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

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

Individual efficiency for the use of feed resources in rabbits

[EN] A Bayesian procedure, which allows consideration of the individual variation in the feed resource allocation pattern, is described and implemented in 2 sire lines of rabbit (Caldes and R). The procedure is based on a hierarchical Bayesian scheme, where the first stage of the model consists of a multiple regression model of feed intake on metabolic BW and BW gain. In a second stage, an animal model was assumed including batch, parity order, litter size, and common environmental litter effects. Animals were reared during the fattening period (from weaning at 32 d of age to 60 d of age) in individual cages on an experimental farm, and were fed ad libitum with a commercial diet. Body weight (g) and cumulative feed intake (g) were recorded weekly. Individual BW gain (g) and average BW (ABW, g) were calculated from these data for each 7-d period. Metabolic BW (g(0.75)) was estimated as ABW(0.75). The number of animals actually measured was 444 and 445 in the Caldes and R lines, respectively. Marginal posterior distributions of the genetic parameters were obtained by Gibbs sampling. Posterior means (posterior SD) for heritabilities for partial coefficients of regression of feed intake on metabolic BW and feed intake on BW gain were estimated to be 0.35 (0.17) and 0.40 (0.17), respectively, in the Caldes line and 0.26 (0.19) and 0.27 (0.14), respectively, in line R. The estimated posterior means (posterior SD) for the proportion of the phenotypic variance due to common litter environmental effects of the same coefficients of regression were respectively, 0.39 (0.14) and 0.28 (0.13) in the Caldes line and 0.44 (0.22) and 0.49 (0.14) in line R. These results suggest that efficiency of use of feed resources could be improved by including these coefficients in an index of selection.Research was supported by INIA SC00-011. The authors acknowledge comments and suggestions made by M. Baselga and A. Blasco from the Universidad Politécnica de Valencia (Spain) and R. Rekaya for his assistance in solving numerical problems.Piles, M.; García-Tomas, M.; Rafel, O.; Ibañez Escriche, N.; Ramon, J.; Varona, L. (2007). Individual efficiency for the use of feed resources in rabbits. Journal of Animal Science. 85(11):2846-2853. https://doi.org/10.2527/jas.2006-218S284628538511Blasco, A. (2001). The Bayesian controversy in animal breeding. Journal of Animal Science, 79(8), 2023. doi:10.2527/2001.7982023xBlasco, A., Piles, M., & Varona, L. (2003). A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits. Genetics Selection Evolution, 35(1). doi:10.1186/1297-9686-35-1-21Cameron, N. D., & Thompson, R. (1986). Design of multivariate selection experiments to estimate genetic parameters. Theoretical and Applied Genetics, 72(4), 466-476. doi:10.1007/bf00289528Estany, J., Camacho, J., Baselga, M., & Blasco, A. (1992). Selection response of growth rate in rabbits for meat production. Genetics Selection Evolution, 24(6), 527. doi:10.1186/1297-9686-24-6-527Gelman, A., & Rubin, D. B. (1992). Inference from Iterative Simulation Using Multiple Sequences. Statistical Science, 7(4), 457-472. doi:10.1214/ss/1177011136Geyer, C. J. (1992). Practical Markov Chain Monte Carlo. Statistical Science, 7(4), 473-483. doi:10.1214/ss/1177011137Gianola, D., & Sorensen, D. (2004). Quantitative Genetic Models for Describing Simultaneous and Recursive Relationships Between Phenotypes. Genetics, 167(3), 1407-1424. doi:10.1534/genetics.103.025734MIGNON-GRASTEAU, S. (1999). Genetic parameters of growth curve parameters in male and female chickens. British Poultry Science, 40(1), 44-51. doi:10.1080/00071669987827Paracchini, V., Pedotti, P., & Taioli, E. (2005). Genetics of Leptin and Obesity: A HuGE Review. American Journal of Epidemiology, 162(2), 101-114. doi:10.1093/aje/kwi174Piles, M., Gianola, D., Varona, L., & Blasco, A. (2003). Bayesian inference about parameters of a longitudinal trajectory when selection operates on a correlated trait1. Journal of Animal Science, 81(11), 2714-2724. doi:10.2527/2003.81112714xPiles, M., Gomez, E. A., Rafel, O., Ramon, J., & Blasco, A. (2004). Elliptical selection experiment for the estimation of genetic parameters of the growth rate and feed conversion ratio in rabbits1. Journal of Animal Science, 82(3), 654-660. doi:10.2527/2004.823654xRauw, W. M., Luiting, P., Verstegen, M. W. A., Vangen, O., & Knap, P. W. (2000). Differences in food resource allocation in a long-term selection experiment for litter size in mice 2. Developmental trends in body weight against food intake. Animal Science, 71(1), 39-47. doi:10.1017/s1357729800054874Rauw, W. M., Knap, P. W., Verstegen, M. W. A., & Luiting, P. (2002). Food resource allocation patterns in lactating females in a long-term selection experiment for litter size in mice. Genetics Selection Evolution, 34(1). doi:10.1186/1297-9686-34-1-83Rekaya, R., Carabaño, M. J., & Toro, M. A. (2000). Bayesian Analysis of Lactation Curves of Holstein-Friesian Cattle Using a Nonlinear Model. Journal of Dairy Science, 83(11), 2691-2701. doi:10.3168/jds.s0022-0302(00)75163-0Rekaya, R., Weigel, K. A., & Gianola, D. (2001). Hierarchical nonlinear model for persistency of milk yield in the first three lactations of Holsteins. Livestock Production Science, 68(2-3), 181-187. doi:10.1016/s0301-6226(00)00239-6Varona, L., Moreno, C., Garcia Cortes, L. A., & Altarriba, J. (1998). Bayesian Analysis of Wood’s Lactation Curve for Spanish Dairy Cows. Journal of Dairy Science, 81(5), 1469-1478. doi:10.3168/jds.s0022-0302(98)75711-xWakefield, J. C., Smith, A. F. M., Racine-Poon, A., & Gelfand, A. E. (1994). Bayesian Analysis of Linear and Non-Linear Population Models by Using the Gibbs Sampler. Applied Statistics, 43(1), 201. doi:10.2307/298612

Genetic parameters for canalisation analysis of litter size and litter weight traits at birth in mice

[EN] The aim of this research was to explore the genetic parameters associated with environmental variability for litter size (LS), litter weight (LW) and mean individual birth weight (IW) in mice before canalisation. The analyses were conducted on an experimental mice population designed to reduce environmental variability for LS. The analysed database included 1976 records for LW and IW and 4129 records for LS. The total number of individuals included in the analysed pedigree was 3997. Heritabilities estimated for the traits under an initial exploratory approach varied from 0.099 to 0.101 for LS, from 0.112 to 0.148 for LW and from 0.028 to 0.033 for IW. The means of the posterior distribution of the heritability under a Bayesian approach were the following: 0.10 (LS), 0.13 (LW) and 0.03 (IW). In general, the heritabilities estimated under the initial exploratory approach for the environmental variability of the analysed traits were low. Genetic correlations estimated between the trait and its variability reached values of -0.929 (LS), -0.815 (LW) and 0.969 (IW). The results presented here for the first time in mice may suggest a genetic basis for variability of the evaluated traits, thus opening the possibility to be implemented in selection schemes.This research was partially financed by a grant from the University Complutense of Madrid, n◦ UCM PR1/03-11650. We thank Dr. Félix Goyache for comments on the manuscript.Gutiérrez, J.; Nieto, B.; Piqueras, P.; Ibañez Escriche, 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):445-462. https://doi.org/10.1051/gse:2006014S44546238

Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data

[EN] Background: Porcine fatty acid composition is a key factor for quality and nutritive value of pork. Several QTLs for fatty acid composition have been reported in diverse fat tissues. The results obtained so far seem to point out different genetic control of fatty acid composition conditional on the fat deposits. Those studies have been conducted using simple approaches and most of them focused on one single tissue. The first objective of the present study was to identify tissue-specific and tissue-consistent QTLs for fatty acid composition in backfat and intramuscular fat, combining linkage mapping and GWAS approaches and conducted under single and multitrait models. A second aim was to identify powerful candidate genes for these tissue-consistent QTLs, using microarray gene expression data and following a targeted genetical genomics approach. Results: The single model analyses, linkage and GWAS, revealed over 30 and 20 chromosomal regions, 24 of them identified here for the first time, specifically associated to the content of diverse fatty acids in BF and IMF, respectively. The analyses with multitrait models allowed identifying for the first time with a formal statistical approach seven different regions with pleiotropic effects on particular fatty acids in both fat deposits. The most relevant were found on SSC8 for C16:0 and C16:1(n-7) fatty acids, detected by both linkage and GWAS approaches. Other detected pleiotropic regions included one on SSC1 for C16:0, two on SSC4 for C16:0 and C18:2, one on SSC11 for C20:3 and the last one on SSC17 for C16:0. Finally, a targeted eQTL scan focused on regions showing tissue consistent effects was conducted with Longissimus and fat gene expression data. Some powerful candidate genes and regions were identified such as the PBX1, RGS4, TRIB3 and a transcription regulatory element close to ELOVL6 gene to be further studied. Conclusions: Complementary genome scans have confirmed several chromosome regions previously associated to fatty acid composition in backfat and intramuscular fat, but even more, to identify new ones. Although most of the detected regions were tissue-specific, supporting the hypothesis that the major part of genes affecting fatty acid composition differs among tissues, seven chromosomal regions showed tissue-consistent effects. Additional gene expression analyses have revealed powerful target regions to carry the mutation responsible for the pleiotropic effects.This work was funded by the MICINN project AGL2011-29821-C02 (Ministerio de Economia y Competitividad). We thank to Fabian Garcia, Anna Mercade and Carmen Barragan for their assistance in DNA preparation and SNP genotyping.Muñoz, M.; Rodríguez, MC.; Alves, E.; Folch, J.; Ibañez Escriche, N.; Silió, L.; Fernández, A. (2013). Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data. BMC Genomics. 14. https://doi.org/10.1186/1471-2164-14-845S14Lichtenstein, A. H. (2003). Dietary Fat and Cardiovascular Disease Risk: Quantity or Quality? Journal of Women’s Health, 12(2), 109-114. doi:10.1089/154099903321576493Jiménez-Colmenero, F., Ventanas, J., & Toldrá, F. (2010). Nutritional composition of dry-cured ham and its role in a healthy diet. Meat Science, 84(4), 585-593. doi:10.1016/j.meatsci.2009.10.029Webb, E. C., & O’Neill, H. A. (2008). The animal fat paradox and meat quality. Meat Science, 80(1), 28-36. doi:10.1016/j.meatsci.2008.05.029Wood, J. D., Enser, M., Fisher, A. V., Nute, G. R., Sheard, P. R., Richardson, R. I., … Whittington, F. M. (2008). Fat deposition, fatty acid composition and meat quality: A review. Meat Science, 78(4), 343-358. doi:10.1016/j.meatsci.2007.07.019Martı́n, L., Timón, M. L., Petrón, M. J., Ventanas, J., & Antequera, T. (2000). Evolution of volatile aldehydes in Iberian ham matured under different processing conditions. Meat Science, 54(4), 333-337. doi:10.1016/s0309-1740(99)00107-2Fernández, A., de Pedro, E., Núñez, N., Silió, L., Garcı́a-Casco, J., & Rodrı́guez, C. (2003). Genetic parameters for meat and fat quality and carcass composition traits in Iberian pigs. Meat Science, 64(4), 405-410. doi:10.1016/s0309-1740(02)00207-3Sellier, P., Maignel, L., & Bidanel, J. P. (2009). Genetic parameters for tissue and fatty acid composition of backfat, perirenal fat and longissimus muscle in Large White and Landrace pigs. animal, 4(4), 497-504. doi:10.1017/s1751731109991261Suzuki, K., Ishida, M., Kadowaki, H., Shibata, T., Uchida, H., & Nishida, A. (2006). Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs. Journal of Animal Science, 84(8), 2026-2034. doi:10.2527/jas.2005-660Clop, A., Ovilo, C., Perez-Enciso, M., Cercos, A., Tomas, A., Fernandez, A., … Noguera, J. L. (2003). Detection of QTL affecting fatty acid composition in the pig. Mammalian Genome, 14(9), 650-656. doi:10.1007/s00335-002-2210-7Nii, M., Hayashi, T., Tani, F., Niki, A., Mori, N., Fujishima-Kanaya, N., … Mikawa, S. (2006). Quantitative trait loci mapping for fatty acid composition traits in perirenal and back fat using a Japanese wild boar × Large White intercross. Animal Genetics, 37(4), 342-347. doi:10.1111/j.1365-2052.2006.01485.xRamayo-Caldas, Y., Mercadé, A., Castelló, A., Yang, B., Rodríguez, C., Alves, E., … Folch, J. M. (2012). Genome-wide association study for intramuscular fatty acid composition in an Iberian × Landrace cross1. Journal of Animal Science, 90(9), 2883-2893. doi:10.2527/jas.2011-4900Uemoto, Y., Soma, Y., Sato, S., Ishida, M., Shibata, T., Kadowaki, H., … Suzuki, K. (2011). Genome-wide mapping for fatty acid composition and melting point of fat in a purebred Duroc pig population. Animal Genetics, 43(1), 27-34. doi:10.1111/j.1365-2052.2011.02218.xGuo, T., Ren, J., Yang, K., Ma, J., Zhang, Z., & Huang, L. (2009). Quantitative trait loci for fatty acid composition in longissimus dorsi and abdominal fat: results from a White Duroc × Erhualian intercross F2population. Animal Genetics, 40(2), 185-191. doi:10.1111/j.1365-2052.2008.01819.xRamos, A. M., Crooijmans, R. P. M. A., Affara, N. A., Amaral, A. J., Archibald, A. L., Beever, J. E., … Groenen, M. A. M. (2009). Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology. PLoS ONE, 4(8), e6524. doi:10.1371/journal.pone.0006524Corominas, J., Ramayo-Caldas, Y., Puig-Oliveras, A., Pérez-Montarelo, D., Noguera, J. L., Folch, J. M., & Ballester, M. (2013). Polymorphism in the ELOVL6 Gene Is Associated with a Major QTL Effect on Fatty Acid Composition in Pigs. PLoS ONE, 8(1), e53687. doi:10.1371/journal.pone.0053687Ponsuksili, S., Jonas, E., Murani, E., Phatsara, C., Srikanchai, T., Walz, C., … Wimmers, K. (2008). Trait correlated expression combined with expression QTL analysis reveals biological pathways and candidate genes affecting water holding capacity of muscle. BMC Genomics, 9(1), 367. doi:10.1186/1471-2164-9-367Steibel, J. P., Bates, R. O., Rosa, G. J. M., Tempelman, R. J., Rilington, V. D., Ragavendran, A., … Ernst, C. W. (2011). Genome-Wide Linkage Analysis of Global Gene Expression in Loin Muscle Tissue Identifies Candidate Genes in Pigs. PLoS ONE, 6(2), e16766. doi:10.1371/journal.pone.0016766C�novas, A., Quintanilla, R., Amills, M., & Pena, R. N. (2010). Muscle transcriptomic profiles in pigs with divergent phenotypes for fatness traits. BMC Genomics, 11(1), 372. doi:10.1186/1471-2164-11-372Uemoto, Y., Sato, S., Ohnishi, C., Terai, S., Komatsuda, A., & Kobayashi, E. (2009). The effects of single and epistatic quantitative trait loci for fatty acid composition in a Meishan × Duroc crossbred population. Journal of Animal Science, 87(11), 3470-3476. doi:10.2527/jas.2009-1917Muñoz, M., Alves, E., Ramayo-Caldas, Y., Casellas, J., Rodríguez, C., Folch, J. M., … Fernández, A. I. (2011). Recombination rates across porcine autosomes inferred from high-density linkage maps. Animal Genetics, 43(5), 620-623. doi:10.1111/j.1365-2052.2011.02301.xQuintanilla, R., Pena, R. N., Gallardo, D., Cánovas, A., Ramírez, O., Díaz, I., … Amills, M. (2011). Porcine intramuscular fat content and composition are regulated by quantitative trait loci with muscle-specific effects1. Journal of Animal Science, 89(10), 2963-2971. doi:10.2527/jas.2011-3974Liaubet, L., Lobjois, V., Faraut, T., Tircazes, A., Benne, F., Iannuccelli, N., … Cherel, P. (2011). Genetic variability of transcript abundance in pig peri-mortem skeletal muscle: eQTL localized genes involved in stress response, cell death, muscle disorders and metabolism. BMC Genomics, 12(1). doi:10.1186/1471-2164-12-548Mitchell-Olds, T. (2010). Complex-trait analysis in plants. Genome Biology, 11(4), 113. doi:10.1186/gb-2010-11-4-113Scoggan, K. A., Jakobsson, P.-J., & Ford-Hutchinson, A. W. (1997). Production of Leukotriene C4in Different Human Tissues Is Attributable to Distinct Membrane Bound Biosynthetic Enzymes. Journal of Biological Chemistry, 272(15), 10182-10187. doi:10.1074/jbc.272.15.10182JAKOBSSON, A., WESTERBERG, R., & JACOBSSON, A. (2006). Fatty acid elongases in mammals: Their regulation and roles in metabolism. Progress in Lipid Research, 45(3), 237-249. doi:10.1016/j.plipres.2006.01.004Iankova, I., Chavey, C., Clapé, C., Colomer, C., Guérineau, N. C., Grillet, N., … Fajas, L. (2008). Regulator of G Protein Signaling-4 Controls Fatty Acid and Glucose Homeostasis. Endocrinology, 149(11), 5706-5712. doi:10.1210/en.2008-0717Angyal, A., & Kiss-Toth, E. (2012). The tribbles gene family and lipoprotein metabolism. Current Opinion in Lipidology, 23(2), 122-126. doi:10.1097/mol.0b013e3283508c3bÓvilo, C., Pérez-Enciso, M., Barragán, C., Clop, A., Rodríguez, C., Oliver, M. A., … Noguera, J. L. (2000). A QTL for intramuscular fat and backfat thickness is located on porcine Chromosome 6. Mammalian Genome, 11(4), 344-346. doi:10.1007/s003350010065Veroneze, R., Lopes, P. S., Guimarães, S. E. F., Silva, F. F., Lopes, M. S., Harlizius, B., & Knol, E. F. (2013). Linkage disequilibrium and haplotype block structure in six commercial pig lines. Journal of Animal Science, 91(8), 3493-3501. doi:10.2527/jas.2012-6052Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences, 100(16), 9440-9445. doi:10.1073/pnas.1530509100Tsai, S., Cassady, J. P., Freking, B. A., Nonneman, D. J., Rohrer, G. A., & Piedrahita, J. A. (2006). Annotation of the Affymetrix1 porcine genome microarray. Animal Genetics, 37(4), 423-424. doi:10.1111/j.1365-2052.2006.01460.xNyholt, D. R. (2004). A Simple Correction for Multiple Testing for Single-Nucleotide Polymorphisms in Linkage Disequilibrium with Each Other. The American Journal of Human Genetics, 74(4), 765-769. doi:10.1086/383251Moskvina, V., & Schmidt, K. M. (2008). On multiple-testing correction in genome-wide association studies. Genetic Epidemiology, 32(6), 567-573. doi:10.1002/gepi.20331Benjamini, Y., & Yekutieli, D. (2005). Quantitative Trait Loci Analysis Using the False Discovery Rate. Genetics, 171(2), 783-790. doi:10.1534/genetics.104.03669

