Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

Detalhes bibliográficos
Autor(a) principal: Silva,Fabyano Fonseca e
Data de Publicação: 2017
Outros Autores: Zambrano,Maria Fernanda Betancur, Varona,Luis, Glória,Leonardo Siqueira, Lopes,Paulo Sávio, Silva,Marcos Vinícius Gualberto Barbosa, Arbex,Wagner, Lázaro,Sirlene Fernandes, Resende,Marcos Deon Vilela de, Guimarães,Simone Eliza Facioni
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162017000100001
Resumo: ABSTRACT: Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied.
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spelling Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curvesSNP markersbody weightlongitudinal dataABSTRACT: Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied.Escola Superior de Agricultura "Luiz de Queiroz"2017-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162017000100001Scientia Agricola v.74 n.1 2017reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2016-0023info:eu-repo/semantics/openAccessSilva,Fabyano Fonseca eZambrano,Maria Fernanda BetancurVarona,LuisGlória,Leonardo SiqueiraLopes,Paulo SávioSilva,Marcos Vinícius Gualberto BarbosaArbex,WagnerLázaro,Sirlene FernandesResende,Marcos Deon Vilela deGuimarães,Simone Eliza Facionieng2016-11-29T00:00:00Zoai:scielo:S0103-90162017000100001Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2016-11-29T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
title Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
spellingShingle Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
Silva,Fabyano Fonseca e
SNP markers
body weight
longitudinal data
title_short Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
title_full Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
title_fullStr Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
title_full_unstemmed Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
title_sort Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
author Silva,Fabyano Fonseca e
author_facet Silva,Fabyano Fonseca e
Zambrano,Maria Fernanda Betancur
Varona,Luis
Glória,Leonardo Siqueira
Lopes,Paulo Sávio
Silva,Marcos Vinícius Gualberto Barbosa
Arbex,Wagner
Lázaro,Sirlene Fernandes
Resende,Marcos Deon Vilela de
Guimarães,Simone Eliza Facioni
author_role author
author2 Zambrano,Maria Fernanda Betancur
Varona,Luis
Glória,Leonardo Siqueira
Lopes,Paulo Sávio
Silva,Marcos Vinícius Gualberto Barbosa
Arbex,Wagner
Lázaro,Sirlene Fernandes
Resende,Marcos Deon Vilela de
Guimarães,Simone Eliza Facioni
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Silva,Fabyano Fonseca e
Zambrano,Maria Fernanda Betancur
Varona,Luis
Glória,Leonardo Siqueira
Lopes,Paulo Sávio
Silva,Marcos Vinícius Gualberto Barbosa
Arbex,Wagner
Lázaro,Sirlene Fernandes
Resende,Marcos Deon Vilela de
Guimarães,Simone Eliza Facioni
dc.subject.por.fl_str_mv SNP markers
body weight
longitudinal data
topic SNP markers
body weight
longitudinal data
description ABSTRACT: Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied.
publishDate 2017
dc.date.none.fl_str_mv 2017-02-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162017000100001
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162017000100001
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-992x-2016-0023
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Escola Superior de Agricultura "Luiz de Queiroz"
publisher.none.fl_str_mv Escola Superior de Agricultura "Luiz de Queiroz"
dc.source.none.fl_str_mv Scientia Agricola v.74 n.1 2017
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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