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Nonadditive genetic effects are currently ignored in national genetic evaluations of farm animals because of ignorance of thelevel of dominance variance for traits of interest and the difficult computational problems involved. Potential gains fromincluding the effects of dominance in genetic evaluations include “purification” of additive values and availability ofpredictions of specific combining abilities for each pair of prospective parents. This study focused on making evaluation withdominance effects feasible computationally and on ascertaining benefits of such an evaluation for dairy cattle, beef cattle,and swine. Using iteration on data, computing costs for evaluation with dominance effects included costs could be less thantwice expensive as with only an additive model. With Method Â, variance components could be estimated for problemsinvolving up to 10 millions equations. Dominance effects accounted for up to 10% of phenotypic variance; estimates werelarger for growth traits. As a percentage of additive variance, the estimate of dominance variance reached 78% for 21-d litterweight of swine and 47% for post weaning weight of beef cattle. When dominance effects are ignored, additive evaluationsare “contaminated”; effects are greatest for evaluations of dams in a single large family. These changes in ranking wereimportant for dairy cattle, especially for dams of full-sibs, but were less important for swine. Specific combining abilitiescannot be included in sire evaluations and need to be computed separately for each set of parents. The predictions of specificcombining abilities could be used in computerized mating programs via the Internet. Gains from including the dominanceeffect in genetic evaluations would be moderate but would outweigh expenditures to produce those evaluations.

We developed and implemented change-point recursive models and compared them with a linear recursive model and a standard mixed model (SMM), in the scope of the relationship between litter size (LS) and number of stillborns (NSB) in pigs. The proposed approach allows us to estimate the point of change in multiple-segment modeling of a nonlinear relationship between phenotypes. We applied the procedure to a data set provided by a commercial Large White selection nucleus. The data file consisted of LS and NSB records of 4,462 parities. The results of the analysis clearly identified the location of the change points between different structural regression coefficients. The magnitude of these coefficients increased with LS, indicating an increasing incidence of LS on the NSB ratio. However, posterior distributions of correlations were similar across subpopulations (defined by the change points on LS), except for those between residuals. The heritability estimates of NSB did not present differences between recursive models. Nevertheless, these heritabilities were greater than those obtained for SMM (0.05) with a posterior probability of 85%. These results suggest a nonlinear relationship between LS and NSB, which supports the adequacy of a change-point recursive model for its analysis. Furthermore, the results from model comparisons support the use of recursive models. However, the adequacy of the different recursive models depended on the criteria used: the linear recursive model was preferred on account of its smallest deviance value, whereas nonlinear recursive models provided a better fit and predictive ability based on the cross-validation approach. Key words: Bayesian analysis, change point, litter size, piglet mortality, recursive model doi: 10.2527/jas.2009-2557

Current selection schemes for livestock improvement use a wide variety of phenotypic traits. Some of them, such as sensory, type, or carcass traits, obtain their records from subjective grading performed by trained technicians. Data from this subjective evaluation usually involve classification under a categorical and arbitrary predefined scale, whose output may lead to strong departures from the Gaussian distribution. In addition, the scale of grading may be different according to different technicians. To study this phenomenon, we have analyzed subjective conformation (CON) and fat cover (FAT) scores in the Pirenaica beef cattle breed from data provided by 12 different slaughterhouses. Three statistical models were used: 1) a Gaussian linear model; 2) an ordered category threshold model; and 3) a specific slaughterhouse ordered category threshold model. These models were analyzed through a Bayesian analysis via a Gibbs sampler with a data augmentation step. Posterior mean estimates of heritability ranged from 0.23 to 0.26 for CON, and from 0.13 to 0.16 for FAT. Statistical models were compared by the deviance information criteria, and the slaughterhouse-specific ordered category threshold model was selected as the most plausible. This result was confirmed by the fact that the threshold estimates differed noticeably between slaughterhouses. Finally, the proposed model for genetic evaluation increased the expected selection response by up to 7.6% for CON and 11.2% for FAT. Key words: Bayesian analysis, conformation score, fat cover, Pirenaica beef cattle, subjective evaluation, threshold model

