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Where causal SNPs (single nucleotide polymorphisms) tend to accumulate withinbiological pathways, the incorporation of prior pathways information into astatistical model is expected to increase the power to detect true associationsin a genetic association study. Most existing pathways-based methods rely onmarginal SNP statistics and do not fully exploit the dependence patterns amongSNPs within pathways. We use a sparse regression model, with SNPs grouped intopathways, to identify causal pathways associated with a quantitative trait.Notable features of our "pathways group lasso with adaptive weights" (P-GLAW)algorithm include the incorporation of all pathways in a single regressionmodel, an adaptive pathway weighting procedure that accounts for factorsbiasing pathway selection, and the use of a bootstrap sampling procedure forthe ranking of important pathways. P-GLAW takes account of the presence ofoverlapping pathways and uses a novel combination of techniques to optimisemodel estimation, making it fast to run, even on whole genome datasets. In acomparison study with an alternative pathways method based on univariate SNPstatistics, our method demonstrates high sensitivity and specificity for thedetection of important pathways, showing the greatest relative gains inperformance where marginal SNP effect sizes are small.

Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps