¿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

Innovación en al aprendizaje estadístico: inferencia Bayesiana amigable en el lenguaje de programación R

Este trabajo describe la implementación del programa runRabbit como herramienta de aprendizaje innovadora para enseñar inferencia Bayesiana aplicada a la genética cuantitativa. El programa, un software didáctico e interactivo diseñado con el lenguaje R, fue utilizado por estudiantes del Máster en Mejora Genética Animal de la Universidad Politécnica de Valencia para resolver un problema estadístico habitual. RunRabbit ayudó a los estudiantes a comprender mejor la materia, que expresaron una gran satisfacción con el programa y el deseo de utilizar la inferencia Bayesiana para resolver nuevos desafíos. A pesar del pequeño tamaño muestral de este estudio preliminar, los resultados sugieren que runRabbit es una herramienta de aprendizaje efectiva para mejorar la comprensión teórica de la inferencia Bayesiana. Para corroborar estos resultados, runRabbit será probado en grupos más grandes en futuros estudios. Su potencial se extiende más allá de la genética cuantitativa y podría aplicarse a cualquier ámbito que utilice estadística bayesiana

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

Genetic parameters and correlations of related feed efficiency, growth, and carcass traits in Hanwoo beef cattle

[EN] Objective: This study aimed to estimate the genetic parameters and genetic correlations for related feed efficiency, growth, and carcass traits in Hanwoo cattle. Methods: Phenotypic data from 15,279 animals born between 1989 and 2015 were con & shy; sidered. The related feed efficiency traits considered were Kleiber ratio (KR) and relative growth rate (RGR). Carcass traits analyzed were backfat thickness (BT), carcass weight, eye muscle area, and marbling score. Growth traits were assessed by the average daily gain (ADG), metabolic body weight (MBW) at mid & shy;test age from 6 to 24 months, and yearling weight (YW). Variance and covariance components were estimated using res & shy; tricted maximum likelihood using nine multi & shy;trait animal models. Results: The heritability estimates for related feed efficiency (0.28 +/- 0.04 for KR and RGR) and growth traits (0.26 +/- 0.02 to 0.33 +/- 0.04) were moderate, but the carcass traits tended to be higher (0.38 +/- 0.04 to 0.61 +/- 0.06). The related feed efficiency traits were positively gene & shy; tically correlated with all the carcass traits (0.37 +/- 0.09 to 0.47 +/- 0.07 for KR, and 0.14 +/- 0.09 to 0.37 +/- 0.09 for RGR), except for BT, which showed null to weak correlation. Conversely, the genetic correlations of RGR with MBW (-0.36 +/- 0.08) and YW (-0.30 +/- 0.08) were negative, and those of KR with MBW and YW were close to zero, whereas the genetic correlations of ADG with RGR (0.40 +/- 0.08) and KR (0.70 +/- 0.05) were positive and relatively moderate to high. The genetic (0.92 +/- 0.02) correlations between KR and RGR were very high. Conclusion: Sufficient genetic variability and heritability were observed for traits of interest. Moreover, the inclusion of KR and/or RGR in Hanwoo cattle breeding programs could improve the feed efficiency without producing any unfavorable effects on the carcass traits.We are grateful to all the staff of the Korean Hanwoo Improvement Center of the National Agricultural Cooperative Federation for supplying the Hanwoo cattle data. This study was supported by grants from the IPET Program (No. 20093068) , Ministry of Agriculture, Food and Rural Affairs, Republic of Korea, and Hankyong National UniversityMehrban, H.; Naserkheil, M.; Lee, DH.; Ibáñez-Escriche, N. (2021). Genetic parameters and correlations of related feed efficiency, growth, and carcass traits in Hanwoo beef cattle. Animal Bioscience (Online). 34(5):824-832. https://doi.org/10.5713/ajas.20.0135S82483234

