Regularized quantile regression for SNP marker estimation of pig growth curves.
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1084057 |
Resumo: | 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. |
id |
EMBR_82cc5ff4e718867baf5cc60e29955de5 |
---|---|
oai_identifier_str |
oai:www.alice.cnptia.embrapa.br:doc/1084057 |
network_acronym_str |
EMBR |
network_name_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository_id_str |
2154 |
spelling |
Regularized quantile regression for SNP marker estimation of pig growth curves.Genome associationPigRegularized quantile regressionQTLGrowth curvesMelhoramento genético animalPorcoSuínoSwineBackground: 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.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.BARROSO, L. M. A.NASCIMENTO, M.NASCIMENTO, A. C. C.SILVA, F. F.SERÃO, N. V. L.CRUZ, C. D.RESENDE, M. D. V. deSILVA, F. L.AZEVEDO, C. F.LOPES, P. S.GUIMARÃES, S. E. F.2018-01-03T23:20:13Z2018-01-03T23:20:13Z2018-01-0320172018-01-03T23:20:13Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article9 p.Journal of Animal Science and Biotechnology, v. 8, n. 59, 2017.http://www.alice.cnptia.embrapa.br/alice/handle/doc/108405710.1186/s40104-017-0187-zenginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2018-01-03T23:20:20Zoai:www.alice.cnptia.embrapa.br:doc/1084057Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542018-01-03T23:20:20falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542018-01-03T23:20:20Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Regularized quantile regression for SNP marker estimation of pig growth curves. |
title |
Regularized quantile regression for SNP marker estimation of pig growth curves. |
spellingShingle |
Regularized quantile regression for SNP marker estimation of pig growth curves. BARROSO, L. M. A. Genome association Pig Regularized quantile regression QTL Growth curves Melhoramento genético animal Porco Suíno Swine |
title_short |
Regularized quantile regression for SNP marker estimation of pig growth curves. |
title_full |
Regularized quantile regression for SNP marker estimation of pig growth curves. |
title_fullStr |
Regularized quantile regression for SNP marker estimation of pig growth curves. |
title_full_unstemmed |
Regularized quantile regression for SNP marker estimation of pig growth curves. |
title_sort |
Regularized quantile regression for SNP marker estimation of pig growth curves. |
author |
BARROSO, L. M. A. |
author_facet |
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. |
author_role |
author |
author2 |
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. |
author2_role |
author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
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. |
dc.contributor.author.fl_str_mv |
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. |
dc.subject.por.fl_str_mv |
Genome association Pig Regularized quantile regression QTL Growth curves Melhoramento genético animal Porco Suíno Swine |
topic |
Genome association Pig Regularized quantile regression QTL Growth curves Melhoramento genético animal Porco Suíno Swine |
description |
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. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2018-01-03T23:20:13Z 2018-01-03T23:20:13Z 2018-01-03 2018-01-03T23:20:13Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Journal of Animal Science and Biotechnology, v. 8, n. 59, 2017. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1084057 10.1186/s40104-017-0187-z |
identifier_str_mv |
Journal of Animal Science and Biotechnology, v. 8, n. 59, 2017. 10.1186/s40104-017-0187-z |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1084057 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
9 p. |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
_version_ |
1794503447231332352 |