¿Can crossbred animals be used for genomic selection?

Ponencia publicada en ITEA, vol.104La producción en poblaciones “puras” suele tener una baja reproducibilidad en sus descendentes “cruzados”. La selección genómica podría utilizarse para evaluar poblaciones “puras” usando los datos de sus descendientes “cruzados”. Sin embargo, en las poblaciones cruzadas quizás el desequilibrio de ligamiento (LD) no esta restringido a marcadores estrechamente ligados al QTL y los efectos de los marcadores podrían ser específicos de cada población. Estos dos problemas podrían solucionarse utilizando un modelo con los alelos de los SNPs específicos para cada población. Para investigar esta idea usamos un modelo con los efectos de los genotipos de los SNPs (modelo 1) y otro modelo con los efectos de alelos de los SNPs específicos para cada población (modelo 2). Ambos modelos se utilizaron para predecir los valores genéticos de las poblaciones “puras” usando datos F1. Tres situaciones fueron simuladas, en las dos primeras se consideró que las dos poblaciones tenían un mismo origen con una diferencia de 50 y 550 generaciones, respectivamente. En la tercera situación se consideró que las dos poblaciones tenían orígenes distintos. En todos los casos las dos poblaciones generaron una población F1 con un tamaño de 1.000 individuos. Los valores fenotípicos de la F1 fueron simulados con una media de 12 QTL segregando y una heredabilidad de 0.3. En el análisis de la F1 y la población “pura” de validación se escogieron 500 marcadores en segregación. Para estimar el efecto de los SNPs se utilizó el método Bayesiano llamado Bayes-B. La precisión media de los valores genéticos obtenida varió entre 0.789 y 0.718. Sin embargo, se observó que conforme las poblaciones estuvieron más alejadas la precisión disminuyó y el modelo 2 dio valores ligeramente superiores que el modelo 1. Estos resultados sugerirían que los animales cruzados pueden ser utilizados para evaluar poblaciones “puras”. Además modelos con origen específico de población darían mejores resultados.Performance of purebred parents can be a poor predictor of performance of their crossbred descendants. However, in crossbred populations linkage disequilibrium may not be restricted to markers that are tightly linked to the QTL and the effects of SNPs may be breed specific. Both these problems can be addressed by using a model with breed-specific SNP effects. To investigate this idea, we used a model with effects of SNP genotypes (model 1) and a model with breed-specific effects of SNP alleles (model 2) to predict purebred breeding values using F1 data. Three scenarios were considered. In the first two, pure breeds were assumed to have a common origin either 50 or 550 generations ago. In the third scenario, the two breeds did not have a common origin. In all these scenarios, the two breeds were used to generate an F1 with 1,000 individuals. Trait phenotypic values controlled by 12 segregating QTL and with a heritability of 0.30 were simulated for the F1 individuals. Further, 500 segregating markers on a chromosome of 1 Morgan were chosen for analysis in the F1s and in the validation population of purebred. A Bayesian method (Bayes-B) was used to estimate the SNP effects. The accuracy of the predictions was between 0.789 and 0.718. However, the accuracy was lower when the populations were more separate and model 2 gave values slightly higher than model 1. These results suggest that crossbred data could be used to evaluate purebreds and breed specific models could give better results

Effect of inbreeding on the longevity of Landrace sows

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

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

Genomic regions influencing intramuscular fat in divergently selected rabbit lines

