gms | German Medical Science

54. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

07. bis 10.09.2009, Essen

Evaluating Strategies for Marker Ranking in Genome-wide Association Studies of Complex Traits

Meeting Abstract

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  • André Scherag - Institut für Medizinische Informatik, Biometrie und Epidemiologie, Essen

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 54. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds). Essen, 07.-10.09.2009. Düsseldorf: German Medical Science GMS Publishing House; 2009. Doc09gmds140

doi: 10.3205/09gmds140, urn:nbn:de:0183-09gmds1407

Published: September 2, 2009

© 2009 Scherag.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Advances in high-throughput genotyping technology lead to the realization of genome-wide association studies (GWAS) which helped identify new susceptibility loci of complex traits. Such studies usually start with genotyping fixed arrays of genetic markers in an initial sample. Out of these markers, some are selected which will be further genotyped in independent samples. Due to the very low a priori probability of a true positive association, the vast majority of all marker signals will turn out to be false positive.

Some of the true and robust marker associations derived from classical candidate gene approaches, however, will not be among those markers with the smallest unadjusted p-values. Consequently, alternative methods to sort marker data have been proposed. In this talk I will introduce and evaluate some properties of the "q-values" [1], the "false positive report probability" [2], and the "Bayesian false-discovery probability" [3] as examples of such alternative ranking methods. In particular, I performed simulation studies based on a population genetic coalescent model and simulations using real data for a genomic region derived from GWAS data sets. Furthermore, I applied all ranking methods to a GWAS case-control data set. For the purpose of selecting markers from a GWAS and within the limits of the simulation, I show the benefits of both the ranking by p-values and the application of a full Bayesian approach for which the explored "Bayesian false-discovery probability" is a first approximation.


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