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

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  • Claude Renaux

    (ETH Zürich)

  • Laura Buzdugan

    (ETH Zürich)

  • Markus Kalisch

    (ETH Zürich)

  • Peter Bühlmann

    (ETH Zürich)

Abstract

We provide a view on high-dimensional statistical inference for genome-wide association studies. It is in part a review but covers also new developments for meta analysis with multiple studies and novel software in terms of an R-package hierinf. Inference and assessment of significance is based on very high-dimensional multivariate (generalized) linear models: in contrast to often used marginal approaches, this provides a step towards more causal-oriented inference.

Suggested Citation

  • Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 1-40, March.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00939-2
    DOI: 10.1007/s00180-019-00939-2
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. 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.
    3. Vincent Plagnol & Joanna M M Howson & Deborah J Smyth & Neil Walker & Jason P Hafler & Chris Wallace & Helen Stevens & Laura Jackson & Matthew J Simmonds & Type 1 Diabetes Genetics Consortium & Polly , 2011. "Genome-Wide Association Analysis of Autoantibody Positivity in Type 1 Diabetes Cases," PLOS Genetics, Public Library of Science, vol. 7(8), pages 1-9, August.
    4. Clive J Hoggart & John C Whittaker & Maria De Iorio & David J Balding, 2008. "Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies," PLOS Genetics, Public Library of Science, vol. 4(7), pages 1-8, July.
    5. Chatterjee, A. & Lahiri, S. N., 2011. "Bootstrapping Lasso Estimators," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 608-625.
    6. Meinshausen, Nicolai & Meier, Lukas & Bühlmann, Peter, 2009. "p-Values for High-Dimensional Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1671-1681.
    7. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    8. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
    9. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    10. Meijer Rosa J. & Krebs Thijmen J.P. & Goeman Jelle J., 2015. "A region-based multiple testing method for hypotheses ordered in space or time," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(1), pages 1-19, February.
    11. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "Rejoinder on: High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 751-758, December.
    12. Frommlet, Florian & Ruhaltinger, Felix & Twaróg, Piotr & Bogdan, Małgorzata, 2012. "Modified versions of Bayesian Information Criterion for genome-wide association studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1038-1051.
    13. Rajen D. Shah & Richard J. Samworth, 2013. "Variable selection with error control: another look at stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 55-80, January.
    14. Peter Bühlmann & Jacopo Mandozzi, 2014. "High-dimensional variable screening and bias in subsequent inference, with an empirical comparison," Computational Statistics, Springer, vol. 29(3), pages 407-430, June.
    15. Nicolai Meinshausen, 2008. "Hierarchical testing of variable importance," Biometrika, Biometrika Trust, vol. 95(2), pages 265-278.
    16. Rajen D. Shah & Peter Bühlmann, 2018. "Goodness‐of‐fit tests for high dimensional linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(1), pages 113-135, January.
    17. Jonas R. Klasen & Elke Barbez & Lukas Meier & Nicolai Meinshausen & Peter Bühlmann & Maarten Koornneef & Wolfgang Busch & Korbinian Schneeberger, 2016. "A multi-marker association method for genome-wide association studies without the need for population structure correction," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
    18. Cun-Hui Zhang & Stephanie S. Zhang, 2014. "Confidence intervals for low dimensional parameters in high dimensional linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 217-242, January.
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