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This article in JAS

  1. Vol. 95 No. 4, p. 1813-1826
    Received: Oct 11, 2016
    Accepted: Jan 03, 2017
    Published: April 13, 2017

    2 Corresponding author(s):


BREEDING AND GENETICS SYMPOSIUM: Breeding for resilience to heat stress effects in dairy ruminants. A comprehensive review1

  1. M. J. Carabaño 2*,
  2. M. Ramón,
  3. C. Díaz*,
  4. A. Molina,
  5. M. D. Pérez-Guzmán and
  6. J. M. Serradilla
  1. * Departamento de Mejora Genética Animal, INIA, Ctra. de La Coruña km 7.5, 28040 Madrid, Spain
     Centro Regional de Selección y Reproducción Animal (CERSYRA)- Instituto Regional de Investigación y Desarrollo Agroalimentario y Forestal de Castilla-La Mancha (IRIAF-JCCM), 13300 Valdepeñas, Spain
     Departamentos de Genética y de Producción Animal, Universidad de Córdoba, Campus de Rabanales. 14071 Córdoba, Spain


Selection for heat tolerant (HT) animals in dairy production has been so far linked to estimation of declines in production using milk recording and meteorological information on the day of control using reaction norms. Results from these models show that there is a reasonable amount of genetic variability in the individual response to high heat loads, which makes feasible selection of HT animals at low costs. However, the antagonistic relationship between level of production and response to heat stress (HS) implies that selection for HT animals under this approach must be done with caution so that productivity is not damaged. Decomposition of the genetic variability in principal components (PC) can provide selection criteria independent of milk production level although biological interpretation of PC is difficult. Moreover, given that response to heat stress for each animal is estimated with very sparse information collected under different physiological and management circumstances, biased (normally underestimation) and lack of accuracy may be expected. Alternative phenotypic characterization of HT can come from the use of physiological traits, which have also shown moderate heritability. However, costs of a large scale implementation based on physiological characteristics has precluded its use. Another alternative is the use of biomarkers that define heat tolerance. A review of biomarkers of HS from more recent studies is provided. Of particular interest are milk biomarkers, which together with infrared spectra prediction equations can provide useful tools for genetic selection. In the ‘omics’ era, genomics, transcriptomics, proteomics and metabolomics have been already used to detect genes affecting HT. A review of findings in these areas is also provided. Except for the slick hair gene, there are no other genes for which variants have been clearly associated with HT. However, integration of omics information could help in pointing at knots of the HS control network and, in the end, to a panel of markers to be used in the selection of HT animals. Overall, HT is a complex phenomenon that requires integration of fine phenotypes and omics information to provide accurate tools for selection without damaging productivity. Technological developments to make on-farm implementation feasible and with greater insight into the key biomarkers and genes involved in HT are needed.

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