This work aims to compare different nonlinear functions for describing the growth curves of Nelore females. The growth curve parameters, their (co)variance components, and environmental and genetic effects were estimated jointly through a Bayesian hierarchical model. In the first stage of the hierarchy, 4 nonlinear functions were compared: Brody, Von Bertalanffy, Gompertz, and logistic. The analyses were carried out using 3 different data sets to check goodness of fit while having animals with few records. Three different assumptions about SD of fitting errors were considered: constancy throughout the trajectory, linear increasing until 3 yr of age and constancy thereafter, and variation following the nonlinear function applied in the first stage of the hierarchy. Comparisons of the overall goodness of fit were based on Akaike information criterion, the Bayesian information criterion, and the deviance information criterion. Goodness of fit at different points of the growth curve was compared applying the Gelfand's check function. The posterior means of adult BW ranged from 531.78 to 586.89 kg. Greater estimates of adult BW were observed when the fitting error variance was considered constant along the trajectory. The models were not suitable to describe the SD of fitting errors at the beginning of the growth curve. All functions provided less accurate predictions at the beginning of growth, and predictions were more accurate after 48 mo of age. The prediction of adult BW using nonlinear functions can be accurate when growth curve parameters and their (co)variance components are estimated jointly. The hierarchical model used in the present study can be applied to the prediction of mature BW in herds in which a portion of the animals are culled before adult age. Gompertz, Von Bertalanffy, and Brody functions were adequate to establish mean growth patterns and to predict the adult BW of Nelore females. The Brody model was more accurate in predicting the birth weight of these animals and presented the best overall goodness of fit. Key words: Bayesian analysis, beef cattle, growth curve, longitudinal data, model choice, nonlinear function

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. Key words: Bayesian analysis, farrowing mortality, genetic parameter, piglet mortality, threshold model, variance component

Although inbreeding (F) is a topic of major concern in animal breeding, estimates of inbreeding depression are usually obtained by modeling the overall F coefficient of each individual, without considering that the recessive (deleterious) genetic load of a given population may be unevenly distributed among the founder genomes. The founder-specific partial F coefficient is calculated as the identity-by-descent probability at any given autosomal locus related to a particular founder and allows a more detailed analysis of inbreeding depression on productive traits. Within this context, birth BW data from 2,459 Ripollesa lambs were analyzed under a hierarchical animal model without F-related covariates (model 0), with inbreeding depression modeled by the overall F coefficient (model F1), or by the partial F coefficient of 9 founders that made a relevant contribution to the population inbreeding (model F2). A straightforward empirical Bayes factor (BF) was developed for testing statistical relevance of each F-related covariate, in which greater-than-1 values favored the model including the covariate. The deviance information criterion (DIC) clearly supported model F1 (5,767.8) rather than model 0 (5,771.2), suggesting that inbreeding depression had a relevant influence on birth BW data. The linear effect of inbreeding depression was statistically relevant in model F1 (BF = 2.52 x [10.sup.35]), with lamb birth BW declining by -13.6 g with each 1% F increase. The quadratic effect of inbreeding depression was almost null in model F1 (BF = 0.02), as suggested by the reduction in DIC (5,766.9) when this effect was removed from model F1. On the other hand, model F2 provided a similar DIC (5,767.9) value, with this parameter decreasing to 5,764.7 when nonrelevant founder-specific inbreeding depression effects were removed. Substantial heterogeneity in founder-specific inbreeding depression was reported by model F2, in which estimates for 4 of the 9 founders did not differ from zero (BF between 0.05 and 0.42), whereas 5 founders originated moderate (-8.2 g for each 1% F increase; BF = 1.42) to large inbreeding depression (-96.2 g for each 1% F increase; BF = 8.80 x [10.sup.19]). The substantial variability between founder estimates suggested that inbreeding depression effects may mainly be due to a few alleles with major deleterious effects. These results contribute valuable information that should help to achieve more accurate management of inbreeding in the Ripollesa breed. Key words: birth weight, empirical Bayes factor, founder, inbreeding depression, Ripollesa sheep

