gms | German Medical Science

Pathway association analysis (PAA) tests for an excess of moderately significant SNPs in genes from a common pathway. Here, we present a Monte-Carlo simulation framework that allows to formulate the main ideas of existing PAA approaches (SNP ratio test, ALIGATOR and GenGen, Fisher combination test) as special cases.

A respective implementation in INTERSNP makes time-consuming communication with standard GWAS software redundant. By additional parallelization with the OpenMP API, we achieve a reduction of running time for PAA by orders of magnitude, making the first power simulation study for pathway association analysis feasible.

We demonstrate that under simple,realistic disease models, PAA can actually strongly outperform the GWAS single-marker approach. PAA methods that make use of the the strength of the SNP association (GenGen, Fisher combination test), in general, perform better than ratio-based methods (ALIGATOR, SNP ratio), whereas the relative performance of gene-based scoring (ALIGATOR, GenGen) and pathway-based scoring (SNP ratio, Fisher combination test) depends on the architecture of the assumed disease model.

Finally, we present a new PAA score that models independent signals from the same gene in a regression framework and show that it is a good compromise that combines the advantages of existing ideas.