False Positives in Imaging Genetics Using Nonstationary Cluster-Size Inference

Poster submitted to the 2010 Organization for Human Brain Mapping (OHBM) conference in Barcelona

Empirical and null pathway (<i>top</i>) and SNP (<i>bottom</i>) selection frequency distributions for the SiMES dataset.

<p>. For both empirical (red) and null (blue) distributions, variables (pathways and SNPs) are ranked along the <i>x</i>-axis in order of their empirical selection frequencies.</p

SiMES dataset: SNP to pathway mapping.

<p>SiMES dataset: SNP to pathway mapping.</p

Separate combinations of regularisation parameters, and used for analysis of the SP2 dataset.

<p>For each , combination, the mean (±SD) number of selected pathways and SNPs across all 1000 subsamples is reported.</p

Comparison of SNP and gene to pathway mappings for the SP2 and SiMES datasets.

<p>Comparison of SNP and gene to pathway mappings for the SP2 and SiMES datasets.</p

Empirical and null pathway selection frequency distributions for all 185 KEGG pathways with the SP2 dataset.

<p>For each scenario, pathways are ranked along the <i>x</i>-axis in order of their empirical pathway selection frequency, . <i>Top: </i>. <i>Bottom: </i>.</p

SGL-CGD vs SGL-BCGD performance, measured across 2000 MC simulations.

<p><i>Top row:</i> Pathway selection performance. (Left) green bars indicate the number of MC simulations where SGL-CGD has greater pathway selection power than SGL. Red bars indicate where SGL-BCGD has greater power than SGL-CGD. (Right) green bars indicate the number of MC simulations where SGL-CGD has a lower FPR than SGL. Red bars indicate the opposite. <i>Bottom row:</i> As above, but for SNP selection performance.</p

SP2 dataset: scatter plots comparing empirical and null selection frequencies presented in Figures 11 and 12.

<p><i>Top row:</i> Pathway selection frequencies with . <i>Bottom row:</i> SNP selection frequencies for the same values. For clarity, SNP selection frequencies are plotted for the top 1000 SNPs (by empirical selection frequency) only. Corresponding correlation coefficients (for all ranked SNPs) are presented in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003939#pgen-1003939-t006" target="_blank">Table 6</a>. Note that pathway and SNP selection frequencies are much higher at the lower value (left hand plots), since many more variables are selected (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003939#pgen-1003939-t005" target="_blank">Table 5</a>.)</p

Simulation study 2: Mean number of pathways and SNPs selected by each model at each effect size, <i>γ</i>, across 2000 MC simulations.

<p>Simulation study 2: Mean number of pathways and SNPs selected by each model at each effect size, <i>γ</i>, across 2000 MC simulations.</p

SGL vs Lasso: distribution over 500 MC simulations of power to detect 5 causal SNPs.

<p>Each plot represents the power distribution at a single data point in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003939#pgen-1003939-g002" target="_blank">Figure 2</a>. The power distribution is discrete, since each method can identify 0, 1, 2, 3, 4 or 5 causal SNPs, with corresponding power 0, 0.2, 0.4, 0.6, 0.8 or 1.0. <i>Top row:</i> Causal SNPs drawn from single causal pathway. <i>Bottom row:</i> Causal SNPs drawn at random.</p