The objective of this work was to evaluate the Nelore beef cattle, growth curve parameters using the Von Bertalanffy function in a nested Bayesian procedure that allowed estimation of the joint posterior distribution of growth curve parameters, their (co)variance components, and the environmental and additive genetic components affecting them. A hierarchical model was applied; each individual had a growth trajectory described by the nonlinear function, and each parameter of this function was considered to be affected by genetic and environmental effects that were described by an animal model. Random samples of the posterior distributions were drawn using Gibbs sampling and Metropolis-Hastings algorithms. The data set consisted of a total of 145,961 BW recorded from 15,386 animals. Even though the curve parameters were estimated for animals with few records, given that the information from related animals and the structure of systematic effects were considered in the curve fitting, all mature BW predicted were suitable. A large additive genetic variance for mature BW was observed. The parameter a of growth curves, which represents asymptotic adult BW, could be used as a selection criterion to control increases in adult BW when selecting for growth rate. The effect of maternal environment on growth was carried through to maturity and should be considered when evaluating adult BW. Other growth curve parameters showed small additive genetic and maternal effects. Mature BW and parameter k, related to the slope of the curve, presented a large, positive genetic correlation. The results indicated that selection for growth rate would increase adult BW without substantially changing the shape of the growth curve. Selection to change the slope of the growth curve without modifying adult BW would be inefficient because their genetic correlation is large. However, adult BW could be considered in a selection index with its corresponding economic weight to improve the overall efficiency of beef cattle production. Key words: Bayesian analysis, beef cattle, growth curve, longitudinal data, selection, Von Bertalanffy

A Bayesian procedure, which allows consideration of the individual variation in the feed resource allocation pattern, is described and implemented in 2 sire lines of rabbit (Caldes and R). The procedure is based on a hierarchical Bayesian scheme, where the first stage of the model consists of a multiple regression model of feed intake on metabolic BW and BW gain. In a second stage, an animal model was assumed including batch, parity order, litter size, and common environmental litter effects. Animals were reared during the fattening period (from weaning at 32 d of age to 60 d of age) in individual cages on an experimental farm, and were fed ad libitum with a commercial diet. Body weight (g) and cumulative feed intake (g) were recorded weekly. Individual BW gain (g) and average BW (ABW, g) were calculated from these data for each 7-d period. Metabolic BW ([g.sup.0.75]) was estimated as [ABW.sup.0.75]. The number of animals actually measured was 444 and 445 in the Caldes and R lines, respectively. Marginal posterior distributions of the genetic parameters were obtained by Gibbs sampling. Posterior means (posterior SD) for heritabilities for partial coefficients of regression of feed intake on metabolic BW and feed intake on BW gain were estimated to be 0.35 (0.17) and 0.40 (0.17), respectively, in the Caldes line and 0.26 (0.19) and 0.27 (0.14), respectively, in line R. The estimated posterior means (posterior SD) for the proportion of the phenotypic variance due to common litter environmental effects of the same coefficients of regression were respectively, 0.39 (0.14) and 0.28 (0.13) in the Caldes line and 0.44 (0.22) and 0.49 (0.14) in line R. These results suggest that efficiency of use of feed resources could be improved by including these coefficients in an index of selection. Key words: Bayesian analysis, feed efficiency, rabbit, selection

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. Key words: FABP4, FAT1, fatness, pig, QTL

Given that correct assumptions on the baseline survival function are determinant for the validity of further inferences, specific tools to test the fit of a model to real data become essential in proportional hazards models. In this sense, we have proposed a parametric bootstrap to test the fit of survival models. Monte Carlo simulations are used to generate new data sets from the estimates obtained through the assumed models, and then bootstrap intervals can be established for the survival function along the time space studied. Significant fitting deficiencies are revealed when the real survival function is not included within the bootstrap interval. We tested this procedure in a survival data set of Bruna dels Pirineus beef calves, assuming 4 parametric models (exponential, Weibull, exponential time-dependent, Weibull time-dependent) and the Cox's semiparametric model. Fitting deficiencies were not observed for the Cox's model and the exponential time-dependent model, whereas the Weibull time-dependent model suffered from moderate overestimation at different ages. Thus, the exponential time-dependent model appears to be preferable because of its correct fit for survival data of beef calves and its smaller computational and time requirements. Exponential and Weibull models were completely rejected due to the continuous over- and underestimation of the survival probability reported. Results here highlighted the flexibility of parametric models with time-dependent effects, achieving a fit comparable to nonparametric models. Key words: model fitting, parametric bootstrap, proportional hazard, survival analysis

