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Comments on: Hierarchical Inference for genome-wide association studies: a view on methodology with software by Paulo C. Rodrigues and Vanda M. Lourenço

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A Rejoinder to this article was published on 08 January 2020

The Original Article was published on 06 January 2020

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References

  • Buzdugan L, Kalisch M, Navarro A, Schunk D, Fehr E, Bühlmann P (2016) Assessing statistical significance in multivariable genome wide association analysis. Bioinformatics 32:1990–2000

    Article  Google Scholar 

  • Edgington ES (1972) An additive method for combining probability values from independent experiments. J Psychol 80:351–363

    Article  Google Scholar 

  • Fisher RA (1934) Statistical methods for research workers, 4th edn. Oliver & Boyd, Edinburgh

    MATH  Google Scholar 

  • Heard NA, Rubin-Delanchy P (2018) Choosing between methods of combining p-values. Biometrika 105:239–246

    Article  MathSciNet  Google Scholar 

  • Klasen J, Barbez E, Meier L et al (2016) A multi-marker association method for genome-wide association studies without the need for population structure correction. Nat Commun 7:13299

    Article  Google Scholar 

  • Meinshausen N (2008) Hierarchical testing of variable importance. Biometrika 95:265–278

    Article  MathSciNet  Google Scholar 

  • Mudholkar G, George E (1979) The logit method for combining probabilities. In: Rustagi J (ed) Symposium on optimizing methods in statistics. Academic Press, New York, pp 345–366

    Google Scholar 

  • Pearson K (1933) On a method of determining whether a sample of size n supposed to have been drawn from a parent population having a known probability integral has probably been drawn at random. Biometrika 25:379–410

    Article  Google Scholar 

  • Peterson CB, Bogomolov M, Benjamini Y, Sabatti C (2016) Many phenotypes without many false discoveries: error controlling strategies for multitrait association studies. Genet Epidemiol 40:45–56

    Article  Google Scholar 

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Rodrigues, P.C., Lourenço, V.M. Comments on: Hierarchical Inference for genome-wide association studies: a view on methodology with software by Paulo C. Rodrigues and Vanda M. Lourenço. Comput Stat 35, 57–58 (2020). https://doi.org/10.1007/s00180-019-00941-8

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