Data/hora: 25/06/2024 11:42:22

Biblioteca(s): 	Embrapa Florestas.	
Data corrente: 	03/01/2018	
Data da última atualização: 	03/01/2018	
Autoria: 	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.	
Afiliação: 	L. M. A. Barroso, UFV; M. Nascimento, UFV; A. C. C. Nascimento, UFV; F. F. Silva, UFV; N. V. L. Serão, Iowa State University; C. D. Cruz, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; F. L. Silva, UFV; C. F. Azevedo, UFV; P. S. Lopes, UFV; S. E. F. Guimarães, UFV.	
Título: 	Regularized quantile regression for SNP marker estimation of pig growth curves.	
Ano de publicação: 	2017	
Fonte/Imprenta: 	Journal of Animal Science and Biotechnology, v. 8, n. 59, 2017.	
Páginas: 	9 p.	
DOI: 	10.1186/s40104-017-0187-z	
Idioma: 	Inglês	
Conteúdo: 	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 (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. Conclusions: RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.	
Palavras-Chave: 	Genome association; Growth curves; Pig; QTL; Regularized quantile regression.	
Thesagro: 	Melhoramento genético animal; Porco; Suíno.	
Thesaurus Nal: 	Swine.	
Categoria do assunto: 	G Melhoramento Genético	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/170172/1/2017-M.Deon-JASB-Regularized.pdf	
Registro original: 	Embrapa Florestas (CNPF)	
Exemplares: 	CNPF - (56206-1) - UPC - AP - DD -  -  -  - DIS