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Methods Inf Med 2010; 49(06): 632-640
DOI: 10.3414/ME09-02-0055
Special Topic – Original Articles
Schattauer GmbH

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

A. Scherag
1   Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany
,
J. Hebebrand
2   Department of Child and Adolescent Psychiatry, University of Duisburg-Essen, Essen, Germany
,
H.-E. Wichmann
3   Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Epidemiology, Neuherberg, Germany
4   Ludwig-Maximilians University Munich, Institute of Medical Data Management, Biometrics and Epidemiology, Chair of Epidemiology, Munich, Germany
,
K.-H. Jöckel
1   Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany
› Author Affiliations
Further Information

Publication History

received: 09 December 2009

accepted: 24 February 2010

Publication Date:
18 January 2018 (online)

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Summary

Background: Genome-wide association studies (GWAS) were highly successful in identifying 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 independentsamples. Due tothevery low a priori probability of a true positive association, the vast majority of all marker signals will turn out to be false positive. Thus, several methods to sort marker data have been proposed which will be evaluated here.

Objectives: We compared statistical properties of ranking by p-values, q-values, the False Positive Report Probability (FPRP) and the Bayesian False-Discovery Probability (BFDP).

Methods: We performed simulation studies for a genomic region derived from GWAS data sets and calculated descriptive statistics as well as mean square errors with regard to the true marker ranking. Additionally, we applied all measures to a GWAS for early onset extreme obesity superimposing a priori information on candidate genes.

Results: Despite the known, more extreme probability results for traditional p-values, we observed that both p-values and the BFDP were more precise in reconstructing the “true” order of the markers in a region. In addition, the BFDP was useful to attenuate unexpected effects at a genome-wide scale.

Conclusions: For the purpose of selecting markers from an initial GWAS and within the limits of this study, we recommend either ranking by p-values or the application of a full Bayesian approach for which the BFDP is a first approximation.

Keywords

Genome-wide association study - p-value - q-value - FPRP - BFDP
 

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