D. Boichard, N. Bouloc, G. Ricordeau, A. Piacere, and F. Barillet, Genetic-parameters for 1st lactation dairy traits in the Alpine and Saanen goat breeds, Genet. Sel. Evol, vol.21, pp.205-215, 1989.

V. Bonfatti, A. Cecchinato, L. Gallo, A. Blasco, and P. Carnier, Genetic analysis of detailed milk protein composition and coagulation properties in Simmental cattle, J. Dairy Sci, vol.94, pp.5183-5193, 2011.

V. Bonfatti, G. D. Martino, and P. Carnier, Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows, J. Dairy Sci, vol.94, pp.5776-5785, 2011.

M. De-marchi, V. Bonfatti, A. Cecchinato, G. D. Martino, and P. Carnier, Prediction of protein composition of individual cow milk using mid-infrared spectroscopy, Ital. J. Anim. Sci, vol.8, issue.S2, pp.399-401, 2009.

M. Ferrand, G. Miranda, S. Guisnel, H. Larroque, O. Leray et al., Determination of protein composition in milk by mid-infrared spectrometry. Pages 41-45 in, Proc. International Strategies and New Developments in Milk Analysis. VI ICAR Reference Laboratory Network Meeting, 2012.

B. Amenu and H. Deeth, The impact of milk composition on Cheddar cheese manufacture, Aust. J. Dairy Technol, vol.62, pp.171-184, 2007.

G. Bittante, C. Cipolat-gotet, and A. Cecchinato, Genetic parameters of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process, J. Dairy Sci, vol.96, pp.7966-7979, 2013.

G. Bittante, M. Penasa, and A. Cecchinato, Invited review: Genetics and modeling of milk coagulation properties, J. Dairy Sci, vol.95, pp.6843-6870, 2012.

J. H. Bland, A. Grandison, and C. Fagan, Evaluation of milk compositional variables on coagulation properties using partial least squares, J. Dairy Res, vol.82, pp.8-14, 2015.

D. Boichard, A. Govignon-gion, H. Larroque, C. Maroteau, I. Palhiere et al., Genetic determinism of milk composition in fatty acids and proteins in ruminants, and selection potential, INRA Prod. Anim, vol.27, pp.283-298, 2014.

V. Bonfatti, L. Degano, A. Menegoz, and P. Carnier, Short communication: Mid-infrared spectroscopy prediction of fine milk composition and technological properties in Italian Simmental, J. Dairy Sci, vol.99, pp.8216-8221, 2016.

V. Bonfatti, D. Vicario, A. Lugo, and P. Carnier, Genetic parameters of measures and population-wide infrared predictions of 92 traits describing the fine composition and technological properties of milk in Italian Simmental cattle, J. Dairy Sci, vol.100, pp.5526-5540, 2017.

A. J. Buitenhuis, U. Sundekilde, N. Poulsen, H. Bertram, L. Larsen et al., Estimation of genetic parameters and detection of quantitative trait loci for metabolites in Danish Holstein milk, J. Dairy Sci, vol.96, pp.3285-3295, 2013.

A. Cecchinato, A. Albera, C. Cipolat-gotet, A. Ferragina, and G. Bittante, Genetic parameters of cheese yield and curd nutrient recovery or whey loss traits predicted using Fourier-transform infrared spectroscopy of samples collected during milk recording cessed bovine milk samples, J. Dairy Sci, vol.96, pp.7980-7990, 2015.

N. Gengler, H. Soyeurt, F. Dehareng, C. Bastin, F. Colinet et al., Capitalizing on fine milk composition for breeding and management of dairy cows, J. Dairy Sci, vol.99, pp.4071-4079, 2016.

M. Glantz, T. Devold, G. Vegarud, H. Mansson, H. Stalhammar et al., Importance of casein micelle size and milk composition for milk gelation, J. Dairy Sci, vol.93, pp.1444-1451, 2010.

A. Govignon-gion, S. Minery, M. Wald, M. Brochard, M. Gelé et al., Paramètres génétiques du taux de calcium, prédit à partir des spectres moyen infrarouge, dans le lait des 3 principales races bovines laitières françaises. Pages 111-114 in Proc. Renc. Rech. Ruminants, 2015.

C. Grelet, J. Pierna, P. Dardenne, V. Baeten, and F. Dehareng, Standardization of milk mid-infrared spectra from a European dairy network, J. Dairy Sci, vol.98, pp.2150-2160, 2015.

C. Grelet, J. Pierna, P. Dardenne, H. Soyeurt, A. Vanlierde et al., Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models, J. Dairy Sci, vol.100, pp.7910-7921, 2017.

F. Gustavsson, M. Glantz, N. Poulsen, L. Wadso, H. Stalhammar et al., Genetic parameters for rennet-and acid-induced coagulation properties in milk from Swedish Red dairy cows, J. Dairy Sci, vol.97, pp.5219-5229, 2014.

M. Haile-mariam and J. E. Pryce, Genetic parameters for lactose and its correlation with other milk production traits and fitness traits in pasture-based production systems, J. Dairy Sci, vol.100, pp.3754-3766, 2017.

C. Hurtaud, H. Rulquin, M. Delaite, and R. Verite, Prediction of cheese yielding efficiency of individual milk of dairy cows-Correlation with coagulation parameters and laboratory curd yield, 1995.

A. Zootech, The World Dairy Situation. Bulletin 485/2016. International Dairy Federation, vol.44, pp.385-398, 2016.

C. Laithier, V. Wolf, M. E. Jabri, P. Trossat, S. Gavoye et al., Prediction of cheesemaking properties of Montbéliarde milks used for PDO/ PGI cheeses production in Franche-Comté by mid-infrared spectrometry, Pages 15-19 in 12th International Meeting on Mountain Cheese, 2017.

A. Logan, L. Day, A. Pin, M. Auldist, A. Leis et al., Interactive effects of milk fat globule and casein micelle size on the renneting properties of milk. Food Bioprocess Technol, vol.7, pp.3175-3185, 2014.

A. Logan, A. Leis, L. Day, S. Oiseth, A. Puvanenthiran et al., Rennet gelation properties of milk: Influence of natural variation in milk fat globule size and casein micelle size, Int. Dairy J, vol.46, pp.71-77, 2015.

J. Luo, Y. Wang, H. Guo, and F. Ren, Effects of size and stability of native fat globules on the formation of milk gel induced by rennet, J. Food Sci, vol.82, pp.670-678, 2017.

K. Meyer, WOMBAT-A tool for mixed model analyses in quantitative genetics by REML, J. Zhejiang Univ. Sci. B, vol.8, pp.815-821, 2007.

N. A. Poulsen, A. Buitenhuis, and L. Larsen, Phenotypic and genetic associations of milk traits with milk coagulation properties, J. Dairy Sci, vol.98, pp.2079-2087, 2015.

M. P. Sanchez, M. Ferrand, M. Gelé, D. Pourchet, G. Miranda et al., Short communication: Genetic parameters for milk protein composition predicted using mid-infrared spectroscopy in the French Montbéliarde, Normande, and Holstein dairy cattle breeds, J. Dairy Sci, vol.100, pp.6371-6375, 2017.

U. K. Sundekilde, P. Frederiksen, M. Clausen, L. Larsen, and H. Bertram, Relationship between the metabolite profile and technological properties of bovine milk from two dairy breeds elucidated by NMR-based metabolomics, J. Agric. Food Chem, vol.59, pp.7360-7367, 2011.

F. Tiezzi, D. Pretto, M. De-marchi, M. Penasa, and M. Cassandro, Heritability and repeatability of milk coagulation properties predicted by mid-infrared spectroscopy during routine data recording, and their relationships with milk yield and quality traits, Animal, vol.7, pp.1592-1599, 2013.

M. S. Ashwell, D. W. Heyen, T. S. Sonstegard, C. P. Van-tassell, Y. Da et al., Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle, Journal of Dairy Science, vol.87, issue.10, pp.468-475, 2004.

, GENOME SCAN OF COW MILK PROTEIN COMPOSITION, vol.8215

A. Bagnato, F. Schiavini, A. Rossoni, C. Maltecca, M. Dolezal et al., Quantitative trait loci affecting milk yield and protein percentage in a three-country Brown Swiss population, J. Dairy Sci, vol.91, pp.767-783, 2008.

S. Blott, J. J. Kim, S. Moisio, A. Schmidt-küntzel, A. Cornet et al., Molecular dissection of a quantitative trait locus: A phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition, Genetics, vol.163, pp.253-266, 2003.

D. Boichard, F. Guillaume, A. Baur, P. Croiseau, M. Rossignol et al., Genomic selection in French dairy cattle, Anim. Prod. Sci, vol.52, pp.115-120, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01000520

M. Boussaha, D. Esquerre, J. Barbieri, A. Djari, A. Pinton et al., Genomewide study of structural variants in bovine Holstein, Montbeliarde and Normande dairy breeds, PLoS ONE, vol.10, p.135931, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01194172

M. Brochard, M. P. Sanchez, A. Govignon-gion, M. Ferrand, M. Gelé et al., Paramètres génétiques pour la composition protéique du lait dans 3 races bovines. Page 158 in Rencontres autour des Recherches sur les Ruminants, vol.20, 2013.

H. D. Daetwyler, A. Capitan, H. Pausch, P. Stothard, R. Van-binsbergen et al., Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle, Nat. Genet, vol.46, pp.858-865, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01193853

T. Druet, S. Fritz, M. Boussaha, S. Ben-jemaa, F. Guillaume et al., Fine mapping of quantitative trait loci affecting female fertility in dairy cattle on BTA03 using a dense single-nucleotide polymorphism map, Genetics, vol.178, pp.2227-2235, 2008.

T. Druet and M. Georges, A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping, Genetics, vol.184, pp.789-798, 2010.

V. ;. Ducrocq, . Inra-gabi, F. Jouy-en-josas, M. Fang, W. Fu et al., A multiple-SNP approach for genome-wide association study of milk production traits in Chinese Holstein cattle, PLoS ONE, vol.9, p.99544, 1998.

