PubMed Health⌕ Search

Biomedical subjects

L Varona

Publications and source records attributed to L Varona.

At least 37 records · Page 2Linked to original sources
PubMed2004

Viability of Iberian x Meishan F2 newborn pigs. II. Survival analysis up to weaning.

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.

Animals↗
PubMed2003

Identification of quantitative trait loci for production traits in commercial pig populations.

The aim of this study was to investigate methods for detecting QTL in outbred commercial pig populations. Several QTL for back fat and growth rate, previously detected in experimental resource populations, were examined for segregation in 10 different populations. Two hundred trait-by-population-by-chromosome tests were performed, resulting in 20 tests being significant at the 5% level. In addition, 53 QTL tests for 11 meat quality traits were declared significant, using a subset of the populations. These results show that a considerable amount of phenotypic variance observed in these populations can be explained by major alleles segregating at several of the loci described. Thus, despite a relatively strong selection pressure for growth and back fat traits in these populations, these alleles have not yet reached fixation. The approaches used here demonstrate that it is possible to verify segregation of QTL in commercial populations by limited genotyping of a selection of informative animals. Such verified QTL may be directly exploited in marker-assisted selection (MAS) programs in commercial populations and their molecular basis may be revealed by positional candidate cloning.

Adipose Tissue↗
PubMed2003

Bayesian inference about parameters of a longitudinal trajectory when selection operates on a correlated trait.

A hierarchical model for inferring the parameters of the joint distribution of a trait measured longitudinally and another assessed cross-sectionally, when selection has been applied to the cross-sectional trait, is presented. Distributions and methods for a Bayesian implementation via Markov Chain Monte Carlo procedures are discussed for the case where information about the selection criterion is available for all the individuals, but longitudinal records are available only in the later generations. Alternative specifications of the residual covariance structure are suggested. The procedure is illustrated with an analysis of correlated responses in growth curve parameters in a population of rabbits selected for increased growth rate. Results agree with those obtained in a previous study using both selected and control populations. The high correlation between samples indicates slow mixing, resulting in small effective sample sizes and high Monte Carlo standard errors.

Animals↗
PubMed2003

Reallocation of body resources in lactating mice highly selected for litter size.

The present study investigated differences in the allocation patterns of body stores in lactating female mice from a line selected for high litter size at birth (S-line, average litter size of 20) and dams from a nonselected control line (C-line, average litter size of 10). Body weight, litter size, litter weight, and absolute and relative lipid and protein mass were measured at peak lactation (2 wk in lactation) and at weaning (3 wk in lactation). Body size in S-line females has been increased as a correlated effect of selection for high litter size at birth, allowing for larger litters and higher absolute milk production. However, these dams produce larger litters relative to their own body weight. At peak lactation, lipid and protein percentage did not differ between lines. At weaning, S-line females had a higher protein percentage (P < 0.001) and lower lipid percentage (P < 0.05) than C-line females. Apparently, S-line females produce more offspring but at a greater cost to their own metabolism. This process was insufficient to supply the offspring with adequate resources, resulting in reduced (P < 0.0001) pup development and increased (P < 0.0001) preweaning mortality rates.

Animals↗
PubMed2003

Full pedigree quantitative trait locus analysis in commercial pigs using variance components.

In commercial livestock populations, QTL detection methods often use existing half-sib family structures and ignore additional relationships within and between families. We reanalyzed the data from a large QTL confirmation experiment with 10 pig lines and 10 chromosome regions using identity-by-descent (IBD) scores and variance component analyses. The IBD scores were obtained using a Monte Carlo Markov Chain method, as implemented in the LOKI software, and were used to model a putative QTL in a mixed animal model. The analyses revealed 61 QTL at a nominal 5% level (out of 650 tests). Twenty-seven QTL mapped to areas where QTL have been reported, and eight of these exceeded the threshold to claim confirmed linkage (P < 0.01). Forty-two of the putative QTL were detected previously using half-sib analyses, whereas 46 QTL previously identified by half-sib analyses could not be confirmed using the variance component approach. Some of the differences could be traced back to the underlying assumptions between the two methods. Using a deterministic approach to estimate IBD scores on a subset of the data gave very similar results to LOKI. We have demonstrated the feasibility of applying variance component QTL analysis to a large amount of data, equivalent to a genome scan. In many situations, the deterministic IBD approach offers a fast alternative to LOKI.

