P. Achlioptas, B. Schölkopf, and K. Borgwardt, Two-locus association mapping in subquadratic time, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining -KDD 11, 2011.

I. Adzhubei, D. M. Jordan, and S. R. Sunyaev, Predicting functional effect of human missense mutations using PolyPhen-2, Current Protocols in Human Genetics, vol.76, issue.1, p.72, 2013.

T. B. Agbabiaka, J. Savovi?, and E. Ernst, Methods for causality assessment of adverse drug reactions, Drug Safety, vol.31, issue.1, p.99, 2008.

J. T. Ahrendsen, D. E. Harlow, L. T. Finseth, J. N. Bourne, S. P. Hickey et al., The protein tyrosine phosphatase shp2 regulates oligodendrocyte differentiation and early myelination and contributes to timely remyelination, The Journal of Neuroscience, vol.38, issue.4, p.69, 2017.

A. L. Antonia, K. D. Gibbs, E. D. Trahair, K. J. Pittman, and A. T. Martin, Pathogen evasion of chemokine response through suppression of CXCL10. Frontiers in Cellular and Infection Microbiology, vol.9, p.74, 2019.

S. Athey, G. W. Imbens, and S. Wager, Approximate residual balancing: debiased inference of average treatment effects in high dimensions, Journal of the Royal Statistical Society: Series B (Statistical Methodology), p.41, 2018.

S. Atwell, Y. S. Huang, B. J. Vilhjálmsson, G. Willems, and M. Horton, Genomewide association study of 107 phenotypes in arabidopsis thaliana inbred lines, Nature, vol.465, issue.7298, pp.627-631, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00468440

P. C. Austin, An introduction to propensity score methods for reducing the effects of confounding in observational studies, Multivariate Behavioral Research, vol.46, issue.3, pp.399-424, 2011.

A. Auton, G. R. Abecasis, D. M. Altshuler, R. M. Durbin, and G. R. Abecasis, A global reference for human genetic variation, Nature, vol.526, issue.7571, pp.68-74, 2015.

C. A. Azencott, D. Grimm, M. Sugiyama, Y. Kawahara, and K. M. Borgwardt, Efficient network-guided multi-locus association mapping with graph cuts, Bioinformatics, vol.29, issue.13, pp.171-179, 2013.

F. R. Bach, Consistency of the group lasso and multiple kernel learning, J. Mach. Learn. Res, vol.9, p.59, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00164735

C. Bank, R. T. Hietpas, J. D. Jensen, and D. N. Bolon, A systematic survey of an intragenic epistatic landscape, Molecular Biology and Evolution, vol.32, issue.1, pp.229-238, 2014.

S. E. Baranzini and J. R. Oksenberg, The genetics of multiple sclerosis: From 0 to 200 in 50 years, Trends in Genetics, vol.33, issue.12, pp.960-970, 2017.

R. F. Barber and E. J. Candès, Controlling the false discovery rate via knockoffs, The Annals of Statistics, vol.43, issue.5, pp.2055-2085, 2015.

W. Bateson and G. Mendel, Mendel s principles of heredity, vol.9, 1909.

A. Beinrucker, U. Dogan, and G. Blanchard, A simple extension of stability feature selection, Lecture Notes in Computer Science, p.24, 2012.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of the Royal Statistical Society. Series B (Methodological), vol.57, issue.1, p.23, 1995.

H. C. Berbee, C. G. Boender, A. H. Ran, C. L. Scheffer, R. L. Smith et al., Hit-and-run algorithms for the identification of nonredundant linear inequalities, Mathematical Programming, vol.37, issue.2, p.52, 1987.

R. Berk, L. Brown, A. Buja, K. Zhang, and L. Zhao, Valid post-selection inference, Ann. Stat, vol.41, issue.2, pp.802-837, 2013.

S. Bershtein, M. Segal, R. Bekerman, N. Tokuriki, and D. S. Tawfik, Robustness-epistasis link shapes the fitness landscape of a randomly drifting protein, Nature, vol.444, issue.7121, pp.929-932, 2006.

K. Bessonov, E. S. Gusareva, and K. V. Steen, A cautionary note on the impact of protocol changes for genome-wide association SNP × SNP interaction studies: an example on ankylosing spondylitis, Human Genetics, vol.134, issue.7, pp.761-773, 2015.

J. Bien, J. Taylor, and R. Tibshirani, A lasso for hierarchical interactions, The Annals of Statistics, vol.41, issue.3, pp.1111-1141, 2013.

G. E. Box and D. R. Cox, An analysis of transformations, Journal of the Royal Statistical Society: Series B (Methodological), vol.26, issue.2, pp.211-243, 1964.

E. A. Boyle, Y. I. Li, and J. K. Pritchard, An expanded view of complex traits: From polygenic to omnigenic, Cell, vol.169, issue.7, p.16, 2017.

M. E. Bunnage, Getting pharmaceutical r&d back on target, Nature Chemical Biology, vol.7, issue.6, p.99, 2011.

P. R. Burton, D. G. Clayton, L. R. Cardon, N. Craddock, and P. Deloukas,

, Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls, Nature, vol.447, issue.7145, pp.661-678, 2007.

C. Bycroft, C. Freeman, D. Petkova, G. Band, and L. T. Elliott, The UK biobank resource with deep phenotyping and genomic data, Nature, vol.562, issue.7726, pp.203-209, 2018.

C. J. Bélisle, H. E. Romeijn, and R. L. Smith, Hit-and-run algorithms for generating multivariate distributions, Mathematics of Operations Research, vol.18, issue.2, pp.255-266, 1993.

R. J. Cabin and R. J. Mitchell, To bonferroni or not to bonferroni: when and how are the questions, Bulletin of the Ecological Society of America, vol.81, issue.3, p.23, 2000.

M. Calzado, S. Bacher, and M. L. Schmitz, NF-κb inhibitors for the treatment of inflammatory diseases and cancer, Current Medicinal Chemistry, vol.14, issue.3, p.76, 2007.

