Search

[EN] This study aimed at identifying differential gene expression conditional on the fatty acid profile of the longissimus thoracis (Lt) muscle, a prime cut of economic relevance for fresh and cured pork production. A population of 110 Iberian (25%)xLandrace (75%) back-crossed pigs was used, because these two breeds exhibit extreme profiles of intramuscular saturated fatty acid, monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) contents. Total RNA from Lt muscle was individually hybridized to GeneChip Porcine Genome arrays (Affymetrix). A principal component analysis was performed with data from the 110 animals to select 40 extreme animals based on the total fatty acid profile and the MUFA composition (MAP). Comparison of global transcription levels between extreme fatty acid profile pigs (n=40) resulted in 219 differentially expressed probes (false discovery rate <0.10). Gene ontology, pathway and network analysis indicated that animals with higher percentages of PUFA exhibit a shift toward a more oxidative muscular metabolism state, with a raise in mitochondria function (PPARGC1A, ATF2), fatty acid uptake and oxidation (FABP5, MGLL). On the other hand, 87 probes were differentially expressed between MUFA composition groups (n=40; false discovery rate <0.10). In particular, muscles rich in n-7 MUFA expressed higher levels of genes involved in lipid metabolism (GLUL, CRAT, PLA2G15) and lower levels of fatty acid elongation genes (ELOVL5). Moreover, the chromosomal position of FABP5, PAQR3, MGLL, PPARGC1A, GLUL and ELOVL5 co-localized with very relevant QTL for fat deposition and composition described in the same resource population. This study represents a complementary approach to identifying genes underlying these QTL effects.

[EN] Although heteroskedasticity has been a main topic of interest in beef cattle during recent decades, the current availability of canalization models provided new insights for animal breeding programs. Within this context, birth BW (BWT) was analyzed in the Bruna dels Pirineus beef cattle breed by implementing canalization models that accounted for heterogeneous residual variances due to 30systematic, permanent environmental effects and additive genetic effects. Analyses were performed on BWT data from 8,130 calves born in 12 commercial breeding herds contributing to the yield recording scheme of the Bruna dels Pirineus breed. Analytical models accounted for direct additive genetic, permanent environmental, and 4 systematic effects (i.e., age of the dam, sex of the calf, birth type, and herd-year-season), and the same effects were evaluated as potential sources of variation in the residual term. Their relevance was checked by the deviance information criterion (DIC), and only residual additive genetic, permanent environmental, birth type, and herd-year-season remained in the operational model, all of them originating relevant reductions in the DIC parameter. Bruna dels Pirineus calves showed a moderate heritability of 0.30 (95% high posterior density, 0.19 to 0.40) for BWT; additional additive genetic variability was revealed in the residual term, this being positively correlated with the direct additive genetic component (0.44; 95% high posterior density, 0.37 to 0.54). Genetic trends were evaluated on both sources of additive genetic variance, and relevant patterns were identified in several herds. Although this breed did not evidence a homogeneous genetic trend for the whole population, herd- specific positive and negative trends were revealed, suggesting the plausibility of genetic selection for canalization on BWT in beef cattle breeds. These results must be viewed as a contribution to the canalization research field, providing relevant information for the b

[EN] Differential gene expression analyses typically focus on departures across mathematical expectations (i.e. mean) from two or more groups of microarrays, without considering alternative patterns of departure. Nevertheless, recent studies in humans and great apes have suggested that differential gene expression could also be characterized in terms of heterogeneous dispersion patterns. This must be viewed as a very interesting genetic phenomenon clearly linked to the regulation mechanisms of gene transcription. Unfortunately, we completely lack information about the incidence and relevance of dispersion-specific differential gene expression in livestock species, although a specific Bayes factor (BF) for testing this kind of differential gene expression (i.e. within-probe heteroskedasticity) has been recently developed. Within this context, our main objective was to characterize the incidence of dispersion-specific differential gene expression in pigs and, if possible, providing the first evidence of this phenomenon in a livestock species. We evaluated dispersion-specific differential gene expression on ovary, uterus and hypophysis samples from 22 F-2 Iberian x Meishan sows, where a total of 15 252 probes were interrogated. For each tissue, heteroskedasticity of probe-specific residual variances was evaluated by three pairwise comparisons involving three physiological stages, that is, heat, 15 days of pregnancy and 45 days of pregnancy. Between 2.9% and 37.4% of the analyzed probes provided statistical evidence of within-tissue across-physiological stages dispersion-specific differential gene expression (BF > 1), and between 0.1% and 3.0% of them reported decisive evidence (BF > 100). It is important to highlight that <8% of the heteroskedastic probes were also linked to differential gene expression in terms of departures among the probe-specific mathematical expectation of each physiological stage. This discarded the disturbance of scale effects in a high percenta

