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“Modelos de Selección y Evaluación de Reproductores’ (MOSEVAR)

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Scientic Supervisor / Contact Person

Name and Surname
Isabel Cervantes Navarro

Localization & Research Area

Faculty / Institute
Faculty of Veterinary Medicine
Animal Production
Research Area
Life Sciences (LIF)

MSCA & ERC experience

Research group / research team hosted any MSCA fellow?
Research group / research team have any ERC beneficiaries?

Research Team & Research Topic

Research Team / Research Group Name (if any)
"Modelos de Selección y Evaluación de Reproductores" (MOSEVAR)
Website of the Research team / Research Group / Department
Brief description of the Research Team / Research Group / Department
The research group was officially created in 2006 and belongs to the Complutense University of Madrid (UCM). The name of the group is "Modelos de Selección y Evaluación de Reproductores" (MOSEVAR) - " Genetic models and breeding value predictions of breeding stock". Nowadays we are four UCM full time researchers, one from an external center (SERIDA), one predoctoral student and three technicians.

The group has published since its creation 130/313works (according to Scopus / Google Scholar of the UCM bibliometric Portal, the complete list can be consulted at: in journals in the areas of genetics, veterinary and animal breeding. Since its foundation, the group has been positively evaluated annually by the Complutense University or Madrid or the delegated institution, and nowadays it is classified as Good. The group has an animal facility in which mice selection experiment can be carried out.

The main objective of our research lines is applying conservation and breeding tools to improve the genetic variability and the genetic response for traits of interest in an animal population. We count with theses specific research lines:

i. Environmental variance divergent selection experiment in Mice
ii. Alpaca breeding program management
iii. Spanish Pure Arab Horse breeding program management
iv. Health traits research in Dairy Cattle
v. Development of free software to preserve genetic variability in a small population
Research lines / projects proposed
The research lines are focused on the management of livestock populations from a genetic point of view, either for selection or for conservation. All genetic selection techniques are aimed at simultaneously promoting and improving the animal welfare.
The group is in charge of the breeding management of several species being available to study different performance datasets and pedigree data: from a population of laboratory mice (characters of homogeneity of weight at birth), a population of alpacas (fiber characters), a dairy cattle population (characters of milk production) and a horse breed (functional traits). For most of these populations we have also DNA samples.

Nowadays, the main research line topic is the genetic control of environmental variance to be applied in livestock breeds, being recognized the importance of the group internationally (participation in ERC projects as Feed-a-gene). Livestock production in EU faces serious challenges because farmers have to deal higher costs for compliance with legislation compared to competitors on the world market. In order to thrive, it is necessary to increase the efficiency of production by utilizing new scientific knowledge and technological development. Hence, there is an increasing interest in the homogeneity of the animal production that would decrease the cost of handling and production that ultimately would increase the profitability of the farm and increase the animal welfare.

For this main objective, nowadays we have DNA samples and it is necessary the application genomic selection techniques. Also debugging of molecular marker data of SNP type and recoding markers and animals (BIG DATA). Data simulation and the management of software for the application of genomic selection to livestock populations.

Application requirements

Professional Experience & Documents
Curriculum vitae with detailed information about: experience in the livestock field and experience with big data management and bioinformatic tools.
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