Relationship between the Porcine Stress Syndrome gene and carcass and performance traits in F2 pigs resulting from divergent crosses

Band, Guilherme de Oliveira; Guimarães, Simone Eliza Facioni; Lopes, Paulo Sávio; Peixoto, J. de O.; Faria, Danielle Assis de; Pires, Aldrin Vieira; Figueiredo, Frederico de Castro; Nascimento, Carlos Souza do; Gomide, Lúcio Alberto de Miranda

doi:10.1590/s1415-47572005000100016

Cited by 30 publications

(27 citation statements)

References 13 publications

(35 reference statements)

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“…A detailed description of the procedures can be found in Band et al (2005). From the F2 population, 345 animals were genotyped for 384 single nucleotide polymorphisms (SNPs).…”

Section: Methodsmentioning

confidence: 99%

Estimating additive and dominance variances for complex traits in pigs combining genomic and pedigree information

et al. 2015

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ABSTRACT. Knowledge of dominance effects should improve genetic evaluations, provide the accurate selection of purebred animals, and enable better breeding strategies, including the exploitation of heterosis in crossbreeds. In this study, we combined genomic and pedigree data to study the relative importance of additive and dominance genetic variation in growth and carcass traits in an F2 pig population. Two GBLUP models were used, a model without a polygenic effect (ADM) and a model with a polygenic effect (ADMP). Additive effects played a greater role in the control of growth and carcass traits than did dominance effects. However, dominance effects were important for all traits, particularly in backfat thickness. The narrow-sense and broadsense heritability estimates for growth (0.06 to 0.42, and 0.10 to 0.51, respectively) and carcass traits (0.07 to 0.37, and 0.10 to 0.76, respectively) exhibited a wide variation. The inclusion of a polygenic effect in the ADMP model changed the broad-sense heritability estimates only E.V. Costa et al. 6304©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (2): 6303-6311 (2015) for birth weight and weight at 21 days of age.

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“…A detailed description of the procedures can be found in Band et al (2005). From the F2 population, 345 animals were genotyped for 384 single nucleotide polymorphisms (SNPs).…”

Section: Methodsmentioning

confidence: 99%

Estimating additive and dominance variances for complex traits in pigs combining genomic and pedigree information

et al. 2015

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“…The desirable weight at slaughter of the animals in this population was around 65 kg (Band et al, 2005). As was the case in the study by Giolo and Demétrio (2011), the exact time it took an animal to gain the desired weight was not known, as daily weighing was impracticable.…”

Section: Methodsmentioning

confidence: 87%

“…A population of 345 F2 animals was produced by outbreed crossing two Brazilian native boars (Piau breed) with 18 commercial females (Landrace x Large White x Pietrain) as described by Band et al (2005).…”

Section: Methodsmentioning

confidence: 99%

Genomic selection for slaughter age in pigs using the Cox frailty model

Vs¹,

Filho²,

Resende³

et al. 2015

Genet. Mol. Res.

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ABSTRACT. The aim of this study was to compare genomic selection methodologies using a linear mixed model and the Cox survival model. We used data from an F2 population of pigs, in which the response variable was the time in days from birth to the culling of the animal and the covariates were 238 markers [237 single nucleotide polymorphism (SNP) plus the halothane gene]. The data were corrected for fixed effects, and the accuracy of the method was determined based on the correlation of the ranks of predicted genomic breeding values (GBVs) in both models with the corrected phenotypic values. The analysis was repeated with a subset of SNP markers with largest absolute effects. The results were in agreement with the GBV prediction and the estimation of marker effects for both models for uncensored data and for normality. However, when considering censored data, the Cox model with a normal random effect (S1) was more appropriate. Since there was no agreement between the linear mixed model and the imputed data (2015) (L2) for the prediction of genomic values and the estimation of marker effects, the model S1 was considered superior as it took into account the latent variable and the censored data. Marker selection increased correlations between the ranks of predicted GBVs by the linear and Cox frailty models and the corrected phenotypic values, and 120 markers were required to increase the predictive ability for the characteristic analyzed.

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“…The F 1 generation consisted of 106 sows and 134 boars (Band et al, 2005). Twelve boars from different litters were randomly selected from the 134 F 1 boars and mated by natural breeding with 54 F 1 sows to produce the F 2 generation.…”

Section: Methodsmentioning

confidence: 99%

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs

et al. 2015

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Relationship between the Porcine Stress Syndrome gene and carcass and performance traits in F2 pigs resulting from divergent crosses

Cited by 30 publications

References 13 publications

Estimating additive and dominance variances for complex traits in pigs combining genomic and pedigree information

Estimating additive and dominance variances for complex traits in pigs combining genomic and pedigree information

Genomic selection for slaughter age in pigs using the Cox frailty model

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs

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