?. {0, 1} m , représentant un haplotype défini sur m loci, p.32

E. A. and E. Maximisation, Expectation Maximization en anglais, p.68

H. Modèle-de-markov-caché, Hidden Markov Model en anglais, p.69

A. J. Brookes, The essence of SNPs, Gene 234, pp.177-186, 1999.

J. D. Wall, K. Et-jonathan, and . Pritchard, Haplotype blocks and linkage disequilibrium in the human genome, Nature Reviews Genetics, vol.20, issue.8, 2003.
DOI : 10.1093/molbev/msg032

J. K. Pritchard, M. Et, and . Przeworski, Linkage Disequilibrium in Humans: Models and Data, The American Journal of Human Genetics, vol.69, issue.1, 2001.
DOI : 10.1086/321275

A. D. Long, H. Charles, and . Langley, The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits, Genome Research, vol.98, pp.720-731, 1999.

K. J. Galinsky, Fast principal components analysis reveals convergent evolution of ADH1B gene in Europe and East Asia, 2015.
DOI : 10.1101/018143

A. Abdellaoui, Population structure, migration and diversifying selection in the Netherlands, European Journal of Human Genetics, vol.34, issue.11, 2013.
DOI : 10.1038/nmeth0508-447

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, The American Journal of Human Genetics, vol.78, issue.4, pp.629-644, 2006.
DOI : 10.1086/502802

S. R. Browning, L. Et-brian, and . 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, issue.5, pp.1084-1097, 2007.
DOI : 10.1086/521987

M. Stephens and P. Scheet, Accounting for Decay of Linkage Disequilibrium in Haplotype Inference and Missing-Data Imputation, The American Journal of Human Genetics, vol.76, issue.3, pp.449-462, 2005.
DOI : 10.1086/428594

H. Jung, New methods for imputation of missing genotype using linkage disequilibrium and haplotype information, Information Sciences, vol.177, issue.3, pp.804-814, 2007.
DOI : 10.1016/j.ins.2006.07.017

P. Donnelly and S. Leslie, The coalescent and its descendants, pp.204-237, 2010.
DOI : 10.1017/CBO9781139107174.011

J. Haldane, The combination of linkage values and the calculation of distances between the loci of linked factors, J Genet, vol.829, pp.299-309, 1919.

J. F. Kingman, On the Genealogy of Large Populations, Journal of Applied Probability, vol.19, 1982.

G. A. Mcvean, N. J. Et, and . Cardin, Approximating the coalescent with recombination, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.18, issue.10, 2005.
DOI : 10.1093/oxfordjournals.molbev.a003733

R. R. Hudson, Properties of a neutral allele model with intragenic recombination, Theoretical population biology 23, pp.183-201, 1983.
DOI : 10.1016/0040-5809(83)90013-8

R. R. Hudson, Generating samples under a Wright-Fisher neutral model of genetic variation, Bioinformatics, vol.18, issue.2, 2002.
DOI : 10.1093/bioinformatics/18.2.337

N. Li and M. Stephens, Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data, Genetics, vol.1654, pp.2213-2233, 2003.

M. Stephens and P. Donnelly, Inference in molecular population genetics, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.62, issue.4, pp.605-635, 2000.
DOI : 10.1111/1467-9868.00254

P. Fearnhead and P. Donnelly, Estimating recombination rates from population genetic data, Genetics, vol.1593, pp.1299-1318, 2001.

S. R. Browning, B. S. Et, and . Weir, Population Structure With Localized Haplotype Clusters, Genetics, vol.185, issue.4, pp.1337-1344, 2010.
DOI : 10.1534/genetics.110.116681
URL : http://www.genetics.org/content/genetics/185/4/1337.full.pdf

M. P. Stumpf, Haplotype diversity and the block structure of linkage disequilibrium, Trends in Genetics, vol.18, issue.5, pp.226-228, 2002.
DOI : 10.1016/S0168-9525(02)02641-0

S. Gravel, Population Genetics Models of Local Ancestry, Genetics, vol.191, issue.2, pp.607-619, 2012.
DOI : 10.1534/genetics.112.139808

