Effect of inbreeding on the longevity of Landrace sows

Ponencia publicada en ITEA, vol.104La consanguinidad es un fenómeno biológico de especial relevancia en las especies domésticas, pudiéndose caracterizar tanto en términos de coeficiente de consanguinidad como fraccionando la contribución de cada individuo fundador en coeficientes de consanguinidad parcial (CP). A partir de los registros de longevidad de 4.226 cerdas de raza Landrace, este trabajo se ha centrado en la modelización de los CP bajo modelos Weibull de riesgos proporcionales y su posterior comparación mediante el DIC (deviance information criterion). Se asumieron tres distribuciones a priori distintas para los efectos de CP, resultando la normal asimétrica (DIC = 55.064,6) claramente preferible a la normal simétrica (DIC = 55.069,2) y a la distribución uniforme (DIC = 55.077,9). Se descartó, también, el modelo estándar con la consanguinidad global de cada individuo (DIC = 55.078,4). En el caso del modelo con DIC mínimo, la distribución posterior de los efectos de CP fue claramente asimétrica, con el 85,15% de las estimas afectando negativamente a la longevidad de las cerdas y el 14,85% restante con efecto neutro o incluso positivo. Señalar por último, que la heredabilidad para el carácter longevidad fue de 0,159.Inbreeding is a biological phenomenon of special relevance in domestic species, where the overall inbreeding coefficient can be partitioned in founder-specific partial inbreeding (PI) coefficients. Taking longevity data of 4,226 Landrace sows as starting point, this research proposed alternative parameterization for PI effects under Weibull proportional hazard models, and compared their performance through the deviance information criterion (DIC). Three different a priori distributions were assumed for PI effects, asymmetric normal (DIC = 55,064.6), symmetric normal (DIC = 55,069.2) and flat (DIC = 55,077.9). Additionally, the standard model accounting for the overall inbreeding coefficient was clearly discarded (DIC = 55,078.4). For the model with asymmetric Gaussian prior, the posterior distribution of PI effects was clearly skewed. An 85.15% of the estimates showed negative effect on sow longevity whereas the remaining 14.85% ones had null or even positive effect on sow survival. Estimated heritability was 0.159

Comparison of economic performance of lead-acid and li-ion batteries in standalone photovoltaic energy systems

Standalone renewable energy systems usually incorporate batteries to get a steady energy supply. Currently, Li-ion batteries are gradually displacing lead-acid ones. In practice, the choice is made without previous comparison of its profitability in each case. This work compares the economic performance of both types of battery, in five real case studies with different demand profiles. For each case, two sets of simulations are carried out. In one of the sets, the energy demand is supplied by a standalone photovoltaic system and, in the other one, by a standalone hybrid photovoltaic-diesel system. Through optimization processes, the economic optimum solutions are obtained. In addition, sensitivity analyses on various parameters have been carried out, seeking the influence in favor of one or another type of battery. The results show that if the type of battery is changed, to achieve the economic optimum the entire system must be resized. In some cases, the economic optimum is reached with Li-ion and in others with lead-acid batteries, depending on the demand profiles. Thus, both types of batteries can be profitable options in standalone energy systems, with a greater tendency to lead-acid in fully photovoltaic systems and to Li-ion in hybrids. The price reductions that would make Li-ion the only choice is quantified. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Bayes factor between Student t and Gaussian mixed models within an animal breeding context

[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.The authors are indebted to Dr. J.L. Noguera and COPAGA for field data on pig weight at six months, and to Dr. J. Piedrafita and Dr. G. Caja for providingadditional field data sets during preliminary tests of the Bayes factor. The research contract of J. Casellas was partially financed by Spain s Ministerio de Educación y Ciencia (Programa Juan de la Cierva).Casellas, J.; Ibáñez-Escriche, N.; Garcia-Cortes, L.; Varona, L. (2008). Bayes factor between Student t and Gaussian mixed models within an animal breeding context. Genetics Selection Evolution. 40(4):395-413. https://doi.org/10.1051/gse:2008007S39541340

