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An analysis of litter size and average piglet weight at birth in Landrace and Yorkshire using a standard two-trait mixed model (SMM) and a recursive mixed model (RMM) is presented. The RMM establishes a one-way link from litter size to average piglet weight. It is shown that there is a one-to-one correspondence between the parameters of SMM and RMM and that they generate equivalent likelihoods. As parameterized in this work, the RMM tests for the presence of a recursive relationship between additive genetic values, permanent environmental effects, and specific environmental effects of litter size, on average piglet weight. The equivalent standard mixed model tests whether or not the covariance matrices of the random effects have a diagonal structure. In Landrace, posterior predictive model checking supports a model without any form of recursion or, alternatively, a SMM with diagonal covariance matrices of the three random effects. In Yorkshire, the same criterion favors a model with recursion at the level of specific environmental effects only, or, in terms of the SMM, the association between traits is shown to be exclusively due to an environmental (negative) correlation. It is argued that the choice between a SMM or a RMM should be guided by the availability of software, by ease of interpretation, or by the need to test a particular theory or hypothesis that may best be formulated under one parameterization and not the other.

The implementation of genetic groups in BLUP evaluations accounts for different expectations of breeding values in base animals. Notwithstanding, many feasible structures of genetic groups exist and there are no analytical tools described to compare them easily. In this sense, the recent development of a simple and stable procedure to calculate the Bayes factor between nested competing models allowed us to develop a new approach of that method focused on compared models with different structures of random genetic groups. The procedure is based on a reparameterization of the model in terms of intraclass correlation of genetic groups. The Bayes factor can be easily calculated from the output of a Markov chain Monte Carlo sampling by averaging conditional densities at the null intraclass correlation. It compares two nested models, a model with a given structure of genetic groups against a model without genetic groups. The calculation of the Bayes factor between different structures of genetic groups can be quickly and easily obtained from the Bayes factor between the nested models. We applied this approach to a weaning weight data set of the Bruna dels Pirineus beef cattle, comparing several structures of genetic groups, and the final results showed that the preferable structure was an only group for unknown dams and different groups for unknown sires for each year of calving.

This study is aimed at improving the analysis of data used in identifying marker-associated effects on quantitative traits, specifically to account for possible departures from a Gaussian distribution of the trait data and to allow for asymmetry of marker effects attributable to phenotypic divergence between parental lines. A Bayesian procedure for analysing marker effects at the whole-genome level is presented. The procedure adopts a skewed t-distribution as a prior distribution of marker effects. The model with the skewed t-process includes Gaussian prior distributions, skewed Gaussian prior distributions and symmetric t-distributions as special cases. A Markov Chain Monte Carlo algorithm for obtaining marginal posterior distributions of the unknowns is also presented. The method was applied to a dataset on three traits (live weight, carcass length and backfat depth) measured in an F2 cross between Iberian and Landrace pigs. The distribution of marker effects was clearly asymmetric for carcass length and backfat depth, whereas it was symmetric for live weight. The t-distribution seems more appropriate for describing the distribution of marker effects on backfat depth.

We identified 22 polymorphisms in the adipocyte fatty-acid binding protein (FABP4) gene, a strong positional candidate gene for the FAT1 locus in porcine chromosome 4. The most informative polymorphism, an insertion/deletion in intron 1, together with a single nucleotide polymorphism in intron 3, was genotyped in a cross between Iberian and Landrace pigs. After performing QTL, single marker, and haplotype analyses, we showed that there were at least 2 quantitative trait genes in the FAT1 region and that the FABP4 polymorphism was tightly associated to fatness. A comparison of allelic frequencies in a panel of pig breeds suggested that the Del2634C polymorphism was under indirect selection. We also showed that FABP4 is tightly associated to fatness but not growth. Furthermore, a haplotype analysis suggests that there is genetic heterogeneity at the FAT1 locus within the Landrace breed.

Malic enzyme 1 (ME1) is a part of the tricarboxylate shuttle that provides NADPH and acetyl-CoA required in fatty acid biosynthesis. The pig ME1 locus maps on the proximal end of chromosome 1, where a quantitative trait loci (QTL) affecting fat deposition has been previously described. We amplified fragments of 1457 and 1459 bp that corresponded to the complete coding region and the 3'-untranslated region (UTR), respectively, of the pig ME1 gene. The sequences of these two fragments in pigs from three breeds (Landrace, Large White and Piétrain) contained five single nucleotide polymorphisms (SNP) in the 3'-UTR: C1706T, G1762T, A1807C, C1857A and T1880A. Three haplotypes were found in two generations of a selected Landrace population: H1 (C1706 G1762 A1807 C1857 A1880), H2 (C1706 G1762 A1807 C1857 T1880) and H3 (T1706 T1762 C1807 A1857 T1880). Using Bayesian association analyses, significant associations (highest posterior density at 95%) between ME1 genotype and backfat (BF) thickness at 171 days and muscular pH were found in a Landrace population.