[EN] Intramuscular fat (IMF) is one of the main meat quality traits for breeding programs in livestock species. The main objective of this study was to identify genomic regions associated with IMF content comparing two rabbit populations divergently selected for this trait, and to generate a list of putative candidate genes. Animals were genotyped using the Affymetrix Axiom OrcunSNP Array (200k). After quality control, the data involved 477 animals and 93,540 single nucleotide polymorphisms (SNPs). Two methods were used in this research: single marker regressions with the data adjusted by genomic relatedness, and a Bayesian multi-marker regression. Associated genomic regions were located on the rabbit chromosomes (OCU) OCU1, OCU8 and OCU13. The highest value for the percentage of the genomic variance explained by a genomic region was found in two consecutive genomic windows on OCU8 (7.34%). Genes in the associated regions of OCU1 and OCU8 presented biological functions related to the control of adipose cell function, lipid binding, transportation and localization (APOLD1, PLBD1, PDE6H, GPRC5D, and GPRC5A) and lipid metabolic processes (MTMR2). The EWSR1 gene, underlying the OCU13 region, is linked to the development of brown adipocytes. The findings suggest that there is a large component of polygenic effect behind the differences in IMF content in these two lines, as the variance explained by most of the windows was low. The genomic regions of OCU1, OCU8 and OCU13 revealed novel candidate genes. Further studies would be needed to validate the associations and explore their possible application in selection programs.The work was funded by project AGL2014-55921-C2-1-P from National Programme for Fostering Excellence in Scientific and Technical Research -Project I+D. BSS was supported by a FPI grant from the Ministry of Economy and Competitiveness of Spain+ (BES-2015-074194). NIB was supported with a "Ramon y Cajal" grant provided by Ministerio de Ciencia e Innovacion (RYC-2016-19764). CSH and PN were supported by the Medical Research Council (United kingdom, grants MC_PC_U127592696 and MC_PC_U127561128). CSH was supported by Biotechnology and Biological Sciences Research Council (United Kingdom, Grant/Award Number: BBS/E/D/30002276).Sosa-Madrid, BS.; Hernández, P.; Blasco Mateu, A.; Haley, CS.; Fontanesi, L.; Santacreu Jerez, MA.; Pena, RN.... (2020). Genomic regions influencing intramuscular fat in divergently selected rabbit lines. Animal Genetics. 51:58-69. https://doi.org/10.1111/age.12873586951Aken, B. L., Ayling, S., Barrell, D., Clarke, L., Curwen, V., Fairley, S., … Searle, S. M. J. (2016). The Ensembl gene annotation system. Database, 2016, baw093. doi:10.1093/database/baw093Aloulou, A., Ali, Y. B., Bezzine, S., Gargouri, Y., & Gelb, M. H. (2012). Phospholipases: An Overview. Methods in Molecular Biology, 63-85. doi:10.1007/978-1-61779-600-5_4Amisten, S., Mohammad Al-Amily, I., Soni, A., Hawkes, R., Atanes, P., Persaud, S. J., … Salehi, A. (2017). Anti-diabetic action of all-trans retinoic acid and the orphan G protein coupled receptor GPRC5C in pancreatic β-cells. Endocrine Journal, 64(3), 325-338. doi:10.1507/endocrj.ej16-0338Astle, W., & Balding, D. J. (2009). Population Structure and Cryptic Relatedness in Genetic Association Studies. Statistical Science, 24(4). doi:10.1214/09-sts307Aulchenko, Y. S., Ripke, S., Isaacs, A., & van Duijn, C. M. (2007). GenABEL: an R library for genome-wide association analysis. Bioinformatics, 23(10), 1294-1296. doi:10.1093/bioinformatics/btm108Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. (2004). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21(2), 263-265. doi:10.1093/bioinformatics/bth457Beissinger, T. M., Rosa, G. J., Kaeppler, S. M., Gianola, D., & de Leon, N. (2015). Defining window-boundaries for genomic analyses using smoothing spline techniques. Genetics Selection Evolution, 47(1). doi:10.1186/s12711-015-0105-9Blasco, A., & Pena, R. N. (2018). Current Status of Genomic Maps: Genomic Selection/GBV in Livestock. Animal Biotechnology 2, 61-80. doi:10.1007/978-3-319-92348-2_4Browning, B. L., & Browning, S. R. (2016). Genotype Imputation with Millions of Reference Samples. The American Journal of Human Genetics, 98(1), 116-126. doi:10.1016/j.ajhg.2015.11.020Carneiro, M., Afonso, S., Geraldes, A., Garreau, H., Bolet, G., Boucher, S., … Ferrand, N. (2011). The Genetic Structure of Domestic Rabbits. Molecular Biology and Evolution, 28(6), 1801-1816. doi:10.1093/molbev/msr003Carneiro, M., Rubin, C.