N. A. Ganai, H. Bovenhuis, J. A. Van-arendonk, and M. H. Visker, Novel polymorphisms in the bovine beta-lactoglobulin gene and their effects on beta-lactoglobulin protein concentration in milk, Anim. Genet, vol.40, pp.127-133, 2009.

G. Gebreyesus, M. S. Lund, L. Janss, N. A. Poulsen, L. B. Larsen et al., Short communication: Multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein, J. Dairy Sci, vol.99, pp.2863-2866, 2016.

M. Gelé, S. Minery, J. M. Astruc, P. Brunschwig, M. Ferrand et al., Phénotypage et génotypage à grande échelle de la composition fine des laits dans les filières bovine, ovine et caprine, Prod. Anim, vol.27, pp.255-268, 2014.

F. E. Grignola, I. Hoeschele, Q. Zhang, and G. Thaller, Mapping quantitative trait loci in outcross populations via residual maximum likelihood. II. A simulation study, Genet. Sel. Evol, vol.28, pp.491-504, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00894149

B. Grisart, W. Coppieters, F. Farnir, L. Karim, C. Ford et al., Positional candidate cloning of a QTL in dairy cattle: Identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition, Genome Res, vol.12, pp.222-231, 2002.

W. Huang, F. Peñagaricano, K. R. Ahmad, J. A. Lucey, K. A. Weigel et al., Association between milk protein gene variants and protein composition traits in dairy cattle, J. Dairy Sci, vol.95, pp.440-449, 2012.

L. Jiang, J. Liu, D. Sun, P. Ma, X. Ding et al., Genome wide association studies for milk production traits in Chinese Holstein population, PLoS ONE, vol.5, p.13661, 2010.

K. E. Kemper, C. M. Reich, P. J. Bowman, C. J. Vander-jagt, A. J. Chamberlain et al., Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions, Genet. Sel. Evol, vol.47, p.29, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01303213

P. Martin, M. Szymanowska, L. Zwierzchowski, and C. Leroux, The impact of genetic polymorphisms on the protein composition of ruminant milks, Reprod. Nutr. Dev, vol.42, pp.433-459, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00900338

T. H. Meuwissen, A. Karlsen, S. Lien, I. Olsaker, and M. E. Goddard, Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping, Genetics, vol.161, pp.373-379, 2002.

T. H. Meuwissen and M. E. Goddard, Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci, Genetics, vol.155, pp.421-430, 2000.

I. Misztal, T. Tsuruta, B. Strabel, B. Auvray, and T. Druet, BLUPF90 and related programs (BGF90). Pages 21-22 in Proc. 7th World Congress on Genetics Applied to Livestock Production, 2002.

F. Montpellier, Editions Quae

G. C. Schopen, J. M. Heck, H. Bovenhuis, M. H. Visker, H. J. Van-valenberg et al., Genetic parameters for major milk proteins in Dutch Holstein-Friesians, J. Dairy Sci, vol.92, pp.1182-1191, 2009.

G. C. Schopen, M. H. Visker, P. D. Koks, E. Mullaart, J. A. Van-arendonk et al., Concordance analysis for QTL detection in dairy cattle: A case study of leg morphology, Genet. Sel. Evol, vol.94, p.31, 2011.

A. Wedholm, L. B. Larsen, H. Lindmark-månsson, A. H. Karlsson, and A. Andrén, Effect of protein composition on the cheesemaking properties of milk from individual dairy cows, J. Dairy Sci, vol.89, pp.3296-3305, 2006.

A. Zimin, A. Delcher, L. Florea, D. Kelley, M. Schatz et al., A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol, vol.10, p.42, 2009.

, Anne Barbat-Leterrier, vol.1

I. Gabi, U. Agroparistech, and F. Paris-saclay,

F. Allice and . Paris, France Genetics Selection Evolution, vol.49, p.68, 2017.

G. C. Schopen, J. M. Heck, H. Bovenhuis, M. H. Visker, H. J. Van-valenberg et al., Genetic parameters for major milk proteins in Dutch Holstein-Friesians, Gènes et variants candidats References, vol.1, pp.1182-91, 2009.

V. Bonfatti, A. Cecchinato, L. Gallo, A. Blasco, and P. Carnier, Genetic analysis of detailed milk protein composition and coagulation properties in Simmental cattle, J Dairy Sci, vol.94, pp.5183-93, 2011.

G. Gebreyesus, M. S. Lund, L. Janss, N. A. Poulsen, L. B. Larsen et al., Short communication: multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein, J Dairy Sci, vol.99, pp.2863-2869, 2016.

M. P. Sanchez, M. Ferrand, M. Gelé, D. Pourchet, G. Miranda et al., Short communication: genetic parameters for milk protein composition predicted using mid-infrared spectroscopy in the French Montbéliarde, Normande, and Holstein dairy cattle breeds, J Dairy Sci, vol.100, pp.6371-6376, 2017.

A. Wedholm, L. B. Larsen, H. Lindmark-månsson, A. H. Karlsson, and A. Andrén, Effect of protein composition on the cheese-making properties of milk from individual dairy cows, J Dairy Sci, vol.89, pp.3296-305, 2006.

V. Bonfatti, D. Martino, G. Carnier, and P. , Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows, J Dairy Sci, vol.94, pp.5776-85, 2011.

M. Ferrand, G. Miranda, S. Guisnel, H. Larroque, O. Leray et al., Determination of protein composition in milk by mid-infrared spectrometry, Proceedings of the international strategies and new developments in milk analysis VI ICAR Reference Laboratory Network Meeting, vol.16, pp.41-46, 2012.

M. P. Sanchez, A. Govignon-gion, M. Ferrand, M. Gele, D. Pourchet et al., Whole-genome scan to detect quantitative trait loci associated with milk protein composition in 3 French dairy cattle breeds, J Dairy Sci, vol.99, pp.8203-8218, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01533901

H. D. Daetwyler, A. Capitan, H. Pausch, P. Stothard, R. Van-binsbergen et al., Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle, Nat Genet, vol.46, pp.858-67, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01193853

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, p.62, 2014.

V. Ducrocq and . Genekit, , 2011.

M. Sargolzaei, J. P. Chesnais, and F. S. Schenkel, A new approach for efficient genotype imputation using information from relatives, BMC Genomics, vol.15, p.478, 2014.

R. Van-binsbergen, M. C. Bink, M. P. Calus, F. A. Van-eeuwijk, B. J. Hayes et al., Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle, Genet Sel Evol, vol.46, p.41, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01341268

A. C. Bouwman and R. F. Veerkamp, Consequences of splitting whole-genome sequencing effort over multiple breeds on imputation accuracy, BMC Genet, vol.15, p.105, 2014.

M. Boussaha, P. Michot, R. Letaief, C. Hoze, S. Fritz et al., Construction of a large collection of small genome variations in French dairy and beef breeds using whole-genome sequences, Genet Sel Evol, vol.48, p.87, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01479216

H. Li, B. Handsaker, A. Wysoker, T. Fennell, J. Ruan et al., The sequence alignment/map format and SAMtools, Bioinformatics, vol.25, pp.2078-2087, 2009.

W. Mclaren, B. Pritchard, D. Rios, Y. Chen, P. Flicek et al., Deriving the consequences of genomic variants with the Ensembl API and SNP effect predictor, Bioinformatics, vol.26, pp.2069-70, 2010.

P. Kumar, S. Henikoff, and P. C. Ng, Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm, Nat Protoc, vol.4, pp.1073-82, 2009.

J. Yang, S. H. Lee, M. E. Goddard, and P. M. Visscher, GCTA: a tool for genome-wide complex trait analysis, Am J Hum Genet, vol.88, pp.76-82, 2011.

W. X. Fu, Y. Liu, X. Lu, X. Y. Niu, X. D. Ding et al., A genome-wide association study identifies two novel promising candidate genes affecting Escherichia coli F4ab/F4ac susceptibility in swine, PLoS One, vol.7, p.32127, 2012.

Y. J. Ferreira, T. Morris, A. P. Medland, and S. E. , Genetic Investigation of ANthropometric Traits (GIANT) Consortium, DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits, Nat Genet, vol.44, pp.369-75, 2012.

W. Mclaren, L. Gil, S. E. Hunt, H. S. Riat, G. R. Ritchie et al., The Ensembl variant effect predictor, Genome Biol, vol.17, p.122, 2016.

D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller et al., STRING v10: protein-protein interaction networks, integrated over the tree of life, Nucl Acids Res, vol.43, pp.447-52, 2015.

M. Gautier, D. Laloe, and K. Moazami-goudarzi, Insights into the genetic history of French cattle from dense SNP data on 47 worldwide breeds, PLoS One, vol.5, p.13038, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01193678

C. Hoze, M. N. Fouilloux, E. Venot, F. Guillaume, R. Dassonneville et al., High-density marker imputation accuracy in sixteen French cattle breeds
URL : https://hal.archives-ouvertes.fr/hal-01001056

, Genet Sel Evol, vol.45, p.33, 2013.

A. J. Chamberlain, V. Jagt, C. J. Hayes, B. J. Khansefid, M. Marett et al., Extensive variation between tissues in allele specific expression in an outbred mammal, BMC Genomics, vol.16, p.993, 2015.

K. E. Kemper, C. M. Reich, P. J. Bowman, C. J. Vander-jagt, A. J. Chamberlain et al., Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions, Genet Sel Evol, vol.47, p.29, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01303213

K. E. Kemper, M. D. Littlejohn, T. Lopdell, B. J. Hayes, L. E. Bennett et al., Leveraging genetically simple traits to identify small-effect variants for complex phenotypes, BMC Genomics, vol.17, p.858, 2016.

L. A. Raven, B. G. Cocks, K. E. Kemper, A. J. Chamberlain, V. Jagt et al., Targeted imputation of sequence variants and gene expression profiling identifies twelve candidate genes associated with lactation volume, composition and calving interval in dairy cattle, Mamm Genome, vol.27, pp.81-97, 2016.