S. I. Candille, D. M. Absher, S. Beleza, M. Bauchet, and B. Mcevoy, Genomewide association studies of quantitatively measured skin, hair, and eye pigmentation in four european populations, PLoS ONE, vol.7, issue.10, p.48294, 2012.

R. M. Cantor, K. Lange, and J. S. Sinsheimer, Prioritizing GWAS results: A review of statistical methods and recommendations for their application, The American Journal of Human Genetics, vol.86, issue.1, pp.6-22, 2010.

. Bibliography,

D. Carvalho-silva, A. Pierleoni, M. Pignatelli, C. Ong, and L. Fumis, Open Targets Platform: new developments and updates two years on, Nucleic Acids Research, vol.47, issue.D1, pp.1056-1065

D. Carvalho-silva, A. Pierleoni, M. Pignatelli, C. Ong, and L. Fumis, Open targets platform: new developments and updates two years on, Nucleic Acids Research, vol.47, issue.D1, pp.1056-1065, 2018.

R. Clarke, H. W. Ressom, A. Wang, J. Xuan, M. C. Liu et al., The properties of high-dimensional data spaces: implications for exploring gene and protein expression data, Nature Reviews Cancer, vol.8, issue.1, p.13, 2008.

O. Combarros, M. Cortina-borja, A. D. Smith, and D. J. Lehmann, Epistasis in sporadic alzheimer s disease, Neurobiology of Aging, vol.30, issue.9, pp.1333-1349, 2009.

A. Conesa and S. Beck, Making multi-omics data accessible to researchers, Scientific Data, vol.6, issue.1, p.98, 2019.

H. J. Cordell, Epistasis: what it means, what it doesn t mean, and statistical methods to detect it in humans, Human Molecular Genetics, vol.11, issue.20, pp.2463-2468

H. J. Cordell, Detecting gene-gene interactions that underlie human diseases, Nature Reviews Genetics, vol.10, issue.6, pp.392-404, 2009.

H. J. Cordell and J. A. Todd, Multifactorial inheritance in type 1 diabetes, Trends in Genetics, vol.11, issue.12, pp.499-504, 1995.

H. J. Cordell, J. A. Todd, S. T. Bennett, Y. Kawaguchi, and M. Farrall, Twolocus maximum lod score analysis of a multifactorial trait: joint consideration of IDDM2 and IDDM4 with IDDM1 in type 1 diabetes, American journal of human genetics, vol.57, issue.4, pp.920-934, 1995.

H. J. Cordell, J. A. Todd, N. J. Hill, C. J. Lord, P. A. Lyons et al., Statistical modeling of interlocus interactions in a complex disease: Rejection of the multiplicative model of epistasis in type 1 diabetes, Genetics, vol.158, issue.1, pp.357-367, 2001.

A. Cornet, E. Bettelli, M. Oukka, C. Cambouris, V. Avellana-adalid et al., Role of astrocytes in antigen presentation and naive t-cell activation, Journal of Neuroimmunology, vol.106, issue.1-2, p.76, 2000.

C. Cotsapas and M. Mitrovic, Genome-wide association studies of multiple sclerosis, Clinical & Translational Immunology, vol.7, issue.6, p.1018, 2018.

N. Couturier, F. Bucciarelli, R. N. Nurtdinov, M. Debouverie, and C. Lebrun-frenay, Tyrosine kinase 2 variant influences t lymphocyte polarization and multiple sclerosis susceptibility, Brain, vol.134, issue.3, pp.693-703, 2011.

T. M. Cover and J. A. Thomas, Elements of Information Theory, 2005.

D. R. Cox, A note on data-splitting for the evaluation of significance levels, Biometrika, vol.62, issue.2, pp.441-444, 1975.

N. J. Cox, M. Frigge, D. L. Nicolae, P. Concannon, C. L. Hanis et al., Loci on chromosomes 2 (NIDDM1) and 15 interact to increase susceptibility to diabetes in mexican americans, Nature Genetics, vol.21, issue.2, pp.213-215, 1999.

N. Dargahi, M. Katsara, T. Tselios, M. E. Androutsou, M. De-courten et al., Multiple sclerosis: Immunopathology and treatment update, Brain Sciences, vol.7, issue.12, p.78, 2017.

N. M. Davies, M. V. Holmes, and G. D. Smith, Reading mendelian randomisation studies: a guide, glossary, and checklist for clinicians, BMJ, p.71, 2018.

J. Davis and M. Goadrich, The relationship between precision-recall and ROC curves, Proceedings of the 23rd international conference on Machine learning -ICML 06, vol.40, 2006.

M. Daya, L. Van-der-merwe, P. D. Van-helden, M. Möller, and E. G. Hoal, Investigating the role of gene-gene interactions in TB susceptibility, PLOS ONE, vol.10, issue.4, p.123970, 2015.

C. A. De-leeuw, J. M. Mooij, T. Heskes, and D. Posthuma, MAGMA: Generalized gene-set analysis of GWAS data, PLOS Computational Biology, vol.11, issue.4, p.92, 2015.

G. Dean, T. W. Yeo, A. Goris, C. J. Taylor, and R. S. Goodman, HLA-DRB1 and multiple sclerosis in malta, Neurology, vol.70, issue.2, pp.101-105, 2007.

. Bibliography,

A. Dehman, C. Ambroise, and P. Neuvial, Performance of a blockwise approach in variable selection using linkage disequilibrium information, BMC Bioinformatics, vol.16, issue.1, p.83, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01193074

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society. Series B (Methodological), vol.39, issue.1, p.102, 1977.

B. Devlin and K. Roeder, Genomic control for association studies, Biometrics, vol.55, issue.4, pp.997-1004, 1999.

R. L. Dobrushin, A general formulation of the fundamental theorem of shannon in the theory of information, Uspekhi Matematicheskikh Nauk, vol.14, issue.6, p.11, 1959.