[EN] The analysis of microarray gene expression data has experienced a remarkable growth in scientific research over the last few years and is helping to decipher the genetic background of several productive traits. Nevertheless, most analytical approaches have relied on the comparison of 2 (or a few) well-defined groups of biological conditions where the continuous covariates have no sense (e. g., healthy vs. cancerous cells). Continuous effects could be of special interest when analyzing gene expression in animal production-oriented studies (e. g., birth weight), although very few studies address this peculiarity in the animal science framework. Within this context, we have developed a recursive linear mixed model where not only are linear covariates accounted for during gene expression analyses but also hierarchized and the effects of their genetic, environmental, and residual components on differential gene expression inferred independently. This parameterization allows a step forward in the inference of differential gene expression linked to a given quantitative trait such as birth weight. The statistical performance of this recursive model was exemplified under simulation by accounting for different sample sizes (n), heritabilities for the quantitative trait (h(2)), and magnitudes of differential gene expression (lambda). It is important to highlight that statistical power increased with n, h(2), and lambda, and the recursive model exceeded the standard linear mixed model with linear (nonrecursive) covariates in the majority of scenarios. This new parameterization would provide new insights about gene expression in the animal science framework, opening a new research scenario where within-covariate sources of differential gene expression could be individualized and estimated. The source code of the program accommodating these analytical developments and additional information about practical aspects on running the program are freely available by request to the

Studies of the variation in recombination rate across the genome provide a better understanding of evolutionary genomics and are also an important step towards mapping and dissecting complex traits in domestic animals. With the recent completion of the porcine genome sequence and the availability of a high-density porcine single nucleotide polymorphism (SNP) array, it is now possible to construct a high-density porcine linkage map and estimate recombination rate across the genome. A total of 416 animals were genotyped with the Porcine SNP60BeadChip, and high-density chromosome linkage maps were constructed using CRI-MAP, assuming the physical order of the Sscrofa10 assembly. The total linkage map length was 2018.79 cM, using 658 meioses and 14 503 SNPs. The estimated average recombination rate across the porcine autosomes was 0.86 cM/Mb. However, a large variation in recombination rate was observed among chromosomes. The estimated average recombination rates (cM/Mb) per chromosome ranged from 0.48 in SSC1 to 1.48 in SSC10, displaying a significant negative correlation with the chromosome sizes. In addition, the analysis of the variation in the recombination rates taking 1-Mb sliding windows has allowed us to demonstrate the variation in recombination rates within chromosomes. In general, a larger recombination rate was observed in the extremes than in the centre of the chromosome. Finally, the ratio between female and male recombination rates was also inferred, obtaining a value of 1.38, with the heterogametic sex having the least recombination. © 2011 The Authors, Animal Genetics © 2011 Stichting International Foundation for Animal Genetics.

[EN] A Bayesian threshold model was fitted to analyze the genetic parameters for farrowing mortality at the piglet level in Large White, Landrace, and Pietrain populations. Field data were collected between 1999 and 2006. They were provided by 3 pig selection nucleus farms of a commercial breeding company registered in the Spanish Pig Data Bank (BDporc). Analyses were performed on 3 data sets of Large White (60,535 piglets born from 4,551 litters), Landrace (57,987 piglets from 5,008 litters), and Pietrain (42,707 piglets from 4,328 litters) populations. In the analysis, farrowing mortality was considered as a binary trait at the piglet level and scored as 1 (alive piglet) or 0 (dead piglet) at farrowing or within the first 12 h of life. Each breed was analyzed separately, and operational models included systematic effects (year-season, sex, litter size, and order of parity), direct and maternal additive genetic effects, and common litter effects. Analyses were performed by Bayesian methods using Gibbs sampling. The posterior means of direct heritability were 0.02, 0.06, and 0.10, and the posterior means of maternal heritability were 0.05, 0.13, and 0.06 for Large White, Landrace, and Pietrain populations, respectively. The posterior means of genetic correlation between the direct and maternal genetic effects for Landrace and Pietrain populations were -0.56 and -0.53, and the highest posterior intervals at 95% did not include zero. In contrast, the posterior mean of the genetic correlation between direct and maternal effects was 0.15 in the Large White population, with the null correlation included in the highest posterior interval at 95%. These results suggest that the genetic model of evaluation for the Landrace and Pietrain populations should include direct and maternal genetic effects, whereas farrowing mortality could be considered as a sow trait in the Large White population.

[EN] 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.