T. Iso-touru, G. Sahana, B. Guldbrandtsen, M. S. Lund, and J. Vilkki, Genome-wide association analysis of milk yield traits in Nordic red cattle using imputed whole genome sequence variants, BMC Genet, vol.17, p.55, 2016.

I. Van-den-berg, D. Boichard, and M. S. Lund, Comparing power and precision of within-breed and multibreed genome-wide association studies of production traits using whole-genome sequence data for 5 French and Danish dairy cattle breeds, J Dairy Sci, vol.99, pp.8932-8977, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01533908

M. D. Littlejohn, K. Tiplady, T. A. Fink, K. Lehnert, T. Lopdell et al., Sequence-based association analysis reveals an MGST1 eQTL with pleiotropic effects on bovine milk composition, Sci Rep, vol.6, p.25376, 2016.

M. Cohen-zinder, E. Seroussi, D. M. Larkin, J. J. Loor, A. Everts-van-der-wind et al., Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle, Genome Res, vol.15, pp.936-980, 2005.

F. Grosclaude, Le polymorphisme génétique des principales lactoprotéines bovines, INRA Prod Anim, vol.1, pp.5-17, 1988.

A. M. Caroli, S. Chessa, and G. J. Erhardt, Invited review: milk protein polymorphisms in cattle: effect on animal breeding and human nutrition, J Dairy Sci, vol.92, pp.5335-52, 2009.

N. A. Ganai, H. Bovenhuis, J. A. Van-arendonk, and M. H. Visker, Novel polymorphisms in the bovine beta-lactoglobulin gene and their effects on betalactoglobulin protein concentration in milk, Anim Genet, vol.40, pp.127-160, 2009.

B. Grisart, W. Coppieters, F. Farnir, L. Karim, C. Ford et al., Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition, Genome Res, vol.12, pp.222-253, 2002.

M. D. Littlejohn, K. Tiplady, T. Lopdell, T. A. Law, A. Scott et al., Expression variants of the lipogenic AGPAT6 gene affect diverse milk composition phenotypes in Bos taurus, PLoS One, vol.9, p.85757, 2014.

D. Harold, R. Abraham, P. Hollingworth, R. Sims, A. Gerrish et al., Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease, Nat Genet, vol.41, pp.1088-93, 2009.

M. P. Sanchez-1*, M. Ferrand, ?. , M. Gelé, ?. et al.,

*. Gabi, . Inra, U. Agroparistech, F. Paris-saclay, and F. De-l'elevage, France Journal of Dairy Science, vol.101, pp.10076-10081, 2018.

D. , A. Govignon-gion, H. Larroque, C. Maroteau, I. Palhiere et al., Genetic determinism of milk composition in fatty acids and proteins in ruminants, and selection potential, Chapitre 4 -Gènes et variants candidats REFERENCES Boichard, vol.27, pp.283-298, 2014.

M. El-jabri, M. P. Sanchez, C. Laithier, E. Doutart, V. Wolf et al., Bayesian regression models and variable selection methods before PLS regression. Application to the prediction of milk cheese-making properties using infared spectral data, Pages 15-17 in Chimiometrie XVIII, 2017.

N. Gengler, H. Soyeurt, F. Dehareng, C. Bastin, F. Colinet et al., Capitalizing on fine milk composition for breeding and management of dairy cows, J. Dairy Sci, vol.99, pp.4071-4079, 2016.

C. Hurtaud, H. Rulquin, M. Delaite, and R. Verite, Prediction dairy cows, J. Dairy Sci, vol.76, issue.93, pp.77640-77647, 1995.

C. Laithier, V. Wolf, M. E. Jabri, P. Trossat, S. Gavoye et al., Prediction of cheesemaking properties of Montbeliarde milks used for PDO/ PGI cheeses production in Franche-Comté by mid-infrared spectrometry, Pages 15-19 in 12th International Meeting on Mountain Cheese, 2017.

C. Li, D. Sun, S. Zhang, L. Liu, M. Alim et al., A post-GWAS confirming the SCD gene associated with milk medium-and long-chain unsaturated fatty acids in Chinese Holstein population, Anim. Genet, vol.47, pp.483-490, 2016.

M. P. Sanchez, M. E. Jabri, S. Minéry, V. Wolf, E. Beuvier et al., Genetic parameters for cheese-making properties and milk composition predicted from mid-infrared spectra in a large dataset of Montbéliarde cows, J. Dairy Sci, pp.2018-14878, 2018.

M. P. Sanchez, M. Ferrand, M. Gele, D. Pourchet, G. Miranda et al., Short communication: Genetic parameters for milk protein composition predicted using mid-infrared spectroscopy in the French Montbéliarde, Normande, and Holstein dairy cattle breeds, J. Dairy Sci, vol.100, pp.6371-6375, 2017.

M. P. Sanchez, A. Govignon-gion, P. Croiseau, S. Fritz, C. Hozé et al., Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle, Genet. Sel. Evol, vol.49, p.68, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01589691

M. Sargolzaei, J. Chesnais, and F. Schenkel, A new approach for efficient genotype imputation using information from relatives, BMC Genomics, vol.15, p.478, 2014.

J. Yang, S. Lee, M. Goddard, and P. Visscher, GCTA: A Tool for genome-wide complex trait analysis, Am. J. Hum. Genet, vol.88, pp.76-82, 2011.

M. De-marchi, V. Toffanin, M. Cassandro, and M. Penasa, Invited review: midinfrared spectroscopy as phenotyping tool for milk traits, J Dairy Sci, vol.97, pp.1171-86, 2014.

. Sanchez, Genet Sel Evol, vol.51, p.34, 2019.

M. P. Sanchez, E. Jabri, M. Minéry, S. Wolf, V. Beuvier et al., Genetic parameters for cheese-making properties and milk composition predicted from mid-infrared spectra in a large dataset of Montbéliarde cows, J Dairy Sci, vol.101, pp.10048-61, 2018.

H. D. Daetwyler, A. Capitan, H. Pausch, P. Stothard, R. Van-binsbergen et al., Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle, Nat Genet, vol.46, pp.858-67, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01193853

M. Fortes, A. Reverter, Y. Zhang, E. Collis, S. H. Nagaraj et al., Association weight matrix for the genetic dissection of puberty in beef cattle, Proc Natl Acad Sci, vol.107, pp.13642-13649, 2010.

A. Reverter and M. B. Fortes, Breeding and genetics symposium: building single nucleotide polymorphism-derived gene regulatory networks: towards functional genomewide association studies, J Anim Sci, vol.91, pp.530-536, 2013.

C. Dadousis, S. Pegolo, G. Rosa, D. Gianola, G. Bittante et al., Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle, J Dairy Sci, vol.100, pp.1223-1254, 2017.

B. Buitenhuis, L. L. Janss, N. A. Poulsen, L. B. Larsen, M. K. Larsen et al.,

, Genome-wide association and biological pathway analysis for milk-fat composition in Danish Holstein and Danish Jersey cattle, BMC Genom, vol.15, p.1112, 2014.

S. Pegolo, C. Dadousis, N. Mach, Y. Ramayo-caldas, M. Mele et al., SNP co-association and network analyses identify E2F3, KDM5A and BACH2 as key regulators of the bovine milk fatty acid profile, Sci Rep, vol.7, p.17317, 2017.

S. Pegolo, N. Mach, Y. Ramayo-caldas, S. Schiavon, G. Bittante et al., Integration of GWAS, pathway and network analyses reveals novel mechanistic insights into the synthesis of milk proteins in dairy cows, Sci Rep, vol.8, p.566, 2018.

R. Gambra, F. Penagaricano, J. Kropp, K. Khateeb, K. A. Weigel et al., Genomic architecture of bovine kappa-casein and beta-lactoglobulin, J Dairy Sci, vol.96, pp.5333-5376, 2013.

M. Ferrand, G. Miranda, S. Guisnel, H. Larroque, O. Leray et al., Determination of protein composition in milk by mid-infrared spectrometry, Proceedings of the VI ICAR reference laboratory network meeting, 2012.

M. Ferrand-calmels, I. Palhiere, M. Brochard, O. Leray, J. M. Astruc et al., Prediction of fatty acid profiles in cow, ewe, and goat milk by midinfrared spectrometry, J Dairy Sci, vol.97, pp.17-35, 2014.

M. P. Sanchez, M. Ferrand, M. Gele, D. Pourchet, G. Miranda et al., Short communication: genetic parameters for milk protein composition predicted using mid-infrared spectroscopy in the French Montbeliarde, Normande, and Holstein dairy cattle breeds, J Dairy Sci, vol.100, pp.6371-6376, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01607785

N. Gengler, H. Soyeurt, F. Dehareng, C. Bastin, F. Colinet et al., Capitalizing on fine milk composition for breeding and management of dairy cows, J Dairy Sci, vol.99, pp.4071-4080, 2016.

V. Ducrocq and . Genekit, INRA GABI, 1998.

M. Sargolzaei, J. P. Chesnais, and F. S. Schenkel, A new approach for efficient genotype imputation using information from relatives, BMC Genom, vol.15, p.478, 2014.

D. Boichard, F. Guillaume, A. Baur, P. Croiseau, M. Rossignol et al., Genomic selection in French dairy cattle, Anim Prod Sci, vol.52, pp.115-135, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01000520

B. Howie, C. Fuchsberger, M. Stephens, J. Marchini, and G. R. Abecasis, Fast and accurate genotype imputation in genome-wide association studies through pre-phasing, Nat Genet, vol.44, pp.955-964, 2012.

R. F. Brondum, B. Guldbrandtsen, G. Sahana, M. S. Lund, and G. Su, Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle, BMC Genom, vol.15, p.728, 2014.

H. Pausch, I. Macleod, R. Fries, R. Emmerling, P. J. Bowman et al., Evaluation of the accuracy of imputed sequence variant genotypes and their utility for causal variant detection in cattle, Genet Sel Evol, vol.49, p.24, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01479153

C. Hoze, M. N. Fouilloux, E. Venot, F. Guillaume, R. Dassonneville et al., High-density marker imputation accuracy in sixteen French cattle breeds
URL : https://hal.archives-ouvertes.fr/hal-01001056

, Genet Sel Evol, vol.45, p.33, 2013.