Analysis of Variance↗
PubMed2002

QTL mapping for growth and carcass traits in an Iberian by Landrace pig intercross: additive, dominant and epistatic effects.

Results from a QTL experiment on growth and carcass traits in an experimental F2 cross between Iberian and Landrace pigs are reported. Phenotypic data for growth, length of carcass and muscle mass, fat deposition and carcass composition traits from 321 individuals corresponding to 58 families were recorded. Animals were genotyped for 92 markers covering the 18 porcine autosomes (SSC). The results from the genomic scan show genomewide significant QTL in SSC2 (longissimus muscle area and backfat thickness), SSC4 (length of carcass, backfat thickness, loin, shoulder and belly bacon weights) and SSC6 (longissimus muscle area, backfat thickness, loin, shoulder and belly bacon weights). Suggestive QTL were also found on SSC1, SSC5, SSC7, SSC8, SSC9, SSC13, SCC14, SSC16 and SSC17. A bidimensional genomic scan every 10 cM was performed to detect interaction between QTL. The joint action of two suggestive QTL in SSC2 and SSC17 led to a genome-wide significant effect in live weight. The results of the bidimensional genomic scan showed that the genetic architecture was mainly additive or the experimental set-up did not have enough power to detect epistatic interactions.

Adipose Tissue↗
PubMed2002

Multivariate analysis of litter size for multiple parities with production traits in pigs: I. Bayesian variance component estimation.

A total of 66,620 records from the first six parities for number of piglets born alive (NBA) from 20,120 Landrace sows and 24,426 records for weight (WT) and backfat thickness (BT) at 175 d of age were analyzed to estimate genetic parameters. The pedigree consisted of 47,186 individuals, including 392 sires and 5,394 dams. Estimates were based on marginal posterior distribution of the genetic parameters obtained using Bayesian inference implemented via the Gibbs sampling procedure with a Data Augmentation step. The posterior means and posterior standard deviation (PSD) for heritability of NBA ranged from 0.064 (PSD 0.005) in the first parity to 0.146 (PSD 0.019) in the sixth parity, always increasing with the order of the parity. The posterior means for genetic correlations of litter size between adjacent parities were, in most cases, greater than 0.80. However, genetic correlation were much lower between nonadjacent parities. For example, the genetic correlation was 0.534 (PSD 0.061) between the fourth and the sixth parity for NBA. The posterior means of heritability for WT and BT were 0.229 (PSD 0.018) and 0.350 (PSD 0.019), respectively. Posterior mean for genetic correlation between WT and BT was 0.339 (PSD 0.044). The posterior means for genetic correlation between production (WT and BT) and reproduction traits (NBA in different parities) were close to zero in most cases. Results from this study suggest that different parities should be considered as different traits. Moreover, selection for growth and backfat should result in no or very little correlated response in litter size.

Adipose Tissue↗
PubMed2002

Multivariate analysis of litter size for multiple parities with production traits in pigs: II. Response to selection for litter size and correlated response to production traits.

