La suplementación de los piensos de las conejas con EPA y DHA mejora el perfil insaturado de los ácidos grasos de la leche y sus parámetros reproductivos

Se ha valorado la influencia del enriquecimiento de las dietas de conejas reproductoras con ácidos grasos poliinsaturados de cadena larga (AGPI) de origen animal (EPA y DHA) durante 2 ciclos sobre sus parámetros reproductivos y la composición de su leche. Un total de 124 conejas se alimentaron desde la recría hasta el segundo destete con dos dietas isofibrosas, isoenergéticas e isoproteicas formuladas con dos fuentes de grasa distintas. El grupo control (C;n=62) recibió un pienso con un 3% de grasa mezcla mientras que el del grupo experimental (P;n=62) contenía un 6% de un suplemento con un 50% de extracto etéreo concentrado en DHA y EPA a partir de aceite de salmón atlántico (Optomega-50, Optivite International Ltd., Barcelona, España)

Effects of feed restriction during pregnancy on maternal reproductive outcome, foetal hepatic IGF gene expression and offspring performance in the rabbit

Primiparous female rabbits have high nutritional requirements and, while it is recommended that they are subjected to an extensive reproductive rhythm, this could lead to overweight, affecting reproductive outcomes. We hypothesised that restricting food intake during the less energetic period of gestation could improve reproductive outcome without impairing offspring viability. This study compares two groups of primiparous rabbit does in an extensive reproductive programme, one in which feed was restricted from Day 0 to Day 21 of gestation (R021), and another in which does were fed ad libitum (control) throughout pregnancy. The mother and offspring variables compared were (1) mother reproductive outcomes at the time points pre-implantation (Day 3 postartificial insemination [AI]), preterm (Day 28 post-AI) and birth; and (2) the prenatal offspring characteristic IGF system gene expression in foetal liver, liver fibrosis and foetus sex ratio, and postnatal factor viability and growth at birth, and survival and growth until weaning. Feed restriction did not affect the conception rate, embryo survival, or the number of morulae and blastocysts recovered at Day 3 post-AI. Preterm placenta size and efficiency were similar in the two groups. However, both implantation rate (P < 0.001) and the number of foetuses (P = 0.05) were higher in the R021 mothers than controls, while there was no difference in foetal viability. Foetal size and weight, the weights of most organs, organ weight/BW ratios and sex ratio were unaffected by feed restriction; these variables were only affected by uterine position (P < 0.05). Conversely, in the R021 does, foetal liver IGBP1 and IGF2 gene expression were dysregulated despite no liver fibrosis and a normal liver structure. No effects of restricted feed intake were produced on maternal fertility, prolificacy, or offspring birth weight, but control females weaned more kits. Litter weight and mortality rate during the lactation period were also unaffected. In conclusion, pre-implantation events and foetal development were unaffected by feed restriction. While some genes of the foetal hepatic IGF system were dysregulated during pregnancy, liver morphology appeared normal, and the growth of foetuses and kits until weaning was unmodified. This strategy of feed restriction in extensive reproductive rhythms seems to have no significant adverse effects on dam reproductive outcome or offspring growth and viability until weaning

Selection for environmental variance of litter size in rabbits

