H. Aliloo, R. Mrode, A. M. Okeyo, G. Ni, M. E. Goddard et al., The feasibility of using low-density marker panels for genotype imputation and genomic prediction of crossbred dairy cattle of East Africa, J. Dairy Sci, vol.101, pp.1-20, 2018.

A. Ontology and . Inra, , 2012.

, Accessed 11, 2015.

R. Carvalheiro, S. A. Boison, H. H. Neves, M. Sargolzaei, F. S. Schenkel et al.,

F. Garcia, Accuracy of genotype imputation in Nelore cattle, Genet. Sel. Evol, vol.46, pp.69-79, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01341240

L. Chen, C. Li, M. Sargolzaei, and F. Schenkel, Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction, PLoS One, vol.9, p.101544, 2014.

M. A. Cleveland and J. M. Hickey, Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation, J. Anim. Sci, vol.91, pp.3583-3592, 2013.

R. Dassonneville, S. Fritz, V. Ducrocq, and D. Boichard, Short communication: Imputation performances of 3 low density marker panels in beef and dairy cattle, J. Dairy Sci, vol.95, pp.4136-4140, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01003420

P. Deelen, M. J. Bonder, K. J. Van-der-velde, H. J. Westra, E. Winder et al., Genotype harmonizer: automatic strand alignment and format conversion for genotype data integration, BMC Res. Notes, vol.7, pp.901-904, 2014.

M. E. Goddard, B. J. Hayes, and T. H. Meuwissen, Using the genomic relationship matrix to predict the accuracy of genomic selection, J. Anim. Breed Genet, vol.128, pp.409-421, 2011.

D. Habier, R. L. Fernando, and J. C. Dekkers, Genomic selection using low-density marker panels, Genetics, vol.182, pp.343-353, 2009.

B. L. Harris and D. L. Johnson, The impact of high density SNP chips on genomic evaluation in dairy cattle, Interbull Bull, vol.42, pp.40-43, 2010.

F. Hérault, J. Yon, F. Herry, S. Allais, and P. Roy, SS4I: select SNP susbset for imputation, 2016.

F. Hérault, F. Herry, A. Varenne, T. Burlot, D. Picard-druet et al., A linkage disequilibrium study in layer and broiler commercial chicken populations, Proc. 11 th World Congr, 2018.

F. Herry, F. Hérault, D. Picard, A. Druet, T. Varenne et al., Design of low density SNP chips for genotype imputation in layer chicken, BMC Genetics, vol.19, pp.108-121, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01962845

, Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution, Nature, vol.432, pp.695-716, 2004.

A. Kranis, A. A. Gheyas, C. Boschiero, F. Turner, L. Yu et al., Development of a high density 600K SNP genotyping array for chicken, BMC Genomics, vol.14, pp.59-71, 2013.

A. Legarra, I. Aguilar, and I. Misztal, A relationship matrix including full pedigree and genomic information, J. Dairy Sci, vol.92, pp.4656-4663, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02668784

H. J. Megens, R. P. Crooijmans, J. W. Bastiaansen, H. H. Kerstens, A. Coster et al.,

. Groenen, Comparison of linkage disequilibrium and haplotype diversity on macroand microchromosomes in chicken, BMC Genetics, vol.10, pp.86-96, 2009.

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.

I. Misztal, S. Tsuruta, T. Strabel, B. Auvray, T. Druet et al., BLUPF90 and related programs (BGF90). Proc. 7 th, 2002.

N. Moghaddar, K. P. Gore, H. D. Daetwyler, B. J. Hayes, and J. H. Van-der-werf, Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction, Genet. Sel. Evol, vol.47, pp.97-108, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01341331

H. A. Mulder, M. P. Calus, T. Druet, and C. Schrooten, Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle, J. Dairy Sci, vol.95, pp.876-889, 2012.

D. Picard-druet, A. Varenne, F. Herry, F. Hérault, S. Allais et al., Properties of genomic evaluation for egg quality traits in layers, Genet. Sel. Evol, 2019.

S. Purcell, B. Neale, K. Todd-brown, L. Thomas, M. A. Ferreira et al., PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses, Am. J. Hum. Genet, vol.81, pp.559-575, 2007.

S. Qanbari, M. Hansen, S. Weigend, R. Preisinger, and H. Simianer, Linkage disequilibrium reveals different demographic history in egg laying chickens, BMC Genetics, vol.11, pp.103-115, 2010.

J. Raoul, A. A. Swan, and J. M. Elsen, Using a very low-density SNP panel for genomic selection in a breeding program for sheep, Genet. Sel. Evol, vol.49, pp.76-87, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01622966

R. Robert, F. Hérault, H. Romé, A. Varenne, H. Chapuis et al., A linkage disequilibrium study in a layer chicken population, Proc. 9 th, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01580637

M. Sargolzaei, J. P. Chesnais, and F. S. Schenkel, A new approach for efficient genotype imputation using information from relatives, BMC Genomics, vol.15, pp.478-489, 2014.

G. Su, R. F. Brøndum, P. Ma, B. Guldbrandsten, G. P. Aamand et al., Comparison of genomic predictions using medium-density (~54,000) and high-density (~777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations, J. Dairy Sci, vol.95, pp.4657-4665, 2012.

P. M. Vanraden, J. R. O'connell, G. R. Wiggans, and K. A. Weigel, Genomic evaluation with many more genotypes, Genet. Sel. Evol, vol.43, pp.10-20, 2011.

P. M. Vanraden, J. D. Null, M. Sargolzaei, G. R. Wiggans, M. E. Tooker et al., Genomic imputation and evaluation using high-density Holstein genotypes, J. Dairy Sci, vol.95, pp.1-11, 2012.

C. Wang, D. Habier, B. L. Peiris, A. Wolc, A. Kranis et al.,

R. L. Garrick, S. J. Fernando, J. C. Lamont, and . Dekkers, Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens, Poult. Sci, vol.92, pp.1712-1723, 2013.

Y. Wang, G. Lin, C. Li, and P. Stothard, Genotype imputation methods and their effects on genomic predictions in cattle, Springer Sci. Rev, vol.4, pp.79-98, 2016.

W. C. Warren, L. D. Hillier, C. Tomlinson, P. Minx, M. Kremitzki et al., , vol.7, pp.109-117, 2017.

K. A. Weigel, G. De-los-campos, O. González-recio, H. Naya, X. L. Wu et al.,

D. Rosa and . Gianola, Predictive ability of direct genomic values for lifetime net 140 merit of Holstein sires using selected subsets of single nucleotide polymorphism markers, 2009.

, J. Dairy Sci, vol.92, pp.5248-5257

K. A. Weigel, G. De-los-campos, A. I. Vazquez, G. J. Rosa, D. Gianola et al., Accuracy of direct genomic values dervied from imputed single nucleotide polymorphism genotypes in Jersey cattle, J. Dairy Sci, vol.93, pp.5423-5435, 2010.

R. Wellmann, S. Preu?, E. Tholen, J. Heinkel, K. Wimmers et al., Genomic selection using low density marker panels with application to a sire line in pigs, Genet. Sel. Evol, vol.45, pp.28-38, 2013.

S. Xu, Estimating polygenic effects using markers of the entire genome, Genetics, vol.163, pp.789-801, 2003.

Z. Zhang, X. Ding, J. Liu, Q. Zhang, and D. J. De-koning, Accuracy of genomic prediction using low-density marker panels, J. Dairy Sci, vol.94, pp.3642-3650, 2011.

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

A. Kranis, A. A. Gheyas, C. Boschiero, F. Turner, L. Yu et al., Development of a high density 600K SNP genotyping array for chicken, BMC Genomics, vol.14, p.59, 2013.

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

R. Dassonneville, R. F. Brøndum, T. Druet, S. Fritz, F. Guillaume et al., Effect of imputing markers from a low density chip on the reliability of genomic breeding values in Holstein populations, J Dairy Sci, vol.94, p.3679, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01000513

R. Dassonneville, S. Fritz, V. Ducrocq, and D. Boichard, Short communication: Imputation performances of 3 low density marker panels in beef and dairy cattle, J Dairy Sci, vol.95, p.4136, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01003420

P. M. Vanraden, J. D. Null, M. Sargolzaei, G. R. Wiggans, M. E. Tooker et al.,

, Genomic imputation and evaluation using high-density Holstein genotypes, J Dairy Sci, vol.95, p.1, 2012.