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

[EN] The implementation of Student t mixed models in animal breeding has been suggested as a useful statistical tool to effectively mute the impact of preferential treatment or other sources of outliers in field data. Nevertheless, these additional sources of variation are undeclared and we do not know whether a Student t mixed model is required or if a standard, and less parameterized, Gaussian mixed model would be sufficient to serve the intended purpose. Within this context, our aim was to develop the Bayes factor between two nested models that only differed in a bounded variable in order to easily compare a Student t and a Gaussian mixed model. It is important to highlight that the Student t density converges to a Gaussian process when degrees of freedom tend to infinity. The two models can then be viewed as nested models that differ in terms of degrees of freedom. The Bayes factor can be easily calculated from the output of a Markov chain Monte Carlo sampling of the complex model (Student t mixed model). The performance of this Bayes factor was tested under simulation and on a real dataset, using the deviation information criterion (DIC) as the standard reference criterion. The two statistical tools showed similar trends along the parameter space, although the Bayes factor appeared to be the more conservative. There was considerable evidence favoring the Student t mixed model for data sets simulated under Student t processes with limited degrees of freedom, and moderate advantages associated with using the Gaussian mixed model when working with datasets simulated with 50 or more degrees of freedom. For the analysis of real data (weight of Pietrain pigs at six months), both the Bayes factor and DIC slightly favored the Student t mixed model, with there being a reduced incidence of outlier individuals in this population.The authors are indebted to Dr. J.L. Noguera and COPAGA for field data on pig weight at six months, and to Dr. J. Piedrafita and Dr. G. Caja for providingadditional field data sets during preliminary tests of the Bayes factor. The research contract of J. Casellas was partially financed by Spain s Ministerio de Educación y Ciencia (Programa Juan de la Cierva).Casellas, J.; Ibáñez-Escriche, N.; Garcia-Cortes, L.; Varona, L. (2008). Bayes factor between Student t and Gaussian mixed models within an animal breeding context. Genetics Selection Evolution. 40(4):395-413. https://doi.org/10.1051/gse:2008007S39541340

Efecto de la línea genética y de dietas enriquecidas en ácido oleico sobre los parámetros productivos del cerdo Ibérico

[ES] La genética y la alimentación son dos de los principales factores que afectan a la producción del cerdo Ibérico, así como a la calidad de su carne y de sus productos cárnicos. El objetivo de este estudio fue evaluar el efecto de la línea genética y de la alimentación durante el cebo, así como su interacción, sobre los parámetros productivos del cerdo Ibérico. Para ello, se utilizaron un total de 96 cerdos Ibéricos machos y castrados, que fueron divididos en 12 lotes (n=8) siguiendo un diseño factorial 4×3 con dos líneas genéticas (Retinto, RR, y Torbiscal, TT) y sus cruces recíprocos (R×T and T×R). Los cerdos fueron cebados en intensivo con tres tipos de piensos enriquecidos con tres niveles de ácido oleico (bajo, L, medio, M y alto, H). Los resultados mostraron un menor (p<0.05) consumo diario y una menor ganancia media diaria en los cerdos RR que en los RT, TR y TT. Así mismo, se observó un menor peso final en los cerdos RR que en los otros grupos genéticos. No se observaron diferencias en el índice de conversión entre los grupos estudiados. Con respecto al efecto de la dieta, los cerdos del lote L presentaron menor consumo diario e índice de conversión que los H y M. El tipo de dieta no afectó a la ganancia media diaria ni al peso final. Por ello, puede concluirse que los parámetros productivos del cruce Retinto×Torbiscal son similares a los de la línea pura Torbiscal, mejorando significativamente los de la línea pura Retinto.[EN] As it is well known, genetic and feeding are two fundamental factors affecting the Iberian pig production, meat and meat product quality. The objective of the present study was to evaluate the effect of genetic and nutrition factors, and their interaction, on productive parameters of Iberian pigs during the final fattening period prior to slaughter. For this purpose, a total of 96 castrated male Iberian pigs were allotted into twelve groups (n=8) following a 4×3 factorial design with two Iberian genetic lines (Retinto, RR, and Torbiscal, TT) and their reciprocal crosses (R×T and T×R) fed in intensive conditions with three different oleic acid enriched diets (low, L, medium, M and high, H levels). The results showed a significant (P<0.05) lower daily intake, average daily gain (ADG) and final weight in RR compared to RT, TR and TT Iberian pigs. Related to diet, L pigs exhibited lower (P<0.05) daily intake and feed conversion ratio than H and M ones, without differences in ADG and final weight. So, it can be concluded that productive parameters of Retinto×Torbiscal crosses are close to those of pure Torbiscal line, improving those of pure Retinto line.The research was supported by the National Institute for Agronomic Research (INIA) assigned to the State Secretariat of Research, Development and Innovation of the Ministry of Economy and Competitiveness of Spain (Project RTA2012-00054-C02).Tejeda, J.; Carrapiso, A.; Noguera, J.; Ibáñez-Escriche, N.; Gonzalez, E. (2018). Productive parameters of Iberian pig as affected by genetic line and oleic acid enriched diets. Archivos de Zootecnia. Sup. 1:41-43. https://doi.org/10.21071/az.v67iSupplement.3569S4143Sup.

