Particularly in studies based on population representative samples, it is of major interest what impact a genetic variant has on the phenotype of interest, which cannot be answered by mere association estimates alone. One possible measure for quantifying the phenotype's variance explained by the genetic variant is R(2). However, for survival outcomes, no clear definition of R(2) is available in the presence of censored observations. We selected three criteria proposed for this purpose in the literature and compared their performance for single nucleotide polymorphism (SNP) data through simulation studies and for mortality data with candidate SNPs in the general population-based KORA cohort. The evaluated criteria were based on: (1) the difference of deviance residuals, (2) the variation of individual survival curves, and (3) the variation of Schoenfeld residuals. Our simulation studies included various censoring and genetic scenarios. The simulation studies revealed that the deviance residuals' criterion had a high dependence on the censoring percentage, was generally not limited to the range [0; 1] and therefore lacked interpretation as a percentage of explained variation. The second criterion (variation of survival curves) hardly reached values above 60%. Our requirements were best fulfilled by the criterion based on Schoenfeld residuals. Our mortality data analysis also supported the findings in simulation studies. With the criterion based on Schoenfeld residuals, we recommend a powerful and flexible tool for genetic epidemiological studies to refine genetic association studies by judging the contribution of genetic variants to survival phenotype.
(c) 2008 Wiley-Liss, Inc.