Testing genetic determinism in rate of hoof growth in pigs using Bayes Factors

Quintanilla, Raquel; Varona, L.; Noguera, J. L.

doi:10.1016/j.livsci.2006.04.030

Cited by 12 publications

(15 citation statements)

References 20 publications

(21 reference statements)

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“…Moreover, this defect is easily detected by farmers, who can preferentially cull these sows. It is important to note that hoof growth has a substantial genetic background in Landrace sows (0.25; Quintanilla et al, 2006), so selective breeding focused on reducing hoof growth could indirectly increase sow longevity.…”

Section: Leg and Teat Conformationmentioning

confidence: 99%

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Effect of leg conformation on survivability of Duroc, Landrace, and Large White sows1

Sevilla

Fàbrega

Tibau

et al. 2008

Journal of Animal Science

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Sow longevity influences farm economy and can be considered an important indicator of animal welfare. Body features such as leg conformation can play a key role in sow longevity, although little is known about its effect on culling decisions. Within this context, longevity data from 587 Duroc, 239 Landrace, and 217 Large White sows were analyzed with special emphasis on the effect of leg conformation. Sow longevity was analyzed twice for each breed, testing the effect of a subjective overall score for leg conformation, or the presence or absence of 6 specific leg conformation defects. Each preliminary model also included a teat conformation score with 3 levels, farm or origin, backfat thickness at 6 mo of age, and 2 continuous sources of variation, namely the age at the first farrowing and the number of piglets born alive at each farrowing. Overall leg conformation score influenced (P < 0.01) sow longevity in Duroc, Landrace, and Large White sows, with a greater hazard ratio (HR) for poorly conformed sows (1.56, 2.16, and 1.79, respectively) than for well-conformed sows (0.32, 0.66, and 0.68, respectively). Abnormal hoof growth reduced survivability in Duroc (HR = 2.78; P < 0.001) and Landrace sows (HR = 1.88; P < 0.01); the presence of splayed feet (P < 0.05) or bumps and injuries (P < 0.001) increased the risk of culling in Duroc sows (HR = 2.08 and 3.57, respectively), whereas the incidence of straight pastern increased the HR in Large White sows (HR = 2.49; P < 0.01). In all 3 breeds, longevity decreased for plantigrade sows, with a greater HR in Duroc (HR = 3.38; P < 0.001) than in Landrace (HR = 1.53; P < 0.10) and Large White sows (HR = 1.73; P < 0.05). Teat conformation did not influence sow longevity (P > 0.10). Estimates of heritability for longevity in Duroc sows ranged from 0.05 to 0.07 depending on the algorithm applied. Leg conformation had a substantial effect on sow longevity, where an accurate removal of poorly leg-conformed candidate gilts before first mating could improve sow survival and reduce culling costs. These moderate estimates of heritability indicated that survivability of Duroc sows could be genetically improved by direct selection for leg conformation.

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Section: Leg and Teat Conformationmentioning

confidence: 99%

“…These heritabilities suggested that direct genetic improvement for sow longevity was feasible, although a small genetic trend was expected. Nevertheless, indirect selection programs could also be useful given the high heritabilities reported for hoof growth (Quintanilla et al, 2006).…”

Section: Heritability For Sow Longevity In Purebred Duroc Sowsmentioning

confidence: 99%

Effect of leg conformation on survivability of Duroc, Landrace, and Large White sows1

Sevilla

Fàbrega

Tibau

et al. 2008

Journal of Animal Science

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“…Although the method was initially developed to test for the genetic background of linear traits [5] and the location of quantitative trait loci (QTL) [23], this Bayes factor has been recently modified to discriminate between linked and pleiotropic QTL [24], to test for the genetic background of threshold traits [2,18], and to compare different structures of random genetic groups [3], all with encouraging results. Previous research highlighted its computational stability [5].…”