A. C. Bouwman and R. F. Veerkamp, Consequences of splitting whole-genome sequencing effort over multiple breeds on imputation accuracy, BMC Genet, vol.15, p.105, 2014.

A. C. Bouwman, H. D. Daetwyler, A. J. Chamberlain, C. H. Ponce, M. Sargolzaei et al., Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals, Nat Genet, vol.50, pp.362-369, 2018.

H. Li, B. Handsaker, A. Wysoker, T. Fennell, J. Ruan et al., The sequence alignment/map format and SAMtools, Bioinformatics, vol.25, pp.2078-2087, 2009.

W. Mclaren, B. Pritchard, D. Rios, Y. Chen, P. Flicek et al., Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor, Bioinformatics, vol.26, pp.2069-70, 2010.

P. Kumar, S. Henikoff, and P. C. Ng, Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm, Nat Protoc, vol.4, pp.1073-82, 2009.

J. Yang, S. H. Lee, M. E. Goddard, and P. M. Visscher, GCTA: a tool for genome-wide complex trait analysis, Am J Hum Genet, vol.88, pp.76-82, 2011.

Y. Ramayo-caldas, G. Renand, M. Ballester, R. Saintilan, and D. Rocha, Multi-breed and multi-trait co-association analysis of meat tenderness and other meat quality traits in three French beef cattle breeds, Genet Sel Evol, vol.48, p.37, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01341371

W. Mclaren, L. Gil, S. E. Hunt, H. S. Riat, G. Ritchie et al., The Ensembl variant effect predictor, Genome Biol, vol.17, p.122, 2016.

A. Reverter and E. K. Chan, Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks, Bioinformatics, vol.24, pp.2491-2498, 2008.

N. S. Watson-haigh, H. N. Kadarmideen, and A. Reverter, PCIT: an R package for weighted gene co-expression networks based on partial correlation and information theory approaches, Bioinformatics, vol.26, pp.411-414, 2010.

P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res, vol.13, pp.2498-504, 2003.

G. Scardoni, M. Petterlini, and C. Laudanna, Analyzing biological network parameters with CentiScaPe, Bioinformatics, vol.25, pp.2857-2866, 2009.

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 Comput Biol, vol.10, p.1003731, 2014.

M. B. Gerstein, A. Kundaje, M. Hariharan, S. G. Landt, K. K. Yan et al., Architecture of the human regulatory network derived from ENCODE data, Nature, vol.489, pp.91-100, 2012.

G. Bindea, B. Mlecnik, H. Hackl, P. Charoentong, M. Tosolini et al., ClueGO: a Cytoscape plug-into decipher functionally grouped gene ontology and pathway annotation networks, Bioinformatics, vol.25, pp.1091-1094, 2009.

F. Grosclaude, M. F. Mahé, J. C. Mercier, and B. Ribadeau-dumas, Localisation des substitutions d'acides aminés différenciant les varianst A et B de la caséine kappa bovine, Ann Genet Sel Anim, vol.4, pp.515-536, 1972.

J. I. Weller, D. M. Bickhart, G. R. Wiggans, M. E. Tooker, J. R. O'connell et al., Determination of quantitative trait nucleotides by concordance analysis between quantitative trait loci and marker genotypes of US Holsteins, J Dairy Sci, vol.101, pp.9089-107, 2018.

M. P. Sanchez, A. Govignon-gion, P. Croiseau, S. Fritz, C. Hozé et al., Within-breed and multi-breed GWAS on imputed whole-genome Page, vol.19, p.19

. Sanchez, 51:34 ? fast, convenient online submission ? thorough peer review by experienced researchers in your field ? rapid publication on acceptance ? support for research data, including large and complex data types ? gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over, Genet Sel Evol, 2019.

, Genet Sel Evol, vol.49, p.68, 2017.

M. P. Sanchez, V. Wolf, E. Jabri, M. Beuvier, E. Rolet-répécaud et al., Short communication: confirmation of candidate causative variants on milk composition and cheesemaking properties in Montbéliarde cows, J Dairy Sci, vol.101, pp.10076-81, 2018.

S. I. Duchemin, H. Bovenhuis, H. J. Megens, J. Van-arendonk, and M. Visker, Fine-mapping of BTA17 using imputed sequences for associations with de novo synthesized fatty acids in bovine milk, J Dairy Sci, vol.100, pp.9125-9160, 2017.

T. M. Knutsen, H. G. Olsen, V. Tafintseva, M. Svendsen, A. Kohler et al., Unravelling genetic variation underlying de novo-synthesis of bovine milk fatty acids, Sci Rep, vol.8, p.2179, 2018.

K. E. Kemper, C. M. Reich, P. J. Bowman, C. J. Vander-jagt, A. J. Chamberlain et al., Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions, Genet Sel Evol, vol.47, p.29, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01303213

J. Jiang, Y. Gao, Y. Hou, W. Li, S. Zhang et al., Whole-genome resequencing of Holstein bulls for indel discovery and identification of genes associated with milk composition traits in dairy cattle, PLoS One, vol.11, p.168946, 2016.

C. Li, D. Sun, S. Zhang, S. Wang, X. Wu et al., Genome wide association study identifies 20 novel promising genes associated with milk fatty acid traits in Chinese Holstein, PLoS One, vol.9, p.96186, 2014.

H. Pausch, R. Emmerling, B. Gredler-grandl, R. Fries, H. D. Daetwyler et al., Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution, BMC Genom, vol.18, p.853, 2017.

T. J. Lopdell, K. Tiplady, M. Struchalin, T. Johnson, M. Keehan et al., DNA and RNA-sequence based GWAS highlights membranetransport genes as key modulators of milk lactose content, BMC Genom, vol.18, p.968, 2017.

K. Schoonjans, B. Staels, and J. Auwerx, The peroxisome proliferator activated receptors (PPARs) and their effects on lipid metabolism and adipocyte differentiation, Biochim Biophys Acta, vol.1302, pp.93-109, 1996.

R. A. Coleman and D. P. Lee, Enzymes of triacylglycerol synthesis and their regulation, Prog Lipid Res, vol.43, pp.134-76, 2004.

P. Martin and C. Leroux, Caprine gene specifying alpha(s1)-casein: a highly suspicious factor with both multiple and unexpected effects, Prod Anim, vol.13, pp.125-157, 2000.

R. Li, P. L. Dudemaine, X. Zhao, C. Lei, and E. M. Ibeagha-awemu, Comparative analysis of the miRNome of bovine milk fat, whey and cells, PLoS One, vol.11, p.154129, 2016.

Z. Li, H. Liu, J. X. Lo, L. Liu, and J. , Expression profiles of microRNAs from lactating and non-lactating bovine mammary glands and identification of miRNA related to lactation, BMC Genom, vol.13, p.731, 2012.

L. Guillou, S. Marthey, S. Laloe, D. Laubier, J. Mobuchon et al., Characterisation and comparison of lactating mouse and bovine mammary gland miRNomes, PLoS One, vol.9, p.91938, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01193894

X. Chen, C. Gao, H. Li, L. Huang, Q. Sun et al., Identification and characterization of microRNAs in raw milk during different periods of lactation, commercial fluid, and powdered milk products, Cell Res, vol.20, pp.1128-1165, 2010.

L. L. Faye, M. J. Machiela, P. Kraft, S. B. Bull, and L. Sun, Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification, PLoS Genet, vol.9, p.1003609, 2013.

. Au-terme-de-ce-manuscrit, Ingénieur d'études, j'ai démarré cette thèse à mi-carrière, un peu plus de trois ans après mon arrivée dans l'équipe génétique et génomique bovine (G2B) de l'unité GABI, après avoir travaillé dans l'équipe de génétique porcine de la même unité. A mon arrivée dans l'équipe G2B, on m'a confié le volet « protéines » du projet PhénoFinlait et quelques années plus tard, le projet From'MIR se mettait en place

, Les résultats qui en découlent sont originaux, ils ont été valorisés dans des articles scientifiques et dans des communications à des congrès mais aussi par des applications directes sur le terrain. La valorisation est à finaliser (article en cours de revue à GSE, l'initiative et dans lequel je me suis épanouie

I. Aguilar, I. Misztal, D. Johnson, A. Legarra, S. Tsuruta et al., Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score, Journal of Dairy Science, vol.93, pp.743-752, 2010.

C. Alais, Science du lait: principes des techniques laitières, 1984.

B. Amenu and H. Deeth, The impact of milk composition on cheddar cheese manufacture, Australian Journal of Dairy Technology, vol.62, pp.171-184, 2007.

, La transformation fermière du lait, AWE, 2018.

C. Bastin, L. Theron, A. Laine, and N. Gengler, On the role of mid-infrared predicted phenotypes in fertility and health dairy breeding programs, Journal of Dairy Science, vol.99, pp.4080-4094, 2016.

A. Baur, S. Fritz, J. Promp, O. Bulot, D. Boichard et al., Implementation of the French official genomic evaluation in Brown Swiss dairy cattle, 10th World Congress of Genetics Applied to Livestock Production, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01193910

M. Beaumont, Etude des possibilités de sélection des vaches de race Montbéliarde sur de nouveaux caractères de fromageabilité du lait. Mémoire de fin d'études, pp.janvier-août, 2018.

D. A. Biggs, Instrumental infrared estimation of fat, protein, and lactose in milk -Collaborative study, Journal of the Association of Official Analytical Chemists, vol.61, pp.1015-1034, 1978.

G. Bittante, C. Cipolat-gotet, and A. Cecchinato, Genetic parameters of different measures of cheese yield and milk nutrient recovery from an individual model cheesemanufacturing process, Journal of Dairy Science, vol.96, pp.7966-7979, 2013.

J. Bland, A. Grandison, and C. Fagan, Evaluation of milk compositional variables on coagulation properties using partial least squares, Journal of Dairy Research, vol.82, pp.8-14, 2015.

G. Bobe, D. Beitz, A. Freeman, and G. Lindberg, Effect of milk protein genotypes on milk protein composition and its genetic parameter estimates, Journal of Dairy Science, vol.82, pp.2797-2804, 1999.

