A. Reverter and M. Fortes, Association Weight Matrix: A Network-Based Approach Towards Functional Genome-Wide Association Studies, Methods Mol Biol, vol.1019, pp.437-484, 2013.
DOI : 10.1007/978-1-62703-447-0_20

M. Sargolzaei, J. Chesnais, and F. Schenkel, A new approach for efficient genotype imputation using information from relatives, BMC Genomics, vol.15, issue.1, p.478, 2014.
DOI : 10.1186/1471-2164-15-478

C. Hoze, M. Fouilloux, E. Venot, F. Guillaume, R. Dassonneville et al., High-density marker imputation accuracy in sixteen French cattle breeds, Genetics Selection Evolution, vol.45, issue.1, p.33, 2013.
DOI : 10.3168/jds.2011-4299

URL : https://hal.archives-ouvertes.fr/hal-01001056

S. Allais, H. Levéziel, J. Hocquette, S. Rousset, C. Denoyelle et al., Fine mapping of quantitative trait loci underlying sensory meat quality traits in three French beef cattle breeds, Journal of Animal Science, vol.92, issue.10, pp.4329-4370, 2014.
DOI : 10.2527/jas.2014-7868

URL : https://hal.archives-ouvertes.fr/hal-01207759

J. Yang, S. Lee, M. Goddard, and P. Visscher, GCTA: A Tool for Genome-wide Complex Trait Analysis, The American Journal of Human Genetics, vol.88, issue.1, pp.76-82, 2011.
DOI : 10.1016/j.ajhg.2010.11.011

J. Vaquerizas, S. Kummerfeld, S. Teichmann, and N. Luscombe, A census of human transcription factors: function, expression and evolution, Nature Reviews Genetics, vol.455, issue.4, pp.252-63, 2009.
DOI : 10.1038/nrg2538

R. Ihaka and R. Gentleman, R: a language for data analysis and graphics, J Comput Graph Stat, vol.5, pp.299-314, 1996.

A. Reverter and E. Chan, Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks, Bioinformatics, vol.24, issue.21, pp.2491-2498, 2008.
DOI : 10.1093/bioinformatics/btn482

P. Shannon, A. Markiel, O. Ozier, N. Baliga, J. Wang et al., Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks, Genome Research, vol.13, issue.11, pp.2498-504, 2003.
DOI : 10.1101/gr.1239303

G. Scardoni, M. Petterlini, and C. Laudanna, Analyzing biological network parameters with CentiScaPe, Bioinformatics, vol.25, issue.21, pp.2857-2866, 2009.
DOI : 10.1093/bioinformatics/btp517

A. Reverter and M. Fortes, BREEDING AND GENETICS SYMPOSIUM: Building single nucleotide polymorphism-derived gene regulatory networks: Towards functional genomewide association studies, Journal of Animal Science, vol.91, issue.2, pp.530-536, 2013.
DOI : 10.2527/jas.2012-5780

R. Janky, A. Verfaillie, H. Imrichova, B. Van-de-sande, L. Standaert et al., iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections, PLoS Computational Biology, vol.3, issue.(8), p.1003731, 2014.
DOI : 10.1371/journal.pcbi.1003731.s015

G. Bindea, B. Mlecnik, H. Hackl, P. Charoentong, M. Tosolini et al., ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks, Bioinformatics, vol.25, issue.8, pp.1091-1094, 2009.
DOI : 10.1093/bioinformatics/btp101

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practi? cal and powerful approach to multiple testing, J R Stat Soc Ser B, vol.57, pp.289-300, 1995.

W. Snelling, R. Cushman, J. Keele, C. Maltecca, M. Thomas et al., BREEDING AND GENETICS SYMPOSIUM: Networks and pathways to guide genomic selection, Journal of Animal Science, vol.91, issue.2, pp.537-52, 2013.
DOI : 10.2527/jas.2012-5784

P. Widmann, A. Reverter, M. Fortes, R. Weikard, K. Suhre et al., A systems biology approach using metabolomic data reveals genes and pathways interacting to modulate divergent growth in cattle, BMC Genomics, vol.14, issue.1, p.798, 2013.
DOI : 10.1186/1471-2164-14-798

T. Chaze, H. Meunier, B. Renand, G. Jurie, C. Chambon et al., Bio? logical markers for meat tenderness of the three main French beef breeds using 2?DE and MS approach, Proteomic in foods: principles and applications, pp.127-173, 2013.