[EN] Background: In recent years, there has been an increasing interest in the genetic determination of environmental variance. In the case of litter size, environmental variance can be related to the capacity of animals to adapt to new environmental conditions, which can improve animal welfare. Results: We developed a ten-generation divergent selection experiment on environmental variance. We selected one line of rabbits for litter size homogeneity and one line for litter size heterogeneity by measuring intra-doe phenotypic variance. We proved that environmental variance of litter size is genetically determined and can be modified by selection. Response to selection was 4.5% of the original environmental variance per generation. Litter size was consistently higher in the Low line than in the High line during the entire experiment. Conclusions: We conclude that environmental variance of litter size is genetically determined based on the results of our divergent selection experiment. This has implications for animal welfare, since animals that cope better with their environment have better welfare than more sensitive animals. We also conclude that selection for reduced environmental variance of litter size does not depress litter size.This research was funded by the Ministerio de Economía y Competitividad (Spain), Projects AGL2014-55921, C2-1-P and C2-2-P. Marina Martínez-Alvaro has a Grant from the same funding source, BES-2012-052655.Blasco Mateu, A.; Martínez Álvaro, M.; García Pardo, MDLL.; Ibáñez Escriche, N.; Argente, MJ. (2017). Selection for environmental variance of litter size in rabbits. Genetics Selection Evolution. 49(48):1-8. https://doi.org/10.1186/s12711-017-0323-4S184948Morgante F, Sørensen P, Sorensen DA, Maltecca C, Mackay TFC. Genetic architecture of micro-environmental plasticity in Drosophila melanogaster. Sci Rep. 2015;5:9785.Sørensen P, de los Campos G, Morgante F, Mackay TFC, Sorensen D. Genetic control of environmental variation of two quantitative traits of Drosophila melanogaster revealed by whole-genome sequencing. Genetics. 2015;201:487–97.Zhang XS, Hill WG. Evolution of the environmental component of the phenotypic variance: stabilizing selection in changing environments and the homogeneity cost. Evolution. 2005;59:1237–44.Mulder HA, Bijma P, Hill WG. Selection for uniformity in livestock by exploiting genetic heterogeneity of residual variance. Genet Sel Evol. 2008;40:37–59.Bodin L, Bolet G, Garcia M, Garreau H, Larzul C, David I. Robustesse et canalisation, vision de généticiens. INRA Prod Anim. 2010;23:11–22.García ML, Argente MJ, Muelas R, Birlanga V, Blasco A. Effect of divergent selection for residual variance of litter size on health status and welfare. In: Proceedings of the 10th World Rabbit Congress. Sharm El-Sheikh; 2012. p. 103–6.Argente MJ, García ML, Zbynovska K, Petruska P, Capcarova M, Blasco A. Effect of selection for residual variance of litter size on hematology parameters as immunology indicators in rabbits. In: Proceedings of the 10th World Congress on genetics applied to livestock production. Vancouver; 2014.García ML, Zbynovska K, Petruska P, Bovdisová I, Kalafová A, Capcarova M, et al. Effect of selection for residual variance of litter size on biochemical parameters in rabbits. In: Proceedings of the 67th annual meeting of the European Federation of Animal Science. Belfast; 2016.Broom DM. Welfare assessment and relevant ethical decisions: key concepts. Annu Rev Biomed Sci. 2008;20:79–90.SanCristobal-Gaudy M, Bodin L, Elsen JM, Chevalet C. Genetic components of litter size variability in sheep. Genet Sel Evol. 2001;33:249–71.Sorensen D, Waagepetersen R. Normal linear models with genetically structured residual variance heterogeneity: a case study. Genet Res. 2003;82:207–22.Mulder HA, Hill WG, Knol EF. Heritable environmental variance causes nonlinear relationships between traits: application to birth weight and stillbirth of pigs. Genetics. 2015;199:1255–69.Ros M, Sorensen D, Waagepetersen R, Dupont-Nivet M, San Cristobal M, Bonnet JC. Evidence for genetic control of adult weight plasticity in the snail Helix aspersa. Genetics. 2004;168:2089–97.Gutiérrez JP, Nieto B, Piqueras P, Ibáñez N, Salgado C. Genetic parameters for components analysis of litter size and litter weight traits at birth in mice. Genet Sel Evol. 2006;38:445–62.Ibáñez-Escriche N, Sorensen D, Waagepetersen R, Blasco A. Selection for environmental variation: a statistical analysis and power calculations to detect response. Genetics. 