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

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...

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Published inScientific 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
LanguageEnglish
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
Online AccessGet full text

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Abstract 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.
AbstractList Abstract 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.
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.
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.
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.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.
ArticleNumber 17480
Author Morita, Akio
Yamashita, Hiroto
Tanaka, Yasuno
Ikka, Takashi
Nagano, Atsushi J.
Uchida, Tomoki
Katai, Hideyuki
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  surname: Yamashita
  fullname: Yamashita, Hiroto
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  givenname: Tomoki
  surname: Uchida
  fullname: Uchida, Tomoki
  organization: Faculty of Agriculture, Shizuoka University
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  surname: Tanaka
  fullname: Tanaka, Yasuno
  organization: Faculty of Agriculture, Shizuoka University, United Graduate School of Agricultural Science, Gifu University
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  givenname: Hideyuki
  surname: Katai
  fullname: Katai, Hideyuki
  organization: Shizuoka Prefectural Research Institute of Agriculture and Forestry, Tea Research Center, Shizuoka Prefecture Chubu Agriculture and Forestry Office
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  givenname: Atsushi J.
  surname: Nagano
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  surname: Morita
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  organization: Faculty of Agriculture, Shizuoka University, Institute for Tea Science, Shizuoka University
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  givenname: Takashi
  surname: Ikka
  fullname: Ikka, Takashi
  email: ikka.takashi@shizuoka.ac.jp
  organization: Faculty of Agriculture, Shizuoka University, Institute for Tea Science, Shizuoka University
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Snippet Effectively using genomic information greatly accelerates conventional breeding and applying it to long-lived crops promotes the conversion to genomic...
Abstract Effectively using genomic information greatly accelerates conventional breeding and applying it to long-lived crops promotes the conversion to genomic...
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StartPage 17480
SubjectTerms 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
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Title Genomic predictions and genome-wide association studies based on RAD-seq of quality-related metabolites for the genomics-assisted breeding of tea plants
URI https://link.springer.com/article/10.1038/s41598-020-74623-7
https://www.ncbi.nlm.nih.gov/pubmed/33060786
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https://pubmed.ncbi.nlm.nih.gov/PMC7562905
https://doaj.org/article/4098fe4f723b4c9db7d38eb18ae8ba73
Volume 10
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