D. Boichard, M. Boussaha, A. Capitan, D. Rocha, C. Hozé et al., Experience from large scale use of the EuroGenomics custom SNP chip in cattle, 2018.

D. Boichard and M. Brochard, New phenotypes for new breeding goals in dairy cattle, Animal, vol.6, pp.544-550, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01019802

D. Boichard, H. Chung, R. Dassonneville, X. David, A. Eggen et al.,

C. T. Hayes, T. S. Lawley, C. P. Sonstegard, P. M. Van-tassell, K. A. Vanraden et al., Design of a bovine low-density SNP array optimized for imputation, PLos One, vol.7, p.34130, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01019774

D. Boichard, F. Guillaume, A. Baur, P. Croiseau, M. Rossignol et al., Genomic selection in French dairy cattle, Animal Production Science, vol.52, pp.115-120, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01000520

V. Bonfatti, A. Cecchinato, L. Gallo, A. Blasco, and P. Carnier, Genetic analysis of detailed milk protein composition and coagulation properties in Simmental cattle, Journal of Dairy Science, vol.94, pp.5183-5193, 2011.

V. Bonfatti, F. Tiezzi, F. Miglior, and P. Carnier, Comparison of Bayesian regression models and partial least squares regression for the development of infrared prediction equations, Journal of Dairy Science, vol.100, pp.7306-7319, 2017.

V. Bonfatti, D. Vicario, L. Degano, A. Lugo, and P. Carnier, Comparison between direct and indirect methods for exploiting Fourier transform spectral information in estimation of breeding values for fine composition and technological properties of milk, Journal of Dairy Science, vol.100, pp.2057-2067, 2017.

V. Bonfatti, D. Vicario, A. Lugo, and R. Carnier, Genetic parameters of measures and population-wide infrared predictions of 92 traits describing the fine composition and technological properties of milk in Italian Simmental cattle, Journal of Dairy Science, vol.100, pp.5526-5540, 2017.

M. Boussaha, P. Michot, R. Letaief, C. Hoze, S. Fritz et al., Construction of a large collection of small genome variations in French dairy and beef breeds using whole-genome sequences, Genetics Selection Evolution, vol.48, p.87, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01479216

A. C. Bouwman, H. D. Daetwyler, A. J. Chamberlain, C. H. Ponce, M. Sargolzaei et al.,

G. Schenkel, A. Sahana, S. Govignon-gion, M. Boitard, H. Dolezal et al., , p.251

P. J. Bowman, B. Thomsen, B. Guldbrandtsen, M. S. Lund, B. Servin et al.,

J. J. Jagt, A. Crowley, D. C. Bieber, D. P. Purfield, R. Berry et al., Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals, Nature Genetics, vol.50, pp.362-367, 2018.

H. Bovenhuis and J. Weller, Mapping and analysis of dairy-cattle quantitative trait loci by maximum-likelihood methodology using milk protein genes as genetic-markers, Genetics, vol.137, pp.267-280, 1994.

B. Buitenhuis, N. Poulsen, L. Larsen, and J. Sehested, Estimation of genetic parameters and detection of quantitative trait loci for minerals in Danish Holstein and Danish Jersey milk, BMC Genetics, vol.16, p.52, 2015.

A. Caroli, S. Chessa, and G. Erhardt, Invited review: Milk protein polymorphisms in cattle: Effect on animal breeding and human nutrition, Journal of Dairy Science, vol.92, pp.5335-5352, 2009.

A. Cecchinato, A. Albera, C. Cipolat-gotet, A. Ferragina, and G. Bittante, Genetic parameters of cheese yield and curd nutrient recovery or whey loss traits predicted using Fourier-transform infrared spectroscopy of samples collected during milk recording on Holstein, Brown Swiss, and Simmental dairy cows, Journal of Dairy Science, vol.98, pp.4914-4927, 2015.

A. Cecchinato and G. Bittante, Genetic and environmental relationships of different measures of individual cheese yield and curd nutrients recovery with coagulation properties of bovine milk, Journal of Dairy Science, vol.99, pp.1975-1989, 2016.

A. Cecchinato, M. De-marchi, L. Gallo, G. Bittante, and P. Carnier, Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk, Journal of Dairy Science, vol.92, pp.5304-5313, 2009.

A. Cecchinato, M. Penasa, M. De-marchi, L. Gallo, G. Bittante et al., Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows, Journal of Dairy Science, vol.94, pp.4205-4213, 2011.

A. J. Chamberlain, B. J. Hayes, R. Xiang, C. J. Vander-jagt, C. M. Reich et al., Identification of regulatory variation in dairy cattle with RNA sequence data, 2018.

C. Cipolat-gotet, A. Cecchinato, M. De-marchi, M. Penasa, and G. Bittante, Comparison between mechanical and near-infrared methods for assessing coagulation properties of bovine milk, Journal of Dairy Science, vol.95, pp.6806-6819, 2012.

, Chiffres clés 2017, Conseil National des Appellations d'Origine, 2018.

, L'économie laitière en chiffres -Edition, Centre National Interprofessionnel de l'Economie, 2018.

F. G. Colinet, T. Troch, O. Abbas, V. Baeten, F. Dehareng et al., Potentiel d'utilisation de la spectrométrie moyen infrarouge pour prédire le rendement fromager du lait et étudier sa variabilité génétique, Rencontres Recherche Ruminants, vol.20, 2013.

M. Coppa, A. Ferlay, C. Leroux, M. Jestin, Y. Chilliard et al., Prediction of milk fatty acid composition by near infrared reflectance spectroscopy, International Dairy Journal, vol.20, pp.182-189, 2010.

W. Coppieters, J. Riquet, J. Arranz, P. Berzi, N. Cambisano et al., A QTL with major effect on milk yield and composition maps to bovine Chromosome 14, Mammalian Genome, vol.9, pp.540-544, 1998.

G. Corrieu, H. Spinnler, Y. Jomier, and D. Picque, Automated system to follow up and control the acidification activity of lactic acid starters, French Patent FR, vol.2, pp.629-612, 1988.

S. Couvreur and C. Hurtaud, Globule milk fat: Secretion, composition, function and variation factors, INRA Productions Animales, vol.20, pp.369-382, 2007.

H. D. Daetwyler, A. Capitan, H. Pausch, P. Stothard, R. Van-binsbergen et al., Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle, Nature Genetics, vol.46, pp.858-867, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01193853

B. Dagnachew, T. Meuwissen, and T. Adnoy, Genetic components of milk Fouriertransform infrared spectra used to predict breeding values for milk composition and quality traits in dairy goats, Journal of Dairy Science, vol.96, pp.5933-5942, 2013.

R. Dal-zotto, M. De-marchi, A. Cecchinato, M. Penasa, M. Cassandro et al., Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy, Journal of Dairy Science, vol.91, pp.4103-4112, 2008.

C. G. De-kruif and C. Holt, Casein Micelle Structure, Functions and Interactions. Pages 233-276 in Advanced Dairy Chemistry-1 Proteins: Part A / Part, 2003.

M. De-marchi, V. Bonfatti, A. Cecchinato, G. D. Martino, and P. Carnier, Prediction of protein composition of individual cow milk using mid-infrared spectroscopy, Italian Journal of Animal Science, vol.8, pp.399-401, 2009.

M. De-marchi, C. Fagan, C. O'donnell, A. Cecchinato, R. Zotto et al., Prediction of coagulation properties, titratable acidity, and pH of bovine milk using mid-infrared spectroscopy, Journal of Dairy Science, vol.92, pp.423-432, 2009.

M. De-marchi, M. Penasa, F. Tiezzi, V. Toffanin, and M. Cassandro, Prediction of milk coagulation properties by Fourier Transform Mid-Infrared Spectroscopy (FTMIR) for genetic purposes, herd management and dairy profitability. Pages 47-53 in International Strategies and New Developments in Milk Analysis, VI ICAR Reference Laboratory Network Meeting, issue.16, 2012.

M. De-marchi, M. Penasa, A. Zidi, and C. Manuelian, Invited review: Use of infrared technologies for the assessment of dairy products-Applications and perspectives, Journal of Dairy Science, vol.101, pp.10589-10604, 2018.

M. De-marchi, M. V.-toffanin, M. Cassandro, and . Penasa, Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits, Journal of Dairy Science, vol.97, pp.1171-1186, 2014.

T. Druet, F. Jaffrezic, and V. Ducrocq, Estimation of genetic parameters for test day records of dairy traits in the first three lactations, Genetics Selection Evolution, vol.37, pp.257-271, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00894503

V. Ducrocq, Genekit, BLUP software. INRA, 2011.

A. Eck, Le fromage, 1984.

M. El-jabri, M. P. Sanchez, C. Laithier, E. Doutart, V. Wolf et al.,

, Bayesian regression models and variable selection methods before PLS regression. Application to the prediction of milk cheese-making properties using infared spectral data, Chimiometrie XVIII

M. El-jabri, M. P. Sanchez, P. Trossat, C. Laithier, V. Wolf et al., Comparison of Bayesian and PLS regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows, Sous presse dans Journal of Dairy Science, vol.102, 2019.

. Encode-project-consortium, An integrated encyclopedia of DNA elements in the human genome, Nature, vol.489, pp.57-74, 2012.

M. Erbe, B. Hayes, L. Matukumalli, S. Goswami, P. Bowman et al., Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels, Journal of Dairy Science, vol.95, pp.4114-4129, 2012.

Z. Fang, M. Visker, G. Miranda, A. Delacroix-buchet, H. Bovenhuis et al., The relationships among bovine alpha(S)-casein phosphorylation isoforms suggest different phosphorylation pathways, Journal of Dairy Science, vol.99, pp.8168-8177, 2016.

R. Fernando, H. Cheng, B. Golden, and D. Garrick, Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and nongenotyped animals, Genetics Selection Evolution, vol.48, p.96, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01479233

A. Ferragina, C. Cipolat-gotet, A. Cecchinato, and G. Bittante, The use of Fouriertransform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from unprocessed bovine milk samples, Journal of Dairy Science, vol.96, pp.7980-7990, 2013.