Inbreeding depression load for litter size in Entrepelado and Retinto Iberian pig varieties

[EN] Individual-specific hidden inbreeding depression load (IDL) can be accounted for in livestock populations by appropriate best linear unbiased prediction approaches. This genetic effect has a recessive pattern and reveals when inherited in terms of identity-by-descent. Nevertheless, IDL inherits as a pure additive genetic background and can be selected using standard breeding values. The main target of this research was to evaluate IDL for litter size in 2 Iberian pig varieties (Entrepelado and Retinto) from a commercial breeding-stock. Analyses were performed on the total number of piglets born (both alive and dead) and used data from 3,200 (8.02 ± 0.04 piglets/litter) Entrepelado and 4,744 Retinto litters (8.40 ± 0.03 piglets/litter). Almost 50% of Entrepelado sows were inbred (1.7% to 25.0%), whereas this percentage reduced to 37.4% in the Retinto variety (0.2% to 25.0%). The analytical model was solved by Bayesian inference and accounted for 2 systematic effects (sow age and breed/variety of the artificial insemination boar), 2 permanent environmental effects (herd-year-season and sow), and 2 genetic effects (IDL and infinitesimal additive). In terms of posterior means (PM), additive genetic and IDL variances were similar in the Entrepelado variety (PM, 0.68 vs. 0.76 piglets2, respectively) and their 95% credibility intervals (95CI) overlapped, although without including zero (0.38 to 0.94 vs. 0.15 to 1.31 piglets2, respectively). The same pattern revealed in the Retinto variety, with IDL variance (PM, 0.41 piglets2; 95CI, 0.07 to 0.88 piglets2) slightly larger than the additive genetic variance (PM, 0.37 piglets2; 95CI, 0.16 to 0.59 piglets2). The relevance of IDL was also checked by a Bayes factor and the deviance information criterion, the model including this effect being clearly favored in both cases. Although the analysis assumed null genetic covariance between IDL and infinitesimal additive effects, a moderate negative correlation (¿0.31) was suggested when plotting the PM of breeding values in the Entrepelado variety; a negative genetic trend for IDL was also revealed in this Iberian pig variety (¿0.25 piglets for 100% inbred offspring of individuals born in 2014), whereas no trend was detected in Retinto breeding-stock. Those were the first estimates of IDL in a commercial livestock population, they giving evidence of a relevant genetic background with potential consequences on the reproductive performance of Iberian sows.The authors gratefully acknowledge the company INGA FOOD SA (Almendralejo, Spain) and its technicians (E. Magallon, M. J. Garcia-Santana, L. Munoz, P. Diaz, D. Iniesta, and M. Ramos), as well as S. Negro (IRTA), for their cooperation and technical support. This research was partially funded by grants CGL2016-80155-R and IDI-20170304 from Spain's Ministry of Science, Innovation and Universities.Casellas, J.; Ibañez Escriche, N.; Varona, L.; Rosas, J.; Noguera, J. (2019). Inbreeding depression load for litter size in Entrepelado and Retinto Iberian pig varieties. Journal of Animal Science. 