2008;180:2209–26.Wolc A, White IM, Avendano S, Hill WG. Genetic variability in residual variation of body weight and conformation scores in broiler chickens. Poult Sci. 2009;88:1156–61.Fina M, Ibáñez-Escriche N, Piedrafita J, Casellas J. Canalization analysis of birth weight in Bruna dels Pirineus beef cattle. J Anim Sci. 2013;91:3070–8.Mulder HA, Rönnegård L, Fikse WF, Veerkamp RF, Strandberg E. Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models. Genet Sel Evol. 2013;45:23.Janhunen M, Kause A, Vehviläinen H, Järvisalom O. Genetics of microenvironmental sensitivity of body weight in rainbow trout (Oncorhynchus mykiss) selected for improved growth. PLoS One. 2012;7:e38766.Sonesson AK, Ødegård J, Rönnegård L. Genetic heterogeneity of within-family variance of body weight in Atlantic salmon (Salmo salar). Genet Sel Evol. 2013;45:41.Garreau H, Bolet G, Larzul C, Robert-Granie C, Saleil G, SanCristobal M, et al. Results of four generations of a canalising selection for rabbit birth weight. Livest Sci. 2008;119:55–62.Pun A, Cervantes I, Nieto B, Salgado C, Pérez-Cabal MA, Ibáñez-Escriche N, et al. Genetic parameters for birth weight environmental variability in mice. J Anim Breed Genet. 2012;130:404–14.Hill WG, Mulder HA. Genetic analysis of environmental variation. Genet Res (Camb). 2010;92:381–95.Yang Y, Christensen OF, Sorensen D. Analysis of a genetically structured variance heterogeneity model using the Box–Cox transformation. Genet Res (Camb). 2011;93:33–46.Piles M, Garcia ML, Rafel O, Ramon J, Baselga M. Genetics of litter size in three maternal lines of rabbits: repeatability versus multiple-trait models. J Anim Sci. 2006;84:2309–15.Estany J, Baselga M, Blasco A, Camacho J. Mixed model methodology for the estimation of genetic response to selection in litter size of rabbits. Livest Prod Sci. 1989;21:67–75.Box GEP, Tiao GC. Bayesian inference in statistical analysis. New York: Wiley; 1973.Searle SR. Matrix algebra useful for statistics. Toronto: Wiley; 1982.Sorensen D, Gianola D. Likelihood, Bayesian and MCMC methods in quantitative genetics. New York: Springer; 2002.Geyer CM. Practical Markov chain Monte Carlo (with discussion). Stat Sci. 1992;7:467–511.Legarra A. TM threshold model. 2008. http://genoweb.toulouse.inra.fr/~alegarra/tm_folder/ . Accessed 02 May 2017.Blasco A. Bayesian data analysis for animal scientists. New York: Springer; 2017.Rönnegård L, Felleki M, Fikse F, Mulder HA, Strandberg E. Genetic heterogeneity of residual variance—estimation of variance components using double hierarchical generalized linear models. Genet Sel Evol. 2010;42:8.Felleki M, Lee D, Lee Y, Gilmour AR, Rönnegård L. Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models. Genet Res (Camb). 2012;94:307–17.Thompson R. Estimation of realized heritability in a selected population using mixed model methods. Genet Sel Evol. 1986;18:475–84.Sorensen DA, Johansson K. Estimation of direct and correlated responses to selection using univariate animal models. J Anim Sci. 1992;70:2038–44.Popper K. The logic of scientific discovery. London: Hutchinson & Co; 1959.Falconer DS, MacKay TFC. An introduction to quantitative genetics. 4th ed. Harlow: Longman Group Ltd; 1996.Formoso-Rafferty N, Cervantes I, Ibáñez-Escriche N, Gutiérrez JP. Correlated genetic trends for production and welfare traits in a mouse population divergently selected for birth weight environmental variability. Animal. 2016;10:1770–7.Ibáñez-Escriche N, Moreno A, Nieto B, Piqueras P, Salgado C, Gutiérrez JP. Genetic parameters related to environmental variability of weight traits in a selection experiment for weight gain in mice; signs of correlated canalised response. Genet Sel Evol. 2008;40:279–93.Mulder HA, Hill WG, Vereijken A, Veerkamp RF. Estimation of genetic variation in residual variance in female and male broiler chickens. Animal. 