Table 3.
Performance of GRAT and Tagger in ENCODE Simulations
| Method | Recall | ReductionCEU | Speedup | Recall | ReductionCHB | Speedup |
|---|---|---|---|---|---|---|
| GRAT | 99.89% | 89.7% | 9.7× | 99.73% | 89.6% | 9.6× |
| Taggerαtag = 1e-8 | 86.25% | 78.9% | 4.7× | 87.78% | 79.7% | 4.9× |
| Taggerαtag = 1e-7 | 95.74% | 78.6% | 4.7× | 97.70% | 79.4% | 4.8× |
| Taggerαtag = 1e-6 | 98.40% | 78.3% | 4.5× | 99.62% | 79.0% | 4.8× |
| Taggerαtag = 1e-5 | 99.30% | 77.8% | 4.5× | 99.97% | 78.4% | 4.6× |
| Method | JPT | YRI | ||||
|---|---|---|---|---|---|---|
| GRAT | 99.63% | 90.2% | 10.2× | 99.72% | 88.4% | 8.6× |
| Taggerαtag = 1e-8 | 88.53% | 80.5% | 5.1× | 87.62% | 65.3% | 2.9× |
| Taggerαtag = 1e-7 | 98.10% | 80.1% | 5.0× | 97.55% | 65.3% | 2.9× |
| Taggerαtag = 1e-6 | 99.52% | 79.6% | 4.9× | 99.39% | 65.1% | 2.9× |
| Taggerαtag = 1e-5 | 99.92% | 79.1% | 4.8× | 99.94% | 65.0% | 2.9× |
In each HapMap population, the average performance of GRAT and Tagger in 500 simulated GWASs are shown. GRAT guarantees to achieve the 99% target recall rate, while reducing the number of tests by 90%. Using Tagger, we test the remainder SNPs that are tagged by the tag-SNPs that exceed a p-value cut-off threshold, αtag. GRAT outperforms the traditional tag-SNPs in all populations.