A Bayesian Multivariate Gametic Model in a Reciprocal Cross with Genomic Information : An Example with Two Iberian Varieties

Altres ajuts: Centro para el Desarrollo Tecnológico Industrial: IDI-20170304INGA FOOD, S.A. initiated a crossbreeding program involving two Iberian pig varieties: Retinto and Entrepelado. The primary objective of this program is to produce an F1 hybrid sow that exhibits enhanced reproductive performance. In a previous investigation, variations in the reproductive performance of sows, specifically litter size, were observed among the reciprocal crosses. These variations indicate the presence of genomic imprinting effects. To assess the influence of genetic origin, we developed a multivariate gametic model to estimate the gametic correlations between paternal and maternal effects. Gametic correlations lower than one could potentially explain the performance differences observed across the reciprocal crosses. Despite having limited data, the study's findings suggest that the gametic correlation estimate between paternal and maternal effects on litter size is lower in the Entrepelado population compared to the Retinto population. INGA FOOD, S.A. initiated a crossbreeding program between two Iberian pig varieties, Retinto (R) and Entrepelado (E), with the goal of producing a hybrid sow (F1). Several studies have been conducted to evaluate its productive performance, and these studies have revealed differences in litter size between the two reciprocal crosses, suggesting the presence of genomic imprinting effects. To further investigate these effects, this study introduces a multivariate gametic model designed to estimate gametic correlations between paternal and maternal effects originating from both genetic backgrounds involved in the reciprocal crosses. The dataset consisted of 1258 records (the total number born-TNB and the number born alive-NBA) from 203 crossbred dams for the Entrepelado (sire) × Retinto (dam) cross and 700 records from 125 crossbred dams for the Retinto (sire) × Entrepelado (dam) cross. All animals were genotyped using the GeneSeek ® GPP Porcine 70 K HDchip (Illumina Inc., San Diego, CA, USA). The results indicated that the posterior distribution of the gametic correlation between paternal and maternal effects was distinctly different between the two populations. Specifically, in the Retinto population, the gametic correlation showed a positive skew with posterior probabilities of 0.78 for the TNB and 0.80 for the NBA. On the other hand, the Entrepelado population showed a posterior probability of a positive gametic correlation between paternal and maternal effects of approximately 0.50. The differences in the shape of the posterior distribution of the gametic correlations between paternal and maternal effects observed in the two varieties may account for the distinct performance outcomes observed in the reciprocal crosses

New insight into the SSC8 genetic determination of fatty acid composition in pigs