A. Ferragina, G. De-los-campos, A. Vazquez, A. Cecchinato, and G. Bittante, Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data, Journal of Dairy Science, vol.98, pp.8133-8151, 2015.

M. Ferrand, G. Miranda, S. Guisnel, H. Larroque, O. Leray et al.,

. Martin, Determination of protein composition in milk by mid-infrared spectrometry. Pages 41-45 in, Proc. International Strategies and New Developments in Milk Analysis. VI ICAR Reference Laboratory Network Meeting, 2012.

M. Ferrand-calmels, I. Palhiere, M. Brochard, O. Leray, J. Astruc et al., Prediction of fatty acid profiles in cow, ewe, and goat milk by mid-infrared spectrometry, Journal of Dairy Science, vol.97, pp.17-35, 2014.

A. Fleming, F. Schenkel, J. Chen, F. Malchiodi, V. Bonfatti et al., Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets, Journal of Dairy Science, vol.100, pp.5073-5081, 2017.

M. Fortes, A. Reverter, Y. Zhang, E. Collis, S. Nagaraj et al., Association weight matrix for the genetic dissection of puberty in beef cattle, Proceedings of National Academy of Sciences USA, vol.107, pp.13642-13647, 2010.

. Franceagrimer, Données et bilans FranceAgriMer: les produits carnés et laitiers -Données statistiques 2017 -France / UE / Monde, 2018.

P. Garnsworthy, S. Feng, A. Lock, and M. Royal, Short communication: Heritability of milk fatty acid composition and stearoyl-CoA desaturase indices in dairy cows, Journal of Dairy Science, vol.93, pp.1743-1748, 2010.

N. Gaudillière, M. Gelé, M. P. Sanchez, M. E. Jabri, V. Wolf et al., La fromageabilité du lait en race Montbéliarde dans les élevages AOP et IGP de Franche-Comté : variabilité et facteurs de variation. in Rencontres Recherche Ruminants, vol.24, 2018.

N. Gengler, H. Soyeurt, F. Dehareng, C. Bastin, F. Colinet et al., Capitalizing on fine milk composition for breeding and management of dairy cows, Journal of Dairy Science, vol.99, pp.4071-4079, 2016.

M. Georges, D. Nielsen, M. Mackinnon, A. Mishra, R. Okimoto et al., Mapping quantitative trait loci controlling milk-production in dairy-cattle by exploiting progeny testing, Genetics, vol.139, pp.907-920, 1995.

E. Giuffra, C. K. Tuggle, and F. Consortium, Functional Annotation of Animal Genomes (FAANG): Current Achievements and Roadmap, Annual Review of Animal Biosciences, vol.7, pp.65-88, 2019.

M. Glantz, T. Devold, G. Vegarud, H. Mansson, H. Stalhammar et al., Importance of casein micelle size and milk composition for milk gelation, Journal of Dairy Science, vol.93, pp.1444-1451, 2010.

M. Goddard and B. Hayes, Mapping genes for complex traits in domestic animals and their use in breeding programmes, Nature Review Genetics, vol.10, pp.381-391, 2009.

C. Grelet, C. Bastin, M. Gele, J. Daviere, M. Johan et al., Development of Fourier transform mid-infrared calibrations to predict acetone, beta-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network, Journal of Dairy Science, vol.99, pp.4816-4825, 2016.

C. Grelet, J. Pierna, P. Dardenne, V. Baeten, and F. Dehareng, Standardization of milk mid-infrared spectra from a European dairy network, Journal of Dairy Science, vol.98, pp.2150-2160, 2015.

C. Grelet, J. Pierna, P. Dardenne, H. Soyeurt, A. Vanlierde et al., Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models, Journal of Dairy Science, vol.100, pp.7910-7921, 2017.

C. Grelet, A. Vanlierde, M. Hostens, L. Foldager, M. Salavati et al.,

E. Sorensen, C. P. Froidmont, C. Ferris, F. Marchitelli, T. Becker et al.,

. Dehareng, Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach, Animal, vol.13, pp.649-658, 2019.

F. Grosclaude, Le polymorphisme génétique des principales lactoprotéines bovines, INRA Productions Animales, vol.1, pp.5-17, 1988.

D. Habier, R. Fernando, K. Kizilkaya, and D. Garrick, Extension of the bayesian alphabet for genomic selection, BMC Bioinformatics, vol.12, p.186, 2011.

M. Haile-mariam and J. Pryce, Genetic parameters for lactose and its correlation with other milk production traits and fitness traits in pasture-based production systems, Journal of Dairy Science, vol.100, pp.3754-3766, 2017.

J. Heck, A. Schennink, H. Van-valenberg, H. Bovenhuis, M. Visker et al., Effects of milk protein variants on the protein composition of bovine milk, Journal of Dairy Science, vol.92, pp.1192-1202, 2009.

B. Howie, C. Fuchsberger, M. Stephens, J. Marchini, and G. Abecasis, Fast and accurate genotype imputation in genome-wide association studies through pre-phasing, Nature Genetics, vol.44, p.955, 2012.

C. Hurtaud, J. Peyraud, G. Michel, D. Berthelot, and L. Delaby, Winter feeding systems and dairy cow breed have an impact on milk composition and flavour of two Protected Designation of Origin French cheeses, Animal, vol.3, pp.1327-1338, 2009.

C. Hurtaud, H. Rulquin, M. Delaite, and R. Verite, Prediction of cheese yielding efficiency of individual milk of dairy cows -correlation with coagulation parameters and laboratory curd yield, Annales de Zootechnie, vol.44, pp.385-398, 1995.

. Idele, Evaluation génétique des taureaux Monbéliards: production laitière, morphologie et caractères fonctionnels, Les chiffres clés du GEB -bovins 2017 -Productions lait et viande. Institut de l'élevage et Confédération Nationale de l'Elevage, vol.17, 2018.

T. Johnson, M. Keehan, C. Harland, T. Lopdell, R. J. Spelman et al.,

L. Smith and C. Couldrey, Short communication: Identification of the pseudoautosomal region in the Hereford bovine reference genome assembly ARS-UCD1.2, Journal of Dairy Science, vol.102, pp.3254-3258, 2019.

R. Karoui, A. Mouazen, E. Dufour, R. Schoonheydt, and J. De-baerdemaeker, Utilisation of front-face fluorescence spectroscopy for the determination of some selected chemical parameters in soft cheeses, Lait, vol.86, pp.155-169, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00895579

P. Kumar, S. Henikoff, and P. Ng, Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm, Nature Protocols, vol.4, pp.1073-1082, 2009.

A. Legarra, A. Ricard, and L. Varona, GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait, G3-Genes Genomes Genetics, vol.8, pp.2301-2308, 2018.

J. Legarto, M. Gelé, A. Ferlay, C. Hurtaud, G. et al., Effets des conduites d'élevage sur la production de lait, les taux butyreux et protéique et la composition en acides gras du lait de vache, chèvre et brebis évaluée par spectrométrie dans le moyen infrarouge, INRA Productions Animales, vol.27, pp.269-282, 2014.

J. Leonil, M. Michalski, and P. Martin, Supramolecular structures of milk: structure and nutritional impact of the casein micelle and the milk fat globule, INRA Productions Animales, vol.26, pp.129-143, 2013.

R. Letaief, E. Rebours, C. Grohs, C. Meersseman, S. Fritz et al., Identification of copy number variation in French dairy and beef breeds using next-generation sequencing, Genetics Selection Evolution, vol.49, p.77, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01622967

A. Lunden, M. Nilsson, and L. Janson, Marked effect of beta-lactoglobulin polymorphism on the ratio of casein to total protein in milk, Journal of Dairy Science, vol.80, pp.2996-3005, 1997.

M. Mahaut, R. Jeantet, and G. Brulé, Initiation à la technologie fromagère, 2000.

M. Malacarne, P. Formaggioni, P. Franceschi, and A. Summer, Seasonal variations of milk quality in Parmigiano-Reggiano cheese manufacture on a period of 10 years, Pages 63-77 in Scienza e Tecnica Lattiero Casearia, vol.55, 2004.

P. Martin, M. Szymanowska, L. Zwierzchowski, and C. Leroux, The impact of genetic polymorphisms on the protein composition of ruminant milks, Reproduction Nutrition Development, vol.42, pp.433-459, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00900338

W. Mclaren, L. Gil, S. Hunt, H. Riat, G. Ritchie et al.,

, The Ensembl Variant Effect Predictor, Genome Biology, vol.17, p.122

S. Mcparland, E. Lewis, E. Kennedy, S. Moore, B. Mccarthy et al., Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows, Journal of Dairy Science, vol.97, pp.5863-5871, 2014.

M. Mesbah-uddin, T. B.-guldbrandtsen, J. Iso-touru, D. Vilkki, D. Koning et al., Genome-wide mapping of large deletions and their populationgenetic properties in dairy cattle, DNA Research, vol.25, pp.49-59, 2018.

T. H. Meuwissen and M. E. Goddard, Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci, Genetics, vol.155, pp.421-430, 2000.

T. H. Meuwissen, B. J. Hayes, and M. E. Goddard, Prediction of total genetic value using genome-wide dense marker maps, Genetics, vol.157, pp.1819-1829, 2001.

K. Meyer, WOMBAT -A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML), Journal of Zhejiang University Science B, vol.8, pp.815-821, 2007.

G. Miranda, N. Boumahrou, L. Bianchi, A. Pinard, B. Saadaoui et al., Understanding milk protein complexity to produce accurate phenotypes, 8th Int. Milk Genomics Consortium Symp, 2011.

M. Montel, S. Buchin, A. Mallet, C. Delbes-paus, D. Vuitton et al., Traditional cheeses: Rich and diverse microbiota with associated benefits, International Journal of Food Microbiology, vol.177, pp.136-154, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02086940

P. Perez and G. De-los-campos, Genome-Wide Regression and Prediction with the BGLR Statistical Package, Genetics, vol.198, pp.483-495, 2014.