A. Bouquet, K. Fève, J. Riquet, and C. Larzul, Précision de l'imputation de génotypages haute densité à partir de puces basse densité pour des individus de race pure et croisés Piétrain, Journées Recherche Porcine, vol.47, p.1, 2015.

M. A. Cleveland and J. M. Hickey, Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation, J Anim Sci, vol.91, p.3583, 2013.

D. A. Grossi, L. F. Brito, M. Jafarikia, F. S. Schenkel, and Z. Feng, Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs, Animal, vol.12, p.2235, 2018.

B. J. Hayes, P. J. Bowman, H. D. Daetwyler, J. W. Kijas, and J. Van-der-werf, Accuracy of genotype imputation in sheep breeds: Genotype imputation in sheep, Anim Genet, vol.43, 2012.

N. Moghaddar, K. P. Gore, H. D. Daetwyler, B. J. Hayes, and J. Van-der-werf, Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction, Genet Sel Evol, vol.47, p.97, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01341331

J. Raoul, A. A. Swan, and J. M. Elsen, Using a very low-density SNP panel for genomic selection in a breeding program for sheep, Genet Sel Evol, vol.49, p.76, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01622966

M. Heidaritabar, M. Calus, A. Vereijken, M. Groenen, and J. Bastiaansen, Accuracy of imputation using the most common sires as reference population in layer chickens, BMC Genetics, vol.16, p.101, 2015.

C. Wang, D. Habier, B. L. Peiris, A. Wolc, A. Kranis et al., Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens, Poult Sci, vol.92, p.1712, 2013.

F. Herry, F. Hérault, P. Druet, D. Varenne, A. Burlot et al., Design of low density SNP chips for genotype imputation in layer chicken, BMC Genetics, vol.19, p.108, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01962845

A. Albrechtsen, F. C. Nielsen, and R. Nielsen, Ascertainment biases in SNP chips affect measures of population divergence, Mol Biol Evol, vol.27, p.2534, 2010.

K. R. Andrews, J. M. Good, M. R. Miller, G. Luikart, and P. A. Hohenlohe, Harnessing the power of RADseq for ecological and evolutionary genomics, Nat Rev Genet, vol.17, p.81, 2016.

J. W. Davey, P. A. Hohenlohe, P. D. Etter, J. Q. Boone, J. M. Catchen et al., Genomewide genetic marker discovery and genotyping using next-generation sequencing, Nat Rev Genet, vol.12, p.499, 2011.

Z. Jiang, H. Wang, J. J. Michall, X. Zhou, B. Liu et al., Genome wide sampling sequencing for SNP genotyping: Methods, challenges and future development, Int J Biol Sci, vol.12, p.100, 2016.

N. A. Baird, P. D. Etter, T. S. Atwood, M. C. Currey, A. L. Shiver et al., Rapid SNP discovery and genetic mapping using sequenced RAD markers, PLoS One, vol.3, p.3376, 2008.

T. M. Beissinger, C. N. Hirsch, R. S. Sekhon, J. M. Foerster, J. M. Johnson et al., Marker Density and Read Depth for Genotyping Populations Using Genotyping-by-Sequencing, Genetics, vol.193, p.1073, 2013.

C. P. Van-tassell, T. P. Smith, L. K. Matukumalli, J. F. Taylor, R. D. Schnabel et al., SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries, Nat Methods, vol.5, pp.247-215, 2008.

P. Andolfatto, D. Davison, D. Erezyilmaz, T. T. Hu, J. Mast et al., Multiplexed shotgun genotyping for rapid and efficient genetic mapping, Genome Res, vol.21, p.610, 2011.

S. Wang, E. Meyer, J. K. Mckay, and M. V. Matz, 2b-RAD: a simple and flexible method for genome-wide genotyping, Nat Methods, vol.9, p.808, 2012.

R. J. Elshire, J. C. Glaubitz, Q. Sun, J. A. Poland, K. Kawamoto et al., A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species, PLoS One, 2011.

C. Chen, Y. Ma, Y. Yang, Z. Chen, R. Liao et al., Genotyping by genome reducing and sequencing for outbred animals, PLoS One, vol.8, p.67500, 2013.

B. K. Peterson, J. N. Weber, E. H. Kay, H. S. Fisher, and H. E. Hoekstra, Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and nonmodel species, PLoS One, vol.7, p.37135, 2012.

K. M. Gardner, P. Brown, T. F. Cooke, S. Cann, F. Costa et al., Fast and costeffective genetic mapping in apple using next-generation sequencing, Genetics, vol.4, p.1681, 2014.

J. A. Poland, P. J. Brown, M. E. Sorrells, and J. L. Jannink, Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach, PLoS One, vol.7, p.32253, 2012.

N. J. Van-orsouw, R. C. Hogers, A. Janssen, F. Yalcin, S. Snoeijers et al., Complexity Reduction of Polymorphic Sequences (CRoPS?): A Novel Approach for Large-Scale Polymorphism Discovery in Complex Genomes, PLoS One, vol.2, p.1172, 2007.

J. Crossa, Y. Beyene, S. Kassa, P. Pérez, J. M. Hickey et al., Genomic prediction in maize breeding populations with genoyping-by-sequencing, G3, vol.3, 1903.

D. Donato, M. Peters, S. O. Mitchell, S. E. Hussain, T. Imumorin et al., Genotyping bysequencing (GBS): A novel, efficient and cost-effective genotyping method for cattle using next-generation sequencing, PLoS One, vol.8, p.62137, 2013.

G. Tosser-klopp, P. Bardou, O. Bouchez, C. Cabau, R. Crooijmans et al., Design and Characterization of a 52K SNP Chip for Goats, PLoS One, vol.9, p.86227, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01602260

Z. Zhai, W. Zhao, C. He, K. Yang, L. Tang et al., SNP discovery and genotyping using restriction-site-associated DNA sequencing in chickens, Anim Genet, vol.46, p.216, 2015.

R. Liao, Z. Wang, Q. Chen, Y. Tu, Z. Chen et al., An efficient genotyping method in chicken based on genome reducing and sequencing, PLoS One, vol.10, pp.137010-216, 2015.

F. Pértille, C. Guerrero-bosagna, V. H. Da-silva, C. Boschiero, J. Nunes et al., High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing, Sci Rep, vol.6, p.26929, 2016.

J. Poland, J. Endelman, J. Dawson, J. Rutkoski, W. Shuangye et al., Genomic selection in wheat breeding using genotyping-by-sequencing, Plant Gen, vol.5, p.103, 2012.

D. Torkameneh and F. Belzile, Scanning and filling: Ultra-dense SNP genotyping combining genotyping-by-sequencing, SNP array and whole-genome resequencing data, PLoS One, vol.10, p.131533, 2015.

I. S. Elbasyoni, A. J. Lorenz, M. Guttieri, K. Frels, P. S. Baenziger et al., A comparison between genotyping-by-sequencing and array-based scoring of SNPs for genomic prediction accuracy in winter wheat, Plant Sci, vol.270, p.123, 2018.

J. S. Brouard, B. Boyle, E. M. Ibeagha-awemu, and N. Bissonnette, Low-depth genotyping-bysequencing (GBS) in bovine population: strategies to maximize the selection of high quality genotypes and the accuracy of imputation, BMC Genetics, vol.18, p.32, 2017.

G. Gorjanc, M. A. Cleveland, R. D. Houston, and J. M. Hickey, Potential of genotyping-bysequencing for genomic selection in livestock populations, Genet Sel Evol, vol.47, p.12, 2015.

Y. Wang, X. Cao, Y. Zhao, J. Fei, X. Hu et al., Optimized double-digest genotyping by sequencing (ddGBS) method with high density SNP markers and high genotyping accuracy for chickens, PLoS One, vol.12, p.179073, 2017.

W. C. Warren, L. Hillier, C. Tomlinson, P. Minx, M. Kremitzki et al., A new chicken genome assembly provides insight into avian genome structure, G3, vol.7, p.109, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01604336

C. C. Chang, C. C. Chow, L. C. Tellier, S. Vattikuti, S. M. Purcell et al., Second-generation PLINK: rising to the challenge of larger and richer datasets, GigaScience, vol.4, p.7, 2015.

