P. M. Visscher, N. R. Wray, Q. Zhang, P. Sklar, M. I. Mccarthy et al., Years of GWAS discovery: biology, function, and translation, Am. J. Hum. Genet, vol.101, pp.5-22, 2017.

A. R. Wood, T. Esko, J. Yang, S. Vedantam, T. H. Pers et al., Defining the role of common variation in the genomic and biological architecture of adult human height, Nat. Genet, vol.46, pp.1173-1186, 2014.

J. D. Eicher, C. Landowski, B. Stackhouse, A. Sloan, W. Chen et al., GRASP v2. 0: an update on the Genome-wide repository of associations between SNPs and phenotypes, Nucleic Acids Res, vol.43, pp.799-804, 2014.

M. J. Li, Z. Liu, P. Wang, M. P. Wong, M. R. Nelson et al., GWASdb v2: an update database for human genetic variants identified by genome-wide association studies, Nucleic Acids Res, vol.44, pp.869-876, 2015.

J. Macarthur, E. Bowler, M. Cerezo, L. Gil, P. Hall et al., The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog), Nucleic Acids Res, vol.45, pp.896-901, 2017.

L. A. Hindorff, P. Sethupathy, H. A. Junkins, E. M. Ramos, J. P. Mehta et al., Potential etiologic and functional implications of genome-wide association loci for human diseases and traits, Proc. Natl. Acad. Sci. U.S.A, vol.106, pp.9362-9367, 2009.

D. L. Nicolae, E. Gamazon, W. Zhang, S. Duan, M. E. Dolan et al., Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS, PLoS Genet, vol.6, p.1000888, 2010.

M. T. Maurano, R. Humbert, E. Rynes, R. E. Thurman, E. Haugen et al., Systematic localization of common disease-associated variation in regulatory DNA, Science, p.1222794, 2012.

F. Spitz and E. E. Furlong, Transcription factors: from enhancer binding to developmental control, Nat. Rev. Genet, vol.13, pp.613-626, 2012.

G. Andrey and S. Mundlos, The three-dimensional genome: regulating gene expression during pluripotency and development, Development, vol.144, pp.3646-3658, 2017.

E. J. Leslie, M. A. Taub, H. Liu, K. M. Steinberg, D. C. Koboldt et al., Identification of functional variants for cleft lip with or without cleft palate in or near PAX7, FGFR2, and NOG by targeted sequencing of GWAS loci, Am. J. Hum. Genet, vol.96, pp.397-411, 2015.

L. D. Ward and M. Kellis, HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants, Nucleic Acids Res, vol.40, pp.930-934, 2011.

S. G. Coetzee, S. K. Rhie, B. P. Berman, G. A. Coetzee, and H. Noushmehr, FunciSNP: an R/bioconductor tool integrating functional non-coding data sets with genetic association studies to identify candidate regulatory SNPs, Nucleic Acids Res, vol.40, p.139, 2012.

E. M. Schmidt, J. Zhang, W. Zhou, J. Chen, K. L. Mohlke et al., GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach, Bioinformatics, vol.31, pp.2601-2606, 2015.

Q. Lu, X. Yao, Y. Hu, and H. Zhao, GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation, Bioinformatics, vol.32, pp.542-548, 2016.

M. Kircher, D. M. Witten, P. Jain, B. J. O'roak, G. M. Cooper et al., A general framework for estimating the relative pathogenicity of human genetic variants, Nat. Genet, vol.46, pp.310-315, 2014.

G. R. Ritchie, I. Dunham, E. Zeggini, and P. Flicek, Functional annotation of noncoding sequence variants, Nat. Methods, vol.11, pp.294-296, 2014.

D. Quang, Y. Chen, and X. Xie, DANN: a deep learning approach for annotating the pathogenicity of genetic variants, Bioinformatics, vol.31, pp.761-763, 2014.

H. A. Shihab, J. Gough, M. Mort, D. N. Cooper, I. N. Day et al., Ranking non-synonymous single nucleotide polymorphisms based on disease concepts, Hum. Genomics, vol.8, p.11, 2014.

L. Chen, P. Jin, and Z. S. Qin, DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles, 2016.

, Genome Biol, vol.17, p.252

D. Smedley, M. Schubach, J. O. Jacobsen, S. K"ohler, T. Zemojtel et al., A Whole-genome analysis framework for effective identification of pathogenic regulatory variants in Mendelian disease, Am. J. Hum. Genet, vol.99, pp.595-606, 2016.