Efecto de la genética y de la dieta sobre el lomo fresco del cerdo Ibérico (m. Longissimus dorsi)

[EN] This study was aimed to evaluate the effect of different genetic lines, diets and their interaction on the weight, yield, intramuscular fat content (IMF) and colour of loin (m. Longissimus dorsi) of Iberian pig. Ninety-six castrated male Iberian pigs were allotted into twelve groups (n=8) following a 4×3 factorial design with two Iberian genetic lines (Retinto, RR, and Torbiscal, TT) and their reciprocal crosses (R×T and T×R) fed in intensive conditions with three different oleic acid enriched diets (low, L, medium, M and high, H levels). Regarding loin weight and yield a significant effect of genetic line was observed due to the higher (P<0.05) scores in TT line compared to RR line, with intermediate levels in R×T and T×R pigs. However, when physicochemical parameters are evaluated, as fat content and meat colour, RR pigs exhibited significantly higher intramuscular fat (IMF) content, a* (redness) and b* (yellowness) values than TT, R×T and T×R pigs. None of the other factors studied, diet and genetic×diet interaction, showed significant effect on weight, yield, IMF and colour of loin.[ES] El objetivo de este estudio fue evaluar el efecto de la línea genética, la dieta y su interacción sobre el peso, rendimiento, contenido en grasa intramuscular y color del lomo (m. Longissimus dorsi) del cerdo Ibérico. Se utilizaron un total de 96 cerdos Ibéricos machos y castrados, que fueron divididos en 12 lotes (n=8) siguiendo un diseño factorial 4×3, con dos líneas genéticas (Retinto, RR, y Torbiscal, TT) y sus cruces recíprocos (R×T y T×R). Los cerdos fueron cebados en intensivo con tres tipos de piensos enriquecidos con tres niveles de ácido oleico (bajo, L, medio, M y alto, H). Se observó un efecto significativo (P<0.05) de la línea genética sobre el peso y el rendimiento del lomo, con valores superiores en ambos parámetros en la línea TT que en la línea RR, presentando valores intermedios los lotes R×T y T×R. Sin embargo, en relación a los parámetros físico-químicos, los lomos de los animales pertenecientes a la línea RR presentaron mayor (p<0.05) contenido en grasa intramuscular (IMF) y valores más elevados de a* (rojo) y b* (amarillo) que los cerdos TT, R×T y T×R. Ni la dieta, ni la interacción dieta×genética afectaron a los parámetros analizados en este estudio sobre el lomo del cerdo Ibérico.The research was supported by the National Institute for Agronomic Research (INIA) assigned to the State Secretariat of Research, Development and Innovation of the Ministry of Economy and Competitiveness of Spain (Project RTA2012-00054-C02).Gonzalez, E.; Carrapiso, A.; Noguera, J.; Ibáñez-Escriche, N.; Tejeda, J. (2018). Effect of genetic and diet on Iberian pig fresh loin (m. Longissimus dorsi). Archivos de Zootecnia. Sup. 1:185-187. https://doi.org/10.21071/az.v67iSupplement.3600S185187Sup.

