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Impacts of the inclusion of runs of homozygosity in the mating strategy processes in a beef cattle simulated population

Grant number: 21/11156-8
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): August 01, 2022
Effective date (End): February 29, 2024
Field of knowledge:Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
Principal Investigator:Ricardo Vieira Ventura
Grantee:Alana Selli
Host Institution: Faculdade de Medicina Veterinária e Zootecnia (FMVZ). Universidade de São Paulo (USP). São Paulo , SP, Brazil


Mating strategies can be included in animal breeding programs in order to reduce inbreeding without compromising the genetic gain of the population. An important method applied to the characterization of autozygosity makes use of the concept runs of homozygosity (ROH), which corresponds to identical copies of haplotypes inherited from the same individual. This method allows the quantification of a genomic inbreeding coefficient specific to each individual and the identification of the location of autozygous loci in the genome. Therefore, the inclusion of ROH may offer an improved degree of details for mating schemes in animal breeding programs, amplifying the genetic gain while reducing inbreeding in the population. The objectives of this project are (i) to apply different selection strategies regarding ROH patterns in a simulated population, (ii) to investigate the impact of such strategies on the genetic gain and parameters related to the population genetic variability, and (iii) to develop an interface which allows the monitoring of the selection process and the observation of its impacts on each component of the system. The AlphaSimR software will be used for the simulation of a beef cattle population, with two quantitative traits with distinct heritabilities (h2 = 0.10 ou 0.30). Some generations will be selected for estimated breeding values and different strategies with ROH, and some parameters regarding the genetic gain and inbreeding level of the population will be analyzed, using computational tools which allows the user to navigate through generations and visualize several components of the system, such as genotypes, phenotypes, genetic clusters, ROH patterns, linkage disequilibrium, etc.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
PAULA DE FREITAS CURTI; ALANA SELLI; DIÓGENES LODI PINTO; ALEXANDRE MERLOS-RUIZ; JULIO CESAR DE CARVALHO BALIEIRO; RICARDO VIEIRA VENTURA. Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview. Animal Reproduction, v. 20, n. 2, . (21/03101-9, 21/11156-8, 16/19514-2)
PINTO, DIOGENES LODI; SELLI, ALANA; TULPAN, DAN; ANDRIETTA, LUCAS TASSONI; GARBOSSA, POLLYANA LEITE MATIOLI; VANDER VOORT, GORDON; MUNRO, JASPER; MCMORRIS, MIKE; ALVES, ANDERSON ANTONIO CARVALHO; CARVALHEIRO, ROBERTO; et al. Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms. LIVESTOCK SCIENCE, v. 267, p. 10-pg., . (20/04461-6, 16/19514-2, 21/11156-8)

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