[EN] Background:Fat content and fatty acid composition in swine are becoming increasingly studied because of their effect on sensory and nutritional quality of meat. A QTL (quantitative trait locus) for fatty acid composition in backfat was previously detected on porcine chromosome 8 (SSC8) in an Iberian x Landrace F-2 intercross. More recently, a genome-wide association study detected the same genomic region for muscle fatty acid composition in an Iberian x Landrace backcross population. ELOVL6, a strong positional candidate gene for this QTL, contains a polymorphism in its promoter region (ELOVL6:c.-533C < T), which is associated with percentage of palmitic and palmitoleic acids in muscle and adipose tissues. Here, a combination of single-marker association and the haplotype-based approach was used to analyze backfat fatty acid composition in 470 animals of an Iberian x Landrace F2 intercross genotyped with 144 SNPs (single nucleotide polymorphisms) distributed along SSC8. Results:Two trait-associated SNP regions were identified at 93 Mb and 119 Mb on SSC8. The strongest statistical signals of both regions were observed for palmitoleic acid (C16:1(n-7)) content and C18:0/C16:0 and C18:1(n-7)/C16:1 (n-7) elongation ratios. MAML3 and SETD7 are positional candidate genes in the 93 Mb region and two novel microsatellites in MAML3 and nine SNPs in SETD7 were identified. No significant association for the MAML3 microsatellite genotypes was detected. The SETD7:c. 700G > T SNP, although statistically significant, was not the strongest signal in this region. In addition, the expression of MAML3 and SETD7 in liver and adipose tissue varied among animals, but no association was detected with the polymorphisms in these genes. In the 119 Mb region, the ELOVL6:c.-533C > T polymorphism showed a strong association with percentage of palmitic and palmitoleic fatty acids and elongation ratios in backfat. Conclusions:Our results suggest that the polymorphisms studied in MAML3 and SETD7 are not the causal mutations for the QTL in the 93 Mb region. However, the results for ELOVL6 support the hypothesis that the ELOVL6:c.-533C > T polymorphism has a pleiotropic effect on backfat and intramuscular fatty acid composition and that it has a role in the determination of the QTL in the 119 Mb region.This work was funded by MICINN AGL2008-04818-C03/GAN and MINECO AGL2011-29821-C02 and the Innovation Programme Consolider-Ingenio 2010 (CSD2007-00036). M. Revilla is a Master's student of Animal Breeding and Biotechnology of Reproduction (Polytechnical University of Valencia and Autonomous University of Barcelona). Y. Ramayo-Caldas was funded by a FPU grant (AP2008-01450), J. Corominas by a FPI scholarship from the Ministry of Education (BES-2009-018223) and A. Puig-Oliveras by a PIF scholarship (458-01-1/2011). This manuscript has been proofread by Chuck Simons, a native English speaking university instructor in English.Revilla, M.; Ramayo-Caldas, Y.; Castelló, A.; Corominas, J.; Puig-Oliveras, A.; Ibañez Escriche, N.; Muñoz, M.... (2014). New insight into the SSC8 genetic determination of fatty acid composition in pigs. Genetics Selection Evolution. 46. https://doi.org/10.1186/1297-9686-46-28S46Clarke, R., Frost, C., Collins, R., Appleby, P., & Peto, R. (1997). Dietary lipids and blood cholesterol: quantitative meta-analysis of metabolic ward studies. BMJ, 314(7074), 112-112. doi:10.1136/bmj.314.7074.112Mensink, R. P., & Katan, M. B. (1992). Effect of dietary fatty acids on serum lipids and lipoproteins. A meta-analysis of 27 trials. Arteriosclerosis and Thrombosis: A Journal of Vascular Biology, 12(8), 911-919. doi:10.1161/01.atv.12.8.911Hunter, J. E., Zhang, J., & Kris-Etherton, P. M. (2009). Cardiovascular disease risk of dietary stearic acid compared with trans, other saturated, and unsaturated fatty acids: a systematic review. The American Journal of Clinical Nutrition, 91(1), 46-63. doi:10.3945/ajcn.2009.27661Astrup, A., Dyerberg, J., Elwood, P., Hermansen, K., Hu, F. B., Jakobsen, M. U., … Willett, W. C. (2011). The role of reducing intakes of saturated fat in the prevention of cardiovascular disease: where does the evidence stand in 2010? The American Journal of Clinical Nutrition, 93(4), 684-688. doi:10.3945/ajcn.110.004622Harris, W. S., Poston, W. C., & Haddock, C. K. (2007). Tissue n−3 and n−6 fatty acids and risk for coronary heart disease events. Atherosclerosis, 193(1), 1-10. doi:10.1016/j.atherosclerosis.2007.03.018Lopez-Huertas, E. (2010). Health effects of oleic acid and long chain omega-3 fatty acids (EPA and DHA) enriched milks. A review of intervention studies. Pharmacological Research, 61(3), 200-207. doi:10.1016/j.phrs.2009.10.007Guo, T., Ren, J., Yang, K., Ma, J., Zhang, Z., & Huang, L. (2009). Quantitative trait loci for fatty acid composition in longissimus dorsi and abdominal fat: results from a White Duroc × Erhualian