S. Durinck, P. T. Spellman, E. Birney, and W. Huber, Mapping identifiers for the integration of genomic datasets with the r/bioconductor package biomart, Nature Protocols, vol.4, p.89, 2009.

D. A. Dyment, Multiple sclerosis in stepsiblings: recurrence risk and ascertainment, Neurosurgery & Psychiatry, vol.77, issue.2, pp.258-259, 2006.

S. Ekins, Y. Nikolsky, A. Bugrim, E. Kirillov, and T. Nikolskaya, Pathway mapping tools for analysis of high content data, High Content Screening: A Powerful Approach to Systems Cell Biology and Drug Discovery, vol.63, pp.319-350, 2006.

A. E. Fish, J. A. Capra, and W. S. Bush, Are interactions between cis -regulatory variants evidence for biological epistasis or statistical artifacts?, The American Journal of Human Genetics, vol.99, issue.4, pp.817-830, 2016.

R. A. Fisher and . Xv, -the correlation between relatives on the supposition of mendelian inheritance, Transactions of the Royal Society of Edinburgh, vol.52, issue.2, pp.399-433, 1919.

C. Fong, C. Hazlett, and K. Imai, Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements. The Annals of, Applied Statistics, vol.12, issue.1, p.99, 2018.

E. Fothergill, J. Guo, L. Howard, J. C. Kerns, and N. D. Knuth, Persistent metabolic adaptation 6 years after "the biggest loser" competition, Obesity, vol.24, issue.8, p.19, 2016.

A. Franke, D. P. Mcgovern, J. C. Barrett, K. Wang, and G. L. Radford-smith,

, Genome-wide meta-analysis increases to 71 the number of confirmed crohn s disease susceptibility loci, Nature Genetics, vol.42, issue.12, pp.1118-1125, 2010.

A. Fraser, C. Macdonald-wallis, K. Tilling, A. Boyd, and J. Golding, Cohort profile: The avon longitudinal study of parents and children: ALSPAC mothers cohort, International Journal of Epidemiology, vol.42, issue.1, pp.97-110, 2012.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software, vol.33, issue.1, p.32

G. Galarza-muñoz, F. B. Briggs, I. Evsyukova, G. Schott-lerner, and E. M. Kennedy, Human Epistatic Interaction Controls IL7R Splicing and Increases Multiple Sclerosis Risk, Cell, vol.169, issue.1, pp.72-84, 2017.

N. M. Gatto, Further development of the case-only design for assessing geneenvironment interaction: evaluation of and adjustment for bias, International Journal of Epidemiology, vol.33, issue.5, pp.1014-1024, 2004.

A. Gilly, L. Southam, D. Suveges, K. Kuchenbaecker, and R. Moore, Very lowdepth whole-genome sequencing in complex trait association studies, vol.35, p.98, 2018.

G. M. Goerg, Lambert w random variables-a new family of generalized skewed distributions with applications to risk estimation, The Annals of Applied Statistics, vol.5, issue.3, pp.2197-2230, 2011.

M. M. Goldenberg, Multiple sclerosis review. P & T : a peer-reviewed journal for formulary management, vol.37, pp.175-184, 2012.

C. E. Gonzalez and M. Ostermeier, Pervasive pairwise intragenic epistasis among sequential mutations in TEM-1 ?-lactamase, Journal of Molecular Biology, vol.431, issue.10, pp.1981-1992, 2019.

S. Gonzalez, J. Gupta, E. Villa, I. Mallawaarachchi, and M. Rodriguez, Replication of genome-wide association study (GWAS) susceptibility loci in a latino bipolar disorder cohort, Bipolar Disorders, vol.18, issue.6, p.97, 2016.

S. G. Gregory, S. Schmidt, P. Seth, J. R. Oksenberg, and J. Hart, Interleukin 7 receptor alpha chain (IL7R) shows allelic and functional association with multiple sclerosis, Nature genetics, vol.39, issue.9, pp.1083-91, 2007.

A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf, Measuring statistical dependence with hilbert-schmidt norms, Algorithmic Learning Theory, pp.63-77, 2005.

H. Springer-berlin, , vol.80

A. Gretton, A. Smola, O. Bousquet, R. Herbrich, and A. Belitski, Kernel constrained covariance for dependence measurement, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, p.47, 2005.

A. Gretton, K. Fukumizu, C. H. Teo, L. Song, B. Schölkopf et al., A Kernel Statistical Test of Independence, Advances in Neural Information Processing Systems, vol.20, pp.585-592, 2008.

B. L. Harty, F. Coelho, S. E. Pease-raissi, A. Mogha, and S. D. Ackerman, Myelinating Schwann cells ensheath multiple axons in the absence of E3 ligase component Fbxw7, Nature Communications, vol.10, issue.1, p.2976, 2019.

Y. Hasin, M. Seldin, and A. Lusis, Multi-omics approaches to disease, Genome Biology, vol.18, issue.1, 2017.

A. C. Haury, F. Mordelet, P. Vera-licona, J. P. Vert, and . Tigress, Trustful inference of gene REgulation using stability selection, BMC Systems Biology, vol.6, issue.1, p.31, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00694218

N. A. Heard and P. Rubin-delanchy, Choosing between methods of combining $p$-values, Biometrika, vol.105, issue.1, p.16, 2018.

R. Heller, N. Chatterjee, A. Krieger, and J. Shi, Post-selection inference following aggregate level hypothesis testing in large-scale genomic data, Journal of the American Statistical Association, vol.113, issue.524, p.18, 2018.

C. Herold, M. Steffens, F. F. Brockschmidt, M. P. Baur, and T. Becker, INTER-SNP: genome-wide interaction analysis guided by a priori information, Bioinformatics, vol.25, issue.24, pp.3275-3281, 2009.

A. S. Hinrichs, The UCSC genome browser database: update, Nucleic Acids Research, vol.34, issue.90001, p.89, 2006.

T. Hofmann, B. Schölkopf, and A. J. Smola, Kernel methods in machine learning, The Annals of Statistics, vol.36, issue.3, p.16, 2008.