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

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

Genotype Imputation To Improve the Cost-Efficiency of Genomic Selection in Rabbits

[EN] Genomic selection uses genetic marker information to predict genomic breeding values (gEBVs), and can be a suitable tool for selecting low-hereditability traits such as litter size in rabbits. However, genotyping costs in rabbits are still too high to enable genomic prediction in selective breeding programs. One method for decreasing genotyping costs is the genotype imputation, where parents are genotyped at high SNP-density (HD) and the progeny are genotyped at lower SNP-density, followed by imputation to HD. The aim of this study was to disentangle the best imputation strategies with a trade-off between genotyping costs and the accuracy of breeding values for litter size. A selection process, mimicking a commercial breeding rabbit selection program for litter size, was simulated. Two different Quantitative Trait Nucleotide (QTN) models (QTN_5 and QTN_44) were generated 36 times each. From these simulations, seven different scenarios (S1-S7) and a further replicate of the third scenario (S3_A) were created. Scenarios consist of a different combination of genotyping strategies. In these scenarios, ancestors and progeny were genotyped with a mix of three different platforms, containing 200,000, 60,000, and 600 SNPs under a cost of EUR 100, 50 and 11 per animal, respectively. Imputation accuracy (IA) was measured as a Pearson's correlation between true genotype and imputed genotype, whilst the accuracy of gEBVs was the correlation between true breeding value and the estimated one. The relationships between IA, the accuracy of gEBVs, genotyping costs, and response to selection were examined under each QTN model. QTN_44 presented better performance, according to the results of genomic prediction, but the same ranks between scenarios remained in both QTN models. The highest IA (0.99) and the accuracy of gEBVs (0.26; QTN_44, and 0.228; QTN_5) were observed in S1 where all ancestors were genotyped at HD and progeny at medium SNP-density (MD). Nevertheless, this was the most expensive scenario compared to the others in which the progenies were genotyped at low SNP-density (LD). Scenarios with low average costs presented low IA, particularly when female ancestors were genotyped at LD (S5) or non-genotyped (S7). The S3_A, imputing whole-genomes, had the lowest accuracy of gEBVs (0.09), even worse than Best Linear Unbiased Prediction (BLUP). The best trade-off between genotyping costs and the accuracy of gEBVs (0.234; QTN_44 and 0.199) was in S6, in which dams were genotyped with MD whilst grand-dams were non-genotyped. However, this relationship would depend mainly on the distribution of QTN and SNP across the genome, suggesting further studies on the characterization of the rabbit genome in the Spanish lines. In summary, genomic selection with genotype imputation is feasible in the rabbit industry, considering only genotyping strategies with suitable IA, accuracy of gEBVs, genotyping costs, and response to selection.This research study was funded by AGL2017-86083-C2-P1 from "Plan Nacional de Investigacion Cientifica" of Spain-Project I+D. B. Samuel Sosa Madrid was supported by FPI grant, number BES-2015-074194, from "Ministerio de Ciencia e Innovacion".Mancin, E.; Sosa-Madrid, BS.; Blasco Mateu, A.; Ibáñez-Escriche, N. (2021). Genotype Imputation To Improve the Cost-Efficiency of Genomic Selection in Rabbits. Animals. 11(3):1-16. https://doi.org/10.3390/ani1103080311611

Genetic Variance Estimation over Time in Broiler Breeding Programmes for Growth and Reproductive Traits

[EN] Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a "double trait covariances" analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999-2001 to 2020-2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance.BSSM is supported by a Margarita Salas grant from funds of the European Union (NextGenerationEU) that are regulated by the Ministerio de Universidades of Spain. The research project was supported by funding from a BBSRC grant (BB/T005408/1).Sosa-Madrid, BS.; Maniatis, G.; Ibáñez-Escriche, N.; Avendaño, S.; Kranis, A. (2023). Genetic Variance Estimation over Time in Broiler Breeding Programmes for Growth and Reproductive Traits. Animals. 13(21). https://doi.org/10.3390/ani13213306132