The novel genomic pathway approach to complex diseases

Background: Examination of the genetic structure of complex diseases hy a "genomic pathway approach"-which applies stepwise model selection to sets of more than 1000 polymorphisms in studies of several hundred suhjects-has recently heen proposed. Models constructed through extensive selection procedures may yield misleading test statistics and measures of predictive performance; we aimed to quantify the extent of such prohlems inherent to stepwise regression on the genomic pathway scale. Methods: We performed permutation analyses and data-splitting approaches using one of the datasets examined in the paper that originally suggested this approach (n = 536; 1195 SNPs in 22 genes) (Lesnick et al. PLoS Genet. 2007;3:e98). Results: The P values for the genetic effects produced hy standard testing severely overestimated the significance, resulting in our example in a standard P value of 3.5 × 10-69and a permutation P of 0.003 (95% confidence interval = 0.001 to 0.009). Furthermore, the apparent validity as measured hy the area under the receiver operating characteristic curve in 90% training datasets (0.935 [interquartile range = 0.918-0.951]) was extremely overoptimistic when compared with the validity estimated from the excluded 10% validation suhsets (0.564 [0.518-0.614]). This validated area under the receiver operating characteristic curve was lower than for models predicting case/control status solely from age and sex while excluding any genetic effects (median difference = -0.040 [95% confidence interval = -0.049 to -0.031]). Conclusions: The application of stepwise model selection on the genomic pathway scale-at least in the simple form currently put forward-is prone to yield highly misleading results. We provide pointers to some promising alternatives.

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