PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data
Figure 3
PUMA methods outperform other tests of association.
Shown here are representative examples of simulation results for single marker analysis (SMA), 2-step conditional regression, a permutation based tuning of MCP (perm-MCP), our approximate Bayesian method (VBAY), and our PUMA methods (Lasso, Adaptive Lasso, LOG, NEG, 1D-MCP, 2D-MCP). Results are shown for 20 replicate datasets from simulations with 5000 individuals, 20 causal markers affecting disease risk and a heritability of 50%. a) The power of each method to recover true associations at a fixed FDR of 5% shown as a function of the marginal heritability of each causal marker. b) Precision-Recall curve for the same simulations as in (a). Note that perm-MCP selected very few markers per simulation so the FDR did not exceed 10%. c) Power to recover true associations at an FDR of 5% for a range of sample sizes.