[EN] Inbreeding is a biological phenomenon of special relevance in domestic species in which its influence has been typically associated with reductions in animal fitness. Nevertheless, recent research suggests substantial founder-specific variability in terms of inbreeding depression on some productive traits. although this centred on a very reduced number of founders. This research focuses on the modelling of founder-specific inbreeding depression (FSID) effects from a more general point of view, characterizing the expected distribution of FSID effects on sow longevity. Under a change-point Weibull proportional hazards model solved through Bayesian inference, a skew-normal a priori distribution for the FSID effects of 19 founders was assumed. In terms of the deviance information criterion, this model was clearly preferred to other prior distributions for FSID effects as well as to a standard analysis of the overall inbreeding depression effect, although all models were consistent with an overall negative genetic effect of inbreeding on sow longevity. The joint posterior distribution of FSID effects showed a skewed pattern with substantial right-tail overexpression, in which the mean (0.036). mode (0.034), S.D. (0.032) and asymmetry parameter (0.045) reported a higher incidence of positive estimates (85.2%) with an unfavourable effect on sow longevity. The founder with the worst inbreeding depression effect reduced sow longevity by 32 days for 1% or 167 days for 10% partial inbreeding. As a whole, our analyses highlighted substantial variability in FSID effects, with unfavourable, neutral and even favourable influences on sow longevity. This heterogeneity could be related to an uneven distribution of the recessive deleterious genetic load among founder genomes, and also with the different selection pressures applied to each founder line. The implementation of skew-normal priors also proved an appealing way to bypass the strict scenario imposed by the standard symme

Caracteres morfológicos como la conformación de los aplomos pueden tener un papel clave en la longevidad de las cerdas. Dentro de este contexto, se evaluó el efecto de los aplomos sobre la longevidad de 587 cerdas Duroc, analizándose tanto la supervivencia global de las cerdas (SG) como la supervivencia bajo tres causas de fallida distintas (competing risk): muerte (BM), baja productividad (BP) y baja fertilidad (BF). La conformación global de los aplomos influyó (p < 0,001) la longevidad de las cerdas en los análisis SG, BP y BF, aumentado el riesgo de fallida a medida que empeoraban los aplomos. El crecimiento anormal de las pezuñas (p < 0,001) y la presencia de golpes o bultos en las patas (p < 0,05) incrementaba el riesgo de fallida en los análisis SG, BP y BF. Las cerdas plantígradas tenían un riesgo mayor de fallida en los análisis SG (p < 0,001) y BP (p < 0,05), las cerdas con hiperextensión de las patas tenían un riesgo mayor en el análisis BF (p < 0,05), mientras que la presencia de pies abiertos aumentaba el riesgo de fallida en el análisis SG (p < 0,05). Las estimaciones de heredabilidad para la longevidad de las cerdas fueron de 0,07 (análisis SG), 0,02 (análisis BP) y 0,08 (análisis BF)., Morphologic traits such as leg conformation can play a key role on sow longevity. Within this context, the effect of leg conformation was evaluated on longevity data from 587 Duroc sows, longevity being characterized as overall longevity (OS) or sow failure due to death (DE), low productivity (LP) or low fertility (LF; competing risk analyses). Overall leg conformation score influenced (P < 0.001) sow longevity in OS, LP and LF analyses, impairing sow longevity when leg conformation got worse. Abnormal hoof growth (P < 0.001) and presence of bumps or injuries in legs (P < 0.001) increased the risk of failure in OS, LP and LF analyses. Plantigrade sows showed a higher culling risk in OS (P < 0.001) and LP (P < 0.05) analysis, sows with sickle-hooked leg had a higher culling risk in the LF analysis (P < 0.05), whereas splayed feet increased sow failure in the OS analysis (P < 0.05). Estimates of heritability for sow longevity were 0.07 (GS analysis), 0.02 (LP analysis) and 0.08 (LF analysis).

[EN] This note presents the program geamm v.1.4 (Gene Expression Analysis with Mixed Models), a versatile software program to analyse appropriately normalized gene expression data by a heteroskedastic mixed model, and allowing for both discrete and continuous effects in model. Although the analysis of gene expression data is an area of intensive software development, available programs are focused on the comparison between discrete¿type effects1 (e.g., health vs. cancerous cells2), whereas continuous effects like age3 cannot be easily accommodated. To our best knowledge, this is the first microarray¿specific program allowing for an easy and flexible modelling of continuous effects on gene expression analysis. Mixed model equations are implemented under Bayesian inference and solved through Gibbs sampling. The program, user's guide and example files can be downloaded free of charge from http://www.bdporc.irta.es/Publicacions/GEAMM.zip. A user's guide is also available as Appendix S1.