97(5):1979-1986. https://doi.org/10.1093/jas/skz084S19791986975Alves, E., Fernández, A., Barragán, C., Ovilo, C., Rodríguez, C., & Silió, L. (2006). Inference of hidden population substructure of the Iberian pig breed using multilocus microsatellite data. Spanish Journal of Agricultural Research, 4(1), 37. doi:10.5424/sjar/2006041-176CABALLERO, A., & TORO, M. A. (2000). Interrelations between effective population size and other pedigree tools for the management of conserved populations. Genetical Research, 75(3), 331-343. doi:10.1017/s0016672399004449Casellas, J. (2017). On individual-specific prediction of hidden inbreeding depression load. Journal of Animal Breeding and Genetics, 135(1), 37-44. doi:10.1111/jbg.12308CASELLAS, J., VARONA, L., IBÁÑEZ-ESCRICHE, N., QUINTANILLA, R., & NOGUERA, J. L. (2008). Skew distribution of founder-specific inbreeding depression effects on the longevity of Landrace sows. Genetics Research, 90(6), 499-508. doi:10.1017/s0016672308009907Charlesworth, D., & Willis, J. H. (2009). The genetics of inbreeding depression. Nature Reviews Genetics, 10(11), 783-796. doi:10.1038/nrg2664Dekkers, J. C. M. (1992). Asymptotic response to selection on best linear unbiased predictors of breeding values. Animal Science, 54(3), 351-360. doi:10.1017/s0003356100020808Esteve-Codina, A., Kofler, R., Himmelbauer, H., Ferretti, L., Vivancos, A. P., Groenen, M. A. M., … Pérez-Enciso, M. (2011). Partial short-read sequencing of a highly inbred Iberian pig and genomics inference thereof. Heredity, 107(3), 256-264. doi:10.1038/hdy.2011.13Fabuel, E., Barragán, C., Silió, L., Rodríguez, M. C., & Toro, M. A. (2004). Analysis of genetic diversity and conservation priorities in Iberian pigs based on microsatellite markers. Heredity, 93(1), 104-113. doi:10.1038/sj.hdy.6800488Fernández, E. N., Legarra, A., Martínez, R., Sánchez, J. P., & Baselga, M. (2017). Pedigree-based estimation of covariance between dominance deviations and additive genetic effects in closed rabbit lines considering inbreeding and using a computationally simpler equivalent model. Journal of Animal Breeding and Genetics, 134(3), 184-195. doi:10.1111/jbg.12267Fuerst, C., & Sölkner, J. (1994). Additive and Nonadditive Genetic Variances for Milk Yield, Fertility, and Lifetime Performance Traits of Dairy Cattle. Journal of Dairy Science, 77(4), 1114-1125. doi:10.3168/jds.s0022-0302(94)77047-8García-Cortés, L. A., Martínez-Ávila, J. C., & Toro, M. A. (2010). Fine decomposition of the inbreeding and the coancestry coefficients by using the tabular method. Conservation Genetics, 11(5), 1945-1952. doi:10.1007/s10592-010-0084-xGelfand, A. E., & Smith, A. F. M. (1990). Sampling-Based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association, 85(410), 398-409. doi:10.1080/01621459.1990.10476213Gulisija, D., Gianola, D., Weigel, K. A., & Toro, M. A. (2006). Between-founder heterogeneity in inbreeding depression for production in Jersey cows. Livestock Science, 104(3), 244-253. doi:10.1016/j.livsci.2006.04.007Hinrichs, D., Meuwissen, T. H. E., Ødegard, J., Holt, M., Vangen, O., & Woolliams, J. A. (2007). Analysis of inbreeding depression in the first litter size of mice in a long-term selection experiment with respect to the age of the inbreeding. Heredity, 99(1), 81-88. doi:10.1038/sj.hdy.6800968Hoeschele, I., & Vollema, A. R. (1993). Estimation of variance components with dominance and inbreeding in dairy cattle. Journal of Animal Breeding