2009;3:1673–80.Ibáñez-Escriche N, Varona L, Sorensen D, Noguera JL. A study of heterogeneity of environmental variance for slaughter weight in pigs. Animal. 2008;2:19–26.Bolet G, Garreau H, Hurtaud J, Saleil G, Esparbié J, Falieres J. Canalising selection on within litter variability of birth weight in rabbits: responses to selection and characteristics of the uterus of the does. In: Proceedings of the 9th World Rabbit Congress. Verona; 2008. p. 51–6.San Cristobal-Gaudy M, Elsen JM, Bodin L, Chevalet C. Prediction of the response to a selection for canalisation of a continuous trait in animal breeding. Genet Sel Evol. 1998;30:423–51.Argente MJ, Santacreu MA, Climent A, Blasco A. Genetic correlations between litter size and uterine capacity. In: Proceeding of the 8th World Rabbit Congress. Valencia; 2000. p. 333–38.Rauw WM. Immune response from a resource allocation perspective. Front Genet. 2012;3:267

Correlated genetic trends for production and welfare traits in a mouse population divergently selected for birth weight environmental variability

The objective of this work was to study the changes that, selecting for environmental variability of birth weight (BW), could bring to other interesting traits in livestock such as: survivability at weaning (SW), litter size (LS) and weaning weight (WW), their variability assessed from standard deviations of LS, standard deviation of WW (SDWW) and also the total litter weight at birth (TLBW) and total litter weight at weaning. Data were registered after eight generations of a divergent selection experiment for BW environmental variability in mice. Genetic parameters and phenotypic and genetic evolution were assessed using linear homoscedastic and heteroscedastic models in which the traits were attributed to the female, except BW and WW that were in some models also attributed to the pup. Genetic correlation between the trait and variability levels was −0.81 for LS and −0.33 for WW. Clear divergent phenotypic trends were observed between lines for LS, WW and SDWW. Although animals were heavier in the high line, TLBW and at weaning was greater in the low line. Despite the negative genetic correlation that was obtained, SDWW was also higher in the high line. Heritabilities were 0.21 and 0.06, respectively, for LS and SW. Both phenotypic and genetic trends showed clear superiority of the low line over the high line for these traits, but inferior for WW. Heteroscedastic model performed similar to the homoscedastic model when there was enough information. Considering LS and survival, the low line was preferred from a welfare point of view, but its superiority from the productivity perspective was not clear. Robustness seemed higher as shown by a low variation and having a benefit to the animal welfare, but this still remains unclear. It was concluded that low variation benefits the welfare of animals.info:eu-repo/semantics/publishedVersio

Modulating birth weight heritability in mice

[EN] Expected genetic response is proportional to the heritability of the trait, and this parameter is considered inherent of a specific trait in a particular population. However, models assuming heterogeneity in residual variance lead to different estimates of heritability across combinations of systematic (environmental) effects. Modifying the residual variance of the birth weight by artificial selection was shown to be feasible in a divergent selection experiment in mice. The objectives of this work were to 1) estimate the evolution of the heritability of birth weight in mice in the mentioned experiment, and 2) estimate different heritability regarding systematic effects. Data came from eleven generations of a divergent selection experiment to modify the residual variability of birth weight in mice. A total of 15,431 birth weight records from 959 females and 1,641 litters in combination with 14,786 pedigree records were used. The model used for analysis included generation, litter size, sex, and parity number as systematic effects. Each