Table 1. Power of different methods, on 1000 data sets and a region with 100 rare SNVs.
Error rate | Coverage per person | C-alpha | Freq-weight | VT | RWAS | LRT_G | VST | LRT |
---|---|---|---|---|---|---|---|---|
0 | 4 | 0.062 | 0.659 | 0.872 | 0.668 | 0.817 | 0.973 | 0.975 |
10 | 0.062 | 0.713 | 0.903 | 0.688 | 0.854 | 0.98 | 0.99 | |
20 | 0.067 | 0.722 | 0.903 | 0.692 | 0.858 | 0.987 | 0.988 | |
0.01 | 4 | 0.051 | 0.174 | 0.208 | 0.269 | 0.286 | 0.634 | 0.745 |
10 | 0.066 | 0.479 | 0.674 | 0.610 | 0.71 | 0.965 | 0.972 | |
20 | 0.067 | 0.686 | 0.887 | 0.690 | 0.848 | 0.988 | 0.993 |
Two error rates (0% and 1%) were considered with three different coverages (4, 10, and 20). Population attributable risk of the region was 0.02, with ci = 0.1 and a significance threshold of 0.05. Tested methods were C-alpha (Neale et al. 2011), Freq-weight similar to the Madsen–Browning method (Madsen and Browning 2009), variable threshold (VT) (Price et al. 2010), RWAS (Sul et al. 2011b), LRT_G (Sul et al. 2011a), and our proposed methods (VST and LRT).