Phenotypic prediction based on metabolomic data for growing pigs from three main european breeds

Rohart, F., Paris, A., Laurent, B., Canlet, C., Molina, J., Mercat, M. J., Tribout, T., Muller, N., Iannuccelli, N., Villa-Vialaneix, N., Liaubet, L., Milan, D. and San Cristoba,l M. (2012) Phenotypic prediction based on metabolomic data for growing pigs from three main european breeds. Journal of Animal Science, 90 13: 4729-4740. doi:10.2527/jas.2012-5338

Author Rohart, F.
Paris, A.
Laurent, B.
Canlet, C.
Molina, J.
Mercat, M. J.
Tribout, T.
Muller, N.
Iannuccelli, N.
Villa-Vialaneix, N.
Liaubet, L.
Milan, D.
San Cristoba,l M.
Title Phenotypic prediction based on metabolomic data for growing pigs from three main european breeds
Journal name Journal of Animal Science   Check publisher's open access policy
ISSN 0021-8812
Publication date 2012
Year available 2012
Sub-type Article (original research)
DOI 10.2527/jas.2012-5338
Open Access Status
Volume 90
Issue 13
Start page 4729
End page 4740
Total pages 12
Place of publication Champaign, IL United States
Publisher American Society of Animal Science
Collection year 2012
Language eng
Subject 1103 Clinical Sciences
1106 Human Movement and Sports Science
1311 Genetics
Abstract Predicting phenotypes is a statistical and biotechnical challenge, both in medicine (predicting an illness) and animal breeding (predicting the carcass economical value on a young living animal). High-throughput fine phenotyping is possible using metabolomics, which describes the global metabolic status of an individual, and is the closest to the terminal phenotype. The purpose of this work was to quantify the prediction power of metabolomic profiles for commonly used production phenotypes from a single blood sample from growing pigs. Several statistical approaches were investigated and compared on the basis of cross validation: raw data vs. signal preprocessing (wavelet transformation), with a single-feature selection method. The best results in terms of prediction accuracy were obtained when data were preprocessed using wavelet transformations on the Daubechies basis. The phenotypes related to meat quality were not well predicted because the blood sample was taken some time before slaughter, and slaughter is known to have a strong influence on these traits. By contrast, phenotypes of potential economic interest (e.g., lean meat percentage and ADFI) were well predicted (R2 = 0.7; P < 0.0001) using metabolomic data.
Keyword Metabolome
Phenotypic prediction
Variable selection
Wavelet transformation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Australian Institute for Bioengineering and Nanotechnology Publications
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