J. Novembre, Genes mirror geography within Europe, Nature, vol.81, issue.7218, p.98, 2008.
DOI : 10.1038/nature07331
URL : http://europepmc.org/articles/pmc2735096?pdf=render

L. L. Cavalli-sforza, A. W. Et, and . Edwards, Phylogenetic Analysis : Models and Estimation Procedures, 1967.
DOI : 10.2307/2406616
URL : http://europepmc.org/articles/pmc1706274?pdf=render

J. K. Pickrell, K. Et-jonathan, and . Pritchard, Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data, PLoS Genetics, vol.26, issue.11, 2012.
DOI : 10.1371/journal.pgen.1002967.s016

J. K. Pritchard, M. Stephens, and P. Donnelly, Inference of Population Structure Using Multilocus Genotype Data, Genetics, vol.1552155, issue.2, pp.945-959, 2000.

D. Falush, M. Stephens, and J. K. Pritchard, Inference of Population Structure Using Multilocus Genotype Data : Linked Loci and Correlated Allele Frequencies, Genetics, vol.1644164, issue.4, pp.1567-1587, 1567.

H. Tang, Reconstructing Genetic Ancestry Blocks in Admixed Individuals, The American Journal of Human Genetics, vol.79, issue.1, 2006.
DOI : 10.1086/504302

B. Padhukasahasram, Inferring ancestry from population genomic data and its applications, Frontiers in Genetics, vol.74, issue.e1002886, 2014.
DOI : 10.1086/421329
URL : http://journal.frontiersin.org/article/10.3389/fgene.2014.00204/pdf

A. L. Price, Sensitive Detection of Chromosomal Segments of Distinct Ancestry in Admixed Populations, PLoS Genetics, vol.299, issue.6, 2009.
DOI : 10.1371/journal.pgen.1000519.s002

A. Sundquist, Effect of genetic divergence in identifying ancestral origin using HAPAA, Genome Research, vol.184, pp.676-682, 2008.

Y. Baran, Fast and accurate inference of local ancestry in Latino populations, Bioinformatics, vol.74, issue.Suppl. 2, pp.1359-1367, 2012.
DOI : 10.1086/421329

Y. Liu, Softwares and methods for estimating genetic ancestry in human populations, Human Genomics, vol.7, issue.1, pp.1479-7364, 2013.
DOI : 10.1164/rccm.200605-596OC

D. Hui, LAIT: a local ancestry inference toolkit, BMC Genetics, vol.93, issue.2, 2017.
DOI : 10.1016/j.ajhg.2013.06.020

A. Brisbin, PCAdmix: Principal Components-Based Assignment of Ancestry Along Each Chromosome in Individuals with Admixed Ancestry from Two or More Populations, Human Biology, vol.84, issue.4, 2012.
DOI : 10.3378/027.084.0401

L. Omberg, Inferring genome-wide patterns of admixture in Qataris using fifty-five ancestral populations, BMC Genetics, vol.13, issue.1, pp.1471-2156, 2012.
DOI : 10.1086/521987

B. Maples, RFMix : A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference " . In : The American Journal of Human Genetics 93, pp.278-288, 2013.

E. Y. Durand, Ancestry Composition : A Novel, Efficient Pipeline for Ancestry Deconvolution, 2014.
DOI : 10.1101/010512

S. Sankararaman, Estimating Local Ancestry in Admixed Populations, The American Journal of Human Genetics 82, pp.290-303, 2008.
DOI : 10.1016/j.ajhg.2007.09.022

A. Y. Ng, I. Michael, T. G. Jordan-de, S. Dietterich, Z. Becker et al., On Discriminative vs Generative Classifiers : A comparison of logistic regression and naive Bayes In : sous la dir, pp.841-848, 2002.

J. J. Yang, Efficient inference of local ancestry, Bioinformatics, vol.79, issue.1, pp.2750-2756, 2013.
DOI : 10.1086/504302

C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), p.387310738, 2006.

