References
- Hume DA, Whitelaw CBA, Archibald AL. The future of animal production: improving productivity and sustainability. J Agric Sci 2011;149:9-16. https://doi.org/10.1017/S0021859610001188
- Zhang W, Aggrey SE. Genetic variation in feed utilization efficiency of meat-type chickens. World Poult Sci J 2003;59:328-39. https://doi.org/10.1079/WPS20030020
- Havenstein GB, Ferket PR, Qureshi MA. Growth, livability, and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poult Sci 2003;82:1500-8. https://doi.org/10.1093/ps/82.10.1500
- Rao Y, Shen X, Xia M, et al. SNP mapping of QTL affecting growth and fatness on chicken GGA1. Genet Sel Evol 2007;39:569-82. https://doi.org/10.1186/1297-9686-39-5-569
- van Kaam JB, Groenen MA, Bovenhuis H, et al. Whole genome scan in chickens for quantitative trait loci affecting growth and feed efficiency. Poult Sci 1999;78:15-23. https://doi.org/10.1093/ps/78.1.15
- Abo-Ismail MK, Vander Voort G, Squires JJ, et al. Single nucleotide polymorphisms for feed efficiency and performance in crossbred beef cattle. BMC Genet 2014;15:14. https://doi.org/10.1186/1471-2156-15-14
- Onteru SK, Gorbach DM, Young JM, et al. Whole genome association studies of residual feed intake and related traits in the pig. PLoS One 2013;8:e61756. https://doi.org/10.1371/journal.pone.0061756
- Xu Z, Ji C, Zhang Y, et al. Combination analysis of genomewide association and transcriptome sequencing of residual feed intake in quality chickens. BMC Genomics 2016;17:594. https://doi.org/10.1186/s12864-016-2861-5
- Isotouru T, Sahana G, Guldbrandtsen B, Lund MS, Vilkki J. Genome-wide association analysis of milk yield traits in Nordic Red Cattle using imputed whole genome sequence variants. BMC Genet 2016;17:55.
- Zhang Z, Xu ZQ, Luo YY, et al. Whole genomic prediction of growth and carcass traits in a Chinese quality chicken population. J Anim Sci 2017;95:72-80.
- Druet T, Macleod IM, Hayes BJ. Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity (Edinb) 2014;112:39-47. https://doi.org/10.1038/hdy.2013.13
- Ye S, Yuan X, Lin X, et al. Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population. J Anim Sci Biotechnol 2018;9:30. https://doi.org/10.1186/s40104-018-0241-5
- Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754-60. https://doi.org/10.1093/bioinformatics/btp324
- Mckenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297-303. https://doi.org/10.1101/gr.107524.110
- Sargolzaei M, Chesnais JP, Schenkel FS. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 2014;15:478. https://doi.org/10.1186/1471-2164-15-478
- Purcell S, Neale B, Toddbrown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75. https://doi.org/10.1086/519795
- Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet 2012;44:821-4. https://doi.org/10.1038/ng.2310
- Madsen P, Sorensen P, Su G, et al. DMU - a package for analyzing multivariate mixed models. In: Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, MG, Brazil, 13-18 August, 2006; 2014. p. 27-11.
- Turner SD. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. Biorxiv 2014 May 14 [Epub]. https://doi.org/10.1101/005165
- Gao X, Starmer J, Martin ER. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol 2008;32:361-9. https://doi.org/10.1002/gepi.20310
- Aggrey SE, Karnuah AB, Sebastian B, Anthony NB. Genetic properties of feed efficiency parameters in meat-type chickens. Genet Sel Evol 2010;42:25. https://doi.org/10.1186/1297-9686-42-25
- Fuchs G, Shema E, Vesterman R, et al. RNF20 and USP44 regulate stem cell differentiation by modulating H2B monoubiquitylation. Mol Cell 2012;46:662-73. https://doi.org/10.1016/j.molcel.2012.05.023
- Low CM, Akthar S, Patel DF, et al. The development of novel LTA4H modulators to selectively target LTB4 generation. Sci Rep 2017;7:44449. https://doi.org/10.1038/srep44449
- Rogers CD, Phillips JL, Bronner ME. Elk3 is essential for the progression from progenitor to definitive neural crest cell. Dev Biol 2013;374:255-63. https://doi.org/10.1016/j.ydbio.2012.12.009
- Ilmer M, Recio BA, Regel I, et al. RSPO2 enhances canonical Wnt signaling to confer stemness-associated traits to susceptible pancreatic cancer cells. Pancreatology 2015;15:S23. https://doi.org/10.1016/j.pan.2015.05.113
- Pachlopnik SJ, Canioni D, Moshous D, et al. Clinical similarities and differences of patients with X-linked lymphoproliferative syndrome type 1 (XLP-1/SAP deficiency) versus type 2 (XLP-2/XIAP deficiency). Blood 2011;117:1522-9. https://doi.org/10.1182/blood-2010-07-298372
- Maeso I, Irimia M, Tena JJ, et al. An ancient genomic regulatory block conserved across bilaterians and its dismantling in tetrapods by retrogene replacement. Genome Res 2012;22:642-55. https://doi.org/10.1101/gr.132233.111
- Jiang K, Hua S, Mohan R, et al. Microtubule minus-end stabilization by polymerization-driven CAMSAP deposition. Dev Cell 2014;28:295-309. https://doi.org/10.1016/j.devcel.2014.01.001
- Hendershott MC, Vale RD. Regulation of microtubule minusend dynamics by CAMSAPs and Patronin. Proc Natl Acad Sci USA 2014;111:5860-5. https://doi.org/10.1073/pnas.1404133111
- Yokoyama S, Ito Y, Ueno-Kudoh H, et al. A systems approach reveals that the myogenesis genome network is regulated by the transcriptional repressor RP58. Dev Cell 2009;17:836-48. https://doi.org/10.1016/j.devcel.2009.10.011
- Santi CM, Ferreira G, Yang B, et al. Opposite regulation of Slick and Slack K+ channels by neuromodulators. J Neurosci 2006;26:5059-68. https://doi.org/10.1523/JNEUROSCI.3372-05.2006
- Sayyah J, Bartakova A, Nogal N, et al. The Ras-related Protein, Rap1A, mediates thrombin-stimulated, integrin-dependent glioblastoma cell proliferation and tumor growth. J Biol Chem 2014;289:17689-98. https://doi.org/10.1074/jbc.M113.536227
- Lautenberger JA, Troyer JL, Nelson GW, et al. Accounting for multiple comparisons in a genome-wide association study(GWAS). BMC Genomics 2010;11:724. https://doi.org/10.1186/1471-2164-11-724
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