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Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows
Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.ORCID iD: 0000-0002-7173-5579
2013 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 8, article id e70256Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
San Fransisco, USA: Public Library Science , 2013. Vol. 8, no 8, article id e70256
National Category
Bioinformatics and Systems Biology
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URN: urn:nbn:se:oru:diva-40610DOI: 10.1371/journal.pone.0070256ISI: 000324470100021PubMedID: 23990900Scopus ID: 2-s2.0-84882661103OAI: oai:DiVA.org:oru-40610DiVA, id: diva2:777908
Available from: 2015-01-09 Created: 2015-01-09 Last updated: 2018-05-22Bibliographically approved

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