  Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within
biological pathways, the incorporation of prior pathways information into a
statistical model is expected to increase the power to detect true associations
in a genetic association study. Most existing pathways-based methods rely on
marginal SNP statistics and do not fully exploit the dependence patterns among
SNPs within pathways. We use a sparse regression model, with SNPs grouped into
pathways, 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 regression
model, an adaptive pathway weighting procedure that accounts for factors
biasing pathway selection, and the use of a bootstrap sampling procedure for
the ranking of important pathways. P-GLAW takes account of the presence of
overlapping pathways and uses a novel combination of techniques to optimise
model estimation, making it fast to run, even on whole genome datasets. In a
comparison study with an alternative pathways method based on univariate SNP
statistics, our method demonstrates high sensitivity and specificity for the
detection of important pathways, showing the greatest relative gains in
performance where marginal SNP effect sizes are small.