J. Holmen-m.fl, The nord-trøndelag health study 1995-97 (HUNT 2), Norsk Epidemiologi, vol.13, issue.1, 2011.

W. H. Hudson, I. M. Vera, J. C. Nwachukwu, E. R. Weikum, and A. G. Herbst, Cryptic glucocorticoid receptor-binding sites pervade genomic NF-?b response elements, Nature Communications, vol.9, issue.1, p.77, 2018.

T. Hughes, A. Adler, J. A. Kelly, K. M. Kaufman, and A. H. Williams, Evidence for gene-gene epistatic interactions among susceptibility loci for systemic lupus erythematosus, Arthritis & Rheumatism, vol.64, issue.2, pp.485-492, 2012.

J. P. Ioannidis, Why most published research findings are false, PLoS Medicine, vol.2, issue.8, p.124, 2005.

I. Ionita-laza, S. Lee, V. Makarov, J. D. Buxbaum, and X. Lin, Sequence kernel association tests for the combined effect of rare and common variants, The American Journal of Human Genetics, vol.92, issue.6, pp.841-853, 2013.

A. Ishkin and . Metabaser, Library of functions to work with Clarivate Analytics' MetaBase, 2019.

K. Iwai, Diverse ubiquitin signaling in NF-?b activation, Trends in Cell Biology, vol.22, issue.7, p.76, 2012.

P. L. Jager, C. Baecher-allan, L. M. Maier, A. T. Arthur, and L. Ottoboni, The role of the CD58 locus in multiple sclerosis, Proceedings of the National Academy of Sciences, vol.106, issue.13, pp.5264-5269, 2009.

I. M. Johnstone and D. M. Titterington, Statistical challenges of high-dimensional data, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.367, p.13, 1906.

J. Judy, Converging evidence for epistasis between ANK3 and potassium channel gene KCNQ2 in bipolar disorder, Frontiers in Genetics, vol.4, 2013.

P. K. Keshari, H. F. Harbo, K. M. Myhr, J. H. Aarseth, S. D. Bos et al., Allelic imbalance of multiple sclerosis susceptibility genes IKZF3 and IQGAP1 in human peripheral blood, BMC Genetics, vol.17, issue.1, p.66, 2016.

. Bibliography,

J. D. Keyser, E. Zeinstra, and N. Wilczak, Astrocytic ?2-adrenergic receptors and multiple sclerosis, Neurobiology of Disease, vol.15, issue.2, p.74, 2004.

J. D. Keyser, G. Laureys, F. Demol, N. Wilczak, J. Mostert et al., Astrocytes as potential targets to suppress inflammatory demyelinating lesions in multiple sclerosis, Neurochemistry International, vol.57, issue.4, p.74, 2010.

D. Kim, R. Li, A. Lucas, S. S. Verma, S. M. Dudek et al., Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma, Journal of the American Medical Informatics Association, vol.10, p.165, 2016.

G. Kimmel and R. Shamir, A block-free hidden markov model for genotypes and its application to disease association, Journal of Computational Biology, vol.12, issue.10, p.101, 2005.

G. King, Unifying Political Methodology, vol.10, 1998.

R. J. Kinsella, A. Kahari, S. Haider, J. Zamora, and G. Proctor, Ensembl BioMarts: a hub for data retrieval across taxonomic space, Database, issue.0, p.72, 2011.

G. D. Kitsios and E. Zintzaras, Genome-wide association studies: hypothesis-"free" or "engaged, Translational Research, vol.154, issue.4, pp.161-164, 2009.

D. Komura, F. Shen, S. Ishikawa, K. R. Fitch, and W. Chen, Genome-wide detection of human copy number variations using high-density DNA oligonucleotide arrays, Genome Research, vol.16, issue.12, pp.1575-1584, 2006.

P. Kraft, E. Zeggini, and J. P. Ioannidis, Replication in genome-wide association studies, Statistical Science, vol.24, issue.4, pp.561-573, 2009.

J. Krüger and R. Westermann, Linear algebra operators for GPU implementation of numerical algorithms, ACM Transactions on Graphics, vol.22, issue.3, p.908, 2003.

L. C. Kwee, D. Liu, X. Lin, D. Ghosh, and M. P. Epstein, A powerful and flexible multilocus association test for quantitative traits, The American Journal of Human Genetics, vol.82, issue.2, pp.386-397, 2008.

T. Laframboise, Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances, Nucleic Acids Research, vol.37, issue.13, pp.4181-4193, 2009.

M. Lagator, C. Igler, A. B. Moreno, C. C. Guet, and J. P. Bollback, Epistatic interactions in the ArabinoseCis-regulatory element, Molecular Biology and Evolution, vol.33, issue.3, pp.761-769, 2015.

M. Lagator, T. Paixão, N. H. Barton, J. P. Bollback, and C. C. Guet, On the mechanistic nature of epistasis in a canonical cis-regulatory element. eLife, 6, 2017.

H. Lassmann and R. M. Ransohoff, The CD4-th1 model for multiple sclerosis: a crucial re-appraisal, Trends in Immunology, vol.25, issue.3, p.75, 2004.

M. Lawrence, R. Gentleman, and V. Carey, rtracklayer: an r package for interfacing with genome browsers, Bioinformatics, vol.25, p.89, 2009.

M. Le-morvan and J. Vert, WHInter: A working set algorithm for high-dimensional sparse second order interaction models, Proceedings of the 35th International Conference on Machine Learning, vol.40, pp.3632-3641, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01711018

J. D. Lee, D. L. Sun, Y. Sun, and J. E. Taylor, Exact post-selection inference, with application to the lasso, The Annals of Statistics, vol.44, issue.3, pp.907-927, 2016.

B. Lehner, Molecular mechanisms of epistasis within and between genes, Trends in Genetics, vol.27, issue.8, pp.323-331, 2011.

