Genetic Association Studies



In genetic association studies, one analyzes associations between a (potentially very large) set of genetic markers and a phenotype of interest. This is a particular multiple test problem which has several challenging aspects, for instance the high dimensionality of the statistical parameter and the discreteness of the statistical model. In this chapter, we discuss how to fine-tune multiple tests that we have described theoretically in Part I in order to address these challenges. In particular, we propose the usage of realized randomized \(p\)-values in data-adaptive multiple tests and show how linkage disequilibrium among genetic markers can be employed to construct simultaneous test procedures and to establish probability bounds which lead to effective numbers of tests. Finally, we analyze (positive) dependency properties among test statistics and the applicability of standard margin-based multiple tests. The methods are applied to two real-life datasets.



This chapter makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from Funding for the Wellcome Trust Case Control Consortium project was provided by the Wellcome Trust under award 076113. Parts of this chapter originated from joint work with Klaus Straßburger, Daniel Schunk, Carlos Morcillo-Suarez, Thomas Illig, Arcadi Navarro and Jens Stange. I am grateful to Mette Langaas and Øyvind Bakke for inviting me and for their hospitality during my visit to Norwegian University of Science and Technology (NTNU), for many fruitful discussions and for some valuable references.


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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Research Group “Stochastic Algorithms and Nonparametric Statistics”Weierstrass Institute for Applied Analysis and StochasticsBerlinGermany

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