C. Zhao, F. Tian, Y. Yu, J. Luo, A. Mitra et al., Functional Genomic Analysis of Variation on Beef Tenderness Induced by Acute Stress in Angus Cattle, Comparative and Functional Genomics, vol.10, issue.20, p.756284, 2012.
DOI : 10.3390/cancers3043799

J. Nagpal, S. Nair, D. Chakravarty, R. Rajhans, S. Pothana et al., Growth Factor Regulation of Estrogen Receptor Coregulator PELP1 Functions via Protein Kinase A Pathway, Molecular Cancer Research, vol.6, issue.5, pp.851-61, 2008.
DOI : 10.1158/1541-7786.MCR-07-2030

M. Saatchi, R. Schnabel, J. Taylor, and D. Garrick, Large-effect pleiotropic or closely linked QTL segregate within and across ten US cattle breeds, BMC Genomics, vol.15, issue.1, p.442, 2014.
DOI : 10.1186/1471-2164-15-442

L. Raven, B. Cocks, and B. Hayes, Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle, BMC Genomics, vol.15, issue.1, p.62, 2014.
DOI : 10.1101/gr.224202

L. Flori, S. Fritz, F. Jaffrezic, M. Boussaha, I. Gut et al., The Genome Response to Artificial Selection: A Case Study in Dairy Cattle, PLoS ONE, vol.6, issue.84, p.6595, 2009.
DOI : 10.1371/journal.pone.0006595.s008

URL : https://hal.archives-ouvertes.fr/hal-01193382

W. Guo, S. Schafer, M. Greaser, M. Radke, M. Liss et al., RBM20, a gene for hereditary cardiomyopathy, regulates titin splicing, Nature Medicine, vol.28, issue.5, pp.766-73, 2012.
DOI : 10.1038/nm.2693

P. Tizioto, J. Decker, J. Taylor, R. Schnabel, M. Mudadu et al., Genome scan for meat quality traits in Nelore beef cattle, Physiological Genomics, vol.45, issue.21, pp.1012-1032, 2013.
DOI : 10.1152/physiolgenomics.00066.2013

Y. Ramayo?caldas, M. Fortes, N. Hudson, L. Porto?neto, S. Bolormaa et al., A marker-derived gene network reveals the regulatory role of PPARGC1A, HNF4G, and FOXP3 in intramuscular fat deposition of beef cattle, Journal of Animal Science, vol.92, issue.7, pp.2832-2877, 2014.
DOI : 10.2527/jas.2013-7484

URL : https://hal.archives-ouvertes.fr/hal-01194020

M. Oury, B. Picard, M. Briand, J. Blanquet, and R. Dumont, Interrelationships between meat quality traits, texture measurements and physicochemical characteristics of M. rectus abdominis from Charolais heifers, Meat Science, vol.83, issue.2, pp.293-301, 2009.
DOI : 10.1016/j.meatsci.2009.05.013

E. Raspe, H. Duez, P. Gervois, C. Fievet, J. Fruchart et al., Transcriptional Regulation of Apolipoprotein C-III Gene Expression by the Orphan Nuclear Receptor ROR??, Journal of Biological Chemistry, vol.276, issue.4, pp.2865-71, 2001.
DOI : 10.1074/jbc.M004982200

P. Lau, P. Bailey, D. Dowhan, and G. Muscat, Exogenous expression of a dominant negative ROR??1 vector in muscle cells impairs differentiation: ROR??1 directly interacts with p300 and MyoD, Nucleic Acids Research, vol.27, issue.2, pp.411-431, 1999.
DOI : 10.1093/nar/27.2.411

B. Guo, P. Greenwood, M. Cafe, G. Zhou, W. Zhang et al., Transcriptome analysis of cattle muscle identifies potential markers for skeletal muscle growth rate and major cell types, BMC Genomics, vol.3, issue.1, p.177, 2015.
DOI : 10.1186/s12864-015-1403-x