Y. Liang, Y. Zhou, and P. Shen, NF-kappaB and its regulation on the immune system, Cellular & molecular immunology, vol.1, issue.5, p.76, 2004.

M. Lim and T. Hastie, Learning interactions via hierarchical group-lasso regularization, Journal of Computational and Graphical Statistics, vol.24, issue.3, p.14, 2015.

X. Lin, Variance component testing in generalised linear models with random effects, Biometrika, vol.84, issue.2, p.92, 1997.

M. R. Lincoln, S. V. Ramagopalan, M. J. Chao, B. M. Herrera, and G. C. Deluca, Epistasis among HLA-DRB1, HLA-DQA1, and HLA-DQB1 loci determines multiple sclerosis susceptibility, Proceedings of the National Academy of Sciences, vol.106, issue.18, pp.7542-7547, 2009.

F. Llinares-lópez, L. Papaxanthos, D. Roqueiro, D. Bodenham, and K. Borgwardt, CASMAP: detection of statistically significant combinations of SNPs in association mapping, Bioinformatics, vol.35, issue.15, pp.2680-2682, 2018.

J. R. Loftus and J. E. Taylor, Selective inference in regression models with groups of variables, vol.46, p.45, 2015.

J. K. Lunceford and M. Davidian, Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study, Statistics in Medicine, vol.23, issue.19, p.28, 2004.

J. Macarthur, E. Bowler, M. Cerezo, L. Gil, and P. Hall, The new NHGRI-EBI catalog of published genome-wide association studies (GWAS catalog), Nucleic Acids Research, vol.45, issue.D1, pp.896-901, 2016.

T. F. Mackay, Epistasis and quantitative traits: using model organisms to study gene-gene interactions, Nature Reviews Genetics, vol.15, issue.1, p.97, 2013.

S. Majumder, L. Zhou, P. Chaturvedi, G. Babcock, S. Aras et al., Regulation of human IP-10 gene expression in astrocytoma cells by inflammatory cytokines, Journal of Neuroscience Research, vol.54, issue.2, p.76, 1998.

T. A. Manolio, F. S. Collins, N. J. Cox, D. B. Goldstein, and L. A. Hindorff, Finding the missing heritability of complex diseases, Nature, vol.461, issue.7265, pp.747-753, 2009.

C. R. Marshall, D. P. Howrigan, D. Merico, and B. Thiruvahindrapuram, Contribution of copy number variants to schizophrenia from a genome-wide study of 41, 321 subjects, Nature Genetics, vol.49, issue.1, pp.27-35, 2016.

J. Masel, . Genetic, and . Drift, Current Biology, vol.21, issue.20, pp.837-838, 2011.

M. Massias, A. Gramfort, and J. Salmon, Celer: a Fast Solver for the Lasso with Dual Extrapolation, ICML 2018 -35th International Conference on Machine Learning, vol.80, pp.3321-3330, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01833398

D. P. Mcgovern, J. I. Rotter, L. Mei, T. Haritunians, and C. Landers, Genetic epistasis of IL23/IL17 pathway genes in crohn's disease, Inflammatory Bowel Diseases, vol.15, issue.6, pp.883-889, 2009.

C. Medina-gomez, J. F. Felix, K. Estrada, M. J. Peters, and L. Herrera, Challenges in conducting genome-wide association studies in highly admixed multiethnic populations: the generation r study, European Journal of Epidemiology, vol.30, issue.4, p.97, 2015.

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.72, issue.4, p.31, 2010.

B. Mieth, M. Kloft, J. A. Rodríguez, S. Sonnenburg, and R. Vobruba, Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies, Scientific Reports, vol.6, issue.1, 2016.

A. Minagar and J. S. Alexander, Blood-brain barrier disruption in multiple sclerosis, Multiple Sclerosis Journal, vol.9, issue.6, p.75, 2003.

R. B. Mokhtari, T. S. Homayouni, N. Baluch, E. Morgatskaya, S. Kumar et al., Combination therapy in combating cancer, Oncotarget, vol.8, issue.23, p.99, 2017.

J. H. Moore and S. M. Williams, Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis, BioEssays, vol.27, issue.6, p.69, 2005.

S. Morgan, P. Grootendorst, J. Lexchin, C. Cunningham, and D. Greyson, The cost of drug development: A systematic review, Health Policy, vol.100, issue.1, p.98, 2011.

S. Nakagawa, A farewell to bonferroni: the problems of low statistical power and publication bias, Behavioral Ecology, vol.15, issue.6, pp.1044-1045, 2004.

P. Nakka, B. J. Raphael, and S. Ramachandran, Gene and network analysis of common variants reveals novel associations in multiple complex diseases, Genetics, vol.204, issue.2, p.89, 2016.

M. R. Nelson, Large-scale validation of single nucleotide polymorphisms in gene regions, Genome Research, vol.14, issue.8, pp.1664-1668, 2004.

. Bibliography,

M. R. Nelson, H. Tipney, J. L. Painter, J. Shen, and P. Nicoletti, The support of human genetic evidence for approved drug indications, Nature Genetics, vol.47, issue.8, pp.856-860, 2015.

P. C. Ng, SIFT: predicting amino acid changes that affect protein function, Nucleic Acids Research, vol.31, issue.13, p.72, 2003.

C. Niel, C. Sinoquet, C. Dina, and G. Rocheleau, A survey about methods dedicated to epistasis detection, Frontiers in Genetics, vol.6, p.23, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01205577

I. M. Nolte, P. J. Van-der-most, B. Z. Alizadeh, P. I. De-bakker, and H. M. Boezen, Missing heritability: is the gap closing? an analysis of 32 complex traits in the lifelines cohort study, European Journal of Human Genetics, vol.25, issue.7, pp.877-885, 2017.

J. R. Oksenberg, Decoding multiple sclerosis: an update on genomics and future directions, Expert Review of Neurotherapeutics, vol.13, issue.sup2, pp.11-19, 2013.

Ö. Carlborg and C. S. Haley, Epistasis: too often neglected in complex trait studies?, Nature Reviews Genetics, vol.5, issue.8, pp.618-625, 2004.