record of birth weight was assigned to the mother of the pup in the model which assumes that the residual variance is heterogeneous and partially under genetic control. Differences in heritability between lines reached values of 0.06 in the last generations. Choosing the most extreme values of systematic effects, the birth weight heritability ranged from 0.04 to 0.22. From these results, the possibility of modulating the heritability for this trait could be explored in 1 of 2 ways: selecting to decrease the residual variability, or choosing the specific levels of the systematic effects.This research has been conducted with a partial funding through a project MEC-INIA (RTA2014-00015-C02-02).This experiment was partially funded by a grant from the Spanish Government (AGL2008 00794).Formoso-Rafferty, N.; Cervantes, I.; Ibañez Escriche, N.; Gutiérrez, J. (2017). Modulating birth weight heritability in mice. Journal of Animal Science. 95(2):531-537. https://doi.org/10.2527/jas.2016.1169S531537952Bolet, G., Garreau, H., Joly, T., Theau-Clement, M., Falieres, J., Hurtaud, J., & Bodin, L. (2007). Genetic homogenisation of birth weight in rabbits: Indirect selection response for uterine horn characteristics. Livestock Science, 111(1-2), 28-32. doi:10.1016/j.livsci.2006.11.012Damgaard, L. H., Rydhmer, L., Løvendahl, P., & Grandinson, K. (2003). Genetic parameters for within-litter variation in piglet birth weight and change in within-litter variation during suckling1. Journal of Animal Science, 81(3), 604-610. doi:10.2527/2003.813604xFernandez, B. J., & Toro, M. A. (1999). The use of mathematical programming to control inbreeding in selection schemes. Journal of Animal Breeding and Genetics, 116(6), 447-466. doi:10.1046/j.1439-0388.1999.00196.xFormoso-Rafferty, N., Cervantes, I., Ibáñez-Escriche, N., & Gutiérrez, J. P. (2015). Genetic control of the environmental variance for birth weight in seven generations of a divergent selection experiment in mice. Journal of Animal Breeding and Genetics, 133(3), 227-237. doi:10.1111/jbg.12174Formoso-Rafferty, N., Cervantes, I., Ibáñez-Escriche, N., & Gutiérrez, J. P. (2016). Correlated genetic trends for production and welfare traits in a mouse population divergently selected for birth weight environmental variability. animal, 10(11), 1770-1777. doi:10.1017/s1751731116000860Garreau, H., Bolet, G., Larzul, C., Robert-Granié, C., Saleil, G., SanCristobal, M., & Bodin, L. (2008). Results of four generations of a canalising selection for rabbit birth weight. Livestock Science, 119(1-3), 55-62. doi:10.1016/j.livsci.2008.02.009Gutiérrez, J., Nieto, B., Piqueras, P., Ibáñez, N., & Salgado, C. (2006). Genetic parameters for canalisation analysis of litter size and litter weight traits at birth in mice. Genetics Selection Evolution, 38(5), 445. doi:10.1186/1297-9686-38-5-445Hill, W. G. (1984). On selection among groups with heterogeneous variance. Animal Science, 39(3), 473-477. doi:10.1017/s0003356100032220Ibáñez-Escriche, N., Sorensen, D., Waagepetersen, R., & Blasco, A. (2008). Selection for Environmental Variation: A Statistical Analysis and Power Calculations to Detect Response. Genetics, 180(4), 2209-2226. doi:10.1534/genetics.108.091678Ibáñez-Escriche, N., Garcia, M., & Sorensen, D. (2009). GSEVM v.2: MCMC software to analyze genetically structured environmental variance models. Journal of Animal Breeding and Genetics, 127(3), 249-251. doi:10.1111/j.1439-0388.2009.00846.xJohnson, R. K., Nielsen, M. K., & Casey, D. S. (1999). Responses in ovulation rate, embryonal survival, and litter traits in swine to 14 generations of selection to increase litter size. Journal of Animal Science, 77(3), 541. doi:10.2527/1999.773541xLamberson, W. R., Johnson, R. K., Zimmerman, D. R., & Long, T. E. (1991). Direct responses to selection for increased litter size, decreased age at puberty, or random selection following selection for ovulation rate in swine. Journal of Animal Science, 69(8), 3129. doi:10.2527/1991.6983129xMoreno, A., Ibáñez-Escriche, N., García-Ballesteros, S., Salgado, C., Nieto, B., & Gutiérrez, J. P. (2012). Correlated genetic trend in the environmental variability of weight traits in mice. Livestock Science, 148(1-2), 189-195. doi:10.1016/j.livsci.2012.05.009Robert-Granié, C., Bonaı̈ti, B., Boichard, D., & Barbat, A. (1999). Accounting for variance heterogeneity in French dairy cattle genetic evaluation. Livestock Production Science, 60(2-3), 343-357. doi:10.1016/s0301-6226(99)00105-0Ros, M., Sorensen, D., Waagepetersen, R., Dupont-Nivet, M., SanCristobal, M., Bonnet, J.