QTL Var % | marker spacing (cM) | sample size | BGF1 | BGF2 | LSR1 | LSR2 |
|---|
2 | 0.1 | 200 | 0.40 | 0.40 | 0.40 | 0.39 |
2 | 0.05 | 200 | 0.42 | 0.42 | 0.42 | 0.41 |
2 | 0.02 | 200 | 0.43 | 0.43 | 0.41 | 0.40 |
2 | 0.1 | 500 | 0.67 | 0.72 | 0.78 | 0.76 |
2 | 0.05 | 500 | 0.74 | 0.76 | 0.79 | 0.77 |
2 | 0.02 | 500 | 0.77 | 0.77 | 0.77 | 0.74 |
5 | 0.1 | 200 | 0.71 | 0.74 | 0.77 | 0.74 |
5 | 0.05 | 200 | 0.75 | 0.76 | 0.79 | 0.78 |
5 | 0.02 | 200 | 0.75 | 0.77 | 0.78 | 0.78 |
5 | 0.1 | 500 | 0.95 | 0.97 | 0.98 | 0.98 |
5 | 0.05 | 500 | 0.97 | 0.98 | 0.99 | 0.99 |
5 | 0.02 | 500 | 0.99 | 0.99 | 0.99 | 0.99 |
- Power to detect a QTL using the gene frequency model (BGF) and the least squares regression model (LSR) with one marker (BGF1, LSR1) or two flanking markers (BGF2, LSR2) for different variances explained by the QTL (% of phenotypic variance), marker spacing, and sample size. For the regression method, the critical value for detecting a QTL was estimated by simulating data sets with no QTL and computing the upper 10% quantile F-value from 1500 replications of F-tests. Power was estimated by simulating 1500 data sets, each with one QTL, and calculating the percentage of F-values that were larger than the estimated critical value. For the gene frequency model, the estimate of QTL variance was used as the statistic to calculate power. The critical value for this test was estimated by simulating data sets with no QTL and computing the upper 10% quantile for the QTL variance from 1500 replications. Power was estimated by simulating 1500 data sets, each with one QTL, and calculating the percentage of estimates of QTL variance that are bigger than the estimated critical value