A. Suarez-gonzalez, (black cottonwood), Molecular Ecology, vol.14, issue.11, 2016.
DOI : 10.1105/tpc.001412

K. Luu, E. Bazin, G. B. Michael, and . Blum, pcadapt : an R package to perform genome scans for selection based on principal component analysis, Molecular Ecology Resources, vol.17, 2017.
DOI : 10.1101/056135
URL : https://hal.archives-ouvertes.fr/hal-01430346

J. K. Pritchard, Association Mapping in Structured Populations, The American Journal of Human Genetics, vol.67, issue.1, 2000.
DOI : 10.1086/302959

H. Xu and S. Shete, Effects of population structure on genetic association studies, BMC Genetics, vol.6, issue.Suppl 1, pp.1471-2156, 2005.
DOI : 10.1186/1471-2156-6-S1-S109

X. Zhou and M. Stephens, Genome-wide efficient mixed-model analysis for association studies, Nature Genetics, vol.23, issue.7, pp.821-824, 2012.
DOI : 10.1186/1297-9686-23-1-67

M. F. Seldin, Admixture mapping as a tool in gene discovery, Current Opinion in Genetics & Development, vol.17, issue.3, pp.177-181, 2007.
DOI : 10.1016/j.gde.2007.03.002

M. F. Seldin, B. Pasaniuc-et-alkes, and L. Price, New approaches to disease mapping in admixed populations, Nature Reviews Genetics, vol.105, issue.8, 2011.
DOI : 10.1073/pnas.0810388105

C. J. Hoggart, Design and Analysis of Admixture Mapping Studies, The American Journal of Human Genetics, vol.74, issue.5, pp.965-978, 2004.
DOI : 10.1086/420855

D. Shriner, Overview of Admixture Mapping In : Current Protocols in Human Genetics, 2013.

M. L. Freedman, Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men, Proceedings of the National Academy of Sciences, vol.437, issue.7063, 2006.
DOI : 10.1038/nature04226

J. T. Bensen, Admixture mapping of prostate cancer in African Americans participating in the North Carolina-Louisiana Prostate Cancer Project (PCaP), The Prostate, vol.7, issue.4, 2013.
DOI : 10.1371/journal.pgen.1001371

A. Darvasi and S. Shifman, The beauty of admixture, Nature Genetics, vol.26, issue.2, pp.118-119, 2005.
DOI : 10.1038/79866

H. Tang, Recent Genetic Selection in the Ancestral Admixture of Puerto Ricans, The American Journal of Human Genetics, vol.81, issue.3, pp.626-633, 2007.
DOI : 10.1086/520769

K. Bryc, Genome-wide patterns of population structure and admixture among Hispanic/Latino populations, Proceedings of the National Academy of Sciences 107.Supplement, 2010.
DOI : 10.2307/2408641

G. Hellenthal, A Genetic Atlas of Human Admixture History, Science, vol.77, issue.1, pp.747-751, 2014.
DOI : 10.1109/5.18626

W. D. Reese, L. Et-elbert, and . Little, Atlas of United States Trees. Volume 1. Conifers and Important Hardwoods, The Bryologist, vol.75, issue.1, 1972.
DOI : 10.2307/3241543

M. Liang, R. Et, and . Nielsen, The Lengths of Admixture Tracts, Genetics, vol.197, issue.3, pp.953-967, 2014.
DOI : 10.1534/genetics.114.162362

S. Carmi and J. , Xue et I. Pe'er (2015) A note on the distribution of admixture segment lengths and ancestry proportions under pulse and two-wave admixture models

Y. Guan, Detecting Structure of Haplotypes and Local Ancestry, Genetics, vol.196, issue.3, pp.625-642, 2014.
DOI : 10.1534/genetics.113.160697

M. W. Smith, A High-Density Admixture Map for Disease Gene Discovery in African Americans, The American Journal of Human Genetics, vol.74, issue.5, pp.1001-1013, 2004.
DOI : 10.1086/420856

L. Huang, Genotype-Imputation Accuracy across Worldwide Human Populations, The American Journal of Human Genetics, vol.84, issue.2, pp.235-250, 2009.
DOI : 10.1016/j.ajhg.2009.01.013
URL : https://doi.org/10.1016/j.ajhg.2009.01.013