intercross F2population. Animal Genetics, 40(2), 185-191. doi:10.1111/j.1365-2052.2008.01819.xUemoto, Y., Soma, Y., Sato, S., Ishida, M., Shibata, T., Kadowaki, H., … Suzuki, K. (2011). Genome-wide mapping for fatty acid composition and melting point of fat in a purebred Duroc pig population. Animal Genetics, 43(1), 27-34. doi:10.1111/j.1365-2052.2011.02218.xClop, A., Ovilo, C., Perez-Enciso, M., Cercos, A., Tomas, A., Fernandez, A., … Noguera, J. L. (2003). Detection of QTL affecting fatty acid composition in the pig. Mammalian Genome, 14(9), 650-656. doi:10.1007/s00335-002-2210-7Ramayo-Caldas, Y., Mercadé, A., Castelló, A., Yang, B., Rodríguez, C., Alves, E., … Folch, J. M. (2012). Genome-wide association study for intramuscular fatty acid composition in an Iberian × Landrace cross1. Journal of Animal Science, 90(9), 2883-2893. doi:10.2527/jas.2011-4900Muñoz, M., Rodríguez, M. C., Alves, E., Folch, J. M., Ibañez-Escriche, N., Silió, L., & Fernández, A. I. (2013). Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data. BMC Genomics, 14(1), 845. doi:10.1186/1471-2164-14-845Ramos, A. M., Crooijmans, R. P. M. A., Affara, N. A., Amaral, A. J., Archibald, A. L., Beever, J. E., … Groenen, M. A. M. (2009). Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology. PLoS ONE, 4(8), e6524. doi:10.1371/journal.pone.0006524Estellé, J., Mercadé, A., Pérez-Enciso, M., Pena, R. N., Silió, L., Sánchez, A., & Folch, J. M. (2009). Evaluation ofFABP2as candidate gene for a fatty acid composition QTL in porcine chromosome 8. Journal of Animal Breeding and Genetics, 126(1), 52-58. doi:10.1111/j.1439-0388.2008.00754.xEstellé, J., Fernández, A. I., Pérez-Enciso, M., Fernández, A., Rodríguez, C., Sánchez, A., … Folch, J. M. (2009). A non-synonymous mutation in a conserved site of theMTTPgene is strongly associated with protein activity and fatty acid profile in pigs. Animal Genetics, 40(6), 813-820. doi:10.1111/j.1365-2052.2009.01922.xPurcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., … Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81(3), 559-575. doi:10.1086/519795Pérez-Enciso, M., & Misztal, I. (2011). Qxpak.5: Old mixed model solutions for new genomics problems. BMC Bioinformatics, 12(1). doi:10.1186/1471-2105-12-202Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences, 100(16), 9440-9445. doi:10.1073/pnas.1530509100Druet, T., & Georges, M. (2009). A Hidden Markov Model Combining Linkage and Linkage Disequilibrium Information for Haplotype Reconstruction and Quantitative Trait Locus Fine Mapping. Genetics, 184(3), 789-798. doi:10.1534/genetics.109.108431Werle, E., Schneider, C., Renner, M., Völker, M., & Fiehn, W. (1994). Convenient single-step, one tube purification of PCR products for direct sequencing. Nucleic Acids Research, 22(20), 4354-4355. doi:10.1093/nar/22.20.4354Ballester, M., Cordón, R., & Folch, J. M. (2013). DAG Expression: High-Throughput Gene Expression Analysis of Real-Time PCR Data Using Standard Curves for Relative Quantification. PLoS ONE, 8(11), e80385. doi:10.1371/journal.pone.0080385Karim, L., Takeda, H., Lin, L., Druet, T., Arias, J. A. C., Baurain, D., … Coppieters, W. (2011). Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature. Nature Genetics, 43(5), 405-413. doi:10.1038/ng.814Oyama, T., Harigaya, K., Sasaki, N., Okamura, Y., Kokubo, H., Saga, Y., … Kitagawa, M. (2011). Mastermind-like 1 (MamL1) and mastermind-like 3 (MamL3) are essential for Notch signaling in vivo. Development, 138(23), 5235-5246. doi:10.1242/dev.062802Pajvani, U. B., Qiang, L., Kangsamaksin, T., Kitajewski, J., Ginsberg, H. N., & Accili, D. (2013). Inhibition of Notch uncouples Akt activation from hepatic lipid accumulation by decreasing mTorc1 stability. Nature Medicine, 19(8), 1054-1060. doi:10.1038/nm.3259Syreeni, A., El-Osta, A., Forsblom, C., Sandholm, N., Parkkonen, M., … Tarnow, L. (2011). Genetic Examination of SETD7 and SUV39H1/H2 Methyltransferases and the Risk of Diabetes Complications in Patients With Type 1 Diabetes. Diabetes, 60(11), 3073-3080. doi:10.2337/db11-0073Chakrabarti, S. K., Francis, J., Ziesmann, S. M., Garmey, J. C., & Mirmira, R. G. (2003). Covalent Histone Modifications Underlie the Developmental Regulation of Insulin Gene Transcription in Pancreatic β Cells. Journal of Biological Chemistry, 278(26), 23617-23623. doi:10.1074/jbc.m303423200Ramayo-Caldas, Y., Mach, N., Esteve-Codina, A., Corominas, J., Castelló, A., Ballester, M., … Folch, J. M. (2012). Liver transcriptome profile in pigs with extreme phenotypes of intramuscular fatty acid composition. BMC Genomics, 13(1), 547. doi:10.1186/1471-2164-13-547Corominas, J., Ramayo-Caldas, Y., Puig-Oliveras, A., Pérez-Montarelo, D., Noguera, J. L., Folch, J. M., & Ballester, M. (2013). Polymorphism in the ELOVL6 Gene Is Associated with a Major QTL Effect on Fatty Acid Composition in Pigs. PLoS ONE, 8(1), e53687. doi:10.1371/journal.pone.005368

