Detection and characterization of quantitative trait loci for meat quality traits in pigs.

Koning, D.J. de; Harlizius, B.; Rattink, A.P.; Groenen, M.A.M.; Brascamp, E.W.; Arendonk, J.A.M. van

doi:10.2527/2001.79112812x

Cited by 134 publications

(132 citation statements)

References 24 publications

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“…The suggestive, 5% and 1% genome-wide threshold values were used. The 5% chromosome-wide threshold obtained following de Koning et al (2001) was considered as the suggestive significance level. The empirical 95% CIs were estimated by the bootstrapping approach with 2000 iterations (Visscher et al, 1996)…”

Section: Discussionmentioning

confidence: 99%

A whole genome scan to detect quantitative trait loci for gestation length and sow maternal ability related traits in a White Duroc × Erhualian F2 resource population

Chen

Guo

Zhang

et al. 2010

Animal

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Gestation length and maternal ability are important to improve the sow reproduction efficiency and their offspring survival. To map quantitative trait loci (QTL) for gestation length and maternal ability related traits including piglet survival rate and average body weight of piglets at weaning, more than 200 F 2 sows from a White Duroc 3 Erhualian resource population were phenotyped. A genome-wide scan was performed with 194 microsatellite markers covering the whole pig genome. QTL analysis was carried out using a composite regression interval mapping method via QTL express. The results showed that total number of born piglets was significantly correlated with gestation length (r 5 20.13, P , 0.05). Three QTL were detected on pig chromosome (SSC)2, 8 and 12 for gestation length. The QTL on SSC2 achieved the 5% genome-wide significant level and the QTL on SSC8 was consistent with previous reports. Four suggestive QTL were identified for maternal ability related traits including 1 QTL for survival rate of piglets at weaning on SSC8, 3 QTL for average body weight of piglet at weaning on SSC3, 11 and 13.

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Section: Discussionmentioning

confidence: 99%

A whole genome scan to detect quantitative trait loci for gestation length and sow maternal ability related traits in a White Duroc × Erhualian F2 resource population

Chen

Guo

Zhang

et al. 2010

Animal

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“…A significant difference in these two estimates is suggestive of a line of origin effect. de Koning et al (2000) initially tested to see if the two estimates were significantly different from zero and reported Quantitative trait loci mapping S. A. Knott 1437 imprinting when the estimate from one sex of parent was significant whereas the other one was not.…”

Section: Application To Structured Outbred Populationsmentioning

confidence: 99%

“…In contrast to statements by Thomsen et al (2004), male or female expression can be determined by considering the direction of the effect of the line of origin estimate compared with the additive estimate. Subsequently, de Koning et al (2000) chose to estimate an effect of the difference in the two alleles inherited through the male parent and through the female parent. A significant difference in these two estimates is suggestive of a line of origin effect.…”

Section: Application To Structured Outbred Populationsmentioning

confidence: 99%

Regression-based quantitative trait loci mapping: robust, efficient and effective

Knott

2005

Phil. Trans. R. Soc. B

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Regression has always been an important tool for quantitative geneticists. The use of maximum likelihood (ML) has been advocated for the detection of quantitative trait loci (QTL) through linkage with molecular markers, and this approach can be very effective. However, linear regression models have also been proposed which perform similarly to ML, while retaining the many beneficial features of regression and, hence, can be more tractable and versatile than ML in some circumstances. Here, the use of linear regression to detect QTL in structured outbred populations is reviewed and its perceived shortfalls are revisited. It is argued that the approach is valuable now and will remain so in the future.Keywords: quantitative trait loci mapping; regression; structured outbred populations HISTORYThe idea of using markers associated with a trait of interest, for example, to predict the performance of individuals in the trait, is not new. Initially, however, the markers used were not identified at the molecular level but rather through the phenotype, for example, coat colour or by the use of simple biochemical procedures such as blood groups. An early implementation in plants is presented by Sax (1923) and by Neimann-Sorensen & Robertson (1961) in livestock. These original studies used a single marker at a time, because there were few markers and information about the location of the markers in the genome was insufficient. The phenotypes could be analysed either by directly fitting the marker genotype in a classic analysis of variance (ANOVA) or linkage could be modelled through a maximum likelihood (ML) approach. Rebaï et al. (1995) showed that these two approaches are asymptotically equivalent in terms of power. The marker effect estimated in the ANOVA approach can be reparametrized as a combination of the effect and recombination distance from the marker, but these two parameters cannot be estimated separately. On the other hand, the ML approach uses information about the distribution of the phenotypes within the marker genotype and, theoretically, can distinguish quantitative trait loci (QTL) with large effect some distance from the marker from one close by with a smaller effect (equivalent models for ANOVA). There is not much information coming from the phenotypic distribution within marker genotype, however, and most evidence for QTL is obtained from differences between the marker means and, hence, the ability of ML to correctly locate the QTL is limited.

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“…However, it was not expected that the genotype of the Piau breed would contribute to an increase of the total loss phenotype (additive effect = 0.27) (Table 1) since the meat of this breed generally has better organoleptic properties than that of commercial breeds. A QTL for drip loss on SSC18 (24 cM) has previously been reported by De Koning et al (2001) in a population resulting from the crossing of Meishan animals and pigs of commercial breeds.…”

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confidence: 99%

Quantitative trait loci for carcass, internal organ and meat quality traits on porcine chromosomes 16, 17 and 18

et al. 2008

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The objective of this study was to map quantitative trait loci (QTL) on porcine chromosomes 16, 17 and 18 and to determine their association with carcass, organ and meat quality traits. An F2 population was produced by crossing two boars of the naturalized Brazilian Piau breed with 18 commercial females (Landrace x Large White x Pietrain). The population was genotyped for 11 microsatellite markers distributed over the three chromosomes and the results were used to construct a marker-specific linkage map for the population. Analysis of the polymorphic information content showed that the microsatellite markers were adequate for the study of quantitative traits. QTL were identified by regression interval mapping using QTL Express software. QTL not previously described in the literature were detected on chromosome 16, whereas QTL described in other populations were detected on chromosomes 17 and 18. The information from the significant QTL identified here will be useful for future fine-mapping studies and should provide a better understanding of productive phenotypes in pigs.

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Detection and characterization of quantitative trait loci for meat quality traits in pigs.

Cited by 134 publications

References 24 publications

A whole genome scan to detect quantitative trait loci for gestation length and sow maternal ability related traits in a White Duroc × Erhualian F2 resource population

A whole genome scan to detect quantitative trait loci for gestation length and sow maternal ability related traits in a White Duroc × Erhualian F2 resource population

Regression-based quantitative trait loci mapping: robust, efficient and effective

Quantitative trait loci for carcass, internal organ and meat quality traits on porcine chromosomes 16, 17 and 18

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