Mapeamento de QTL nos cromossomos 1, 2, 3, 12, 14, 15 e X em suínos: características de carcaça e qualidade de carne

Paixão, Débora Martins; Carneiro, Paulo Luız Souza; Paiva, Samuel Rezende; Sousa, Katiene Régia Silva; Verardo, Lucas Lima; Neto, José Braccini; Pinto, Ana Paula Gomes; Hidalgo, A.M.; Nascimento, Carlos Souza do; Périssé, Iuri Viótti; Lopes, Paulo Sávio; Guimarães, Simone Eliza Facioni

doi:10.1590/s0102-09352012000400026

Cited by 3 publications

(5 citation statements)

References 31 publications

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“…Eight of these had not been reported in the consulted literature: SF and LUNG on SSC4; SBF, PBF and LRBF on SSC8; BW on SSC17; SIL and BCW on SSCX (PigQTLdb). Compared to previous studies that used the same F2 population and relied only on microsatellite markers ( Paixão et al , 2008 , 2012 , 2013 ; Silva et al , 2008 ; Sousa et al , 2011 ), only seven of 40 QTL were confirmed in the present analysis. For these confirmed QTL the confidence intervals were narrowed down on average by 23.9 cM using the dataset with increased marker coverage.…”

Section: Discussioncontrasting

confidence: 87%

“…On SSCX a QTL associated with meat color, A, identified by Paixão et al (2012) was confirmed. The peak of the QTL remained located between markers SW1943 and S0218.…”

Section: Discussionmentioning

confidence: 78%

“…These final 237 SNPs were distributed as follows: SSC1 (56), SSC4 (54), SSC7 (59), SSC8 (31), SSC17 (25), and SSCX (12). Genotypes from 37 microsatellite markers were available from previous studies ( Paixão et al , 2008 , 2012 , 2013 ; Silva et al , 2008 ; Sousa et al , 2011 ) and included in the analyses ( Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies from our research group were conducted on the same population and detected QTL by means of microsatellite markers. A combined total of 40 QTL for growth performance, meat quality, internal organs, cut yield, and carcass composition were found in studies by Paixão et al ( 2008 , 2012 , 2013 ), Silva et al (2008) , and Sousa et al (2011) : five on chromosome 1 (SSC1), 12 on chromosome 4 (SSC4), nine on chromosome 7 (SSC7), eight on chromosome 8 (SSC8), three on chromosome 17 (SSC17), and three on chromosome X (SSCX). The sparse genetic maps that were used led to the detection of QTL with large confidence intervals.…”

Section: Introductionmentioning

confidence: 94%

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Fine mapping and single nucleotide polymorphism effects estimation on pig chromosomes 1, 4, 7, 8, 17 and X

et al. 2013

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Fine mapping of quantitative trait loci (QTL) from previous linkage studies was performed on pig chromosomes 1, 4, 7, 8, 17, and X which were known to harbor QTL. Traits were divided into: growth performance, carcass, internal organs, cut yields, and meat quality. Fifty families were used of a F2 population produced by crossing local Brazilian Piau boars with commercial sows. The linkage map consisted of 237 SNP and 37 microsatellite markers covering 866 centimorgans. QTL were identified by regression interval mapping using GridQTL. Individual marker effects were estimated by Bayesian LASSO regression using R. In total, 32 QTL affecting the evaluated traits were detected along the chromosomes studied. Seven of the QTL were known from previous studies using our F2 population, and 25 novel QTL resulted from the increased marker coverage. Six of the seven QTL that were significant at the 5% genome-wide level had SNPs within their confidence interval whose effects were among the 5% largest effects. The combined use of microsatellites along with SNP markers increased the saturation of the genome map and led to smaller confidence intervals of the QTL. The results showed that the tested models yield similar improvements in QTL mapping accuracy.

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Section: Discussioncontrasting

confidence: 87%

“…On SSCX a QTL associated with meat color, A, identified by Paixão et al (2012) was confirmed. The peak of the QTL remained located between markers SW1943 and S0218.…”

Section: Discussionmentioning

confidence: 78%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

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Fine mapping and single nucleotide polymorphism effects estimation on pig chromosomes 1, 4, 7, 8, 17 and X

et al. 2013

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“…Figure 1 shows that the best methodology was BLASSO, and that the second factor, "fat", showed the most accurate value (0.56) of all four factors. Given the higher accuracy, and its practical relevance for the pig industry (Silva et al, 2011;Paixão et al, 2012;Azevedo et al, 2013aAzevedo et al, , 2015, only the second factor ("fat") will be discussed. The results for the other factors showed the same pattern, and are available at http:// www.det.ufv.br/~moyses/links.php.…”

Section: Resultsmentioning

confidence: 99%

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs

Teixeira

Nascimento

et al. 2016

Genet. Mol. Res.

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ABSTRACT. The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genomewide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: "weight", "fat", "loin", and "performance". These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.

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Regressão via componentes independentes aplicada à seleção genômica para características de carcaça em suínos

Azevedo

Resende

Silva

et al. 2013

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar a eficiência do método de regressão via componentes independentes (ICR) na estimação de valores genéticos genômicos e dos efeitos de marcadores SNP para características de carcaça de uma população F 2 de suínos (Piau x linhagem comercial). Os métodos foram avaliados por meio da concordância entre os valores genéticos preditos e os fenótipos corrigidos, observados por validação cruzada, e também foram comparados com outros métodos geralmente utilizados para os mesmos propósitos, tais como RR-BLUP, PCR e PLS. Os métodos ICR e PCR apresentam resultados similares, mas o método ICR apresenta maiores valores de acurácia.Termos para indexação: Sus scrofa, BLUP, marcadores SNP, regressão aleatória, redução dimensional. Independent component regression applied to genomic selection for carcass traits in pigsAbstract -The objective of this work was to evaluate the efficiency of the independent component regression (ICR) method for the estimation of genomic values and of SNP marker effects for carcass traits in a F 2 pig population (Piau x commercial line). The methods were evaluated by the agreement between the genetic predicted values and the corrected phenotypes observed by cross-validation, and they were also compared with other methods generally used for the same purposes, such as RR-BLUP, PCR, and PLS. The ICR and PCR methods show similar results, but ICR has the highest accuracy prediction values.

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Mapeamento de QTL nos cromossomos 1, 2, 3, 12, 14, 15 e X em suínos: características de carcaça e qualidade de carne

Cited by 3 publications

References 31 publications

Fine mapping and single nucleotide polymorphism effects estimation on pig chromosomes 1, 4, 7, 8, 17 and X

Fine mapping and single nucleotide polymorphism effects estimation on pig chromosomes 1, 4, 7, 8, 17 and X

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs

Regressão via componentes independentes aplicada à seleção genômica para características de carcaça em suínos

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