and Genetics, 110(1-6), 93-104. doi:10.1111/j.1439-0388.1993.tb00720.xIbáñez-Escriche, N., Varona, L., Magallón, E., & Noguera, J. L. (2014). Crossbreeding effects on pig growth and carcass traits from two Iberian strains. Animal, 8(10), 1569-1576. doi:10.1017/s1751731114001712Kass, R. E., & Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773-795. doi:10.1080/01621459.1995.10476572Legarra, A., & Vitezica, Z. G. (2015). Genetic evaluation with major genes and polygenic inheritance when some animals are not genotyped using gene content multiple-trait BLUP. Genetics Selection Evolution, 47(1). doi:10.1186/s12711-015-0165-xLeroy, G. (2014). Inbreeding depression in livestock species: review and meta-analysis. Animal Genetics, 45(5), 618-628. doi:10.1111/age.12178Martınez, A. M., Delgado, J. V., Rodero, A., & Vega-Pla, J. L. (2000). Genetic structure of the Iberian pig breed using microsatellites. Animal Genetics, 31(5), 295-301. doi:10.1046/j.1365-2052.2000.00645.xNagy, I., Gorjanc, G., Curik, I., Farkas, J., Kiszlinger, H., & Szendrő, Z. (2012). The contribution of dominance and inbreeding depression in estimating variance components for litter size in Pannon White rabbits. Journal of Animal Breeding and Genetics, 130(4), 303-311. doi:10.1111/jbg.12022Ober, C., Hyslop, T., & Hauck, W. W. (1999). Inbreeding Effects on Fertility in Humans: Evidence for Reproductive Compensation. The American Journal of Human Genetics, 64(1), 225-231. doi:10.1086/302198Perez-Enciso, M., & Gianola, D. (1992). Estimates of genetic parameters for litter size in six strains of Iberian pigs. Livestock Production Science, 32(3), 283-293. doi:10.1016/s0301-6226(12)80007-8Pujol, B., Zhou, S.-R., Sanchez Vilas, J., & Pannell, J. R. (2009). Reduced inbreeding depression after species range expansion. Proceedings of the National Academy of Sciences, 106(36), 15379-15383. doi:10.1073/pnas.0902257106Quaas, R. L. (1976). Computing the Diagonal Elements and Inverse of a Large Numerator Relationship Matrix. Biometrics, 32(4), 949. doi:10.2307/2529279Saccheri, I., Kuussaari, M., Kankare, M., Vikman, P., Fortelius, W., & Hanski, I. (1998). Inbreeding and extinction in a butterfly metapopulation. Nature, 392(6675), 491-494. doi:10.1038/33136Saura, M., Fernández, A., Rodríguez, M. C., Toro, M. A., Barragán, C., Fernández, A. I., & Villanueva, B. (2013). Genome-Wide Estimates of Coancestry and Inbreeding in a Closed Herd of Ancient Iberian Pigs. PLoS ONE, 8(10), e78314. doi:10.1371/journal.pone.0078314Saura, M., Fernández, A., Varona, L., Fernández, A. I., de Cara, M., Barragán, C., & Villanueva, B. (2015). Detecting inbreeding depression for reproductive traits in Iberian pigs using genome-wide data. Genetics Selection Evolution, 47(1), 1. doi:10.1186/s12711-014-0081-5Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583-639. doi:10.1111/1467-9868.00353Varona, L., Legarra, A., Herring, W., & Vitezica, Z. G. (2018). Genomic selection models for directional dominance: an example for litter size in pigs. Genetics Selection Evolution, 50(1). doi:10.1186/s12711-018-0374-1Wang, C., Rutledge, J., & Gianola, D. (1994). Bayesian analysis of mixed linear models via Gibbs sampling with an application to litter size in Iberian pigs. Genetics Selection Evolution, 26(2), 91. doi:10.1186/1297-9686-26-2-9