H. Pagès and . Snplocs, Hsapiens.dbSNP144.GRCh37: SNP locations for Homo sapiens, p.66

A. Pakman and L. Paninski, Exact hamiltonian monte carlo for truncated multivariate gaussians, Journal of Computational and Graphical Statistics, vol.23, issue.2, p.52, 2014.

J. Pearl, Causal inference in statistics: An overview, Statistics Surveys, vol.3, issue.0, p.16, 2009.

J. Peters, D. Janzing, and B. Schlkopf, Elements of Causal Inference: Foundations and Learning Algorithms, vol.17, p.9780262037310

R. A. Peterson and J. E. Cavanaugh, Ordered quantile normalization: a semiparametric transformation built for the cross-validation era, Journal of Applied Statistics, p.82, 2019.

P. C. Phillips, Epistasis -the essential role of gene interactions in the structure and evolution of genetic systems, Nature Reviews Genetics, vol.9, issue.11, pp.855-867, 2008.

W. W. Piegorsch, C. R. Weinberg, and J. A. Taylor, Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies, Statistics in Medicine, vol.13, issue.2, pp.153-162, 1994.

R. K. Plowright, S. H. Sokolow, M. E. Gorman, P. Daszak, and J. E. Foley, Causal inference in disease ecology: investigating ecological drivers of disease emergence, Frontiers in Ecology and the Environment, vol.6, issue.8, p.99, 2008.

G. Ponath, C. Park, and D. Pitt, The role of astrocytes in multiple sclerosis, Frontiers in Immunology, vol.9, p.76, 2018.

A. Poon and L. Chao, The rate of compensatory mutation in the DNA bacteriophage ?x174, Genetics, vol.170, issue.3, pp.989-999, 2005.

S. Prabhu and I. Pe-er, Ultrafast genome-wide scan for SNP-SNP interactions in common complex disease, Genome Research, vol.22, issue.11, pp.2230-2240, 2012.

A. L. Price, N. J. Patterson, R. M. Plenge, M. E. Weinblatt, N. A. Shadick et al., Principal components analysis corrects for stratification in genomewide association studies, Nature Genetics, vol.38, issue.8, p.12, 2006.

J. K. Pritchard, The allelic architecture of human disease genes: common diseasecommon variant, Human Molecular Genetics, vol.11, issue.20, pp.2417-2423, 2002.

S. Purcell, B. Neale, K. Todd-brown, L. Thomas, and M. A. Ferreira, PLINK: A tool set for whole-genome association and population-based linkage analyses, The American Journal of Human Genetics, vol.81, issue.3, pp.559-575, 2007.

L. Qi, Gene-diet interaction and weight loss, Current Opinion in Lipidology, vol.25, issue.1, pp.27-34, 2014.

C. Qian, H. An, Y. Yu, S. Liu, and X. Cao, TLR agonists induce regulatory dendritic cells to recruit th1 cells via preferential IP-10 secretion and inhibit th1 proliferation, Blood, vol.109, issue.8, p.74, 2006.

L. R. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition, Proceedings of the IEEE, vol.77, issue.2, p.31, 1989.

. Bibliography,

P. Rastas, M. Koivisto, H. Mannila, and E. Ukkonen, A hidden markov technique for haplotype reconstruction, Lecture Notes in Computer Science, p.101, 2005.

P. Raynor and B. I. Group, Born in Bradford, a cohort study of babies born in Bradford, and their parents: Protocol for the recruitment phase, BMC Public Health, vol.8, issue.1, p.327, 2008.

S. Reid, J. Taylor, and R. Tibshirani, A general framework for estimation and inference from clusters of features, Journal of the American Statistical Association, vol.113, issue.521, pp.280-293, 2017.

R. Reuss, M. Mistarz, A. Mirau, J. Kraus, R. H. Bödeker et al., FADD is upregulated in relapsing remitting multiple sclerosis. Neuroimmunomodulation, vol.21, p.69, 2014.

S. Ripke, B. M. Neale, A. Corvin, J. T. Walters, and K. H. Farh, Biological insights from 108 schizophrenia-associated genetic loci, Nature, vol.511, issue.7510, pp.421-427, 2014.

N. Risch and K. Merikangas, The future of genetic studies of complex human diseases, Science, vol.273, issue.5281, pp.1516-1517, 1996.

I. Rivals, L. Personnaz, L. Taing, and M. C. Potier, Enrichment or depletion of a GO category within a class of genes: which test?, Bioinformatics, vol.23, issue.4, pp.401-407, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00801557

Y. Rochlani, M. H. Khan, M. Banach, and W. S. Aronow, Are two drugs better than one? a review of combination therapies for hypertension, Expert Opinion on Pharmacotherapy, vol.18, issue.4, p.99, 2017.

P. Romagnani, F. Annunziato, E. Lazzeri, L. Cosmi, and C. Beltrame, Interferon-inducible protein 10, monokine induced by interferon gamma, and interferon-inducible t-cell alpha chemoattractant are produced by thymic epithelial cells and attract t-cell receptor (TCR) ??+CD8+ single-positive t cells, TCR??+ t cells, and natural killer-type cells in human thymus, Blood, vol.97, issue.3, p.74, 2001.

D. B. Rubin, Causal inference using potential outcomes, Journal of the American Statistical Association, vol.100, issue.469, pp.322-331, 2005.

K. Rupp, P. Tillet, F. Rudolf, J. Weinbub, A. Morhammer et al., ViennaCL-linear algebra library for multi-and many-core architectures, SIAM Journal on Scientific Computing, vol.38, issue.5, pp.412-439, 2016.

T. Saito and M. Rehmsmeier, Precrec: fast and accurate precision-recall and ROC curve calculations in r, Bioinformatics, vol.33, issue.1, pp.145-147, 2016.

