Genetically controlled environmental variance for sternopleural bristles in Drosophila melanogaster - an experimental test of a heterogeneous variance model

This is an Accepted Manuscript of an article published by Taylor & Francis in Acta Agriculturae Scandinavica Section A - Animal Science on2007, available online: http://doi.org/10.1080/09064700801959403[EN] objective of this study was to test the hypothesis that the environmental variance of sternopleural bristle number in Drosophila melanogaster is partly under genetic control. We used data from 20 inbred lines and 10 control lines to test this hypothesis. Two models were used: a standard quantitative genetics model based on the infinitesimal model, and an extension of this model. In the extended model it is assumed that each individual has its own environmental variance and that this heterogeneity of variance has a genetic component. The heterogeneous variance model was favoured by the data, indicating that the environmental variance is partly under genetic control. If this heterogeneous variance model also applies to livestock, it would be possible to select for animals with a higher uniformity of products across environmental regimes. Also for evolutionary biology the results are of interest as genes affecting the environmental variance may be important for adaptation to changing environmental conditions.Sørensen, AC.; Kristensen, TN.; Loeschcke, V.; Ibañez Escriche, N.; Sorensen, D. (2007). Genetically controlled environmental variance for sternopleural bristles in Drosophila melanogaster - an experimental test of a heterogeneous variance model. Acta Agriculturae Scandinavica Section A - Animal Science. 57(4):196-201. https://doi.org/10.1080/09064700801959403S19620157

Study of using marker assisted selection on a beef cattle breeding program by model comparison

[EN] A data set of a commercial Nellore beef cattle selection program was used to compare breeding models that assumed or not markers effects to estimate the breeding values, when a reduced number of animals have phenotypic, genotypic and pedigree information available. This herd complete data set was composed of 83,404 animals measured for weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait analyses were performed by MTDFREML software to estimate fixed and random effects solutions using this complete data. The additive effects estimated were assumed as the reference breeding values for those animals. The individual observed phenotype of each trait was adjusted for fixed and random effects solutions, except for direct additive effects. The adjusted phenotype composed of the additive and residual parts of observed phenotype was used as dependent variable for models' comparison. Among all measured animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three models were compared in terms of changes on animals' rank, global fit and predictive ability. Model 1 included only polygenic effects, model 2 included only markers effects and model 3 included both polygenic and markers effects. Bayesian inference via Markov chain Monte Carlo methods performed by TM software was used to analyze the data for model comparison. Two different priors were adopted for markers effects in models 2 and 3, the first prior assumed was a uniform distribution (U) and, as a second prior, was assumed that markers effects were distributed as normal (N). Higher rank correlation coefficients were observed for models 3_U and 3_N, indicating a greater similarity of these models animals' rank and the rank based on the reference breeding values. Model 3_N presented a better global fit, as demonstrated by its low DIC. The best models in terms of predictive ability were models 1 and 3_N. Differences due prior assumed to markers effects in models 2 and 3 could be attributed to the better ability of normal prior in handle with collinear effects. The models 2_U and 2_N presented the worst performance, indicating that this small set of markers should not be used to genetically evaluate animals with no data, since its predictive ability is restricted. In conclusion, model 3_N presented a slight superiority when a reduce number of animals have phenotypic, genotypic and pedigree information. It could be attributed to the variation retained by markers and polygenic effects assumed together and the normal prior assumed to markers effects, that deals better with the collinearity between markers. (C) 2012 Elsevier B.V. All rights reserved.We are grateful to the Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Merialilgenity and Conselho Nacional de apoio a Pesquisa (CNPq) for the financial support, to Agro-Pecuaria CFM for data set and the Institut de Investigacion y Tecnologia Agroalimentarias de Cataluña (IRTA) as the host institution for its full backing while preparing the research and the manuscript.Rezende, F.; Ferraz, J.; Eler, J.; Silva, R.; Mattos, E.; Ibáñez-Escriche, N. (2012). Study of using marker assisted selection on a beef cattle breeding program by model comparison. Livestock Science. 147(1-3):40-48. https://doi.org/10.1016/j.livsci.2012.03.017S40481471-

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. 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Genetic control of the environmental variance for birth weight in seven generations of a divergent selection experiment in mice

[EN] Data from seven generations of a divergent selection experiment designed for environmental variability of birth weight were analysed to estimate genetic parameters and to explore signs of selection response. A total of 10 783 birth weight records from 638 females and 1127 litters in combination with 10 007 pedigree records were used. Each record of birth weight was assigned to the mother of the pup in a heteroscedastic model, and after seven generations of selection, evidence of success in the selection process was shown. A Bayesian analysis showed that success of the selection process started from the first generation for birth weight and from the second generation for its environmental variability. Genetic parameters were estimated across generations. However, only from the third generation onwards were the records useful to consider the results to be reliable. The results showed a consistent positive and low genetic correlation between the birth weight trait and its environmental variability, which could allow an independent selection process. This study has demonstrated that the genetic control of the birth weight environmental variability is possible in mice. Nevertheless, before the results are applied directly in farm animals, it would be worth confirming any other implications on other important traits, such as robustness, longevity and welfare.This manuscript was partially funded by a grant from the Spanish Government (AGL2008-00794). The experiment will be continued with partial funding of Feed-agene and a grant from MEC- INIA (RTA2014- 00015-C02-01)Formoso-Rafferty, N.; Cervantes, I.; Ibañez Escriche, N.; Gutiérrez, J. (2016). 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. https://doi.org/10.1111/jbg.12174S227237133