H. Youn, C. Grozinger, and J. Liu, Calcium Regulates Transcriptional Repression of Myocyte Enhancer Factor 2 by Histone Deacetylase 4, Journal of Biological Chemistry, vol.275, issue.29, pp.22563-22570, 2000.
DOI : 10.1074/jbc.C000304200

T. Mckinsey, C. Zhang, and E. Olson, Activation of the myocyte enhancer factor-2 transcription factor by calcium/calmodulin-dependent protein kinase-stimulated binding of 14-3-3 to histone deacetylase 5, Proceedings of the National Academy of Sciences, vol.97, issue.26, pp.14400-14405, 2000.
DOI : 10.1073/pnas.260501497

Z. Gu, S. Eleswarapu, and H. Jiang, Identification and characterization of microRNAs from the bovine adipose tissue and mammary gland, FEBS Letters, vol.65, issue.5, pp.981-989, 2007.
DOI : 10.1016/j.febslet.2007.01.081

N. Hudson, A. Reverter, Y. Wang, P. Greenwood, and B. Dalrymple, Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks, PLoS ONE, vol.99, issue.3, p.7249, 2009.
DOI : 10.1371/journal.pone.0007249.s006

C. Alfieri, H. Evans?anderson, and K. Yutzey, Developmental regulation of the mouse IGF-I exon 1 promoter region by calcineurin activation of NFAT in skeletal muscle, AJP: Cell Physiology, vol.292, issue.5, pp.1887-94, 2007.
DOI : 10.1152/ajpcell.00506.2006

T. Yang, H. Suk, X. Yang, O. Olabisi, R. Yu et al., Role of Transcription Factor NFAT in Glucose and Insulin Homeostasis, Molecular and Cellular Biology, vol.26, issue.20, pp.7372-87, 2006.
DOI : 10.1128/MCB.00580-06

S. Bae, K. Lee, Y. Zhang, and Y. Ito, Intimate Relationship Between TGF-??/BMP Signaling and Runt Domain Transcription Factor, PEBP2/CBF, The Journal of Bone and Joint Surgery-American Volume, vol.83, pp.48-55, 2001.
DOI : 10.2106/00004623-200100001-00007

S. Li, M. Czubryt, J. Mcanally, R. Bassel?duby, J. Richardson et al., Requirement for serum response factor for skeletal muscle growth and maturation revealed by tissue-specific gene deletion in mice, Proceedings of the National Academy of Sciences, vol.102, issue.4, pp.1082-1089, 2005.
DOI : 10.1073/pnas.0409103102

C. Hsieh, H. Liu, Y. Huang, L. Kang, H. Chen et al., ADAR1 deaminase contributes to scheduled skeletal myogenesis progression via stage-specific functions, Cell Death and Differentiation, vol.10, issue.5, pp.707-726, 2014.
DOI : 10.1038/nmeth.1226

Y. Jiao, C. Bishop, and B. Lu, Mex3c regulates insulin-like growth factor 1 (IGF1) expression and promotes postnatal growth, Molecular Biology of the Cell, vol.23, issue.8, pp.1404-1417, 2012.
DOI : 10.1091/mbc.E11-11-0960

Y. Jiao, S. George, Q. Zhao, M. Hulver, S. Hutson et al., Mex3c Mutation Reduces Adiposity and Increases Energy Expenditure, Molecular and Cellular Biology, vol.32, issue.21, pp.4350-62, 2012.
DOI : 10.1128/MCB.00452-12

Q. Gu, S. Nagaraj, N. Hudson, B. Dalrymple, and A. Reverter, Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle, BMC Genomics, vol.5, issue.Suppl 1, p.23, 2011.
DOI : 10.1038/nrg1272

R. Maier, G. Moser, G. Chen, and S. Ripke, Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder, The American Journal of Human Genetics, vol.96, issue.2, pp.283-94, 2015.
DOI : 10.1016/j.ajhg.2014.12.006

B. Maher, ENCODE: The human encyclopaedia, Nature, vol.489, issue.7414, pp.46-54, 2012.
DOI : 10.1038/489046a

G. Renand and A. Fisher, Comparison of methods for estimating carcass fat content of young Charolais bulls in performance testing station, Livestock Production Science, vol.51, issue.1-3, pp.205-218, 1997.
DOI : 10.1016/S0301-6226(97)00060-2