S. Atwell, Y. S. Huang, B. J. Vilhjálmsson, G. Willems, M. Horton et al., Genome-wide 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

F. R. Bach, Consistency of the group lasso and multiple kernel learning, J. Mach. Learn. Res, vol.9, pp.1179-1225, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00164735

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, pp.184-207, 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.

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

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

A. Gretton, A. Smola, O. Bousquet, R. Herbrich, A. Belitski et al., Kernel constrained covariance for dependence measurement, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pp.1-8, 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.

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.

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.

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

A. Pakman and L. Paninski, Exact hamiltonian monte carlo for truncated multivariate gaussians, Journal of Computational and Graphical Statistics, vol.23, issue.2, pp.518-542, 2014.

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. L. Smith, Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions, Operations Research, vol.32, issue.6, pp.1296-1308, 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, 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.

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

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.

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 group-sparse linear models, Advances in Neural Information Processing Systems, pp.2469-2477, 2016.

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