-C., & Mallard, J. (2004). Evidence for Genetic Control of Adult Weight Plasticity in the Snail Helix aspersa. Genetics, 168(4), 2089-2097. doi:10.1534/genetics.104.032672Ruíz-Flores, A., & Johnson, R. K. (2001). Direct and correlated responses to two-stage selection for ovulation rate and number of fully formed pigs at birth in swine. Journal of Animal Science, 79(9), 2286. doi:10.2527/2001.7992286xSanCristobal-Gaudy, M., Elsen, J.-M., Bodin, L., & Chevalet, C. (1998). Prediction of the response to a selection for canalisation of a continuous trait in animal breeding. Genetics Selection Evolution, 30(5), 423. doi:10.1186/1297-9686-30-5-423SORENSEN, D., & WAAGEPETERSEN, R. (2003). Normal linear models with genetically structured residual variance heterogeneity: a case study. Genetical Research, 82(3), 207-222. doi:10.1017/s0016672303006426Ziadi, C., Mocé, M. L., Laborda, P., Blasco, A., & Santacreu, M. A. (2013). Genetic selection for ovulation rate and litter size in rabbits: Estimation of genetic parameters and direct and correlated responses1. Journal of Animal Science, 91(7), 3113-3120. doi:10.2527/jas.2012-604

Correlated genetic trends for production and welfare traits in a mouse population divergently selected for birth weight environmental variability

[EN] The objective of this work was to study the changes that, selecting for environmental variability of birth weight (BW), could bring to other interesting traits in livestock such as: survivability at weaning (SW), litter size (LS) and weaning weight (WW), their variability assessed from standard deviations of LS, standard deviation of WW (SDWW) and also the total litter weight at birth (TLBW) and total litter weight at weaning. Data were registered after eight generations of a divergent selection experiment for BW environmental variability in mice. Genetic parameters and phenotypic and genetic evolution were assessed using linear homoscedastic and heteroscedastic models in which the traits were attributed to the female, except BW and WW that were in some models also attributed to the pup. Genetic correlation between the trait and variability levels was -0.81 for LS and -0.33 for WW. Clear divergent phenotypic trends were observed between lines for LS, WW and SDWW. Although animals were heavier in the high line, TLBW and at weaning was greater in the low line. Despite the negative genetic correlation that was obtained, SDWW was also higher in the high line. Heritabilities were 0.21 and 0.06, respectively, for LS and SW. Both phenotypic and genetic trends showed clear superiority of the low line over the high line for these traits, but inferior for WW. Heteroscedastic model performed similar to the homoscedastic model when there was enough information. Considering LS and survival, the low line was preferred from a welfare point of view, but its superiority from the productivity perspective was not clear. Robustness seemed higher as shown by a low variation and having a benefit to the animal welfare, but this still remains unclear. It was concluded that low variation benefits the welfare of animals.This paper was partially funded by a grant from the Spanish Government (AGL2008-00794). The experiment will be continued with partial funding of Feed-a-gene and a grant from MEC-INIA (RTA2014-00015-C02-01). The authors wish to thank the detailed work of an anonymous reviewer who has contributed greatly to improving this work.Formoso-Rafferty, N.; Cervantes, I.; Ibañez Escriche, N.; Gutiérrez, J. (2016). Correlated genetic trends for production and welfare traits in a mouse population divergently selected for birth weight environmental variability. Animal. 