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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

A new approach for applied nutritional models: Computing parameters of dynamic mechanistic growth models using genorne-wide prediction

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Autor(es):
Freua, Mateus Castelani ; de Almeida Santana, Miguel Henrique ; Sterman Ferraz, Jose Bento
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: LIVESTOCK SCIENCE; v. 190, p. 131-135, AUG 2016.
Citações Web of Science: 1
Resumo

Nutritional models have long been used as decision support tools by the livestock industry. Despite the advance of genomic prediction, these two disciplines have evolved separately. Because model parameters are responsible to describe between-animal variability, we propose an integration of nutritional models with genomics by means of such parameters. Two dynamic mechanistic models of cattle growth were used: Cornell Cattle Value Discovery System (CVDS) and Davis Growth Model (DGM). We estimated SNP marker effects for their parameters and also for observed phenotypes. Then, we compared what would be the best prediction scenario - model simulation with parameters computed from genomic data or genomic prediction directly on higher phenotypes. We found that genomic prediction on dry matter intake (DMI) and average daily gain (ADG) are still a better approach than using CVDS for predictions. Dry matter required (DMR), a CVDS-predicted value for DMI had higher correlation (r=0.253) with observed DMI than results from genomic prediction (r=0.07). DGM had better predictive ability (r=038) than genomic prediction on ADG (r=0.098). This is also the case for whole-body protein (r=0.496) and fat at slaughter (r=0.505) whose predictions were better with DGM than genomic prediction performed on the observed traits (r=0.194 and r=0.183, respectively). When contrasting simulations with genomically predicted parameters to those with regularly computed ones, CVDS showed moderate correlation and low bias between simulations of DMR (r=0.966; b=0.9%) and ADG (r=0.645; b=5.5%). Although further model development is necessary, the DGM with subject-specific parameters computed from genotypic data was a better option for predicting phenotypes than genomic prediction alone. In addition, simulations with genomically and regularly computed parameters match at a reasonable extend. This is the main argument to call attention from the research community that our approach may pave the way for the development of a new generation of applied nutritional models, especially towards individual-based simulations with subject-specific parameters computed from genomic information. (C) 2016 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 13/20571-2 - Genômica funcional da ingestão e eficiência alimentar de bovinos da raça Nelore
Beneficiário:Miguel Henrique de Almeida Santana
Linha de fomento: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 13/26902-0 - Uso da variância genética em modelos mecanicistas dinâmicos de crescimento para predizer o desempenho e a composição da carcaça de bovinos confinados
Beneficiário:Mateus Castelani Freua
Linha de fomento: Bolsas no Brasil - Mestrado
Processo FAPESP: 14/07566-2 - Genômica aplicada à produção de ruminantes
Beneficiário:José Bento Sterman Ferraz
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 12/02039-9 - Estudo genômico da ingestão e eficiência alimentar em bovinos da raça Nelore
Beneficiário:José Bento Sterman Ferraz
Linha de fomento: Auxílio à Pesquisa - Regular