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Journal of Animal Science Abstract - Animal Growth, Physiology, and Reproduction

Phenotypic prediction based on metabolomic data for growing pigs from three main European breeds1

 

This article in JAS

  1. Vol. 90 No. 13, p. 4729-4740
     
    Received: Mar 30, 2012
    Accepted: Sept 21, 2012
    Published: January 20, 2015


    2 Corresponding author(s): magali.san-cristobal@toulouse.inra.fr
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doi:10.2527/jas.2012-5338
  1. F. Rohart*†,
  2. A. Paris,
  3. B. Laurent,
  4. C. Canlet§,
  5. J. Molina§,
  6. M. J. Mercat#,
  7. T. Tribout,
  8. N. Muller,
  9. N. Iannuccelli*,
  10. N. Villa-Vialaneix**,
  11. L. Liaubet*,
  12. D. Milan* and
  13. M. San Cristobal 2
  1. INRA, UMR444 Laboratoire de Génétique Cellulaire, F-31326 Castanet Tolosan, France
    INSA, Département de Génie Mathématiques, and Institut de Mathématiques, Université de Toulouse (UMR 5219), F-31077 Toulouse, France
    INRA, Met@risk, F-75231 Paris Cedex 05, France
    INRA, UMR 1331 Toxalim (Research Centre in Food Toxicology), INRA/INP/UPS, F-31027 Toulouse, France
    BIOPORC, 75595 PARIS Cedex 12
    INRA GABI, F-78351 Jouy-en-Josas cedex, France
    INRA UE450 Testage - Porcs, F-35653 Le Rheu, France
    SAMM, Université Paris 1, 75013 Paris, France

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.

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