10(11):1770-1777. https://doi.org/10.1017/S1751731116000860S177017771011Wolf, J., Žáková, E., & Groeneveld, E. (2008). Within-litter variation of birth weight in hyperprolific Czech Large White sows and its relation to litter size traits, stillborn piglets and losses until weaning. Livestock Science, 115(2-3), 195-205. doi:10.1016/j.livsci.2007.07.009Mesa, H., Safranski, T. J., Cammack, K. M., Weaber, R. L., & Lamberson, W. R. (2006). Genetic and phenotypic relationships of farrowing and weaning survival to birth and placental weights in pigs1. Journal of Animal Science, 84(1), 32-40. doi:10.2527/2006.84132xGarreau, H., Bolet, G., Larzul, C., Robert-Granié, C., Saleil, G., SanCristobal, M., & Bodin, L. (2008). Results of four generations of a canalising selection for rabbit birth weight. Livestock Science, 119(1-3), 55-62. doi:10.1016/j.livsci.2008.02.009Garcı́a, M. ., & Baselga, M. (2002). Estimation of correlated response on growth traits to selection in litter size of rabbits using a cryopreserved control population and genetic trends. Livestock Production Science, 78(2), 91-98. doi:10.1016/s0301-6226(02)00093-3Hill, W. G., & Caballero, A. (1992). Artificial Selection Experiments. Annual Review of Ecology and Systematics, 23(1), 287-310. doi:10.1146/annurev.es.23.110192.001443García M , David I , Garreau H , Ibáñez-Escriche N , Mallard J , Masson JP , Pommeret D , Robert-Granié C and Bodin L 2009. Comparisons of three models for canalising selection or genetic robustness. Proceedings of the 60th Annual Meeting of European Association for Animal Production, August 2009, Barcelona, Spain, 599pp.Bolet, G., Garreau, H., Joly, T., Theau-Clement, M., Falieres, J., Hurtaud, J., & Bodin, L. (2007). Genetic homogenisation of birth weight in rabbits: Indirect selection response for uterine horn characteristics. Livestock Science, 111(1-2), 28-32. doi:10.1016/j.livsci.2006.11.012SanCristobal-Gaudy, M., Elsen, J.-M., Bodin, L., & Chevalet, C. (1998). Prediction of the response to a selection for canalisation of a continuous trait in animal breeding. Genetics Selection Evolution, 30(5), 423. doi:10.1186/1297-9686-30-5-423Bayon, Y., Fuente, L., & Primitivo, F. S. (1987). Direct and correlated responses to selection for large and small 6-week body weight in mice. Genetics Selection Evolution, 19(4), 445. doi:10.1186/1297-9686-19-4-445Högberg, A., & Rydhmer, L. (2000). A Genetic Study of Piglet Growth and Survival. Acta Agriculturae Scandinavica, Section A - Animal Science, 50(4), 300-303. doi:10.1080/090647000750069494Legarra A 2008. TM Threshold Model. Retrieved on 16 July 2015 from http://acteon.webs.upv.es/.Gutiérrez, J., Nieto, B., Piqueras, P., Ibáñez, N., & Salgado, C. (2006). Genetic parameters for canalisation analysis of litter size and litter weight traits at birth in mice. Genetics Selection Evolution, 38(5), 445. doi:10.1186/1297-9686-38-5-445Fernández, J., Moreno, A., Gutiérrez, J. P., Nieto, B., Piqueras, P., & Salgado, C. (1998). Direct and correlated selection response for litter size and litter weight at birth in the first parity in mice. Livestock Production Science, 53(3), 217-223. doi:10.1016/s0301-6226(97)00146-2Zomeño, C., Hernández, P., & Blasco, A. (2013). Divergent selection for intramuscular fat content in rabbits. I. Direct response to selection1. Journal of Animal Science, 91(9), 4526-4531. doi:10.2527/jas.2013-6361Mormede, P., & Terenina, E. (2012). Molecular genetics of the adrenocortical axis and breeding for robustness. Domestic Animal Endocrinology, 43(2), 116-131. doi:10.1016/j.domaniend.2012.05.002Ibáñez-Escriche, N., Garcia, M., & Sorensen, D. (2009). GSEVM v.2: MCMC software to analyze genetically structured environmental variance models. Journal of Animal Breeding and