C. H. Sandholt, K. H. Allin, U. Toft, A. Borglykke, and R. Ribel-madsen, The effect of GWAS identified BMI loci on changes in body weight among middleaged danes during a five-year period, Obesity, vol.22, issue.3, pp.901-908, 2013.

S. Sawcer, G. Hellenthal, M. Pirinen, C. C. Spencer, and N. A. Patsopoulos, Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis, Nature, vol.476, issue.7359, pp.214-219, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00996686

S. Sawcer, R. J. Franklin, and M. Ban, Multiple sclerosis genetics, The Lancet Neurology, vol.13, issue.7, pp.700-709, 2014.

D. J. Schaid, W. Chen, and N. B. Larson, From genome-wide associations to candidate causal variants by statistical fine-mapping, Nature Reviews Genetics, vol.19, issue.8, pp.491-504, 2018.

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, p.17, 2006.

H. Schmid, A. Boucherot, Y. Yasuda, A. Henger, and B. Brunner, Modular activation of nuclear factor-b transcriptional programs in human diabetic nephropathy, Diabetes, vol.55, issue.11, p.74, 2006.

B. Schölkopf, M. Scholkopf, K. Tsuda, M. P. Zur-förderung-der-wissenschaften, and J. Vert, Kernel Methods in Computational Biology. A Bradford book. Bradford Bks, vol.9780262195096, p.18, 2004.

N. J. Schork, S. S. Murray, K. A. Frazer, and E. J. Topol, Common vs. rare allele hypotheses for complex diseases, Current Opinion in Genetics & Development, vol.19, issue.3, pp.212-219, 2009.

M. Sesia, C. Sabatti, and E. J. Candès, Gene hunting with hidden markov model knockoffs, Biometrika, vol.101, 2018.

R. L. Shah, Q. Li, W. Zhao, M. S. Tedja, and J. W. Tideman, A genome-wide association study of corneal astigmatism: The CREAM Consortium, Molecular vision, vol.24, p.89, 2018.

B. S. Shastry and . Snps, Impact on gene function and phenotype, Methods in Molecular Biology, pp.3-22, 2009.

M. Simmonds and S. Gough, The HLA region and autoimmune disease: Associations and mechanisms of action, Current Genomics, vol.8, issue.7, pp.453-465, 2007.

M. Slatkin, Linkage disequilibrium -understanding the evolutionary past and mapping the medical future, Nature Reviews Genetics, vol.9, issue.6, p.24, 2008.

L. Slim, C. Chatelain, C. A. Azencott, and J. P. Vert, Novel methods for epistasis detection in genome-wide association studies, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01984919

L. Slim, C. Chatelain, C. A. Azencott, and J. P. Vert, kernelPSI: a post-selection inference framework for nonlinear variable selection, Proceedings of the 36th International Conference on Machine Learning, vol.97, pp.9-15, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02441304

E. Smalley, Clinical trials go virtual, big pharma dives in, Nature Biotechnology, vol.36, issue.7, p.98, 2018.

R. L. Smith, Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions, Operations Research, vol.32, issue.6, p.53, 1984.

L. Song, A. Smola, A. Gretton, K. M. Borgwardt, and J. Bedo, Supervised feature selection via dependence estimation, Proceedings of the 24th international conference on Machine learning -ICML '07, vol.81, 2007.

C. C. Spencer, Z. Su, P. Donnelly, and J. Marchini, Designing genome-wide association studies: Sample size, power, imputation, and the choice of genotyping chip, PLoS Genetics, vol.5, issue.5, p.1000477, 2009.

B. E. Stranger, E. A. Stahl, and T. Raj, Progress and promise of genome-wide association studies for human complex trait genetics, Genetics, vol.187, issue.2, p.97, 2010.

Z. Su, J. Marchini, and P. Donnelly, HAPGEN2: simulation of multiple disease SNPs, Bioinformatics, vol.27, issue.16, pp.2304-2305, 2011.

J. H. Sul, L. S. Martin, and E. Eskin, Population structure in genetic studies: Confounding factors and mixed models, PLOS Genetics, vol.14, issue.12, p.97, 2018.

S. Sun, C. M. Greenwood, and R. M. Neal, Haplotype inference using a bayesian hidden markov model, Genetic Epidemiology, vol.31, issue.8, p.101, 2007.

X. Sun, X. Wang, T. Chen, T. Li, and K. Cao, Myelin activates FAK/akt/NF?b pathways and provokes CR3-dependent inflammatory response in murine system, PLoS ONE, vol.5, issue.2, p.69, 2010.

G. J. Székely, M. L. Rizzo, and N. K. Bakirov, Measuring and testing dependence by correlation of distances, The Annals of Statistics, vol.35, issue.6, pp.2769-2794, 2007.

J. Taylor and R. J. Tibshirani, Statistical learning and selective inference, Proc. Natl. Acad. Sci. U.S.A, vol.112, pp.7629-7634, 2015.

G. A. Thanei, N. Meinshausen, and R. D. Shah, The xyz algorithm for fast interaction search in high-dimensional data, Journal of Machine Learning Research, vol.19, issue.37, p.23, 2018.

L. Tian, A. A. Alizadeh, A. J. Gentles, and R. Tibshirani, A simple method for estimating interactions between a treatment and a large number of covariates, Journal of the American Statistical Association, vol.109, issue.508, pp.1517-1532, 2014.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological), vol.58, issue.1, pp.267-288, 1996.

R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, p.83, 2001.

R. J. Tibshirani, J. Taylor, R. Lockhart, and R. Tibshirani, Exact post-selection inference for sequential regression procedures, Journal of the American Statistical Bibliography Association, vol.111, issue.514, pp.600-620, 2016.

R. Tokunaga, W. Zhang, M. Naseem, A. Puccini, and M. D. Berger, CXCL9, CXCL10, CXCL11/CXCR3 axis for immune activation -a target for novel cancer therapy, Cancer Treatment Reviews, vol.63, p.74, 2018.

U. Traugott and P. Lebon, Demonstration of ?, ?, and ? interferon in active chronic multiple sclerosis lesions, Annals of the New York Academy of Sciences, vol.540, issue.1, p.74, 1988.

