Aken, 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/baw093
Aloulou, 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_4
Amisten, 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.ej16-0338
[+]
Aken, 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/baw093
Aloulou, 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_4
Amisten, 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.ej16-0338
Astle, W., & Balding, D. J. (2009). Population Structure and Cryptic Relatedness in Genetic Association Studies. Statistical Science, 24(4). doi:10.1214/09-sts307
Aulchenko, 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/btm108
Barrett, 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/bth457
Beissinger, 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/s12711-015-0105-9
Blasco, 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_4
Browning, 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.2015.11.020
Carneiro, 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/msr003
Carneiro, 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.1253714
Cesar, 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:10.1186/1471-2156-15-39
Chaves, 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.2011.05.018
Chen, 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:10.1086/521580
Claire 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:10.1186/2049-1891-4-43
Do, 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.2014.00307
Do, 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/s41598-018-31427-0
Fan, 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.03
Gao, 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.2007.03.026
Garrick, D. J. (2011). The nature, scope and impact of genomic prediction in beef cattle in the United States. Genetics Selection Evolution, 43(1). doi:10.1186/1297-9686-43-17
Garrick, 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_11
Gotoh, 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.2014-0033
Gotoh, 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.18.0333
Hocquette, 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/s1751731109991091
Hopkins, 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.2011.09.036
Jiao, 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/bts251
Jin, 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.2017.08.142
Kass, R. E., & Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773-795. doi:10.1080/01621459.1995.10476572
Kim, 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.2015-8879
Kuleshov, 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/gkw377
Lander, 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/ng1195-241
Lionikas, 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:10.1186/1471-2164-13-592
López 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/s00439-014-1461-1
Marras, 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.12485
Martínez-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.2016-0590
Mateescu, 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.2014-8405
McLarenD.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.2018.01.018
Miller, 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:10.2174/1389203715666140221115135
Mortimer, 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.2013.09.007
Nyima, 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/s12864-016-2424-9
Ochsner, 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.2016.1231
Pannier, 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.2018.06.035
Peña, 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.e40
Pena, 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/ijms17122100
Purcell, 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:10.1086/519795
Ros-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.0152496
Sahana, 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.2010-3381
Schmid, 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-60-335-2017
Song, 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.31726
Spencer, 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.1000477
Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681-690. doi:10.1038/nrg2615
Sukegawa, 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.12108
Sul, 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.1007309
Swierczynski, 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.i9.2279
Toosi, 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/s12711-018-0402-1
Uemoto, 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.x
Visscher, 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.2017.06.005
Vitti, 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-111212-133526
Wahl, 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/nature20784
Wang, W., & Seale, P. (2016). Control of brown and beige fat development. Nature Reviews Molecular Cell Biology, 17(11), 691-702. doi:10.1038/nrm.2016.96
Wang, 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.2016.04.003
Wang, 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.10557
Wang, 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.2017.1296333
Won, 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.17.0218
Zhang, 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:10.1186/2049-1891-3-26
Zhang, 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.2013.06.024
Zomeño, 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.2013-6361
[-]