Genetics, 127(3), 249-251. doi:10.1111/j.1439-0388.2009.00846.xCervantes, I., Gutiérrez, J. P., Fernández, I., & Goyache, F. (2010). Genetic relationships among calving ease, gestation length, and calf survival to weaning in the Asturiana de los Valles beef cattle breed1. Journal of Animal Science, 88(1), 96-101. doi:10.2527/jas.2009-2066Perrier G 2003. Influence de l’homogénéité de la portée sur la croissance et la viabilité des lapereaux de faible poids à la naissance. Proceedings of the 10èmes Journées de la recherche cunicole, 19–20 November 2003, Paris, France, pp. 119–122.Moreno, A., Ibáñez-Escriche, N., García-Ballesteros, S., Salgado, C., Nieto, B., & Gutiérrez, J. P. (2012). Correlated genetic trend in the environmental variability of weight traits in mice. Livestock Science, 148(1-2), 189-195. doi:10.1016/j.livsci.2012.05.009Damgaard, L. H., Rydhmer, L., Løvendahl, P., & Grandinson, K. (2003). Genetic parameters for within-litter variation in piglet birth weight and change in within-litter variation during suckling1. Journal of Animal Science, 81(3), 604-610. doi:10.2527/2003.813604xHILL, W. G., & MULDER, H. A. (2010). Genetic analysis of environmental variation. Genetics Research, 92(5-6), 381-395. doi:10.1017/s0016672310000546Jaffrezic, F., White, I. M. S., Thompson, R., & Hill, W. G. (2000). A Link Function Approach to Model Heterogeneity of Residual Variances Over Time in Lactation Curve Analyses. Journal of Dairy Science, 83(5), 1089-1093. doi:10.3168/jds.s0022-0302(00)74973-3García ML , Argente MJ , Muelas R , Birlanga V and Blasco A 2012. Effect of divergent selection for residual variance of litter size on health status and welfare. Proceedings of the 10th World Rabbit Congress, 3–6 September 2012, Sharm El- Sheikh, Egypt, pp. 103–106.Larzul, C., Ducrocq, V., Tudela, F., Juin, H., & Garreau, H. (2014). The length of productive life can be modified through selection: An experimental demonstration in the rabbit1. Journal of Animal Science, 92(6), 2395-2401. doi:10.2527/jas.2013-721

Feed restriction on growth of mice divergently selected for birth weight environmental variability

Nowadays, the selection for feed efficiency is one of the main aims in animal breeding to decrease the production costs. On the other hand, selection for less sensitivity with respect to environmental effects, as indicated by a low variation around the optimum trait value, may have benefits in terms of productivity and animal welfare. Therefore, the objective of this work was to analyze the influence of food restriction, understood as an environmental challenge, on weight at different ages in two lines divergently selected for birth weight variability in mouse lines with either a low variability (LV) or high variability (HV). A total of 40 females (four full-sib females from 10 random different litters from the 12, 13, and 14 generations of selection), were chosen within lines and fed either ad libitum or restricted from 21 to 77 days. Restriction consisted of feeding with 75%, 90%, or 85% of ad libitum feed consumption in the respective three studied generations. Weekly weights from 21 to 77 days were analyzed. The model was adjusted for the diet (restricted or ad libitum), mouse line, generation, and litter size, and included also the interaction between the line, generation, and the diet. The ASReml Release 4.1 program was used for the analysis. Animals fed ad libitum of the LV line had similar weights in all generations unlike those of the HV line, which had lower weights in successive generations. The feed restriction had a negative effect on the body weight of the animals but the interaction between line and diet was significantly different only after day 35, showing a differential response of the lines to the environmental challenge. Animals from the LV line were less sensitive to the feed restriction. This study is part of the Feed-a-Gene project and received funding from the European Union’s H2020 program under grant agreement no. 633531