Y. Huang, B. Gulko, and A. Siepel, Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data, Nat. Genet, vol.49, pp.618-624, 2017.

Y. Fu, Z. Liu, S. Lou, J. Bedford, X. J. Mu et al., FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer, Genome Biol, vol.15, p.480, 2014.

J. Zhou and O. G. Troyanskaya, Predicting effects of noncoding variants with deep learning-based sequence model, Nat. Methods, vol.12, pp.931-934, 2015.

B. Gulko, M. J. Hubisz, I. Gronau, and A. Siepel, A method for calculating probabilities of fitness consequences for point mutations across the human genome, Nat. Genet, vol.47, pp.276-283, 2015.

I. Ionita-laza, K. Mccallum, B. Xu, and J. D. Buxbaum, A spectral approach integrating functional genomic annotations for coding and noncoding variants, Nat. Genet, vol.48, pp.214-220, 2016.

J. Wang, A. Z. Ullah, and C. Chelala, IW-Scoring: an Integrative weighted scoring framework for annotating and prioritizing genetic variations in the noncoding genome, Nucleic Acids Res, vol.46, p.47, 2018.

C. A. Bodea, A. A. Mitchell, A. Bloemendal, A. G. Day-williams, H. Runz et al., PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants, 2018.

, Genome Biol, vol.19, p.173

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, 2015.

A. Griffon, Q. Barbier, J. Dalino, J. Van-helden, S. Spicuglia et al., Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape, Nucleic Acids Res, vol.43, p.27, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01219379

J. Chèneby, M. Gheorghe, M. Artufel, A. Mathelier, and B. Ballester, ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments, Nucleic Acids Res, vol.46, pp.267-275, 2017.

M. V. Kuleshov, M. R. Jones, A. D. Rouillard, N. F. Fernandez, Q. Duan et al., Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Res, vol.44, pp.90-97, 2016.

I. V. Kulakovskiy, I. E. Vorontsov, I. S. Yevshin, R. N. Sharipov, A. D. Fedorova et al., HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis, Nucleic Acids Res, vol.46, pp.252-259, 2017.

J. A. Castro-mondragon, S. Jaeger, D. Thieffry, M. Thomas-chollier, and J. Van-helden, RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections, Nucleic Acids Res, vol.45, p.119, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01624366

A. Medina-rivera, M. Defrance, O. Sand, C. Herrmann, J. A. Castro-mondragon et al., RSAT 2015: regulatory sequence analysis tools, vol.43, pp.50-56, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01624369

E. M. Smigielski, K. Sirotkin, M. Ward, and S. T. Sherry, dbSNP: a database of single nucleotide polymorphisms, Nucleic acids Res, vol.28, pp.352-355, 2000.

G. P. Consortium, A global reference for human genetic variation, Nature, vol.526, pp.68-74, 2015.

M. J. Landrum, J. M. Lee, M. Benson, G. Brown, C. Chao et al., ClinVar: public archive of interpretations of clinically relevant variants, Nucleic Acids Res, vol.44, pp.862-868, 2016.

R. Andersson, C. Gebhard, I. Miguel-escalada, I. Hoof, J. Bornholdt et al., An atlas of active enhancers across human cell types and tissues, Nature, vol.507, pp.455-461, 2014.

J. Lonsdale, J. Thomas, M. Salvatore, R. Phillips, E. Lo et al., The genotype-tissue expression (GTEx) project, Nat. Genet, vol.45, pp.580-585, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01374823

E. C. Roadmap, A. Kundaje, W. Meuleman, J. Ernst, M. Bilenky et al., Integrative analysis of 111 reference human epigenomes, Nature, vol.518, pp.317-330, 2015.

D. Hnisz, B. J. Abraham, T. I. Lee, A. Lau, V. Saint-andré et al., Super-enhancers in the control of cell identity and disease, Cell, vol.155, pp.934-947, 2013.

J. K-¨-oster and S. Rahmann, Snakemake-a scalable bioinformatics workflow engine, Bioinformatics, vol.28, pp.2520-2522, 2012.

W. J. Kent, C. W. Sugnet, T. S. Furey, K. M. Roskin, T. H. Pringle et al., The human genome browser at UCSC, Genome Res, vol.12, pp.996-1006, 2002.

