Characterization of Langmuir and Langmuir-Blodgett films of a thiomacrocyclic ionophore by surface pressure and AFM

Preprin

Implementation and effectiveness of the HACCP and pre-requisites in food establishments

[EN] The aim of this paper was to identify the most important weaknesses in the implementation and effectiveness of the pre-requisites and HACCP found in food establishments. To cover these objectives, official control audits of the manuals and their implementation in 1350 small and 66 medium size organizations: restaurants, hotels and cafeterias in one area of the Valencian region (Spain) were carried out from 2007 to 2010. The microbiological quality of 1054 ready-to-consume dishes was also evaluated as an indicator of the effectiveness of the control at Critical Control Points. The results showed that the main deficiencies in the implementation of the pre-requisites and HACCP were found in conditions and structural design followed by hygiene & cleaning. Moreover, the analysis of Listeria monocytogenes in dishes at the time of consumption shows that 99.6% were of good microbiological quality. This indicates that in relation to this hazard, the implementation of safety management systems in the majority of the food establishments was effective. These results demonstrate the crucial role played by official control to ensure the welfare of consumers and how it facilitates continuous improvement in the safety management of these businesses. © 2011 Elsevier Ltd.Doménech Antich, EM.; Amorós, J.; Pérez Gonzalvo, M.; Escriche Roberto, MI. (2011). Implementation and effectiveness of the HACCP and pre-requisites in food establishments. Food Control. 22(8):1419-1423. doi:10.1016/j.foodcont.2011.03.001S1419142322

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

Dificultades algebraicas en la resolución de problemas por transferencia

La enseñanza de resolución de problemas en ciencias y matemáticas se realiza en general mediante estrategias de transferencia (transfer): se resuelve y explica un conjunto de problemas y después se pide a los estudiantes que resuelvan otros problemas análogos a los ejemplos trabajados. Los profesores de Secundaria con frecuencia asumen que las relaciones analógicas entre los problemas resueltos y los problemas propuestos son sencillas de comprender y establecer, y atribuyen el fracaso a la falta de dominio de los procedimientos matemáticos de resolución. En este trabajo se realiza un experimento para probar si esta atribución causal es adecuada o no. Los resultados demuestran que la causa principal de las dificultades debe tener su origen en la construcción de un modelo de la situación y/o de un modelo del problema, adecuados

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

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

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

Practical procedure for discriminating monofloral honey with a broad pollen profile variability using an electronic tongue

Colour and floral origin are key parameters that may influence the honey market. Monofloral light honey are more demanded by consumers, mainly due to their flavour, being more valuable for producers due to their higher price when compared to darker honey. The latter usually have a high anti-oxidant content that increases their healthy potential. This work showed that it is possible to correctly classify monofloral honey with a high variability in floral origin with a potentiometric electronic tongue after making a preliminary selection of honey according their colours: white, amber and dark honey. The results showed that the device had a very satisfactory sensitivity towards floral origin (Castanea sp., Echium sp., Erica sp., Lavandula sp., Prunus sp. and Rubus sp.), allowing a leave-one-out cross validation correct classification of 100%. Therefore, the E-tongue shows potential to be used at analytical laboratory level for honey samples classification according to market and quality parameters, as a practical tool for ensuring monofloral honey authenticity

Comparison of conventional BLUP and single-step genomic BLUP evaluations for yearling weight and carcass traits in Hanwoo beef cattle using single trait and multi-trait models