H. Li and R. Durbin, Fast and accurate short read alignment with Burrows-Wheeler Transform, Bioinformatics, vol.25, p.1754, 2009.

A. Mckenna, M. Hanna, E. Banks, A. Sivachenko, K. Cibulskis et al., The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data, Genome Res, vol.20, p.1297, 2010.

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.

A. Ontology and . Inra, , 2012.

, Accessed 11, 2015.

W. Huber, V. J. Carey, R. Gentleman, S. Anders, M. Carlson et al., Orchestrating high-throughput genomic analysis with Bioconductor, Nat Methods, vol.12, pp.115-217, 2015.

M. A. Quail, Y. Gu, H. Swerdlow, and M. Mayho, Evaluation and optimisation of preparative semi-automated electrophoresis systems for Illumina library preparation

, Electrophoresis, vol.33, p.3521, 2012.

F. Herry, P. Druet, D. Hérault, F. Varenne, A. Burlot et al., Interest of using imputation for genomic evaluation in layer chicken, Poult Sci. In submission
URL : https://hal.archives-ouvertes.fr/hal-02624102

A. Legarra, I. Aguilar, and I. Misztal, A relationship matrix including full pedigree and genomic information, J Dairy Sci, vol.92, p.4656, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02668784

I. Misztal, S. Tsuruta, T. Strabel, B. Auvray, T. Druet et al., BLUPF90 and related programs (BGF90), Proceedings of the 7th World Congress of Genetics Applied to Livestock Production, 2002.

A. Gurgul, A. Miksza-cybulska, T. Szmato?a, I. Jasielczuk, A. Piestrzy?ska-kajtoch et al., Genotyping-by-sequencing performance in selected livestock species, Genomics, vol.111, p.186, 2019.

H. J. Megens, R. Crooijmans, J. Bastiaansen, H. Kerstens, A. Coster et al., Comparison of linkage disequilibrium and haplotype diversity on macro-and microchromosomes in chicken, BMC Genetics, vol.10, p.86, 2009.

S. Qanbari, M. Hansen, S. Weigend, R. Preisinger, and H. Simianer, Linkage disequilibrium reveals different demographic history in egg laying chickens, BMC Genetics, vol.11, p.103, 2010.

F. Hérault, F. Herry, A. Varenne, T. Burlot, D. Picard-druet et al., A linkage disequilibrium study in layer and broiler commercial chicken populations, Proceedings of the 11th World Congress, 2018.

K. G. Ardlie, L. Kruglyak, and M. Seielstad, Patterns of linkage disequilibrium in the human genome, Nat Rev Genet, vol.3, p.299, 2002.

J. Aerts, H. J. Megens, T. Veenendaal, I. Ovcharenko, R. Crooijmans et al., International Chicken Genome Sequencing Consortium. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution, Cytogenet Genome Res, vol.117, p.695, 2004.

M. A. Gore, J. M. Chia, R. J. Elshire, Q. Sun, E. Z. Ersoz et al., A first-generation haplotype map of maize, Science, vol.326, p.1115, 2009.

L. Chen, C. Li, M. Sargolzaei, and F. Schenkel, Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction, PLoS One, vol.9, p.101544, 2014.

. Affymetrix, Axiom® Genome-Wide Chicken Genotyping Array, Consulté le 31 juillet 2019 sur, 2013.

. Affymetrix, Axiom® Porcine Genotyping Array, Consulté le 30 juillet sur, 2015.

. Affymetrix, Axiom® Trout Genotyping Array, Consulté le 2 août 2019 sur, 2015.

. Affymetrix, Axiom® Equine Genotyping Array, Consulté le 31 juillet 2019 sur, 2017.

I. Aguilar, I. Misztal, D. L. 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.
URL : https://hal.archives-ouvertes.fr/hal-02668766

J. Aerts, H. J. Megens, T. Veenendaal, I. Ovcharenko, R. Crooijmans et al., Extent of linkage disequilibrium in chicken. Cytogenetic and Genome Research, vol.117, pp.338-345, 2007.

A. Albrechtsen, F. C. Nielsen, and R. Nielsen, Ascertainment Biases in SNP Chips Affect Measures of Population Divergence, Molecular Biology and Evolution, vol.27, pp.2534-2547, 2010.

D. H. Alexander, J. Novembre, and K. Lange, Fast model-based estimation of ancestry in unrelated individuals, Genome Research, vol.19, pp.1655-1664, 2009.

H. Aliloo, R. Mrode, A. M. Okeyo, G. Ni, M. E. Goddard et al., The feasibility of using lowdensity marker panels for genotype imputation and genomic prediction of crossbred dairy cattle of East Africa, Journal of Dairy Science, vol.101, pp.9108-9127, 2018.

H. Aliloo, J. E. Pryce, O. González-recio, B. G. Cocks, and B. J. Hayes, Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits, Genetics Selection Evolution, vol.48, pp.8-18, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01341342

S. Allais, Détection et validation de marqueurs génétiques impliqués dans la qualité de la viande bovine, AgroParisTech-ABIES, 2011.

P. Andolfatto, D. Davison, D. Erezyilmaz, T. T. Hu, J. Mast et al., Multiplexed shotgun genotyping for rapid and efficient genetic mapping, Genome Research, vol.21, pp.610-617, 2011.

K. R. Andrews, J. M. Good, M. R. Miller, G. Luikart, and P. A. Hohenlohe, Harnessing the power of RADseq for ecological and evolutionary genomics, Nature Reviews Genetics, vol.17, pp.81-92, 2016.

K. G. Ardlie, L. Kruglyak, and M. Seielstad, Patterns of linkage disequilibrium in the human genome, Nature Reviews Genetics, vol.3, pp.299-309, 2002.

A. Ontology and . Inra, Consulté le 12 février 2019 sur, 2012.

Y. S. Aulchenko, S. Ripke, A. Isaacs, and C. M. Van-duijn, GenABEL: an R library for genome-wide association analysis, Bioinformatics, vol.23, pp.1294-1296, 2007.

Y. M. Badke, R. O. Bates, C. W. Ernst, C. Schwab, and J. P. Steibel, Estimation of linkage disequilibrium in four US pig breeds, BMC Genomics, vol.13, pp.24-33, 2012.

N. A. Baird, P. D. Etter, T. S. Atwood, M. C. Currey, A. L. Shiver et al., Rapid SNP Discovery and Genetic Mapping Using Sequenced RAD Markers, PLoS ONE, vol.3, p.3376, 2008.

B. Bapst, C. Baes, F. R. Seefried, A. Bieber, H. Simianer et al., Effect of cows in the reference population: First results in Swiss Brown Swiss, Interbull Bulletin, pp.187-191, 2013.

J. Bartholomé, J. Van-heerwaarden, F. Isik, C. Boury, M. Vidal et al., Performance of genomic prediction within and across generations in maritime pine, BMC Genomics, vol.17, pp.604-617, 2016.

J. Bastiaansen, A. Coster, M. Calus, J. Van-arendonk, and H. Bovenhuis, Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures, Genetics Selection Evolution, vol.44, pp.3-15, 2012.

. Biofidal, MiSeq Illumina sequencing system at Lyon, Consulté le 5 sept 2019 sur, 2016.

D. Boichard, L. Roy, P. Levéziel, H. Elsen, and J. M. , Utilisation des marqueurs moléculaires en génétique animale, INRA Productions Animales, vol.11, pp.67-80, 1998.

D. Boichard, H. Chung, R. Dassonneville, X. David, A. Eggen 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. N. 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-01000202

D. Boichard, La sélection génomique, une opportunité pour améliorer la santé des animaux d'élevage. Bulletin de l'Académie Vétérinaire de France, vol.166, pp.25-31, 2013.

D. Boichard, V. Ducrocq, and S. Fritz, Sustainable dairy cattle selection in the genomic era, Journal of Animal Breeding and Genetics, vol.132, pp.135-143, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01194086

D. Boichard, V. Ducrocq, P. Croiseau, and S. Fritz, Genomic selection in domestic animals: Principles, applications and perspectives, Comptes Rendus Biologies, vol.339, pp.274-277, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02641310

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, 11th World Congress of Genetics Applied to Livestock Production. Auckland, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02737523

S. Bolormaa, K. Gore, J. Van-der-werf, B. J. Hayes, and H. D. Daetwyler, Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy, Animal Genetics, vol.46, pp.544-556, 2015.

