Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

Collection Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

Organizer(s) Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne
Date(s) 15/06/2020 - 19/06/2020
linked URL https://www.cirm-math.com/cirm-virtual-event-2146.html
00:00:00 / 00:00:00
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Knockoff genotypes: value in counterfeit

By Chiara Sabatti

The framework of knockoffs has been recently proposed to perform variable selection under rigorous type-I error control, without relying on strong modeling assumptions. We extend the methodology of knockoffs to a rich family of problems where the distribution of the covariates can be described by a hidden Markov model. We develop an exact and efficient algorithm to sample knockoff variables in this setting and then argue that, combined with the existing selective framework, this provides a natural and powerful tool for performing principled inference in genomewide association studies with guaranteed false discovery rate control. To handle the high level of dependence that can exist between SNPs in linkage disequilibrium, we propose a multi-resolution analysis, that simultaneously identifies loci of importance and provides results analogous to those obtained in fine mapping.

This is joint work with Matteo Sesia, Eugene Katsevich, Stephen Bates and Emmanuel Candes.

Information about the video

Citation data

  • DOI 10.24350/CIRM.V.19642903
  • Cite this video Sabatti, Chiara (03/06/2020). Knockoff genotypes: value in counterfeit. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19642903
  • URL https://dx.doi.org/10.24350/CIRM.V.19642903

Domain(s)

Bibliography

  • SESIA, Matteo, SABATTI, Chiara, et CANDÈS, Emmanuel J. Gene hunting with hidden Markov model knockoffs. Biometrika, 2019, vol. 106, no 1, p. 1-18. - https://doi.org/10.1093/biomet/asy033
  • SESIA, Matteo, KATSEVICH, Eugene, BATES, Stephen, et al. Multi-resolution localization of causal variants across the genome. Nature communications, 2020, vol. 11, no 1, p. 1-10. - http://dx.doi.org/10.1038/s41467-020-14791-2
  • KATSEVICH, Eugene et SABATTI, Chiara. Multilayer knockoff filter: Controlled variable selection at multiple resolutions. The annals of applied statistics, 2019, vol. 13, no 1, p. 1. - http://dx.doi.org/10.1214/18-AOAS1185
  • KATSEVICH, Eugene, SABATTI, Chiara, et BOGOMOLOV, Marina. Controlling FDR while highlighting selected discoveries. arXiv preprint arXiv:1809.01792, 2018. - https://arxiv.org/abs/1809.01792

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