A study of heterogeneity of environmental variance for slaughter weight in pigs

[EN] This work presents an analysis of heterogeneity of environmental variance for slaughter weight (175 days) in pigs. This heterogeneity is associated with systematic and additive genetic effects. The model also postulates the presence of additive genetic effects affecting the mean and environmental variance. The study reveals the presence of genetic variation at the level of the mean and the variance, but an absence of correlation, or a small negative correlation, between both types of additive genetic effects. In addition, we show that both, the additive genetic effects on the mean and those on environmental variance 19 have an important influence upon the future economic performance of selected individuals.Financial support was provided by the IRTA, Spain (grant 0502- 21102). The authors gratefully acknowledge the cooperative COPAGA for its collaboration and would particularly like to thank Sergi Illa´ n, Eva Ramells, Eva Roca and Fe´ lix Grau.Ibañez Escriche, N.; Varona, L.; Sorensen, D.; Noguera, J. (2008). A study of heterogeneity of environmental variance for slaughter weight in pigs. animal. 2(1):19-26. https://doi.org/10.1017/S1751731107001000S19262

Crossbreeding effects on pig growth and carcass traits from two Iberian strains

[EN] An experiment of a 2 x 2 full diallelic cross between two contemporary Iberian pig strains (Retinto: RR, and Torbiscal: TT) was conducted to estimate the crossbreeding effects for growth and carcass traits. Phenotypic records were obtained under intensive management and consisted of two different data sets. The first set comprised growth traits until weaning and was collected at two different farms (6236 and 1208 records, respectively). Specific data included individual piglet weight at birth and at weaning at 28 days and average daily gain from birth to weaning at 28 days (ADG28) for both RR and TT and their reciprocal crosses. The second set comprised growth data from birth to slaughter (similar to 340 days and similar to 160 kg) and carcass traits from 349 individuals (randomly) sampled at weaning from the first dataset. Data were analyzed through a Bayesian analysis by using a reparameterization of Dickerson's model that allowed estimation of the posterior distributions of the following crossbreeding effects: average maternal breed effect (gM), average paternal breed effect (gP) and individual heterosis (hI). Results showed that the relative magnitude of crossbreeding effects depends on the trait analyzed. Crosses where Torbiscal strain was used as mother (RT and TT) achieved the greatest performance for all growth traits at weaning, leading to remarkable gM effects. The most outstanding example is the case of ADG28 where the probability of relevance was one. In contrast, TR cross showed the greatest differences from RR cross for all growth at slaughter and carcass traits. These differences were mainly due to hI and gP crossbreeding parameters. In particular, the posterior mean of hI was more noticeable for live weight at slaughter, average daily gain at slaughter and carcass length, while gP was more relevant for hams (kg) and loins (kg) representing from 3% to10% of average performance of traits. Hence, growth traits at weaning did not reveal any notable advantage of the crossbreeding scheme because of the superiority of the Torbiscal strain with respect to its mothering ability and the small hI. However, results from growth and carcass traits at slaughter would support the implementation of a TR crossbred system. It would allow exploitation of both the gP of the Torbiscal strain and the hI between these two Iberian pig strains. Additionally, gP estimates and phenotypic differences between reciprocal crosses might suggest signs of the presence of paternal genetic imprinting in primal cuts traits.The authors acknowledge the technicians of the IRTA (M.J. Garcia-Santana) and of the INGAFOOD Company (Manuel Ramos, Lourdes Munoz and Pilar Diaz) for cooperating in the experimental protocol and their technical support. The work was partially funded by the Center for Industrial Technological Development (CDTI) grant IDI-20100447 and by the grant RTA2012-00054-C02-01 from the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA), Spain.Ibañez Escriche, N.; Varona, L.; Magallon, E.; Noguera, J. (2014). Crossbreeding effects on pig growth and carcass traits from two Iberian strains. animal. 8(10):1569-1576. https://doi.org/10.1017/S1751731114001712S1569157681

Selection for Environmental Variation: A Statistical Analysis and Power Calculations to Detect Response

[EN] Data from uterine capacity in rabbits (litter size) were analyzed to determine whether the environmental variance was partly genetically determined. The fit of a classical homogeneous variance mixed linear (HOM) model and that of a genetically Structured heterogeneous variance mixed linear (HET) model were compared. Various methods to assess the quality of fit favor the HET model. The posterior mean (95% posterior interval) of the additive genetic variance affecting the environmental variance was 0.16 (0.10; 0.25) and the corresponding number for the coefficient of correlation between genes affecting mean and variance was -0.74 (-0.90; -0.52). It is argued that stronger support for the HET model than that derived from statistical analysis of data would be provided by a successful selection experiment designed to modify the environmental variance. A simple selection criterion is Suggested (average squared deviation front the mean of repeated records within individuals) and its predicted response and variance tinder the HET model are derived. This is used to determine the appropriate size and length of a selection experiment designed to change tire environmental variance. Results from the analytical expressions are compared with those obtained using Simulation. There is good agreement provided selection intensity, is not intense.Ibañez Escriche, N.; Sorensen, D.; Waagepetersen, R.; Blasco Mateu, A. (2008). Selection for Environmental Variation: A Statistical Analysis and Power Calculations to Detect Response. Genetics. 180(4):2209-2226. https://doi.org/10.1534/genetics.108.09167822092226180