S. Bolormaa, A. J. Chamberlain, M. Khansefid, P. Stothard, A. A. Swan et al., Accuracy of imputation to whole-genome sequence in sheep, Genetics Selection Evolution, vol.51, pp.1-17, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02445124

J. S. Brouard, B. Boyle, E. M. Ibeagha-awemu, and N. Bissonnette, Low-depth genotyping-bysequencing (GBS) in a bovine population: strategies to maximize the selection of high quality genotypes and the accuracy of imputation, BMC Genetics, vol.18, pp.32-45, 2017.

S. R. Browning and B. L. Browning, Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering, The American Journal of Human Genetics, vol.81, pp.1084-1097, 2007.

B. L. Browning and S. R. Browning, A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals, The American Journal of Human Genetics, vol.84, pp.210-223, 2009.

M. Calus, Y. De-haas, and R. F. Veerkamp, Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies, Journal of Dairy Science, vol.96, pp.6703-6715, 2013.

M. Calus, A. C. Bouwman, J. M. Hickey, R. F. Veerkamp, and H. A. Mulder, Evaluation of measures of correctness of genotype imputation in the context of genomic prediction: a review of livestock applications, Animal, vol.8, pp.1743-1753, 2014.

C. Carillier, H. Larroque, I. Palhière, V. Clément, R. Rupp et al., A first step toward genomic selection in the multi-breed French dairy goat population, Journal of Dairy Science, vol.96, pp.7294-7305, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02651011

C. Carillier-jacquin, A. Bouquet, Y. Labrune, P. Brenaut, J. Riquet et al., Using 1K SNP panel for genomic selection in 3 French pig breeds: Accuracy of Imputation and estimation of genomic breeding values using 1K SNP panel, designed for several breeds in French pig populations, Proceedings, 11th World Congress of Genetics Applied to Livestock Production. Auckland, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02733718

R. Carvalheiro, S. A. Boison, H. Neves, M. Sargolzaei, F. S. Schenkel et al., Accuracy of genotype imputation in Nelore cattle, Genetics Selection Evolution, vol.46, pp.69-79, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01341240

C. C. Chang, C. C. Chow, L. C. Tellier, S. Vattikuti, S. M. Purcell et al., Second-generation PLINK: rising to the challenge of larger and richer datasets, GigaScience, vol.4, pp.7-22, 2015.

L. Chen, C. Li, M. Sargolzaei, and F. Schenkel, Impact of Genotype Imputation on the Performance of GBLUP and Bayesian Methods for Genomic Prediction, PLoS ONE, vol.9, p.101544, 2014.

Q. Chen, Y. Ma, Y. Yang, Z. Chen, R. Liao et al., Genotyping by Genome Reducing and Sequencing for Outbred Animals, PLoS ONE, vol.8, p.67500, 2013.

S. A. Clark, J. M. Hickey, H. D. Daetwyler, and J. Van-der-werf, The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes, Genetics Selection Evolution, vol.44, pp.4-12, 2012.

M. A. Cleveland and J. M. Hickey, Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation1, Journal of Animal Science, vol.91, pp.3583-3592, 2013.

W. Coppieters, J. Riquet, J. 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.

I. Croué, Évaluation génétique et génomique de nouveaux caractères en bovins laitiers, 2017.

H. D. Daetwyler, R. Pong-wong, B. Villanueva, and J. A. Woolliams, The Impact of Genetic Architecture on Genome-Wide Evaluation Methods, Genetics, vol.185, pp.1021-1031, 2010.

R. Dassonneville, R. F. Brøndum, T. Druet, S. Fritz, F. Guillaume et al., Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations, Journal of Dairy Science, vol.94, pp.3679-3686, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01000513

R. Dassonneville, S. Fritz, V. Ducrocq, and D. Boichard, Short communication: Imputation performances of 3 low-density marker panels in beef and dairy cattle, Journal of Dairy Science, vol.95, pp.4136-4140, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01003420

R. Dassonneville, Sélection génomique des vaches laitières, 2012.

R. Dassonneville, A. Baur, S. Fritz, D. Boichard, and V. Ducrocq, Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment, Genetics Selection Evolution, vol.44, pp.40-47, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01000386

D. Donato, M. Peters, S. O. Mitchell, S. E. Hussain, T. Imumorin et al., Genotyping-by-Sequencing (GBS): A Novel, Efficient and Cost-Effective Genotyping Method for Cattle Using Next-Generation Sequencing, PLoS ONE, vol.8, p.62137, 2013.

A. De-roos, Genomic selection in dairy cattle, p.259, 2011.

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.

T. Druet, C. Schrooten, and A. De-roos, Imputation of genotypes from different single nucleotide polymorphism panels in dairy cattle, Journal of Dairy Science, vol.93, pp.5443-5454, 2010.

T. Druet, I. M. Macleod, and B. J. Hayes, Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions, Heredity, vol.112, pp.39-47, 2014.

F. X. Du, A. C. Clutter, and M. M. Lohuis, Characterizing linkage disequilibrium in pig populations, International Journal of Biological Sciences, vol.3, pp.166-178, 2007.

R. J. Elshire, J. C. Glaubitz, Q. Sun, J. A. Poland, K. Kawamoto et al., Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species, PLoS ONE, vol.6, p.19379, 2011.

. Ensembl, Ensembl Variation -Data sources

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

J. Ertl, A. Legarra, Z. G. Vitezica, L. Varona, C. Edel et al., Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle, Genetics Selection Evolution, vol.46, pp.40-49, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01341269

J. Ferraz, X. Wu, H. Li, J. Xu, R. Ferretti et al., Design of a low-density SNP chip for Bos indicus: GGP indicus technical characterization and imputation accuracy to higher density SNP genotypes, Proceedings, 11th World Congress of Genetics Applied to Livestock Production. Auckland, 2018.

R. A. Fisher, The Correlation between Relatives on the Supposition of Mendelian Inheritance, Transactions of the Royal Society of Edinburgh, vol.52, pp.399-433, 1918.

W. Fu, J. Dekkers, W. R. Lee, and B. Abasht, Linkage disequilibrium in crossbred and pure line chickens. Genetic Selection Evolution, vol.47, pp.11-22, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01341288

C. Fuchsberger, G. R. Abecasis, and D. A. Hinds, minimac2: faster genotype imputation, Bioinformatics, vol.31, pp.782-784, 2015.

K. M. Gardner, P. Brown, T. F. Cooke, S. Cann, F. Costa et al., Fast and Cost-Effective Genetic Mapping in Apple Using Next-Generation Sequencing

, Genes|Genomes|Genetics, vol.4, pp.1681-1687, 2014.

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

G. Gorjanc, M. A. Cleveland, R. D. Houston, and J. M. Hickey, Potential of genotyping-by-sequencing for genomic selection in livestock populations, Genetics Selection Evolution, vol.47, pp.12-24, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01341290

M. Groenen, H. H. Cheng, N. Bumstead, B. F. Benkel, W. E. Briles et al., A consensus linkage map of the chicken genome, Genome Research, vol.10, pp.137-147, 2000.
URL : https://hal.archives-ouvertes.fr/hal-02697258

M. Groenen, P. Wahlberg, M. Foglio, H. H. Cheng, H. J. Megens et al., A high-density SNP-based linkage map of the chicken genome reveals features correlated with recombination rate, Genome Research, vol.19, pp.510-519, 2008.

M. Groenen, H. J. Megens, Y. Zare, W. C. Warren, L. Hillier et al., The development and characterization of a 60K SNP chip for chicken, BMC Genomics, vol.12, pp.274-282, 2011.

L. F. Groeneveld, J. A. Lenstra, H. Eding, M. A. Toro, B. Scherf et al., Genetic diversity in farm animals -a review, Animal Genetics, vol.41, pp.6-31, 2010.

F. Grosclaude, M. F. Mahé, G. Brignon, D. Stasio, L. Jeunet et al., A Mendelian polymorphism underlying quantitative variations of goat ?s1-casein, Genetics Selection Evolution, vol.19, pp.399-412, 1987.
URL : https://hal.archives-ouvertes.fr/hal-02727018

G. Duarte, J. Bates, R. O. Ernst, C. W. Raney, N. E. Cantet et al., Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels, BMC Genetics, vol.14, pp.38-50, 2013.

