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Concern about pork quality has increased during last decades. Given the influence of fat content and composition on sensorial, nutritional, and technological variables of pork meat, an accurate knowledge about genetic control of pig lipid metabolism is required. This study focused on providing a broad characterization for serum and meat lipid trait heritability estimates in pigs. Analyses were performed on a population of 370 Duroc barrows and measured the additive polygenic background for the serum concentrations of cholesterol, triglyceride, and low- and high-density lipoproteins at 45 and 190 d of age (at slaughter), as well as intramuscular fat, cholesterol content, and C:12 to C:22 fatty acid content in longissimus thoracis et lumborum and gluteus medius muscles at slaughter. These traits were analyzed under Bayesian univariate animal linear models, and the statistical relevance of heritability estimates was evaluated through Bayes factor (BF); the model with polygenic additive effects was favored when BF >1. All serum lipid traits showed relevant genetic determinism, but the BF reached greater values at 190 d of age. Serum lipid traits displayed moderate modal estimates for heritability that ranged from 0.18 to 0.30. On the other hand, the genetic determinism for meat quality traits showed a heterogeneous behavior with large and less-than-1 BF. In general, longissimus thoracis et lumborum and gluteus medius muscles showed a similar pattern, with strong evidence of polygenic additive effects for intramuscular fat and palmitic, stearic, and cis-vaccenic fatty acids content, whereas oleic and muscle cholesterol content showed moderate to weak BF with moderate heritabilities. Similarly, results regarding linoleic, arachidonic, n-3, and n-6 fatty acids suggested a moderate genetic determinism, but only in gluteus medius muscle. For the remaining traits (myristic and palmitoleic fatty acids in both muscles, along with linoleic, arachidonic, n-3, and n-6 fatty acids in the longissimus thoracis et lumborum muscle), no statistical evidence for genetic control was observed in this study. As a whole, these results confirm the complexity of lipid metabolism in pigs. Key words: Bayes factor, cholesterol, fatty acid, heritability, lipoprotein, meat quality doi: 10.2527/jas.2009-2205

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

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 QTL analysis of fat androstenone levels from a three-generation experimental cross between Large White and Meishan pig breeds was carried out. A total of 485 [F.sub.2] males grouped in 24 full-sib families, their 29 parents and 12 grandparents were typed for 137 markers distributed over the entire porcine genome. The [F.sub.2] male population was measured for fat androstenone levels at 100, 120, 140, and 160 d of age and at slaughter around 80 kg liveweight. Statistical analyses were performed using two interval mapping methods: a line-cross (LC) regression method, which assumes alternative alleles are fixed in founder lines, and a half- full-sib (HFS) maximum likelihood method, where allele substitution effects were estimated within each half- and full-sib family. Both methods revealed genomewide significant gene effects on chromosomes 3, 7, and 14. The QTL explained, respectively, 7 to 11%, 11 to 15%, and 6 to 8% of phenotypic variance. Three additional significant QTL explaining 4 to 7% of variance were detected on chromosomes 4 and 9 using LC method and on chromosome 6 using HFS method. Suggestive QTL were also obtained on chromosomes 2, 10, 11, 13, and 18. Meishan alleles were associated with higher androstenone levels, except on chromosomes 7, 10, and 13, although 10 and 13 additive effects were near zero. The QTL had essentially additive effects, except on chromosomes 4, 10, and 13. No evidence of linked QTL or imprinting effects on androstenone concentration could be found across the entire porcine genome. The steroid chromosome P450 21-hydroxylase (CYP21) and cytochrome P450 cholesterol side chain cleavage subfamily XIA (CYP11A) loci were investigated as possible candidate genes for the chromosome 7 QTL. No mutation of coding sequence has been found for CYP21. Involvement of a candidate regulatory mutation of CYP11A gene proposed by others can be excluded in our animals. Key Words: Androstenone, Boar Taint, Gene Mapping, Pigs, Quantitative Trait Loci

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(super -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 (h(super 2)(sub d)), .107 (.033) for maternal heritability (h(super 2)(sub m)), .047 (.020) for the proportion of variance due to maternal environmental effects (C(super 2)(sub m)), and -.034 (.043) for the genetic correlation between direct and maternal effects (r(sub 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 h(super 2)(sub m)estimates, lower C(super 2)(sub m) and h(super 2)(sub d) estimates, and less negative values for the marginal posterior distribution of r(sub 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. Key Words: Maternal Effects, Bayesian Theory, Gibbs Sampling, Beef Cattle, Weaning Weight

Preweaning piglet mortality (PWM), a trait highly related to litter size, is one of the main concerns associated with productive efficiency and animal welfare in commercial pig farms. The objectives of this work were to study piglet survival at the farm level, to establish a survival rate (SR) as a target indicator to be improved, and to model it based on other reproductive parameters. Analyzed data corresponded to 580 Spanish commercial farms with a total inventory of 809,768 sows. These farms showed a mean SR of 85.70% piglets born alive (BA), which decreased to 81.81% when total piglets born (TB) were considered. The SR was strongly associated with prolificacy (P < 0.01), the parities with the highest prolificacy being those that had the lowest SR. Thus, the highest correlations were for the SR of piglets BA in the third and fourth parities (r = -0.460 and r = -0.452, respectively, P < 0.01), and for the SR of piglets TB in the fourth parity (r = -0.546, P < 0.01), which was the one with the highest prolificacy. The values corresponding to the quartile of farms with the highest SR within the most productive farms were established as targets to be improved, which were ≥88.5% of piglets BA and 83.2% of piglets TB. Nevertheless, the direct associations shown between the piglet's survival and prolificacy and other productive factors, such as the age of piglets at weaning, the farrowings per sow and year and the farrowing interval, suggest the convenience of modeling the risk of PWM on farms to have its own target of survival index to be improved., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)