A. R. Quinlan, BEDTools: the Swiss-army tool for genome feature analysis, Curr. Protoc. Bioinformatics, vol.47, 2014.

J. T. Robinson, H. Thorvaldsdóttirthorvaldsd´thorvaldsdóttir, W. Winckler, M. Guttman, E. S. Lander et al., Integrative genomics viewer, Nat. Biotechnol, vol.29, pp.24-26, 2011.

T. Chen and T. He, Higgs Boson discovery with boosted trees, Proceedings of the 2014 International Conference on High-Energy Physics and Machine Learning, vol.42, pp.69-80, 2014.

M. M. Hoffman, J. Ernst, S. P. Wilder, A. Kundaje, R. S. Harris et al., Integrative annotation of chromatin elements from ENCODE data, Nucleic Acids Res, vol.41, pp.827-841, 2013.

M. A. Beer, Predicting enhancer activity and variant impact using gkm-SVM, Hum. Mutat, vol.38, pp.1251-1258, 2017.

B. Schuster-b"ockler and B. Lehner, Chromatin organization is a major influence on regional mutation rates in human cancer cells, Nature, vol.488, pp.504-507, 2012.

S. Heinz, C. E. Romanoski, C. Benner, and C. K. Glass, The selection and function of cell type-specific enhancers, Nat. Rev. Mol. Cell Biol, vol.16, pp.144-154, 2015.

Z. Wang, C. Zang, J. A. Rosenfeld, D. E. Schones, A. Barski et al., Combinatorial patterns of histone acetylations and methylations in the human genome, Nat. Genet, vol.40, pp.897-903, 2008.

Q. Song and A. D. Smith, Identifying dispersed epigenomic domains from ChIP-Seq data, Bioinformatics, vol.27, pp.870-871, 2011.

H. Xi, H. P. Shulha, J. M. Lin, T. R. Vales, Y. Fu et al., Identification and characterization of cell type-specific and ubiquitous chromatin regulatory structures in the human genome, 2007.

, PLoS Genet, vol.3, p.136

K. J. Gaulton, T. Nammo, L. Pasquali, J. M. Simon, P. G. Giresi et al., A map of open chromatin in human pancreatic islets, Nat. Genet, vol.42, pp.255-259, 2010.

H. Zeng, M. D. Edwards, Y. Guo, and D. K. Gifford, Accurate eQTL prioritization with an ensemble-based framework, Hum. Mutat, vol.38, pp.1259-1265, 2017.

J. I. Bass, N. Sahni, S. Shrestha, A. Garcia-gonzalez, A. Mori et al., Human gene-centered transcription factor networks for enhancers and disease variants, Cell, vol.161, pp.661-673, 2015.

E. Mangold, K. U. Ludwig, S. Birnbaum, C. Baluardo, M. Ferrian et al., Genome-wide association study identifies two susceptibility loci for nonsyndromic cleft lip with or without cleft palate, Nat. Genet, vol.42, pp.24-26, 2010.

K. C. Lim, G. Lakshmanan, S. E. Crawford, Y. Gu, F. Grosveld et al., Gata3 loss leads to embryonic lethality due to noradrenaline deficiency of the sympathetic nervous system, Nat. Genet, vol.25, pp.209-212, 2000.

L. Bernardini, L. Sinibaldi, A. Capalbo, I. Bottillo, B. Mancuso et al., HDR (Deafness, Renal dysplasia) syndrome associated to GATA3 gene duplication, Clin. Genet, vol.76, pp.117-119, 2009.

K. Sheehan-rooney, M. E. Swartz, F. Zhao, D. Liu, and J. K. Eberhart, Ahsa1 and Hsp90 activity confers more severe craniofacial phenotypes in a zebrafish model of hypoparathyroidism, sensorineural deafness and renal dysplasia (HDR), Dis. Models Mech, vol.6, pp.1285-1291, 2013.

C. L. Smith, J. A. Blake, J. A. Kadin, J. E. Richardson, C. J. Bult et al., Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse, Nucleic Acids Res, vol.46, pp.836-842, 2018.

A. Hamosh, A. F. Scott, J. S. Amberger, C. A. Bocchini, and V. A. Mckusick, Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders, Nucleic Acids Res, vol.33, pp.514-517, 2005.

M. M. Suzuki and A. Bird, DNA methylation landscapes: provocative insights from epigenomics, Nat. Rev. Genet, vol.9, p.465, 2008.