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Methods and Tools in Genome-wide Association Studies

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Computational Cell Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1819))

Abstract

Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual’s risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies—or associations—between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.

Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.

This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.

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Authors and Affiliations

  1. Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland

    Anja C. Gumpinger, Damian Roqueiro, Dominik G. Grimm & Karsten M. Borgwardt

  2. SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Anja C. Gumpinger, Damian Roqueiro, Dominik G. Grimm & Karsten M. Borgwardt

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  1. Anja C. Gumpinger
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  2. Damian Roqueiro
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  3. Dominik G. Grimm
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  4. Karsten M. Borgwardt
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Correspondence to Anja C. Gumpinger or Karsten M. Borgwardt .

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Editors and Affiliations

  1. NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Louise von Stechow

  2. Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Alberto Santos Delgado

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Gumpinger, A.C., Roqueiro, D., Grimm, D.G., Borgwardt, K.M. (2018). Methods and Tools in Genome-wide Association Studies. In: von Stechow, L., Santos Delgado, A. (eds) Computational Cell Biology. Methods in Molecular Biology, vol 1819. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8618-7_5

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