F. Guillaume, Intégration de l'information moléculaire dans l'évaluation génétique, AgroParisTech-ABIES, 2009.

Y. Guo, J. He, S. Zhao, H. Wu, X. Zhong et al., Illumina human exome genotyping array clustering and quality control, Nature Protocols, vol.9, pp.2643-2662, 2014.

A. P. Gutierrez, F. Turner, K. Gharbi, R. Talbot, N. R. Lowe et al., Development of a Medium Density Combined-Species SNP Array for Pacific and European Oysters (Crassostrea gigas and Ostrea edulis). G3; Genes|Genomes|Genetics, vol.7, pp.2209-2218, 2017.

D. Habier, R. L. Fernando, and J. Dekkers, The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values, Genetics, vol.177, pp.2389-2397, 2007.

D. Habier, R. L. Fernando, and J. Dekkers, Genomic Selection Using Low-Density Marker Panels, Genetics, vol.182, pp.343-353, 2009.

D. Habier, J. Tetens, F. R. Seefried, P. Lichtner, and G. Thaller, The impact of genetic relationship information on genomic breeding values in German Holstein cattle, Genetics Selection Evolution, vol.42, pp.5-16, 2010.

D. Habier, R. L. Fernando, and D. J. Garrick, Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction, Genetics, vol.194, pp.597-607, 2013.

C. S. Haley and P. M. Visscher, Strategies to Utilize Marker-Quantitative Trait Loci Associations, Journal of Dairy Science, vol.81, pp.85-97, 1998.

G. H. Hardy, Mendelian proportions in a mixed population, Science, vol.28, pp.49-50, 1908.

B. L. Harris and D. L. Johnson, The impact of high density SNP chips on genomic evaluation in dairy cattle, Interbull Bulletin, pp.40-43, 2010.

B. J. Hayes and M. E. Goddard, The distribution of the effects of genes affecting quantitative traits in livestock, Genetics Selection Evolution, vol.33, pp.209-229, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00894372

B. J. Hayes, P. J. Bowman, A. J. Chamberlain, and M. E. Goddard, Invited review: Genomic selection in dairy cattle: Progress and challenges, Journal of Dairy Science, vol.92, pp.433-443, 2009.

B. J. Hayes, P. J. Bowman, A. C. Chamberlain, K. Verbyla, and M. E. Goddard, Accuracy of genomic breeding values in multi-breed dairy cattle populations, Genetics Selection Evolution, vol.41, pp.51-59, 2009.

B. J. Hayes, Course Notes: Genomic Selection. Toulouse, 2011.

B. J. Hayes, P. J. Bowman, H. D. Daetwyler, J. W. Kijas, and J. Van-der-werf, Accuracy of genotype imputation in sheep breeds: Genotype imputation in sheep, Animal Genetics, vol.43, pp.72-80, 2012.

B. J. Hayes, I. M. Macleod, H. D. Daetwyler, P. J. Bowman, A. J. Chamberlain et al., Genomic Prediction from Whole Genome Sequence in Livestock: the 1000 Bull Genomes Project, Proceedings, 10th World Congress of Genetics Applied to Livestock Production. Vancouver, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01193911

L. N. Hazel, The genetic basis for constructing selection indexes, Genetics, vol.28, pp.476-490, 1943.

S. R. Head, H. K. Komori, S. A. Lamere, T. Whisenant, F. Van-nieuwerburgh et al., Library construction for next-generation sequencing: Overviews and challenges, BioTechniques, vol.56, pp.61-77, 2014.

M. Heidaritabar, M. Calus, A. Vereijken, M. Groenen, and J. Bastiaansen, High imputation accuracy in layer chicken from sequence data on a few key ancestors, Proceedings, 10th World Congress of Genetics Applied to Livestock Production. Vancouver, 2014.

M. Heidaritabar, M. Calus, H. J. Megens, A. Vereijken, M. Groenen et al., Accuracy of genomic prediction using imputed whole-genome sequence data in white layers, Journal of Animal Breeding and Genetics, vol.133, pp.167-179, 2016.

M. Heidaritabar, A. Wolc, J. Arango, J. Zeng, P. Settar et al., Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers, Journal of Animal Breeding and Genetics, vol.133, pp.334-346, 2016.

C. R. Henderson, Best Linear Unbiased Estimation and Prediction under a Selection Model, Biometrics, vol.31, pp.423-447, 1975.

C. R. Henderson, A Simple Method for Computing the Inverse of a Numerator Relationship Matrix Used in Prediction of Breeding Values, Biometrics, vol.32, pp.69-83, 1976.

F. Hérault, F. Herry, A. Varenne, T. Burlot, D. Picard-druet et al., A linkage disequilibrium study in layer and broiler commercial chicken populations, Proceedings, 11th World Congress of Genetics Applied to Livestock Production. Auckland, 2018.

F. Herry, F. Hérault, P. Druet, D. Varenne, A. Burlot et al., Design of low density SNP chips for genotype imputation in layer chicken, BMC Genetics, vol.19, pp.108-121, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01962845

F. Herry, P. Druet, D. Hérault, F. Varenne, A. Burlot et al., Interest of using imputation for genomic evaluation in layer chicken, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02624102

J. M. Hickey, B. P. Kinghorn, B. Tier, J. F. Wilson, N. Dunstan et al., A combined longrange phasing and long haplotype imputation method to impute phase for SNP genotypes, Genetics Selection Evolution, vol.43, pp.12-24, 2011.

J. M. Hickey, J. Crossa, R. Babu, and G. De-los-campos, Factors Affecting the Accuracy of Genotype Imputation in Populations from Several Maize Breeding Programs, Crop Science, vol.52, pp.654-663, 2012.

W. G. Hill and A. Robertson, Linkage disequilibrium in finite populations, Theoretical and Applied Genetics, vol.38, pp.226-231, 1968.

L. Hosking, S. Lumsden, K. Lewis, A. Yeo, L. Mccarthy et al., Detection of genotyping errors by Hardy-Weinberg equilibrium testing, European Journal of Human Genetics, vol.12, pp.395-399, 2004.

R. D. Houston, J. B. Taggart, T. Cézard, M. Bekaert, N. R. Lowe et al., Development and validation of a high density SNP genotyping array for Atlantic salmon (Salmo salar), BMC Genomics, vol.15, pp.90-102, 2014.

B. N. Howie, P. Donnelly, and J. Marchini, A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies, PLoS Genetics, vol.5, p.1000529, 2009.

B. N. Howie, C. Fuchsberger, M. Stephens, J. Marchini, and G. R. Abecasis, Fast and accurate genotype imputation in genome-wide association studies through pre-phasing, Nature Genetics, vol.44, pp.955-959, 2012.

C. Hozé, M. N. Fouilloux, E. Venot, F. Guillaume, R. Dassonneville et al., High-density marker imputation accuracy in sixteen French cattle breeds, Genetics Selection Evolution, vol.45, pp.33-45, 2013.

C. Hozé, Développement d'évaluations génomiques multiraciales chez les bovins laitiers, AgroParisTech-ABIES, 2014.

C. Hozé, S. Fritz, F. Phocas, D. Boichard, V. Ducrocq et al., Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population, Journal of Dairy Science, vol.97, pp.3918-3929, 2014.

C. Hozé, S. Fritz, F. Phocas, D. Boichard, V. Ducrocq et al., Genomic evaluation using combined reference populations from Montbéliarde and French Simmental breeds, Proceedings, 10th World Congress of Genetics Applied to Livestock Production. Vancouver, 2014.

. Illumina, OvineSNP50 Genotyping BeadChip, Consulté le 30 juillet 2019 sur, 2008.

. Illumina, Illumina Porcine60K BeadChip, 2009.

. Illumina, BovineLD v2.0 Genotyping BeadChip, Consulté le 30 juillet 2019 sur, 2011.

. Illumina, BovineSNP50 Genotyping BeadChip, Consulté le 30 juillet 2019 sur, 2011.

. Illumina, GoldenGate® Bovine3K Genotyping BeadChip, Consulté le 30 juillet 2019 sur, 2011.