Genetic parameters and direct, maternal and heterosis effects on litter size in a diallel cross among three commercial varieties of Iberian pig

[EN] The Iberian pig is one of the pig breeds that has the highest meat quality. Traditionally, producers have bred one of the available varieties, exclusively, and have not used crosses between them, which has contrasted sharply with other populations of commercial pigs for which crossbreeding has been a standard procedure. The objective of this study was to perform an experiment under full diallel design among three contemporary commercial varieties of Iberian pig and estimate the additive genetic variation and the crossbreeding effects (direct, maternal and heterosis) for prolificacy. The data set comprised 18 193 records for total number born and number born alive from 3800 sows of three varieties of the Iberian breed (Retinto, Torbiscal and Entrepelado) and their reciprocal crosses (Retinto × Torbiscal, Torbiscal × Retinto, Retinto × Entrepelado, Entrepelado × Retinto, Torbiscal × Entrepelado and Entrepelado × Torbiscal), and a pedigree of 4609 individuals. The analysis was based on a multiple population repeatability model, and we developed a model comparison test that indicated the presence of direct line, maternal and heterosis effects. The results indicated the superiorities of the direct line effect of the Retinto and the maternal effect of the Entrepelado populations. All of the potential crosses produced significant heterosis, and additive genetic variation was higher in the Entrepelado than it was in the other two populations. The recommended cross for the highest yield in prolificacy is a Retinto father and an Entrepelado mother to generate a hybrid commercial sow.The work was partially funded by the Center for Industrial Technological Development (CDTI) via grant IDI-20170304 and by grant CGL-2016-80155 from the Ministry of Economy, Industry and Competitiveness (MINECO), Spain.Noguera, J.; Ibáñez-Escriche, N.; Casellas, J.; Rosas, J.; Varona, L. (2019). Genetic parameters and direct, maternal and heterosis effects on litter size in a diallel cross among three commercial varieties of Iberian pig. Animal. 13(12):2765-2772. https://doi.org/10.1017/S1751731119001125S276527721312Tsai, T.-S., Rajasekar, S., & St. John, J. C. (2016). The relationship between mitochondrial DNA haplotype and the reproductive capacity of domestic pigs (Sus scrofa domesticus). BMC Genetics, 17(1). doi:10.1186/s12863-016-0375-4Wolf, J. B., & Wade, M. J. (2009). What are maternal effects (and what are they not)? Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1520), 1107-1115. doi:10.1098/rstb.2008.0238Srirattana, K., McCosker, K., Schatz, T., & St. John, J. C. (2017). Cattle phenotypes can disguise their maternal ancestry. BMC Genetics, 18(1). doi:10.1186/s12863-017-0523-5Fernández, A., Rodrigáñez, J., Zuzúarregui, J., Rodríguez, M. C., & Silió, L. (2008). Genetic parameters for litter size and weight at different parities in Iberian pigs. Spanish Journal of Agricultural Research, 6(S1), 98. doi:10.5424/sjar/200806s1-378García-Casco, J. M., Fernández, A., Rodríguez, M. C., & Silió, L. (2012). Heterosis for litter size and growth in crosses of four strains of Iberian pig. Livestock Science, 147(1-3), 1-8. doi:10.1016/j.livsci.2012.03.005Cameron, N. M. (2011). Maternal Programming of Reproductive Function and Behavior in the Female Rat. Frontiers in Evolutionary Neuroscience, 3. doi:10.3389/fnevo.2011.00010Noguera, J. L., Rodríguez, C., Varona, L., Tomàs, A., Muñoz, G., Ramírez, O., … Sánchez, A. (2009). A bi-dimensional genome scan for prolificacy traits in pigs shows the existence of multiple epistatic QTL. BMC Genomics, 10(1), 636. doi:10.1186/1471-2164-10-636Southwood, O. I., & Kennedy, B. W. (1990). Estimation of direct and maternal genetic variance for litter size in Canadian Yorkshire and Landrace swine using an animal model. Journal of Animal Science, 68(7), 1841. doi:10.2527/1990.6871841xMartınez, A. M., Delgado, J. V., Rodero, A., & Vega-Pla, J. L. (2000). Genetic structure of the Iberian pig breed using microsatellites. Animal Genetics, 31(5), 295-301. doi:10.1046/j.1365-2052.2000.00645.xDekkers, J. C. M., Mathur, P. K., & Knol, E. F. (s. f.). Genetic improvement of the pig. The genetics of the pig, 390-425. doi:10.1079/9781845937560.0390Haley, C. S., Lee, G. J., & Ritchie, M. (1995). Comparative reproductive performance in Meishan and Large White pigs and their crosses. Animal Science, 60(2), 259-267. doi:10.1017/s1357729800008420Hwang, J. H., An, S. M., Kwon, S., Park, D. H., Kim, T. W., Kang, D. G., … Kim, C. W. (2017). DNA methylation patterns and gene expression associated with litter size in Berkshire pig placenta. PLOS ONE, 