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Use of genetic variance in dynamic mechanistic models of growth to predict cattle performance and carcass composition under feedlot conditions

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Mateus Castelani Freua
Total Authors: 1
Document type: Master's Dissertation
Press: Pirassununga.
Institution: Universidade de São Paulo (USP). Faculdade de Zootecnica e Engenharia de Alimentos (FZE/BT)
Defense date:
Examining board members:
José Bento Sterman Ferraz; Joanir Pereira Eler; Ricardo Vieira Ventura
Advisor: José Bento Sterman Ferraz

The prediction of phenotypic variance is important for beef cattle operations to increase profitability by optimizing resource use. Dynamic mechanistic models of cattle growth have been used as decision support tools for individual cattle management systems. However, the application of such models is still based on population parameters, with no further approach to capture between-subject variability. By assuming that mechanistic models are able to simulate environmental deviations components of phenotypic variance and considering that SNPs markers may predict the genetic component of this variance, this project aimed at evolving towards a mathematical model that takes between-animal variance to its genetic level. Following the concepts of computational physiological genomics, we assumed that genetic variance of the complex trait (i.e. outcome of model behavior) arises from component traits (i.e. model parameters) in lower hierarchical levels of biological systems. This study considered two mechanistic models of cattle growth - Cornell Cattle Value Discovery System (CVDS) and Davis Growth Model (DGM) - and verified their expected biological interpretation by asking whether model parameters would map genomic regions that harbors QTLs already described for the complex trait. This provided a proof of concept that CVDS and DGM parameters are indeed phenotypes whose expected interpretations may be stated by means of their mapped genomic regions. A method of genomic prediction to compute parameters for CVDS and DGM was then used. SNP marker effects were estimated both for their parameters and observed phenotypes. We looked for the best prediction scenario - model simulation with parameters computed from genomic data or genomic prediction on complex phenotypes directly. We found that while genomic prediction on complex phenotypes may still be a better option than predictions from growth models, simulations conducted with genomically computed parameters are in accordance with those performed with parameters obtained from regular methods. This is the main argument to call attention from the research community that this approach may pave the way for the development of a new generation of applied nutritional models capable of representing genetic variability among beef cattle under feedlot conditions and performing simulation with inputs from individual\'s genotypes. To our knowledge, this project is the first of this kind in Brazil and the first using Bos indicus genotypes to study the application of genomics integrated with mechanistic models for the management and marketing of commercial livestock. (AU)

FAPESP's process: 13/26902-0 - Use of genetic variance in dynamic mechanistic models of growth to predict cattle performance and carcass composition under feedlot conditions
Grantee:Mateus Castelani Freua
Support Opportunities: Scholarships in Brazil - Master