Table 1.
Summary of some effect size distributions that have been proposed for polygenic modeling.
Figure 1.
Comparison of PVE estimates from LMM (blue), BVSR (red), and BSLMM (purple) in two simulation scenarios.
The x-axis show the number of causal SNPs (Scenario I) or the number of medium/small effect SNPs (Scenario II). Results are based on 20 replicates in each case. (A) (true PVE = 0.2) and (C) (true PVE = 0.6) show RMSE of PVE estimates. (B) (true PVE = 0.2) and (D) (true PVE = 0.6) show boxplots of PVE estimates, where the true PVE is shown as a horizontal line. Notice a break point on the y-axis in (C).
Figure 2.
Comparison of prediction performance of LMM (blue), BVSR (red), and BSLMM (purple) in two simulation scenarios, where all causal SNPs are included in the data.
Performance is measured by Relative Predictive Gain (RPG). True PVE = 0.6. Means and standard deviations (error bars) are based on 20 replicates. The x-axis show the number of causal SNPs (Scenario I) or the number of medium/small effect SNPs (Scenario II).
Table 2.
PVE and PGE estimates for five human traits.
Figure 3.
Comparison of prediction performance of LMM (blue), BVSR (red), and BSLMM (purple) for seven diseases in the WTCCC dataset.
Performance is measured by area under the curve (AUC), where a higher value indicates better performance. The order of the diseases is based on the performance of BSLMM. The mean and standard deviation of AUC scores for BSLMM in the seven diseases are 0.60 (0.02) for HT, 0.60 (0.03) for CAD, 0.61 (0.03) for T2D, 0.65 (0.02) for BD, 0.68 (0.02) for CD, 0.72 (0.01) for RA, 0.88 (0.01) for T1D.
Figure 4.
Comparison of prediction performance of several models with BSLMM for three traits in the heterogenous stock mouse dataset.
Performance is measured by RMSE difference with respect to BSLMM, where a positive value indicates worse performance than BSLMM. The x-axis shows two different ways to split the data into a training set and a test set, each with 20 replicates. The mean RMSE of BSLMM for the six cases are 0.70, 0.80, 0.79, 0.90, 0.98 and 0.99, respectively.
Table 3.
Mean computation time, in hours, of various methods for the mouse dataset.
Table 4.
Mean computation time, in hours, of BVSR and BSLMM in all examples used in this study.