12(9), e0184539. doi:10.1371/journal.pone.0184539Fabuel, E., Barragán, C., Silió, L., Rodríguez, M. C., & Toro, M. A. (2004). Analysis of genetic diversity and conservation priorities in Iberian pigs based on microsatellite markers. Heredity, 93(1), 104-113. doi:10.1038/sj.hdy.6800488Serra, X., Gil, F., Pérez-Enciso, M., Oliver, M. ., Vázquez, J. ., Gispert, M., … Noguera, J. . (1998). A comparison of carcass, meat quality and histochemical characteristics of Iberian (Guadyerbas line) and Landrace pigs. Livestock Production Science, 56(3), 215-223. doi:10.1016/s0301-6226(98)00151-1Gelfand, A. E., & Smith, A. F. M. (1990). Sampling-Based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association, 85(410), 398-409. doi:10.1080/01621459.1990.10476213Irgang, R., Fávero, J. A., & Kennedy, B. W. (1994). Genetic parameters for litter size of different parities in Duroc, Landrace, and large white sows. Journal of Animal Science, 72(9), 2237-2246. doi:10.2527/1994.7292237xOgawa, S., Konta, A., Kimata, M., Ishii, K., Uemoto, Y., & Satoh, M. (2018). Estimation of genetic parameters for farrowing traits in purebred Landrace and Large White pigs. Animal Science Journal, 90(1), 23-28. doi:10.1111/asj.13120Ibáñez-Escriche, N., Magallón, E., Gonzalez, E., Tejeda, J. F., & Noguera, J. L. (2016). Genetic parameters and crossbreeding effects of fat deposition and fatty acid profiles in Iberian pig lines1. Journal of Animal Science, 94(1), 28-37. doi:10.2527/jas.2015-9433Peripato, A. C., De Brito, R. A., Matioli, S. R., Pletscher, L. S., Vaughn, T. T., & Cheverud, J. M. (2004). Epistasis affecting litter size in mice. Journal of Evolutionary Biology, 17(3), 593-602. doi:10.1111/j.1420-9101.2004.00702.xSerrano, M. P., Valencia, D. G., Nieto, M., Lázaro, R., & Mateos, G. G. (2008). Influence of sex and terminal sire line on performance and carcass and meat quality of Iberian pigs reared under intensive production systems. Meat Science, 78(4), 420-428. doi:10.1016/j.meatsci.2007.07.006Quaas, R. L. (1976). Computing the Diagonal Elements and Inverse of a Large Numerator Relationship Matrix. Biometrics, 32(4), 949. doi:10.2307/2529279Quinton, V. M., Wilton, J. W., Robinson, J. A., & Mathur, P. K. (2006). Economic weights for sow productivity traits in nucleus pig populations. Livestock Science, 99(1), 69-77. doi:10.1016/j.livprodsci.2005.06.002Barea, R., Nieto, R., Vitari, F., Domeneghini, C., & Aguilera, J. F. (2010). Effects of pig genotype (Iberian v. Landrace × Large White) on nutrient digestibility, relative organ weight and small intestine structure at two stages of growth. animal, 5(4), 547-557. doi:10.1017/s1751731110002181Bidanel, J. P. (s. f.). Biology and genetics of reproduction. The genetics of the pig, 218-241. doi:10.1079/9781845937560.0218Boletín Oficial del Estado 2014. Real Decreto 4/2014, de 10 de enero, por el que se aprueba la norma de calidad para la carne, el jamón, la paleta y la caña de lomo ibérico. BOE-A-2014-318.Putz, A. M., Tiezzi, F., Maltecca, C., Gray, K. A., & Knauer, M. T. (2015). Variance component estimates for alternative litter size traits in swine. Journal of Animal Science, 93(11), 5153-5163. doi:10.2527/jas.2015-9416Cassady, J. P., Young, L. D., & Leymaster, K. A. (2002). Heterosis and recombination effects on pig reproductive traits. Journal of Animal Science, 80(9), 2303. doi:10.2527/2002.8092303xGilles, G. (2009). Dry cured ham quality as related to lipid quality of raw material and lipid changes during processing: a review. Grasas y Aceites, 60(3), 297-307. doi:10.3989/gya.130908Perez-Enciso, M., & Gianola, D. (1992). Estimates of genetic parameters for litter size in six strains of Iberian pigs. Livestock Production Science, 32(3), 283-293. doi:10.1016/s0301-6226(12)80007-8Rodriguez, C., Rodrigañez, J., & Silio, L. (1994). Genetic analysis of maternal ability in Iberian pigs. Journal of Animal Breeding and Genetics, 111(1-6), 220-227. doi:10.1111/j.1439-0388.1994.tb00461.xSpiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583-639. doi:10.1111/1467-9868.00353Coster, A., Madsen, O., Heuven, H. C. M., Dibbits, B., Groenen, M. A. M., van Arendonk, J. A. M., & Bovenhuis, H. (2012). The Imprinted Gene DIO3 Is a Candidate Gene for Litter Size in Pigs. PLoS ONE, 7(2), e31825. doi:10.1371/journal.pone.0031825Willham, R. L. (1972). The Role of Maternal Effects in Animal Breeding: III. Biometrical Aspects of Maternal Effects in Animals. Journal of Animal Science, 35(6), 1288-1293. doi:10.2527/jas1972.3561288xFerraz, J. B. S., & Johnson, R. K. (1993). Animal model estimation of genetic parameters and response to selection for litter size and weight, growth, and backfat in closed seedstock populations of large white and Landrace swine2. Journal of Animal Science, 71(4), 850-858. doi:10.2527/1993.714850