. Illumina, BovineHD Bead Chip datasheet, Consulté le 30 juillet 2019 sur, 2012.

. Illumina, EquineSNP50 Genotyping BeadChip, Consulté le 31 juillet 2019 sur, 2012.

. Illumina, International Chicken Genome Sequencing Consortium. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution, Nature, vol.432, pp.695-716, 2004.

, International Chicken Polymorphism Map Consortium. A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms, Nature, vol.432, pp.717-722, 2004.

. Itavi, Situation du marché des oeufs et ovoproduits -Édition avril 2018. Note de conjoncture Poules Pondeuses, 2018.

K. S. Khanyile, E. F. Dzomba, and F. C. Muchadeyi, Population genetic structure, linkage disequilibrium and effective population size of conserved and extensively raised village chicken populations of Southern Africa, Frontier in Genetics, vol.6, pp.1-11, 2015.

A. Kong, G. Masson, M. L. Frigge, A. Gylfason, P. Zusmanovich et al., Detection of sharing by descent, long-range phasing and haplotype imputation, Nature Genetics, vol.40, pp.1068-1075, 2008.

A. Kranis, A. A. Gheyas, C. Boschiero, F. Turner, L. Yu et al., Development of a high density 600K SNP genotyping array for chicken, BMC Genomics, vol.14, pp.59-71, 2013.

M. T. Kuhn, P. J. Boettcher, and A. E. Freeman, Potential Biases in Predicted Transmitting Abilities of Females from Preferential Treatment, Journal of Dairy Science, vol.77, pp.2428-2437, 1994.

D. Laloë, La genèse et le développement des concepts de l'évaluation génétique classique, INRA Productions Animales, vol.24, pp.323-330, 2011.

R. Lande and R. Thompson, Efficiency of marker-assisted selection in the improvement of quantitative traits, Genetics, vol.124, pp.743-756, 1990.

A. Legarra, C. Robert-granié, E. Manfredi, and J. M. Elsen, Performance of Genomic Selection in Mice, Genetics, vol.180, pp.611-618, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02751918

A. Legarra, I. Aguilar, and I. Misztal, A relationship matrix including full pedigree and genomic information, Journal of Dairy Science, vol.92, pp.4656-4663, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02668784

A. Legarra, G. Baloche, F. Barillet, J. M. Astruc, C. Soulas et al., Within-and acrossbreed genomic predictions and genomic relationships for Western Pyrenees dairy sheep breeds Latxa, Manech, and Basco-Béarnaise, Journal of Dairy Science, vol.97, pp.3200-3212, 2014.

A. Legarra, O. F. Christensen, I. Aguilar, and I. Misztal, Single Step, a general approach for genomic selection, Livestock Science, vol.166, pp.54-65, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02635203

L. Roy, P. Naveau, J. Elsen, J. M. Sellier, and P. , Evidence for a new major gene influencing meat quality in pigs, Genetical Research, vol.55, pp.33-40, 1990.
URL : https://hal.archives-ouvertes.fr/hal-02706738

L. Roy, P. Chapuis, H. Guemene, and D. , Sélection génomique : quelles perspectives pour les filières avicoles, INRA Production Animales, vol.27, pp.331-336, 2014.

R. C. Lewontin, The Interaction of Selection and Linkage. I. General Considerations; Heterotic Models, Genetics, vol.49, pp.49-67, 1964.

R. C. Lewontin, On measures of gametic disequilibrium, Genetics, vol.120, pp.849-852, 1988.

R. Liao, Z. Wang, Q. Chen, Y. Tu, Z. Chen et al., An Efficient Genotyping Method in Chicken Based on Genome Reducing and Sequencing, PLOS ONE, vol.10, p.137010, 2015.

M. Lillehammer, T. Meuwissen, and A. K. Sonesson, Genomic selection for maternal traits in pigs, Journal of Animal Science, vol.89, pp.3908-3916, 2011.

R. Liu, S. Xing, J. Wang, M. Zheng, H. Cui et al., A new chicken 55K SNP genotyping array, BMC Genomics, vol.20, pp.410-421, 2019.

Z. Liu, F. R. Seefried, F. Reinhardt, S. Rensing, G. Thaller et al., Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction, Genetics Selection Evolution, vol.43, pp.19-27, 2011.

D. Lourenco, S. Tsuruta, B. O. Fragomeni, Y. Masuda, I. Aguilar et al., Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus, Journal of Animal Science, vol.93, pp.2653-2662, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02639641

D. Lu, M. Sargolzaei, M. Kelly, C. Li, V. Voort et al., Linkage disequilibrium in Angus, Charolais and Crossbred beef cattle, Frontier in Genetics, vol.3, pp.1-10, 2012.

P. Ma, R. F. Brøndum, Q. Zhang, M. S. Lund, and G. Su, Comparison of different methods for imputing genome-wide marker genotypes in Swedish and Finnish Red Cattle, Journal of Dairy Science, vol.96, pp.4666-4677, 2013.

J. Marchini and B. Howie, Genotype imputation for genome-wide association studies, Nature Reviews Genetics, vol.11, pp.499-511, 2010.

A. T. Marees, H. De-kluiver, S. Stringer, F. Vorspan, E. Curis et al., A tutorial on conducting genome-wide association studies: Quality control and statistical analysis, International Journal of Methods in Psychiatric Research, vol.27, p.1608, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01904753

L. K. Matukumalli, C. T. Lawley, R. D. Schnabel, J. F. Taylor, M. F. Allan et al., Development and Characterization of a High Density SNP Genotyping Assay for Cattle, PLoS ONE, vol.4, p.5350, 2009.

M. E. Mccue, D. L. Bannasch, J. L. Petersen, J. Gurr, E. Bailey et al., A High Density SNP Array for the Domestic Horse and Extant Perissodactyla: Utility for Association Mapping, Genetic Diversity, and Phylogeny Studies, PLoS Genetics, vol.8, p.1002451, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01191252

I. Misztal, S. Tsuruta, T. Strabel, B. Auvray, T. Druet et al., BLUPF90 and related programs (BGF90), Proceedings, 7th World Congress of Genetics Applied to Livestock Production. Montpellier, 2002.

T. 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.

T. Meuwissen, B. Hayes, and M. Goddard, Genomic selection: A paradigm shift in animal breeding, Animal Frontiers, vol.6, pp.6-14, 2016.

N. Moghaddar, K. P. Gore, H. D. Daetwyler, B. J. Hayes, and J. Van-der-werf, Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction, Genetics Selection Evolution, vol.47, pp.97-108, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01341331

N. Moghaddar and J. Van-der-werf, Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations, Journal of Animal Breeding and Genetics, vol.134, pp.453-462, 2017.

W. M. Muir, Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters: Comparison of BLUP and GEBV selection, Journal of Animal Breeding and Genetics, vol.124, pp.342-355, 2007.

W. M. Muir, G. K. Wong, Y. Zhang, J. Wang, M. Groenen et al., Review of the initial validation and characterization of a 3K chicken SNP array. World's, Poultry Science Journal, vol.64, pp.219-226, 2008.

H. A. Mulder, M. Calus, T. Druet, and C. Schrooten, Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle, Journal of Dairy Science, vol.95, pp.876-889, 2012.

. Neogen, GeneSeek® Genomic Profiler TM Bovine 50K. 2012. Consulté le 30 juillet, 2019.

. Neogen, T. M. Geneseek-®-genomic-profiler, and . Indicus, Consulté le 13 août 2019 sur, 2018.

. Neogen, GeneSeek® Genomic Profiler Porcine. Consulté le 30 juillet, 2019.

G. Ni, D. Cavero, A. Fangmann, M. Erbe, and H. Simianer, Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture, Genetics Selection Evolution, vol.49, pp.8-21, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01479131

M. Nishio and M. Satoh, Including Dominance Effects in the Genomic BLUP Method for Genomic Evaluation, PLoS ONE, vol.9, p.85792, 2014.

K. M. Olson, P. M. Vanraden, and M. E. Tooker, Multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss, Journal of Dairy Science, vol.95, pp.5378-5383, 2012.