A QTL analysis of multibreed experiments (i.e., crossed populations involving more than two founder breeds) offers clear advantages over classical two-breed crosses, among them increased power and a more comprehensive coverage of the total genetic variability in the species. An alternative to designed multibreed crosses is to reanalyze jointly several experiments involving different breeds. We report a multibreed, multitrait QTL analysis of SSCX that involves five different crosses, six breeds, and almost 3,000 genotyped individuals using a truly multibreed strategy to allow for any number of founder breed origins. Traits analyzed were growth, fat thickness, car-cass length, and shoulder and ham weights. Generally, the joint analysis resulted in more significant QTL than the single-experiment analyses. We show that the QTL for fatness, which is highly significant (nominal P < [10.sup.-43]), is of Asiatic origin (Meishan). The next most significant QTL (nominal P < [10.sup.15]) affected ham weight and seems to be segregating only between Large White and the rest of the breeds. A multitrait, multi-QTL analysis suggests that these are two distinct loci. Additionally, a locus segregating only between Iberian and Landrace affects live weight. The advantages of joint, multibreed analyses clearly outweigh their potential risks. Key Words: Fatness, Growth, Pig, Quantitative Trait Locus, Sex Chromosome

We characterized the nearly complete coding sequence of the pig 2,4-dienoyl CoA reductase 1 (DECR1) gene, which encodes an enzyme involved in the [beta]-oxidation of polyunsaturated fatty enoyl-CoA esters and maps on a linoleic QTL located on Chromosome 4 Sequencing of a 937-bp fragment encompassing exons 2 and 10 revealed the existence of two missense SNP at exon 2 ([C.sub.181] [right arrow] [G.sub.181]) and exon 5 ([C.sub.458] [right arrow] [G.sub.458]). These two SNP are associated with Val (C) [right arrow] Leu (G) and Ser (C) [right arrow] Thr (G) conservative AA replacements at positions 61 and 153 of the DECR1 protein, respectively. Moreover, DECR1 genotyping in a representative sample of 184 pigs from the Large White, Pietrain, Iberian, Duroc, and Landrace breeds demonstrated the existence of disequilibrium linkage between these two SNP (Haplotype 1: [C.sub.181][C.sub.458]; Haplotype 2: [G.sub.181][G.sub.458]). An association analysis between DECR1 genotype and growth, carcass, and meat quality traits in a highly selected Landrace population (n = 470) revealed differences among genotypes for isocitrate dehydrogenase activity (highest posterior density [HPD] of 90%), longissimus thoracis pH (HPD of 95%), lightness (HPD of 90 to 95%), and redness (HPD of 95%). Because these associations were not consistently found in the three available genotype comparisons, we believe that exon 2 and 5 polymorphisms at the DECR1 gene might be in linkage disequilibrium with the true causal mutation influencing isocitrate dehydrogenase activity and muscle color and pH. Key Words: Carcass Traits, Fatty Acids, Meat Quality, Pig, Quantitative Trait Loci, Reductase

The genetic basis of the main components of boar taint was investigated in intact male pigs in a commercial population. We analyzed fat androstenone and skatole concentrations from 217 males of an outbred Landrace population. Records were normalized using a logarithm transformation and tested for norreality using a Wilk-Shapiro test. Bayesian analysis was then used to map QTL in 10 candidate regions previously selected on chromosomes 1, 2, 3, 4, 6, 7, 8, 9, 10, and 13. The criterion for QTL detection was the Bayes factor (BF) between polygenic models with and without QTL effects. Both traits had considerable genetic determination, with posterior means of total heritabilities ranging from 0.59 to 0.73 for androstenone and from 0.74 to 0.89 for skatole. Positive evidence for a fat skatole QTL was detected on SSC6 (BF = 5.16); however, no QTL for androstenone were found in any of the 10 chromosomal regions analyzed. With the detection of a QTL for the fat skatole concentration segregating in this population, marker-assisted selection or even gene-assisted selection could be used once the causal mutation of the QTL was identified. Key Words: Androstenone, Bayes Factor, Boar Taint, Quantitative Trail Loci, Skatole