N. A. Poulsen, A. J. Buitenhuis, and L. B. Larsen, Phenotypic and genetic associations of milk traits with milk coagulation properties, Journal of Dairy Science, vol.98, pp.2079-2087, 2015.

D. Pretto, N. Lopez-villalobos, M. Penasa, and M. Cassandro, Genetic response for milk production traits, somatic cell score, acidity and coagulation properties in Italian Holstein-Friesian population under current and alternative selection indices and breeding objectives, Livestock Science, vol.150, pp.59-66, 2012.

D. Pretto, M. Vallas, E. Parna, A. Tanavots, H. Kiiman et al., Short communication: Genetic correlation and heritability of milk coagulation traits within and across lactations in Holstein cows using multiple-lactation random regression animal models, Journal of Dairy Science, vol.97, pp.7980-7984, 2014.

R. Rahman, Etudes des variants des protéines du lait de vache. Mémoire de L2, p.2017, 2017.

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, p.62, 2014.

M. Rutten, H. Bovenhuis, K. Hettinga, H. Van-valenberg, and J. Van-arendonk, Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer, Journal of Dairy Science, vol.92, pp.6202-6209, 2009.

M. Salque, P. Bogucki, J. Pyzel, I. Sobkowiak-tabaka, R. Grygiel et al., Earliest evidence for cheese making in the sixth millennium BC in northern Europe, Nature, vol.493, pp.522-525, 2013.

M. P. Sanchez, M. Ferrand, M. Gele, D. Pourchet, G. Miranda et al., Short communication: Genetic parameters for milk protein composition predicted using mid-infrared spectroscopy in the French Montbeliarde, Normande, and Holstein dairy cattle breeds, Journal of Dairy Science, vol.100, pp.6371-6375, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01607785

M. P. Sanchez, A. Govignon-gion, P. Croiseau, S. Fritz, C. Hozé et al., Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle, Genetics Selection Evolution, vol.49, p.68, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01589691

M. P. Sanchez, A. Govignon-gion, M. Ferrand, M. Gele, D. Pourchet et al., Whole-genome scan to detect quantitative trait loci associated with milk protein composition in 3 French dairy cattle breeds, Journal of Dairy Science, vol.99, pp.8203-8215, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01533901

M. P. Sanchez, D. Jonas, A. Baur, V. Ducrocq, C. Hozé et al., Implementation of genomic selection in three French regional dairy cattle breeds, Proc. 67th European Association of Animal Production, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01531682

M. P. Sanchez, Y. Ramayo-caldas, V. Wolf, C. Laithier, M. E. Jabri et al., Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows, Genetics Selection Evolution, vol.51, p.34, 2019.

M. P. Sanchez, V. Wolf, M. E. Jabri, E. Beuvier, O. Rolet-répécaud et al., Short communication: Confirmation of candidate causative variants on milk composition and cheesemaking properties in Montbéliarde cows, Journal of Dairy Science, vol.101, pp.10076-10081, 2018.

M. Sargolzaei, J. Chesnais, and F. Schenkel, A new approach for efficient genotype imputation using information from relatives, BMC Genomics, vol.15, p.478, 2014.

R. Schnabel, H. Daetwyler, and A. Chamberlain, Bovine haplotype reference consortium 1000 bulls and GTEx: international projects to advance bovine genomic research. in Plant and Animal Genome XXVII, 2019.

G. C. Schopen, J. M. Heck, H. Bovenhuis, M. H. Visker, H. J. Van-valenberg et al., Genetic parameters for major milk proteins in Dutch Holstein-Friesians, Journal of Dairy Science, vol.92, pp.1182-1191, 2009.

H. Soyeurt, D. Bruwier, J. Romnee, N. Gengler, C. Bertozzi et al.,

, Potential estimation of major mineral contents in cow milk using mid-infrared spectrometry, Journal of Dairy Science, vol.92, pp.2444-2454

H. Soyeurt, P. Dardenne, F. Dehareng, G. Lognay, D. Veselko et al., Estimating fatty acid content in cow milk using mid-infrared spectrometry, Journal of Dairy Science, vol.89, pp.3690-3695, 2006.

H. Soyeurt, F. Dehareng, N. Gengler, S. Mcparland, E. Wall et al.,

. Dardenne, Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries, Journal of Dairy Science, vol.94, pp.1657-1667, 2011.

H. Soyeurt, I. Misztal, and N. Gengler, Genetic variability of milk components based on mid-infrared spectral data, Journal of Dairy Science, vol.93, pp.1722-1728, 2010.

R. Spelman, W. Coppieters, L. Karim, J. , and H. Bovenhuis, Quantitative trait loci analysis for five milk production traits on chromosome six in the Dutch Holstein-Friesian population, Genetics, vol.144, pp.1799-1807, 1996.

U. Sundekilde, P. Frederiksen, M. Clausen, L. Larsen, and H. Bertram, Relationship between the Metabolite Profile and Technological Properties of Bovine Milk from Two Dairy Breeds Elucidated by NMR-Based Metabolomics, Journal of Agricultural and Food Chemistry, vol.59, pp.7360-7367, 2011.

V. Toffanin, M. De-marchi, N. Lopez-villalobos, and M. Cassandro, Effectiveness of mid-infrared spectroscopy for prediction of the contents of calcium and phosphorus, and titratable acidity of milk and their relationship with milk quality and coagulation properties, International Dairy Journal, vol.41, pp.68-73, 2015.

V. Toffanin, M. Penasa, S. Mcparland, D. Berry, M. Cassandro et al., Genetic parameters for milk mineral content and acidity predicted by mid-infrared spectroscopy in Holstein-Friesian cows, Animal, vol.9, pp.775-780, 2015.

M. Vallas, H. Bovenhuis, T. Kaart, K. Parna, H. Kiiman et al., Genetic parameters for milk coagulation properties in Estonian Holstein cows, Journal of Dairy Science, vol.93, pp.3789-3796, 2010.

M. Vallas, T. Kaart, S. Varv, K. Parna, I. Joudu et al., Composite beta-kappa-casein genotypes and their effect on composition and coagulation of milk from Estonian Holstein cows, Journal of Dairy Science, vol.95, pp.6760-6769, 2012.

I. Van-den-berg, D. Boichard, and M. Lund, Comparing power and precision of withinbreed and multibreed genome-wide association studies of production traits using wholegenome sequence data for 5 French and Danish dairy cattle breeds, Journal of Dairy Science, vol.99, pp.8932-8945, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01533908

K. J. Van-hulzen, R. C. Sprong, R. Van-der-meer, and J. A. Van-arendonk, Genetic and nongenetic variation in concentration of selenium, calcium, potassium, zinc, magnesium, and phosphorus in milk of Dutch Holstein-Friesian cows, Journal of Dairy Science, vol.92, pp.5754-5759, 2009.

A. Vanlierde, H. Soyeurt, N. Gengler, F. Colinet, E. Froidmont et al., Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers, Journal of Dairy Science, vol.101, pp.7618-7624, 2018.

P. M. Vanraden, Invited review: Selection on net merit to improve lifetime profit, Journal of Dairy Science, vol.87, pp.3125-3131, 2004.

R. F. Veerkamp, L. Kaal, Y. D. Haas, and J. D. Oldham, Breeding for robust cows that produce healthier milk: RobustMilk, Advances in Animal Biosciences, vol.4, pp.594-599, 2013.

G. Visentin, A. Mcdermott, S. Mcparland, D. Berry, O. Kenny et al., Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows, Journal of Dairy Science, vol.98, pp.6620-6629, 2015.

G. Visentin, S. Mcparland, M. De-marchi, A. Mcdermott, M. Fenelon et al., Processing characteristics of dairy cow milk are moderately heritable, Journal of Dairy Science, vol.100, pp.6343-6355, 2017.

M. Visker, B. Dibbits, S. Kinders, H. Van-valenberg, J. Van-arendonk et al., Association of bovine beta-casein protein variant I with milk production and milk protein composition, Animal Genetics, vol.42, pp.212-218, 2011.

P. Visscher, N. Wray, Q. Zhang, P. Sklar, M. Mccarthy et al., 10 Years of GWAS Discovery: Biology, Function, and Translation, American Journal of Human Genetics, vol.101, pp.5-22, 2017.

P. Walstra, Casein sub-micelles: do they exist ?, International Dairy Journal, vol.9, pp.189-192, 1999.

Y. Wang, D. Veltkamp, and B. Kowalski, Multivariate Instrument Standardization, Analytical Chemistry, vol.63, pp.2750-2756, 1991.

H. Westra and L. Franke, From genome to function by studying eQTLs, Biochimica Et Biophysica Acta-Molecular Basis of Disease, vol.1842, pp.1896-1902, 2014.

J. Yang, T. Ferreira, A. Morris, S. Medland, P. Madden et al.,

A. Trai, D. G. Meta-a, G. I. Trai, D. G. Meta, and -. , Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits, Nature Genetics, vol.44, pp.369-375, 2012.

J. Yang, S. Lee, M. Goddard, and P. Visscher, GCTA: a tool for genome-wide complex trait analysis, American Journal of Human Genetics, vol.88, pp.76-82, 2011.

, Liste des publications, pp.2016-2019

M. Sanchez, A. Govignon-gion, M. Ferrand, M. Gelé, D. Pourchet et al., Identification of QTL and candidate mutations affecting major milk proteins in three French dairy cattle breeds, Journal of Dairy Science, vol.99, pp.8203-8215, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01193917

M. Sanchez, M. Ferrand, M. Gelé, D. Pourchet, G. Miranda et al., Short-communication: Genetic parameters for milk protein composition predicted using mid-infrared spectroscopy in the French Montbéliarde, Normande and Holstein dairy cattle breeds, Journal of Dairy Science, vol.100, pp.6371-6375, 2017.

M. Sanchez, A. Govignon-gion, P. Croiseau, S. Fritz, C. Hozé et al., Within-breed and multibreed GWAS on imputed whole genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle, Genetics Selection Evolution, vol.49, p.68, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01589691

M. Sanchez, M. El-jabri, S. Minéry, V. Wolf, E. Beuvier et al., Genetic parameters for cheese-making properties and milk composition predicted from mid-infrared spectrometry in a large dataset of Montbéliarde cows, Journal of Dairy Science, vol.101, pp.10048-10061, 2018.