J. Owen, Karyotype studies on Gallus domesticus, Chromosoma, vol.16, pp.601-608, 1965.

Y. Palti, G. Gao, S. Liu, M. P. Kent, L. S. Miller et al., The development and characterization of a 57K single nucleotide polymorphism array for rainbow trout, Molecular Ecology Resources, vol.15, pp.662-672, 2015.

C. Patry and V. Ducrocq, Evidence of biases in genetic evaluations due to genomic preselection in dairy cattle, Journal of Dairy Science, vol.94, pp.1011-1020, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01000597

R. J. Pengelly, A. A. Gheyas, R. Kuo, E. Mossotto, E. G. Seaby et al., Commercial chicken breeds exhibit highly divergent patterns of linkage disequilibrium, Heredity, vol.117, pp.375-382, 2016.

O. Pérez, A. M. 'brien, G. Mészáros, Y. T. Utsunomiya, T. S. Sonstegard et al., Linkage disequilibrium levels in Bos indicus and Bos taurus cattle using medium and high density SNP chip data and different minor allele frequency distributions, Livestock Science, vol.166, pp.121-132, 2014.

F. Pértille, C. Guerrero-bosagna, V. H. Silva, C. Boschiero, J. Nunes et al., Highthroughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing, Scientific Reports, vol.6, pp.1-12, 2016.

B. K. Peterson, J. N. Weber, E. H. Kay, H. S. Fisher, and H. E. Hoekstra, Double Digest RADseq: An Inexpensive Method for De Novo SNP Discovery and Genotyping in Model and Non-Model Species, PLoS ONE, vol.7, p.37135, 2012.

P. Druet, D. Varenne, A. Herry, F. Hérault, F. Allais et al., Relevance of genomic evaluation for egg quality traits in layers, BMC Genetics, 2019.

M. L. Piccoli, J. Braccini, F. F. Cardoso, M. Sargolzaei, S. G. Larmer et al., Accuracy of genome-wide imputation in Braford and Hereford beef cattle, BMC Genetics, vol.15, pp.157-171, 2014.

J. A. Poland, P. J. Brown, M. E. Sorrells, and J. L. Jannink, Development of High-Density Genetic Maps for Barley and Wheat Using a Novel Two-Enzyme Genotyping-by-Sequencing Approach, PLoS ONE, vol.7, p.32253, 2012.

J. Poland, J. Endelman, J. Dawson, J. Rutkoski, S. Wu et al., Genomic Selection in Wheat Breeding using Genotyping-by-Sequencing, The Plant Genome Journal, vol.5, pp.103-113, 2012.

J. K. Pritchard and M. Przeworski, Linkage disequilibrium in humans: models and data, American Journal of Human Genetics, vol.69, pp.1-14, 2001.

J. E. Pryce, B. J. Hayes, and M. E. Goddard, Genotyping dairy females can improve the reliability of genomic selection for young bulls and heifers and provide farmers with new management tools, Proceedings, 38th ICAR Session, vol.28, 2012.

M. Pszczola, H. A. Mulder, and M. Calus, Effect of enlarging the reference population with (un)genotyped animals on the accuracy of genomic selection in dairy cattle, Journal of Dairy Science, vol.94, pp.431-441, 2011.

M. Pszczola, T. Strabel, H. A. Mulder, and M. Calus, Reliability of direct genomic values for animals with different relationships within and to the reference population, Journal of Dairy Science, vol.95, pp.389-400, 2012.

S. Qanbari, M. Hansen, S. Weigend, R. Preisinger, and H. Simianer, Linkage disequilibrium reveals different demographic history in egg laying chickens, BMC Genetics, vol.11, pp.103-115, 2010.

. R-core-team, R: A language and environment for statistical computing, 2017.

A. M. Ramos, R. Crooijmans, N. A. Affara, A. J. Amaral, A. L. Archibald et al., Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology, PLoS ONE, vol.4, p.6524, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02665746

J. Raoul, A. A. Swan, and J. M. Elsen, Using a very low-density SNP panel for genomic selection in a breeding program for sheep, Genetics Selection Evolution, vol.49, pp.76-87, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01622966

A. Rasheed, Y. Hao, X. Xia, A. Khan, Y. Xu et al., Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives, Molecular Plant, vol.10, pp.1047-1064, 2017.

C. Robert-granié, A. Legarra, and V. Ducrocq, Principes de base de la sélection génomique, INRA Productions Animales, vol.24, pp.331-340, 2011.

M. Sargolzaei, F. S. Schenkel, G. B. Jansen, and L. R. Schaeffer, Extent of linkage disequilibrium in Holstein Cattle in North America, Journal of Dairy Science, vol.91, 2008.

M. Sargolzaei, J. P. Chesnais, and F. S. Schenkel, A new approach for efficient genotype imputation using information from relatives, BMC Genomics, vol.15, pp.478-489, 2014.

R. J. Schaefer, M. Schubert, E. Bailey, D. L. Bannasch, E. Barrey et al., Developing a 670k genotyping array to tag ~2M SNPs across 24 horse breeds, BMC Genomics, vol.18, pp.565-582, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02626769

L. R. Schaeffer, Strategy for applying genome-wide selection in dairy cattle, Journal of Animal Breeding and Genetics, vol.123, pp.218-223, 2006.

P. Scheet and M. Stephens, A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase, Am J Hum Genet, vol.78, pp.629-644, 2006.

C. Schrooten, R. Dassonneville, V. Ducrocq, R. F. Brøndum, M. S. Lund et al., Error rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip, Genetics Selection Evolution, vol.46, pp.10-18, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01189869

F. Sitzenstock, F. Ytournel, A. R. Sharifi, D. Cavero, H. Täubert et al., Efficiency of genomic selection in an established commercial layer breeding program, Genetics Selection Evolution, vol.45, pp.29-39, 2013.

T. R. Solberg, A. K. Sonesson, J. A. Woolliams, and T. Meuwissen, Genomic selection using different marker types and densities, Journal of Animal Science, vol.86, pp.2447-2454, 2008.

T. R. Solberg, A. K. Sonesson, J. A. Woolliams, J. Ødegard, and T. Meuwissen, Persistence of accuracy of genome-wide breeding values over generations when including a polygenic effect, Genetics Selection Evolution, vol.41, pp.53-60, 2009.

G. Su, R. F. Brøndum, P. Ma, B. Guldbrandtsen, G. P. Aamand et al., Comparison of genomic predictions using medium-density (?54,000) and high-density (?777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations, Journal of Dairy Science, vol.95, pp.4657-4665, 2012.

C. Sun, X. L. Wu, K. A. Weigel, G. Rosa, S. Bauck et al., An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle, Genetics Research, vol.94, pp.133-150, 2012.

C. Sun, P. M. Vanraden, J. B. Cole, O. Connell, and J. R. , Improvement of Prediction Ability for Genomic Selection of Dairy Cattle by Including Dominance Effects, PLoS ONE, vol.9, p.103934, 2014.

N. Thébault, J. Riquet, C. Diot, S. Brard-fudulea, D. Guéméné et al., Développement d'une puce de génotypage haute densité 600K pour le canard commun et le canard de barbarie, Treizièmes Journées de la Recherche Avicole et Palmipèdes à Foie Gras, pp.75-79, 2019.

D. Torkamaneh and F. Belzile, Scanning and Filling: Ultra-Dense SNP Genotyping Combining Genotyping-By-Sequencing, SNP Array and Whole-Genome Resequencing Data, PLOS ONE, vol.10, p.131533, 2015.

D. Torkamaneh, B. Boyle, and F. Belzile, Efficient genome-wide genotyping strategies and data integration in crop plants, Theoretical and Applied Genetics, vol.131, pp.499-511, 2018.

G. Tosser-klopp, P. Bardou, O. Bouchez, C. Cabau, R. Crooijmans et al., Design and Characterization of a 52K SNP Chip for Goats, PLoS ONE, vol.9, p.86227, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01602260

T. Tribout, Perspectives d'application de la sélection génomique dans les schémas d'amélioration génétique porcins, INRA Productions Animales, vol.24, p.369, 2011.

T. Tribout, J. P. Bidanel, F. Phocas, S. Schwob, F. Guillaume et al., La sélection génomique : principe et perspectives d'utilisation pour l'amélioration des populations porcines, Journées Recherche Porcine, pp.13-25, 2011.