-J., Di Palma, F., Albert, F. W., Alföldi, J., Barrio, A. M., … Andersson, L. (2014). Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication. Science, 345(6200), 1074-1079. doi:10.1126/science.1253714Cesar, A. S., Regitano, L. C., Mourão, G. B., Tullio, R. R., Lanna, D. P., Nassu, R. T., … Coutinho, L. L. (2014). Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle. BMC Genetics, 15(1). doi:10.1186/1471-2156-15-39Chaves, V. E., Frasson, D., & Kawashita, N. H. (2011). Several agents and pathways regulate lipolysis in adipocytes. Biochimie, 93(10), 1631-1640. doi:10.1016/j.biochi.2011.05.018Chen, W.-M., & Abecasis, G. R. (2007). Family-Based Association Tests for Genomewide Association Scans. The American Journal of Human Genetics, 81(5), 913-926. doi:10.1086/521580Claire D’Andre, H., Paul, W., Shen, X., Jia, X., Zhang, R., Sun, L., & Zhang, X. (2013). Identification and characterization of genes that control fat deposition in chickens. Journal of Animal Science and Biotechnology, 4(1). doi:10.1186/2049-1891-4-43Do, D. N., Strathe, A. B., Ostersen, T., Pant, S. D., & Kadarmideen, H. N. (2014). Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake. Frontiers in Genetics, 5. doi:10.3389/fgene.2014.00307Do, D. N., Schenkel, F. S., Miglior, F., Zhao, X., & Ibeagha-Awemu, E. M. (2018). Genome wide association study identifies novel potential candidate genes for bovine milk cholesterol content. Scientific Reports, 8(1). doi:10.1038/s41598-018-31427-0Fan, B., Du, Z.-Q., Gorbach, D. M., & Rothschild, M. F. (2010). Development and Application of High-density SNP Arrays in Genomic Studies of Domestic Animals. Asian-Australasian Journal of Animal Sciences, 23(7), 833-847. doi:10.5713/ajas.2010.r.03Gao, Y., Zhang, R., Hu, X., & Li, N. (2007). Application of genomic technologies to the improvement of meat quality of farm animals. Meat Science, 77(1), 36-45. doi:10.1016/j.meatsci.2007.03.026Garrick, D. J. (2011). The nature, scope and impact of genomic prediction in beef cattle in the United States. Genetics Selection Evolution, 43(1). doi:10.1186/1297-9686-43-17Garrick, D. J., & Fernando, R. L. (2013). Implementing a QTL Detection Study (GWAS) Using Genomic Prediction Methodology. Genome-Wide Association Studies and Genomic Prediction, 275-298. doi:10.1007/978-1-62703-447-0_11Gotoh, T., Takahashi, H., Nishimura, T., Kuchida, K., & Mannen, H. (2014). Meat produced by Japanese Black cattle and Wagyu. Animal Frontiers, 4(4), 46-54. doi:10.2527/af.2014-0033Gotoh, T., Nishimura, T., Kuchida, K., & Mannen, H. (2018). The Japanese Wagyu beef industry: current situation and future prospects — A review. Asian-Australasian Journal of Animal Sciences, 31(7), 933-950. doi:10.5713/ajas.18.0333Hocquette, J. F., Gondret, F., Baéza, E., Médale, F., Jurie, C., & Pethick, D. W. (2010). Intramuscular fat content in meat-producing animals: development, genetic and nutritional control, and identification of putative markers. Animal, 4(2), 303-319. doi:10.1017/s1751731109991091Hopkins, D. L., Fogarty, N. M., & Mortimer, S. I. (2011). Genetic related effects on sheep meat quality. Small Ruminant Research, 101(1-3), 160-172. doi:10.1016/j.smallrumres.2011.09.036Jiao, X., Sherman, B. T., Huang, D. W., Stephens, R., Baseler, M. W., Lane, H. C., & Lempicki, R. A. (2012). DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics, 28(13), 1805-1806. doi:10.1093/bioinformatics/bts251Jin, C., Wang, W., Liu, Y., & Zhou, Y. (2017). RAI3 knockdown promotes adipogenic differentiation of human adipose-derived stem cells by decreasing β-catenin levels. Biochemical and Biophysical Research Communications, 493(1), 618-624. doi:10.1016/j.bbrc.2017.08.142Kass, R. E., & Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773-795. doi:10.1080/01621459.1995.10476572Kim, E.-S., Ros-Freixedes, R., Pena, R. N., Baas, T. J., Estany, J., & Rothschild, M. F. (2015). Identification of signatures of selection for intramuscular fat and backfat thickness in two Duroc populations1. Journal of Animal Science, 93(7), 3292-3302. doi:10.2527/jas.2015-8879Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., … Ma’ayan, A. (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research, 44(W1), W90-W97. doi:10.1093/nar/gkw377Lander, E., & Kruglyak, L. (1995). Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nature Genetics, 11(3), 241-247. doi:10.1038/ng1195-241Lionikas, A., Meharg, C., Derry, J. M., Ratkevicius, A., Carroll, A. M., Vandenbergh, D. J., & Blizard, D. A. (2012). Resolving candidate genes of mouse skeletal muscle QTL via RNA-Seq and expression network analyses. BMC Genomics, 13(1), 592. doi:10.1186/1471-2164-13-592López de Maturana, E., Ibáñez-Escriche, N., González-Recio, Ó., Marenne, G., Mehrban, H., Chanock, S. J., … Malats, N. (2014). Next generation modeling in GWAS: comparing different genetic architectures. Human Genetics, 133(10), 1235-1253. doi:10.1007/s00439-014-1461-1Marras, G., Rossoni, A., Schwarzenbacher, H., Biffani, S., Biscarini, F., & Nicolazzi, E. L. (2016). zanardi: an open-source pipeline for multiple-species genomic analysis of SNP array data. Animal Genetics, 48(1), 121-121. doi:10.1111/age.12485Martínez-Álvaro, M., Hernández, P., & Blasco, A. (2016). Divergent selection on intramuscular fat in rabbits: Responses to selection and genetic parameters1. Journal of Animal Science, 94(12), 4993-5003. doi:10.2527/jas.2016-0590Mateescu, R. G., Garrick, D. J., Garmyn, A. J., VanOverbeke, D. L., Mafi, G. G., & Reecy, J. M. (2015). Genetic parameters for sensory traits in longissimus muscle and their associations with tenderness, marbling score, and intramuscular fat in Angus cattle1. Journal of Animal Science, 93(1), 21-27. doi:10.2527/jas.2014-8405McLarenD.G.&SchultzC.M.(1992)Genetic Selection to Improve the Quality and Composition of Pigs. In45th Reciprocal Meat Conferences Proceedings. Colorado State University pp.115–21.Migdał, Ł., Kozioł, K., Pałka, S., Migdał, W., Otwinowska-Mindur, A., Kmiecik, M., … Bieniek, J. (2018). Single nucleotide polymorphisms within rabbits ( Oryctolagus cuniculus ) fatty acids binding protein 4 ( FABP4 ) are associated with meat quality traits. Livestock Science, 210, 21-24. doi:10.1016/j.livsci.2018.01.018Miller, I., Rogel-Gaillard, C., Spina, D., Fontanesi, L., & de Almeida, A. (2014). The Rabbit as an Experimental and Production Animal: From Genomics to Proteomics. Current Protein & Peptide Science, 15(2), 134-145. doi:10.2174/1389203715666140221115135Mortimer, S. I., van der Werf, J. H. J., Jacob, R. H., Hopkins, D. L., Pannier, L., Pearce, K. L., … Pethick, D. W. (2014). Genetic parameters for meat quality traits of Australian lamb meat. Meat Science, 96(2), 1016-1024. doi:10.1016/j.meatsci.2013.09.007Nyima, T., Müller, M., Hooiveld, G. J. E. J., Morine, M. J., & Scotti, M. (2016). Nonlinear transcriptomic response to dietary fat intake in the small intestine of C57BL/6J mice. BMC Genomics, 17(1). doi:10.1186/s12864-016-2424-9Ochsner, K. P., MacNeil, M. D., Lewis, R. M., & Spangler, M. L. (2017). Economic selection index development for Beefmaster cattle I: Terminal breeding objective1. Journal of Animal Science, 95(3), 1063-1070. doi:10.2527/jas.2016.1231Pannier, L., Gardner, G. E., O’Reilly, R. A., & Pethick, D. W. (2018). Factors affecting lamb eating quality and the potential for their integration into an MSA sheepmeat grading model. Meat Science, 144, 43-52. doi:10.1016/j.meatsci.2018.06.035Peña, F., Juárez, M., Bonvillani, A., García, P., Polvillo, O., & Domenech, V. (2011). Muscle and genotype effects on fatty acid composition of goat kid intramuscular fat. Italian Journal of Animal Science, 10(3), e40. doi:10.4081/ijas.2011.e40Pena, R., Ros-Freixedes, R., Tor, M., & Estany, J. (2016). Genetic Marker Discovery in Complex Traits: A Field Example on Fat Content and Composition in Pigs. International Journal of Molecular Sciences, 17(12), 2100. doi:10.3390/ijms17122100Purcell, 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:10.1086/519795Ros-Freixedes, R., Gol, S., Pena, R. N., Tor, M., Ibáñez-Escriche, N., Dekkers, J. C. M., & Estany, J. (2016). Genome-Wide Association Study Singles Out SCD and LEPR as the Two Main Loci Influencing Intramuscular Fat Content and Fatty Acid Composition in Duroc Pigs. PLOS ONE, 11(3), e0152496. doi:10.1371/journal.pone.0152496Sahana, G., Guldbrandtsen, B., & Lund, M. S. (2011). Genome-wide association study for calving traits in Danish and Swedish Holstein cattle. Journal of Dairy Science, 94(1), 479-486. doi:10.3168/jds.2010-3381Schmid, M., & Bennewitz, J. (2017). Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs. Archives Animal Breeding, 60(3), 335-346. doi:10.5194/aab-60-335-2017Song, H., Sun, B., Liao, Y., Xu, D., Guo, W., Wang, T., … Deng, J. (2018). GPRC5A deficiency leads to dysregulated MDM2 via activated EGFR signaling for lung tumor development. International Journal of Cancer, 144(4), 777-787. doi:10.1002/ijc.31726Spencer, C. C. A., Su, Z., Donnelly, P., & Marchini, J. (2009). Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip. PLoS Genetics, 5(5), e1000477. doi:10.1371/journal.pgen.1000477Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681-690. doi:10.1038/nrg2615Sukegawa, S., Miyake, T., Ibi, T., Takahagi, Y., Murakami, H., Morimatsu, F., & Yamada, T. (2013). Multiple marker effects of single nucleotide polymorphisms in three genes,AKIRIN2,EDG1andRPL27A, for marbling development in Japanese Black cattle. Animal Science Journal, 85(3), 193-197. doi:10.1111/asj.12108Sul, J. H., Martin, L. S., & Eskin, E. (2018). Population structure in genetic studies: Confounding factors and mixed models. PLOS Genetics, 14(12), e1007309. doi:10.1371/journal.pgen.1007309Swierczynski, J. (2014). Role of abnormal lipid metabolism in development, progression, diagnosis and therapy of pancreatic cancer. World Journal of Gastroenterology, 20(9), 2279. doi:10.3748/wjg.v20.i9.2279Toosi, A., Fernando, R. L., & Dekkers, J. C. M. (2018). Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis. Genetics Selection Evolution, 50(1). doi:10.1186/s12711-018-0402-1Uemoto, Y., Nakano, H., Kikuchi, T., Sato, S., Ishida, M., Shibata, T., … Suzuki, K. (2011). Fine mapping of porcine SSC14 QTL and SCD gene effects on fatty acid composition and melting point of fat in a Duroc purebred population. Animal Genetics, 43(2), 225-228. doi:10.1111/j.1365-2052.2011.02236.xVisscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. The American Journal of Human Genetics, 101(1), 5-22. doi:10.1016/j.ajhg.2017.06.005Vitti, J. J., Grossman, S. R., & Sabeti, P. C. (2013). Detecting Natural Selection in Genomic Data. Annual Review of Genetics, 47(1), 97-120. doi:10.1146/annurev-genet-111212-133526Wahl, S., Drong, A., Lehne, B., Loh, M., Scott, W. R., Kunze, S., … Yang, Y. (2016). Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature, 541(7635), 81-86. doi:10.1038/nature20784Wang, W., & Seale, P. (2016). Control of brown and beige fat development. Nature Reviews Molecular Cell Biology, 17(11), 691-702. doi:10.1038/nrm.2016.96Wang, B., Yang, Q., Harris, C. L., Nelson, M. L., Busboom, J. R., Zhu, M.-J., & Du, M. (2016). Nutrigenomic regulation of adipose tissue development — role of retinoic acid: A review. Meat Science, 120, 100-106. doi:10.1016/j.meatsci.2016.04.003Wang, X., Tucker, N. R., Rizki, G., Mills, R., Krijger, P. H., de Wit, E., … Boyer, L. A. (2016). Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures. eLife, 5. doi:10.7554/elife.10557Wang, J., Shi, Y., Elzo, M. A., Su, Y., Jia, X., Chen, S., & Lai, S. (2017). Myopalladin gene polymorphism is associated with rabbit meat quality traits. Italian Journal of Animal Science, 16(3), 400-404. doi:10.1080/1828051x.2017.1296333Won, S., Jung, J., Park, E., & Kim, H. (2018). Identification of genes related to intramuscular fat content of pigs using genome-wide association study. Asian-Australasian Journal of Animal Sciences, 31(2), 157-162. doi:10.5713/ajas.17.0218Zhang, H., Wang, Z., Wang, S., & Li, H. (2012). Progress of genome wide association study in domestic animals. Journal of Animal Science and Biotechnology, 3(1). doi:10.1186/2049-1891-3-26Zhang, G.-W., Gao, L., Chen, S.-Y., Zhao, X.-B., Tian, Y.-F., Wang, X., … Lai, S.-J. (2013). Single nucleotide polymorphisms in the FTO gene and their association with growth and meat quality traits in rabbits. Gene, 527(2), 553-557. doi:10.1016/j.gene.2013.06.024Zomeño, 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.2013-636

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-