<p>We simulated quantitative phenotype data with between two and five causal variants using genotype data from the T1D dataset for the <i>IL2RA</i> region. The simulated data sets were analysed using forward stepwise regression, GUESSFM, the lasso, the group lasso and the elastic net. GUESSFM produces credible sets for each variant chosen using the snp.picker algorithm described in Materials and Methods. We defined pseudo “credible sets” for the other approaches as the set of SNPs with <i>r</i><sup>2</sup> > 0.8 with a selected SNP. We calculated the discovery rate (the proportion of causal variants within at least one credible set, y axis) and false discovery rate (proportion of detected variants whose credible sets did not contain any cau...
Background: Accurately modeling LD in simulations is essential to correctly evaluate new and existin...
Comparison of (A) AUC, (B) statistical power to detect true marginal and joint risk-associated SNPs ...
<p>Upper panel: Behavior of the learning approaches in terms of their predictive accuracy (<i>y</i>-...
<p>We simulated datasets consisting of 10 K genotypes over one hundred 10 KB loci using three synthe...
<p>Fraction of the simulations where the fine-mapped set is reduced to fewer than 10 variants for th...
<p>Methods were benchmarked using the average number of SNPs per locus selected to find (50%,90%) of...
<p>Shown is the true positive rate as a function of false positive rate for correct identification o...
We simulated 13082 phenotypes using 100 loci of ∼200 SNPs, as described in the main text. Panels (a)...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.6 with contributi...
<p>A-D, KW, MLM and LM. The “Power” was defined as the detection frequency in 500 repeats for a cert...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.2 with contributi...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.2 with only contr...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.2 with only contr...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.6 with only contr...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.6 with only contr...
Background: Accurately modeling LD in simulations is essential to correctly evaluate new and existin...
Comparison of (A) AUC, (B) statistical power to detect true marginal and joint risk-associated SNPs ...
<p>Upper panel: Behavior of the learning approaches in terms of their predictive accuracy (<i>y</i>-...
<p>We simulated datasets consisting of 10 K genotypes over one hundred 10 KB loci using three synthe...
<p>Fraction of the simulations where the fine-mapped set is reduced to fewer than 10 variants for th...
<p>Methods were benchmarked using the average number of SNPs per locus selected to find (50%,90%) of...
<p>Shown is the true positive rate as a function of false positive rate for correct identification o...
We simulated 13082 phenotypes using 100 loci of ∼200 SNPs, as described in the main text. Panels (a)...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.6 with contributi...
<p>A-D, KW, MLM and LM. The “Power” was defined as the detection frequency in 500 repeats for a cert...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.2 with contributi...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.2 with only contr...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.2 with only contr...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.6 with only contr...
Here, quantitative traits are simulated to have broad-sense heritability of H2 = 0.6 with only contr...
Background: Accurately modeling LD in simulations is essential to correctly evaluate new and existin...
Comparison of (A) AUC, (B) statistical power to detect true marginal and joint risk-associated SNPs ...
<p>Upper panel: Behavior of the learning approaches in terms of their predictive accuracy (<i>y</i>-...