A Bayesian analysis with a threshold model was performed for fertility defined as a binary trait (1 = successful mating, 0 = unsuccessful mating) in two populations of rabbits of different reproductive potential and different genetic origin: Line P selected for litter size and Line C selected for growth rate. There were 20,793 records of natural mating (86.2% successful) in Line C between 1983 and 2003, and 17,548 records (80.5% successful) in Line P, between 1992 and 2003. Data related to 5,388 and 3,848 females and 1,021 and 685 males in Lines C and P, respectively. The pedigree included 6,409 and 4,533 individuals in Lines C and P, respectively. The binary response was modeled under a probit approach. The model for the latent variable included male and female additive genetic effects, male and female permanent environmental effects, and the year-season and physiological status of the female (nulliparous, multiparous lactating, or multiparous nonlactating) as systematic effects. Means (standard deviation in parentheses) of the estimated marginal posterior distribution (EMPD) of male heritability were 0.013 (0.006) and 0.010 (0.008) in Lines C and P, respectively, and those of EMPD of female heritability were 0.056 (0.013) and 0.062 (0.018) in Lines C and P, respectively. Means of the EMPD of the proportion of the phenotypic variance due to environmental male and female effects were, respectively, 0.031 (0.007) and 0.128 (0.018) in Line C and 0.053 (0.010) and 0.231 (0.024) in Line P. Means (standard deviations in parentheses) of the EMPD of genetic correlation between male and female fertility were 0.733 (0.197) in Line C and 0.434 (0.381) in Line P. The posterior distribution of genetic correlations presents a huge dispersion, and the estimates should be taken with caution because of the almost negligible estimate of the male genetic component. Results indicate that little genetic variation exists for female fertility, and practically none for male fertility. It would, therefore, be possible to improve reproductive performance by including female fertility in a breeding program, but response to selection would be very small. Key Words: Bayesian Theory, Fertility, Genetic Parameters, Rabbit, Threshold Model

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.sub.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. Key Words: Bayes Factor, Neonate Metabolism, Survival, Vitality

The identification of QTL related to production traits that are relevant for the pig industry has been mostly performed by using divergent crosses. The main objective of the current study was to investigate whether these growth, fatness, and meat quality QTL, previously described in diverse experimental populations, were segregating in a Landrace commercial population selected for litter size, backfat thickness, and growth performance. We have found QTL for carcass weight (posterior P > 0.75), cutlet weight (posterior P > 0.99), weight of ham (posterior P > 0.75), shoulders weight (posterior probability > 0.99), and shear firmness (posterior P > 0.99) on pig Chromosome 2. Moreover, QTL with posterior P > 0.75 for fat thickness between the 3rd and 4th ribs (Chromosome 7), rib weights (Chromosome 8), backfat thickness (Chromosomes 8, 9, and 10), and b Minolta color component (Chromosome 7) were identified. These results indicate that commercial purebred populations retain a significant amount of genetic variation, even for traits that have been selected for many generations. Key Words: Pigs, Quantitative Trait Loci, Selected Population

Iberian x Meishan [F.sub.2] 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 ( Key Words: Categorical Survival Analysis, [F.sub.2] Piglets, Preweaning Survival, Proportional Hazards

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.

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. Key Words: Bayesian Theory, Correlated Responses, Markov Processes, Monte Carlo Method, Selection

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. Key Words: Best Linear Unbiased Prediction, Genomes, Least Squares, Pigs, Quantitative Trait Loci, Variance Components

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.

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. Key Words: Body Composition, Lactation, Mice, Resource Allocation, Selection

An experimental [F.sub.2] 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 [F.sub.2] animals with records for pH 24 h postmortem (pH 24 h), muscle color Minolta measurements [L.sup.*] (lightness), [a.sup.*] (redness), and [b.sup.*] (yellowness), [H.sup.*] (hue angle), [C.sup.*] (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.sup.*] (SSC4 and SSC7; F =16.42 and 7.17 respectively), and [a.sup.*] (SSC4 and SSC8; F = 8.05 and 7.36 respectively). No QTL were observed for the color measurements [b.sup.*], [H.sup.*], and [C.sup.*]. 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. Key Words: Meat Quality, Pigs, Quantitative Trait Loci

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. Key Words: Bayesian Theory, Pigs, Prolificacy, Selection Responses