S. Ali, S. Sremath-tirumala, and A. Sarrafzadeh, Ensemble learning methods for decision making: Status and future prospects, 2015 International Conference on Machine Learning and Cybernetics (ICMLC), 2015.
DOI : 10.1109/ICMLC.2015.7340924
URL : http://unitec.researchbank.ac.nz/bitstream/10652/3364/1/ENSEMBLE%20LEARNING%20METHODS%20FOR%20DECISION%20MAKING%20STATUS%20AND%20FUTURE%20PROSPECTS.pdf

T. Chen and T. He, Higgs Boson Discovery with Boosted Trees, Proceedings of the 2014 International Conference on High-Energy Physics and Machine Learning, 2014.

T. Chen and C. Guestrin, XGBoost, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, 2016.
DOI : 10.1109/SSDBM.2007.27
URL : http://arxiv.org/pdf/1603.02754

L. Breiman, Bagging predictors, Machine Learning 24, 1996.
DOI : 10.2307/1403680

B. Efron and R. J. Tibshirani, In : An Introduction to the Bootstrap, pp.978-979, 1993.
DOI : 10.1007/978-1-4899-4541-9

S. R. Browning, L. Et-brian, and . Browning, Haplotype phasing: existing methods and new developments, Nature Reviews Genetics, vol.447, issue.10, 2011.
DOI : 10.1038/nature05911
URL : http://europepmc.org/articles/pmc3217888?pdf=render

E. P. Xing, Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006.
DOI : 10.1145/1143844.1143976

K. Kalpakis, P. Et, and . Namjoshi, Haplotype Phasing Using Semidefinite Programming, Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05), 2005.
DOI : 10.1109/BIBE.2005.36
URL : http://www.csee.umbc.edu/~nam1/papers/kalpakis-haplotype.pdf

O. Delaneau, J. Marchini, and J. Zagury, A linear complexity phasing method for thousands of genomes, Nature Methods, vol.165, issue.2, pp.179-181, 2011.
DOI : 10.1086/500808

J. Roach, Chromosomal Haplotypes by Genetic Phasing of Human Families, The American Journal of Human Genetics, vol.89, issue.3, pp.382-397, 2011.
DOI : 10.1016/j.ajhg.2011.07.023

D. G. Hert, P. Christopher, A. E. Fredlake, and . Barron, Advantages and limitations of next-generation sequencing technologies: A comparison of electrophoresis and non-electrophoresis methods, ELECTROPHORESIS, vol.299, issue.23, pp.4618-4626, 2008.
DOI : 10.1002/elps.200800456

A. G. Clark, Inference of Haplotypes from PCR-amplified Samples of Diploid Populations, pp.111-133, 1990.

E. Hubbel, Finding a Maximum Parsimony Solution to Haplotype Phase Is NP-Hard, 2000.

P. Bertolazzi, Solving haplotyping inference parsimony problem using a new basic polynomial formulation, Computers & Mathematics with Applications, vol.55, issue.5, pp.900-911, 2008.
DOI : 10.1016/j.camwa.2006.12.095
URL : https://hal.archives-ouvertes.fr/hal-01255553

D. G. Brown, M. Et-ian, and . Harrower, A New Integer Programming Formulation for the Pure Parsimony Problem in Haplotype Analysis, Algorithms in Bioinformatics, pp.254-265978, 2004.
DOI : 10.1007/978-3-540-30219-3_22

L. Wang, Y. Et, and . Xu, Haplotype inference by maximum parsimony, Bioinformatics, vol.19, issue.14, 2003.
DOI : 10.1093/bioinformatics/btg239
URL : https://academic.oup.com/bioinformatics/article-pdf/19/14/1773/679339/btg239.pdf

I. Wang and . Yang, Haplotyping populations by pure parsimony based on compatible genotypes and greedy heuristics, Applied Mathematics and Computation, vol.217, issue.23, pp.9798-9809, 2011.
DOI : 10.1016/j.amc.2011.04.073

L. Excoffier, M. Et, and . Slatkin, In : Molecular Biology and Evolution, 1995.

Z. S. Qin, T. Niu-et-jun, and S. Liu, Partition-Ligation???Expectation-Maximization Algorithm for Haplotype Inference with Single-Nucleotide Polymorphisms, The American Journal of Human Genetics, vol.71, issue.5, pp.1242-1247, 2002.
DOI : 10.1086/344207