The aim of this study was to investigate chromosomal regions affecting the number of teats in pigs and possible epistatic interactions between the identified quantitative trait loci (QTL). An experimental F2 cross between Iberian and Chinese Meishan lines was used for this purpose. A genomic scan was conducted with 117 markers covering the 18 porcine autosomes. Linkage analyses were performed by interval mapping using an animal model to estimate QTL and additive polygenic effects. Complementary analyses with models fitting two QTL were also carried out. The results showed three genomewide significant QTL mapping on chromosomes 5, 10 and 12, whose joint action control up to 30% of the phenotypic variance of the trait. Meishan alleles had a positive additive effect on teat number, and a positive-additive x additive-epistatic interaction was detected between QTL on chromosomes 10 and 12.

A crossed population between Iberian x Landrace pigs consisting of 321 F2, 87 F3, and 85 backcross individuals has been analyzed to refine the number and positions of quantitative trait loci (QTL) affecting shape, growth, fatness, and meat quality traits in SSC4. A multitrait multi-QTL approach has been used. Our results suggest that carcass length and shoulder weight are affected by two loci. The first one, close to the AFABP gene, has a very strong pleiotropic effect on fatness, whereas the second one, in the interval between S0073 and S0214, also affects live weight, although to a lesser extent. This latter QTL would correspond to the FAT1 locus described initially in pigs. It seems that SSC4's loci play an important role in redistributing total weight, and the Landrace allele increases shoulder weight and carcass length much more than ham or total weight. Furthermore, there is also strong evidence of additional loci influencing pH and color in more distant, telomeric positions.

The Bayes factor (BF) procedure was applied to examine the additive genetic component of several physiological and vitality variables for newborn pigs. Nine variables were studied: heart rate, arterial oxygen saturation, rectal temperature (all at birth and 60 min later), birth weight, interval between birth and first teats contact, and interval between birth and first colostrum intake. The available numbers of data ranged from 288 (heart rate at 60 min) to 839 records (birth weight) from F(2) Iberian x Meishan newborn pigs. We compared a model with zero heritability (nonheritable) with the one where the additive genetic background was included. The BF was used to discriminate between both candidate models. Very strong evidence of genetic background was detected for heart rate 60 min after birth (BF = 48.90), and strong evidence was detected for rectal temperature at birth (BF = 13.82). Posterior modes (means) of heritabilities were 0.29 (0.32) and 0.40 (0.39), respectively. In addition, substantial evidence of absence of genetic background was detected for arterial oxygen saturation at birth.

Iberian x Meishan F2 piglet's preweaning survivability was analyzed using categorical data regression procedures within the proportional hazards assumption. A frailty sire model was assumed with the litter effect treated as an additional random source of variation. Moreover, the relative birth weight within litter and the litter effect were considered time-dependent covariates that changed their values in the second day of life due to cross fostering carried out to standardize litters. Six variables had a significant effect on survivability: birth weight (P < 0.01), relative birth weight within litter (P < 0.001), rectal temperature 60 min after birth (P < 0.01), type of presentation at birth (P < 0.05), presence of stillbirths (P < 0.001), and presence of mummified fetuses (P < 0.001). Small piglets (<0.98 kg) suffered a high hazard ratio (6.57; P < 0.001), with this variable being clearly lower for the rest of birth weight categories. Piglets that were small in relation to their siblings (relative birth weight within litter) also suffered an increased death risk, with a hazard ratio of 1.81 (P < 0.05), which was similar to animals with posterior presentations at birth (hazard ratio = 1.80; P < 0.05). Piglets with a rectal temperature lower than 35.4 degrees C 60 min after birth showed the highest hazard ratio (7.18; P < 0.01). Furthermore, the presence of mummified fetuses decreased the survivability of the remaining siblings, with a hazard ratio of 2.03 (P < 0.01), as did the presence of stillbirths (hazard ratio = 3.55; P < 0.001). The inclusion of the two random effects allowed us to estimate the mode of the joint posterior density of the sire variance (0.08) and the litter variance (1.98). The estimated heritability of preweaning survival reached a value of 0.03. We conclude that piglet survival involves several systematic influences related to birth weight, thermoregulatory ability, and injuries suffered during gestation and farrowing. The genetic variance was small compared with those generated by the common environment, for which the genetic improvement of piglet survival seems difficult.