Section: Discussionmentioning

confidence: 99%

Bayes factor between Student t and Gaussian mixed models within an animal breeding context

et al. 2008

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-The implementation of Student t mixed models in animal breeding has been suggested as a useful statistical tool to effectively mute the impact of preferential treatment or other sources of outliers in field data. Nevertheless, these additional sources of variation are undeclared and we do not know whether a Student t mixed model is required or if a standard, and less parameterized, Gaussian mixed model would be sufficient to serve the intended purpose. Within this context, our aim was to develop the Bayes factor between two nested models that only differed in a bounded variable in order to easily compare a Student t and a Gaussian mixed model. It is important to highlight that the Student t density converges to a Gaussian process when degrees of freedom tend to infinity. The two models can then be viewed as nested models that differ in terms of degrees of freedom. The Bayes factor can be easily calculated from the output of a Markov chain Monte Carlo sampling of the complex model (Student t mixed model). The performance of this Bayes factor was tested under simulation and on a real dataset, using the deviation information criterion (DIC) as the standard reference criterion. The two statistical tools showed similar trends along the parameter space, although the Bayes factor appeared to be the more conservative. There was considerable evidence favoring the Student t mixed model for data sets simulated under Student t processes with limited degrees of freedom, and moderate advantages associated with using the Gaussian mixed model when working with datasets simulated with 50 or more degrees of freedom. For the analysis of real data (weight of Pietrain pigs at six months), both the Bayes factor and DIC slightly favored the Student t mixed model, with there being a reduced incidence of outlier individuals in this population.Bayes factor / Gaussian distribution / mixed model / Student t distribution / preferential treatment *

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“…Given the reduced genetic variability reported for sow survival in previous studies (Tholen et al, 1996;López-Serrano et al, 2000;Yazdi et al, 2000a,b;Fernàndez de Sevilla et al, 2008), indirect genetic selection through specific leg conformation defects could be viewed as an appealing alternative to attempts to genetically improve sow longevity. Nevertheless, studies relating to the heritability of overall or specific leg conformation defects in sows are limited (Jørgensen and Andersen, 2000;Serenius et al, 2001;Quintanilla et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Genetic background and phenotypic characterization over two farrowings of leg conformation defects in Landrace and Large White sows1

Sevilla

Fàbrega

Tibau

et al. 2009

Journal of Animal Science

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A Bayesian threshold animal model was applied to evaluate the prevalence over 2 farrowings and genetic background of overall leg conformation score and the presence or absence of 6 specific leg defects (abnormal hoof growth, splay footed, plantigradism, straight pasterns, sickle-hocked legs, and the presence of swelling or injuries) in purebred Landrace and Large White sows. Data sets contained phenotypic records from 2,477 and 1,550 Landrace and Large White females, respectively, at the end of the growing period. Leg conformation data from first and second farrowings were available for 223 and 191 Landrace sows and 213 and 193 Large White sows, respectively. Overall leg conformation deteriorated with age, with statistically relevant differences between females at the end of the growing period, first farrowing (FF), and second farrowing (SF). In a similar way, the prevalence of the 6 specific leg defects increased between the end of the growing period and FF (with the exception of straight pasterns in the Landrace population). Differences between FF and second farrowing were statistically relevant for hoof growth (highest posterior density regions at 95% did not overlap), plantigradism, sickle-hocked legs, and overall leg conformation score in Landrace and for sickle-hocked leg and overall leg conformation score in Large White. The statistical relevance of the genetic background was tested through the Bayes factor (BF) between the model with the additive genetic component and the model with 0 heritability (nonheritable). Heritability (h(2)) was discarded (BF < 1) for sickle-hocked leg in both breeds, whereas decisive evidence (BF > 100) of genetic background was obtained for overall leg conformation score in Landrace and Large White sows (h(2) = 0.27 and 0.38, respectively), hoof growth in both breeds (h(2) = 0.22 and 0.26, respectively), and plantigradism (h(2) = 0.34) and the presence of swelling or injuries in Landrace (h(2) = 0.27). Note that a BF > 100 implies that the model with infinitesimal genetic effects was more than 100 times more suitable than the model without genetic effects, a conclusive estimate within the Bayesian framework. The remaining traits (splay footed and straight pasterns) registered BF values ranging from 11.6 to 35.1 and h(2) values ranging from 0.09 to 0.19. These results indicated a moderate genetic determinism for leg conformation in Landrace and Large White sows.

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Testing genetic determinism in rate of hoof growth in pigs using Bayes Factors

Cited by 12 publications

References 20 publications

Effect of leg conformation on survivability of Duroc, Landrace, and Large White sows1

Effect of leg conformation on survivability of Duroc, Landrace, and Large White sows1

Bayes factor between Student t and Gaussian mixed models within an animal breeding context

Genetic background and phenotypic characterization over two farrowings of leg conformation defects in Landrace and Large White sows1

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