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Invited review: Advances and applications of random regression models: From quantitative genetics to genomics.
Oliveira, H R; Brito, L F; Lourenco, D A L; Silva, F F; Jamrozik, J; Schaeffer, L R; Schenkel, F S.
Afiliación
  • Oliveira HR; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada; Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil.
  • Brito LF; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada; Department of Animal Sciences, Purdue University, West Lafayette, IN 47907.
  • Lourenco DAL; Department of Animal and Dairy Science, University of Georgia, Athens 30602.
  • Silva FF; Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil.
  • Jamrozik J; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada; Canadian Dairy Network, Guelph, ON, N1K 1E5, Canada.
  • Schaeffer LR; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada.
  • Schenkel FS; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada. Electronic address: schenkel@uoguelph.ca.
J Dairy Sci ; 102(9): 7664-7683, 2019 Sep.
Article en En | MEDLINE | ID: mdl-31255270
An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cruzamiento / Carácter Cuantitativo Heredable / Genómica Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Animals Idioma: En Revista: J Dairy Sci Año: 2019 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cruzamiento / Carácter Cuantitativo Heredable / Genómica Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Animals Idioma: En Revista: J Dairy Sci Año: 2019 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos