Identification of functional mutations associated with environmental variance of litter size in rabbits

[EN] Background Environmental variance (V-E) is partly under genetic control and has recently been proposed as a measure of resilience. Unravelling the genetic background of the V-E of complex traits could help to improve resilience of livestock and stabilize their production across farming systems. The objective of this study was to identify genes and functional mutations associated with variation in V-E of litter size (LS) in rabbits. To achieve this, we combined the results of a genome-wide association study (GWAS) and a whole-genome sequencing (WGS) analysis using data from two divergently selected rabbit lines for high and low V-E of LS. These lines differ in terms of biomarkers of immune response and mortality. Moreover, rabbits with a lower V-E of LS were found to be more resilient to infections than animals with a higher V-E of LS. Results By using two GWAS approaches (single-marker regression and Bayesian multiple-marker regression), we identified four genomic regions associated with V-E of LS, on chromosomes 3, 7, 10, and 14. We detected 38 genes in the associated genomic regions and, using WGS, we identified 129 variants in the splicing, UTR, and coding (missense and frameshift effects) regions of 16 of these 38 genes. These genes were related to the immune system, the development of sensory structures, and stress responses. All of these variants (except one) segregated in one of the rabbit lines and were absent (n = 91) or fixed in the other one (n = 37). The fixed variants were in the HDAC9, ITGB8, MIS18A, ENSOCUG00000021276 and URB1 genes. We also identified a 1-bp deletion in the 3 ' UTR region of the HUNK gene that was fixed in the low V-E line and absent in the high V-E line. Conclusions This is the first study that combines GWAS and WGS analyses to study the genetic basis of V-E. The new candidate genes and functional mutations identified in this study suggest that the V-E of LS is under the control of functions related to the immune system, stress response, and the nervous system. These findings could also explain differences in resilience between rabbits with homogeneous and heterogeneous V-E of litter size.This study was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) with the Projects AGL2014-55921, C2-1-P and C2-2-P, and AGL2017-86083, C2-1-P and C2-2-P and the Grant RYC-2016-19764.Casto-Rebollo, C.; Argente, MJ.; García, ML.; Pena, R.; Ibáñez-Escriche, N. (2020). Identification of functional mutations associated with environmental variance of litter size in rabbits. Genetics Selection Evolution. 52(1):1-9. https://doi.org/10.1186/s12711-020-00542-wS19521Ibáñez-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.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 H, Hill W, Vereijken A, Veerkamp R. Estimation of genetic variation in residual variance in female and male broiler chickens. Animal. 2009;3:1673–80.Ros M, Sorensen D, Waagepetersen R, Dupont-Nivet M, Sancristobal M, Bonnet J, et al. Evidence for genetic control of adult weight plasticity in the snail Helix aspersa. Genetics. 2004;168:2089–97.Rönnegård L, Felleki M, Fikse WF, Mulder HA, Strandberg E. Variance component and breeding value estimation for genetic heterogeneity of residual variance in Swedish Holstein dairy cattle. J Dairy Sci. 2013;96:2627–36.Falconer DS, Mackay TFC. Introduction to quantitative genetics. 4th ed. Harlow: Prentice Hall; 1996.Garreau H, Bolet G, Larzul C, Robert-Granié 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.Formoso-Rafferty N, Cervantes I, Ibáñez-Escriche N, Gutiérrez JP. Genetic control of the environmental variance for birth weight in seven generations of a divergent selection experiment in mice. J Anim Breed Genet. 2016;133:227–37.Blasco A, Martínez-Álvaro M, García ML, Ibáñez-Escriche N, Argente MJ. Selection for environmental variance of litter size in rabbit. Genet Sel Evol. 2017;49:48.Berghof TVL, Poppe M, Mulder HA. Opportunities to improve resilience in animal breeding programs. Front Genet. 2019;9:692.Mulder HA. Genomic selection improves response to selection in resilience by exploiting genotype by environment interactions. Front Genet. 2016;7:178.Colditz IG, Hine BC. Resilience in farm animals: biology, management, breeding and implications for animal welfare. Anim Prod Sci. 2016;56:1961–83.Sell-Kubiak E, Duijvesteijn N, Lopes MS, Janss LLG, Knol EF, Bijma P, et al. Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population. BMC Genomics. 2015;16:1049.Morgante F, Sørensen P, Sorensen DA, Maltecca C, Mackay TFC. Genetic architecture of micro-environmental plasticity in Drosophila melanogaster. Sci Rep. 2015;5:9785.Wijga S, Bastiaansen JWM, Wall E, Strandberg E, de Haas Y, Giblin L, et al. Genomic associations with somatic cell score in first-lactation Holstein cows. J Dairy Sci. 2012;95:899–908.Owen JA, Punt J, Stranford SA, Jones PP, Kuby J. Immunology. 7th ed. New York: Freeman; 2013.Yang J, Loos RJF, Powel JE, Medland SE, Elizabeth K, Chasman DI, et al. FTO genotype is associated with phenotypic variability of body mass index. Nature. 2012;490:267–72.Feng Y, Wang F, Pan H, Qiu S, Lu J, Wu L, et al. Obesity-associated gene FTO rs9939609 polymorphism in relation to the risk of tuberculosis. BMC Infect Dis. 2014;14:592.Hermesch S, Dominik S, editors. Breeding focus 2014—improving resilience. Armidale: University of New England; 2014.Argente MJ, García ML, Zbyňovská K, Petruška P, Capcarová M, Blasco A. Correlated response to selection for litter size environmental variability in rabbits’ resilience. Animal. 2019;13:2348–55.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.Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.Browning BL, Browning SR. Genotype imputation with millions of reference samples. Am J Hum Genet. 2016;98:116–26.Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.Yang J, Weedon MN, Purcell S, Lettre G, Estrada K, Willer CJ, et al. Genomic inflation factors under polygenic inheritance. Eur J Hum Genet. 2011;19:807–12.Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995;11:241–7.Garrick DJ, Fernando RL. Genome-wide association studies and genomic prediction. Methods Mol Biol. 2013;1019:275–98.Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995;90:773–95.VanLiere JM, Rosenberg NA. Mathematical properties of the r2 measure of linkage disequilibrium. Theor Pop Biol. 2008;74:130–7.Elston RC. Preprocessing and quality control for whole-genome sequences from the Illumina HiSeq X platform. In: Wright MN, Gola D, Ziegler A, editors. Statistical human genetics methods in molecular biology, vol. 1666. New York: Humana Press; 2017. p. 629–47.Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler Transform. Bioinformatics. 2009;25:1754–60.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. 1000 Genome project data processing subgroup. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.Broad Institute. Picard tools. version 2.17.8; Broad Institute, GitHub repository. http://broadinstitute.github.io/picard/. Accessed 21 Feb 2018.McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.Cingolani P, Platts A, le Wang L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6:80–92.Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018;46:D754–61.Stelzer G, Rosen R, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards suite: from gene data mining to disease genome sequence analysis. Curr Protoc Bioinformatics. 2016;54:1–30.López de Maturana E, Ibáñez-Escriche N, González-Recio Ó, Marenne G, Mehrban H, Chanock SJ, et al. Next generation modelling in GWAS: comparing different genetic architectures. Hum Genet. 2014;133:1235–53.Chen Y, Meng F, Wang B, He L, Liu Y, Liu Z. Dock2 in the development of inflammation and cancer. Eur J Immunol. 2018;48:915–22.Kelly A, Gunaltay S, McEntee CP, Shuttleworth EE, Smedley C, Houston SA, et al. Human monocytes and macrophages regulate immune tolerance via integrin αvβ8–mediated TGFβ activation. J Exp Med. 2018;215:2725–36.de Zoeten EF, Wang L, Sai H, Dillmann WH, Hancock WW. Inhibition of HDAC9 increases T regulatory cell function and prevents colitis in mice. Gastroenterology. 2010;138:583–94.Zhang X, Mosser D. Macrophage activation by endogenous danger signals. J Pathol. 2009;214:161–78.Enerbäck S, Nilsson D, Edwards N, Heglind M, Alkanderi S, Ashton E, et al. Acidosis and deafness in patients with recessive mutations in FOXI1. J Am Soc Nephrol. 2018;29:1041–8.James G, Foster SR, Key B, Beverdam A. The expression pattern of EVA1C, a novel slit receptor, is consistent with an axon guidance role in the mouse nervous system. PLoS One. 2013;28:e74115.Liu A, Li JYH, Bromleigh C, Lao Z, Niswander LA, Joyner AL. FGF17b and FGF18 have different midbrain regulatory properties from FGF8b or activated FGF receptors. Development. 2003;130:6175–85.Williams CB, Phelps-Polirer K, Dingle IP, Williams CJ, Rhett MJ, Eblen ST, et al. HUNK phosphorylates EGFR to regulate breast cancer metastasis. Oncogene. 2020;39:1112–24.Costa AM, Leite M, Seruca R, Figueiredo C. Adherens junctions as targets of microorganisms: a focus on Helicobacter pylori. FEBS Lett. 2013;587:259–65

Selection for environmental variance shifted the gut microbiome composition driving animal resilience

Background. Understanding how the host’s microbiome shapes phenotypes and participates in the host response to selection is fundamental for evolutionists and animal and plant breeders. Currently, selection for resilience is considered a critical step in improving the sustainability of livestock systems. Environmental variance (VE), the withinindividual variance of a trait, has been successfully used as a proxy for animal resilience. Selection for reduced VE could effectively shift gut microbiome composition; reshape the inflammatory response, triglyceride, and cholesterol levels; and drive animal resilience. This study aimed to determine the gut microbiome composition underlying the VE of litter size (LS), for which we performed a metagenomic analysis in two rabbit populations divergently selected for low (n = 36) and high (n = 34) VE of LS. Partial least square-discriminant analysis and alpha- and beta-diversity were computed to determine the differences in gut microbiome composition among the rabbit populations. Results. We identified 116 KEGG IDs, 164 COG IDs, and 32 species with differences in abundance between the two rabbit populations studied. These variables achieved a classification performance of the VE rabbit populations of over than 80%. Compared to the high VE population, the low VE (resilient) population was characterized by an underrepresentation of Megasphaera sp., Acetatifactor muris, Bacteroidetes rodentium, Ruminococcus bromii, Bacteroidetes togonis, and Eggerthella sp. and greater abundances of Alistipes shahii, Alistipes putredinis, Odoribacter splanchnicus, Limosilactobacillus fermentum, and Sutterella, among others. Differences in abundance were also found in pathways related to biofilm formation, quorum sensing, glutamate, and amino acid aromatic metabolism. All these results suggest differences in gut immunity modulation, closely related to resilience. Conclusions. This is the first study to show that selection for VE of LS can shift the gut microbiome composition. The results revealed differences in microbiome composition related to gut immunity modulation, which could contribute to the differences in resilience among rabbit populations. The selection-driven shifts in gut microbiome composition should make a substantial contribution to the remarkable genetic response observed in the VE rabbit population

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

Five genomic regions have a major impact on fat composition in Iberian pigs

Abstract The adipogenic nature of the Iberian pig defines many quality attributes of its fresh meat and dry-cured products. The distinct varieties of Iberian pig exhibit great variability in the genetic parameters for fat deposition and composition in muscle. The aim of this work is to identify common and distinct genomic regions related to fatty acid composition in Retinto, Torbiscal, and Entrepelado Iberian varieties and their reciprocal crosses through a diallelic experiment. In this study, we performed GWAS using a high density SNP array on 382 pigs with the multimarker regression Bayes B method implemented in GenSel. A number of genomic regions showed strong associations with the percentage of saturated and unsaturated fatty acid in intramuscular fat. In particular, five regions with Bayes Factor >100 (SSC2 and SSC7) or >50 (SSC2 and SSC12) explained an important fraction of the genetic variance for miristic, palmitoleic, monounsaturated (>14%), oleic (>10%) and polyunsaturated (>5%) fatty acids. Six genes (RXRB, PSMB8, CHGA, ACACA, PLIN4, PLIN5) located in these regions have been investigated in relation to intramuscular composition variability in Iberian pigs, with two SNPs at the RXRB gene giving the most consistent results on oleic and monounsaturated fatty acid content

Efficient generation of transgenic pigs using equine infectious anaemia virus (EIAV) derived vector

AbstractTraditional methods of transgene delivery in livestock are inefficient. Recently, human immunodeficiency virus (HIV-1) based lentiviral vectors have been shown to offer an efficient transgene delivery system. We now extend this method by demonstrating efficient generation of transgenic pigs using an equine infectious anaemia virus derived vector. We used this vector to deliver a green fluorescent protein expressing transgene; 31% of injected/transferred eggs resulted in a transgenic founder animal and 95% of founder animals displayed green fluorescence. This compares favourably with results using HIV-1 based vectors, and is substantially more efficient than the standard pronuclear microinjection method, indicating that lentiviral transgene delivery may be a general tool with which to efficiently generate transgenic mammals

Quantitative trait loci analysis of a Duroc commercial population highlights differences in the genetic determination of meat quality traits at two different muscles

We performed a whole-genome scan with 110 informative microsatellites in a commercial Duroc population for which growth, fatness, carcass and meat quality phenotypes were available. Importantly, meat quality traits were recorded in two different muscles, that is, gluteus medius (GM) and longissimus thoracis et lumborum (LTL), to find out whether these traits are determined by muscle-specific genetic factors. At the whole-population level, three genome-wide QTL were identified for carcass weight (SSC7, 60 cM), meat redness (SSC13, 84 cM) and yellowness (SSC15, 108 cM). Within-family analyses allowed us to detect genome-wide significant QTL for muscle loin depth between the 3rd and 4th ribs (SSC15, 54 cM), backfat thickness (BFT) in vivo (SSC10, 58 cM), ham weight (SSC9, 69 cM), carcass weight (SSC7, 60 cM; SSC9, 68 cM), BFT on the last rib (SSC11, 48 cM) and GM redness (SSC8, 85 cM; SSC13, 84 cM). Interestingly, there was low positional concordance between meat quality QTL maps obtained for GM and LTL. As a matter of fact, the three genome-wide significant QTL for colour traits (SSC8, SSC13 and SSC15) that we detected in our study were all GM specific. This result suggests that QTL effects might be modulated to a certain extent by genetic and environmental factors linked to muscle function and anatomical location. © 2012 The Authors, Animal Genetics © 2012 Stichting International Foundation for Animal Genetics.This study was funded with grants AGL2002-04271-C03 and AGL2007-66707-C02-01 (Ministerio de Ciencia e Innovación, Spain).Peer Reviewe

Porcine intramuscular fat content and composition are regulated by quantitative trait loci with muscle-specific effects

Intramuscular fat (IMF) storage is a biological process with a strong impact on nutritional and technological properties of meat and also with rel- evant consequences on human health. The genetic ar- chitecture of IMF content and composition phenotypes has been thoroughly studied in pigs through the iden- tification of QTL and the estimation of genetic param- eters. A question that has not been elucidated yet is if the genetic determinants of IMF-related phenotypes are muscle specific or, conversely, have broad effects on the whole skeletal muscle compartment. We have ad- dressed this question by generating lipid QTL maps for 2 muscles with a high commercial value, gluteus medius (GM) and longissimus thoracis et lumborum (LTL), in a Duroc commercial population (n = 350). Our data support a lack of concordance between the GM and LTL QTL maps, suggesting that the effects of poly- morphisms influencing IMF, cholesterol, and fatty acid contents are modulated to some extent by complex spa- tial factors related to muscle location, metabolism, and function. These results have important implications on the implementation of genomic selection schemes aimed to improve the lipid profile of swine meat. © 2011 American Society of Animal Science, All rights reserved.This study was funded with grants AGL2007-66707-C02-01 and AGL2010-22208-C02-01 (Ministerio de Ciencia e Innovación, Spain). D. Gallardo and A. Cánovas were supported by fellowships from the Universitat Autònoma de Barcelona and the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria of Spain, respectively.Peer Reviewe

Carcass lean-yield effects on the fatty acid and amino acid composition of Duroc pork and its technological quality after vacuum-ageing

Eighty purebred Duroc castrated male pigs slaughtered at 210 days of age were used to evaluate the effect of lean yield (European Union carcass grading based on lean content; or R, O and P classes) on the fatty acid and amino acid composition of raw pork (Day 1 post-mortem), and technological meat quality after vacuum aging up to 4, 6 and 8 days. A strong relationship between slaughter weight and carcass lean-yield was observed. Carcasses graded as having a lower lean yield were fatter with higher intramuscular fat concentration, and differences in proportions of fatty acids with increased monounsaturated fatty acid and decreased polyunsaturated fatty acid percentage, but without adverse effect on ultimate pH, drip loss or colour attributes. There were no effects of carcass lean-yield on amino acid composition of raw pork, with valine being the limiting amino acid relative to lysine by ~30–35%. Vacuum aging did not reduce the shear force of raw pork, which may not be indicative of cooked pork response. The lipid oxidation had an inverse relationship with the polyunsaturated fatty acid content of each pork class, and it did not increase due to vacuum aging up to 8 days. Meat fatness did not affect its amino acid balance and technological quality (colour, drip loss, shear force and lipid stability) but modified intramuscular fat composition.The authors thank T. Giró, A. Ñaco and L. Villagrasa for their technical assistance during collection and analyses of samples. This work was supported by the Ministry of Economy and Competitiveness of Spain and the European Union Regional Development Funds (AGL2012–33529 and AGL2015–65846-R)