[EN] Hanwoo, an important indigenous and popular breed of beef cattle in Korea, shows rapid growth and has high meat quality. Its yearling weight (YW) and carcass traits (backfat thickness, carcass weight- CW, eye muscle area, and marbling score) are economically important for selection of young and proven bulls. However, measuring carcass traits is difficult and expensive, and can only be performed postmortem. Genomic selection has become an appealing procedure for genetic evaluation of these traits (by inclusion of the genomic data) along with the possibility of multi-trait analysis. The aim of this study was to compare conventional best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP) methods, using both single-trait (ST-BLUP, ST-ssGBLUP) and multi-trait (MT-BLUP, MT-ssGBLUP) models to investigate the improvement of breeding-value accuracy for carcass traits and YW. The data comprised of 15,279 phenotypic records for YW and 5,824 records for carcass traits, and 1,541 genotyped animals for 34,479 single-nucleotide polymorphisms. Accuracy for each trait and model was estimated only for genotyped animals by five-fold cross-validation. ssGBLUP models (ST-ssGBLUP and MT-ssGBLUP) showed ~19% and ~36% greater accuracy than conventional BLUP models (ST-BLUP and MT-BLUP) for YW and carcass traits, respectively. Within ssGBLUP models, the accuracy of the genomically estimated breeding value for CW increased (19%) when ST-ssGBLUP was replaced with the MT-ssGBLUP model, as the inclusion of YW in the analysis led to a strong genetic correlation with CW (0.76). For backfat thickness, eye muscle area, and marbling score, ST- and MT-ssGBLUP models yielded similar accuracy. Thus, combining pedigree and genomic data via the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions, especially among young animals, for ongoing Hanwoo cattle breeding programs. MT-ssGBLUP is highly recommended when phenotypic records are limited for one of the two highly correlated genetic traits.Mehrban, H.; Lee, DH.; Naserkheil, M.; Moradi, MH.; Ibáñez-Escriche, N. (2019). Comparison of conventional BLUP and single-step genomic BLUP evaluations for yearling weight and carcass traits in Hanwoo beef cattle using single trait and multi-trait models. PLoS ONE. 1-13. https://doi.org/10.1371/journal.pone.0223352S113Choi, T. J., Alam, M., Cho, C. I., Lee, J. G., Park, B., Kim, S., … Roh, S. H. (2015). Genetic parameters for yearling weight, carcass traits, and primal-cut yields of Hanwoo cattle1. Journal of Animal Science, 93(4), 1511-1521. doi:10.2527/jas.2014-7953Choy, Y. H., Park, B. H., Choi, T. J., Choi, J. G., Cho, K. H., Lee, S. S., … Kim, H. S. (2012). Estimation of Relative Economic Weights of Hanwoo Carcass Traits Based on Carcass Market Price. Asian-Australasian Journal of Animal Sciences, 25(12), 1667-1673. doi:10.5713/ajas.2012.12397Joo, S.-T., Hwang, Y.-H., & Frank, D. (2017). Characteristics of Hanwoo cattle and health implications of consuming highly marbled Hanwoo beef. Meat Science, 132, 45-51. doi:10.1016/j.meatsci.2017.04.262Park, B., Choi, T., Kim, S., & Oh, S.-H. (2013). National Genetic Evaluation (System) of Hanwoo (Korean Native Cattle). Asian-Australasian Journal of Animal Sciences, 26(2), 151-156. doi:10.5713/ajas.2012.12439Chen, L., Vinsky, M., & Li, C. (2014). Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle. Animal Genetics, 46(1), 55-59. doi:10.1111/age.12238Rolf, M. M., Garrick, D. J., Fountain, T., Ramey, H. R., Weaber, R. L., Decker, J. E., … Taylor, J. F. (2015). Comparison of Bayesian models to estimate direct genomic values in multi-breed commercial beef cattle. Genetics Selection Evolution, 47(1). doi:10.1186/s12711-015-0106-8Mehrban, H., Lee, D. H., Moradi, M. H., IlCho, C., Naserkheil, M., & Ibáñez-Escriche, N. (2017). Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture. Genetics Selection Evolution, 49(1). doi:10.1186/s12711-016-0283-0Misztal, I., Aggrey, S. E., & Muir, W. M. (2013). Experiences with a single-step genome evaluation. Poultry Science, 92(9), 2530-2534. doi:10.3382/ps.2012-02739Hayes, B. J., Bowman, P. J., Chamberlain, A. J., & Goddard, M. E. (2009). Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal of Dairy Science, 92(2), 433-443. doi:10.3168/jds.2008-1646VanRaden, P. M., Van Tassell, C. P., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F., & Schenkel, F. S. (2009). Invited Review: Reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science, 92(1), 16-24. doi:10.3168/jds.2008-1514Misztal, I., Legarra, A., & Aguilar, I. (2009). Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. Journal of Dairy Science, 92(9), 4648-4655. doi:10.3168/jds.2009-2064Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 92(9), 4656-4663. doi:10.3168/jds.2009-2061Aguilar, I., Misztal, I., Johnson, D. L., Legarra, A., Tsuruta, S., & Lawlor, T. J. (2010). Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 93(2), 743-752. doi:10.3168/jds.2009-2730Ibáñez-Escriche, N., Forni, S., Noguera, J. L., & Varona, L. (2014). Genomic information in pig breeding: Science meets industry needs. Livestock Science, 166, 94-100. doi:10.1016/j.livsci.2014.05.020Onogi, A., Ogino, A., Komatsu, T., Shoji, N., Simizu, K., Kurogi, K., … Iwata, H. (2014). Genomic prediction in Japanese Black cattle: Application of a single-step approach to beef cattle. Journal of Animal Science, 92(5), 1931-1938. doi:10.2527/jas.2014-7168Gordo, D. G. M., Espigolan, R., Tonussi, R. L., Júnior, G. A. F., Bresolin, T., Magalhães, A. F. B., … de Albuquerque, L. G. (2016). Genetic parameter estimates for carcass traits and visual scores including or not genomic information1. Journal of Animal Science, 94(5), 1821-1826. doi:10.2527/jas.2015-0134Lee, J., Cheng, H., Garrick, D., Golden, B., Dekkers, J., Park, K., … Fernando, R. (2017). Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle. Genetics Selection Evolution, 49(1). doi:10.1186/s12711-016-0279-9Tsuruta, S., Misztal, I., Aguilar, I., & Lawlor, T. J. (2011). Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Journal of Dairy Science, 94(8), 4198-4204. doi:10.3168/jds.2011-4256Forni, S., Aguilar, I., & Misztal, I. (2011). Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution, 43(1). doi:10.1186/1297-9686-43-1Xiang, T., Nielsen, B., Su, G., Legarra, A., & Christensen, O. F. (2016). Application of single-step genomic evaluation for crossbred performance in pig1. Journal of Animal Science, 94(3), 936-948. doi:10.2527/jas.2015-9930Chen, C. Y., Misztal, I., Aguilar, I., Legarra, A., & Muir, W. M. (2011). Effect of different genomic relationship matrices on accuracy and scale1. Journal of Animal Science, 89(9), 2673-2679. doi:10.2527/jas.2010-3555Guo, G., Zhao, F., Wang, Y., Zhang, Y., Du, L., & Su, G. (2014). Comparison of single-trait and multiple-trait genomic prediction models. BMC Genetics, 15(1), 30. doi:10.1186/1471-2156-15-30Jia, Y., & Jannink, J.-L. (2012). Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy. Genetics, 192(4), 1513-1522. doi:10.1534/genetics.112.144246Browning, S. R., & Browning, B. L. (2007). Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering. The American Journal of Human Genetics, 81(5), 1084-1097. doi:10.1086/521987Aguilar, I., Misztal, I., Legarra, A., & Tsuruta, S. (2011). Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. Journal of Animal Breeding and Genetics, 128(6), 422-428. doi:10.1111/j.1439-0388.2010.00912.xVanRaden, P. M. (2008). Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 91(11), 4414-4423. doi:10.3168/jds.2007-0980Saatchi, M., McClure, M. C., McKay, S. D., Rolf, M. M., Kim, J., Decker, J. E., … Taylor, J. F. (2011). Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genetics Selection Evolution, 43(1), 40. doi:10.1186/1297-9686-43-40Lee, S. H., Choi, B. H., Lim, D., Gondro, C., Cho, Y. M., Dang, C. G., … Hong, S. K. (2013). Genome-Wide Association Study Identifies Major Loci for Carcass Weight on BTA14 in Hanwoo (Korean Cattle). PLoS ONE, 8(10), e74677. doi:10.1371/journal.pone.0074677Gordo, D. G. M., Espigolan, R., Bresolin, T., Fernandes Júnior, G. A., Magalhães, A. F. B., Braz, C. U., … Albuquerque, L. G. (2018). Genetic analysis of carcass and meat quality traits in Nelore cattle1. Journal of Animal Science, 96(9), 3558-3564. doi:10.1093/jas/sky228Visscher, P. M., Medland, S. E., Ferreira, M. A. R., Morley, K. I., Zhu, G., Cornes, B. K., … Martin, N. G. (2006). Assumption-Free Estimation of Heritability from Genome-Wide Identity-by-Descent Sharing between Full Siblings. PLoS Genetics, 2(3), e41. doi:10.1371/journal.pgen.0020041Christensen, O. F., Madsen, P., Nielsen, B., Ostersen, T., & Su, G. (2012). Single-step methods for genomic evaluation in pigs. Animal, 6(10), 1565-1571. doi:10.1017/s1751731112000742Aguilar, I., Misztal, I., Tsuruta, S., Wiggans, G. R., & Lawlor, T. J. (2011). Multiple trait genomic evaluation of conception rate in Holsteins. Journal of Dairy Science, 94(5), 2621-2624. doi:10.3168/jds.2010-3893Baloche, G., Legarra, A., Sallé, G., Larroque, H., Astruc, J.-M., Robert-Granié, C., & Barillet, F. (2014). Assessment of accuracy of genomic prediction for French Lacaune dairy sheep. Journal of Dairy Science, 97(2), 1107-1116. doi:10.3168/jds.2013-7135Lourenco, D. A. L., Tsuruta, S., Fragomeni, B. O., Masuda, Y., Aguilar, I., Legarra, A., … Misztal, I. (2015). Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus1. Journal of Animal Science, 93(6), 2653-2662. doi:10.2527/jas.2014-8836Calus, M. P., & Veerkamp, R. F. (2011). Accuracy of multi-trait genomic selection using different methods. Genetics Selection Evolution, 43(1). doi:10.1186/1297-9686-43-26Jiang, J., Zhang, Q., Ma, L., Li, J., Wang, Z., & Liu, J.-F. (2015). Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model. Heredity, 115(1), 29-36. doi:10.1038/hdy.2015.9Ismael, A., Løvendahl, P., Fogh, A., Lund, M. S., & Su, G. (2017). Improving genetic evaluation using a multitrait single-step genomic model for ability to resume cycling after calving, measured by activity tags in Holstein cows. Journal of Dairy Science, 100(10), 8188-8196. doi:10.3168/jds.2017-13122Cheng, H., Kizilkaya, K., Zeng, J., Garrick, D., & Fernando, R. (2018). Genomic Prediction from Multiple-Trait Bayesian Regression Methods Using Mixture Priors. Genetics, 209(1), 89-103. doi:10.1534/genetics.118.300650Kemper, K. E., Bowman, P. J., Hayes, B. J., Visscher, P. M., & Goddard, M. E. (2018). A multi-trait Bayesian method for mapping QTL and genomic prediction. Genetics Selection Evolution, 50(1). doi:10.1186/s12711-018-0377-yGoddard, M. E., & Hayes, B. J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics, 10(6), 381-391. doi:10.1038/nrg257