Table 2.
Computational efficiency for quantitative trait association
| Samples | Method | Step 1 | Step 2 | Total time | Total memorya | Cost on RAPb |
|---|---|---|---|---|---|---|
| (h) | (h) | (h) | (GB) | (£) | ||
| 50,000 | Quickdraws | 9.6 | 9 | 18.6 | 48 (16) | 7.9 |
| BOLT-LMM | 127 | 671.5 | 798.5 | <15 | 158.4 | |
| REGENIE | 1.1 | 19.4 | 20.5 | <15 | 4.0 | |
| FastGWA | – | 20.2 | 20.2 | <15 | 3.6 | |
| 405,088 | Quickdraws | 97.7 | 51.5 | 149.3 | 63 (16) | 93.0 |
| BOLT-LMM | 1,150 | 7,250 | 8,400 | 46 | 7,500.0 | |
| REGENIE | 4.7 | 45.3 | 50.0 | <15 | 24.2 | |
| FastGWA | – | 128.4 | 128.4 | <15 | 37.3 |
We compared the computational requirements for recent GWAS algorithms with Quickdraws to generate summary statistics for 13.3 million variants and 50 quantitative traits with either N = 50,000 or N = 405,088. aTotal memory includes CPU RAM memory and GPU memory; only Quickdraws requires GPU memory which is reported separately in brackets. bA more detailed cost analysis can be found in Supplementary Table 13. Running times are computed using the same hardware for all methods (mem2_ssd1_v2_x8, with 32 GB of RAM, 8-core processor for N = 50,000 and mem1_ssd1_v2_x36, 72 GB of RAM, 36-core processor for N = 405,088 datasets).