|
|
| BARROSO, L. M. A.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; SILVA, F. F.; SERÃO, N. V. L.; CRUZ, C. D.; RESENDE, M. D. V. de; SILVA, F. L.; AZEVEDO, C. F.; LOPES, P. S.; GUIMARÃES, S. E. F.. |
| Background: Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). Results: The regularized quantile regression (RQR) enabled the discovery, at different levels of interest... |
| Tipo: Artigo de periódico |
Palavras-chave: Genome association; Pig; Regularized quantile regression; QTL; Growth curves.; Melhoramento genético animal; Porco; Suíno; Swine.. |
| Ano: 2017 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1084057 |
| |
