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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

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The performance of conditional tests in associated regions derived from genome-wide association studies

Meeting Abstract

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  • Brandon Greene - Institut der Medizinischen Biometrie und Epidemiologie der Philipps-Universität Marburg, Marburg
  • Ivonne Jarick - Institut der Medizinischen Biometrie und Epidemiologie der Philipps-Universität Marburg, Marburg
  • Anke Hinney - Department of Child and Adolescent Psychiatry and Psychotherapy, University of Duisburg-Essen, Essen
  • Johannes Hebebrand - Department of Child and Adolescent Psychiatry and Psychotherapy, University of Duisburg-Essen, Essen
  • Helmut Schäfer - Institut der Medizinischen Biometrie und Epidemiologie der Philipps-Universität Marburg, Marburg

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. Doc09gmds144

doi: 10.3205/09gmds144, urn:nbn:de:0183-09gmds1443

Veröffentlicht: 2. September 2009

© 2009 Greene et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.

Gliederung

Text

Genome-wide association studies (GWAS) have been used successfully to identify genetic loci associated with complex diseases and phenotypes. So far this association often takes the form of several significant signals (such as small p-values) in a univariate analysis at various markers within a single region that may or may not be located within a gene. Once these associations have been confirmed, this leads to the question if a single markers tags the association signal of another, functionally relevant variant (indirect mapping) or if the single marker tags a functionally relevant haplotype.

To deal with this question, several conditional analysis methods for family data have been proposed and implemented in software packages. Examples are methods based on logistic regression [1], adaptations of the multi-allelic extended transmission/disequilibrium test [2] or weighted haplotype likelihood methods [3].

Whereas between marker linkage disequilibrium (LD) is necessary in a univariate analysis GWAS in order to discover association by indirect mapping, it can often result in a loss of power when using conditional tests. We examine some advantages and disadvantages of a selection of conditional methods with regard to model assumptions and the effects of LD in the region.

Gliederung

References

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Cordell HJ, Clayton DG. A Unified Stepwise Regression Procedure for Evaluating the Relative Effects of Polymorphisms within a Gene using Case/Control or Family Data: Application to HLA in Type 1 Diabetes. Am J Hum Genet. 2002;70(1):124-41.
2.
Koeleman BPC, Dudbridge F, Cordell HJ, Todd JA. Adaptation of the extended transmission/disequilibrium test to distinguish disease associations of multiple loci: the Conditional Extended Transmission/Disequilibrium Test. Ann Hum Genet. 2000;64(3):207-13.
3.
Becker T, Knapp M. Maximum-Likelihood Estimation of Haplotype Frequencies in Nuclear Families. Genet Epidemiol. 2004;27(1):21-32.