A. L. Van-eenennaam, K. A. Weigel, A. E. Young, M. A. Cleveland, and J. Dekkers, Applied Animal Genomics: Results from the Field, Annual Review of Animal Biosciences, vol.2, pp.105-139, 2014.

N. J. Van-orsouw, R. Hogers, A. Janssen, F. Yalcin, S. Snoeijers et al., Complexity Reduction of Polymorphic Sequences (CRoPS TM ): A Novel Approach for Large-Scale Polymorphism Discovery in Complex Genomes, PLoS ONE, vol.2, p.1172, 2007.

P. M. Vanraden, Efficient Methods to Compute Genomic Predictions, Journal of Dairy Science, vol.91, pp.4414-4423, 2008.

P. M. Vanraden, J. R. O'connell, G. R. Wiggans, and K. A. Weigel, Genomic evaluations with many more genotypes, Genetics Selection Evolution, vol.43, pp.10-20, 2011.

P. M. Vanraden, D. J. Null, M. Sargolzaei, G. R. Wiggans, M. E. Tooker et al., Genomic imputation and evaluation using high-density Holstein genotypes, Journal of Dairy Science, vol.96, pp.668-678, 2012.

C. P. Van-tassell, T. Smith, L. K. Matukumalli, J. F. Taylor, R. D. Schnabel et al., SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries, Nature Methods, vol.5, pp.247-252, 2008.

L. Varona and I. Misztal, Prediction of Parental Dominance Combinations for Planned Matings, Methodology, and Simulation Results, Journal of Dairy Science, vol.82, pp.2186-2191, 1999.

R. V. Ventura, D. Lu, F. S. Schenkel, Z. Wang, C. Li et al., Impact of reference population on accuracy of imputation from 6K to 50K single nucleotide polymorphism chips in purebred and crossbreed beef cattle1, Journal of Animal Science, vol.92, pp.1433-1444, 2014.

A. Vereijken, G. Albers, and J. Visscher, Imputation of SNP genotypes in chicken using a reference panel with phased haplotypes, Proceedings, 9th World Congress of Genetics Applied to Livestock Production. Leipzig, 2010.

A. Vignal, État de la carte de la poule, Hors-série « Génétique moléculaire : principes et applications aux populations animales, pp.113-114, 2000.

H. Wang, I. Misztal, I. Aguilar, A. Legarra, and W. M. Muir, Genome-wide association mapping including phenotypes from relatives without genotypes, Genetics Research, vol.94, pp.73-83, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02652510

S. Wang, E. Meyer, J. K. Mckay, and M. V. Matz, 2b-RAD: a simple and flexible method for genomewide genotyping, Nature Methods, vol.9, pp.808-810, 2012.

W. C. Warren, L. W. Hillier, C. Tomlinson, P. Minx, M. Kremitzki et al., New Chicken Genome Assembly Provides Insight into Avian Genome Structure. G3, vol.7, pp.109-117, 2017.

K. A. Weigel, G. De-los-campos, O. González-recio, H. Naya, X. L. Wu et al., Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers, Journal of Dairy Science, vol.92, pp.5248-5257, 2009.

K. A. Weigel, G. De-los-campos, A. I. Vazquez, G. Rosa, D. Gianola et al., Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle, Journal of Dairy Science, vol.93, pp.5423-5435, 2010.

K. A. Weigel, C. P. Van-tassell, O. Connell, J. R. Vanraden, P. M. Wiggans et al., Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms, Journal of Dairy Science, vol.93, pp.2229-2238, 2010.

B. S. Weir and C. C. Cockerham, Estimating F-Statistics for the analysis of population structure, Evolution, pp.38-1358, 1984.

K. M. Weiss and A. G. Clark, Linkage disequilibrium and the mapping of complex human traits, Trends in Genetics, vol.18, pp.19-24, 2002.

R. Wellmann and J. Bennewitz, Bayesian models with dominance effects for genomic evaluation of quantitative traits, Genetics Research, vol.94, pp.21-37, 2012.

R. Wellmann, S. Preuß, E. Tholen, J. Heinkel, K. Wimmers et al., Genomic selection using low density marker panels with application to a sire line in pigs, Genetics Selection Evolution, vol.45, pp.28-38, 2013.

Z. Weng, M. Saatchi, R. D. Schnabel, J. F. Taylor, and D. J. Garrick, Recombination locations and rates in beef cattle assessed from parent-offspring pairs, Genetics Selection Evolution, vol.46, pp.34-47, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01341266

Z. Weng, A. Wolc, X. Shen, R. L. Fernando, J. Dekkers et al., Effects of number of training generations on genomic prediction for various traits in a layer chicken population, Genetics Selection Evolution, vol.48, pp.22-31, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01341356

G. R. Wiggans, T. A. Cooper, P. M. Vanraden, and J. B. Cole, Technical note: Adjustment of traditional cow evaluations to improve accuracy of genomic predictions, Journal of Dairy Science, vol.94, pp.6188-6193, 2011.

G. R. Wiggans, T. A. Cooper, P. M. Vanraden, K. M. Olson, and M. E. Tooker, Use of the Illumina Bovine3K BeadChip in dairy genomic evaluation, Journal of Dairy Science, vol.95, pp.1552-1558, 2012.

G. R. Wiggans, T. A. Cooper, C. P. Van-tassell, T. S. Sonstegard, and E. B. Simpson, Technical note: Characteristics and use of the Illumina BovineLD and GeneSeek Genomic Profiler lowdensity bead chips for genomic evaluation, Journal of Dairy Science, vol.96, pp.1258-1263, 2013.

G. R. Wiggans, J. B. Cole, S. M. Hubbard, and T. S. Sonstegard, Genomic Selection in Dairy Cattle: The USDA Experience, Annual Review of Animal Biosciences, vol.5, pp.309-327, 2017.

A. Wolc, J. Arango, P. Settar, J. E. Fulton, N. P. O'sullivan et al., Persistence of accuracy of genomic estimated breeding values over generations in layer chickens, Genetics Selection Evolution, vol.43, pp.23-30, 2011.

A. Wolc, H. H. Zhao, J. Arango, P. Settar, J. E. Fulton et al., Response and inbreeding from a genomic selection experiment in layer chickens, Genetics Selection Evolution, vol.47, pp.59-70, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01341296

A. Wolc, A. Kranis, J. Arango, P. Settar, J. E. Fulton et al., Implementation of genomic selection in the poultry industry, Animal Frontiers, vol.6, pp.23-31, 2016.

D. Wragg, J. M. Mwacharo, J. A. Alcalde, P. M. Hocking, and O. Hanotte, Analysis of genome-wide structure, diversity and fine mapping of Mendelian traits in traditional and village chickens, Heredity, vol.109, pp.6-18, 2012.

S. Wright, The genetical structure of populations, Annals of Eugenics, vol.15, pp.323-354, 1951.

S. Wright, Variability Within and Among Natural Populations, Evolution and the Genetics of Populations, vol.4, 1978.

T. Xiang, O. F. Christensen, Z. G. Vitezica, and A. Legarra, Genomic evaluation by including dominance effects and inbreeding depression for purebred and crossbred performance with an application in pigs, Genetics Selection Evolution, vol.48, pp.92-105, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01479227

F. Ytournel, Déséquilibre de liaison et cartographie de QTL en population sélectionnée, AgroParisTech-ABIES, 2008.

Z. Zhai, W. Zhao, C. He, K. Yang, L. Tang et al., SNP discovery and genotyping using restriction-site-associated DNA sequencing in chickens, Animal Genetics, vol.46, pp.216-219, 2015.

X. Zhang, D. Lourenco, I. Aguilar, A. Legarra, and I. Misztal, Weighting Strategies for Single-Step Genomic BLUP: An Iterative Approach for Accurate Calculation of GEBV and GWAS, Frontiers in Genetics, vol.7, pp.743-756, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01602498

Z. Zhang and T. Druet, Marker imputation with low-density marker panels in Dutch Holstein cattle, Journal of Dairy Science, vol.93, pp.5487-5494, 2010.

Z. Zhang, X. Ding, J. Liu, Q. Zhang, and D. De-koning, Accuracy of genomic prediction using lowdensity marker panels, Journal of Dairy Science, vol.94, pp.3642-3650, 2011.