Litter size and production trait responses to experimental selection for increased litter size in a Landrace pig population are reported. The numbers of sows and litters available for the first cycle of selection were 3,034 and 961, respectively. Selection was carried out using a BLUP repeatability animal model for number of piglets born alive (NBA). The experiment included one selection and one control line, each with three nonoverlapping generations. The selection line (H) consisted of the 160 sows with the highest breeding values and one boar from each of 25 full-sib families with the highest breeding values. The control line (C) consisted of 160 sows and 25 boars randomly chosen. The two subsequent generations in each line were obtained by random selection. A Bayesian analysis of genetic response using a multivariate model was carried out by Gibbs sampler. Marginal posterior distributions were obtained for direct response in NBA, and for correlated response in weight (WT), and backfat thickness (BT) at 175 d of age. The posterior means and posterior standard deviation (PSD) for direct genetic response of NBA ranged from 0.32 (PSD 0.08) in the first parity to 0.64 (PSD 0.08) in the fourth. The posterior means for correlated genetic response in WT and BT were -0.66 kg (PSD 0.36) and 0.20 mm (PSD 0.10), respectively. For WT and BT, the 95% highest posterior density regions (HPD) contain zero-correlated genetic response. Marginal posterior distributions of selection differentials were investigated. The posterior means for standardized selection differentials for NBA in different parities ranged from 0.70 (PSD 0.12) to 0.94 (PSD 0.06) in females for line H, from 0.22 (PSD 0.19) to 0.34 (PSD 0.10) in males for line H, and from 0.08 (PSD 0.08) to 0.13 (PSD 0.07) in females for line C. All available males were used in line C. Results from this experiment showed that selection for increased litter size is effective. Responses to selection were heterogeneous across parities, suggesting that litter size in each parity may have a different genetic background. No correlated genetic response to growth and backfat thickness was observed.

Adipose Tissue↗
PubMed2002

Quantitative trait locus mapping for meat quality traits in an Iberian x Landrace F2 pig population.

An experimental F2 cross between Iberian and Landrace pig strains was performed to map quantitative trait loci (QTL) for diverse productive traits. Here we report results for meat quality traits from 369 F2 animals with records for pH 24 h postmortem (pH 24 h), muscle color Minolta measurements L* (lightness), a* (redness), and b* (yellowness), H* (hue angle), C* (chroma), intramuscular fat (IMF) and haematin pigment content measured in the longissimus thoracis. Pigs were genotyped for 92 markers covering the 18 porcine autosomes (SSC). Results of the genome scan show evidence for QTL for IMF (SSC6; F = 27.16), pH 24 h (SSC3; F = 7.73), haematin pigments (SSC4 and SSC7; F = 8.68 and 9.47 respectively) and Minolta color measurements L* (SSC4 and SSC7; F =16.42 and 7.17 respectively), and a* (SSC4 and SSC8; F = 8.05 and 7.36 respectively). No QTL were observed for the color measurements b*, H*, and C*. Alternative models fitting epistasis between QTL were also tested, but detected epistatic interactions were not significant at a genome-wise level. In this work we identify genomic regions related with meat quality traits. Improvement by traditional selection methods is complicated, and finer mapping would be required for their application in introgression programs.

Animals↗
PubMed2000

Quantitative trait loci mapping in F(2) crosses between outbred lines.

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.

Chromosome Mapping↗
PubMed2000

Genetic analysis of growth curve parameters for male and female chickens resulting from selection on shape of growth curve.

The objectives of this research were to evaluate gender differences and selection on body weight as they affect growth curves of chickens. Marginal posterior densities of growth curve parameters were studied by Gibbs sampling on 10,671 male and female chickens originating from five lines. Line X-+ was selected on low body weight at 8 wk (BW8) and high body weight at 36 wk (BW36), line X+- on high BW8 and low BW36, X++ on high BW8 and BW36, X-- on low BW8, and BW36, and X00 was an unselected control line. Growth was modeled by a Gompertz function. Heritabilities and genetic correlations among parameters of the Gompertz curve were estimated. Marginal posterior densities were drawn for parameters of the growth curve and for sexual dimorphism at ages ranging from hatching to 1 yr. Lines selected for a higher BW8 had higher initial specific growth rates (L), higher maturation rates (K), and lower ages at inflection (T(I)). Lines selected for a higher BW36 had higher asymptotic body weights (A). Estimates of A, L, and T(I) were higher in males and K was higher in females. Difference between sexes for A was greater in the line selected for a lower BW8 and a higher BW36. Dimorphism for L and K was the lowest in lines X++ and X--, respectively. The greatest difference in T(I) was observed in the line selected for lower BW8 and BW36. Sexual dimorphism of body weight was lower at most ages in the lightest line. Before 15 wk, sexual dimorphism in X++ line was lower than in the line selected for higher BW8 and lower BW36. The increase in sexual dimorphism with body weight could be reduced by selecting animals on body weight at two ages instead of one, as is usually done in commercial lines.

