Extended Data Fig. 2: FPR for SAIGE, fastGWA-GLMM and REGENIE quantified using the null common variants in simulations.
From: A generalized linear mixed model association tool for biobank-scale data
Three methods, SAIGE, fastGWA-GLMM, and REGENIE, are compared. The y-axis represents the FPR computed from the null common variants (that is, all the common variants on the even chromosomes), and the x-axis represents different levels of prevalence of the simulated binary phenotypes (prevalence \(= n_{case}/(n_{case} + n_{control})\)). FPR is evaluated at five different alpha levels (α=0.05, 0.005, 5×10−4, 5×10−5, and 5×10−6), as shown in panels from a) to e), respectively. The dashed lines indicate the expected FPRs (that is, the alpha levels). Each boxplot represents the distribution of FPR across 100 simulation replicates. The line inside each box indicates the median value, notches indicate the 95% confidence interval, central box indicates the interquartile range (IQR), whiskers indicate data up to 1.5 times the IQR, and outliers are shown as separate dots. In all the analyses, we used a one-sided \(\chi _{\mathrm{d.f.} = 1}^2\) statistic to test against the null hypothesis of no association.
