pone.0070256.pdf 300,82KB
1000 Titel
  • Integrating Milk Metabolite Profile Information for the Prediction of Traditional Milk Traits Based on SNP Information for Holstein Cows
1000 Autor/in
  1. Melzer, Nina |
  2. Repsilber, Dirk |
  3. Wittenburg, Dörte |
1000 Erscheinungsjahr 2013
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2013-08-21
1000 Erschienen in
1000 Quellenangabe
  • 8(8): e70256
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2013
1000 Lizenz
1000 Verlagsversion
  • |
  • |
1000 Ergänzendes Material
  • |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype).
1000 Sacherschließung
lokal Quantitative trait loci
lokal Metabolomics
lokal Protein metabolism
lokal Metabolites
lokal Molecular genetics
lokal Heredity
lokal Fats
lokal Milk
1000 Fachgruppe
  1. Biologie |
  2. Agrarwissenschaften |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
1000 Label
1000 Förderer
  1. Federal ministry of Education and Research, Germany (BMBF) |
1000 Fördernummer
  1. 0315137
1000 Förderprogramm
  1. FUGATO plus
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Federal ministry of Education and Research, Germany (BMBF) |
    1000 Förderprogramm FUGATO plus
    1000 Fördernummer 0315137
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6404533.rdf
1000 Erstellt am 2017-09-21T12:58:16.389+0200
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1000 Zuletzt bearbeitet Thu Jan 30 15:53:50 CET 2020
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1000 Oai Id
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