000058454 001__ 58454
000058454 005__ 20190709135424.0
000058454 0247_ $$2doi$$a10.1590/1678-992x-2016-0023
000058454 0248_ $$2sideral$$a97457
000058454 037__ $$aART-2017-97457
000058454 041__ $$aeng
000058454 100__ $$ae Silva, F.F.
000058454 245__ $$aGenome association study through nonlinear mixed models revealed new candidate genes for pig growth curves
000058454 260__ $$c2017
000058454 5060_ $$aAccess copy available to the general public$$fUnrestricted
000058454 5203_ $$aGenome 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.
000058454 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000058454 590__ $$a1.383$$b2017
000058454 591__ $$aAGRICULTURE, MULTIDISCIPLINARY$$b14 / 56 = 0.25$$c2017$$dQ1$$eT1
000058454 592__ $$a0.578$$b2017
000058454 593__ $$aAnimal Science and Zoology$$c2017$$dQ2
000058454 593__ $$aAgronomy and Crop Science$$c2017$$dQ2
000058454 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000058454 700__ $$aZambrano, M.F.B.
000058454 700__ $$0(orcid)0000-0001-6256-5478$$aVarona, L.$$uUniversidad de Zaragoza
000058454 700__ $$aGlória, L.S.
000058454 700__ $$aLopes, P.S.
000058454 700__ $$aSilva, M.V.G.B.
000058454 700__ $$aArbex, W.
000058454 700__ $$aLázaro, S.F.
000058454 700__ $$ade Resende, M.D.V.
000058454 700__ $$aGuimarães, S.E.F.
000058454 7102_ $$11001$$2420$$aUniversidad de Zaragoza$$bDpto. Anatom.,Embri.Genét.Ani.$$cÁrea Genética
000058454 773__ $$g74, 1 (2017), 1-7$$pSci. agric.$$tSCIENTIA AGRICOLA$$x0103-9016
000058454 8564_ $$s263412$$uhttps://zaguan.unizar.es/record/58454/files/texto_completo.pdf$$yVersión publicada
000058454 8564_ $$s113457$$uhttps://zaguan.unizar.es/record/58454/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000058454 909CO $$ooai:zaguan.unizar.es:58454$$particulos$$pdriver
000058454 951__ $$a2019-07-09-11:28:05
000058454 980__ $$aARTICLE