M. Sanchez, V. Wolf, M. El-jabri, E. Beuvier, O. Rolet-répécaud et al., Short Communication: Confirmation of candidate causative variants on milk composition and cheese-making properties in Montbéliarde cows, Journal of Dairy Science, vol.101, pp.10076-10081, 2018.

M. Sanchez, Y. Ramayo-caldas, V. Wolf, C. Laithier, M. El-jabri et al., Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows, Genetics Selection Evolution, vol.51, p.34, 2019.

E. Jabri, M. Sanchez, M. Trossat, P. Laithier, C. Wolf et al., Comparison of Bayesian and PLS regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows, Sous presse dans Journal of Dairy Science, vol.102, 2019.

M. Sanchez, V. Wolf, C. Laithier, M. El-jabri, E. Beuvier et al., Analyse génétique de la fromageabilité du lait de vache prédite par spectrométrie moyen infrarouge en race Montbéliarde, 2019.

M. Sanchez, A. Govignon-gion, A. Barbat, M. Gelé, S. Fritz et al., Using whole genome sequences to identify candidate mutations of milk fatty acids and proteins in dairy cattle. 24 th Plant & animal Genome, 2016.

D. Boichard, M. Sanchez, A. Govignon-gion, A. Barbat, M. Boussaha et al., Identification of candidate causal variants underlying QTL in dairy cattle through GWAS and Bayesian approach at the sequence level. 24 th Plant & animal Genome, 2016.

M. Sanchez, Genetic analysis of bovine milk composition and cheese-making abilities predicted from MIR spectra, Séminaire des doctorants du département de génétique animale INRA, pp.16-17, 2016.

E. Jabri, M. Sanchez, M. Laithier, C. Doutart, E. Wolf et al., Bayesian regression models and variable selection methods before PLS regression. Application to the prediction of mill cheese-making properties using infrared spectral data, Chimiometrie XVIII, 2017.

M. Sanchez, Genetic analysis of bovine milk composition and cheese-making abilities predicted from MIR spectra. Séminaire des doctorants du département de génétique animale INRA, 2017.

M. Sanchez, V. Wolf, M. El-jabri, E. Beuvier, O. Rolet-répécaud et al., Validation of candidate causative variants on milk composition and cheese-making properties in Montbéliarde cows, proceedings of the 11 th World Congress on Genetics Applied to Livestock Production: 11th WCGALP, pp.12-16, 2018.

M. Sanchez, V. Wolf, M. El-jabri, M. Boussaha, S. Taussat et al., GWAS on whole genome sequences for cheese-making traits and milk composition in Montbéliarde cows. 69 th EAAP meeting, 2018.

M. Sanchez, V. Wolf, M. El-jabri, M. Boussaha, S. Taussat et al., GWAS on whole genome sequences for cheese-making traits and milk composition in Montbéliarde cows, Journées scientifiques du département de génétique animale, 2018.

N. Gaudilliere, M. Gelé, M. Sanchez, M. El-jabri, V. Wolf et al., La fromageabilité du lait en race Montbéliarde dans les élevages AOP et IGP de Franche-Comté : variabilité et facteurs de variation, pp.7-8, 2018.

E. Jabri, M. Rolet-répécaud, O. Sanchez, M. Wolf, V. Beuvier et al., Bayes and partial least square regression methods for infrared prediction of dairy vat milk casein micelle size. 12th EFITA, 2019.

M. Sanchez, T. Tribout, V. Wolf, M. El-jabri, N. Gaudillière et al., Towards a genomic evaluation of cheese-making traits including candidate SNP in Montbéliarde cows. Accepté à 70th EAAP meeting, 2019.

D. Aarhus, , 2019.

R. Rahman, Etudes des variants des protéines du lait de vache. Mémoire de L2, p.2017, 2017.

M. Beaumont, Etude des possibilités de sélection des vaches de race Montbéliarde sur de nouveaux caractères de fromageabilité du lait. Mémoire de fin d'études, pp.janvier-août, 2018.

, Autres résultats sur la période, pp.2016-2019

, Articles scientifiques

J. D. Ducrocq, V. Fritz, S. Baur, A. Sanchez, M. Croiseau et al., Genomic evaluation of regional dairy cattle breeds in single-breed and multibreed contexts, Journal of animal breeding and genetics, vol.134, pp.3-13, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01484842

M. Teissier, M. Sanchez, M. Boussaha, A. Barbat-leterrier, C. Hozé et al., Use of meta-analyses and joint analyses to select variants in whole genome sequences for genomic evaluation: an application in milk production of French dairy cattle breeds, Journal of Dairy Science, vol.101, pp.3126-3139, 2017.

, Communications à des congrès / séminaires

M. P. Sanchez, D. Jonas, A. Baur, V. Ducrocq, C. Hoze et al., Mise en place d'une évaluation génomique en races, 2016.

T. Abondance and . Vosgienne, Rencontres Recherches Ruminants, 2016.

M. Sanchez, R. Guatteo, A. Davergne, C. Grohs, A. Capitan et al., Whole genome association analysis of resistance / susceptibility to paratuberculosis in French Holstein and Normande cattle, 13 th International Colloquium on Paratuberculosis, pp.20-24, 2016.

A. Marete, M. Boussaha, D. Rocha, S. Fritz, P. Michot et al., Candidate variant confirmation in three French dairy breeds: GWAS exploits on big data. 5 th ICQG, pp.12-17, 2016.

M. Sanchez, R. Guatteo, A. Davergne, C. Grohs, A. Capitan et al., GWAS of resistance to paratuberculosis in French Holstein and Normande cattle. 67 th EAAP meeting, 2016.

M. Sanchez, D. Jonas, A. Baur, V. Ducrocq, C. Hozé et al., Implementation of genomic selection in three French regional dairy cattle breeds. 67 th EAAP meeting, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01531682

T. Tribout, M. Barbat, A. Govignon-gion, A. Launay, R. Lefebvre et al., Using whole genome sequences to identify QTL for udder health and morphology in French dairy cattle. 67 th EAAP meeting, 2016.

E. Venot, D. Boichard, V. Ducrocq, P. Croiseau, S. Fritz et al., French genomic experience: genomics for all ruminant species. Presented at 40. Biennal session of ICAR, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01606324

P. Croiseau, T. Tribout, D. Boichard, M. P. Sanchez, and S. Fritz, Use of whole sequence GWAS to improve genomic evaluation in dairy cattle. 25 th Plant & animal Genome, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01608241

. D. Boichard, M. Boussaha, A. Capitan, D. Rocha, C. Hozé et al., Experience from large scale use of the EuroGenomics custom SNP chip in cattle, proceedings of the 11 th World Congress on Genetics Applied to Livestock Production: 11th WCGALP, pp.12-16, 2018.

P. Croiseau, T. Tribout, . D. Boichard, M. Sanchez, and S. Fritz, Use of whole sequence GWAS to improve genomic evaluation in dairy cattle, proceedings of the 11 th World Congress on Genetics Applied to Livestock Production: 11th WCGALP, pp.12-16, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01608241

M. P. Sanchez, R. Guatteo, A. Davergne, C. Grohs, S. Taussat et al., Sequence-based association study of resistance to paratuberculosis in Holstein and Normande cattle. 69 th EAAP meeting, 2018.

P. Croiseau, C. Hozé, S. Fritz, M. Sanchez, and T. Tribout, Inclusion of candidate mutations in genomic evaluation for French dairy cattle breeds. 69th EAAP meeting, 2018.

C. Bourdon, M. Boussaha, R. Rupp, M. Sanchez, T. Tribout et al., Recherche d'associations entre microARNs, variants génétiques et QTL laitiers chez les bovins, caprins et ovins, Rencontres Recherches, pp.7-8, 2018.

, Les travaux de l'UMT eBIS dans

, Umotest est un lieu de concertation et de décisions pour conduire le programme de sélection au quotidien mais également pour anticiper les besoins des éleveurs et imaginer les services, outils, indicateurs qui leur seront utiles. Dans ce cadre-là, Umotest est très actif en matière de R&D partenariale. Elle a été à l'initiative ou motrice de plusieurs innovations récentes (génomique, sexage de la semence?, Umotest, est une union de coopératives agricoles (insémination) qui diffuse ses taureaux Montbéliards auprès de 15 000 éleveurs français et dont l'activité de ses coopératives adhérentes représente 80% de l'insémination française dans cette race

, Rôle d'Umotest dans FROM'MIR

, Umotest a accompagné et soutenu le Conseil Elevage 25-90 pour monter et conduire le projet FROM'MIR. Umotest s'est fortement impliquée dans FROM'MIR, par la participation de MM. Hervé Bole (éleveur administrateur) et Tristan Gaiffe (directeur général) au pilotage et de Mickaël Brochard (responsable R&D) dans le suivi et la réalisation du programme. Umotest a également mis à disposition du projet l'intégralité des génotypes de femelles (et mâles) dont elle disposait. Le rôle de Mickaël Brochard a consisté à animer et participer, avec l'Institut de l'Elevage, l'INRA et Allice (UMTeBIS), aux travaux du 3 ème volet de FROM'MIR dédié à l'analyse des facteurs génétiques, dont les principaux résultats sont présentés dans cette newsletter

, Jura Conseil Elevage) réalisent le contrôle de performances en élevage. Cette mission consiste à prélever un échantillon de lait par vache traite à fréquence régulière dans les élevages, Cela répond à deux objectifs : ? Un objectif génétique et collectif : collecter des phénotypes nécessaires pour conduire les programmes génétiques et établir les index

, ? Un objectif économique et individuel : élaborer un suivi et le pilotage de l'atelier lait

. Le-coeur-de-métier-de-ces-trois-entreprises, Les conseillers techniques valorisent les données issues du contrôle de performance et apportent une expertise dans les élevages laitiers de la région pour améliorer la rentabilité des exploitations laitières. Le conseil technique porte notamment sur l'alimentation, les fourrages, la qualité du lait, la reproduction