A simple procedure to calculate the Bayes factor between linked and pleiotropic QTL models is presented. The Bayes factor is calculated from the marginal prior and posterior densities of the locations of the QTL under a linkage and a pleiotropy model. The procedure is computed with a Gibbs sampler, and it can be easily applied to any model including the location of the QTL as a variable. The procedure was compared with a multivariate least-squares method. The proposed procedure showed better results in terms of power of detection of linkage when low information is available. As information increases, the performance of both procedures becomes similar. An example using data provided by an Iberian by Landrace pig intercross is presented. The results showed that three different QTL segregate in SSC6: a pleiotropic QTL affects myristic, palmitic, and eicosadienoic fatty acids; another pleiotropic QTL affects palmitoleic, stearic, and vaccenic fatty acids; and a third QTL affects the percentage of linoleic acid. In the example, the Bayes factor approach was more powerful than the multivariate least-squares approach.

We present a QTL genome scan for fatty acid composition in pigs. An F2 cross between Iberian x Landrace pigs and a regression approach fitting the carcass weight as a covariate for QTL identification was used. Chromosomes (Chrs) 4, 6, 8, 10, and 12 showed highly significant effects. The Chr 4 QTL influenced the linoleic content and both the fatty acid double-bond index and peroxidability index. In Chr 6 we found significant associations with the double-bond index and the unsaturated index of fatty acids. Chr 8 showed clear effects on the percentages of palmitic and palmitoleic fatty acids as well as the average chain length of fatty acids. In Chr 10 we detected a significant QTL for the percentage of myristic fatty acid, with an F value that was slightly above the genomewide threshold. The percentage of linolenic fatty acid was affected by a region on Chr 12. A nearly significant QTL for the content of gadoleic fatty acid was also detected in Chr 12. We also analyzed the genomic QTL distribution by a regression model that fits the backfat thickness as a covariate. Some of the QTL that were detected in our analysis could not be detected when the data were corrected by backfat thickness. This work shows how critical the selection of a covariate can be in the interpretation of results. This is the first report of a genome scan detection of QTL directly affecting fatty acid composition in pigs.

Gompertz growth curves were fitted to the data of 137 rabbits from control (C) and selected (S) lines. The animals came from a synthetic rabbit line selected for an increased growth rate. The embryos from generations 3 and 4 were frozen and thawed to be contemporary of rabbits born in generation 10. Group C was the offspring of generations 3 and 4, and group S was the contemporary offspring of generation 10. The animals were weighed individually twice a week during the first four weeks of life, and once a week thereafter, until 20 weeks of age. Subsequently, the males were weighed weekly until 40 weeks of age. The random samples of the posterior distributions of the growth curve parameters were drawn by using Markov Chain Monte Carlo (MCMC) methods. As a consequence of selection, the selected animals were heavier than the C animals throughout the entire growth curve. Adult body weight, estimated as a parameter of the Gompertz curve, was 7% higher in the selected line. The other parameters of the Gompertz curve were scarcely affected by selection. When selected and control growth curves are represented in a metabolic scale, all differences disappear.

We present a very flexible method that allows us to analyze X-linked quantitative trait loci (QTL) in crosses between outbred lines. The dosage compensation phenomenon is modeled explicitly in an identity-by-descent approach. A variety of models can be fitted, ranging from considering alternative fixed alleles within the founder breeds to a model where the only genetic variation is within breeds, as well as mixed models. Different genetic variances within each founder breed can be estimated. We illustrate the method with data from an F(2) cross between Iberian x Landrace pigs for intramuscular fat content and meat color component a*. The Iberian allele exhibited a strong overdominant effect for intramuscular fat in females. There was also limited evidence of one or more regions affecting color component a*. The analysis suggested that the QTL alleles were fixed in the Iberian founders, whereas there was some evidence of segregation in Landrace for the QTL affecting a* color component.

