomic information greatly accelerates conventional breeding and applying it to long-lived crops promotes the conversion to genomic breeding. Because tea plants are bred using conventional methods, we evaluated the potential of genomic predictions (GPs) and genome-wide association studies (GWASs) for the genetic breeding of tea quality-related metabolites using genome-wide single nucleotide polymorphisms (SNPs) detected from restriction site-associated DNA sequencing of 150 tea accessions. The present GP, based on genome-wide SNPs, and six models produced moderate prediction accuracy values ( r ) for the levels of most catechins, represented by ( −)-epigallocatechin gallate ( r  = 0.32–0.41) and caffeine ( r  = 0.44–0.51), but low r values for free amino acids and chlorophylls. Integrated analysis of GWAS and GP detected potential candidate genes for each metabolite using 80–160 top-ranked SNPs that resulted in the maximum cumulative prediction value. Applying GPs and GWASs to tea accession traits will contribute to genomics-assisted tea breeding.