IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v35y2020i1d10.1007_s00180-019-00942-7.html
   My bibliography  Save this article

Comments on: Hierarchical inference for genome-wide association studies: a view on methodology with software

Author

Listed:
  • Ruth Heller

    (Tel-Aviv University)

Abstract

No abstract is available for this item.

Suggested Citation

  • Ruth Heller, 2020. "Comments on: Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 51-55, March.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00942-7
    DOI: 10.1007/s00180-019-00942-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-019-00942-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-019-00942-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. M Sesia & C Sabatti & E J Candès, 2019. "Gene hunting with hidden Markov model knockoffs," Biometrika, Biometrika Trust, vol. 106(1), pages 1-18.
    2. Ruth Heller & Amit Meir & Nilanjan Chatterjee, 2019. "Post‐selection estimation and testing following aggregate association tests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(3), pages 547-573, July.
    3. Yekutieli, Daniel, 2008. "Hierarchical False Discovery RateControlling Methodology," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 309-316, March.
    4. Yoav Benjamini & Ruth Heller, 2008. "Screening for Partial Conjunction Hypotheses," Biometrics, The International Biometric Society, vol. 64(4), pages 1215-1222, December.
    5. M Sesia & C Sabatti & E J Candès, 2019. "Rejoinder: ‘Gene hunting with hidden Markov model knockoffs’," Biometrika, Biometrika Trust, vol. 106(1), pages 35-45.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anders Bredahl Kock & David Preinerstorfer, 2021. "Superconsistency of Tests in High Dimensions," Papers 2106.03700, arXiv.org, revised Jan 2022.
    2. Emmanuel Candès & Chiara Sabatti, 2020. "Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 60-63, December.
    3. Zihuai He & Linxi Liu & Michael E. Belloy & Yann Guen & Aaron Sossin & Xiaoxia Liu & Xinran Qi & Shiyang Ma & Prashnna K. Gyawali & Tony Wyss-Coray & Hua Tang & Chiara Sabatti & Emmanuel Candès & Mich, 2022. "GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    4. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
    5. Steven Phillips & Yuji Takeda & Archana Singh, 2012. "Visual Feature Integration Indicated by pHase-Locked Frontal-Parietal EEG Signals," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    6. Qingyun Cai & Hock Peng Chan, 2017. "A Double Application of the Benjamini-Hochberg Procedure for Testing Batched Hypotheses," Methodology and Computing in Applied Probability, Springer, vol. 19(2), pages 429-443, June.
    7. Ferreira José A. & Berkhof Johannes & Souverein Olga & Zwinderman Koos, 2009. "A Multiple Testing Approach to High-Dimensional Association Studies with an Application to the Detection of Associations between Risk Factors of Heart Disease and Genetic Polymorphisms," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-56, January.
    8. T. Tony Cai & Wenguang Sun, 2017. "Optimal screening and discovery of sparse signals with applications to multistage high throughput studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 197-223, January.
    9. Chang, Chiu-Lan & Cai, Qingyun, 2023. "Stock return anomalies identification during the Covid-19 with the application of a grouped multiple comparison procedure," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 168-183.
    10. David Amar & Ron Shamir & Daniel Yekutieli, 2017. "Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-22, August.
    11. Yoav Benjamini, 2010. "Discovering the false discovery rate," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 405-416, September.
    12. Hillary Koch & Cheryl A. Keller & Guanjue Xiang & Belinda Giardine & Feipeng Zhang & Yicheng Wang & Ross C. Hardison & Qunhua Li, 2022. "CLIMB: High-dimensional association detection in large scale genomic data," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    13. T. Tony Cai & Wenguang Sun & Weinan Wang, 2019. "Covariate‐assisted ranking and screening for large‐scale two‐sample inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 187-234, April.
    14. Cai, Qingyun, 2018. "A scoring criterion for rejection of clustered p-values," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 180-189.
    15. Eric F. Lock & David B. Dunson, 2017. "Bayesian genome- and epigenome-wide association studies with gene level dependence," Biometrics, The International Biometric Society, vol. 73(3), pages 1018-1028, September.
    16. Qunhua Li & Feipeng Zhang, 2018. "A regression framework for assessing covariate effects on the reproducibility of high‐throughput experiments," Biometrics, The International Biometric Society, vol. 74(3), pages 803-813, September.
    17. Rina Foygel Barber & Aaditya Ramdas, 2017. "The p-filter: multilayer false discovery rate control for grouped hypotheses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1247-1268, September.
    18. Goeman Jelle J. & Finos Livio, 2012. "The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-18, January.
    19. Philippe Vindras & Michel Desmurget & Pierre Baraduc, 2012. "When One Size Does Not Fit All: A Simple Statistical Method to Deal with Across-Individual Variations of Effects," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-15, June.
    20. Antoine Bichat & Christophe Ambroise & Mahendra Mariadassou, 2022. "Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process," Computational Statistics, Springer, vol. 37(3), pages 995-1013, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00942-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.