M. Stephens, N. J. Smith, and P. Donnelly, A New Statistical Method for Haplotype Reconstruction from Population Data, The American Journal of Human Genetics, vol.68, issue.4, pp.978-989, 2001.
DOI : 10.1086/319501

E. Halperin, E. Et, and . Eskin, Haplotype reconstruction from genotype data using Imperfect Phylogeny, Bioinformatics, vol.20, issue.12, pp.1842-1849, 2004.
DOI : 10.1093/bioinformatics/bth149
URL : https://academic.oup.com/bioinformatics/article-pdf/20/12/1842/579314/bth149.pdf

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.84, issue.6, 2009.
DOI : 10.1371/journal.pgen.1000529.s004

N. Arivazhagan, H. J. Kim, and E. Yuan, Local Ancestry Inference in Admixed Populations, 2015.

F. Pedregosa, Scikit-learn : Machine Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

B. E. Boser, M. Isabelle, V. N. Guyon, and . Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, 1992.
DOI : 10.1145/130385.130401

M. Burrows, D. J. Et, and . Wheeler, A block-sorting lossless data compression algorithm, 1994.

B. Langmead, Ultrafast and memory-efficient alignment of short DNA sequences to the human genome, Genome Biology, vol.10, issue.3, 2009.
DOI : 10.1186/gb-2009-10-3-r25

R. Durbin, Efficient haplotype matching and storage using the positional Burrows-Wheeler transform (PBWT), Bioinformatics, vol.23, issue.5, pp.1266-1272, 2014.
DOI : 10.1093/bioinformatics/btl681

G. Lunter, Fast haplotype matching in very large cohorts using the Li and Stephens model In : bioRxiv. doi : 10.1101/048280. eprint : https: //www.biorxiv.org/content/early, p.48280, 2016.
DOI : 10.1101/048280

P. Loh, Reference-based phasing using the Haplotype Reference Consortium panel, Nature Genetics, vol.4811, 2016.

Y. Brandvain, A. M. Kenney, L. Flagel, G. Coop, and A. L. Sweigart, Speciation and Introgression between Mimulus nasutus and Mimulus guttatus, PLoS Genetics, vol.26, issue.6, p.1004410, 2014.
DOI : 10.1371/journal.pgen.1004410.s025
URL : https://doi.org/10.1371/journal.pgen.1004410

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680

S. R. Browning and B. L. Browning, Rapid and accurate haplotype phasing and missing-data inference for 307 whole-genome association studies by use of localized haplotype clustering. The American Journal of Human Ge- 308 netics 81, pp.1084-1097, 2007.

K. Bryc, E. Durand, J. M. Macpherson, D. Reich, and J. Mountain, The Genetic Ancestry of African Americans, Latinos, and European Americans across the United States, The American Journal of Human Genetics, vol.96, issue.1, pp.37-53, 2015.
DOI : 10.1016/j.ajhg.2014.11.010

C. A. Buerkle and C. Lexer, Admixture as the basis for genetic mapping. Trends in ecology & evolution 313, pp.686-694, 2008.

R. Corbett-detig and R. Nielsen, A Hidden Markov Model Approach for Simultaneously Estimating Local Ancestry and Admixture Time Using Next Generation Sequence Data in Samples of Arbitrary Ploidy, PLOS Genetics, vol.28, issue.1, pp.1006529-316, 2017.
DOI : 10.1371/journal.pgen.1006529.s014

S. Gravel, Population Genetics Models of Local Ancestry, Genetics, vol.191, issue.2, pp.607-619, 2012.
DOI : 10.1534/genetics.112.139808

M. B. Hufford, P. Lubinksy, T. Pyhäjärvi, M. T. Devengenzo, N. C. Ellstrand et al., The Genomic Signature of Crop-Wild Introgression in Maize, PLoS Genetics, vol.78, issue.5, p.1003477, 2013.
DOI : 10.1371/journal.pgen.1003477.s013