One QTL affecting backfat thickness (BF), intramuscular fat content (IMF) and eye muscle area (MA) was previously localized on porcine chromosome 6 in an F2 cross between Iberian and Landrace pigs. This work was done to study the effect of two positional candidate genes on these traits: H-FABP and LEPR genes. The QTL mapping analysis was repeated with a regression method using genotypes for seven microsatellites and two PCR-RFLPs in the H-FABP and LEPR genes. H-FABP and LEPR genes were located at 85.4 and 107 cM respectively, by linkage analysis. The effects of the candidate gene polymorphisms were analyzed in two ways. When an animal model was fitted, both genes showed significant effects on fatness traits, the H-FABP polymorphism showed significant effects on IMF and MA, and the LEPR polymorphism on BF and IMF. But when the candidate gene effect was included in a QTL regression analysis these associations were not observed, suggesting that they must not be the causal mutations responsible for the effects found. Differences in the results of both analyses showed the inadequacy of the animal model approach for the evaluation of positional candidate genes in populations with linkage disequilibrium, when the probabilities of the parental origin of the QTL alleles are not included in the model.

A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor.

The testing of Bayesian point null hypotheses on variance component models have resulted in a tough assigmment for which no clear and generally accepted method exists. In this work we present what we believe is a succeeding approach to such a task. It is based on a simple reparameterization of the model in terms of the total variance and the proportion of the additive genetic variance with respect to it, as well as on the explicit inclusion on the prior probability of a discrete component at origin. The reparameterization was used to bypass an arbitrariness related to the impropriety of uninformative priors onto unbounded variables while the discrete component was necessary to overcome the zero probability assigned to sets of null measure by the usual continuous variable models. The method was tested against computer simulations with appealing results.

We develop a mixed-model approach for QTL analysis in crosses between outbred lines that allows for QTL segregation within lines as well as for differences in mean QTL effects between lines. We also propose a method called "segment mapping" that is based in partitioning the genome in a series of segments. The expected change in mean according to percentage of breed origin, together with the genetic variance associated with each segment, is estimated using maximum likelihood. The method also allows the estimation of differences in additive variances between the parental lines. Completely fixed random and mixed models together with segment mapping are compared via simulation. The segment mapping and mixed-model behaviors are similar to those of classical methods, either the fixed or random models, under simple genetic models (a single QTL with alternative alleles fixed in each line), whereas they provide less biased estimates and have higher power than fixed or random models in more complex situations, i.e., when the QTL are segregating within the parental lines. The segment mapping approach is particularly useful to determining which chromosome regions are likely to contain QTL when these are linked.

We describe a 26 year-old woman with cervical plexopathy with damage of left superficial or cutaneous branches, and left cervical sensory dorsal rami. Picture began acutely and vanished few days later. We ruled out known etiologies like traumatisms, tumours and surgical and anaesthetic complicated procedures, therefore the patient suffered an idiopathic cervical plexopathy. We hypothesize two possibilities for this clinical picture: an autoimmune reaction or a muscular entrapment.

We present a 60-year-old man with a complete right IX-XII nerve palsy (Collet-Sicard syndrome) due to spontaneous right internal carotid artery (ICA) dissection. Magnetic resonance imaging (MRI) and MR angiography (MRA) showed signs of subadventitial dissection of the right ICA with a mural haematoma that expanded the circumference of the vessel at the level of the retrostyloid space, adjacent to the IX-XII nerves. No narrowing of the lumen or aneurysms was found. Clinical recovery was excellent after treatment with only antiplatelet drugs. Cervical internal carotid artery dissection should be included in the differential diagnosis of lower cranial nerve palsies. MRI and MRA are noninvasive, reliable methods for diagnosis and follow-up, especially in subadventitial dissections.

Interest in progressive focal cerebral syndromes associated with classical degenerative diseases has increased in recent years. Descriptions of posterior cortical atrophy with progressive visual agnosia are relatively rare. We present 5 patients (2 women) ranging in age between 57 and 72 years old. In all cases symptoms began and progressed with no known etiology. All cases were sporadic. The main clinical signs are difficulty in recognizing objects, colors, persons or places; topographical disorientation and visual memory alterations; alexia, simultagnosia, loss of ocular fixing and optic ataxia. Some patients presented other disturbances of praxis or memory and 2 progressed to global dementia. Language function was preserved and behavioral disturbances did not develop. The amplitude of the P100 visual evoked potential was low but latency was normal in 4 patients and prolonged in 1. Brain images showed atrophy and hypoperfusion in the parieto-occipital area. The neuropathology status of these patients is unknown.