Animals↗
PubMed2000

Method R estimates of additive genetic, dominance genetic, and permanent environmental fraction of variance for yield and health traits of Holsteins.

Fractions of variance accounted for by additive genetic, dominance genetic, and permanent environmental effects for milk, fat, and protein yields; somatic cell score; and productive life were estimated from Holstein data used for national genetic evaluations. Contemporary group assignments were determined using the national procedure. Data included 1,973,317 milk and fat records for 812,659 cows, 1,019,421 protein records for 462,067 cows, 468,374 lactation average somatic cell score (SCS) records for 232,909 cows, and 735,256 cows with productive-life records. Variance components were estimated with the JAADOM program, which uses iteration on data and second-order Jacobi iteration for obtaining solutions to the mixed-model equations and Method R for estimation of variance components. Ten different random data subsets were used to estimate parameters for each trait. Estimated additive genetic, dominance genetic, and permanent environmental fractions of variance were 0.34, 0.05, and 0.10 for milk yield; 0.34, 0.05, and 0.11 for fat yield; 0.31, 0.05, and 0.10 for protein yield; and 0.17, 0.01, and 0.16 for lactation average SCS. Estimated additive genetic and dominance genetic fractions of variance were 0.12 and 0.06 for productive life. Mean empirical standard errors of additive genetic, dominance genetic, and permanent environmental variance fractions were 0.003, 0.006, and 0.006.

Animals↗
PubMed1999

Maternal animal model with correlation between maternal environmental effects of related dams.

A procedure to take into account the nongenetic relationship between maternal effects in adjacent generations is presented. It considers a correlation between maternal environments provided by a dam and its daughters (lambda). The dispersion structure of the maternal animal model was modified to include a correlation matrix (E) that relates the maternal permanent environmental effects. The structures of the E matrix and its inverse (E(-1)) are described. Both matrices are completely defined by the correlation coefficient lambda. An algorithm to compute these matrices from pedigree information was also developed. Furthermore, a Bayesian analysis of this model including the lambda parameter was developed using Gibbs sampling, with Metropolis steps for the nonstandard conditional distributions. With simulated data, the proposed model reduced the bias in all estimates of dispersion parameters when an antagonism between the maternal effects received by a daughter and its future maternal environment existed. This model also provides an estimate of the environmental relationship between the maternal effects of dams and daughters by the lambda parameter. The same Bayesian analysis was also carried out with weaning weight data of the Bruna dels Pirineus breed. The posterior means (standard deviation) of (co)variance ratios were .214 (.081) for direct heritability (h2d), .107 (.033) for maternal heritability (h2m), .047 (.020) for the proportion of variance due to maternal environmental effects (c2m), and -.034 (.043) for the genetic correlation between direct and maternal effects (r(dm)). The posterior mean of lambda parameter was -.190, and 76% of its marginal posterior distribution took negative values. As occurred with simulated data, considering the maternal environmental correlation in the analysis implied higher h2m estimates, lower c2m and h2d estimates, and less negative values for the marginal posterior distribution of r(dm). These results were considered as evidence of the environmental antagonism between maternal effects provided by a dam and its daughters to weaning weight of their progeny in the Bruna dels Pirineus breed.

Animal Husbandry↗
PubMed1999

Threshold-linear versus linear-linear analysis of birth weight and calving ease using an animal model: I. Variance component estimation.

Birth weight and calving difficulty were analyzed with Bayesian methodology using univariate linear models, a bivariate linear model, a threshold model for calving difficulty, and a joint threshold-linear model using a probit approach. Field data included 26,006 records of Gelbvieh cattle. Simulated populations were generated using parameters estimated from the field data. The Gibbs sampler was used to obtain estimates of the marginal posterior mean and standard deviation of the (co)variance components, heritabilities, and correlations. In the univariate analyses, the posterior mean of direct heritability for calving difficulty was .23 with the threshold model and .18 with the linear model. Maternal heritabilities were .10 and .08, respectively. In the bivariate analysis, posterior means of direct heritability for calving difficulty were .21 and .18 for the bivariate linear-threshold and linear-linear model, respectively. Maternal heritabilities were .09 and .06, respectively. Direct heritability for birth weight was .25 for the univariate model and .26 for bivariate models. Maternal heritability was .05 for the linear-threshold model and the univariate model and .06 for the bivariate linear model. Genetic correlation between direct genetic effects in both traits was .81 for the linear-threshold model and .79 for the bivariate linear. Residual correlation was .35 for the bivariate linear model and .50 for the bivariate linear-threshold. A simulation study confirmed that the posterior mean of the marginal distribution was suitable as a point estimate for univariate threshold and bivariate linear-threshold models.

