gms | German Medical Science

Kongress Medizin und Gesellschaft 2007

17. bis 21.09.2007, Augsburg

Quantification of the contribution of genetic variants in association analysis with survival outcome: three methods in comparison

Meeting Abstract

  • A. Martina Müller - Ludwig-Maximilians-Universität-IBE/GSF, Insitut für Epidemiologie, Neuherberg
  • Helmut Küchenhoff - Ludwig-Maximilians-Universität, Institut für Statistik, München
  • Claudia Lamina - GSF, Institut für Epidemiologie, Neuherberg
  • Dörthe Malzahn - Georg-August-Universität, Abteilung für Genetische Epidemiologie, Göttingen
  • Heike Bickeböller - Georg-August-Universität, Abteilung für Genetische Epidemiologie, Göttingen
  • Thomas Illig - GSF, Institut für Epidemiologie, Neuherberg
  • H.-Erich Wichmann - Ludwig-Maximilians-Universität-IBE/GSF, Insitut für Epidemiologie, Neuherberg
  • Iris M. Heid - Ludwig-Maximilians-Universität-IBE/GSF, Insitut für Epidemiologie, Neuherberg

Kongress Medizin und Gesellschaft 2007. Augsburg, 17.-21.09.2007. Düsseldorf: German Medical Science GMS Publishing House; 2007. Doc07gmds278

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gmds2007/07gmds278.shtml

Veröffentlicht: 6. September 2007

© 2007 Müller 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 vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

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Introduction: Quantifying the contribution of genetic variants, i.e. alleles, genotypes, or haplotypes, to disease outcome or to quantitative traits is of great interest in order to interpret the impact of findings from genetic association analyses. In linear regression, the contribution of the genetic variants to the model can be quantified by the proportion of the phenotype’s variance that is explained by the variant, R². However, it is difficult to define a comparable criterion for survival analysis, due to the censored observations. Thus, it is difficult to judge the contribution of genetic variants to survival outcome.

Methods: A variety of answers is currently available for this topic. Our requirements for an appropriate criterion include: (a) limitation to the range [0;1] for interpretation as percentage of variation explained by the variant, (b) robustness against censoring percentage, and (c) values increasing with the associated hazard ratio. In simulation studies with a variety of scenarios (varying genotype frequencies, censoring percentages, and hazard ratios), criteria based on three different approaches have been compared with respect to the above stated requirements: (1) residuals formulated by means of the cumulative hazard, (2) variation of individual survival curves, (3) Schoenfeld residuals which measure the difference of observed and expected covariate values.

Results: The first approach (residuals based on cumulative hazard) was highly dependent on censoring percentage and showed a tendency to systematically exceed the desired range of values. The second approach (variation of survival curves) had a tendency to low values. Our requirements were best fulfilled by the criterion based on Schoenfeld residuals (approach 3). For this criterion, we also show how extended models adjusted for environmental variables or gene-environment interactions can be judged.

Conclusion: We present a powerful tool for judging the contribution of genetic variants as well as gene-environment interactions within the survival context.