B. Van-der-waerden, Order tests for the two-sample problem and their power, Indagationes Mathematicae (Proceedings), vol.55, pp.453-458, 1952.

T. J. Vanderweele and M. A. Hernan, Causal inference under multiple versions of treatment, Journal of Causal Inference, vol.1, issue.1, p.41, 2013.

T. J. Vanderweele and M. J. Knol, A tutorial on interaction. Epidemiologic Methods, vol.3, issue.1, 2014.

S. V. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt, Graph kernels, Journal of Machine Learning Research, vol.11, p.99, 2010.

P. M. Visscher, M. A. Brown, M. I. Mccarthy, and J. Yang, Five years of GWAS discovery, The American Journal of Human Genetics, vol.90, issue.1, pp.7-24, 2012.

P. M. Visscher, N. R. Wray, Q. Zhang, P. Sklar, M. I. Mccarthy et al., 10 years of GWAS discovery: Biology, function, and translation, The American Journal of Human Genetics, vol.101, issue.1, pp.5-22, 2017.

U. Võsa, A. Claringbould, H. J. Westra, M. J. Bonder, and P. Deelen, Unraveling the polygenic architecture of complex traits using blood eqtl metaanalysis. bioRxiv, 2018.

X. Wan, C. Yang, Q. Yang, H. Xue, X. Fan et al., BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies, The American Journal of Human Genetics, vol.87, issue.3, pp.325-340, 2010.

Q. Wang, R. Chen, F. Cheng, Q. Wei, and Y. Ji, A bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data, Nature Neuroscience, vol.22, issue.5, p.98, 2019.

R. L. Wasserstein and N. A. Lazar, The ASA statement on p-values: Context, process, and purpose, The American Statistician, vol.70, issue.2, p.16, 2016.

W. H. Wei, G. Hemani, and C. S. Haley, Detecting epistasis in human complex traits, Nature Reviews Genetics, vol.15, issue.11, pp.722-733, 2014.

D. M. Weinreich, Darwinian evolution can follow only very few mutational paths to fitter proteins, Science, vol.312, issue.5770, pp.111-114, 2006.

D. Welter, J. Macarthur, J. Morales, T. Burdett, and P. Hall, The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Research, vol.42, pp.1001-1006, 2013.

J. Whittaker, Graphical models in applied multivariate statistics, p.11, 2009.

A. Williams, G. Piaton, and C. Lubetzki, Astrocytes-friends or foes in multiple sclerosis?, Glia, vol.55, issue.13, p.76, 2007.

D. Witt, Recent developments in disulfide bond formation, Synthesis, issue.16, pp.2491-2509, 2008.

M. C. Wu, P. Kraft, M. P. Epstein, D. M. Taylor, S. J. Chanock et al., Powerful SNP-set analysis for case-control genome-wide association studies, The American Journal of Human Genetics, vol.86, issue.6, pp.929-942, 2010.

M. C. Wu, S. Lee, T. Cai, Y. Li, M. Boehnke et al., Rare-variant association testing for sequencing data with the sequence kernel association test, The American Journal of Human Genetics, vol.89, issue.1, pp.82-93, 2011.

J. B. Xia, G. H. Liu, Z. Y. Chen, C. Z. Mao, and D. C. Zhou, Hypoxia/ischemia promotes CXCL10 expression in cardiac microvascular endothelial cells by NFkB activation, Cytokine, vol.81, p.74, 2016.

A. Xue, Y. Wu, Z. Zhu, and F. Zhang, Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nature Communications, vol.9, 2018.

. Bibliography,

Y. Xue, G. Cooper, C. Cai, S. Lu, B. Hu et al., Tumourspecific causal inference discovers distinct disease mechanisms underlying cancer subtypes, Scientific Reports, vol.9, issue.1, p.99, 2019.

M. Yamada, Y. Umezu, K. Fukumizu, and I. Takeuchi, Post selection inference with kernels, Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, vol.84, pp.9-11, 2018.

F. Yang, R. F. Barber, P. Jain, and J. Lafferty, Selective inference for groupsparse linear models, Advances in Neural Information Processing Systems, vol.46, p.45, 2016.

Q. Yang, M. J. Khoury, F. Sun, and W. D. Flanders, Case-only design to measure gene-gene interaction, Epidemiology, vol.10, issue.2, pp.167-70, 1999.

S. Yang, G. W. Imbens, Z. Cui, D. E. Faries, and Z. Kadziola, Propensity score matching and subclassification in observational studies with multi-level treatments, Biometrics, vol.72, issue.4, p.99, 2016.

I. K. Yeo, A new family of power transformations to improve normality or symmetry, Biometrika, vol.87, issue.4, pp.954-959, 2000.

L. S. Yung, C. Yang, X. Wan, and W. Yu, GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies, Bioinformatics, vol.27, issue.9, p.23, 2011.

D. V. Zaykin and L. A. Zhivotovsky, Ranks of genuine associations in whole-genome scans, Genetics, vol.171, issue.2, pp.813-823, 2005.

Y. Zeng and P. Breheny, The biglasso package: A memory-and computationefficient solver for lasso model fitting with big data in r, p.37, 2017.

Q. Zhang, S. Filippi, A. Gretton, and D. Sejdinovic, Large-scale kernel methods for independence testing, Statistics and Computing, vol.28, issue.1, pp.113-130, 2018.

Y. Zhao, D. Zeng, A. J. Rush, and M. R. Kosorok, Estimating individualized treatment rules using outcome weighted learning, Journal of the American Statistical Association, vol.107, issue.499, pp.1106-1118, 2012.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, issue.2, p.31, 2005.

O. Zuk, E. Hechter, S. R. Sunyaev, and E. S. Lander, The mystery of missing heritability: Genetic interactions create phantom heritability, Proceedings of the National Academy of Sciences, vol.109, issue.4, pp.1193-1198, 2012.