Genomic selection of purebreds for crossbred performance

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Quality stability assessment of a strawberry-gel product during storage

A strawberry-gel product was formulated by using osmotic treatment. The osmotic solution (OS) used to dehydrate the fruit was mixed with carrageenan and employed to formulate the gel. In order to prevent a further dehydration of the fruit during product storage, the OS was previously diluted so that its water activity is the same as the dehydrated fruit. Changes in water, soluble solids, citric acid, ascorbic acid and anthocyanin contents, water activity, surface color, mechanical properties and volatile profile during 15 days of storage (5C) were evaluated. The use of the OS increased the nutritive and functional properties of the product. Changes in volatile profile, mechanical properties and color of the strawberry occur mainly in the first 2 days of storage and are not due to the presence of the gel matrix, as they occur also in the samples not placed in gel. The flux of anthocyanins from the fruit to the gel produces redness, giving a more attractive aspect to the formulated product. © 2009 Wiley Periodicals, Inc.The authors thank the Ministerio de Educacion y Ciencia and the Fondo Europeo de Desarrollo Regional (FEDER) for financial support throughout the projects AGL2002-01793 and AGL 2005-05994.Martín-Esparza, M.; Escriche Roberto, MI.; Penagos, L.; Martínez Navarrete, N. (2011). Quality stability assessment of a strawberry-gel product during storage. Journal of Food Process Engineering. 34(2):204-223. https://doi.org/10.1111/j.1745-4530.2008.00349.xS20422334

Characterization of Langmuir and Langmuir-Blodgett films of a thiomacrocyclic ionophore by surface pressure and AFM

Preprin