Animals↗
PubMed1999

Threshold-linear versus linear-linear analysis of birth weight and calving ease using an animal model: II. Comparison of models.

Several models were evaluated in terms of predictive ability for calving difficulty. Data included birth weight and calving difficulty scores provided by the American Gelbvieh Association from 26,006 calves born to first-parity cows and five simulated populations of 6,200 animals each. Included in the model were fixed age of dam x sex interaction effects, random herd-year-season effects, and random animal direct and maternal effects. Bivariate linear-threshold and linear-linear models for birth weight/calving ease and univariate threshold and linear models for calving ease were applied to the data sets. For each data set and model, one-half of calving ease records were randomly discarded. Predictive ability of the different models was defined with the mean square error (MSE) for the difference between a deleted calving ease score and its prediction obtained from the remaining data. In terms of correlation between simulated and predicted breeding values, the threshold models had a 1% advantage for direct genetic effects and 3% for maternal genetic effects. In simulation, the average MSE was .29 for linear-threshold, .32 for linear-linear, .37 for threshold, and .39 for linear model. For the field data set, the MSE was .31, .33, .39, and .40, respectively. Although the bivariate models for calving ease/birth weight were more accurate than univariate models, the threshold models showed a greater advantage under the bivariate model. For the purpose of genetic evaluation for calving difficulty in beef cattle, the use of the linear-threshold model seems justified. In dairy cattle, the evaluation for calving ease can benefit from recording birth weight.

Animals↗
PubMed1999

Prediction of parental dominance combinations for planned matings, methodology, and simulation results.

Optimal use of dominance information requires a mating system and predictions of specific combining abilities for each set of prospective parents. Current evaluation procedures provide such predictions only for a limited number of parents. A procedure is described that predicts the specific combining ability for any parents. In this procedure, for each set of parents and their ancestors, the additive relationship matrix is created as a dense matrix. This matrix is then used to create a parental dominance matrix in a sparse matrix form, in which the rows of the matrix correspond to all parental combinations for which predictions are already available. Each new prediction requires a solution of the system of equations with the parental dominance matrix as the left-hand side. The efficiency of the mating system that accounts for dominance was evaluated in a simulation study. The simulated data files varied with respect to proportion of males and females selected, proportion of cattle born through embryo transfer, and additive and dominance variance. Sires and dams were preselected based on the additive merits only, but specific matings were arranged based on the combined additive plus dominance merit. The response to selection with consideration of dominance increased from 3.8 to 16.6% of the response from one generation of additive selection. The response was greater when the additive variance was smaller, the dominance variance was larger, the intensity of additive selection was lower, and the proportion of full sibs was greater. Use of dominance in the mating system is feasible and results in an additional genetic response to selection.

Animals↗
PubMed1998

Bayesian inference of variance components for litter size in Rasa Aragonesa sheep.

Variance components were estimated for litter size in Rasa Aragonesa sheep, a meat breed from northern Spain, to determine whether selective breeding for litter size is a reasonable strategy to improve reproductive performance. We assumed an animal mixed effect threshold model with a binary response variable. Marginal estimates of the genetic parameters were obtained in the underlying scale using Bayesian inference, implemented via the Gibbs sampling procedure and a data augmentation approach. Posterior marginal means of heritability and repeatability were .077 and .141, respectively. Moreover, the 95% highest marginal posterior density region of heritability ranged from .051 to .101. Therefore, we conclude that litter size is a trait that could be selected for in breeding programs. The effect of the loss of pedigree information, a common feature of sheep production, on the estimation of the genetic parameters was also studied using simulation. The results indicate that the lack of pedigree information has little effect on our estimates of heritability.

Animals↗